development of environment statisticsfdes framework for the development of environment statistics...

DEVELOPMENT OFENVIRONMENT STATISTICSin Developing Asian and Pacific Countries

Asian Development Bank1999

Asian Development BankP.O. Box 7890980 Manila

Published by the Asian Development Bank© Asian Development Bank 1999

Printed in the Philippines

ISBN No. 971-561-165-7Publication Stock No. 010798

Copies (US$30) of this publication can be obtained from: InformationOffice, Asian Development Bank, P.O. Box 789, 0980 Manila, Philippines;e-mail: [email protected]; Fax No.: ++63-2-6362648

Foreword

The medium-term development objectives of the Bank accord highpriority to environmental concerns. In recent years the Bank has

been actively providing technical assistance in the area ofenvironment protection to its developing member countries (DMCs).Such assistance can become more effective if policy makers’ decisionsare based on reliable quantifiable information. However, there is ageneral paucity of environment statistics in the DMCs.

In 1995, the Bank provided Regional Technical Assistance (RETA)for Institutional Strengthening and Collection of Environment Statis-tics in 11 selected DMCs. In the course of RETA implementation, anumber of technical papers and documents were prepared. In view ofthe still sparse literature in the relatively new field of environmentstatistics, it was decided that organizing the documents in publicationform would make them useful references for the national statistical andenvironmental agencies charged with compiling environment statistics.

The chapters of this publication were contributed partly bythe staff of the Statistics and Data Systems Division of the Economicsand Development Resource Center and partly by a consultant fromthe Tata Energy Research Institute of India. The data were based onRETA outputs such as country-specific frameworks for the develop-ment of environment statistics, technical papers from the variousRETA workshops, and the compendiums on environment statisticsprepared by the participating countries. Guidance and coordinationin bringing out this publication were provided by the Statistics andData Systems Division. Nevertheless, the views expressed in thispublication are those of the authors, and do not necessarily reflectthe views and policies of the Bank.

I would like to thank the people concerned for theircontributions to this publication. It is hoped that all those involvedin the development, collection, and compilation of environmentstatistics in the Bank’s DMCs will find this handbook useful.

Chief EconomistEconomics and Development Resource Center

Acknowledgments

We are very grateful to the statistical and environment agenciesin the DMCs that participated in RETA 5555: Institutional

Strengthening and Collection of Environment Statistics for theircontinued cooperation and valuable support in terms of time andservices to make this project successful.

Bangladesh Bangladesh Bureau of StatisticsDepartment of Environment

India Central Statistical OrganizationMinistry of Environment and Forests

Indonesia Central Bureau of StatisticsCentre for Information Development and

Environmental Compliance

Malaysia Department of Statistics, EnvironmentStatistics Section

Nepal Central Bureau of Statistics

Pakistan Federal Bureau of StatisticsMinistry of Environment, Local

Government and Rural Development

Philippines National Statistical Coordination BoardDepartment of Environment and

Natural Resources

Samoa Government of Samoa, Treasury Department

Sri Lanka Census and StatisticsCentral Environment Authority

Vanuatu Statistics Office

Viet Nam General Statistics Office

TATA Consultancy Services (TCS)

Pacific Rim Innovation and Management Exponents, Inc.(PRIMEX)

TATA Energy Research Institute (TERI)

Contents

Foreword ................................................................................................. iAcknowledgments ................................................................................ iiAbbreviations ........................................................................................ v

Chapter 1 – Introduction ................................................................... 1

Background .................................................................................. 1Environmental Issues in the Asian and Pacific Region ....... 2RETA 5555 .................................................................................... 9

Chapter 2 – Development of Environment Statistics in DMCs:Issues and Problems .................................................. 21

Types and Uses of Environmental Data andStatistics ................................................................................ 21

Framework for the Development of EnvironmentStatistics ................................................................................ 32

Compendium of Environment Statistics ................................ 36State-of-Environment Statistics in Developing

Member Countries ............................................................... 37Problems and Issues in the Collection of

Environment Statistics ........................................................ 40Conclusion .................................................................................. 51

Chapter 3 – Framework for the Development of EnvironmentStatistics ....................................................................... 53

Introduction ................................................................................ 53Framework for Environment Statistics .................................. 53Conceptual Model for Compiling Environment

Statistics ................................................................................ 65Implementation of an Environment Statistics Program ...... 68

viii DEVELOPMENT OF ENVIRONMENT STATISTICS

Chapter 4 – Core Environment Statistics and MethodologicalIssues ............................................................................ 75

Land Use .................................................................................... 75Deforestation .............................................................................. 76Soil Degradation........................................................................ 79Water Resources Availability and Use ................................... 83Water Quality Degradation ...................................................... 86Urban Air Pollution ................................................................... 88Greenhouse Gases..................................................................... 90Human Settlements .................................................................. 93Bioresources ............................................................................... 95Methodological Issues in Developing Environment

Statistics ................................................................................ 99Basic Data Standards ............................................................. 110

Chapter 5 – Country Experiences in the Development ofEnvironment Statistics in DMCs .......................... 119

Status of Environment Statistics in Selected CountriesBefore the RETA ................................................................. 119

Institutional Framework for Collecting EnvironmentStatistics Prior to the RETA ............................................. 124

An Approach to Developing Environment StatisticsAdopted by DMCs ............................................................. 127

Present Status .......................................................................... 129

Chapter 6 – Future Directions in the Development ofEnvironment Statistics ............................................ 135

Priorities in Strengthening Environmental StatisticalSystems ................................................................................ 135

Linking the Environment Statistics Framework with PolicyThrough State-of-the-Environment Reporting .............. 144

Institutional Issues .................................................................. 147Handbook and Manuals ........................................................ 157Recommendations of the Concluding Workshop ............... 159Future Work .............................................................................. 162

Introduction ix

Appendixes ....................................................................................... 167

Appendix 1 – Key Environmental Indicators Identifiedby the Asian Development Bank forInclusion in the SDBS .................................... 167

Appendix 2 – Air and Water Quality Indexes .................... 172Appendix 3 – Ecosystem Structure Indices ........................ 173

References ......................................................................................... 175

Glossary of Environment Terms................................................... 179

Environment Statistics and Indicators ....................................... 198

Index to the Bibliography ...................................................... 198A Bibliography ......................................................................... 206Useful Web Sites ..................................................................... 220

Contents ix

x DEVELOPMENT OF ENVIRONMENT STATISTICS

Abbreviations

ADB Asian Development BankASEAN Association of Southeast Asian NationsBFDES Bangladesh Framework for Development of

Environment StatisticsBOD biological oxygen demandCA coordinating agencyCBS Central Bureau of Statistics (Indonesia)CDIAC Carbon Dioxide Information Analysis CenterCES compendium of environment statisticsCFC chlorofluorocarbonCOD chemical oxygen demandCSO Central Statistical Organization (India)DENR Department of Environment and Natural ResourcesDMC developing member countryDOE Department of EnergyECE Economic Commission for EuropeEIA environmental impact assessmentEPA Environmental Protection AgencyESCAP Economic and Social Commission for Asia

and the PacificFAO Food and Agriculture OrganizationFDES framework for the development of environment statisticsFDES-NEP Framework for Development of Environment Statistics

– NepalGAW Global Atmosphere WatchGEMS Global Environmental Monitoring SystemGESAMP Group of Experts on the Scientific Aspects of Marine

PollutionGGI greenhouse gas indexGIS geographic information systemGLASOD Global Assessment of Soil DegradationGNP gross national productGSO General Statistics Office (Viet Nam)IAC-TWG Interagency Technical Working Group

Introduction xi

MOSTE Ministry of Science, Technology and Environment (Malaysia)

NEMS National Environment Management StrategyNGO nongovernment organizationNNP net national productNSCB National Statistical Coordination BoardNSO national statistics officeOECD Organization for Economic Co-operation and

DevelopmentPAHs polynuclear aromatic hydrocarbonsPESC preliminary environment statistics compendiumPFDES Philippines Framework for the Development of

Environment StatisticsPRC People’s Republic of ChinaPSR pressure-state-response (model)RAM rapid assessment methodRETA regional technical assistanceSACEP South Asian Cooperative Environment ProgrammeSDBS statistical database systemSEEA system of integrated environmental and economic

accountingSNA System of National AccountsSOER state-of-the-environment reportSPM suspended particulate matterTEC target environment componentUN United NationsUNCED United Nations Conference on the Environment and

DevelopmentUNDP United Nations Development ProgrammeUNEP United Nations Environment ProgrammeUN-FDES United Nations Framework for the Development of

Environment StatisticsUNSD United Nations Statistics DivisionVNESF Viet Nam Environment Statistics FrameworkVOC volatile organic compoundWHO World Health OrganizationWMO World Meteorological OrganizationWRI World Resources Institute

Abbreviations xi

Chapter 1

Introduction

Background

The United Nations Conference on the Human Environment heldin Stockholm in June 1972 noted that environmental concerns

have increasingly become the subject of mainstream socioeconomicpolicies, both at the national and international levels. As part of itsEarthwatch Programme, the conference recommended thegathering of data on specific environmental variables to determineand predict important environmental conditions and trends. Twentyyears later, at the United Nations Conference on Environment andDevelopment (UNCED) held in Rio de Janeiro in June 1992, aconsensus was reached that strategies for sustainable developmentshould integrate environmental issues into development plans andpolicies. Such integration needs to be supported by integratedenvironmental and socioeconomic data and statistics. Specificrecommendations by UNCED’s Agenda 21 refer to the develop-ment and implementation of (i) integrated environmental andeconomic accounting, and (ii) indicators of sustainabledevelopment.

The rapid pace of modernization, urbanization, andindustrialization has led to serious environmental concerns in theAsian and Pacific region’s developing countries. The environmentalsituation has significantly deteriorated over the years, thusnecessitating the development of broad-based environmentalmanagement policies for those countries. But before a national policyand programs for abatement and control of pollution can beformulated, the environment problems will have to be well defined.This will require the development of a reliable environment databaseand widely accepted ways of presenting environment data for theuse of national planners and decision makers. Authentic statisticaldata relating to existing environmental conditions are vital in makingappropriate decisions on environmental planning and management.The Bank’s developing member countries (DMCs) will therefore need

2 DEVELOPMENT OF ENVIRONMENT STATISTICS

to vigorously pursue the collection and collation of environmentstatistics on an urgent basis. The collection and compilation ofenvironment statistics are prerequisite to the development ofenvironmental indicators that are needed for monitoring the levelsand effects of environmental pollution as well as for assessing thecurrent state of the environment.

Environmental Issues in the Asian and Pacific Region

This section provides an overview of the common environmentalproblems and issues that are likely to be the focus of information-gathering efforts in the countries of the Asian and Pacific region. Akey premise is that efforts to collect environment statistics should befocused on priority environmental concerns, not just in order toeconomize on the usually limited resources for data collection andenvironmental monitoring in developing countries, but moreimportantly, to ensure that the data collected are the ones needed bythose who make policies or decisions affecting the environment. TheAsian and Pacific region is vast, and the economies of the regionshare a wide range of environmental problems and issues. The majorenvironmental issues identified by most developing countries in theirstate-of-the-environment reports (SOERs) are deforestation, landdegradation, land desertification, landslides, land salinization, wastedisposal, inland water pollution, marine water pollution, depletionof coastal and marine resources, loss of biodiversity, loss of aquaticlife, air pollution, depletion of energy resources, and natural disasterssuch as floods, droughts, cyclones, and earthquakes. Some of theseissues are briefly discussed here.

Deforestation

Deforestation is the most serious and widespread problem inthe Asian and Pacific region. Its rate has accelerated from 2 millionhectares per year during 1976-1981 to 3.9 million hectares peryear during 1981-1990. The countries experiencing the most rapiddeforestation are Bangladesh, Indonesia, Pakistan, Philippines,and Thailand (FAO 1993). In the early 1990s, Indonesia alonehad a deforestation rate of 0.6 million hectares per year, while

Introduction 3

Malaysia, Myanmar, Philippines, and Thailand each lost more than0.3 million hectares a year for the period 1981-1990. The countriesof the South Pacific subregion have the lowest rate of deforestation,except Papua New Guinea where it is severe (FAO 1993).

Land Degradation

Land degradation is also serious and widespread in theregion. The majority of the developing countries suffer fromvarying degrees of soil erosion and degradation mainly due torapid rates of deforestation, poor irrigation and drainage practices,inadequate soil conservation, steep slopes, and overgrazing.Extensive and severe water erosion occurs throughout the region.In India alone, about 18 percent of the total agricultural land isaffected. In the dry belt stretching from Iran to Pakistan and India,wind erosion causes extensive and severe land degradation. About59 million hectares are affected by wind erosion in the countriesof South Asia alone, as well as in Afghanistan and Iran (UNEP1997).

Population growth has already put tremendous pressureon land resources in most of those countries. Increased dependenceon intensive agriculture and irrigation is likely to result in sali-nization, alkalinization, and waterlogging in the irrigated areas.If the newly irrigated lands, which are expected to increasesignificantly by the year 2000, are not managed properly, morecultivable soil will eventually become waterlogged and subject toalkalinization.

Inland Water Pollution

Inland water pollution has become a serious problem in manyDMCs although the region is well endowed with water resources.Water quality parameters include indicators of the availability ofoxygen like biological oxygen demand (BOD) and chemical oxygendemand (COD), indicators of nutrient level like nitrogen andphosphorus; and indicators of heavy metals like cadmium, chromium,copper, lead, mercury, and zinc. These pollutants are the productsof excessive use of agrochemicals, sedimentation, domestic industrial


wastes, saltwater intrusion due to overpumping of groundwater, andwaterlogging. The severity of the water quality problems in the regionis summarized in Table 1.1.

Pathogens generally come from domestic sewage that isdischarged untreated into watercourses. The primary sources oforganic matter pollution are domestic sewage and industrial effluentsfrom tanneries, and paper and textile factories. South Asia and thePeople’s Republic of China (PRC) are most severely affected byorganic matter pollution due mainly to effuent from the pulp, paper,and food industries. The discharge of mine tailings and thedevelopment of industrial areas where pollutants are dischargeddirectly into neighboring river systems have resulted in localizedareas of heavy metal pollution throughout the Asian and Pacificregion. In the small island countries in the South Pacific, ground-water resources are suffering from severe salinization due to theintrusion of seawater. As a result, the capacity of rivers to supportaquatic life is threatened by pollution, loss of oxygen associatedwith the decomposition of pollutants , and eutrophicationstimulated by nutrient runoff.

Table 1.1Water Quality Problems in the Asian and Pacific Region

People’s Australia,Water Quality South Southeast Pacific Rep. of Japan, &

Problems Asia Asia Islands China New Zealand

Pathogenic agents 1-3 1-2 2-3 1-3 0-1Organic matter 1-3 0-2 0-1 1-3 0-1Salinization 0-1 0-1 0-3 0-2 0-1Nitrate 0-1 0-1 1-2 0-2 0-1Fluoride 0-1 0 0 0-2 0Eutrophication 0-1 0-3 0 0-2 0-1Heavy metals 0-1 0-2 0-1 0-2 0-2Pesticides 0-1 0-1 0-1 0-1 0-1Sediment load 0-2 0-2 0-1 0-1 0-1Acidification 0 0-1 0 0-1 0-1

Note: 0 = no pollution or irrelevant, 1 = some pollution, 2 = major pollution, 3 = severe pollution.

Source: ESCAP (1991).

Introduction 5

Marine Water Pollution

Marine water pollution is critical in the coastal areas of mostdeveloping countries in the region. Generally, the main pollutionproblems occur along the margins of the sea, around large cities andindustrial complexes, and in the vicinity of the mouths of large rivers.There is a substantial loss of coastal habitats, specially in SoutheastAsian countries, as mangrove swamps are converted into shrimpponds or used for rice cultivation. These activities indirectly affectthe commercial demersal fisheries that rely on the mangrove swampsas nursery areas. Coastal and marine water pollution in the regionare due to direct discharge from rivers, surface runoff and drainagefrom port areas, domestic and industrial effluent discharges throughoutfalls, and various contaminants from ships. Rivers in the regionare generally contaminated with municipal sewage, industrialeffluent, and sediments. Asian rivers account for nearly 50 percentof the total sediment load transported by the world’s rivers.Unfortunately, most of the coastal cities in the region discharge theirdomestic and industrial wastes directly into the sea, without anytreatment (UNEP1997).

Biological Diversity

Widespread deforestation and the degradation of coral reefsin the developing countries are leading to loss of biodiversity. Lossof biodiversity may take many forms, but the most fundamental andirreversible loss is the extinction of species. Species extinction is anatural process, which occurs without human intervention. However,extinction caused by human beings is occurring at a rate that farexceeds any reasonable estimates of natural extinction. Overhuntingis the most obvious direct cause of extinction of animals as it hasaffected several large and well-known species. Severe loss inbiodiversity is a great loss from the economic, aesthetic, and scientificpoints of view and will greatly limit future genetic potential.

The flora and fauna of the region are increasingly threatened.The drive for increased agricultural production has resulted in theloss of genetic diversity. The coastal habitat loss and degradation,combined with increased discharge of sediments, nutrients, andpollutants into coastal areas, are a major concern to all coastal


countries. The rates of loss of coral reefs and mangrove habitatsin the region are among the highest in the world. It is clear thatloss of biodiversity is widespread and serious in the developingcountries of the region. The high rates of population and economicgrowth in these countries suggest that even greater losses couldoccur in the coming years unless decisive action is taken (ESCAP1995).

Air Pollution

Air pollution as a consequence of rapid urbanization andindustrialization in recent years has become a major concern in mostcountries of the region. Urban air pollution is due mainly to sulfurdioxide, nitrogen oxides, carbon monoxide, and suspended particu-late matter (SPM, including lead). Apart from the direct health impacton human beings, air pollution also creates three major global en-vironmental issues: global warming, ozone depletion, and acid rain.

A recent survey by the World Health Organization/UnitedNations Environment Programme (WHO/UNEP) revealed that 10 of11 major cities of the region had exceeded dangerous levels of SPM.The problems of sulfur dioxide, lead, and carbon monoxide pollutionwere also prevalent (Table 1.2).

In addition to the megacities, a large number of medium-size and small cities have serious problems resulting from their de-velopment and industrial centers. Air pollution is expected to increasein most of the cities of the developing countries irrespective of theirsize. Vehicular emissions are a significant problem in all major cit-ies. In recent years, most governments have attempted to addressthis issue by (i) implementing programs for the development ofenvironment-friendly mass transit systems such as the light rail transit,mass rail transit, and subways, (ii) setting emission standards forvehicles, (iii) requiring manufacturers to meet strict emission stan-dards for all new vehicles, (iv) limiting the number of vehicles onthe road, and (v) encouraging vehicle owners to use unleaded petrol.These measures have, no doubt, contributed to reduce air pollutionsubstantially. Nevertheless, it is crucial that the governments andenvironment planners consider these increasingly severe air pollu-tion inputs in their strategies and specific investment plans so as toprevent future problems.

Introduction 7

Acidification of the environment has, so far, been regardedas a problem only in Europe and North America. But it also has thepotential to affect the environment in the Asian and Pacific region.Increased emission of sulfur dioxide as a result of human activitiesis the major precursor of acid rain. It can damage vegetation, fabrics,and structures either directly or when dissolved in rain to form dilutesulfuric and nitric acids.

Energy

Energy is a very important sector of the national economy inall countries. Depletion of certain nonrenewable energy resourcessuch as metals and fossil fuels due to a growing demand and somehuman activities has become a serious concern. The rapid growthof the population, residential activities, industrial activities, commer-cial activities, transportation activities, and oil spill problems are themajor reasons for the depletion of energy resources. In the future,energy or environment questions, or both, will continue to be raised

Table 1.2Air Quality in Megacities of the Asian and Pacific Region

City SO2 SPM Pb CO

Bangkok L H M LBeijing H H L LCalcutta L H L LDelhi L H L LJakarta L H M MKarachi L H H LManila L H M LMumbai L H L LSeoul H H L LShanghai M H L LTokyo L L L L

SO2 = sulfur dioxide, SPM = suspended particulate matter, Pb = lead, CO = carbon monoxide.

Note: H = serious problem, M = moderate to heavy, L = low pollution (according to WHOGuidelines).

Source: WHO/ UNEP (1992).


and energy consumption will become an increasingly important factorin environmental impact analysis.

Natural Hazards

Many developing countries of the Asian and Pacific regionare situated in the world’s hazardous belts and are subject to variousnatural disasters such as landslides, floods, droughts, cyclones, andearthquakes. The major natural disasters that occur periodically inthis region are largely due to climatic and seismic factors.Vulnerability to natural disasters has increased in urban centersbecause of environmental degradation and lack of planning andpreparedness. Some of the natural disasters that occur frequently inthe region are briefly discussed here.

Landslides have been severe in countries like PRC, India,Nepal, and Thailand. Most landslides often result from heavy rainfall,volcanic activity, and earthquakes. Some factors associated withlandslides are deforestation, overpumping of groundwater,compressibility of the soil, geological formations, degree of naturalslope, infrastructural activities such as road construction, and miningactivities. Overpumping causes water levels in aquifers to decline.If the aquifer is not immediately recharged, large voids form aboveit, and the land subsides and slides. Earth slides can block streamsand cause water backup. The result is upstream damage due to agradual rise in water level, and extensive downstream damage dueto rapid release of water when the slide is overtopped. Earth slidesalso destroy vegetation, increase sediment loads in streams, anddisrupt transportation routes.

Floods are natural hydrologic events that become severe due toincompatible human activities in the floodplain. Flood disasters areincreasing because of deforestation, poor land drainage, and heavyrainfall. Floods have become more frequent and more severe; they alsocome with less warning as in the case of the Himalayan watershed.Annual flood losses in India today are 14 times those in the 1950s.Floodwaters covered 60 percent of Bangladesh in 1988 and abnormallevels of flooding are now an annual event. In Bangkok at least 1.2 millionpeople live in slums on swampy ground prone to flooding (UNEP 1997).

Droughts are hydrologic events causing acute water shortage.They are detrimental to human, plant, and animal existence in the

Introduction 9

affected areas. Several countries in the region are susceptible todrought hazard intermittently. In many countries, the majority of thepopulation are dependent on agriculture and are rural based. In caseof droughts, this group is severely affected.

Tropical cyclones occur more frequently in the Asian andPacific region than in any other part of the world. In this region,tropical cyclones frequently form over the northwest Pacific Oceanjust east of the Philippines. Each year, Bangladesh is hit by cyclonesthat cause heavy damage to life and property. Cyclones originatefrom areas of low pressure or depressions, and the cyclone hazardis aggravated by wind velocity and topography. For example,depressions that form in the Bay of Bengal in the south of Bangladeshduring premonsoon, monsoon, and postmonsoon periods can leadto cyclones. Cyclones are associated with heavy rainfall, gusty winds,and, sometimes, storm surges.

Earthquakes are one of the deadliest natural disasters. Twothirds of the world’s devastating earthquakes occur in the circum-Pacific belt. The next most important earthquake zone stretches fromIndonesia, through the Himalayas, and along the axis of theMediterranean. Some 75 percent of the world’s earthquake deathsoccur in this zone, which is more densely populated than the circum-Pacific belt. Overpopulation and large-scale construction activitiesin tectonic zones increase the earthquake hazard. The primary effectsof earthquakes are violent ground motions accompanied by fracturing,which may shear or collapse large buildings, bridges, dams, tunnels,and other massive structures. The secondary effects include short-range events such as fires, landslides, and floods, and long-rangeevents, such as regional subsidence, uplift of landmasses, and regionalchanges in groundwater hydrology.

A 1991 Asian Development Bank (ADB) report assessed therelative significance and severity of the enumerated issues for selectedcountries in South and Southeast Asia, and the Pacific region. Theresults are summarized in Figure 1.1.

RETA 5555

Prior to 1995, environment statistics were generally lackingin many of the Bank’s DMCs. Although several line ministries andspecialized agencies were collecting subsets of environment-related


Figure 1.1Relative Significance of Resource and Environmental

Issues in Selected DMCs

Introduction 11

data, they were doing so without coordination. In most DMCs, noagency was primarily responsible for the collection and disseminationof environment statistics. Environment statistics being a new area,methodologies for data collection were not yet well-developed in anyof these countries. Neither did they have conceptual frameworks thatcould be used as a guide in identifying the environment data to collect.Adequate expertise and resources for compiling environment statisticswere also unavailable.

In 1995, the Bank initiated the regional technical assistance(RETA)1 for institutional strengthening and collection of environmentstatistics to assist 11 selected DMCs in developing their institutionalcapabilities to collect, compile, and disseminate environmentstatistics. The RETA aimed to assist the respective DMCs in (i) settingup organizational linkages among different units involved in thecollection of environment-related statistics; (ii) establishing their ownframework for the development of environment statistics (FDES) andeventually integrating environment statistics with socioeconomic anddemographic statistics; and (iii) bringing out a compendium ofenvironment statistics, based on the country-specific framework, byorganizing the environment data that are already available fromexisting sources.

In the process of implementing the Project, the Bankorganized a number of meetings for representatives of nationalstatistics offices (NSOs) and departments of environment (DOEs) ofthe participating countries. An inception workshop that the Bankorganized in September 1995 discussed the work plan, organizationallinkages, and issues and problems relating to the collection andcompilation of environment statistics. The workshop identified manyissues and elicited valuable suggestions.

Procedural and Methodological Issues

The main procedural and methodological issues wereidentified in the workshop:

1 RETA 5555: Institutional Strengthening and Collection of Environment Statisticsin Selected Developing Member Countries, for $900,000, approved on 18 November1993.


(i) the choice between a comprehensive framework and aframework based on data available in the country, and

(ii) the need for standard definitions and classifications ofenvironment statistics.

In addition, the participants addressed (i) the problem ofselecting core environment statistics and variables and bringing theminto accord with the country FDES, and (ii) the steps in initiatingthe implementation of the technical assistance (TA) program in theselected DMCs.

The following recommendations were made:(i) Each DMC should start with the United Nations (UN)

FDES2 as the broad framework for developing thecountry’s own framework for environment statisticscollection and compilation.

(ii) The framework that the country adopts should providea logical way for developing statistics to meet its specificrequirements, e.g., addressing national concerns,formulating policies.

(iii) The country should start by making a list of variablesusing various existing references, e.g., UNEP/EAP-APState of the Environment Database, Economic and SocialCommission for Asia and the Pacific (ESCAP) handbook(to be published shortly), ESCAP guidelines for thepreparation of SOE, and other relevant documents.

(iv) To the extent possible, the country should adoptinternationally accepted classifications, definitions, andmethods such as those of, e.g., International StandardClassification, Economic Commission for Europe/Conference of European Statisticians, UNEP/GlobalEnvironmental Monitoring System, (UNEP/GEMS),Food and Agriculture Organization (FAO), World HealthOrganization (WHO), World Meteorological Organiza-tion (WMO), Habitat.

2 The UN framework recommends a list of environment statistics that a countrymay want to collect and maintain. Since environment statistics aremultidisciplinary in nature, various data sources need to be tapped and variousmethods have to be adopted to develop the database.

Introduction 13

(v) Since many international classifications have alreadybeen developed by First World countries, DMCs couldsimply adapt those suited to their own requirementsinstead of starting from scratch.

(vi) Each country should take the following general steps:(a) inventory environmental concerns and prioritize

issues;(b) delineate the scope of the issues (i.e., national or global);(c) identify data gaps and the need for institutional

capacity building to make the statistics more use-oriented (institutional strengthening may requireconducting case studies and developing models tounderstand the dynamics, responses, and reactionsof the environmental decision-making process); andsubsequently develop handbooks or guidebooks,and conduct training;

(d) organize an environment statistics compendiumbased on the country’s requirements althoughchecklists may be used as guides; and

(e) collect and regularly update statistical data at afrequency to be determined by each country basedon its needs and resources.

Institutional Linkage Issues

Three major linkage issues were identified and recommen-dations for addressing each were made:

Issue #1: Lack of effective coordination between and among theconcerned agencies in each DMC with respect to thecollection and compilation of environment statistics

Recommendations:(i) Each DMC should designate an agency (coordinating

agency [CA]) to coordinate efforts in environmentstatistics collection on a continuing basis.(a) Countries that have already identified their CAs

should make those agencies serve as the focal pointsfor the RETA;


(b) The governments of countries that are yet to identifytheir CAs may opt to designate their central statisticsoffice as the CA; and

(c) For countries that neither have identified nor areready to identify their CAs, agencies that coordinatesimilar activities (e.g., the National Commission onSustainable Development) or the activities ofinternational organizations (e.g., ESCAP, SouthAsian Cooperative Environment Programme[SACEP], South Pacific Regional EnvironmentProgramme [SPREP], United Nations StatisticsDivision [UNSD]) may be named as CA. Nonethe-less, the decision on which office is to serve as theCA rests with the respective governments.

(ii) Each DMC should recognize the importance of havinga pool of resource persons who can provide the continuityneeded for the sustained development of environmentstatistics in the country.

(iii) A coordinating mechanism should be established ineach country to ensure effective and efficient collec-tion, analysis, and dissemination of environmentstatistics.(a) An interagency committee should be set up,

composed of the designated CA as chair, withrepresentatives from the different agencies doingwork in the environment or environment-relatedfields as members;

(b) This committee should be headed by a high-rankinggovernment official to ensure that its recommenda-tions are implemented;

(c) The committee should be provided with secretariatsupport by the CA to assist in its various tasks; and

(d) The committee should be assigned the followingbroad tasks:• give policy advice on matters relating to envi-

ronment statistics;• direct and coordinate the activities of the various

agencies involved in environment statisticscollection and dissemination; and

Introduction 15

• review, and monitor and evaluate the progressof work under the project, including reportingto ADB.

Issue #2: Lack of technical and financial resources in the CA andthe concerned environment agencies for environmentstatistics collection and dissemination

Recommendations:(i) The governments should be made aware of this

deficiency and urged to take the necessary steps toimprove the capability of the concerned agencies incollecting and disseminating environment statistics.

(ii) The governments of the concerned DMCs should seethe ADB RETA as a first step in that direction (note that,in addition to financial assistance, the TA had provisionsfor technical advice and information exchange, and fieldreview missions as required).

Issue #3: Lack of communication among the different countries inthe region (e.g. Association of Southeast Asian Nations[ASEAN], South Asia), among international organizations,and even among the different divisions of the same or-ganization regarding their respective activities in the samefield (i.e., environment statistics)

Recommendation: There should be reciprocity in informationexchange among the concerned organizations so as to preventduplication of efforts and ensure a more efficient utilization of resources.

Two subregional workshops, one for participating South Asiancountries and another for Southeast Asian and Pacific island countries,were organized in December 1996 and March 1997, respectively,to review the progress made by the participating countries and todiscuss the detailed outlines of the proposed frameworks andcompendiums of environment statistics. Finally, a concludingworkshop was organized to discuss the final outcome of the RETAas well as the future courses of action to be taken by the countriesparticipating in it.


The NSOs are responsible for collecting, compiling, anddisseminating social, economic, and demographic statistics in allcountries. Hence, they were selected as focal points for implementingthe RETA for the development of environment statistics. However,various environment agencies were responsible for collectingenvironment-related statistics in their specific sectoral/subsectoralenvironmental areas. It was felt that it would be appropriate for theNSOs to coordinate the collection of environment-related statisticsas well. Since environmental science is a highly specialized subject,it was thought logical and appropriate to also involve the DOEs,together with NSOs, in the implementation of the RETA.

RETA Outputs

As the scope of environment statistics is very broad, asystematic approach was needed to develop the system of environ-mental data collection. The Bank provided assistance in preparingcountry-specific frameworks. As recommended during the inceptionworkshop, the country frameworks were based on the broadframework of the UN-FDES to promote uniformity and comparabilityacross the countries. The preparation of country-specific frameworkswas expected to facilitate the (i) review of the country’s environmentalconcerns and identification of those that can be measured inquantifiable terms; (ii) listing of variables that can be used to measureaspects of environmental concerns; (iii) evaluation of datarequirements, availability, and sources; and (iv) presentation of aplan of action for data collection and dissemination.

All the participating countries have now prepared the country-specific FDES, one of the major outputs of the RETA (Table 1.3). Thesespecific frameworks were based on the UN-FDES. Some countrieshave already finalized their frameworks after discussing them innational workshops, whereas others are expected to do so in the nearfuture. All frameworks present their environmental activities in termsof six components (viz., flora, fauna, atmosphere, water, land or soil,and human settlements) with four information categories in eachcomponent, namely: (i) social economic activities, natural events;(ii) environmental impacts of activities and natural events;(iii) responses to environmental impacts; and (iv) inventories, stocks,and background conditions.

Introduction 17

The UN-FDES is a comprehensive document that covers allfacets of environment statistics. In preparing the country-specificframeworks, some countries, however, could not include allinformation categories because of the unavailability of required data.Since the environmental problems as well as availability of data variedsignificantly among the participating countries, the countryframeworks show major differences in terms of the appropriatevariables and indicators, units of measurements, and the nature andtype of the data included.

Another important output of the Project is the compendiumsof environment statistics prepared by the participating countries,using approaches depending on their convenience. Some countriesorganized the data in the compendium along the lines of their ownframeworks broadly following the UN-FDES format, some adoptedthe media approach, while others adopted the pressure-state-response(PSR) approach. Large amounts of environment-related statistics arecollected by the countries on an ad hoc basis, and are available in

Table 1.3Status of Environment Statistics Development Activities

in the RETA Participating Countries

Environ-Inter- ment

Country FDES Compen- National agency Technical Statisticsdium Workshop Committee Committee Unit

Bangladesh y y Y y x yIndia ya y Y y x yIndonesia y y Y y x yMalaysia y y Y y x yNepal y y Y y x yPakistan y y Y y x yPhilippines y y Y y y ySamoa y y Y y x ySri Lanka y y Y y x yVanuatu y y Y y x xViet Nam y y Y y x y

x = does not exist, y = does exist.a First framework prepared in 1986.


various administrative records. When appropriately organized, thesestatistics will certainly be useful to makers of policies and decisions,particularly, environment ministries in preparing SOERs. Thepreparation of such compendiums will help prevent duplication ofdata collection activities among agencies.

The Bank recently identified a short list of environmentalvariables or indicators that are important to Bank operations as wellas to the countries in monitoring and assessing the state of theirenvironment (Appendix 1). A part of the environment statistics andindicators collected and compiled by the countries participating inthe RETA will be incorporated into the Bank’s statistical databasesystem to make the data more widely available to users both withinand outside the Bank. The Compendium of Environment Statisticswill be the primary source of data for these Bank-identified indicators.The indicators will be regularly updated on the basis of the availabilityof environment statistics in the DMCs. Hence, it will be helpful ifthe compendiums were also updated on a regular basis.

Some nine participating countries have establishedenvironment statistics cells in their NSOs while others have identifiedsome professional staff to work on environment statistics. These stepsare necessary for the systematic development and growth ofenvironment statistics, and are expected to facilitate the smoothcompilation and collection of environment statistics in closecoordination with various environment-related agencies. Mostcountries have formed high-level steering or interagency committeesthat are expected to play an important role in providing not onlyeffective coordination between various agencies but also technicalguidance to the NSOs and DOEs in developing environment statistics.At least one country, the Philippines, has also formed technicalworking groups to provide technical guidance to statisticians andenvironment experts involved in the collection and compilation ofenvironment statistics (Table 1.3). The formation of technical groupswill be very helpful, for not many statisticians are familiar withenvironmental concepts and definitions.

The Bank, jointly with the Central Statistical Organization(CSO) of the Government of India, recently conducted a short-termtraining course on environment statistics in India for the benefit ofthe countries participating in the RETA. The course was attended by23 participants from eight countries. The participants judged the

Introduction 19

training as very useful. Although the course was of short duration,it was particularly successful in providing the participants withvaluable insights into the issues involved in developing environ-ment statistics.

The overriding objective of the Bank’s technical assistanceprogram is capacity building in its DMCs. In this light, one specificpurpose of the RETA was to play a catalytic role in the sustaineddevelopment of environment statistics in the DMCs; the bulk of futurework rests on the concerned agencies of the DMCs. While the BankRETA has been successful in creating the basic infrastructure, thework needs to be continued and further developed by the countriesthemselves. It is the purpose of this publication to assist DMCs insetting up statistical systems that will support the regular productionof environmental compendiums.

Chapter 2

Development ofEnvironment Statistics in DMCs:

Issues and Problems

This chapter reviews some common issues and problems that arelikely to occur during the process of developing environment

statistics in DMCs. It starts by giving a generalized picture of thedifferent forms of environment data so as to facilitate a descriptionof these forms in their common setting. The objective of the wholeexercise is to delineate the role and possible contents of environmentstatistics as well as provide some insight into the derived uses ofenvironment data.

Types and Uses of Environmental Data and Statistics

The major users of environment data and statistics are policy makers,planners, scientists, students, and the general public.

State-of-the-Environment Report

The position of environment statistics might be betterappreciated by contrasting it with state-of-environment reports(SOERs). SOERs are not neutral instruments as they are explicitlymeant to assess the condition of the environment, and determine thecauses of the condition and possible cures. The text of an SOER givesan interpretation of existing data and knowledge so as to suggestwhat should be main trends. An example is ESCAP’s SOE Report(ESCAP 1992). UNCED country reports can also be regarded as such(UNCED 1992).

Reporting on the state of the environment is becoming verycommon. Two types of products are usually produced by governments:SOERs and environmental statistical publications. The main distinc-


tion between the two is that environmental statistical publicationsare largely numerical, whereas SOERs contain substantialexplanatory text. Scientists’ need for detailed environmental statisticsis obvious. But what is the purpose of the more general SOERs?

Policy makers need basic references on the state of theenvironment. Because SOERs are abstracted from a huge mass ofreports and data, they do not normally contain all the environmentalinformation available for a particular geographical region. However,they do provide a general picture of the state of the environment,from which progress in dealing with environmental issues can bededuced. In some countries, such as Italy, simply having to producesuch a report has galvanized governments into action. SOERs alsoallow the environmental record of governments to be open to scrutiny.In developing countries, SOERs often represent the main source ofinformation on the environment. Presently, many countries haveenvironmental administrations, but not all publish regular SOERs.Of course, producing an SOER does not necessarily indicate thatprudent environmental policies are being followed.

SOERs indicate where urgent problems may lie or where“unfinished business” remains. SOERs can also help policy makersto identify opportunities for improving the environment and to setpriorities.

In essence, the SOER divides the big environmental problemsinto coherent specific problems and links them with significant actors(called target groups). Themes have been formulated for specificenvironmental problems such as the greenhouse effect, ozonedepletion (global level), acidification (continental level),eutrophication (regional level), noise (local level), and target groupshave been identified: households, energy plants, industry, agriculture,and car owners, groups to which more or less uniform measures canbe applied. Further policies might be applied to specific areas, e.g.,mountainous areas prone to erosion. Needless to say, an SOER forsuch an environment tends to lead toward a more specific and preciseassessment of the goals laid out in the EPP.

Forms of Environment Statistics

The primary audience of environment statistics (and statisticsin general, for that matter) is the policy makers. For the needs of this

Development of Environment Statistics in DMCs 23

audience, basic, straightforward environmental data are presentedin derived forms. To clarify the importance of environment statistics,the most important forms (statistics, indicators, and indices), frombasic to derived, for presenting environmental data are discussed.In practice, the forms are not mutually exclusive.

Environment statistics, briefly defined, refers to statistics onthe state of the environment, as influenced by man’s doings andnatural causes, thereby showing causes, consequences, and remedialmeasures.

Such a definition is as broad as it is vague. In practice,environment statistics are a system of selected data organized onthe basis of some framework so as to reflect certain natural or logicalconnections. Thus, all published environment statistics indicate someexplicit or implicit framework due to the assumed connection andselection of items. The UN Framework for the Development ofEnvironment Statistics (FDES) sketches the generalized traits ofcountry publications (UN 1984). A unique feature of environmentstatistics is that they combine data from the natural sphere with thosefrom the social and economic realms. For example, air pollution ispresented as a chemical and physical phenomenon that is causedby man’s economic activities and has impacts on man, plants, andanimals alike.

A framework should be seen as a tool for organizing andinterpreting data statistics and, therefore, a framework cannot be aneutral device. Although a carefully developed framework is veryimportant and useful for organizing and highlighting a country’sspecial needs, it should be borne in mind that there is no idealframework.3 A very general framework is reproduced in Figure 2.1.The framework basically describes the cause-effect chain.

Environmental indicators

Environmental indicators are an efficient way of measuringthe environment issues in a country. Properly derived indicators canserve to highlight changes in environmental conditions that warrant

3 In contrast to economic statistics, which are guided by the system of nationalaccounts and whose framework is based on a set of rigorougly tested economicprinciples.


further investigations. Potentially, indicators can signal the healthof the environment and can help in formulating actions to serve thelong-term needs of the environment and the community. Environ-mental indicators can be classified into three groups: pressure, state,and response indicators. Pressure indicators show the causes ofenvironmental problems. Certain flow quantities such as emissions,use of raw materials, products, and energy, or interventions in theenvironment, for instance, infrastructural activities that place a burdenon the environment, are charted by means of pressure or stressindicators. State indicators reflect the quality of the environment inrelation to the effects of human action. Response indicators pertainto measures taken by society to improve the environment. Theseindicators can tell whether things are getting better or worse, whetherproblems are growing, or whether current policies are achieving thedesired goals.

It is not always possible to define an ideal state or norm foreach indicator. Nonetheless, the indicator still provides useful insightsinto the trend of the state of an environmental resource and associated

Figure 2.1A General Framework for Environment Statistics

Note: Reactions may be directed at any step discerned in the first line. It seems evident thatcontributions toward sustainability are best directed toward “population” and“activities,” being the fundamental variables. Abatement of emissions consists mostlyof end-of-pipe measures (e.g., flue gas scrubbing) and less of fundamental changesin production methods (nonwasting; non- or low-polluted river sludge).

Population/geography

Emissions/land usechanges

Activities/natural

disasters

Altered SOE/economic

losses

Responses byindividualsand society

Concentrations

Æ Æ Æ

Æ Æ


concerns, if any. For example, it is difficult to establish how muchambient concentration of carbon dioxide (greenhouse gas) isacceptable. However, the increase or decrease in ambient carbondioxide concentration gives an indication about global warming. Byrestricting the definition of an indicator to a simple structure asdiscussed, information provided by indicators would be objective.However, this information would be relevant to only one aspect ofan environmental resource. Fragmented information provided byindicators may help in making environmental decisions, but doesnot provide a comprehensive view of the various dimensions of theenvironment for decision making. Environment indices attempt toovercome this particular limitation of environmental indicators.

Environmental indicators are not easy to formulate, and theamount of work involved in developing an agreed-upon set ofindicators for a country should not be underestimated. The UnitedNations Commission on Environment and Sustainable Development(UNCESD), together with the UNSD, has already achieved someprogress in identifying key indicators on the environment andsustainable development for the benefit of the member countries.Therefore, it is advisable that developing countries instead ofreinventing the wheel should start developing indicators specific totheir countries, proceeding from the work already done at theinternational level.

Environmental indices

An index combines a number of variables into a single value.The ability of an index to provide information at a level thatencompasses information on a number of variables in the form of asingle value makes the concept of an index attractive for a numberof functions. An environmental index is necessary to reflect the stateof an environmental resource; to understand the dynamics of anenvironmental system or the relationship between differentenvironmental components as part of scientific investigation; tofacilitate the analysis of trade-offs between objectives, i.e., develop-ment and environmental protection; and to assist in making resourceallocation and policy decisions.

The components of an environmental index vary with thepurpose or use of the index. If the task is a simple ranking of countries,


fewer problems are encountered than if the index is to be used fordetailed comparisons. On the other hand, if one wants to comparethe level of one country’s environment quality with another’s, thenaccurate and appropriate measurement techniques are critical. Foran index to capture the environmental quality of a country,methodologies that take into account the discrepancies caused bydifferences in population, area, and income, among other factors,must be developed. Most important, however, is the selection of theset of environmental quality indicators that will be used in the index.Although the parameters that are chosen should be good indicatorsof environmental quality, it is most important that the availability ofdata be taken into consideration.

The Asian Development Bank has been promoting policiesand programs to enhance the environment performance of its DMCs.The Bank’s recent policies for reflecting the growing emphasis onenvironmental and social considerations in its funded technicalassistance have brought about the need for new methods in assessingthe environmental repercussions and the sustainability of economicdevelopment. The Bank therefore initiated a study in 1994 to developtools for monitoring environmental change in the DMCs. A set ofenvironmental indices, namely, cost of remediation, environmentalelasticity, and the environmental diamond, were developed and testedin six selected countries of the region. All three indices were developedon the basis of four principal environmental components, i.e., air,water, land, and the ecosystem, using principal component analysistechniques. Each of the three new indices is designed to characterizedifferent aspects of environmental quality. Each has differentimplications for data requirements. Without good-quality environmentdata, it would not be possible to make any judgment on the usefulnessof these indices. In addition, the technical complexities and methodsfor constructing the indices also vary. The ultimate test for theseindices is, however, their usefulness as tools for conducting futureenvironment planning and devising effective policies.

Environmental accounting

The environmental degradation associated with economicdevelopment and population growth is visible in many countries ofthe Asian and Pacific region. The change in quality of land, air, and


water as well as the loss of flora and fauna makes one concernedabout the costs of progress. Questions about the desirability ofuntrammelled economic development initially elicited the response“poverty is the worst pollutant.” While there is much truth in thisobservation, our understanding of environment issues has becomesomewhat more sophisticated over the past few years. Theenvironmental outlook of the developing countries of the region wouldbe gloomy indeed had countries not been alerted now to the needto protect their environment and natural resources. While thesignificance and the degree of severity of these environmentalproblems vary from country to country, their mitigation inevitablyrequires the integration of environmental considerations into theplanning process and developmental activities.

The preparation of environmental accounts and their regularpublication could bring a much greater degree of accountability inpublic policy. Environmental accounting aims at measuring the realincome of a nation, which takes into account how much nations borrow(or take) from nature. Changes in environmental quality and stocksof natural resources that occur as a result of economic and socialdevelopment must be taken into consideration so that developmentdecisions can satisfy the needs of present and future generations.

The ongoing exploration of the usefulness of accountingtechniques for the organization and compilation of aggregateenvironment statistics gives rise to a multitude of different approaches.These can be classified into either physical or monetary accountingschemes, but many countries in the region appear to be faced withthe need to advance in both types of accounting frameworks. It seemsthat the demand for monetary accounting primarily emanates frompolitical quarters and some economists and environmentalists, whilethe more technically oriented subject-matter experts appear to besceptical of the possibilities for aggregation, for monetary valuation,or for both. Demands for the compilation of accounting aggregatesare more often than not associated with quests for developing statisticspermitting the conceptualization of sustainable development.

The conventional system of national accounting does notadequately reflect the effects of a degrading environment on theeconomy, or of depleting stocks of natural resources; indeed it is notdesigned to. Various approaches to environmental and resourceaccounting have been proposed to deal with the shortcomings


identified in national accounting systems with respect to theenvironment. The revised 1993 System of National Accounts (SNA)considers environment as a “satellite account,” separate from thecore accounts, but developed in a framework entirely compatible withthe SNA. The UNSD has prepared the Handbook of IntegratedEnvironmental and Economic Accounting (United Nations 1993),which provides guidelines for producing “satellite” integratedenvironmental accounts. The handbook describes a system ofintegrated environmental and economic accounting (SEEA) that wastested and revised following case studies in Mexico, Papua NewGuinea, and Thailand. Essentially, the new satellite system providesguidance in the treatment of natural capital depletion (land, water,air). For goods and services that have no market prices, alternativevaluation methods are suggested to impute the cost of depletion ordegradation. Using SEEA, planners incorporate environmentalinformation into production, income, and balance sheet accounts. Itexplicitly allocates all environmental impacts to the separate economicactivities that cause (or bear) them. Environmental information isthus made available for integration into the entire array of economicpolicy and management analyses that the national accounting systemserves.

The UN satellite environmental accounts will certainlyencourage interested countries to begin constructing environmentalaccounts that can easily be integrated into the core system, but theyare yet to be fully tested. The few case studies undertaken were basedon far-from-perfect data — sometimes only rough estimates, thoughreasonably comprehensive — focusing on minerals, forests, and somepollution charges. As the countries where the methodology is testedcome to some agreement about what the international standard ofgreen accounting should be, the possibility of reforming the coreUN system to include environmental information will grow.

Contents of Environment Statistics

UN-FDES wisely abstains from presenting model contentsfor environment statistics, but restricts itself to presenting a generalframework within which actual concerns are listed. But for one facedwith the problem of devising a handbook, breaking up the field intodefinite chapters is inevitable. Furthermore, as people are unfailingly


bewildered about the possible scope of environment statistics, abroad sketch of the contents of environment statistics may behelpful. It should be stressed that the following is not in any waymeant to be a prescription, but is merely a tool to provide clarityon the subject.

Core statistics

Core statistics, which are considered the main subject matterof environment statistics, comprise the physical statistics describingemissions from human society into nature as well as changes in landuse and their consequences. They cover the following areas ofconcern:

(i) emissions to, concentrations in, or effects on air, water,soil

(ii) land use, soil degradation, deforestation, agriculture, orsalinization

(iii) natural environment: plants, animals, or ecosystems

At present, the best developed methodology is for emissionsand concentrations, the latter often referred to as quality data.4

Probably for historical and institutional reasons, emission andconcentration monitoring developed independently. Thus theopportunity to link the two and thereby provide policy makers withmore coherent data was lost. For example, one may know that a riveris polluted, but if one cannot identify and quantify the relevantpollution sources, corrective measures are bound to be vague andprobably even misguided.

At a later stage, when sufficient data are available, data fordifferent media (air, water, soil) could be integrated by following thefate of specific substances in so-called substance balance sheets.Many effects can be readily measured, but their relation to causescan often be stated in general terms only. One example is theincidence of lung diseases and air pollution.

4 Between emissions and ambient concentrations, dispersion and emission can beinterposed. Dispersion is the way emitted substances are distributed from theirpoint of release. Emission is the process in which substances pass over to an-other medium where they may reside for some time, e.g., air emissions to soil,water emissions to soils.


As most developing countries are still highly dependent onagriculture, the importance of land use or land cover statistics inconnection with soil degradation and other soil-related problems canhardly be underrated. In countries poor in minerals, oil, gas, and thelike, soil is the most precious resource. Unfortunately, no unifiedmethodology has been developed for assembling reliable data in acommon framework. However, efforts being exerted by FAO andothers appear promising (Earthwatch/GEMS 1994).

Statistics on the natural environment are strongly related toland use statistics (in this case, formulated in terms of habitat andecosystems) as a consequence of changing land use patterns ratherthan of pollution. And much like effect statistics, many data can beproduced (e.g., abundance and distribution of plant and animalspecies) without their making much sense for lack of an appropriateframework, even on a national or subnational scale. Meanwhile,species inventories and reed lists seem to have less meaning in thetropics than in the higher latitudes, as there are simply too manyspecies to count. Ecosystem protection might be a better option inthis case. The most important questions then are (i) Do currentprotected areas match the threatened ecosystem? (ii) Have boundariesof protected areas been suitably delineated? (iii) Are enough areasunder protection? (Braatz 1992).

Explanatory and background statistics

Starting from the assumption that environmental problemsare caused by man and his actions or activities (Figure 2.1) andmodified by natural circumstances and wealth (amount of productionand consumption), the following statistics should provide explanatoryvariables to emissions, concentration, or land use statistics:

(i) population (absolute level, density, rate of change);(ii) activities (including transport, energy, and elements of

SNA); and(iii) geography and climate (including natural disasters).

The methodology for these statistics has already beenestablished (e.g., population statistics, SNA). The statistical processesconsist mainly of selecting and restructuring existing data, in thelight of some framework for environment statistics. The same applies


to geographic and climate data. For natural disasters, for example,WMO methodology and data may be used for a start.

Integrated statistics

There is concern that categorizing environmentalinformation by subject areas such as air, water, land, etc., maylead away from an integrated approach to environmental issues.It is necessary to recognize that the environment and the economyare closely interrelated. The databases that are developed shouldbe based on the dynamic real world and not on an arbitrary setof subject areas.

Statistics on environmental costs and physical resourceaccounting are most useful in a direct way to developing countries.The first consists of surveying the outlays made to prevent, or restrict,environmental pollution and damages and to restore the environment.Physical resources accounts represent gains and losses in the resourcestocks (oil, iron ore, wood, fish) in the course of a year, and thus offeradditional information to the SNA.

Statistics in environment frameworks

Judging from the work done by UNSD, UNEP, and Habitat,the following frameworks seem to be important to developingcountries:

(i) human settlement statistics linking socioeconomic dataand statistics with environmental services andinfrastructure; and

(ii) urban area statistics.

In contrast to integrated statistics, such statistics offer a crosssection of several statistics in a more or less defined framework. Atthe lowest level, it may consist of a national ordering of existingstatistics. The combination of social variables with environmentalones is typical, while human settlement statistics seem to stress linkingthe social with environmental variables. At the household level (familycomposition, income, education, health, versus access to water,cooking fuels used, waste disposal, etc.), urban statistics tend toconcentrate on environmental problems typical to densely populated


areas like megacities, including spatial planning issues. Noise, trafficemissions, and waste generation and treatment pose a much greaterproblem in cities than in rural areas. Furthermore, more and betterenvironmental data may be expected to be available for cities thanfor rural areas (ESCAP 1993).

Framework for the Development ofEnvironment StatisticsAs discussed earlier, environment statistics are multidisciplinary incharacter. Their sources are dispersed and a variety of methods areapplied in their compilation. Better coordination and organizationare needed for developing this complex area of statistics. To this end,statistical frameworks and systems have been successfully appliedin the fields of social, demographic, and economic statistics. A similarframework would be useful to provide a systematic approach to thedevelopment of environment statistics.

The statistical framework is an instrument by which datacoming from various data-collecting institutions are compiled andintegrated in such a way as to be more useful in the formulation andvaluation of socioeconomic and environmental programs and policies.Different conceptual frameworks are in vogue in different countries.A preliminary review of all those environment statistics frameworkssuggests that four conceptual models are being used:

(i) environmental media-based framework,(ii) resource accounting model,

(iii) ecological model, and(iv) stress-response model.

The media-based framework organizes environmental issuesfrom the perspective of the major environmental components of air,water, land or soil, and the man-made environment. It aims atassessing the state of environmental media at different points in timerather than at monitoring the processes of environmental changecontinuously. The media approach complies with conventionalstatistical and administrative concepts and classifications, and thepopular perception of the environment. The main criticism againstthe media-based model is that it is more concerned with the natural


environment than with the human aspect, and that the interrelation-ships among the components are not considered.

The resource accounting model keeps track of stocks and flowsof natural resources from the time of their extraction from theenvironment, through successive stages of processing and final use,to their return to the environment and waste or to the economic sectorfor recycling. This system has also been made compatible with the1993 SNA. Although this model appears to be appealing, theoreticallythere is difficulty in implementing it to trace the complete life cycleof resources, a task that requires a high level of coordination amongvarious agencies.

The ecological approach to statistical data collection andanalysis, on the other hand, includes a variety of models, monitoringtechniques, and ecological indices. They deal with such diverse topicsas the assessment of population diversity and dynamics, of biomassproduction; and of the productivity, stability, and resilience ofecosystems.

The stress-response model was developed in recognition ofthe inadequacy of the media approach for describing the processesof environmental change. The stress-response model focuses onimpacts of human intervention within the environment (stress) andthe environment’s subsequent transformation (environmentalresponse). By establishing cause-effect relationships betweenactivities and subsequent environmental impacts, the frameworkhelps in developing statistics that are useful for taking both preventiveand curative measures for protecting the environment and mitigatingthe adverse environmental impacts of development activities. Manyexisting frameworks are broadly based on this conceptual approach.The main disadvantage of this approach is that it represents a sim-plistic view of the environment by ignoring interactions amongenvironmental components. The nature and severity of the impactof an activity on an environmental resource could depend on boththe existing status of that resource and the interactions that thisresource has with other environmental components. In fact, theinteractions are quite complex, and, as a result, one impact couldtrigger a chain of other environmental impacts.

The United Nations Framework for the Development ofEnvironment Statistics (UN-FDES) is based on the pressure-state-response model, which attempts to establish a relationship between


human activities and the resulting environmental impacts. Thisapproach guides the (i) development of a statistical system formeasuring human activities that cause stresses on the environ-ment, and (ii) measurement of actual impacts that have becomedistinct over time and space. In addition, it takes the policyresponse, mitigating measures, and stocks or inventories intoconsideration.

The UN-FDES was designed with a view to assist countriesin the development, coordination, and organization of environmentstatistics. The use of such a framework is envisaged for these specificpurposes:

(i) review of environmental problems and concerns anddetermination of their quantifiable aspects;

(ii) determination of variables for statistical descriptions ofthe quantifiable aspect of environmental concerns;

(iii) assessment of data requirements, sources andavailability; and

(iv) structuring of databases, information systems, andstatistical publications.

The FDES format has evolved from the joint considerationof the scope and nature of environment statistics, and the purposesand properties of such a framework. A synthesis of these factors hasresulted in the format of a two-way table that relates the basiccomponents of the environment to the various categories ofinformation (Figure 2.2).

The components of the environment are based on thedescription of the coverage of environment statistics as perceived bydeveloping countries. The natural environment includes theenvironmental media of air, water, and land or soil, as well as thebiota in these media. The man-made environment includes humansettlements, which consist of the physical elements shelter andinfrastructure, and services to which these elements provide thematerial support. The information categories are anchored on therecognition that environmental problems are the result of humanactivities and natural events. Relevant information refers to socialand economic activities and natural events, their impacts on theenvironment, the responses to these impacts by governments,nongovernment organizations, enterprises, and individuals.


The usefulness of any FDES could be determined by thefollowing factors:

(i) adaptability of the framework to the existing institutionalframeworks and mechanisms of the countries;

(ii) organization of information to facilitate the flow of datafrom data-producing agencies to the user agencies;

(iii) provision in the framework to preserve the interlinkagesamong statistics compiled for different environmentaldomains; and

(iv) provision in the framework of a logical framework foridentifying relevant agencies that will compilenontraditional statistics, the need for which has arisenbecause the interactions among various environmentalcomponents have become important.

Among the various frameworks discussed here, the UN-FDES,despite its limitations, has been found most operational. Thisframework has already been tested in some countries, and the resultsare satisfactory. One of the objectives of the Bank’s RETA is to establisha sound foundation for an environment statistics system, so thatenvironment statistics are compiled on a continuing basis by the

Inventories

Figure 2.2Format of the FDES Framework

1. Flora2. Fauna3. Atmosphere4. Water5. Land/soil6. Human settlements

Componentsof theEnvironment

Social andEconomicActivities,NaturalEvents

EnvironmentalImpacts ofActivities/Events

Responses toEnvironmentalImpacts


participating countries. During the inception meeting of the RETA,the selection and adoption of an environment statistics system werediscussed in detail by the participants. In view of the framework’ssimplicity, flexibility, and adaptability, the RETA participatingcountries decided to pattern their national framework on environmentstatistics on the UN-FDES. It does not mean, however, that thecountries are required to adapt this framework forever. Theframeworks will need to be modified over time to reflect the increasingand changing demands of environment statistics.

Compendium of Environment Statistics

When the national framework for the development of environmentstatistics is finished, the next logical step for any developing countrywill be to prepare a compendium of environment statistics based onthat framework. A large amount of environment-related statistics arecollected in the countries on an ad hoc basis. These statistics needto be organized systematically to make them useful for policy makersand decision makers. The compendium should attempt to identifystatistical parameters and variables for each component andinformation category of the environment as laid out in the nationalFDES. The objective of the compendium should therefore be to(i) improve the utilization and value of existing information of theNSO and other environment agencies; (ii) provide a comprehensive,easy-to-understand reference of environmental information for thecountry; (iii) provide a directory of environmental information sources;(iv) raise awareness and understanding of the environmental issuesof the broader community including government, industry, nongov-ernment organizations (NGOs), media, and the wider public; (v) setup a process of cooperation, consultation, and coordination amongdata providers of the country for the ongoing development ofenvironmental information in an effective and cost-efficient way; and(vi) develop the skills of the NSO staff in compiling, collating, andpresenting environmental information.

Collecting data on each of the areas of environment could becumbersome and resource demanding. The best action will be to startcompiling data from existing sources. Different environment-relatedagencies undertake a large number of surveys or studies, often without


much coordination among themselves; thus, they contribute to theduplication of activities in many developing countries. These agenciesalso compile a large amount of environment statistics forimplementing various environment-related projects. It is clear thata large amount of environment-related statistics could be compiledby scanning various ad hoc studies and administrative records ofthe government agencies.

The presentation of environment statistics is not among themost important considerations when starting; what should be stressedis the use of the explanatory text to avoid misinterpretation, tointroduce technical matters, and to point out links with other statistics,among others.

The FDES prepared by the participating countries hasidentified a long and comprehensive list of variables for eachcomponent of environment and information categories. They havealso identified units of measurement, computation levels, dataavailability, sources of data, and data collection methods for each ofthe identified variables. The outcome will provide a comprehensiveknowledge of the status of environment data in a particular country.What is important now is the organization of future studies andsurveys in a manner that the collection of the important environment-related information becomes systematic and data can be updated ona regular basis. It would also seem important for each NSO to examinethe possibility of including some relevant environment-relatedquestions in the schedules of various surveys that the officeundertakes on a regular basis.

State-of-Environment Statistics in DevelopingMember CountriesRobust environmental and socioeconomic data provide the foundationfor the analysis and interpretation of the state of the environment.In the absence of such data, any report on the state of the environmentis reduced to a descriptive, anecdotal, and nonsystematic observation,which is not an acceptable basis for rational decision making. Thetype of data required covers a wide spectrum. Data on natural resourcestocks and environmental conditions are essential. Similarly,statistics on human activities impacting on the environment, the


emission of pollutants, natural events, and human responses toenvironmental changes are equally important in assessing theecosystem interactions.

Environmental and socioeconomic data tend to be collectedindependently by diverse agencies, using different methods andclassifications, and for quite specific purposes. Data on theenvironment are usually derived from monitoring programs and theinterpretation of remotely sensed images. Socioeconomic data arecollected from statistically designed surveys and from administrativerecords. From a state-of-the-environment perspective, particularlyat national, regional, and global levels, the spatial resolution andtemporal dimensions of much of the data are often limited. Muchof the available data relate to individual environmental or humanactivity components rather than to a synergistic, ecosystemperspective. For example, databases for commercial forest areas tendto emphasize the production mandate of the forestry managementagency, not adequately reflecting the diverse values of forest ecozones,which include their role in terms of habitat and biodiversity, waterconservation, and traditional and alternative land uses.

Under these circumstances, do the countries need more dataor should they use existing data more efficiently? Some people wouldcontend that the developing countries have insufficient data, othersthink that they have too much. We know comparatively little aboutthe biota of the planet, particularly microorganisms; yet masses ofraw data come to earth daily from observation satellites. Our databasesfor social, economic, and demographic conditions and trends arerelatively well developed and integrated compared with that forenvironmental data. However, it is clear that we do not have adequateand accurate data to answer some basic questions related tosustainability. Data limitations in terms of both balance and qualityseverely hinder the quantitative assessment of and reporting on thestate of the environment. In today’s world, data must support thedevelopment of more holistic information, understanding, andknowledge (Rump 1996).

Although high-quality data are vital for credible information,a systematic approach to their generation is largely lacking. Theacquisition, processing, and storage of environmental data is time-consuming and expensive, and is not a priority for most governments.Consequently, baseline and trend data related to the ways the


ecosystem functions and its components interact are insufficient. Thedata we do have tend to be scattered and difficult to obtain, whileproprietary and security factors can inhibit dissemination and openaccess. Environmental and socioeconomic data do not generally existin usable and integrated formats for reporting. There is a commondeficiency of infrastructure and standards to facilitate the easyexchange and correlation of data from different jurisdictions anddisciplines.

What can the environmental agencies do to improve thesituation, recognizing that they are usually not agencies for gatheringprimary data? The various sources of environmental data includebodies responsible for environmental and resource monitoring,statistical surveys, mapping, and remote sensing. There are no quicksolutions, but many agencies are taking immediate and longer termsteps to consolidate data to facilitate more efficient monitoring andreporting. There is a basic choice between (i) designing from theoutset a new monitoring and data processing system based onecosystems, to allow the reporting of environmental conditions andtrends; and (ii) incrementally adapting existing monitoring and surveysystems to meet today’s more holistic needs. Both approaches havebeen proposed. The suggestion in this chapter tends to be modestand is primarily directed at building on existing data and data-gathering networks to improve access and dissemination. Thisapproach is seen as being more practical and more suited to theresource realities faced by most national and regional reportingprograms.

The collection and compilation of environment statisticsconstitute a recent phenomenon in most of the developing Asian andPacific countries. The present system of data collection in all thesecountries is weak, unorganized, and poorly funded. Most of thecountries do not collect core environment statistics; what they haveis environment-related statistics. Where some core environment dataexist, their quality, comparability, and accessibility normally fall shortof the standard required for decision making. There is a wide varianceamong countries with respect to the extent of their expertise andknowledge. There are also variations in their interpretations ofterminologies, classifications and standards, estimation methods, thetraining they provide to their personnel, and the resources theyallocate for data collection.


Despite some data gaps, there have been some efforts byinternational organizations to compile and publish global/regionalenvironmental data. Since there is a general dearth of environmentaldata in all the developing countries, it is always likely thatinternational data will show gaps. For example, no internationalorganization reports the extent of tropical deforestation on an annualbasis, primarily because most countries do not make annualassessments of their forest resources. FAO assesses deforestation onlyonce every ten years. No amount of international efforts can thereforesucceed in compiling regional/global statistics unless the countries’capabilities to produce environment statistics are improved.

Problems and Issues in the Collection ofEnvironment StatisticsThe organization of environment statistics offers challenges to thestatisticians who are highly familiar with the techniques of collectingsocioeconomic and demographic data, but not so much with thetechniques of collecting environment data. Since the environmentis all-encompassing and ill understood, an ongoing debate amongstatisticians is whether they should be publishing imperfect orincomplete data. Most national jurisdictions and internationalorganizations, however, define environment data to include a broadspectrum of information, thereby implying that, excepting certainautonomous natural events, the environmental changes of concernto citizens and governments are the result of human activities.

Some Methodological Issues

This section aims to give readers an overview of the scientificissues and complexities that arise during the measurement,interpretation, and presentation of environmental data. A moredetailed description of these issues is given in Chapter 4.

Measurement errors

There are certain important differences between traditionalsocioeconomic data and physical data about the environment. In


socioeconomic data, most of which are obtained from surveys, themeasurement error relates to questionnaire design and theunderstanding of the human cognitive processes. Quantifying thedifferences between what one intends to measure and what one reallymeasures with a survey may be a problem. On the other hand, mostenvironmental data are measured using scientific instruments forwhich calibration standards can ensure repeatability.

Many air quality statistics are usually obtained throughmonitoring. This implies that statistical generalizations from the dataare impeded by the site-specificity of monitoring data, thecharacteristics of measurement sites, sampling design and specifi-cation, calibration, and sampling and analytical methods used. Therepercussions of these factors on data reliability and comparabilityare known only to some extent. A remedy for this situation could bethe incorporation of statistical concerns in the design of monitoringsystems, such as the selection of monitoring stations with a view toobtaining spatially representative measurements. While someprogress along this road is slowly materializing in some countries,the overall situation remains unsatisfactory. Future works should bringabout clarification on this respect. Of special interest are studies intothe relationship between the site characteristics and levels ofmeasured data. Furthermore, future methodological studies shouldshed further light on the possibilities for valid generalizations ofmonitoring data to air quality statistics (Hamilton 1991).

Sampling

The question of how to sample from the universe of industrialestablishments is a particular methodological concern, and a numberof approaches have tentatively been used. Large industrial enterprisesare primarily responsible for the generation of the bulk of wastes;they ought to be adequately represented in the samples to permitthe making of valid estimations. On the other hand, particular typesof waste may only be generated in small or medium-size forms sothat samples confined to large units would disregard the types ofwaste concerned. A relationship exists in this regard between thedetails asked in the statistical inquiry and the importance of thequestion. It appears that the use of fairly detailed lists of wastes ina survey calls for the inclusion of small and medium-size


establishments in the sample. It is hoped that further insight intothe question of how to sample will become available once relevantwork in progress in several countries is carefully evaluated in thisregard.

In collecting socioeconomic data, simple statistical techniquescan be used to compute relevant basic statistics. These industrialsystems allow the use of the enumerative measurement techniquesfor collecting data. In principle, measurements for such systems cancover the entire population, as these systems are made ofdistinguishable statistical units. Sample surveys are conducted atregular intervals to measure other socioeconomic parameters, suchas the economically active population, household income andexpenditure, and employment. These surveys are based on probabilitysamples, with sample size ranging from hundreds to tens of thousandsof households. Large sample sizes for these surveys allow the useof the Central Limit Theorem to make the assumption that sampleaverages follow normal distribution. Therefore statistics computedfrom these samples could be used to make an inference about theentire population within a known error bound.

However, descriptive statistics cannot be used for environ-mental resources such as the atmosphere, water, and ecologicalsystems. Since air and water resources are contiguous andmeasurement units are not distinguishable, samples of air and waterare to be taken for the measurement of pollutant concentrations inthese resources or media. Statistics computed from these samplescould be used to make an inference about the air quality of a regionor the water quality of a water resource. For ecological systems,samples also need to be taken for identifying and counting maintaxa, to make an inference about the population of flora and faunain a region. Measuring the entire population of the region would beprohibitively expensive (ADB 1995).

For ecological systems, it is even more important to take intoaccount spatial structure and dynamics when selecting the samplinglocations or sites. Ecological systems are complex systems made upof an assemblage of communities and abiotic elements, continuouslyinteracting with each other. Hence, statistics on these systems dealwith both structural and functional measures. The selection ofsampling locations should capture information on community frontiersand allow monitoring of the movement of these frontiers. Sampling


sites should also take into account the changes close to theboundary of the ecosystem to monitor the impact of human settlementpressures or the influence of the buffer zone on the expansion ofthe ecosystem.

Ignoring the spatial structure of ecological systems whenselecting random sample sites will result in the loss of informationon the structure of an ecosystem. Surveys have shown that theecosystem may be divided into several zones to carry out stratifiedrandom sampling. For developing reliable statistics within eachstratified zone, a probability distribution function of the parameteror variable of interest should be known to determine the minimumnumber of samples required to provide statistics of a given confidencelevel. In principle, it may be possible to determine the probabilitydistribution function and the minimum number of samples forcomputing reasonably reliable estimates. However, budgetaryconstraints may limit the number of samples and thereby influencethe reliability of the statistics.

Aggregation

Many socioeconomic data are extensive (number ofindividuals, value of shipments, etc.), and so may be summed intoaggregates. A considerable proportion of environmental data areintensive (concentrations of pollutants in water, air, or soil, etc.) andtherefore may not be summarized or averaged without auxiliaryinformation. While environmental change and its causes may beglobal, a significant proportion of environmental problems arelocalized in space or time. Therefore, in most instances, each datain an environmental database should have a geographic reference.For reporting environmental data, natural geographic boundariessuch as ecological zones, watersheds, soil and climatological regions,wetland boundaries, forest zones, etc. may be preferable toadministrative zones. However, introducing geographic locationas an attribute of data may open up whole new areas of potentialstatistical error, including the accuracy of geographic referencingand of summarization of data to environmental boundaries(Hamilton 1991).

Natural environmental resources also show spatial andtemporal variations in their various characteristics. These variations


can arise due to both natural and human-induced forces. For example,uneven distribution of rainfall over a region can be attributed tonatural forces. Spatial variations in air and water quality, on the otherhand, can be due to the uneven distribution of pollution sources aswell as natural factors like wind direction, speed, and topographyfor air quality; and the volumetric flow rate of a river and rivergeometry for water quality. The scale of temporal variations for theatmosphere can be as short as a few seconds or as long as a year.For a water resource, the scale of temporal variations is determinedby the pattern of pollutant discharges into the water body as well asby seasonal and annual weather cycles. For ecological systems, thescale of temporal variations could be several decades, as is evidentfrom ecological succession.

Both spatial and temporal variations need to be captured bystatistics in such a manner as to provide useful information withrespect to the concerns about the uses of these environmentalresources. For example, to monitor the trend in global warming, bothspatial and temporal aggregations of greenhouse gas concentrationsare required. Spatial aggregation is required over a large area insuch a manner that the statistics would be free from influences ofpeak concentrations due to individual sources. To capture all scalesof temporal variations, aggregation must also be carried out formeasurements taken over a year. On the other hand, for statisticsrepresenting exposure of the population to pollution, aggregation isrequired to determine the maximum one-hour, eight-hour, daily, andannual pollutant concentrations to reflect the risk from both acuteand chronic health effects.

If spatial and temporal variations of resource attributes areof a random nature, or if these attributes follow normal distribution,random samples taken in space and time could be used to inferstatistics on the environment resources, or a part thereof. However,the selection of random samples without considering theheterogeneity of the system due to natural or human-induced forcescould result in loss of valuable information. For example, spatialaggregation of rainfall data based on random samples taken over alarge area without considering the differences in climatological orrainfall regimes would provide statistics that are useful neither foragricultural planning nor for the management of water resources(ADB 1995).


Spatial aggregation

Spatial aggregation of data is required to compute statisticsthat are representative of an area domain, such as the sulfur oxideconcentration in an urban area, the dissolved oxygen level of a lake,rainfall intensity in a watershed, or the species diversity of anecosystem. To preserve the heterogeneity of the resource whilecomputing statistics, areas within a region need to be delineated insuch a manner that for the parameters or variables of interest, thevariation within each area is low, while variations across areas arehigh. Samples taken from each area can then be used to computestatistics representative of that particular area. If the central tendencyof a variable or an attribute over the entire region is required, statisticscompiled for individual areas may be used to determine the medianor the arithmetic mean, as may be appropriate for the type ofdistribution in the area.

Ideally, the delineation of heterogeneous areas should bebased on data collected over a long period of time with the use ofa dense monitoring network. These data can be analyzed using suchtechniques as spatial correlation analysis, cluster analysis, or principalcomponent analysis to delineate areas that show significantdifferences with respect to given attributes of an environment system.For atmospheric systems, representative statistics for an area couldalso be determined by plotting contours of equal attribute values ona map representing a region. Thus, the average pollutantconcentrations represented by two consecutive isopleths could beused to represent pollutant concentration in the area enclosed bythese isopleths. Pollutant concentrations estimated in this mannercould be used to determine the exposure of the population living inthe area. Similarly, average rainfall intensity represented by twoisopleths (lines joining equal rainfall points) could represent therainfall of the area enveloped by these isopleths. The representativerainfall intensity could be used to calculate surface runoff over thearea enclosed by isohyets.

Temporal aggregation

The temporal aggregation of data should capture temporalvariations in the variables in such a way that the statistics can provide


information for different purposes. For the atmosphere, the scale oftemporal variations ranges from minutes to years. With regard to airquality, minute-to-minute variations are caused by changes in winddirection and speed. Daily variations in air quality result from diurnalvariations of the atmosphere as well as from daily emission patterns.Macroscale weather fluctuations last a few days and are importantfrom the viewpoint of air pollution episodes. Seasonal and annualweather cycles also influence the air quality of a place. Temporalvariations in the quality of a water resource may be caused by thedaily pollution discharge pattern. The dilution capacity of a waterresource may change in response to seasonal and annual weathercycles, which give rise to variations in water quality. Hence, theaggregations of air or water pollutant concentrations over differenttime scales may be required to derive statistics for different uses. Forexample, it may be necessary to determine episodic air pollution levelsto shut down some industrial units in a region that is susceptible toair pollution episodes.

Similarly, it may be of interest to determine peak waterpollution levels and the volumetric flow of a water body during thedry season for water resource planning. If pollution levels in air andwater are continuously monitored over time, average concentrationsover time intervals and their frequency distributions could easily bedetermined. However, cost constraints may prevent the continuousmonitoring of all pollutants at all monitoring stations. Therefore,knowledge about the frequency distribution function is required todetermine the frequency of sampling necessary to obtain reliableaverage concentrations for different time intervals. Budgetaryconstraints may not allow the monitoring to be carried out at thefrequency determined on the basis of statistical analysis (ADB 1995).

Rapid Assessment Method

It is clear from the preceding discussion that there is a lackof core environmental data in most DMCs. In the face of thisinadequacy or general lack of both quantitative and qualitativeenvironmental data, undertaking environmental planning andmanagement measures as well as formulating the requisiteenvironment policy will be rather difficult. The treatment of water,air, and soil pollution cannot be compartmentalized. It is necessary


to approach these pollution problems from an integrative perspective.However, reliable data on these pollutive emissions and dischargesin most cases are rather scarce because the requisite pollutionmonitoring for generating such data is both difficult and costly. Suchmonitoring assumes the existence of an organization with highlytrained and skilled technical staff. For developing countries, therefore,the intensive use of these actual air quality measurements for theformulation of a preliminary emission inventory may not seem to bea valid option. Thus, field data for air quality monitoring may haveto be collected only for restricted areas and selected air qualityparameters and variables. In view of this, an indirect approach toair quality monitoring through emission inventories is thereforesuggested. This approach, which is called the Rapid AssessmentMethod (RAM), was developed by the World Health Organization(WHO), especially for the benefit of developing countries.

RAM provides an effective way of assessing air, water, andsolid waste emissions and discharges generated by each source, orgroups of similar pollution sources, within the study area. It permitsconvenient assessment of the efficiency and effectiveness ofalternative pollution control options. The method is based ondocumented and often extensive past experience with the nature andquantities of pollutants generated from each pollution source, withand without associated pollution control systems.

The main advantage of this method is convenience of use.It permits the conduct of integrated inventories of air, water, andland emission sources in highly complex situations within areasonable period of time and at fairly modest resources. Despitethe simplicity of the method, the end result has often been foundrelatively reliable compared with that of a direct source monitoringprogram, especially in cases where shortcuts to data collection havebeen taken. Another significant advantage is the possibility ofestimating the effectiveness of alternative emission control schemesfor their emission reduction potential. The latter constitutes a majorinput for formulating rational pollution control strategies (ESCAP1998). However, a major disadvantage of the RAM approach is thestatistical validity of its inventory predictions. More specifically, thepredictions for any given pollution source will have to be consideredas only indicative in many cases on account of the significant variationevident in normalized emissions between similar pollution sources.


Consequently, control measures adopted immediately following theapplication of RAM need to be viewed as preliminary, and subjectto subsequent and more detailed analysis prior to implementationof pollution control strategies.

For any given industrial activity, the emission factors varyfrom one pollution source to another, the variations sometimes beingsignificant. Such variations are often the result of different operationspractices, but may also reflect differences in the design and layoutof the process equipment. The emission factors provided are usuallyselected to represent average or typical conditions as much aspossible. As a result, the calculated emission for any individualpollution source can be expected to occasionally differ significantlyfrom the actual waste loads operated. However, overall pollution loadpredictions for a number of similar industrial plants, e.g., the totalpollution loads in the effluents of many tanneries operating in a givenarea, should be reasonably accurate.

The question that often arises is on the validity of theseemission factors across different countries, especially when thosederived from industrialized countries are applied to developingcountries. For example, because of differences in source inspectionand the maintenance of specific industrial plants, or because ofdifferences in the size of a “typical” industrial plant, the use ofsomewhat higher emission factors in developing countries could bejustified. However, the extensive use of RAM for over a decade inmany parts of the world has shown that this is not a significant problem(WHO 1992). The general conclusion so far has been that theapplication of RAM can be expected to produce acceptable accuracyfor environmental planning and management purposes. Suchaccuracy could be improved in cases where information about localemission factors is available, with the requisite pollution assessmentsbeing derived from them whenever possible. Such refinements, alongwith the increase in the number and quality of trained andexperienced personnel, are expected to improve results that couldserve as useful inputs for purposes of environmental qualityimprovement and management.

RAM is a technical means to gain fairly quick insights intothe various significant aspects of total pollution load, in terms ofemissions and discharges, exerted by anthropogenic activities. Thewidespread use of this methodology is expected to lay the groundwork


for better environmental policies, and improved reporting of local,national, and international environment statistics. However, to makefiner distinctions between data sets and to facilitate the formulationof time series data sets that are more closely reflective of real worldemissions, the use of RAM should also be complemented by additionalinstruments that will ensure that emissions of large polluters(refineries, etc.) are monitored more directly; also, collective emissionsby diffuse sources (i.e., from traffic) should be reflected more faithfullyin environment statistics. What this implies therefore is that the RAMresults themselves offer a good starting point for the undertakingand formulation of emission and discharge inventories that areadapted to the specific needs and circumstances of various countries.

Institutional Problems

Environment statistics cut across many sectors and subsectors.A number of agencies are involved in the collection and compilationof environment-related statistics. Lack of coordination among theseagencies may hinder the collection and compilation of environmentstatistics. Hence, it is necessary to think about various strategiesand mechanisms to achieve effective coordination between data-collecting agencies. National statistics agencies have traditionallybeen responsible for collecting, compiling, and disseminatingsocioeconomic and demographic statistics in all countries. Therefore,it would seem logical and most appropriate to entrust the respon-sibility for compiling and collating environment-related statistics aswell to the NSOs. To provide guidance to the NSOs and to improvecoordination among the data-collecting agencies, it is necessary toestablish a high-power steering committee consisting of heads ofthese agencies. Such a committee will be helpful in sorting out anydifferences in sharing the data from the various agencies.

Environment being a highly specialized subject, statisticiansare not quite well-versed with the concepts and technical terms ofenvironmental science. To enable the NSOs to effectively collect andcompile environment statistics, it is necessary that they acquirefamiliarity with the basic concepts and definitions in environmentalscience. Close interaction between statisticians and environmentscientists is necessary. Creation of a number of technical committeeswill be helpful not only in providing technical guidance to the staff


of NSO, but also in maintaining working relations between the staffof NSO and other environment agencies. The collection ofenvironment statistics will be facilitated if separate environmentstatistics units are established in the NSOs and are manned by well-trained professionals. These could be statisticians with adequatetraining in environmental science, or environment experts with sometraining in statistics. Since the field of environment statistics is new,regular in-country training in environment statistics should beorganized utilizing the services of the various experts available inthe countries. To meet the growing demand for trained manpowerin this specialized area, such training could be supplemented byregional and international training programs, as and when necessary.

It is important to develop environment statistics within thecontext of regional collaboration. This can be achieved by theexchange of environment statistics reports and other products suchas discussion papers and survey designs, and through the exchangeof views and experiences at regional meetings and workshops. Thework of the NSO of a country could benefit from the examples ofother countries. The NSOs should establish contact with colleaguesin the region for the purpose of acquiring copies of those countries’technical reports. The NSOs would also benefit from examples fromcountries with more developed environment statistics programs suchas Australia, Canada, and Netherlands. Access to Internet may alsohelp NSOs to keep abreast of international developments in the fieldof environment statistics.

There is general lack of adequate financial resources for thecollection and dissemination of statistics including environmentstatistics in most countries. Thus there is a need to draw attentionto this deficiency and to urge governments to take the necessarysteps to improve the capability of the agencies involved in thecollection, compilation, dissemination, and analysis of environmentstatistics.

Some countries of the region are more advanced than othersin developing environment statistics. Therefore, the latecomers canreally benefit from the experience of the early players of the game.There is, however, a tremendous communication gap among differentcountries of the region, among international organizations, and evenamong different divisions of the same organization on their respectiveactivities in the field of environment statistics. It is desirable that


there should be reciprocity in information exchange among theconcerned organizations so as to prevent duplication of efforts andensure a more efficient utilization of resources.

Conclusion

The preceding discussion attempts to point out some of themethodological issues that may be confronted in the collection andcompilation of environment statistics. The preparation of the nationalframeworks and compendiums of environment statistics should beseen as the start of the development of environment statistics in thecountries participating in the RETA. As the collection of environmentstatistics becomes part of regular data collecting activities, both thestatisticians and environment experts will need to address all theoutstanding methodological issues. When that happens, a contribu-tion will have been made to the development of environment statisticsin the countries along with other statistics.

Chapter 3

Framework for the Developmentof Environment Statistics

Introduction

This chapter discusses frameworks for compiling environmentstatistics. The discussion is based on a review of the work done

by various international organizations in developing environmentalinformation systems, and environmental indicators and indices. Thechapter argues that because of the large volume of environmentaldata or data needs that are likely to be encountered, the informationsystem should be systematically structured in such a way that itfacilitates data aggregation, information organization (or groupingunder a logical framework), analysis, and communication.

This chapter is not written specifically for environmentspecialists or resources managers. Rather, it is intended mainly forstatisticians who may not yet be fully apprised of environmentalmanagement issues. Therefore, issues as well as the mechanics ofenvironmental data collection and processing themselves arepresented together. The aim is to ensure a strong link among effortsat data collection and their usefulness for actually solvingenvironmental problems.

Framework for Environment Statistics

A framework is important to systematically sort out environmentaldata (particularly on air, water, and soil conditions), which are oftenvery large in volume. The framework provides a suggested approachto organizing environmental data into types, levels of aggregation,issue relevance, uses, and potential users.

The preliminary framework presented here represents acollection of various subframeworks. For instance, one subframework


presents levels of data aggregation and uses or users. Anotherpresents ways of grouping the data according to whether they pertainto states of the environment, the pressures or stresses creating suchstates, and the responses (policy and program interventions) of theconcerned authority. These data groupings are further organizedaccording to related key issues. Finally, a third subframework ispresented for analyzing linkages among issues and, in general, thelink between the environment and socioeconomic development.

The Information Pyramid

As used here, the term environment statistics refers to acollection of data and information organized into layers, as in apyramid with a wide base and an apex, to indicate increasing levelsof data aggregation. The layers are not independent categories.Rather, the lower layers are used for building the upper layers – apattern that results in increasing information content (as well asdegree of consolidation and simplification) as one moves toward thetop of the pyramid. In itself, the pyramid is the model of theinformation system (Figure 3.1).

At the base of the pyramid are primary data. Primary data forwater, for example, may refer to daily measured concentrations ofvarious key pollutants in a river sampling station. Other primary data

Figure 3.1The Information Pyramid

Indices

Indicators

Analyzed data

Primary data

Framework for the Development of Environment Statistics 55

for the river might include the volume rate of flow, water temperature,dissolved oxygen, suspended solids, and so on. On top of this primarylayer is a second level of aggregation, representing the analysis andinitial consolidation of primary data. The analyzed data may includeannual averages, variabilities, and totals for each of the waterparameters. Usually, the analysis is reported as time series information(showing changes in the parameter measurements over time at agiven location). Also at this second level, the primary data would becombined so as to, say, generate information on the pollution loadin the river (this is obtained by combining data on pollutantconcentration with volume rate of flow). Thus, the two aspects ofanalysis involved here are those of data aggregation (e.g., obtainingaverages of measurements or depicting patterns in time and space)and data combination (putting together two or more parameters toderive new information).

For the most part, the two base layers of the environmentalinformation pyramid–the primary data and the analyzed data–arethe familiar ones. The methodologies for primary data collection andanalysis are well known, especially for physical parameters (soil,water, and air). The main concerns have had to do with whether thedata themselves are actually being collected and, if so, whether theyare being collected adequately, and analyzed and reported instandard, comparable fashion. Ultimately, the test is whether theinformation actually influences decision making.

In recent years, environmental advocates have realized thatto capture the attention of both policy makers and the public, whatis needed is a compact core set of environmental indicators (derivedfrom the huge amount of primary, multisectoral data) that are capableof conveying key information in a simple and integrated manner.The inspiration for environmental indicators derives from experiencewith economic indicators. In any country, for instance, the keyeconomic indicator reported is GNP (gross national product), whichcommands literally everyone’s attention. Such an indicatordemonstrates what a single number can do when its significance iswidely understood.

The task is how to develop similar indicators for theenvironment so that they are analytically sound, comprehensive,easily grasped, and derived from a disciplined methodology of dataaggregation and combination. What complicates the task is the fact


that the building blocks for deriving indicators are disproportionatelylarge in comparison with the small number of key indicators andeven smaller number of indices desired.

In such a case, it is more realistic to view the conventionalinformation pyramid as an inverted funnel with a disproportionatelylarge base and a very narrow tip. Add to this the characteristic thatthe base itself is very heterogeneous -–a characterization used hereto indicate that there are many actors or data producers involved atthe base. In most countries, it is unclear who or which agency is incharge of consolidating the mountains of separately collected andanalyzed data to come up with aggregate indicators. More funda-mentally, of course, there remains the question of how to derive theindicators themselves. Various approaches to deriving environmen-tal indicators are described in the references listed in the bibliography.

Parallel to the information pyramid is a model of informationuse. This model shows relationships among the quantity ofinformation, the degree of data consolidation, and the types of users(Figure 3.2). One sees that as the type of users changes from technical

Figure 3.2Relationship Between Data, Indicators, and Information

to Meet Users’ Needs

total quantity of information

Source: UNEP Environmental Assessment Program for Asia-Pacific (1995)

�

Increasingconcentration

data

��

Indicators for public information

Indicators for policy makers

Indicators for scientists


users (e.g., researchers and scientists) to regulators to policy makersand, finally, to the general public, the degree of informationconsolidation and simplification increases. That may seem obvious.However, what is important to note is that although the informationbecomes increasingly simplified to improve communication, themeasures remain quantitative. Quantification throughout all levelsof the pyramid is important because it instills systematic dataconsolidation and facilitates comparison. By itself, quantitativeinformation helps in rapidly assessing trends and patterns containedin the data.

An expansion of the user model is shown in Figure 3.3. Thismodel is adapted from an information systems framework developed

Figure 3.3Environmental Information Systems Model

ActionPlansLegislationState of the

Environment

ToolsExpertsSystems

IndicatorsIndices

Emerging issues

ToolsGIS/RS

EnvironmentMinistry

DecisionMaking

Information

Data Bio-Physical Socio-Economic

TopographyHydrologyGeologyLand useAtmosphereSoilsFloraFaunaetc.

PopulationTradeInfrastructureAdministrative BoundariesSettlementsHealthPovertyEducationetc.

DecentralizedNationalNetwork



by the UNEP Environmental Assessment Programme for Asia-Pacific.It superimposes three elements: (i) the activities (data collection,information generation, and decision making); (ii) the nature of thedata and information produced and the level of aggregation; and(iii) the method of data collection and aggregation. A notable aspectis that the information collected, analyzed, interpreted, andaggregated is oriented to the users’ needs. In short, it is utility thatdrives information collection and aggregation, rather than the otherway around.

The Pressure-State-Response Model

While the information pyramid provides a basic model forenvironment statistics in terms of levels of data aggregation, a logicalframework is needed to organize the contents of the informationsystem to make it relevant to policy making and environmentalproblem solving at the national level and, increasingly, at the globallevel. The information base is often overwhelming in terms of thevariety of data related in one way or another to the environment. Itis important to sort out the information base to make explicit theconnections among the data elements – for instance, in terms of cause-and-effect linkages, or in terms of interactions (i.e., the interactionbetween man and the biophysical environment).

A framework that is gaining increasing acceptance is thepressure-state-response (PSR) model, which was developed by theOrganization for Economic Co-operation and Development (OECD).The framework uses a cause-effect-response structure in whichparameters or indicators are grouped according to whether theypertain to cause, effect, or response (Figure 3.4). Essentially, theframework addresses the following fundamental questions:

(i) What is happening to the state of the environment, andhow is this state measured in terms of key indicators ofconditions or trends?

(ii) What are the causes responsible for this state of theenvironment; in particular, what pressures do humanactivities bring to bear on the environment?

(iii) What is being done to manage both the state of theenvironment and the human pressures that it issubjected to?


Environmental parameters may thus be categorized as stateindicators, pressure indicators, or response indicators. Thesecategories provide a logical framework for environment indicators.However, the framework need not apply exclusively to aggregatedindicators. It is also applicable to organizing primary and secondarydata (secondary, meaning data that have been subjected to analysis)before these are aggregated or combined to form indicators. The PSRmodel supplements the information pyramid model. In the followingdiscussion, the term “parameter” is used to refer generally to eitherdata or aggregated measures, depending on the level of the infor-mation pyramid that is pertinent.

Figure 3.4OECD Pressure State Response Model

PRESSURE

HUMAN ACTIVITIESAND IMPACTS

EnergyTransportIndustry

AgricultureFisheriesOthers

STATE

STATE OR CONDITIONSOF THE ENVIRONMENT

AirWater

Land resourcesBiodiversity

Human settlementsCulture and heritage

RESPONSE

INSTITUTIONAL ANDINDIVIDUAL RESPONSES

LegislationEconomic instruments

New technologiesChanging community valuesInternational obligations

Others

Information Information

Social responses(decisions-actions)

Social responses(decisions-actions)

Pressures

Resources


“Human activities exert pressure on the environment and change its state or condition.Society responds to this changed state by developing and implementing policies.”


State parameters measure changes or trends in the physicalor biological state of the natural environment. State in this senserefers to the condition of a particular environmental aspect (e.g., waterquality in a river stretch as measured by average dissolved oxygenconcentration) or to whole ecosystems (e.g., wetlands, whose statemay be characterized in terms of remaining areal extent). A stateparameter could be either quantitative or qualitative. To fullyappreciate the state of a particular environmental element, however,one has to consider both qualitative and quantitative parameters.

Pressure parameters show the causes of the existing state ofthe environment, as well as probable future states that can be dis-cerned from past trends and current states of the environment. Forexample, the poor quality of a particular water environment may betraced to discharges of industrial wastes, the reduction in wetlandarea may be traced to the expansion of fishpond operations or toconversion for urban settlements, and the reduction in fish stocksmay be traced to excessive fishing efforts or to the degradation ofthe fishing area. In all three examples, the pressure comes from humanactivity. Pressure parameters generally refer to human activitiesaffecting the environment. However, it is not always easy to relatepressure parameters to state parameters in a one-to-one correspon-dence. A given state may be the effect of multiple pressures whoseseparate contributions are difficult to isolate. The pressures them-selves might be linked to one another. For example in tracing thecause of deforestation, the principal causes frequently pointed to areindustrial logging and shifting cultivation, yet it is not possible toascertain exactly how much each contributes to deforestation. Loggingby itself does not cause deforestation. On the other hand, loggingactivities provide access (through roads) for farmers to enter theforests, such that while shifting cultivation may be an immediatefactor in forestland degradation, logging plays an initiating role.

This example suggests that the PSR model needs to besupplemented by submodels that capture the nature of the cause-and-effect linkage. Such linkage models must be developed aroundspecific environmental issues. In fact, most PSR formulations areorganized around key environmental issues or problems (Tables 3.1and 3.2). Here, in addition to grouping data according to pressure,state, or response categories, they are also organized according toissues. In the example of deforestation, a submodel of the linkage


Table 3.1OECD/UNEP Matrix of Issue-Based Environmental Indicators

Issue Pressure State Response

Fish resources Fish catches Sustainable stocks Quotas

Forest resources Use intensity Area of degradedforest, use/sustain-able growth ratio

Protected areaforest, sustainablelogging

Water resources Demand/useintensity inresidences industryagriculture

Demand/supplyratio, quality

Expenditures,water pricing,savings policy

Waste Waste generationmunicipal, indus-trial, agricultural

Soil/groundwaterquality

Collection rate,recycling invest-ments/cost

Urban environ-mental quality

VOC, NOx, Sox)emissions

(VOC, NOx, SOx)concentrations

Expenditures,transportationpolicy

Toxiccontamination

(POC, heavy metal)emissions

(POC, heavy metal)concentrations

Recovery ofhazardous waste,investments/cost

Acidification (SOx, NOx, NH3)emissions

Deposition;concentrations

Investments,signed agreements

Eutrophication (N,P, water, soil)emissions

(N, P, BOD) con-centrations

Treatmentconnection, invest-ments/costs

Ozone depletion (Halocarbon)emissions;production

(Chlorine)concentrations;O3 column

Protocol signed,CFC recovery, fundcontribution

Climate change (GHG) emissions Concentrations Energy intensity,environmentalmeasures

Species abundancecompared withthat in a virgin area

Biodiversity Land conversion;land fragmentation

Protected areas

connecting human activities to loss of forests may be formulated tounderstand the forces or factors driving deforestation.


Oceans/coastalzones

Emissions, oilspills, depositions

Water quality Coastal zonemanagement,ocean protection

Soil degradation Land use changes Topsoil loss Rehabilitation/protection

Table 3.1 (continued)OECD/UNEP Matrix of Issue-Based Environmental Indicators


EnvironmentalIndex

Pressure index State index Response index

BOD= biological oxygen demand, CFC= chlorofluoro carbons, GHG= greenhouse gases,N= nitrogen, NH3= ammonia, NOx= oxides of nitrogen , OECD= Organization for Economic Coop-eration and Development, P= phosphorus, POC= persistent organic compounds, SOx= oxides ofsulphur, UNEP= United Nations Environment Programme, VOC= volatile organic compounds.

Source: Hammond et al. 1995.

Table 3.2World Bank Matrix of Issue-Based Environmental Indicators


A. Sourceindicators

1. Agriculturea. Land qualityb. Others

2. Forest

3. Marineresources

4. Water

Rural/urban termsof trade

Input/output ratio,main users;recycling rates

Percent coverageof internationalprotocols/conven-tions

Water efficiencymeasures

Cropland aspercent of wealth,climatic classesand soil constraints

Area, volumes,distribution;value of forest

Stock of marinespecies

Accessibility topopulation(weighted percentof total)

Value added/grossoutput human-induced soildegradation

Land use changes

Contaminants,demand for fishas food

Intensity of use


Emissions of CO2

Apparent con-sumption of CFCs

Emissions of SOx,NOx

Use of phosphates(P), nitrates (N)

Generation ofhazardous waste/load

1. Climate changea. Greenhouse

b. Stratosphericozone

2. Acidification

3. Eutrophication

4. Toxification

Atmosphericconcentration ofgreenhouse gases

Atmosphericconcentration ofCFCs

Concentration ofpH, SOx, NOx inprecipitation

Biological oxygendemand; P, N inrivers

Concentration oflead, cadmium,etc. in rivers

Energy efficiencyof NNP

Percent coverageof internationalprotocols/conven-tions

Expenditures onpollution abate-ment

Percent populationwith waste treat-ment

Percent petrolunleaded

Table 3.2 (continued)World Bank Matrix of Issue-Based Environmental Indicators


5. Subsoil assets

a. Fossil fuels

b. Metals andminerals

Extraction rates

Extraction rates

Extraction rates

Subsoil assets,% wealthProven reserves

Proven reserves

Material balances/NNPReverse energysubsidiesInput/output ratio,main users; recy-cling rates

B. Sink or pollu-tion indicators

C. Life supportindicators

1. Biodiversity

2. Oceans

3. Special Lands(e.g., wetland)

Land use changes

Threatened, extinctspecies percenttotal

Protected areas aspercent threatened


Response parameters identify and assess efforts to mitigateundesirable environmental states or to curb pressures that create thesestates. They may include a wide array of actions involving policies,regulatory efforts, budgetary commitments, management plans,economic incentives, research and development, imposition of quotas,ratification of conventions, and so on. Activities of a more direct natureand which aim to improve the state of the environment, such asreforestation of denuded areas, establishment of artificial reefs, orrehabilitation of rivers, also fall under this category. Like pressureand state parameters, response parameters are grouped accordingto the set of issues being addressed.

Table 3.2 (continued)World Bank Matrix of Issue-Based Environmental Indicators


CFCS= chlorofluorocarbons, DALY S= disability adjusted life years, NNP= net national product,NR= natural resources

Source: Hammond et al. 1995.

D. Human impactindicators

1. Health

a. Water quality

b. Air quality

c. Occupationalexposures, etc.

2. Food securityand quality

3. Housing/Urban

4. Waste

5. Natural disaster

Burden of disease(DALYs/person)

Energy demand

Population density(persons/km2)

Generation ofindustrial, munici-pal waste

Life expectancy atbirth

Dissolved oxygen,fecal coliformConcentration ofparticulates, SO2,etc.

Accumulation todate

Percent NNP spenton health, vaccina-tionAccess to safewaterAccess to safewater

Percent NNP spenton housing

Expenditure oncollection andtreatment, recylingrates


Conceptual Model for Compiling EnvironmentStatisticsThis section presents a conceptual model for preparing an FDES.The conceptual model sees each environmental component as aresource that has use or value to human society. Based on this resourceperspective, the environment is categorized into two types ofresources: natural or environmental and human resources. Natural/environmental resources include land/soil, atmosphere, water, floraand fauna, and ecosystems. Human resources refer to populations,human settlements, and the economy. The model recognizesinteractions among various environmental resources and theseinteractions should be reflected in any FDES.

Two principles form the basis of a conceptual model:(i) There should be a purpose for compiling environment

statistics.(ii) The characteristics (which determine the nature of

variations) of an environmental resource should be takeninto account in compiling statistics relevant to theresource.

The purpose for compiling environment statistics can begleaned from the concerns or themes associated with individualenvironmental domains, as statistics will eventually be required toaddress these concerns or themes. The conceptual modelsystematically identifies the characteristics of environmentalresources so as to develop a logical framework for identifyingappropriate statistics to represent each environmental resource aswell as the interactions of this resource with other components ofthe environment.

The main purpose of a framework for environment statisticsis to provide a reliable and easily accessible information system tofacilitate the incorporation of environmental factors in public decisionmaking. A public decision invariably involves making a choice—orselecting an alternative—to achieve an objective or a set of objectivesin the interest of the public. These objectives are broadly aimed atarresting the deterioration of, or enhancing the quality of life.

Typically, any public action uses resources to achieve socialobjectives or benefits. However, the use of a resource deprives society


of deriving other benefits from the same resource, which is generallyscarce. The benefits so forgone are the costs of the resource used toachieve the objectives of the public action. Thus, any public actionis associated with both costs and benefits. A rational choice wouldthen involve selecting that action or alternative that maximizes thenet benefit to society. To make this choice, costs and benefitsassociated with the resource use should be quantifiable.

Costs and benefits associated with a resource use, whichconverts the resource into marketable goods or services, can easilybe quantified since the market allows the costs of resources andbenefits from goods and services to be measured in monetary valuesthrough pricing mechanisms. However, a resource use cannot alwaysbe translated into a marketable commodity. Two such examples arethe use of air and a visit to a scenic spot for recreational purposes.Resource use could also cause the degradation of the social or naturalenvironment or both, which would result in the forgoing of benefitsthat the society would have derived had the degradation not takenplace. For example, the deterioration of land and water quality dueto a mining activity could deprive local people of the use of theiragricultural lands and also adversely affect their health. These socialcosts are not captured by traditional market mechanisms. In the sameway, a public action may result in incidental benefits to society, whichare not marketable. Traditional market mechanisms are also blind totime dimensions in the sense that they fail to consider the value ofa resource to future generations, or the desire of the present generationto sustain the resource for future generations.

One way of internalizing social and environmental costs andbenefits (which are external to the market mechanism) in the decision-making process is to find surrogate ways of giving values to thesecosts and benefits in monetary terms. An action, which results inhigher net benefit after internalizing social/environmental benefitsand costs, would then be the rational choice of a decision maker. Theobjective or objectives of an action would then be treated as a benefitto society. Expressing costs and benefits in monetary terms allowsthe comparison of net benefits associated with various alternatives,and thus facilitates decision making. However, widely acceptablesurrogate methods for putting a monetary value on each resourceuse or on the resulting degradation of a resource are not yet well-established.


Another method to incorporate social/environmental costs andbenefits in decision making involves defining a set of “norms” or“ideal states” for each resource or environmental component. In thiscase, two decision-making options are available:

(i) to select a least-cost alternative that does not violate anyenvironmental norm; or

(ii) to select an alternative that causes minimum adverseenvironmental impacts (measured with respect to thenorms) while meeting the cost constraint.

There are problems associated with this method. It may notbe possible to define “norms” or “ideal states” for an environmentalcomponent due to lack of complete understanding of the exactimplications that the component’s various states will have for humansociety. For example, as of now, we do not have an ideal level ofgreenhouse gases in the atmosphere. This type of difficulty is,however, temporarily circumvented by specifying targets (e.g., a targetmay be set to reduce carbon dioxide emission levels by 25 percentof the baseline level in the next five years) and selecting a publicaction that will meet the targets.

Even if norms could be established, it may be difficult to finda course of action that satisfies all the norms. In fact, one alternativemay satisfy one set of norms and not others, whereas another optionmay have the opposite effect. As a result, it is difficult to comparethe impacts of different alternatives. Attempts are made to developenvironmental indexes that provide a single value to reflect the overallstate of the environment, and thereby allow the comparison ofpotential environmental impacts due to various alternatives.

The underlying premise for both methods of incorporatingsocial/environmental factors in decision making–the method basedon cost-benefit analysis and the one using social/environmentalobjectives explicitly–is that we should be able to identify and measurethose parameters or variables of a resource or environmentalcomponent that determine its use to human society. Valuatingresource uses in monetary terms or establishing targets for achievingenvironmental and social goals is the next step.

As a first step toward facilitating the decision-making process,an environment statistics system should provide data with respectto the current and potential uses of a resource or environmental


component. This system should identify the variables or parametersas well as the appropriate statistics for each variable or parameterselected, so that the data become useful in deciding the particularuse of that resource.

It would also be desirable for the environment statisticsframework to add to the quality of decision making by providing astructure to the data so that it can be used to identify data gaps;define norms, targets, and indexes with respect to the various usesof a resource; and relate these statistics to social or environmentalconcerns and themes.

Implementation of an EnvironmentStatistics ProgramOne of the objectives of the Bank’s technical assistance is to establisha sound foundation for an environment statistics system so thatenvironment statistics are compiled on a continuing basis by theparticipating countries. The successful implementation of the envi-ronment statistics program can be ensured by the following factors:

(i) adaptability of the FDES to the existing institutionalframework and mechanisms of the participatingcountries;

(ii) organization of information to facilitate the flow of datafrom the statistics-compiling agencies to the statistics-user agencies;

(iii) provisions in the FDES to preserve the linkages amongstatistics compiled for different environmental domains; and

(iv) provision in the FDES for a logical framework foridentifying relevant agencies that will compilenontraditional statistics, the need for which has arisenbecause the interactions among various environmentalcomponents have become critical.

Organization and Flow of Information

Information or statistics for individual resources or domainsare usually compiled by departments or agencies that use theinformation. Therefore, the data collected and processed meet the


specifications of the agency that collects them. For example, anagriculture department is likely to compile soil information usefulfor agriculture only. Different departments may collect similarinformation for their respective uses. Information on hydrology maybe compiled by both irrigation and power departments. The FDESwill play an important role by identifying statistics compiled by oneagency but also useful to other departments or agencies, so thatduplication of efforts is avoided. Then, whether a single agency ormultiple agencies are given the responsibility of compiling statisticscould depend on the existing institutional framework of theparticipating country.

As shown in the information pyramid in Figure 3.2, differentresolutions and levels of information may be required at various userlevels. The resolution of information is very high at the base leveland decreases as one moves up the pyramid. At the same time, theinformation content of statistics or data compiled at base level is low.This lower level information is processed and becomes useful fordecision making at the middle and higher levels. Thus, whereascarbon dioxide emissions from each major individual source may bemonitored or estimated at a local level, sectorwise carbon dioxideemissions per unit gross domestic product (GDP) may be relevantto the formulation of a national policy on global warming. Therefore,it is desirable that the FDES explicitly show the flow of informationbetween and across various levels.

Two types of systems – centralized and decentralized – canbe envisaged for organizing information flow. Each has advantagesand disadvantages. In both systems, the traditional user agencieswould continue to collect or compile data at the base level. In thecentralized system, all data from the data collection agencies wouldbe transmitted to an apex agency, where the information would beprocessed. Various user agencies could then access this centralizeddatabase to retrieve the information they require for decision making.The advantages of the centralized system are that (i) all informationwill be available at one nodal point, and (ii) coordination betweendata collection agencies and the apex agency is going to be simple.The disadvantages of the system are that (i) processing andmaintaining all information will put on the apex agency a heavyburden that the agency may not be able to handle; (ii) most of theinformation will be used at the regional and local levels and, thus,


processed information will have to be transmitted back to the regionaland local user agencies; and (iii) the noninvolvement of user agenciesin compiling and processing statistics may make them averse to theuse of centrally prepared statistics in their decision making.

In a decentralized system, a statistics/data collection agencyat the local level would transmit data/statistics to its regional headoffices, where the information would be processed for the agency’sown use. The regional office would disseminate the relevant informationto other user agencies. All regional user departments or agencies wouldtransmit relevant information to the apex agency. In this model,information would be processed at all levels and only relevant informa-tion would be transmitted between the levels and across a level. Inthis model, information will flow from the bottom to the top anddecisions will be transmitted from the top to the lower levels. Theadvantages of the decentralized system are that (i) user agencieswould be involved in the system and, therefore, would tend to favorthe use of statistics, (ii) user agencies would have expertise in processinginformation for their own use, and (iii) information at each level wouldbe more manageable. The main disadvantage of the system is thatcoordination between all the concerned agencies could be complex.

Preservation of Linkages Among Statistics

As discussed earlier, environmental resources interact witheach other in a number of ways. A development activity could haveadverse impacts on a number of environment components, which,in turn, may trigger a series of higher order impacts. The particularstate of an environmental resource may further exacerbate an impacton that environmental resource. Therefore, in selecting a developmentalternative or a response action for environmental protection,decisions will have to be based on relevant statistics from all inter-acting environmental resources. Since the required statistics couldbelong to different environmental domains, these statistics are likelyto be compiled by different agencies. An FDES should provide a meansto show the interlinkages among statistics maintained by differentinstitutions so that the statistics will be useful for decision making.

The significance of preserving the association among statisticsfor better planning or decision making is demonstrated by thefollowing example. Suppose a city planning office is preparing a plan


to reduce air pollution in the city. To develop various alternativesand finally select the most cost-effective option, the office shouldhave statistics or information from different domains. Pollutantemissions from different industries, locations and types of individualindustries, and the cost of reducing the unit level of a pollutant fromeach industry type are required to relate industrial emissions toambient air quality. The cost of industrial pollution control shouldalso be considered. Statistics on vehicle density on various roads,vehicle types, and emission factors for each vehicle type are requiredto account for air pollution from vehicular sources. The distributionof ambient air pollution levels in the city and the incidence of airpollution-related diseases in different areas of the city must be knownbefore pollution control strategies can be assessed.

The city planning office will have to rely on secondary sourcesto obtain this information. It is logical to expect industry-relatedinformation to come from the department of industry or an equivalentagency, ambient pollution statistics from the local or regional pollutioncontrol agency, and health information from the department of health.However, this information will be meaningful only if it could bespatially related. The information should show the relationship betweenair pollutant emissions and ambient air pollutant concentrations atdifferent locations, and air pollution exposure of the population andthe incidence of respiratory diseases to justify the cost of, say, settingup a railway transit system or relocating some industries.

One way of providing or preserving spatial relationshipsamong statistics is to lay a grid over the region under consideration.The statistics for which spatial dimensions are important are assigneda grid identification number corresponding to the location on thegrid to which the statistics belong. Statistics belonging to the samegrid but compiled and maintained by different agencies could thenbe related through this grid identification number. Any proposedFDES should provide a mechanism for determining information needsto be shown in a grid format.

Logical Framework for Delineating Responsibilityfor Compiling Statistics

Four types of variables represent environmental resources andinteractions: stock variables, rate variables, state variables, and


activity or event variables. Stock, rate, and state variables pertain toan environmental resource; therefore, statistics on them should becompiled by the agencies designated for compiling statistics on thatparticular environmental resource. Since activities and events giverise to interactions among environmental resources, the responsibilityfor compiling statistics on these variables needs to be delineated.Any proposed FDES should organize information on an activity orevent under the resource in which an activity or event originates.

Summary

The basic elements of a framework have been presented: (i) theinformation pyramid model for showing hierarchical levels of dataaggregation and use; (ii) the pressure-state-response model forproviding a logical approach to organizing data and grouping themby issues; and (iii) an environment-development linkage model, whichprovides a framework for analysis of how key issues interact withone another.

One characteristic of the environmental information pyramidis that its base is very wide; that is, there is a great magnitude ofdata to contend with, often to the point of overwhelming the users.A key concern is to ensure that the mass of information on numerousenvironmental parameters is actually used to influence policies andprograms and, on the whole, governments’ environmental manage-ment capabilities. To promote this, work is under way on developinga core set of environmental indicators that can be used to focusattention on key issues and, at the same time, be more manageable.Those responsible for compiling environment statistics should perhapsfocus on the upper layers of the information pyramid, that is, on dataconsolidation and the formulation of core indicators.

An added feature of the large environment database is that,invariably, data are collected and analyzed by different sets of people,or by different agencies of government. Going back to the exampleon water, quantity data (river flows, groundwater supplies) are oftencollected by an agency different from the agency in charge ofmonitoring water quality. The problem itself is not in the diffusemanner in which data are being collected — since, for practicalreasons, such a decentralized system is the de facto arrangement in


many countries, and is probably not going to change — but in thelack of systematic consolidation of the mass of data on numerousaspects of the environment. Work on environment statistics needs tostart with an effective arrangement for managing and consolidatinginformation generated from a largely decentralized informationnetwork. National statistics agencies working closely with counterpartnational planning agencies are the key places to performconsolidation. They can ensure that the information compiled on,say, key indicators are tied in with national issues of concern to thegovernment, particularly socioeconomic development issues.

In the end, only the users know what information they need,and how such information will be put to use. While this itself is abasic principle in designing information systems, it is not alwaysfollowed and cannot be taken for granted. The implication is that akey step is the identification of the target users of environmentstatistics. As discussed earlier, the projected use of the data shoulddrive the development of the information system.

Incorporating an issue dimension into a framework forenvironment statistics helps ensure that information made availableis attuned to the needs of those who will use the system. The issuesare not defined after the environmental data are collected andindicators developed. Rather, the parameters (indicators and variousother categories of data aggregating) are formulated after the keyissues have been identified. This makes the information system issue-based. Data generation and combination to come up with indicatorsmay then be done around specific environmental issues.

Chapter 4

Core Environment Statisticsand Methodological Issues

This chapter identifies and describes specific parameters for air,soil, water, human settlements, and biodiversity statistics relevant

to anticipated priority environmental issues in the countries coveredby this RETA. Included here are definitions of the various parameters,classification systems, measurement techniques, computationalmethods, and reporting formats. Examples from databases establishedby international organizations illustrate how the parameters arederived. The limitations of current data collection efforts are assessed,including problems likely to be encountered with regard to datacollection capabilities, standardization of methods, data aggregation,and derivation of regional or global indicators. Solutionsrecommended by international organizations are included.

Land Use

Environment statistics on land use should be able to track the ratesof land use conversions from one type to another to be useful forpolicy making. To accomplish this, land use data would ideally becompiled from an accounting perspective so that attention is givento “flows” of changes from one use to another, rather than just to“stocks” or status of use at any one time. Conventional reportingfocuses on the existing area and percent change over a period fora given land use. Data on conversions from one type of use to anotherusually are not available.

Unfortunately, attempting to calculate land use conversionsfrom one type to another using past data may not always providereliable results since definitions of land use categories are oftensubstantially revised from time to time (e.g., as FAO did in 1985 whenit excluded from the cropland category land used for shiftingcultivation but currently lying fallow). Apparent changes could


therefore indicate differences in land classification and data reporting,rather than actual conditions.

Categories for reporting land use vary considerably from onecountry to another. In general, however, five categories are commonlyrecognized: cropland, permanent pasture, forest and woodland, built-up areas, and other land. This classification system is the one adoptedby FAO (FAO Production Yearbook) and by the World ResourcesInstitute (World Resources Report). Within each general category,there may be additional subclassifications. For example, forest andwoodlands may be further classified into closed and open forests(if canopy cover is the criterion), or into rain forest and dry forest(if rainfall is the criterion).

Deforestation

The status of deforestation may be reported with reference to totalforests, or with reference to types of forests. FAO reports deforestationwith reference to various types of forest formations (also called zonesor ecozones). There are two general forest types: lowland forests,and hill and mountain forests. Lowland forests may be furtherclassified into tropical rain forest, moist deciduous forest, drydeciduous forest, very dry forest, and desert forest.

FAO makes two other important distinctions of forest types:one between closed and open forests, and another between naturaland plantation forests. These additional classifications may overlapwith the forest formation (or ecozone) classification system. A closedforest is defined as land where trees cover a high proportion of theground. An open forest is defined as one consisting of mixed treesand grasses, with at least 10 percent tree cover and the rest of thearea covered by continuous grass cover. In the FAO definitions fortropical forests, “natural” refers to all stands except plantations,including forest areas degraded by fire, logging, or agriculture.Plantations refer to stands established by afforestation andreforestation for industrial and nonindustrial use.

The classification “other wooded area” also appears in somestatistical compilations on forest cover. This classification oftenrefers to areas where shrubs and stunted trees cover more than20 percent of the area. In FAO definitions, trees are distinguished

Core Environment Statistics and Methodological Issues 77

from shrubs on the basis of height (a mature tree is taller thanseven meters).

FAO’s statistical compilation on forest cover and deforestationrates in various countries is based on a survey of 76 tropicaldeveloping countries. Work started in 1980 and was expanded to129 countries in 1988. Most of the data were provided by nationalforestry services in the countries covered. Among the countries in-cluded in this RETA, FAO assessed Malaysia and Nepal to have verygood or good data on both closed forest areas and deforestation rates.Malaysia (Sarawak), Philippines, Sri Lanka, and Viet Nam wereassessed by FAO as having very good or good data on forest coverand satisfactory or poor data on deforestation rates. For the othercountries, FAO assessed the resulting estimates of both forest coverand deforestation rates as satisfactory or poor. Globally, however, FAOassessed Asia to have the best information obtainable on forestresources.

FAO’s 1990 tropical forest resources assessment providesestimates of the extent of forest areas and deforestation ratesbetween 1981 and 1990. A model was used to adjust baseline forestinventory data from each country to a common year. Existing forestinventory data were reviewed and adjusted to a common set ofclassifications and combined in a database. A geographic informationsystem (GIS) was used to integrate statistical and map data for thispurpose.

Various environmental state indicators may be derived fromdeforestation rate statistics. One indicator is deforestation rate percapita. ESCAP’s SOER for Asia and the Pacific (1991) states that theaverage size of deforested area per person (based on the deforestationestimates of FAO) is about 15 square meters (m2) per year. Notablecountries were Lao People’s Democratic Republic (288 m2 per personper year), Malaysia (176), Thailand (63), and Nepal (60).

The causes of deforestation are familiar, even if the monitoringand statistical compilation of the data related to the causes themselvesare not standardized. The five recognized major causes arecommercial logging, shifting cultivation, weak institutions(enforcement, regulatory resources), fuelwood consumption, and highpopulation growth. Except for population statistics, the other causesare less systematically monitored and their statistics vary in definitionand data reliability from country to country.


Statistics on commercial logging are reported in terms of botharea of forests affected and volume of wood extracted. The WorldResource Institute (WRI), for example, provides compiled statisticson “managed closed forests” in various countries. These forests aredefined as those managed on the basis of a plan and where thereis some control of use such as harvesting regulations and silviculturaltreatments. These areas are usually covered by forest concessions.The volume of wood extracted is internationally reported in termsof roundwood and processed wood production. Each country willhave individual classifications within each of these two generalcategories (e.g., processed wood may be reported as sawn wood orpanels). Statistics on the production of fuelwood and charcoal areinadequate in many countries. FAO uses population data and country-specific per capita consumption figures to derive estimates.

The major drain from the forest is cutting for fuelwood (e.g.,some 200 million cubic meters are consumed annually within ASEAN,more than twice the volume of roundwood production). Statistics oninstitutional capacities — or lack of capacities when examining thecauses of deforestation — may properly fall under the category ofresponse in the PSR model. They include forestry laws andregulations, personnel, budgets, and programs. However, data onthese are hard to come by on a standardized reporting basis acrosscountries. By far, the most difficult statistic to obtain is the extent ofshifting cultivation, for quite obvious reasons: it is in the informalsector where there is no systematic recording system, and the activityinvolves numerous small actors moving from place to place and,hence, inherently difficult to track.

Aside from strengthening institutional capacities formanaging forests, responses to deforestation also include theestablishment of protected forests/areas, revegetation of denudedlands (reforestation and afforestation), establishment of forestplantations to divert pressure away from natural forests, and localcommunity involvement in forest protection and utilization. Statisticson reforestation and afforestation are often reported as forestplantation data. Plantation forests are defined as man-made forestsused for industrial or nonindustrial (e.g., watershed protection)purposes. Data are usually in terms of size of area planted, thoughoften the forest area actually reestablished is considerably smallerthan the area planted (because of such causes as mortality or fire).


In protecting forest areas, the widely adopted approach is toincorporate multiple objectives and resource use. In the context ofoverall environmental management, protecting the forests shouldmeet the objectives of maintaining natural habitats, preventing landdegradation, and providing for the needs of local communities.

The World Conservation Union (formerly known as theInternational Union for the Conservation of Nature or IUCN), anindependent global body that oversees efforts to conserve biodiversity,has developed a system of national protected area classificationaccording to management objectives. Categories I and II refer,respectively, to strict nature reserves and national parks which are,by definition, protected from human exploitation. Other categories(III-VII) accommodate sustainable forms of human activity or landuse. For example, multiple-use areas (category VII) may provide foroutdoor recreation or even for timber production. Statistics may thenbe compiled on the extent of the areas under each protection category.

Soil Degradation

Various factors may be used to express the nature, cause, and degreeof impairment in soil productivity. Regardless of variations in theirdefinition, degraded soils are usually characterized by poor orundesirable physical, chemical, and biological features.Socioeconomic conditions in areas of such soils are invariably pooras well.

In most developing countries, there is an almost universallack of reliable, quantitative data on soil properties (and variationsin time and space). There is a general lack of data on soil qualityat the national level. Keeping track of national soil resources andmonitoring changes in relation to human activities have, up to now,received low priority in many national environmental monitoringprograms. Databases need to be established to determine which landuse systems are causing soil degradation, to assess the effects ofvarious forms of pollution, and to derive meaningful measures ofsoil quality in relation to human activities.

Currently, standard procedures for evaluating soil quality arestill deficient (UNEP 1993). For instance, there is as yet no universallyaccepted set of criteria for evaluating changes in soil quality. The


science for standardizing methods for soil investigation is not asadvanced as that for air and water. More fundamentally, the detaileddata needed to evaluate soil quality at the national level are oftenlacking. Various types of soil degradation are present in the countriescovered by the present study, and indicators are needed to monitorstatus, causes, consequences, and effectiveness of remedial actions.To produce these indicators, improved soils databases andstandardized methods for soil description and analysis are needed.

In general, the three basic soil quality issues that need to beaddressed are (i) productivity (e.g., soil fertility, toxicity);(ii) environment (e.g., water quality and contaminant leaching); and(iii) health (e.g., effects on animals and humans). In the past, emphasiswas placed on productivity for agriculture. It is now widely recognizedthat the perspective needs to be broadened to include environmentalquality as well as human and animal health aspects. Soil qualityindices should capture all these elements. Essentially, nutritionalquality and management would be positive items in such an index,and toxicants would be negative.

To date, the Global Assessment of Soil Degradation (GLASOD)Project coordinated by the International Soil Reference andInformation Centre (ISRIC) and sponsored by UNEP provides themost comprehensive compilation of global information on the statusand causes of soil degradation. The study, completed in 1991, definedthree broad categories of soil degradation: (i) erosion and terraindeformation, (ii) chemical degradation, and (iii) physical degradation.

Actually, GLASOD further divided the first category erosionand terrain deformation into two separate categories: wind and watererosion. Erosion refers to the removal of topsoil due to wind action orthrough sheet erosion caused by water. Terrain deformation refers tocreation of dunes or hollows, and to the scarring of land due to water-caused rill and gully formation, landslides, and collapse of riverbanks.By far, water is the main agent of topsoil erosion and terrain defor-mation in most of the tropical countries covered by the present study.

Environment statistics for eroded soils are usually reportedas the aggregate extent of affected areas (and as a proportion to totalland). The degree of erosion may be classified as slight, moderate,or severe. Unfortunately, there is no standard set of definitions forwhat these classifications mean. Reporting organizations adopt theirown definitions, often formulated in terms of ranges of estimated


soil loss rates (e.g., severe erosion might be defined as soil loss ratein excess of 50 metric tons a year).

The second category, chemical degradation, has four types:nutrient loss, salinization, pollution, and acidification. Nutrient lossrefers to the loss of organic matter in the soil as a result of the clearingof vegetation, burning, and physical removal of fertile topsoil by theaction of wind and water. Salinization is an increase in the salt contentof the soil due to human-induced activities that cause salt toaccumulate in soils. One cause is the inadequate provision of drainageto complement irrigation schemes in semiarid areas. Salts transportedwith the irrigation water accumulate in the soil as the waterevaporates. Salinization may also be caused by saltwater intrusioninto groundwater due to overpumping near coastal areas. Intensivecultivation results in rapid water evaporation rates that transport saltfrom saline groundwater. Pollution refers to the contamination of soilsby pesticides, wastes, and substances that are toxic to soilmicroorganisms. The pollution of soils is often accompanied by thepollution of groundwater. Acidification is the lowering of the soil pH,which may result from the overapplication of fertilizers.

The third category of soil degradation is physical degradation,of which there are three types: compaction, waterlogging, and landsubsidence. Soil compaction is the result of trampling by cattle orheavy machinery. This action destroys the soil structure and sealsthe spaces between soil particles, leaving the soil unable to drainwater. Waterlogging is a condition associated with prolonged periodsof inundation due to lack of drainage (except for paddy fieldspurposely flooded). Waterlogging is often accompanied by salinitybuildup. Land subsidence results from excessive removal of waterfrom aquifers under pressure (e.g., areas in Bangkok), or the excessivedrainage and/or oxidation of organic soils in agricultural areas.

An extreme form of soil degradation (associated with all threecategories) is desertification, which is a major concern in arid,semiarid, and dry subhumid areas. Among the 11 countries coveredby this RETA, India and Pakistan are the countries with desertificationproblems. The total affected area in both countries is about 1.7 millionhectares (ESCAP 1995).

Theoretically, statistics on the extent of areas affected by soildegradation according to the various types and subtypes could becompiled. However, precise information on the extent of such


degraded soils is not usually available. In addition, the types of soildegradation often overlap in any one area, and merely adding upthe areas falling under various degradation types could result inmisinterpretation or overestimation of the degradation problems.Nonetheless, where data are available, statistics could be compiledfor the areal extent of soil degradation under each category.

One approach to environment statistics for soil degradationis based simply on determining the aggregate areas according to asystematic classification of the qualitative degree of soil degradationcaused by the separate or combined effects of the various types ofdegradation. The classification system would include considerationof the soil’s rehabilitation potential, and not just the soil’s presentcondition. For example, the GLASOD Project defined four degreesof degradation (Table 4.1). The GLASOD classification systemconsiders the agricultural suitability of the soil, maintenance of bioticfunctions, and feasibility of soil restoration. Thus, environmentstatistics on soil degradation would be compiled as the annual orsome periodic estimates of the aggregate size of areas falling undereach of the four qualitative categories.

Table 4.1Degrees of Soil Degradation According to theGlobal Assessment of Soil Degradation Project

Degree Description

(i) Light There has been only a small decline in agricultural produc-tivity. Biotic functions are largely intact. Soils can be fullyrestored with changes in ongoing land use practices.

(ii) Moderate Still permits continuing agricultural use of an area, but withgreatly reduced productivity. Biotic functions are only partlydestroyed. Restoration is possible with major changes inland use practices.

(iii) Strong Agricultural use under local land use management is nolonger possible and most biotic functions have beendestroyed. Restoration is possible, but at a high cost.

(iv) Extreme The area has become unsuitable for agriculture and isbeyond restoration. Biotic functions are completelydestroyed.

Source: World Resources Institute (1993).


Water Resources Availability and Use

Although most countries conduct regular and systematic collectionand analysis of hydrometeorological, hydrological, andhydrogeological data, not one specific international organization hasa mandate to coordinate national efforts and compile globalinformation, particularly on freshwater resources. Also, in mostcountries, the collection of water quantity and quality data is notintegrated (data collection is done by separate agencies). As a result,policy making and planning for water quantity and quality manage-ment are often fragmented.

The renewable water supply of a country is derived from twosources: (i) the rainfall that falls directly on its land area, and (ii) thewater that flows in rivers originating from outside the country (alsoreferred to as external water sources). The former is measured interms of “annual internal renewable water resources,” which is theaverage annual freshwater flow of rivers and groundwater producedfrom rainfall that falls within a country. WRI publishes available dataor estimates of this parameter. UNEP has supplemented the databasewith its own estimates, derived from model calculations that useavailable values of determining factors such as cropland area underirrigation, livestock numbers, and rainfall amounts as inputs. Thereliability of these estimates varies considerably from country tocountry, thus limiting the extent to which comparisons can be made.

About two thirds of the internal renewable water is in theform of flood runoff, which quickly flows out to the sea. Only aboutone third is “available” as usable surface and underground watersupply. The derived available water resources volume does not implyuniform availability throughout the year or across space. Considerableseasonal variations occur, particularly in river flows. There is alsoconsiderable spatial variation even within one country. An essentialpart of the water supply statistical database, then, is an indicationof time variation, expressed as average monthly river flows, forexample. Recorded river flows may then be combined with catchmentarea to derive indicators of water flow (or runoff) per unit area.

Although most of the countries in the present study dependmainly on internal water resources, others (Bangladesh, India,Pakistan, and Thailand) depend, to a large extent, on external sources(flows from neighboring countries). For instance, nearly 70 cubic


kilometers (km3) of water flows in from neighboring countries intoThailand every year, an inflow equivalent to more than half of thiscountry’s internal water resources.

The renewable water resources statistic is an aggregate annualaverage figure that is useful mainly as basis for developing indicatorsof water supply availability. A critical level of utilization is consideredreached when the water withdrawal rate reaches or exceeds theaverage annual available water supply (ESCAP 1992). Another usefulindicator is the ratio of available water supply to the total population.The resulting indicator is “per capita available water supply.” Basedon this indicator, countries may be ranked according to relative percapita water availability or scarcity.

Within each country, of course, the water supply databasewill be more extensive and desegregated. Normally, each countrywould have a network of rainfall and river flow measuring stationsoperated by functional agencies (the public works or irrigationdepartments). These same agencies also routinely analyze the primarydata to derive totals, averages, dependable flows, and time seriesindicators (of trend and variability), including possibly mapping theinformation (as in isohyetal maps of rainfall intensities or monthlytotals). Here, measures of availability are more refined. For instance,available water may be defined as the water flowing in a river thatis available 90 percent of the time (also called “dependable water”in irrigation water terminology). Time series data are important fordeveloping indices of water supply sustainability.

Internal water resources include recharge to the groundwater.Ultimately, the source of all internal water is rainfall, part of whichrecharges the groundwater. Groundwater supplies rivers with a “baseflow” during the months without rainfall. The inability to adequatelyrecharge groundwater (because of soil compaction and removal ofvegetation) is an important factor contributing to droughts andagricultural failures, and is a concern that needs to be monitoredclosely.

Because groundwater acts essentially as a natural reservoir(with stored volume, inflow, and outflow), a water balance approachor an accounting approach to database development seems ideal.Unfortunately, this ideal approach is not always easy to implement.Data may be available on estimates of groundwater volume (whichmay be expressed in terms of exploitable potential per year derived


from analysis). Indonesia, for example, has an estimated exploitablepotential of about 460 km3 per year, nearly 18 percent of its annualinternal renewable water resources, while Malaysia and thePhilippines have about 30 and 40 km3 a year, respectively (ESCAP1992). Locally, changes in usable groundwater volume may be gleanedfrom measurements of depth to water table or changes in pressurelevels for pressurized aquifers.

However, data on inflow (recharge) is harder to estimate. Themost accurate data may be that on outflow, derived from records ofgroundwater withdrawal (pumping), including measurements of riverbase flows. Still, an accounting approach represents the most logicalway of tracking the groundwater supply status and human-inducedpressures.

For island or archipelagic countries, in particular, themonitoring of groundwater withdrawals is very important becauseof their vulnerability to saltwater intrusion. On islands, especiallysmall ones, groundwater occurs as shallow freshwater lenses thatfloat and hold back the surrounding saline water. If too muchgroundwater pumping occurs (or if there is considerably reducedrecharge), the holding effect of the freshwater lens is reduced andsalinity advances inland.

Water availability data portray the status aspect of the watersupply issue. Human pressure is reflected by data on water utilization.Utilization data may be compiled using a system for classifying wateruse. Water use is commonly categorized as either consumptive ornonconsumptive in nature. Consumptive use involves taking waterout of the source, whereas nonconsumptive use involves use on site.Consumptive uses include irrigation, domestic use, and industrialwater. Groundwater use, in particular, is mainly consumptive.Nonconsumptive uses include hydropower generation, fisheries,navigation, and recreation.

Statistics on consumptive use are usually reported as waterwithdrawals by sector: agriculture, domestic (including householdsand commercial and public establishments), and industrial (includingprocess water and cooling water). Unfortunately, comparable updateddata on water withdrawals across countries are hard to come by.

Consumptive use data may also be desegregated into sourceof water: surface water or groundwater. Groundwater is an importantsource for domestic use because it usually has better quality compared


with surface water; it is often free of pathogens and is more acceptablein terms of turbidity and color. In India, groundwater supplies 80 per-cent of rural drinking water compared with 60 percent in Nepal andthe Philippines (ESCAP 1995). Surface water, obtained from naturalor man-made reservoirs, is a major source for industrial use. InMalaysia, surface water supplies most of the industrial demand, butin India and Nepal, groundwater provides up to 80 percent ofindustrial water (ESCAP 1995). Water used for agriculture ispredominantly surface water, except in arid and semiarid regionswhere groundwater supplies a considerable volume (e.g., Pakistan).

Since nonconsumptive use does not involve water removal,data are not reported in terms of water use rates or volumes. Rather,the data are in the form of values derived from the use (e.g., electricitygeneration for hydropower plants). These data commonly includehydropower potential (measured in gigawatt-hours per year), numberof dams installed, and production from freshwater fisheries as wellas aquaculture (in metric tons per year).

Water Quality Degradation

Overall, water quality in the 11 countries covered by the RETA isbeing degraded by the combined effects of sewage, industrialeffluents, urban and agricultural runoff, and saltwater intrusion. Thedraft 1995 SOER for the ESCAP Region ranks the severity of waterquality issues. Water pollution issues ranked as “major” to “severe”in significance in the Indian subcontinent cover pathogenic agents,organic matter pollution, and high sediment loads in rivers. InSoutheast Asia, the major to severe problems are pathogenic agents,organic matter pollution, eutrophication, heavy metal contamination,and high sediment loads. In the Pacific islands, the correspondingmain issues are pathogenic agents, salinization, and nitratecontamination.

In general, water quality may be measured in terms ofphysical, chemical, and biological parameters or variables. Forpurposes of monitoring, water quality is usually considered in relationto the use of the water and its expected impacts on a water body, orin relation to maintaining a desired quality for a water-basedecosystem. Where it is possible to find sampling locations that are


unaffected by human activity, the data collected provide usefulbaseline information on natural or background water quality, fromwhich the impact of human activity can be better understood.

Specific uses of water - such as for drinking, irrigation, orindustrial use – usually have minimum acceptable quality standardsdefined for selected measurable variables. These are selectedaccording to their known or anticipated effects on human health andecological values. Usually, water quality monitoring is undertakento meet certain objectives; these objectives may differ for variouswater quality management programs. Thus, the water qualitymonitoring system and the type of data generated across countriesmay not be the same in terms of the scope of water quality variables,target levels, and standards assigned to them. Differences are to beexpected in laboratory techniques (sampling, analytical methods,frequency) as well as the precision and accuracy of equipment (e.g.,detection limits). These differences reflect varying availabilities andqualities of monitoring equipment, as well as the varying objectivesof specific programs, which are mainly local or national in extent.In response to specific local issues (e.g., eutrophication), governmentsundertake monitoring programs or special surveys of specific waterbodies. The issues faced by individual countries, however, areincreasingly becoming common. The situation provides anopportunity to coordinate methods for data collection and reporting.

The UN Economic Commission for Europe (ECE) has devel-oped and tested a system for the standard statistical classificationof ecological freshwater quality. The ECE’s proposed classificationsystem provides a framework for the compilation and presentationof the water quality in bodies of surface water. It defines five qualityclasses for variables organized into seven groups of key parameters.The classes range from “excellent” (Class I) to “bad” (Class V). Theparameters are oxygen regime, eutrophication, acidification, heavymetals, pesticide residues and other harmful substances, radioactivity,and microbial pollution. Data are reported as annual arithmetic meansor medians.

The classification is termed “ecological” because theclassification limits and variables selected are based on therequirements for maintaining aquatic life; that is, limits are set onthreshold concentrations of substances to keep them from producingan adverse effect on aquatic life in all its forms and life stages. The


system is currently being improved using the results of tests doneon selected major transboundary rivers in Europe and NorthAmerica. A similar approach may be used to develop a commonwater quality classification system applicable to the countriescovered by this RETA.

Inland freshwater bodies are not the only ones threatenedwith water pollution. Coastal waters throughout the Asian and Pacificregion are similarly exposed to the combined effects of rapidurbanization, industrialization, pollution, and overexploitation ofresources. In its 1990 report, the Group of Experts on the ScientificAspects of Marine Pollution (GESAMP) concluded that the world’sopen seas are still relatively clean, but that nutrient pollution fromland-based sources poses the gravest and most prevalent threat tothe marine environment (GESAMP 1990).

Systematic data on organic pollution of coastal waters mustbe included in national environment statistics. At present, the datagenerated are usually the product of special studies. Data collectionusually stops when the project funds dry up.

Urban Air Pollution

For the 11 countries covered by this RETA, the air pollutants judgedto be of high significance are suspended particulate matter (SPM),lead, and sulfur dioxide. Of low to moderate significance are nitrogenoxides, carbon monoxide, and ozone. However, although the currentlevels of the latter three gaseous pollutants are not high (i.e., levelsare below ambient standards in most cities), potential increases inemissions require that they be monitored closely. SPM and sulfurdioxide are associated with respiratory illnesses. For SPM, themonitoring of particles smaller than ten microns (denoted as SPM10)is important because these are the particles within the respirablerange. The chemical composition of suspended particulates alsodetermines the seriousness of health effects. Elemental carbon,polynuclear aromatic hydrocarbons (PAHs), and toxic base metalsare among the constituents of particulate matter that are importantin this regard.

Lead, added to gasoline as an antiknock agent, is releasedto the atmosphere by motor vehicle emissions. Lead is known to


accumulate in the human body, causing damage to the brain, thenervous system, and other vital organs. Children are particularlyvulnerable because of potential irreversible damage to theirdeveloping nervous system. Carbon monoxide reduces the capacityof the blood to absorb oxygen; almost all of it is traceable to motorvehicles. Nitrogen oxides are a key agent in the occurrence ofphotochemical smog. As with carbon monoxide, the principal sourcesof nitrogen oxides in urban areas are motor vehicles. Nitrogen dioxide,a respiratory irritant, and ozone are secondary pollutants derivedfrom nitrogen oxide emissions. Ozone is a major constituent ofphotochemical smog.

The air pollution characteristics of cities in different countriescan vary considerably due to differences in emission structures (e.g.,sources of emissions) and meteorological conditions. Atmosphericdispersion characteristics are different for cities located in rivervalleys, coastal areas, or mountain valleys. Differences in air samplingand meteorological instrumentation in different countries are alsoa factor. Consequently, cities may have distinctive air pollutionpatterns that are not directly comparable. Even within one city, airpollution patterns can differ from one part of the city to another,depending on the terrain, elevation, meteorological conditions, andland use (e.g., core urban areas where traffic is congested registerhigher air pollution levels). Variations in air pollution concentrationscould also be significant during different hours of the day and duringdifferent months of the whole year. Thus, it is not possible to deriveand average air quality in a city unless a fairly dense network ofmeasurement stations exists with frequent air sampling. Such anetwork is not usually available in most countries. In any case, ifothers want to derive and average concentrations, the data wouldhave to undergo further analysis using dispersion modeling or othermethods of spatial generalization.

For those reasons, data on air pollution are reported stationby station, rather than as area-aggregated indicators. Essentially, thestations “represent themselves.” Data interpretation therefore needsto be supplemented by knowledge of the local conditions aroundeach station. Some stations may monitor a larger number of pollutantsthan other stations do (e.g., station in the urban core area). Withineach station, data compilation is often reported as arithmetic meansover specified periods, as well as percentile distributions for each


pollutant during the same periods. Trend information is derived fromthe mean values. These mean values may then be compared withguidelines for acceptable long-term exposure of the affectedpopulation to the pollutant. Such guidelines are derived from studies(primarily done by WHO at present) on the potential health effectsof various air pollutants. Percentile values (e.g., 95 or 98 percentile)are used to measure the occurrence of peak concentrations. (A 95percentile value means that 95 percent of the data fall below thatvalue.) An alternative to the computed percentile value is an observedhigh value itself, usually, the second or third highest measurementfor a sample period. These data are used to assess the risk of short-term exposure by obtaining the number of days per year (at a station)during which a guideline value or threshold for short-term exposureis exceeded.

Statistics provided by mean and percentile values, bythemselves, cannot be used to draw precise conclusions on the actualexposure of individuals. Actual exposure is a function of the exposuresin different locations (at home, when commuting, and at work) andat different times of the day. These differences are not reflected byoutdoor measurements at fixed locations.

A more informative (and also concise) way of reporting orsummarizing air pollution data would show average air pollutionconcentrations and changes in their values over time, preferably ina graphic format. Levels are typically expressed as means calculatedon the basis of daily or monthly values for each year. The summarywould also describe or identify the type of the station (e.g., thesurrounding land use or average traffic condition), its geographiclocation (elevation, terrain), dispersion characteristics based on localmeteorology, method used for air sampling and analysis, and themajor emission sources (e.g., motor vehicles, industry). The reportfor each station would also include the number of days per year theguidelines for the pollutants are exceeded.

Greenhouse Gases

Greenhouse gases are associated with global warming concerns thatare increasingly drawing national and international attention. Thethree principal greenhouse gases are carbon dioxide, methane, and


the halocarbons. The impact of greenhouse gases on global warmingis usually examined through the use of complex models. The accuracyof these models depends, to a large extent, on the quality of thedatabase, particularly emissions inventories of the major greenhousegases.

The Framework Convention on Climate Change, formulatedduring the 1992 UNCED, includes a requirement for the preparationof inventories of greenhouse gas emissions (as well as sinks).Assistance to individual countries in carrying out this work is beingmade through a Global Environment Facility (GEF) project. Globalefforts toward reducing greenhouse gas emissions are bound tointensify in the coming years. And as the implications for nationaleconomies, particularly for developing countries, become evident(for instance, if international agreements have to be reached forlimiting industrial gas emissions in individual countries), nationalstatistical agencies will have to improve their capability for estimating(or coordinating the estimate of) national greenhouse gasemissions instead of relying solely on estimates generated byoutside groups.

Routine monitoring of atmospheric concentrations of themajor greenhouse gases is conducted through global networksoperated by international agencies and research bodies. WMOhas established the Global Atmosphere Watch (GAW) as an umbrellaprogram for integrating and coordinating such monitoring activities.When fully implemented, the GAW will comprise 30 global stationsand 300 regional stations. The data obtained will be stored at theWMO World Data Centre for Greenhouse Gases in Tokyo. However,this global monitoring system looks primarily at overallatmospheric conditions (i.e., ambient conditions) and not at thesources of emissions. Emissions inventories are needed to correlatewith data on atmospheric conditions so as to arrive at policyprescriptions.

Much of the greenhouse gas inventory work is done byinternational research organizations (e.g., Carbon DioxideInformation Analysis Centre [CDIAC] on carbon dioxide emissions).Emissions are generally computed in much the same way as are urbanair pollutants, that is, by multiplying the activity levels of sourcesby their respective emission factors. By itself, the estimate processis prone to considerable uncertainty because sources are difficult to


characterize and properly document, especially at the global level.Current calculations involve making a number of major assumptions,which can provoke disagreement over the results, especially whenderiving conclusions on national accountability (i.e,. contributionsby individual countries) for global emissions. However, if nationalagencies can improve their capability to prepare estimates or verifyoutside estimates, the database would improve (particularly theemission factor), and so would the validity of assumptions and theacceptability of the results.

Two examples of a greenhouse gas index are the GlobalWarming Potential (GWP) developed by the Intergovernmental Panelon Climate Change (IPCC), and the Greenhouse Gas Index (GGI)developed by WRI. The GWP provides a measure of the relativewarming effect of key greenhouse gases relative to that of carbondioxide. For instance, one kilogram of methane has 11 times(earlier estimated at 21 times) the warming effect of the same amountof carbon dioxide, whereas CFC-11 has 3,400 times the warmingeffect of carbon dioxide. Indices such as these provide policymakers with a basis for evaluating options for controlling emissionof various greenhouse gases. These indices are also used to weighnational emissions of the various greenhouse gases to determineaccountability for global warming among various countries. TheGGI of WRI uses the GWP values (and national emissionsinventories, such as those prepared by CDIAC) to derive an indexthat represents the contribution of individual countries to the globalsum of emissions of greenhouse gas. According to this index, the UShas the highest percent share of global greenhouse gas emissions;India is in 7th place; Indonesia, 9th; Malaysia, 27th; and thePhilippines, 30th.

The approaches described here have limitations of one kindor another. Results have been the subject of intense debate,particularly the ranking of countries according to accountability forglobal warming. If accountability is to be used as the basis for futurerestriction on emissions, criteria and methodologies acceptable tothe majority of the countries need to be found. In turn, the need toharmonize estimation methods will arise. Guidance is being providedby the IPCC, which is developing an internationally acceptablemethodology for calculating and reporting national net emissions(considering both sources and sinks) of greenhouse gases.


Human Settlements

It is essential that a system for collecting and interpreting humansettlements statistics be established in all countries. Such statisticsmust be readily available, easily understood, and designed toprovide a quantitative basis for assessing human settlementsconditions.

The quality of the environment and the performance of humansettlements are inextricably linked. Important elements of the livingenvironment include quality of housing, water supply, sewerage anddrainage facilities, energy, and transport, as well as the spatialdistribution of housing, all of which have consequences on thesustainability of the environment. An examination of humansettlements, especially urban settlements, reveals a strong correlationbetween poverty and environmental quality. Slums, dilapidatedneighborhoods, and squatter settlements are often the places withthe poorest environmental quality.

Many factors affect the availability and reliability of humansettlements statistics. Statistical systems in many developing countriesare still weak while statistical methods, coverage, practices, anddefinitions differ among countries. The Housing and Urban IndicatorsProgram, which started in 1990 as a joint initiative of the UN Centrefor Human Settlements and the World Bank, seeks to implement apermanent data collection facility that will permit regular analysisof statistics on human settlements.

Given the multitude of factors affecting human settlements,separate descriptions of these concerns cannot provide theinformation required for planning, policy making, and monitor-ing of performances. However, it is necessary to identify the keysubject areas of the physical and service elements that comprisea human settlement and which can, at least, be subjectedtheoretically to statistical assessment. The key subject areas (listedbelow) are not the only possible items for statistical assessment.Particular situations and concerns in countries may demanddifferent selections or priorities. The important subject areas arethe following:

(i) Housing(ii) Land use(iii) Urbanization


(iv) Environmental infrastructure: water supply, sanitation,drainage, and solid waste management

(v) Use of energy(vi) Transport(vii) Construction industry activities(viii) Education(ix) Population growth and change

The collection and interpretation of human settlementsstatistics involve several general methodological questions orissues.

A major difficulty in interpreting human settlements data isthe diversity of conditions within both rural and urban areas in mostcountries. Statistical averages for all rural areas or urban centersfrequently show large differences among towns and rural areas.Another problem is that poor living conditions, particularly inurban centers, are averaged for an entire city or urban center andmay tend to hide large individual differences that are relevant forpolicy making.

Another important methodological issue is the genericdefinition and classification of low-income settlements. Slums, shantysettlements, marginal settlements, and illegal settlements greatlydiffer in characteristics, and their relationship to informal sectoractivities is complex. Data availability and collection from suchsources are a general problem of human settlements statistics. Dataavailability and compatibility are major obstacles in the assessmentof marginal settlements, which may differ in size, location, density,growth rates, terrain, and type and age of construction, sanitation,or infrastructure. In addition, there are other differences that cannotbe determined by mere physical data, such as the degree of socialcohesion among inhabitants, their ethnic composition, and theiraspiration, skills, and health conditions. These factors may vary fromcountry to country, within countries, and even from one part of asettlement to another.

As mentioned earlier in this chapter, available statistics onaccess to potable water and sanitation can be misleading. The variousinterpretations of “access” and “safe” make statistics on thesefacilities an inadequate indicator for policy making and monitoring.The statistics on access to toilets or latrines are often inadequate


because those statistics do not capture poor design, poor mainte-nance, or sharing by large numbers, which can be major causes ofinfection.

In human settlements statistics, data on solid waste areclassified on the basis of either the physical or chemical characteristicsof the materials contained in solid waste, or the activities that generatesolid waste. Waste generation is always measured by solid wastecollected; thus, uncollected or dumped waste is not accounted forin the statistical information.

Bioresources

South and Southeast Asia, in which the 11 countries of this RETA(Bangladesh, India, Indonesia, Malaysia, Nepal, Pakistan,Philippines, Samoa, Sri Lanka, Vanuatu, and Viet Nam) are found,are important regions of the world. The regions are not industrializedlike Western Europe or North America, but are essentially biomass-based with some amount of industry – bio-industrial, rather thanpurely industrial. Thus, natural resources like air, water, land andsoil, forests, and flora and fauna are critical to their well-being. Theirflora and fauna constitute bioresources that are generally regardedas renewable. However, the unfavorable state of a host ofenvironmental factors on which flora and fauna depend could leadto the extinction of such resources. These resources are, therefore,“conditionally sustainable” (UN 1991).

Biogeographic Realms

The biosphere is the part of the earth where all terrestrialand aquatic organisms (plants, animals, and microorganisms) exist.Taken in relation to geography, the biosphere could be divided intodiscrete biogeographical realms. Often, these realms are regionsthe size of a continent or a subcontinent. Biogeographic realms arethe highest category in biology. According to Udvardy (1975),biogeographic realms are more or less based on individualecosystems, which share common features of geography as well asof flora and fauna.


Flora and Fauna

Databases for flora and fauna should be established includingdescriptions of families, genera, and individual species and givingthe geographic boundaries of the species. Electronic databases willbe advantageous for this group because they are dynamic andexpandable systems and can be updated periodically.

Biodiversity

Biological diversity, or biodiversity, reflects the living thingsthat form an interacting system with the atmosphere (air), thelithosphere (land and soil), and the hydrosphere (water, both freshand marine). The building block of biodiversity is genetic diversity,which refers to the heritable variation within and between populationsof a species.

Species diversity, another form of biodiversity, measuresspecies richness or the number of species of plants, animals, andmicroorganisms in a given habitat or country or region.

Thus far, roughly 1.7 million species have been describedworldwide. A conservative or working estimate gives the number ofspecies at about 12.5 million (WCMC 1993). Species are composedof individuals that occur in populations, which are, in turn, sets ofinterbreeding individuals belonging to the same species. A set ofdifferent species of plants, animals, and microorganisms interactingwithin a system and sharing the same habitat constitute a community,and a set of communities living as an interacting system in aecological region constitutes an ecosystem. The ecological aspectis important because it exercises control over the structure of thecommunity and the overall composition of biodiversity. For instance,in the Alpine Himalayas, a tree stands by itself with a few lichenson its bark. On the other hand, a single tree in the humid tropicsis part of a rich biotic resource and may be host to many speciesof algae, fungi, lichens, liverworts, mosses, epiphytic ferns, andflowering plants. In addition, such a tree may also harbor manyspecies of invertebrate (ants, in particular) and some vertebrateanimals. Each tree is indeed a mini-ecosystem. Thus, incalculablelosses could occur with the destruction of even a single tree inthe humid tropics.


Although the exact measurement of biodiversity in anecosystem is difficult, biodiversity is usually assessed on the basisof the number of species and the level of uniqueness. Historically,there have been changes in biodiversity, that is, in the compositionor mix of species in any ecosystem. However, it has not been possibleto establish a rate of extinction in exact terms.

While there are reasonable estimates of the number of higherplants and animals in a country, no such estimates are available forlower plants and animals. Microorganisms have altogether beenexcluded even if it is known that the earth would be a very dirtyhabitat without these microorganisms.

Based on recent reports, the diversity of plants and animalscan been documented. Information on geographic distribution wouldreveal the areas in need of surveys on a priority basis.

Fisheries

Innumerable statistics are needed to design policies that willmake fishing sustainable. There is a need for timely and reliablestatistics to calculate the allowable catch to prevent overfishing. Ingeneral, however, fish catch is not reported properly. One importantinformation is the market price of the catch, which determinesprofitability and therefore influences catch intensity. There is a needto analyze catch data to determine any appropriate changes ingovernment regulations on licensing, allowable catch or quotas,and so on.

To properly manage fish stocks and to accurately calculatetotal allowable catch, information must be reliable and timely. Dataare needed on the growth cycle of specific fish stocks, nutrient cycles,changes in ocean currents and water temperature, and the effect ofpollution. There is already alarming evidence of shrinking fish stocksin both fresh and marine waters. Aquatic ecosystems are highlyvulnerable compared with terrestrial ecosystems.

Wetlands

Cowdrin (1979) defines wetlands as “transitional areasbetween aquatic and terrestrial ecosystems where the water table isusually at or near the surface or where the land is covered by shallow


water.” Thus, marshes, floodplains, bogs, peat lands, shallow ponds,the littoral zone of large bodies of water, and tidal marshes areclassified as wetlands. Wetlands also have one or more of the followingattributes: the area must be predominantly or periodically inundated,must support hydrophytic vegetation, and should have hydric soil.

By and large, the productivity of wetlands per unit of areaand time is very high. There are communities living around wetlandsand drawing sustenance from them. The important parameters fora database for the management of wetlands are as follows:

(i) general features (name, ownership - government orprivate);

(ii) geographic location (state, district, latitude, longitude,altitude, area);

(iii) morphometry (length, width, depth, volume, bathymetricmap depicting depth profiles);

(iv) hydrology (inflow, outflow, siltation rate, evapotranspi-ration, soil, moisture, rainfall pattern, temperaturecharacteristics);

(v) ecological features (natural, man-made, temporary,permanent);

(vi) physicochemical features of soil, water, and vegetation;(vii) landscape type (forests, scrubs, grassland, desert, delta,

estuary, lagoon, mangrove, backwater, coastal zone);(viii) water source (spring, river, irrigation channel, runoff

from surrounding area, estuary, sea, etc.);(ix) flora and fauna with special economic importance, and

status of migratory and resident birds and rare orendemic and endangered species;

(x) extraction of economic species and their end use;(xi) major use of wetlands for local people, annual revenue

from seed gathering, fishing, grazing, agriculture,irrigation, drinking water, and tourism;

(xii) legislation, if any, for protection from overfishing,overhunting, or any other activity that threatens the area;

(xiii) threats to wetlands due to weeds, domestic sewage,deforestation, overfishing, shooting, factory effluents,solid waste, inadequate drainage, overgrazing, siltation;

(xiv) educational awareness; and(xv) conservation measures planned and undertaken.


Methodological Issues in DevelopingEnvironment StatisticsThe purpose of this section is to highlight the methodological issuesassociated with the development of environment statistics. It doesnot intend to cover the entire range of methodological problems, butto provide an insight into the types of issues involved in developingenvironment statistics. The identification of these issues should helpin developing programs that will promote the use of standardizedmethods across the participating DMCs and encourage the systematicdevelopment of an environment statistics system.

The methodological issues may arise from two sources:(i) complexities that are inherent in various environmental systems,and (ii) issues involved in developing higher level statistics such asindicators and indices.

Inherent Complexity of Environmental Resources

The purpose of an environment statistics system is to providerepresentative and reliable statistics to serve the objectives for whichthe statistics are compiled. The representativeness and reliability ofenvironment statistics depend on the adequacy of the methodologiesused for computing statistics. The methodological issues indeveloping environment statistics can be attributed to three factors:

(i) inherent characteristics of natural environmentalresources or systems that do not allow measurement byenumeration;

(ii) lack of understanding of the complexity of the factorsresponsible for variations in the system attributes, whichthe statistics should capture; and

(iii) cost constraints that limit the number of measurementsand thereby influence the reliability of the statistics.

For resources or systems such as population and humansettlements, simple statistical techniques can be used to computerelevant basic statistics. These systems allow the use of the enumera-tive measurement technique for collecting data. In principle, themeasurement for such systems can cover the entire population asthe systems are made of distinguishable statistical units. For example,


a population census covers the entire population and measures severalof its attributes. Information on such systems could be representedby descriptive statistics, such as mean, median, mode, standarddeviation, interquartile range, and percentile.

A population census is usually conducted at five- or ten-yearintervals, for it involves huge expenses. Periodic surveys are con-ducted during intermediate periods to update census information.Sample surveys are also conducted at regular intervals to measureother socioeconomic parameters - economically active population,household income and expenditure, employment, and so on – if theseare not covered by the census. Usually, these surveys are based onrandom samples, with the sample size ranging from hundreds tothousands of people. Large sample sizes for these surveys allow theuse of the central limit theorem to make the assumption that sampleaverages follow a normal distribution. Therefore, statistics computedfrom these samples could be used to make an inference about theentire population within a known margin of error.

Descriptive statistics cannot be used for environmental re-sources such as the atmosphere, water, and ecological systems. Sinceair and water resources are contiguous and measurement units arenot distinguishable, samples of air and water need to be taken forthe measurement of pollutant concentrations in these resources ormedia. Statistics computed from these samples could be used to makean inference about the air quality of a region or the water qualityof a water resource. For ecological systems, samples also need to betaken for identifying and counting main taxa, to make an inferenceabout the population of flora and fauna in a region. Measuring entirepopulations of the region would be highly expensive.

Natural environmental resources also show spatial and tem-poral variations in their characteristics. These variations can arisefrom both natural and man-induced forces. For example, the unevendistribution of rainfall over a region can be attributed to natural forces.Spatial variations in air and water quality, on the other hand, canbe due to the uneven distribution of pollution sources as well asnatural factors like wind direction, speed, and topography for airquality; and the volumetric flow rate of a river and river geometryfor water quality. The scale of temporal variations for the atmosphereis as short as a few seconds and as long as a year. For a water re-source, the scale of temporal variations is determined by the pattern


of pollutant discharges into the water body as well as by seasonaland annual weather cycles. For ecological systems, the scale oftemporal variations could be several decades, as is evident fromecological succession.

Both spatial and temporal variations need to be captured bystatistics in such a manner as to provide useful information withrespect to the concerns about the uses of these environmentalresources. For example, to monitor the trend in global warming, bothspatial and temporal aggregations of greenhouse gas concentra-tions are required. Spatial aggregation is required over a largearea so that the statistics would be free from influences of peakconcentrations due to individual sources. To capture all scales oftemporal variations, aggregation must be carried out formeasurements taken over a year. On the other hand, for statisticsrepresenting pollution exposure of the population, aggregation isrequired to determine maximum one-hour, eight-hour, daily, andannual pollutant concentrations to reflect the risk of both acuteand chronic health effects.

In spatial and temporal variations of a resource, attributes orcharacteristics are of a random nature and follow a normal distribu-tion. Samples taken at random over space and time could be usedto infer statistics on the environmental resource, or a part thereof.However, the selection of random samples without considering theheterogeneity of the system due to natural and man-induced forcescould result in the loss of valuable information. For example, spatialaggregation of rainfall data based on random samples taken over alarge area without considering differences in climatological or rain-fall regimes would provide statistics that are useful neither foragricultural planning nor for the management of water resources.

For ecological systems, it is even more important to take intoaccount spatial structure and dynamics when selecting samplinglocations or sites. Ecological systems are complex systems made upof an assemblage of communities and abiotic elements that arecontinuously interacting with each other. Hence, statistics on thesesystems deal with both structural and functional measures. Theselection of sampling locations should capture information on com-munity frontiers to monitor the movement of these frontiers. Thechoice of the sampling sites should also take into account the changesclose to the boundary of the ecosystem to monitor the impact of human


settlement pressures or the influence of the buffer zone on theexpansion of the ecosystem.

Ignoring the spatial structure of ecological systems whileselecting random sample sites will result in loss of information onthe structure and dynamics of these systems. Thus, preliminarysurveys are generally carried out to gain an understanding of thestructure of an ecosystem. Based on this survey, the ecosystem maybe divided into several zones to carry out stratified random samplingby a method that is appropriate for the given purpose. Theclassification of these systems is done by experienced surveyors, butis generally subjective. For developing reliable statistics within eachstratified zone, a probability distribution function of the parameteror variable of interest should be known to determine the minimumnumber of samples required to provide statistics of a given confidencelevel. In principle, it may be possible to determine the probabilitydistribution function and the minimum number of samples forcomputing reasonably reliable statistics. However, budgetaryconstraints may limit the number of samples and thereby influencethe reliability of the statistics.

To understand the dynamics of an ecosystem, it is obviousthat sampling should be repeated after intervals of several years.However, for repetitive sampling to provide maximum information,some of the previous sampling locations may be changed every timethe sampling is repeated.

For a better understanding of the influence of spatial andtemporal variations on the development of statistics for air and waterresources, a discussion of spatial and temporal aggregation of dataor statistics is provided in the following paragraphs.

Spatial aggregation of statistics

Spatial aggregation of data is required to compute statisticsthat are representative of an area or a domain, e.g., the sulfur dioxideconcentration in an urban area, the dissolved oxygen level of a lake,rainfall intensity in a watershed, or the species diversity of anecosystem. To preserve the heterogeneity of the resource whilecomputing the statistics, areas within a region need to be delineatedin such a manner that, for the parameters or variables of interest,variations within each area are low while variations across the areas


are high. Samples taken from each can then be used to computestatistics representative of that particular area. If the central tendencyof a variable or an attribute over the entire region is required, statisticscompiled for individual areas may be used to determine the medianor arithmetic mean, as may be appropriate for the type of distributionin the area.

Ideally, the delineation of heterogeneous areas should bebased on data collected over a long period of time using a densemonitoring network. These data can be analyzed using suchtechniques as spatial correlation analysis, cluster analysis, or principalcomponent analysis to delineate areas showing significant differenceswith respect to given attributes of an environmental system.

For atmospheric systems, representative statistics for an areacould also be determined by plotting contours of equal attribute valueson a map representing a region. Thus, the average pollutantconcentration represented by two consecutive isopleths could be usedto represent the pollutant concentration of the area enclosed by theseisopleths. Pollutant concentrations estimated in this manner may beused to determine exposure of the population living in the area.Similarly, average rainfall intensity represented by two isohyets (linesjoining points of equal rainfall) could stand for the representativerainfall of the area enveloped by these isohyets. Representative rainfallintensity could be used to calculate surface runoff over the areaenclosed by isohyets.

Statistics should be not only representative of an area butalso reliable. For computing reliable statistics, the probabilitydistribution function of the concerned variable in the area should beknown to determine the minimum number of samples required tocompute statistics with a given confidence level. For example, if avariable follows a log normal distribution within the area, then theminimum number of sampling stations required to estimate a realmean within a specified confidence level is given by the equation.

n = (CGv)2 x t2/p2

Where:t = the specified confidence level,p = the allowable percent departure from the true mean.

CGv is the coefficient of geographic variation and is givenby CGv = 100 antilog Sg - 100


One major shortcoming of the above approach for computingspatially representative and reliable statistics is that an extensivedatabase is required to delineate areas, determine the appropriateprobability distribution function, and find the minimum number ofsampling stations for computing statistics within a given confidencelevel. The existence of such extensive databases is not likely to bepresent in most of the participating DMCs. In addition, the requirednumber of sampling locations, based on statistical requirements, maynot be met by the budget for monitoring.

Temporal aggregation of statistics

The temporal aggregation of data or statistics should capturethe temporal characteristics of variables so that the statistics can beused for different purposes. For the atmosphere, the scale of temporalvariations ranges from minutes to a year. For air quality, minute-to-minute variations are caused by changes in wind direction and speed.The daily variations in air quality result from both diurnal variationsof the atmosphere as well as daily emission patterns. Macroscaleweather fluctuations last for a few days and are important from theviewpoint of air pollution episodes. Seasonal and annual weathercycles also influence the air quality of a place.

Temporal variations in the quality of a water resource maybe caused by the daily pollutant discharge pattern. The dilutioncapacity of a water resource may change in response to seasonaland annual weather cycles, giving rise to variations in the waterquality.

Hence, the aggregation of air or water pollutantconcentrations over different time scales may be required to derivestatistics for different uses. For example, it may be necessary todetermine episodic air pollution levels to shut down some industrialunits in a region that is susceptible to air pollution episodes. Similarly,it may be of interest to determine the peak water pollution levels andthe volumetric flow of a water body during the dry season for waterresource planning.

If pollution levels in the air and water media are continuouslymonitored over time, average concentrations over time intervals andtheir frequency distribution could easily be determined. However,due to cost constraints, the continuous monitoring of all pollutants


at all monitoring stations may not be possible. Therefore, knowledgeabout the frequency distribution functions is required to determinethe frequency of sampling needed to obtain reliable averageconcentrations for different time intervals. Budgetary constraints maynot allow the monitoring to be carried out at the frequency determinedon the basis of statistical analysis.

The methodological issues due to the inherent complexity ofenvironmental systems can be summarized as follows:

(i) Descriptive statistics cannot be used for naturalenvironmental resources such as air, water, and ecologicalsystems, either because their measurement units to coverlarge contiguous areas are not distinguishable or becausethe cost of measuring an entire population is prohibitive.The latter is true for ecological systems.

(ii) Natural environmental systems show both spatial andtemporal variations, which statistics on these systems mustcapture. However, while capturing these variations, theheterogeneity of the system should be preserved so thatvaluable information is not lost. Therefore, a survey isrequired to stratify or delineate the system beforestatistics can be computed for each part of that system.

(iii) For developing representative and reliable statistics, anextensive database is required to delineate areas withsignificantly different characteristics and to determinethe probability distribution functions for the variables.Such a database is not usually available, especially indeveloping countries. Therefore, analytical techniques(e.g., simulation modeling) and expert judgment maybe required to decide where and when monitoring shouldtake place.

Development of Higher Level Statistics

A socioeconomic decision can have implications on both thesocioeconomic and natural environmental systems. A large amountof basic-level statistics pertaining to different domains can overwhelma decision maker. To facilitate the decision-making process, basicstatistics need to be processed into higher level statistics, which shouldencompass a variety of relevant information with reference to a


context. The development of indicators and indices is an attempt toprocess basic statistics into a form that would facilitate decisionmaking.

In accordance with Wayne Ott’s definition, an indicator isderived from a single variable representing an attribute of a system.Indicators are developed to have a simple measure for the state ofan environmental resource or system, which otherwise needs to becharacterized by a number of variables. An indicator may bedescriptive or normative. A descriptive indicator describes a state,and may be used to monitor the trend of or change in the state dueto human activity. A normative indicator, on the other hand, providesa measure of the state with respect to a norm. Thus, the percentageof people below the poverty line is a normative indicator as it providesa measure of the state of poverty with respect to a minimum desirablelevel, i.e., the poverty line. On the other hand,, tropospheric ozoneconcentration, used as an indicator for photochemical oxidants,merely describes the state of the air quality of an area.

While an indicator provides a simple and easilyunderstandable measure for assessing the state of an environmentalresource, the information provided by the indicator may not besufficient for decision making, which has to consider the implicationsof the decision on various environmental resources. In manysituations, it may not even be possible to represent a state by a single,variable-based indicator. In such situations, variables representinga state need to be aggregated in some form to represent the overallstate of the environment system.

In contrast with an indicator, which is based on a singlevariable, an index combines statistics on a number of variables intoa single value. Its ability to consolidate information on a number ofvariables into a single value makes an index attractive for decisionmaking. By allowing the implications of a decision on a particularstate or different states of the environment to be represented by asingle value, an index facilitates the comparison of variousalternatives for selecting the most cost-effective option.

An index may use aggregation, a functional relation, or astructural relation to combine statistics on various variables. A varietyof indices have been developed to be used in different contexts. Theseindices represent states of environmental systems such as air andwater (Appendix 2). One common aspect of these indices is that


weights have been used in aggregating statistics on different variablesto reflect the relative importance of the variable in determining thestate of the environment. When no weights are used, the variablesare assumed to be equally important.

Ideally, weights used for the aggregation of variables to forman index should have a scientific basis. An index so computed wouldbe noncontroversial and can be used across countries. Very often,however, weights are determined by using subjective methods,making the indices open to criticism. There are indices for whichweights cannot have a scientific basis since the weights representvalue attached to the variables. Values for such weights should bearrived at by consensus. An example of such an index would be“knowledge,” a measure of educational achievement as used in thehuman development index (HDI). This measure of educationalachievement is given as:

E = a1 Literacy + a2 Years of schoolingwhere: a1 = 2/3 and a2 = 1/3.

There is no scientific way of determining values for a1 anda2 in the above indicator/index. These values have to be based ongeneral consensus.

Another class of indices represent the structure and dynamicsof environmental systems. Examples are the niche breadth and nicheoverlap indices, which represent information on resources partitioningof an ecological system. This information would be valuable inunderstanding the dynamics of an ecological system. Species richnessindices provide information on the composition or structure of sucha system.

Indices based on functional relationships, on the other hand,represent the interaction among various environmental systems. Thisclass of indices is, in fact, based on the mathematical simulation ofvarious processes representing these interactions. Many times,interactions among environmental systems are too complex or notwell understood to be represented by a simple mathematical equation.In such cases, empirical equations may be used to represent theinteractions, as is depicted by the soil loss equation in Appendix 3.For these empirical constants to be valid across different geographicalregions, standard classifications of environmental resources are


required. For example, a standard classification for soils, topography,and vegetation cover is desirable for uniformly interpreting erodibilityacross geographical regions.

In summary,(i) Indicators and indices provide information in a simple

and compact form and, therefore, could facilitate decisionmaking.

(ii) Indicators are based on statistics derived from onevariable or attribute of a system. They could bedescriptive or normative. The former describes a statein a particular context, whereas the latter provides ameasure of a state with respect to a norm.

(iii) An index combines information on a number of variablesinto a single value, which makes it attractive for decisionmaking. By representing various implications of adecision in a single value, an index facilitates thecomparison of various alternatives/actions.

(iv) An index may combine information on various variablesby simple aggregation or through structural andfunctional relationships. In the aggregation of variables,weights are used to reflect the relative importance ofthese variables in determining the state represented bythe index. These weights should have a scientific basisso that the indices can be used across countries. Forindices where weights reflect value attached to thevariable, the value for weights should be determinedthrough consensus.

(v) Indices based on structural and functional relationshipsare more useful for understanding the structure anddynamics of systems.

One of the objectives of this RETA is to promote thedevelopment and use of standard methods for compiling environmentstatistics so that the statistics are comparable across countries. Theneed for comparable statistics arises for the following reasons:

(i) Many environmental concerns have assumed globaldimensions. Statistics needed for understanding andaddressing many of these concerns are compiled atnational levels. Standard methods for developing


statistics are required so that statistics can be comparedacross countries.

(ii) Environmental systems or resources transcend politicalboundaries. As such, the compilation of statistics for themanagement of these systems or resources should beundertaken in a coordinated manner. Examples of suchsystems are bioreserves, watersheds, rivers, and lakes,which may be shared by different countries and thereforerequire coordinated monitoring and management.

This discussion of methodological issues should be seen inthe light of the objective of promoting the use of standard methodsfor developing environmental statistics in the participating DMCs.The points that emerge from the discussion are the following:

(i) Knowledge of the domain is essential to compile statisticswithout losing valuable information. In view of budgetaryconstraints and the lack of data on various domains,statistical methods alone cannot be used for determiningwhere, when, and how data need to be collected. Math-ematical models that simulate the dynamics of the system,along with expert judgment, may have to be used to gainmaximum information with a minimum number of sam-pling stations. Therefore, for each domain, handbooks needto be developed to guide the development of statistics.

(ii) Indicators and indices can facilitate decision making.However, they should be constructed in such a way thatthey can be used across countries. Standardizedclassifications of resources should be adopted by all thecountries to allow the use across countries of indicesbased on empirical relations. The areas requiring theestablishment of standardized classifications shouldtherefore be identified. It should be determined ifinternational standardized classifications are availablefor particular areas. It is also important that a minimumor core set of indicators and indices be identified andaccepted by participating countries to be used forpresenting information.


Basic Data Standards

It is important to consider data standards because better informationwill enable a nation to better address its environmental issues at alllevels. Environmental indicators, which track changes in environmen-tal conditions over time, are generally developed from multisectoralenvironmental data or statistics generated by various agenciesthrough their monitoring programs. Furthermore, some raw data aredirectly used for assessing environmental trends and projections to beused in SOERs. Consistent national, subregional, and regionalinformation is needed for critical indicators of air, soil, and waterquality; and of natural resource degradation processes. To be useful,the information should clarify environmental issues as well as providefeedback on the effects of management initiatives, without any bias.

In the context of SOERs and environmental indicators, thedata standard indirectly refers to standard data definition, standardunits (measuring unit and geographical unit), methods ofmeasurement, sampling standards (sampling locations and frequencyof sampling), classification standard, spatial data standards (scale,geographic coverage, and processing), time series requirements, andmeta-data standards.

Data Definition

The definitions of the various categories of data vary fromcountry to country and region to region. Aggregating such data forthe purpose of SOERs might amount to adding apples and oranges.For example, the definition of open or closed forest varies from countryto country in terms of the crown cover percentage. While some countriesuse the FAO definition, others use their own definition. A similarcase exists in the definition of poverty. Thus, aggregatinginformation at a subregional or a regional level may cause a bigproblem. In the context of spatial data, the variables being measuredare mostly well-defined in photogrammetry, remote sensing,surveying, and other measurement-oriented spatial sciences. But forthe more interpretational spatial sciences, such as geology, soilscience, forestry, or geography, this is far from true, and oneperson’s freshwater marshes can be another ’s peat lands as bothare primarily waterlogged areas.


Standard Units

Standard units are very important in the context of spatialanalysis. The units may be countries, states, provinces, watersheds,floodplains, or river basins. To show or assess soil erosion status, awatershed unit could be more appropriate than a country or stateunit. Similarly, to assess the degree of flood hazard, a floodplain ofa 100-year flood could be more appropriate than one of a 50-year(return period) flood. With regard to statistical data, different unitsare frequently used for the same parameters, causing many problemsin aggregation, interpretation, conversion, and comparison across asubregion or region. For example, per capita commercial energyconsumption — which refers to the aggregate final consumption ofcoal and other solid fuels, petroleum products, gases, electricity —is expressed in kilograms of coal equivalent per capita or in kilo-grams of oil equivalent per capita. Similarly, the level of acidity inrainwater is expressed in pH value, or simply in terms of sulfate ionconcentration, or sulfate ion concentration as equivalent of sulfurdioxide in a volume of air. Decision makers encounter difficulties incomparing this value with the internationally accepted standards ofacidity level in rainwater when this value is expressed in various units.

Methods of Measurement

The quality of data significantly depends on the method ofmeasurement. Information derived from raw data obtained from faultymeasurements could mislead a person about the real situation. Inwater quality assessment, for example, various standard methods ofmeasurement should be followed. The concentration of pollutants inwater is strongly, sometimes inversely, correlated with flow rates.Therefore, sampling and velocity measurements should be carried outas close in time as possible. By sampling a volume of one liter or morewithin a minute of the velocity measurement, this variation is reducedsignificantly. If multiple-point sampling over the cross section of ariver is necessary, the hydrological measurements and samplingshould be carried out in the same cross section. Water quality data forassessing the potential effects on aquatic life should be based notonly on the mean annual flow rate calculated from the daily rate, butalso on the minimum flow rate. The minimum flow rate could be


operationally defined as the 20th percentile of the distribution of theaverage daily flow rates for a period that spans many years. Thus,standardization of measurements for different flow conditions is abso-lutely essential to cover a wider topic than raw water quality data only.

Another example is the measurement of the concentration offecal coliforms in freshwater bodies to assess the quality of wateravailable to communities for basic needs. Microbial examinationprovides the most sensitive indication of pollution by fecal matter.Because the growth medium, the conditions of incubation, and thenature and age of the water sample can influence microbiologicalanalysis, the accuracy of results may be variable and thestandardization of methods and laboratory procedures is extremelyimportant. Established standard methods are available through theInternational Organization for Standardization (ISO), AmericanPublic Health Association (APHA), the UK Department of Healthand Social Security, and WHO. Determination of the sample size isthe first important step in this examination. The source of the samplewill determine, in the first instance, the concentration of organisms.For example, under normal conditions, the volume of sample for alake or reservoir would be 100 milliliters (ml); in the case of rawmunicipal sewage, only 0.001 ml would be required. Larger sampleswould result in a volume too large to make counting possible. Thereis also need to ensure that the samples are not exposed to light andare kept preferably at 4-10oC to avoid changes in their bacterialcharacteristics. Such precautions are particularly important in tropicalclimates where ambient temperatures are high and sunlight(ultraviolet radiation) is brightest.

Similarly, the selection of an appropriate method is importantto achieve the objectives of air quality assessment. In scientific usage,concentrations of most air pollutants are commonly expressed in unitsof weight-to-volume ratio, such as micrograms per cubic meter; oras volume-to-volume ratio, such as parts per million (ppm). Weight-to-volume measurements depend on temperature and pressure.Therefore, it is common practice to standardize measurements to agiven pressure (usually one atmosphere) and temperature.Unfortunately, different national agencies standardize to differenttemperatures, usually 0oC, 15oC, 20oC, or 25oC. Measurements givenin units of weight-to-volume standardized at different temperaturescannot be directly compared. Similarly, the conversion from weight-


to-volume unit to volume-to-volume unit, or vice-versa, requiresknowledge of temperature and pressure. Different sampling (activesampling or passive sampling) or laboratory methodologies areappropriate for different gaseous species. Established standardsampling and analytical methods are available through the ISO,WMO, Economic Commission for Europe (ECE), EuropeanMonitoring and Evaluation Program, WHO, GEMS/Air of UNEP, andCouncil for Mutual Economic Assistance (CMEA).

In the context of spatial data, for example, land cover classesdetermined through aerial photography or remote sensing often haveknown confusion matrices, which give the probability that any onecover class may be confused with any other. Such errors enter theraw data, and stay with them through the entire process of databasecreation and further use for analysis purpose or in an SOER.

Sampling Standards

The quality of data collected depends first and foremost onhow good the sample is, i.e., how well it represents the quality ofthe body from which it is collected, and whether or not contaminationis avoided. There are various methods or techniques for samplingsoil, water, and air to assess their quality and condition. The mostreliable techniques for collecting samples and for making fieldmeasurements contribute to the good quality of the data, increasetheir precision and accuracy, contribute to the overall improvementof the air or water quality management process, and facilitateinternational harmonization of environmental measurement.

Sampling techniques generally vary from country to country.However, at least some minimum and necessary sampling techniquesshould be followed. It is important to consider (i) sampling locations,(ii) frequency and time of sampling, and (iii) sampling procedures.For example, in the assessment of river water quality, the samplingstations must be situated before one end of the human settlementas well as at the other end. It will be observed that the level ofpollutants will significantly vary from point 1 to 2 or 3. The level ofpollutants at point 2 will be more than that at point 1 because of theload from human settlements. Similarly, the level of pollutants atpoint 3 will be more than that of at 1 and 2, and so on. For example,the Yamuna River in India receives an estimated 200 million liters


of untreated sewage everyday as it passes through New Delhi. Thecoliform count starts from 7,500 per 100 ml above the city and risesto 24 million per 100 ml below the city (WRI 1995). Similarly, withair quality assessment in major cities, four representative locationscould be chosen in each of the cities, with sites in a city centercommercial area, an inner suburb residential area, a city center besidea busy road, and a residential area primarily influenced by industrialemissions. Measurements at similar locations in different cities wouldbe comparable.

Frequency and time of sampling are important as the qualityof water in rivers or water bodies is rarely, if ever, constant, but issubject to change. In measuring the mean, maximum, and minimumvalues of water quality variables over a period of time, the closenessof the monitored values to the true values will largely depend on thevariability of the variable and the number of samples taken. The largerthe number of samples from which the mean is derived, the narrowerwill be the limits of the probable difference between the observedand true means. To double the reliability of a mean value, the numberof samples must be increased to fourfold. With regard to samplingtime, the sampling program may stipulate random sampling times,but these should be spread more or less evenly throughout the year,covering all seasons of different activities such as agricultural andindustrial activities.

Standard sampling procedures should also be followed toobtain reliable results. For example, specific procedures are followedfor river water sampling, lake water sampling, and groundwatersampling. For a river with nonhomogeneous lengths, individualsamples may not be representative of the whole water body. It willbe necessary then to sample a cross section of the river to obtainaverage values. Average values are either simple averages or weightedaverages. Considering the river length as a series of vertical sectionsacross the chosen site, discrete samples are taken in each sectionand analyzed separately. The results from all the samples for eachsection are analyzed separately. To get the average value of theconcerned water quality parameter for that site, the results from allthe samples are added and the sum is divided by the number ofsamples.

However, weighted averages are preferable. Flow volume ineach section is measured at the time of sampling. To obtain a weighted


average, the cross-sectional area of each section must be known andthe velocity profiles for each must be prepared. The flow is multipliedby the sample value, the results for all sections are added, and thesum is divided by the total flow to give the flow weighted average.In the case of lakes, many exhibit the phenomenon of seasonal thermalstratification. When stratification exists, a number of samples shouldbe taken vertically according to the position of the metalimnion orthermocline. To measure the lake’s temperature or dissolved oxygenlevels so as to assess the potential effects on aquatic life, samplesshould be taken along the vertical profile of the stratification:(i) immediately below the water surface, (ii) immediately above theepilimnion, (iii) immediately below the epilimnion, (iv) at mid-hypolimnion, and (v) one meter above the sediment/water interface.

Classification Standard

Classification provides a structure into which availablenational information can be cast to arrive at internationallycomparable data. The first objective is to provide conceptual andmethodological guidance for collection and compilation of statisticson air, water, soil, and land quality and biota statistics for use inrelevant international data collections as well as in developingenvironmental indicators for state-of-the-environment reporting. Forexample, the overall classification of surface water relates to two waterquality aspects that have considerable impacts on aquatic life: theecological consequences of the regulation of water bodies such asdams, and the freshwater quality of surface water bodies. In the firstinstance, classification attempts to provide a framework for systematiccompilation and presentation of water quality data on water bodiesof international importance. Such water bodies are defined as thosewhose basins are shared by several countries or substantiallycontribute to coastal pollution. To assess the potential effects onaquatic life, a range of statistical variables could be included underwater quality classification as per the principles of oxygen regime,eutrophication, acidification, heavy metals, chlorinatedmicropollutants and other hazardous substances, radioactivity, etc.Further, the statistical variables under each of the above criteria couldbe grouped into several classes ranging from acute toxicity tominimum toxicity levels according to potential effects on aquatic life.


Similarly, to determine and to quantify factors with harmfuleffects on air pollution and to assess possible abatement measures,air pollution can be seen in three broad phases: (i) emission ofpollutants, (ii) their concentration in ambient air, and (iii) theirdeposition. Emission statistics are widely used in the estimation andmodeling of air pollution. Air emissions can be measured directly orcan be estimated on the basis of fuel and material consumption dataand other industrial processes. For this purpose, emissionclassification could cover emission from stationary sources such asprocesses (combustion of fuels in power plants and other industrialestablishments, domestic heating, and several economic activities)and from activity sources such as roads, railways, and other mobilesources. Data on concentration are primarily used for the monitoringof environmental problems with air quality at various scales. Theclassification for concentration in ambient air could coverconcentration (i) at impact stations (sulfur oxides expressed as sulfurdioxide, nitrogen oxides expressed as nitrogen dioxide, carbonmonoxide, volatile organic compounds [VOCs], lead, mercury,cadmium, SPM); (ii) at regional background stations (sulfur oxide,nitrogen oxides, particulate sulfur, nitric acid, particulate NO3

-

(nitrates), ozone [tropospheric], ammonia, VOCs, chemicalcomposition of precipitation such as pH/H+, ammonium, NO3-, Cl-(chlorine), sulfate, Na+ (sodium), K+ (potassium), Mg+(magnesium),Ca+ (calcium), electrical conductivity, etc.); and (iii) at globalbackground stations (carbon dioxide, ozone [stratospheric], methane,CFCs, Ha, nitrous oxide, SPM, etc.). Deposition statistics, togetherwith available information on critical loads of areas or ecosystemsconcerned, are increasingly requested for research and managementpurposes. Recommended deposition statistics classifications coverwet deposition of acids and acidifying compounds (sulfur dioxide,sulfate expressed in sulfur content; nitrogen dioxide, nitric acid , NO3expressed as nitrogen content; pH/H+, ammonia and ammoniumcompounds expressed as nitrogen content). The volume of acidifyingdeposition is calculated as the product of the concentration of thegiven component in the precipitation and the volume of precipitation.

With the ecosystem constantly evolving, it is important toprovide a temporal context for decision makers by reporting on past,current, and future trends. A common historical trend period, forexample, 20-25 years, can be used as a guide, but considerable


flexibility is required to reflect ecosystem and decision-makingcircumstances. It is important to anticipate and forecast futureconsequences of pressures on the environment. To predict future statesof the environment, various time scales ranging from 5 to 50 yearsmay be appropriate in terms of different scenarios. This is importantbecause there may be some considerable time delay before theecosystem responds to new or additional pressure. For example, theeffect of fertilizer application on the state of groundwater may notbe evident for several years. Preventive action may be too late if thesituation is not addressed on time. Furthermore, the implications ofnew policy and development options on environmental conditionsneed to be examined prior to implementation.

Meta-Data Standards

Broadly, a meta-database contains data about data, whichdescribe content, quality, condition, and other characteristics of data.The meta-database is required as a quality control measure tomaintain updated information. In this sense, the meta-databasebecomes an SOER product itself with external value. Meta-data areimportant because they help ensure an organization’s investment indata and help reduce wasteful duplication of data amongorganizations. Considering the nature of the data/information, it couldbe useful to maintain two distinct meta-databases: (i) a meta-databasefor environment statistics data, which could cover the followinginformation: data file name, summary description of contents, purposeof the database, name and address of organization, contact persons,access methods, parameters/variables included, geographic coverage,geographic projection, data acquisition methods, units of measure-ment, period of record, update frequency, database hardware/software,output formats (maps, tables, etc.), language, restrictions andconditions, price information, user guides/manual available,keywords, etc.; (ii) a meta-database for spatial (GIS) data, which couldcover data code, title/theme, type-vector or raster, location/geographical name, date, scale, file size, sources, keywords, updatefrequency, etc.

Chapter 5

Country Experiences in theDevelopment of Environment

Statistics in DMCs

This chapter aims to document the changes that the RETA hasbrought about in the participating DMCs. The status of

environment statistics is examined before and after the RETA. Thechapter begins by reporting the status of statistics in some core areas.Subsequently, the institutional setups that existed prior to the RETAare analyzed. The chapter then reports on the approach that the DMCsadopted to develop the FDES and the compendium, largely as a resultof the consensus steps agreed upon in the inception workshop. Theexperience of each country during the course of the RETA is thendescribed. Lastly, a critical examination is made of the final outcomeof the FDES and the compendium, specially on the status of the datafor the core environment statistics.

Status of Environment Statistics in SelectedCountries Before the RETA

Environment Statistics for Land-Related Issues

The first level of data reflecting land-related problemsconcerns land use. Land use describes the utilization of land for crops,pastures, forests and woodlands, and wilderness areas. Although thedata on pastures and cropland are complete, no data exist forwilderness areas in Bangladesh, Nepal, Philippines, Sri Lanka,and Viet Nam. This lack prevents those countries from assessingthe current situation properly, especially considering thatBangladesh lists the loss of habitat for its precious species as a


problem and Sri Lanka worries about soil erosion due toinappropriate land use.

Land degradation is observed to be a major problem forcountries, but data on land quality are either incomplete orunavailable. A good parameter for judging the trend in land qualityis soil productivity. It is recommended that statistics on soilproductivity be collected.

To understand or predict the changes taking place in landuse, data on the movement of population are very important. However,such data are incomplete in almost all the selected countries.

Deforestation is a major problem faced by the participatingcountries. Fortunately, all of them monitor the total area under forestregularly. Good data exist for productive forest areas and fuelwoodproduction. Data on average annual reforestation are also wellmaintained; however, data on forest quality are not easily available.Areas classified as forest area may be already degraded forest andsome indication of the quality of the forest is necessary.

The data sets on wildlife (large animals) seem to be more orless complete. All countries have data on the number of known speciesin a particular region and on a number of endangered species. Habitatloss is recorded by all the countries. However, the data on the factorsthat threaten wildlife are incomplete. For example estimates of tradein raw ivory, which is a major cause of poaching for elephants, arenot available for Bangladesh, Indonesia, Nepal, and Viet Nam.Figures on trade in mammal skin are important for the same reason.Data on this are not available for Indonesia, Malaysia, Pakistan,Philippines, Sri Lanka, Nepal, and Viet Nam. It seems that Nepaland Viet Nam do not collect any data on trade involving wildlife,which includes trade in live primates and reptiles, mammal skin,and ivory.

However, wildlife in forests do not consist of just large animals.Data on other forms of wildlife like plants and their diversity, insecthabitants and so on, as well as on ecology are missing altogether inany of the data sets.

India faces the problem of an energy shortage, Malaysia islooking for more efficient ways to produce and consume energy. Suchproblems can be studied from the available data on production,consumption, and trade of energy, but time series data are notavailable for most of the countries.

Country Experiences in the Development of Environment Statistics in DMCs 121

The most crucial energy problem faced by the world is theaccelerated use of nonrenewable fossil fuels. To address this concern,data on coal and oil reserves are needed. The selected countriesmaintain data on coal reserve, but some (like Bangladesh) measurereserves in place while others (e.g., Indonesia) keep data on just therecoverable reserves. A decision is needed on the uniform basis formeasuring reserves.

The production of uranium and nuclear energy might be verysmall for some of the countries, but no information on uranium isavailable for Indonesia, Malaysia, Nepal, Pakistan, Philippines,Sri Lanka, and Viet Nam. Pakistan does not have data on a nuclearreactor under construction either.

For the rural areas, the principal energy source is biomass-based fuels. It is observed that some data are available on the shortageof fuelwood, which is indicative of the seriousness of the problem.However, systematic data collection on sources of fuelwood and theirtrends is not done in any of the selected countries. Such data willbe useful for analyzing the fuel shortage problem.

Waste disposal is becoming a major problem for all the selectedcountries. However, data on waste generation is difficult to come by.For example, India and Malaysia do not have any data on annualmunicipal waste or industrial waste generation. Only data for haz-ardous waste are maintained. It is realized that the disposal ofhazardous waste is a more difficult task, but data on the generationof other waste will give some idea of the size of the problem of wastegeneration and disposal.

Data for Water-Related Issues

Foremost on the list of water-related issues is the availabilityof potable water. Satisfactory data for total availability of renewablewater resources and water withdrawal rates are also availablesectorwise for all the concerned countries.

Data on surface water quality are collected by all the countries,but, in the absence of a uniform structure, the indices are not thesame. The presence of coliform in water is a well-accepted indexthat is available for all the countries.

Another problem noted is the consistency of using the samemethodology for collecting data as well as the regularity of data


collection. It is observed that the river sampling station does notremain the same, that is, the samples are taken from a different sourcein the third year. This is true mainly for India and Malaysia. Suchinconsistencies need to be corrected.

Data on groundwater consumption need to be generated inthe region in the light of acknowledged problems such as the loweringof water tables, land subsidence, and saltwater intrusion.

Data on total fish production are available for all the countries,but data for individual fish species are sketchy. A decision has to bemade on the type of data needed to assess fish yield as well as theoverexploitation of certain fish species.

Some indices also have to be devised for assessing the con-dition of the mangrove swamps, coral reefs, and sea grasses becausemost urban sewage is being dumped into the sea. And in view of theoccurrence of oil spills, data on marine pollution are important andthe carrying capacity of coastal water should not be overestimated.

Natural disasters like floods, tsunamis, and cyclones arecommon in the selected countries. Bangladesh, Nepal, and Vanuatucite them as major environmental concerns. The selected countriesmaintain a record of the time and types of disasters and, more oftenthan not, the number of deaths caused by these natural disasters(though the accuracy of the data is not always ensured); however,the total number of people injured and affected is not well recorded.Without these data, it is difficult to estimate the extent of success inprotecting people against such natural disasters.

Air Pollution Data

Since Agenda 21, more efforts are being exerted to collectmore reliable data to estimate net additions to global warming,however, not all the indices for estimating and comparing air qualityacross countries are available. A case in point is the data on smoke,which is lacking for all the large countries covered by the RETA (India,Indonesia, Malaysia, and Philippines). As far as emissions areconcerned, proper data are available for carbon, sulfur dioxide, andmethane emissions, but the data on consumption of chlorofluorocar-bons are incomplete for many countries including Bangladesh, India,Indonesia, Nepal, and Pakistan. Malaysia seems to be the only countrywith good quality data on air pollution. Uniform air pollution data


must be integrated with data on the exposure of people to suchpollution.

To summarize, air quality data are still difficult to obtain inmost of the countries. It is desirable that gaps be filled soon. Inaddition, worldwide estimates of emissions are of uneven quality.The problem is further aggravated by the fact that data are oftencollected and analyzed on an ad hoc basis.

Environment Statistics Related to Social Economic Indicators

The relation between population pressure and environmentaldegradation has been discussed in the previous sections. It has beendemonstrated that population-related data are essential to assess theexisting and predicted pressures that will be exerted on theenvironment.

Although the collection of environment statistics is a relativelynew task for nations, the collection of socioeconomic informationhas a much longer history. Basic demographic data are collected byall the 11 countries. Hence, it does not come as a surprise thatcomplete data exist for population growth; labor force distributionof the population by age; birth and death rates; life expectancy; fertilityrate; infant, child, and maternal mortality rates, and so on. However,data on the factors that create the trends in the basic demographicindicator differ in structure from country to country and are not alwaysavailable. For example, Bangladesh and the Philippines do not collectdata on the number of births attended by trained personnel. Manycountries (India, Indonesia, Nepal, Sri Lanka, and Viet Nam) do nothave data on the average number of children as affected by themother ’s years of education. Uniform indices have to be developedfor comparing global and regional trends.

The most common indicator used to determine the economicstatus of a country is its GNP. The selected countries seem to keepa good account of GNP, but the indices for more detailed analysisare not always complete. For example, the change in real GNP overthe years is not recorded by Viet Nam. The distribution of GNP amongthe different sectors, i.e., agriculture, industry, and services, was notrecorded by Malaysia and Viet Nam in 1987.

Environmental degradation is often closely related to thedeterioration of human health. Hence, health statistics form an


important part of environment statistics. Accessibility of the basicinfrastructure facilities is another aspect of environment statisticsthat should not be underrated. Lack of a proper sanitation systemleads to water pollution; therefore, countries will need to collect dataon health and access to infrastructure facilities.

The current status of health and infrastructure statistics showsthat much needs to be improved on this front. Viet Nam does nothave data on sanitation services. Most of the selected countries(Bangladesh, India, Indonesia, Malaysia, Nepal, Philippines, andSri Lanka) do not have information on the percentage of thepopulation with access to health services. However, records on peopleaffected by major diseases like malaria and cholera are adequatelymaintained.

Institutional Framework for CollectingEnvironment Statistics Prior to the RETA

The purpose of this section is to describe the institutional frameworkfor the collection of environment statistics that existed in each of theselected countries prior to the RETA.

Most of the countries have well established NSOs, known byvarious names. The NSOs have been collecting, collating, andreporting primarily socioeconomic data. Much of these data arerelevant to environmental analysis, mainly because they describethe extent of human activities (the pressure aspect of the pressure-state-response framework).

In nearly all the selected countries, the responsibility forcollecting core environment statistics rests with other line ministriessuch as those of environment, agriculture, forests, fisheries, etc. Butthere is little evidence to suggest that formal systems are in placefor environmental information to be routinely transmitted to the NSOs.Certainly, in none of the countries is there any legislation or executiveorders that make communication of such data mandatory.

In some countries, notably India, Indonesia, and Philippines,substantial amounts of environmental data were available, thoughin a scattered manner, owing to previous activities related to state-of-environment reporting, national environmental action plans,


National Agenda 21, and general environmental management-capacity-building exercises. Yet, none of these activities actuallygenerated new data; the results were largely recompilations of existingdata and used secondary data to a large extent. These activities werecommonly funded by international agencies such as UNEP, the WorldBank, and United Nations Development Programme (UNDP). Thus,the funds for new surveys and monitoring are still supplied by internalsources, which are, in all countries, very meager.

From an assessment of the country papers prepared by theparticipating DMCs before the launching of the Project, some morespecific points emerge.

(i) In most of the countries, except India, Indonesia, andMalaysia, there was a general lack of coordination/collaboration among the agencies responsible forcollecting data for environment statistics. Moreover, nosingle unit or agency was officially entrusted with theresponsibility for collecting, collating, and compilingdata for environment statistics. In India, an interdisci-plinary working group under the Director General of theCentral Statistical Organization (CSO), comprisingvarious government agencies, was involved in collectingand disseminating data for environment statistics. Noseparate unit or division within CSO, however, wasentrusted with the responsibility for working on variousaspects of environment statistics.

(ii) In Indonesia, coordination among various data collectionagencies was good, but there were no formal institutionallinkages among them. The Central Bureau of Statistics(CBS) had been responsible for publishing environmentstatistics data on a regular basis since 1982. In Malaysia,some informal institutional linkages were in place amongvarious government agencies within the Ministry ofScience, Technology, and Environment (MOSTE). TheDepartment of Environment under MOSTE wasresponsible for compiling environment statistics data;NGOs were directly or indirectly involved with collection.No separate unit within the Department of Statistics(DOS) was given the responsibility for dealing withenvironment statistics.


(iii) There was no formal framework for environment statisticsin any of the participating DMCs, barring India,Indonesia, and Philippines. In India, although aframework based on UN-FDES was in place, interlinkingof regular and time series data for appropriateenvironmental indicators (EIs) was absent. In Indonesia,the Government adopted UN-FDES in 1993. In thePhilippines, no UN-FDES was adopted but there was aframework for Philippine environmental accounts(through a UNDP-Department of Environment andNatural Resources [DENR] project) and the Bureauof Agricultural Statistics (BAS) had a database onenvironmental accounting (through a USAID-fundedproject).

(iv) Except for Indonesia, whose CBS published acompendium of environment statistics (CES) in 1982(updated in subsequent years), no other participatingDMCs had prepared a formal CES.

The collection of environment statistics is important to assessthe current environmental problems of a country and to makeinformed decisions about environmental policies. To make policiesfor a group of countries, a common statistical framework for all thesecountries is necessary. Such a framework is the goal that ADB wouldlike its member countries to achieve.

Some environmental concerns are common to all the membercountries: deforestation, water pollution, excessive water withdrawal,and air pollution. In addition, each country has some uniqueproblems. Land subsidence, saltwater intrusion into groundwater,reservoir desertification, slums, and lack of access to basic servicesare categories of such individual problems. The aim is to reflect theseconcerns in the common statistical framework for the selectedcountries.

The selected countries maintain adequate statistics on forests,water availability, wildlife animal population, occurrence of disastersand the resulting casualties, as well as on basic economic anddemographic indicators. However, not much is available beyond thebasic statistics. For example, data on forest quality, flora and someforms of fauna (e.g., insects), and trade in wildlife skins and other


parts are not available in many countries. Information on aspects ofland use, population movement, waste generation, water and airquality, and health is difficult to get in all the selected countries. Acommon statistical framework needs to be designed to identify thebasic statistical parameters as well as to determine the data needsfor defining higher order indicators. In each of the selected countries,an institution will have to be identified and given the responsibilityfor compiling all necessary data on the environment.

An Approach to Developing Environment StatisticsAdopted by DMCs

The general paucity of environment statistics in DMCs is the resultof several factors:

(i) the nondesignation of a focal agency with responsibilityfor collecting environment statistics and lack ofcoordination between the various government agenciesinvolved in environment statistics;

(ii) lack of a clear definition of terms, concepts, andclassifications within the environment and in statisticsagencies in the countries concerned;

(iii) lack of clear-cut methodologies for developingenvironment statistics; and

(iv) lack of government support for the collection ofenvironment statistics.

All countries face similar problems, such as the lack of(i) adequately trained staff,(ii) technical documents including operational manuals on

environment statistics and methodological andprocedural guidelines, and

(iii) computer hardware and software.

For the initial stages, these needs were identified:(i) training for concerned personnel, possibly with financial

support from ADB, ESCAP, or similar agencies;(ii) logistical resources such as vehicles and computers;


(iii) technical documents;(iv) short-term consulting services, and(v) study tours.

To complete the process of developing an environmentstatistics system, each participating country undertook the follow-ing key actions:

(i) finalization of the FDES and the compendium ofenvironment statistics,

(ii) operationalization of institutional linkages,(iii) organization of training courses, and(iv) acquisition of additional facilities.

At an early stage, the following recommendations for the RETAwere made:

(i) Various training programs and study tours bothin-country and on a subregional level should beorganized and conducted for concerned personnel of theparticipating DMCs.

(ii) Relevant technical documentation on operationalguidelines, methodologies, and procedures onenvironment statistics should be disseminated by theBank to the participating countries.

(iii) Training materials should be collated and documented,and a training curriculum should be prepared forin-country or subregional courses.

(iv) Technical assistance to the participating DMCs to enablethem to finalize their FDES or compendium should beprovided by ADB.

(v) Interdepartmental linkages within each DMC withrespect to the collection of environment statistics shouldbe strengthened.

(vi) Information exchange or sharing and other forms ofcooperation in the area of environment statistics shouldbe fostered or promoted between DMCs.


Present Status

The status of the three major aspects of environment statistics(institutional linkages, FDES, and compendium of environmentstatistics [CES]) has changed considerably during the course of RETA5555, as indicated by the responses to the questionnaire survey andother supporting documents (FDES and CES) obtained from theparticipating DMCs. All the participating DMCs have establishedinstitutional linkages among various agencies responsible for datacollection and the users of environment statistics. Most of theparticipating DMCs have established separate units or equivalentbodies responsible for matters relating to the collection of environmentstatistics (data from various agencies, collation and compilation ofsuch data, and preparation of FDES and CES in the future).

Most of the participating countries, as revealed in thesurvey, would need additional surveys, research programs, etc. toupdate their CES and FDES. Due to lack of adequate data onvarious environmental issues, most of the DMCs can supply onlypartial data to ADB on a regular basis, as per the format providedin Appendix 1.

The major differences among the DMCs include theirchoice of appropriate environmental issues, their units ofmeasurement, and availability of data on the issues; nature of data(time series vs one-time measurements); level of technicalknowledge, skills, or expertise on environment statistics availablein individual DMCs; and availability of infrastructure for primarydata collection for environmental issues. Since all of these featuresare related to the FDES, these were considered in assessing thequality of the FDES prepared by the DMCs.

Framework for Developing Environment Statistics

The FDESs prepared by Indonesia, Malaysia, and Philippinesare well-structured, contain appropriate environmental issues (alongwith their units of measurement) to describe various environmentalcomponents under different information categories as per theUN-FDES format, and have adequate time series data.

The FDESs prepared by India and Nepal have a fairly widecoverage of environmental issues and have adequate data in some


form or another (time series or one-time measurements) on most ofthe issues. Nevertheless, improvement could be made in the structureof the FDES, units of measurement of the issues, and availability oftime series data for a larger number of environmental issues.Institutional linkages across data-collecting agencies (bothgovernment and NGOs) need to be strengthened further to help createimproved versions of the FDES in the future. Both India and Nepalhave the required expertise (trained and skilled manpower) fordeveloping environment statistics. Better coordination among thesetrained and skilled personnel, and a more systematic and focusedattention on environment statistics are required, however, to sustainthe progress achieved through RETA 5555.

The FDESs prepared by Bangladesh, Pakistan, Samoa,Sri Lanka, Vanuatu, and Viet Nam could be considered preliminaryas they lack adequate data on the environmental issues identifiedand included in the present version. Also, a majority of the data areeither secondary or one-time data. This is mainly due to lack ofadequate knowledge, skills or expertise, and infrastructure in thesecountries for collecting or generating primary and time-series data.Moreover, the concept of environment statistics in these countriesis still in its infancy. Nevertheless, given the above constraints, thesecountries should be commended for the efforts they have exerted todate to bring out their present FDES.

Compendium of Environment Statistics

An overall qualitative assessment is made of the data on coreenvironment statistics presented in the compendiums. The criteriafor evaluation are mainly depth and specificity of coverage.

The presentation of data related to the atmosphericenvironment (Table 5.1) clearly shows that inventories of greenhousegases and ozone-depleting substances have not been presented ingreat detail. Countries such as Nepal, Pakistan, Sri Lanka, and VietNam also need to improve the section on regional emissions. Mostcountries have adequately presented whatever information on localair quality information was available to them.

Water availability being perhaps the most crucial problem ofthe region, it is surprising that most countries have not presentedthe data in an adequate manner - in terms of either availability across


sources or demand across sectors (Table 5.2). Data pertaining to waterquality was presented only for rivers, and not for lakes andgroundwater.

Table 5.1Core Environment Data Status: Atmospheric Environment

Global Level(Greenhouse

Country Gases and Regional Level Local LevelOzone Depleting (Emissions) (Urban Air

Substances) Pollution)

Bangladesh Needs improvement Satisfactory Needs improvement

India Satisfactory Satisfactory Satisfactory

Indonesia Needs improvement Satisfactory Satisfactory

Malaysia Needs improvement Satisfactory Satisfactory

Nepal Not available Needs improvement Satisfactory

Pakistan Needs improvement Needs improvement Needs improvement

Philippines Needs improvement Satisfactory Satisfactory

Samoa Not available Not relevant Not relevant

Sri Lanka Needs improvement Needs improvement Needs improvement

Viet Nam Satisfactory Needs improvement Needs improvement

Table 5.2Core Environment Data Status: Aquatic Environment

Country Water Resource Use Water Quality

Bangladesh Satisfactory Needs improvement

India Satisfactory Needs improvement

Indonesia Needs improvement Satisfactory

Malaysia Satisfactory Satisfactory

Nepal Needs improvement Needs improvement

Pakistan Needs improvement Needs improvement

Philippines Satisfactory Satisfactory

Samoa Satisfactory Not relevant

Sri Lanka Needs improvement Satisfactory

Viet Nam Needs improvement Needs improvement


Table 5.3Core Environment Data Status: Terrestrial Environment

Flora and CoastalCountry Forests Fauna Environment

Bangladesh Satisfactory Satisfactory Needs improvement

India Satisfactory Satisfactory Needs improvement

Indonesia Satisfactory Satisfactory Needs improvement

Malaysia Satisfactory Needs improvement Satisfactory

Nepal Satisfactory Satisfactory Not relevant

Pakistan Needs improvement Needs improvement Needs improvement

Philippines Satisfactory Satisfactory Satisfactory

Samoa Satisfactory Satisfactory Satisfactory

Sri Lanka Satisfactory Satisfactory Satisfactory

Viet Nam Needs improvement Needs improvement Needs improvement

Data related to forests are restricted to the current amountof land under various types of forests (Table 5.3). Rate of deforestationis rarely presented. The data on number of species, endangeredspecies, etc., are fairly adequately presented. But many countries

facing acute coastal and marine problems have not provided anydata related to either pressure factors or status.

Basic data related to the very important urban problem ofsanitation has hardly been reported (Table 5.4). Data related tomunicipal solid waste have been presented, but in a rathersimplistic way.

In the case of all four areas of core environment statistics—atmospheric, aquatic, terrestrial, and urban environment—whereverdata presentation is inadequate, it is not clear from the compendiumswhether data do not exist or whether there were problems in accessingthe data.


Table 5.4Core Environment Data Status: Urban Environment

Country Sanitation Solid Waste Management

Bangladesh Needs improvement Needs improvement

India Satisfactory Needs improvement

Indonesia Satisfactory Needs improvement

Malaysia Needs improvement Satisfactory

Nepal Needs improvement Satisfactory

Pakistan Satisfactory Needs improvement

Philippines Needs improvement Satisfactory

Samoa Needs improvement Satisfactory

Sri Lanka Needs improvement Needs improvement

Viet Nam Needs improvement Needs improvement

Chapter 6

Future Directionsin the Development ofEnvironment Statistics

Priorities in Strengthening EnvironmentalStatistical Systems

In most developing countries, environmental policy is being redefinedand efforts to develop national policy frameworks are under way.The common elements of emerging environmental policies are

(i) a commitment to the goal of sustainable development;(ii) a strengthened role for environmental institutions vis-

a-vis sectoral and central government agencies;(iii) a firm legal basis for setting policies, their implemen-

tation, and their enforcement;(iv) increased responsibilities of regional and local

governments for environmental management;(v) separation of the regulatory and economic development

roles of government;(vi) adoption of the polluter-pays principle;(vii) recognition of “new actors” involved in environmental

issues: the public, NGOs, sector interest groups, privateenterprise, independent research institutes, and laborunions; and

(viii) strengthening of cooperation with internationalinstitutions and programs, with which contact in thepast has been limited.

The new context for environmental policy is also transformingthe roles of and expectations about environmental data, statistics,and information. Environmental information is no longer a tool inthe preparation and implementation of government plans. Instead,


strengthened environmental statistical systems focus on severalobjectives, providing (i) accurate and reliable environmentalinformation to meet national and international demands; (ii) a toolfor monitoring and enforcing compliance with regulations andenvironmental policies; (iii) an instrument for policy integration; and(iv) a means of communicating with and informing decision makers,the public, the private sector, NGOs, and interest groups.

Priority should be attached to strengthening the availabilityof quality environmental information in those areas or regions withthe greatest risks to human health and irreversible environmentalchanges. The information generated should support the implementa-tion of priority environmental action programs. The close cooperationof health and relevant sectoral ministries, together with environmentministries, is essential for this purpose.

Reliable information is required by foreign and domestic inves-tors. For example, environmental factors can restrict investment, andappropriate information is required to resolve difficulties that may arise.

On the basis of priorities and resource availability, steps shouldbe taken to promote medium-term to longer term consolidation andexpansion of environmental information systems. Attention shouldconcentrate primarily on environmental conditions and trends. Todate, much of this information has not reached managers. There isopportunity for environment statistics to strengthen sector policyintegration efforts, especially in the priority energy sector. Objective,credible information can support dialogue between and coordinatedpolicy actions by environment and sectoral ministries.

Strengthening the environmental statistical systems in theselected countries requires a framework with clear objectives and astrategy for implementation and evaluation of performance. Thechallenge is to improve existing systems by upgrading the qualityof present arrangements; where necessary, eliminating or reassigningelements that do not meet users’ needs or are not cost-effective; andprogressively filling in the most important gaps.

Several weaknesses in the coverage of existing environmentalinformation systems are apparent. At the overall system level, theprincipal weakness in the developing countries is a lack of acomprehensive and integrated information system linked spatially(across ecoregions) and temporally. Data weaknesses are alsoidentifiable: poor coverage of biological indicators of water quality,

Future Directions in the Development of Environment Statistics 137

and water pollution in rivers and heavy metal levels in lakes; limiteddata on marine pollutants originating from the coast; little data onpesticide use on arable and crop land; and gaps in air quality dataconcerning estimates of national carbon monoxide and hydrocarbonemissions, lead emissions, CFC usage, and urban air quality. Dataon population exposure to noise from traffic, airports, and othersources are deficient. Wastewater treatment information does not givethe number of households connected to sewage schemes, capacityof treatment systems, and degree of treatment prior to disposal. Solidwaste and hazardous waste data are weak in terms of specifyingvolumes and sources (household, industrial, construction sites etc).Data on ecosystems and biodiversity are sparse.

Once the framework and priority elements of theenvironmental statistical system have been established, attentionshould focus on the methods used to collect information. Explicitcriteria to guide the choice of data collection methods andtechnologies should be specified. Such criteria should include cost-effectiveness, flexibility for future modification and extension, abilityto deliver essential and reliable information on priority environmentalissues to decision makers, and ability to harmonize with internationalstandards and classifications. The tendency for state-of-the-arttechnology to “drive” environmental information systems should befirmly resisted.

Existing approaches to data collection and environmentalmonitoring need to be improved, particularly integrated monitoringsystems. The extension of monitoring networks is a priority task inall of the reviewed countries. There is an urgent need to improve thecompatibility, comparability, reliability, and accessibility of data bylinking various sectoral networks and to slowly extend their spatialcoverage. Critical issues requiring attention include the number anddistribution of monitoring sites, balance between ambient and pointsource monitoring, and reliability of the data generated by monitoringstations. Complementing this effort, sample surveys should beencouraged for preparing SOERs. This will require training of statisticiansand environmental professionals in sample survey techniques.

The potential for cross-media and multiple-exposuremonitoring should be assessed. Such monitoring can clarifytransmedia movement of pollutants and their synergistic effects onenvironmental quality and human health. Biological monitoring, such


as for effluent discharges, can often be applied more easily thanphysical-chemical monitoring, and at much less cost. Otherpossibilities for data collection and monitoring include the use ofenvironmental impact assessments (EIAs) and environmental auditsof firms’ performance, and independent monitoring by enterprisesand research institutes.

Institutional Arrangements and Decision Making

Environment ministries are emerging as key actors indeveloping and implementing environmental statistical systems.Statistical offices will continue to have important roles to play,including coordinating the collection of data by various governmentdepartments, integrating the national environmental monitoringsystem with the national statistical system, reinforcing the consistentuse of internationally agreed-upon definitions and terminology,and meeting specific information requests. These new responsi-bilities need to be clearly distinguished from those of environ-ment ministries.

It is to be noted that much of the statistical analysis ofenvironment data will be done by the agencies producing those datathemselves, not by the statistics agency. As noted earlier, much ofthe statistical analysis methodologies require skills found in the data–producing agencies themselves. Staff of the statistics agency maybenefit from some training in those methodologies, but it would beimpractical to try to transfer those skills from the data producers tothe statistics agency. For example, ways of expressing and quantifyingland degradation are the expertise of agriculture personnel.Observations and measurements of data on water are best done bythe water resource department; those on pollution, by the environmentdepartment. The statistics agency’s job is mainly to compile thestatistics generated by the responsible sectoral or functional agencies(e.g., forestry, mining, agriculture, industry, etc.)

The various sector agencies collecting environmentalinformation require a framework for the coordination of informationflows within and between ministries. The framework is essential forenvironment and sector policies to be better integrated. Ministriesof health and social affairs, agriculture, forestry, transport, watermanagement, energy, and industry and trade may all be collect-


ing environmental information for their specific sectors. This maycreate problems of administration, duplication, and nonsharingof information.

Decentralization of power is an integral part of democrati-zation in the countries. As a result, debates on centralization versusdecentralization of environmental management are emerging, oftencentered around the appropriate mix of decision-making powersbetween different levels of government (including local cityauthorities). Ensuring that local needs are balanced against nationalneeds in a coherent, comprehensive environmental informationsystem is a particular concern.

Strengthening environmental statistical systems also involvesextensive training and extension of the existing skills base. In general,there exists a relatively highly educated workforce with technicalskills in natural resource conservation and environmental scienceand technology, the so-called “hard sciences.” Nevertheless,monitoring personnel do not always have appropriate training,guidance, or support. Bilateral and multilateral training efforts incapacity building, EIA, and air and water quality monitoringtechniques are expanding the existing technical skills. The need fortraining and support is particularly acute at the subnational level.

Strengthening Partnerships

Research institutes play an important role in the collectionof environmental data. These institutes were well supported in thepast and were usually attached to specific ministries. As a result, thestaff are often well trained and motivated, and have good contactswith international scientific networks. Where research institutes havea demonstrated capacity to generate quality environmentalinformation, governments should continue to provide support at anadequate level. Multilateral and bilateral programs could also providesupport by working, where appropriate, with research institutes.

The strengthening of environmental statistical systems musttake place within the perspective of their costs and benefits.Implementing the polluter-pays principle to “internalize” the costsof use, or degradation, of environmental resources is thecost-allocation principle adopted in Western countries. In theory, thepolluters should pay the full cost of damages caused by their activities.


Where monitoring activities can clearly reveal specificpolluting activities, the polluter-pays principle suggests that the costof monitoring should be borne by the polluter. This will be the casefor emissions from large stationary sources. Regulations requiringfirms to disclose emissions data could be enacted. However, thereare many cases where this is not feasible, such as when the polluteris not identifiable, or when the enterprises are small or medium size.Nevertheless, all over the world monitoring of ambient environmentalconditions at present generally is financed by public sources.

The allocation of expenses between central agencies orministries and local authorities’ budgets is often made according tothe division of responsibilities. Some transfer of resources from thenational budget to local authorities may be justified when the lattercarry out national responsibilities locally, such as operating part ofan environmental monitoring network. The manner in which moneyfrom public budgets is allocated to different national environmentalprograms (air, water resources management, fauna and flora, etc.)should be consistent with environmental policy priorities. Sufficientfunding should be made available within those different programsfor monitoring work at the field and policy levels.

External assistance from bilateral and multilateral sources todevelop and extend environmental statistical systems has beenimportant. Such support can provide only a fraction of the resourcesrequired, but is nevertheless significant because of the transfer ofexperience, their demonstration effect, and the provision of modelsthat can be adapted as appropriate. Activities include establishingmodel air and water quality monitoring systems in the most severelypolluted regions, assistance in setting up and using environmentaldatabases, equipping regional laboratories, promoting staffsecondments to work in advanced countries and internationalorganizations, and funding the participation of countryrepresentatives at international meetings. Generally, the greatestneeds appear to be for technical assistance, training, and exchangesof personnel, especially at the local level.

In strengthening environmental information systems,opportunities exist for establishing partnerships between the publicand private sectors to meet information supply needs. For example,enterprises could collect environmental information in accordancewith government-specified guidelines and standards, and subject to


verification procedures. Opportunity would then exist for enterprisesto choose whether to collect the information using in-house specialistsor to contract the work out to private research institutes orconsultancies. Other possibilities for information collection includecooperative arrangements between the government and NGOs, laborgroups, and the scientific community. This could be a cost-effectiveand efficient way to obtain and disseminate environmentalinformation, which reduces as well the financial burden forgovernment administrative and statistical offices.

The emergence of distinct public and private sectors requiresnew institutional arrangements to ensure that governments haveaccess to the information that they require for policy purposesand that the rights of private enterprises and individuals areadequately protected. In a more market-oriented economy,enterprises may be reluctant to supply information to public bodiesbecause of commercial confidentiality and cost. Principles of confi-dentiality must be well defined, usually reinforced by appropriatelegislation, with guaranteed protection of information on individualsurvey forms. Public authorities for their part will need to be clearas to what types of information they require and why, ensure thatcost-effective approaches are used so as to minimize reportingburdens on the private sector, and develop appropriate informationdissemination strategies that take account of confidentiality concerns.For all groups that will be involved, this is very much a new experience.

Participating in international cooperative efforts inenvironmental information collection and dissemination is an integralpart of system design. The significance of such cooperation derivesfrom three principal reasons. First, the transboundary nature of somepollution problems (such as greenhouse gases, ozone, and acid rain)highlights the importance of having a well-developed domesticmonitoring system linked to international monitoring systems, whichpermits meaningful comparisons of information. Second, to survey thecompliance of countries signatory to international conventions andagreements, comparable information must be collected on an interna-tional scale. Third, links to international information systems providecountries with an opportunity to cooperate in developing cost-effectivetechnical, institutional, and financial approaches to problems.Regional activities, often cofunded bilaterally or by internationalinstitutions, are already evident and provide a solid base for extension.


Efforts to further integrate the environmental statisticalsystems of the selected countries into the largest internationalframework could focus on several initiatives. First, the developmentof regional information networks will help reduce duplication, enableinformation to be collected and compared on the basis of standardmethodologies, and provide opportunity for staff secondment tobroaden experience and skills.

Second, because international comparisons of environmentaldata rely on the availability of high quality data inputs, there is aresponsibility to strengthen domestic capabilities in and coordinationof information collection, treatment, and dissemination. Where dataare to be supplied on a regular basis, a single permanent contactpoint should be established. Some sharing of responsibilities betweenenvironment ministries and central statistical offices is necessary,and the division of labor should be clear. This would preventduplication of efforts and reduce at the international level theconfusion resulting from having different sets of data originating inone country.

Summary

It is evident that serious efforts are being made to strengthenand restructure environmental statistical systems in these countries,concomitant with reforming existing institutional arrangements andupgrading technical skills. Some progress has been achieved, butmuch still remains to be done.

(i) Despite the existence of a wealth of data, the purposeof collection and its coverage need to be better focusedto support the work of decision makers. The role ofenvironmental information needs to be reoriented tobetter match the governments’s new role of monitoringand regulating market-based economic activity. Timelyand reliable information can have an importantinfluence in integrating environmental considerationsinto economic sector restructuring and in monitoringoutcomes.

(ii) There is a need to improve the quality of the datacollected and to establish confidence in the reliabilityof environmental information. Data collection mecha-


nisms have to be reviewed and appropriate technologyfor environmental statistical systems chosen. The cost-effectiveness of technology should be a major criterion.

(iii) Institutional arrangements for environmentalinformation need to be improved and the respectiveinstitutional roles unambiguously defined. A cleardivision of tasks between environment and statisticaloffices has to be established. Much more than they didin the past, all relevant agencies should supply credible,objective information to support decision making forsustainable development.

(iv) The responsibilities of central and local governmentsneed to be made clearer, taking account of therequirements for coordination, cost-effectiveness, andresponsiveness to decision making needs at the differentlevels of government.

(v) Further efforts are needed to integrate the environmentalstatistical systems of these countries into the largerinternational framework. Such integration will benefitfrom specific activities jointly conducted by countriesand international organizations such as ADB, EuropeanCommunity (EC), International Bank for Reconstructionand Development (IBRD), OECD, UNSD, and WHO.

(vi) Imaginative and broadly targeted environmentalinformation and communication programs need to bedevised to improve public awareness of environmentalissues. Opportunities include consolidating the limitedexperience with state-of-the-environment reporting anddeveloping environmental indicators.

Linking the Environment Statistics Framework withPolicy Through State-of-the-Environment ReportingThe SOER, whether at the national, subregional, or regional level,is commonly prepared to monitor trends in achieving the goals ofenvironmentally sound and sustainable development. The assessmentof the state of the environment should, through statistics, aim at thefollowing:


(i) informing governments and concerned organizationsin the region and elsewhere about the state of theenvironment with respect to both the prevailing humanconditions and the status of the natural resources, in aconsistent and comprehensive manner;

(ii) providing information, based on empirical evidence, onthe various stresses placed on the human condition andthe natural resource base;

(iii) assisting in the process of informed planning bypresenting information in a framework that is compre-hensive and easily understood, thereby facilitatinganalysis of cause-effect relationships, and forming thebasis of policies, strategies, and action plans;

(iv) illustrating policies, strategies, and action plansundertaken at national, subregional, and regional levels,including both institutional and technological aspects;

(v) indicating, as far as practicable, gaps in the present stateof knowledge and information and the need for newinformation, as well as for investments in research anddevelopment; and

(vi) Improving public understanding about the state of theenvironment through a better informed public debateabout these issues at all levels.

A framework should help in integrating multisectoral data,identify areas where data are inadequate, and indicate weak linksin institutional networks that need strengthening. To supportenvironmental assessment activities, the framework should possessa good database, with data from relevant and appropriate case studies.Importantly, the database will need to be sensitive to the level ofapplication of the information.

A well-documented format can serve as the basis ofinformation for the monitoring of state-of-the-environment reporting.The database can be the knowledge base for spatial and tabular datafor catalyzing decision making on environment and developmentissues. It can also serve as a tool in implementing and following upnational environment management strategies that were producedfor member countries. The database can be developed under fourcategories:


(i) biophysical environment, i.e., land use, topography, landtenure, soil types, village boundaries, conservationareas, flora and fauna, etc.;

(ii) socioeconomic environment, i.e., human activities,agriculture, forestry, transportation, energy and tourism;and human conditions, size, growth, and distribution,health aspects;

(iii) natural disasters, i.e., floods, droughts, susceptibility tocyclones, earthquakes, etc.; and

(iv) policies and institutions, i.e., responses by governmentsand government agencies.

To ensure the participation of all sectors, the framework canbe broken down into seven components as follows:

(i) Data processing and information flow, including themechanism to process data collected from varioussectors into useful information.

(ii) Integration of biophysical and socioeconomic data, toassess the impact of human activities on the conditionof natural resources. Integration of these two types ofdata will identify, according to the problems posed, thecritical linkages between them. For problems to beaddressed appropriately, the linkage needs to be aunique cause-and-effect relationship.

(iii) Technological support or utilization of GIS to integratemultisectoral data so as to derive useful information tocatalyze decision making; selection of technologybased on the capacity of data that it can analyze,frequency of the data, cost-effectiveness, and userfriendliness.

(iv) Development of indicators/indices and identification ofissues. Given the pressures on environment anddevelopment, the traditional sole reliance on economicindicators as a means of measuring progress is no longersufficient. Hence, environment indicators are also beingdeveloped to present information on environmentconditions and natural resources. These indicators canbe of various types designed to(a) reflect the quality of the environment;


(b) indicate the impact or stress on the environment,resulting from human actions;

(c) evaluate the costs and benefits of environmentalmeasures; and

(d) indicate sustainable development trends.(v) Outputs-inputs for state-of-the-environment reporting,

legislation and action plans. Analysis of the problemsand the information regarding the options will provideinputs to various SOERs, legislative and regulatorymeasures, and action plans at national, regional, andglobal levels. Similarly, a model should be developedto test the effectiveness of different policies andstrategies.

(vi) Establishment and strengthening of a decentralizednetwork of institutions to collect and analyze data onthe environment. The objective of the network is toimprove the acquisition, storage, analysis, exchange,and dissemination of environment data.

(vii) National perspectives. At the national level, the frame-work aims to(a) ensure an integrated national system for

measurement of environment quality,(b) maintain a data set for assessing the state of the

environment, and(c) develop national baseline data to evaluate the

effective integration of environment anddevelopment information.

Most importantly, the framework should be designed to enablegovernments to meet their environment reporting obligationsefficiently and to formulate action plans realistically.

Institutional Issues

This section mainly addresses the question of what to include in acompendium of environment statistics, how best to organize thatcompendium, and what framework to apply. For this discussion, thereader is referred to Figure 6.1, which shows the conventional


information pyramid. The important questions are (i) What shouldbe included in the environment statistics compendium? (ii) What arethe respective roles of the statistics agency and the environmentagency? (iii) Why should the database be structured in terms ofcollection, analysis, compilation, interpretation, and communication?(iv) How will the compendium be sustained? (v) How is a frameworkfor the development of environment statistics formulated and whyis it important?

Structuring the Database

Figure 6.1 shows a “pile” of environment-related data orinformation organized into layers, as in a pyramid, with a very widebase and a very narrow top to indicate increasing levels of dataaggregation (from base to top). Because the lower layers are usedto build the upper layers, the result is increasing informationcontent (i.e., degree of consolidation and simplification) as onemoves to the top of the pyramid. The actual number of indicatorsand indices is very much smaller than the amount of primary andsecondary data.

Figure 6.1Data Aggregations and Their Uses

Indices

Indicators

Analyzed data

Primary data

Advocacy

Policy

ManagementInformation

Planning andResearch

Basic Data

Time Series andSpatial Accounts

Modeling

ByEnvironmental

Media

EcologicalSustainability

Pressure State-Response

Issue-Based

StatisticalAnalysis

EnvironmentalAnalysis

Collection

Communication

InterpretationCompilation, and

Classification

Information Hierarchy Framework Operative Activity

�

�

DecisionMaking

�

�

EnvironmentStatistics

Compendium


Primary data, at the base of the pyramid, are data obtainedfrom monitoring of field activities. An example is daily measuredconcentrations of various key pollutants in a river sampling station,or an inventory of plant species found along a transect. These areraw data and as yet not analyzed. The operative activity here is datacollection. Obviously collection will remain a decentralized activity.

On top of the base of primary data is a layer that may bereferred to as “information,” that is, data that have been analyzed(e.g., screened for reliability and accuracy as well as aggregated toshow averages or total over time). There are, of course, many waysof analyzing environmental interactions. Data might be combined toderive new information as in combining rainfall and river flow datato derive so-called rainfall/runoff ratios. The process may be referredto as environmental analysis. Another way of analyzing is to simplyaggregate the data to show time-series patterns or cross-sectional patternsof the aggregated data. The latter is referred to as statistical analysis.For the purpose of this project, interest is limited to statistical analy-sis and the operative activity is data compilation and classification.

Statistical analysis of environmental data is not as simple astaking averages, deriving measures of variability, or adding up totals.For the most part as in statistical hydrology, a discipline underlinesthe process. The expertise for statistical analysis of data would remainwith the data producers themselves (e.g., the water department) andit seems impractical to attempt to transfer that expertise to the statisticsagency, which has its own area of expertise, usually demographyand socioeconomics. Following these premises much of the analysis–including statistical analysis–of environment data would remain withthe data producers. Selected information would be passed on to thestatistics agency for inclusion in the compendium. The statisticsagency’s role is mainly to compile and classify the information.

It should be noted that, invariably, the contents of the primarydata and analyzed data layers of the pyramid would be organizedby media, that is to say, water, land, or air – which also reflects thefunctional areas of the data-producing agencies (e.g., irrigationdepartment forestry department, etc.). They may also be organizedby sector as in industry, human settlements, or energy.

The next level is the so-called indicator. An indicator may bean aggregation of several statistics, or it may simply be a selectedstatistic. The point to remember is that an indicator must indicate


something. In other words, while primary and analyzed data may toa large extent be characterized as basic or neutral information,indicators are purposeful information. They are purposeful in thesense that they are linked to defined environmental managementconcerns and objectives, say, the control of pollution or theconservation of biodiversity. For example, statistics on sulfuremissions are a relevant indicator for assessing the degree ofpollution from power plants or diesel-based motor vehicles. In short,indicators are issue driven. They are not neutral information thatmay be used for any arbitrary purpose. Common issues found inDMCs include deforestation, land degradation, water supply, and waterand air pollution.

The implication is that environmental indicators should beorganized to indicate the environmental concern and managementobjectives they are related to. The next implication is that it is theenvironment agency that is in the best position to identify thoseindicators. However, the indicators may have to be derived frominformation produced by a variety of agencies. Here the statistics agencyis in a better position to both compile and disseminate the indicators.

Unlike primary and analyzed data that may be organizedaccording to media or economic sector, indicators are preferablyorganized by environmental issue. This is where a framework becomesuseful, that is a framework that will allow one to classify theindicators according to some logical structure of cause, effect, andremedy. The operative activity here is interpretation, specifically forpolicy-making and decision-making purposes. Note that this is alsowhat a SOER is supposed to do. In fact, indicators provide the basisfor an SOER.

The uppermost layer of the pyramid consists of indices thatare more aggregated indicators, such as a water quality index, whichwould combine elements of quantity and quality. Environmental orecological indices, in general, attempt to capture aspects of theecosystem or resource sustainability. The important thing toremember with indices is that there should not be a large numberof them and that they should be able to convey information in asimple yet comprehensive manner. Take the example of grossnational product (GNP). It shows what a single number can dowhen its significance is widely understood. Here the operativeactivity is communication.


What to Include in the Compendium

An issue that keeps coming back is how to define the coverageof environment statistics. Its coverage does not refer to merelyphysical, biological, and socioeconomic types of information. It isobviously impractical to attempt to compile everything in theinformation pyramid; a compendium on environment statistics cannotaim to cover the whole of the information pyramid.

With reference to the pyramid, only the upper layer ofinformation needs to be in the environment statistics compendium.These include the ecological indices, the indicators, and part of theanalyzed data. That part of the analyzed data to be included in thecompendium are those available as time-series statistics such asmonthly or annual averages, and variations and totals for variousenvironment parameters (e.g., stream flow). They may also be resourceand environment accounts (e.g. forest accounts).

Through a consultative process during the course of RETA5555, a number of crucial indicators were identified for the region(Appendix 1). DMCs may wish to include all or some of the indicatorsin their compendium.

Organizing the Compendium

The next question is how to organize the environmentstatistics. The compendium is essentially a well-organizedcompilation of statistics, tables, and charts, accompanied by briefexplanatory notes on the information. It is not a textual or narrativeinterpretation of environmental information, or an attempt to explainwhy a certain environmental problem is occurring or what is beingdone about it. The explanation is for the SOER to make with the useof the statistics compendium.

The point is that the compendium and the SOER are differentpublications. The compendium is a simple characterization whilethe SOER is 80 percent explanation and 20 percent data. Thecompendium could serve as the database of the SOER, and assuch can be used as a reference for readers of the SOER. Thecompendium may thus be organized (i.e., the content classified)so that it supports the analytical framework of the SOER, e.g., aPSR format.


Also, the compendium is a publication of the statistics agency,whereas the SOER is a publication of the environment agency. Sayingthat the compendium and the SOER are different publications doesnot mean to undermine their interdependence. On the one hand,the compendium could serve as the official database of the SOER.A common criticism of existing SOERs, especially those written byconsultants, is the use of data source from outside of the country(e.g., data from WRI or the World Bank, some of which may becontroversial and open to challenge). This is often becauseinformation from those external sources are better organized or aremore readily available. The ideal is for the SOER to depend on oneofficial database that is also reliable and readily available.

On the other hand, use of the compendium to support anSOER could provide a useful framework for organizing the contentof the compendium, as in the adoption of a PSR format typical ofmost SOERs. The SOER also provides a ready user of the compendiumand one capable of explaining the information supplied by thecompendium. For policy makers and the general public, it is theexplanation or interpretation of environment issues that matters, notso much the source statistics. This is not to suggest that the SOERis the most important use of the compendium, nor that it shoulddrive the design of the compendium. However, it is the presentmost relevant use for a compendium and, especially by itself, is animportant step toward institutionalizing environment statistics.

As for the compendium, its contents can be organized intoessentially three parts. Part I will be a summary of important ecologicalindices (if already developed) as well as selected environmentindicators. This summary can be published separately. Part 2 will bea compilation, with brief explanatory notes, of core environmentindicators and statistics so arranged that they show a logical matrixof cause, effect, and remedy (e.g., the PSR format of the SOER) andfurther organized under headings identifying key environment issues.The environment issues may, in turn, be grouped according to physicalenvironment media (e.g., land, air, water), biological media (e.g.,biodiversity), or economic sector (e.g., human settlements, industry,energy) to show trends more readily. Much of the information shouldbe presented in the form of charts or diagrams. Part 3 can includea compilation of supporting statistics mostly in tabular form (e.g.,resource and environment physical account).


For now, core indicators will have to be identified or developedin a supply-driven fashion, i.e., relying on whatever data are availableto develop the indicators. As such, the indicators may not yet adequatelyreflect the nature of the environment issue or management objectivethat such indicators are supposed to show over time. Nevertheless,a more demand-driven approach may evolve, whereby data collec-tion becomes tailored to supply information for the key indicator.

Need for a Framework for Developing Environment Statistics

A framework is useful to focus environment statistics onimportant issues and to identify relevant statistical variables tocharacterize those issues in terms of causes, effects, and remedies.Because of the very wide area that environment statistics must cover,a framework helps to avoid the mere compilation of voluminousinformation. An underlying structure will allow use of suchinformation for policy making, SOER, and public information.

In addition to providing a structure that will relateenvironment statistics to key environment concerns, a frameworkshould also specify the arrangements for coordinating and organizingthe data collection inasmuch as many sources will be involved. Thetasks will include assessing the data requirements and inventoryingthe data available as well as their sources; and specifying theprocedures for setting up and maintaining the database, andpublishing the compendium itself.

The framework itself does not have to state explicitly thestatistical parameters, methodologies, or detailed procedures fortabulating data. It is primarily a structure or format for howinformation is to be organized so that it is clear to the user how thestatistics address important issues and how they show linkages ofcauses and effects. It also provides a strategy for integrating widelydispersed environmental data. Guidance on data collection andtabulation methodologies can be obtained from guidelines orhandbooks available from various sources such as the UN statisticsoffice and ESCAP. Such details need not be specified in the framework.The framework contents are mainly statistical topics related to keyenvironmental issues identified.

It is also important to make a distinction here between astatistical framework and a statistical system. A framework is mainly


a general design, which can be adapted to a country’s specificconcerns, including data limitation. In contrast, a statistical systemsuch as the SNA is founded on standardized concepts, datadefinitions, and classification. This is possible with SNA becausethe theoretical aspects of economic accounting on which it is basedare fairly well established, plus the fact that in SNA we are dealingwith a standard monetary numeraire. In the case of the environment,the scope is very broad, environment system linkages are not wellunderstood, and there is no standard numeraire.

Thus, the FDES should not be confused with a statisticalsystem even though over time, as more information becomes availableand as the understanding of environment system linkages improves,the framework could facilitate the evolution of a statistical sys-tem. Even then, it may not be possible to expect that such a systemwould be the same for all countries. This is again because envi-ronmental statistics are issues-based and, although globalenvironmental concerns may be common, the way environmentalissues are identified and assessed may differ from one country toanother. Still, a common framework by which to understand thecauses and effects (as well as remedies) underlying those issuesis possible.

In sum, a framework for developing environment statisticsshould cover

(i) the environmental problem and concerns that areimportant for the country, possibly grouped accordingto environmental media (i.e., land, water, air);

(ii) the statistical topics by which to describe and quantifythose concerns in terms of their underlying causes andeffects as well as responses or remedies; and

(iii) an action plan for coordinating and organizing thedatabase, and assessing data availability and theirsources.

It is also important to keep in mind certain properties thatthe framework should possess:

(i) The framework should be flexible; that is, it should bekept at a sufficiently general level so that it can beexpanded or modified to suit local environmentconditions, priorities, and data limitations.


(ii) It should be comprehensive and allow the coverageof a broad range of environmental concerns and link-ages between environment, economic, and socialsystems.

(iii) It should have a consistent structure so that for anyenvironmental issue identified, the information can begrouped according to cause, effect, and responses, ina consistent and logical manner.

Initiating the Development of Environment Statistics

The following discussion highlights some points to bear inmind in the development of environment statistics.

Institutional coordination

Focal points are important for national and internationalcommunication and between the statistics agency (e.g., CBS) andthe environment office (e.g., Environmental Protection Agency[EPA]5). EPA can supply monitoring data and technical expertise,while CBS can offer the benefits of its statistical system to give thedata a wider meaning and a wider audience. To create and controlsuch efforts, a steering committee outside EPA and CBS may be setup, preferably at a high level, to promote closer collaboration andprevent turf wars.

A typical feature of environment statistics is that many datarefer to physical parameters (e.g., air quality, emissions to water)that the CBS never measures directly, but instead obtains from thetechnical agencies that measure them. Further, the CBS is notqualified to use raw data but relies on validated data appearing intechnical reports, e.g., water quality in the X river in 199Y. The typicaltask of the CBS is to select and aggregate data, with a view to fittingthem into the environment statistics framework.

5 EPA is taken here as a generic term for environmental registering, measuring,and monitoring agencies. A list and description of these agencies may constitutean important step in setting up environment statistics. Likewise, CBS should betaken as a generic name for all national statistical bureaus.


Producing environment statisticians

Most statisticians are not well-versed in the natural sciences(e.g., physics, chemistry, ecology), but are trained in the socialsciences and economics. This makes it very important that statisticiansdevoting themselves to this new field of statistics acquire a minimumof knowledge in the sciences. Introductory literature in theenvironmental sciences is available in many countries. UNEP forexample, has published many instructive books and reports.In-country courses are more useful, if available. Agencies such asEPA usually give local training courses, suggest literature on certaintopics, and are able to help where books fail to instruct.

Setting up the database for environment statistics

The following is a logical order of activities for setting up adatabase for environment statistics:

(i) Make an inventory of the environmental problems ofthe country.

(ii) Choose the most pressing problems that could very wellbe addressed through a national meeting of all importantagencies and institutions involved, thus making theactivities widely known to the people that matter. Inthis case, press coverage should be ensured so as toreach a larger audience.

(iii) Compile an overview of currently available data to serveas a first publication and as hard evidence of the activitiesand a useful catalogue on which to base further steps.

(iv) Start the statistical process, not by devising new andcostly surveys, but by utilizing existing data sources first(especially applicable to activity statistics: production,traffic, import/export, etc.) and identifying data gaps andomissions. Existing statistics could thus be used at aminimal cost and with maximum possibilities to linkup with other existing statistics. Then, and only then,can an agency say that it has exhausted existingpossibilities and advocate new surveys. Even then,setting up new surveys, e.g., on emissions—as anaddition to quality monitoring data from EPA—might


be too costly and take too long. In that case, shortcutsshould be taken to produce data in a consistent, althoughsimplified way. An example of such an approach wouldbe the WHO Rapid Assessment Method for emissionsto air, water, and soil, which makes use of emissionfactors (Economopoulos 1983).

(v) Disseminate and discuss results.

Extent of coverage

An important consideration, especially for large countries, isthe coverage of environment statistics in terms of both subject andgeographical areas. Assuming that only limited funds, staff,knowledge, and experience are available, a reasonable scope of workshould be defined. Rivers for which no water quality monitoringsystem is available can hardly be covered by statistics. As for areacoverage, urban zones and other areas where several environmentalproblems appear combined or where severe single problems arise,and for which data are available may be selected rather than try tocover the country as a whole. In any case, it would seem better togain experience on a small scale at first.

Linkup with existing international monitoring programs

Several UN bodies are active in environmental monitoring,both in developing methodology and in gathering, processing, anddisseminating data. UNEP and WHO, which are working togetherin the GEMS, and FAO, which is working on soil quality, should bementioned in this context. The finer details of methodology are notso much a cause of concern for CBS as for EPA. UNEP publishes manybooklets and reports that are accessible to a wide audience. Being linkedto global monitoring programs like GEMS has these advantages:

(i) International comparability is enhanced.(ii) International contacts linked to methodological issues

are better ensured.

A focal point should be identified for the purpose of nationaland international networking, and simple media like printednewsletters, or the new medium Internet may be considered.


First compendium as a tactical goal

A first compendium is relatively easy to assemble, butsustained efforts to produce regular additions to meaningful time-series data will prove much harder.

Handbooks and Manuals

Handbooks and manuals help in identifying important variablesnecessary for monitoring the state of the environment and indeveloping appropriate methodologies and standards for thecollection and compilation of environmental data. Environmentalconditions and statistical priorities in particular countries may welldemand different selections and formulations of statistical topics andrelated variables. The list of variables identified should therefore bebroad enough to accommodate all needs. Handbooks can also beuseful to environmental experts who are directly or indirectly involvedin the collection and compilation of environment statistics but haveno adequate background in statistics.

Compared with social, demographic, and economic statistics,the development of environment statistics is still in its infancy, andthe methods, techniques, and choice of variables will improve overtime with the interaction of producers and users of the data.Handbooks can be helpful by including an important area ofenvironment statistics, namely: emission and environmental quality-related statistics. A number of other important areas such as landuse, land degradation, desertification, biodiversity and wildlife,natural disasters and marine environment, which are of major concernto the developing countries of the region, should also be incorporatedin the handbooks.

It is important to recall that national statistics offices do notgenerally collect biophysical data. A large part of their effort is devotedto identifying data sources and making the arrangements for regulardata acquisition. For example, they can send questionnaires toenvironmental agencies to select parameters from large in-house databanks such as those maintained by a meteorological office.Handbooks can assist in the identification and selection of these datasources and relevant statistical variables.


Statistics offices basically collect and compile statisticaldata from such sources as surveys of households, agriculturalfarms, and manufacturing establishments. A good portion ofenvironment databases can be created by recasting such data intoenvironmentally relevant categories. There is also an opportunity toobtain statistics by modifying questionnaires and by redesigningthe surveys. For example, questions on fuelwood uses and sourcescan be added to household surveys. Of course, there is also thepossibility of introducing new surveys devoted to the collection ofenvironment statistics, such as surveys of industrial pollutionabatement practices, recycling activities, and waste generationand deposition. Handbooks should provide numerous examplesof such surveys.

Recommendations of the Concluding Workshop

It is evident that RETA 5555 has successfully achieved its objectives.Nevertheless, more effort needs to be exerted by the participatingDMCs as well as by ADB to sustain the progress achieved throughthis Project.

Workshop participants made the following recommendations:(i) An informal Manila Group for Environment Statistics/

Indicators and Environmental Accounting could beestablished to discuss the problems or constraints facedby various participating DMCs working on environmentstatistics and to share knowledge and experience inaddressing problems that they encounter. Thecontributions of Indonesia, Malaysia, and Philippineswill be very valuable and useful in these discussionssince these countries are already in a relatively advancedstage in the development of this type of statistics. ThisManila Group may have, as its core committee members,experts from ADB’s Statistics Division and the Officeof Environment and Social Development; otherinternational organizations such as the UN-ESCAP,UNSD, SACEP, etc.; and consultants. Representativesfrom the DMCs may be included as members of thiscommittee. This group could have annual meetings in


various countries, with the host country providinglogistical support for organizing workshops. The Bankmay consider providing some financial assistancetoward meeting the expenses for organizing theseworkshops. The annual meetings will certainly help inknowledge sharing and enhancing the learning processon various aspects of environment statistics in theparticipating countries.

(ii) Phase 2 of the Project could cover more DMCs wherethe concept of environment statistics could beestablished and the process of institutionalstrengthening for this development initiated. This wouldnot only extend the domain or coverage of the conceptand awareness of environment statistics into a widerarena but also help strengthen the ADB StatisticalDatabase System (SDBS) further.

(iii) Under RETA 5555, all participating DMCs identifiedtheir present and future priority environmental concerns.As the countries indicated in response to thequestionnaire survey, the indicators for addressing thepresent environmental concerns have generally beenidentified. The identification of country-specificenvironmental indicators representing futureenvironmental concerns could be included as an activityunder phase 2 of the Project.

(iv) Another priority task that could be taken up duringphase 2 of the Project is the development of a commonset of core indicators for all participating DMCs. Asstated earlier, the infrastructure and training needed tocollect data on these indicators, especially for countriesthat are weak in these aspects, may also be taken upin this phase.

(v) Finally, the key environmental indicators identified bythe Bank (Appendix 1) could also include additionalindicators for flora and fauna to address these twoenvironmental components as per the UN-FDES format.This will assist the Bank in monitoring the status ofbiodiversity in the DMCs and in developing a data-base on genetic diversity within its SDBS. Assess-


ment of these indicators will also assist the Bank inplanning future strategies for the preservation ofbiodiversity.

Need for Training

Training will benefit all groups involved in collecting anddisseminating environment statistics.

(i) Basic training covering terminology, definitions,environmental sampling, and analysis, is needed forstatisticians in the field of environment for betterappreciation of environmental issues. This will also helpDMCs to identify the environmental indicators mostappropriate for them.

(ii) Basic appreciation training for policy makers is requiredto educate them on the importance of environmentstatistics.

(iii) Training will strengthen the interrelationships betweenstatisticians and environmentalists which will result, inturn, in a better understanding of the type of environmentstatistics data required and their units of measurementto develop appropriate environmental indicators.

Training Modalities

National proposals for training may be submitted to ADB byvarious DMCs, indicating priority areas of training, itemized costsof training in those priority areas, and the necessary infrastructure.Technical assistance from the Bank could be provided for these high-priority areas for individual countries. This could be considered byADB in phase 2.

Subregional training in various DMCs that possess adequatetraining facilities and expertise could be arranged for theirneighboring countries, e.g., India for SAARC countries, Philippinesfor ASEAN countries, Vanuatu for South Pacific countries, etc. Sucharrangements would maximize gains and minimize costs.

Environment specialists should handle the training. Extractsfrom the UN/ESCAP Operational Handbook would be useful trainingmaterials for sampling and analytical methods.


Scope and Content of Training

The training should start with identifying the target group.Training for appreciation should be given to decision makers andsenior-level executives; Compilers and users of environment statisticsdata should be given rigorous training, including field visits(sampling) and laboratory demonstration (analysis) programs.

Future Work

The overriding objective of the Bank’s technical assistance programis capacity building in the DMCs. In this light, one specific purposeof the RETA was to play a catalytic role in the sustained developmentof environment statistics in the DMCs. The bulk of future work nowrests on the concerned agencies of the DMCs. While the Bank RETAwas successful in creating the basic infrastructure, the work needsto be continued and further developed by the countries themselves.

All the participating countries have prepared their FDESsbased on their own requirements. These requirements might changeover time as the countries progress through the different phases ofdevelopment. The frameworks will therefore have to be revised andupdated periodically, as and when required. From the point of viewof convenience and comparability across the countries, theparticipating countries were encouraged to adopt the UN-FDES.However, it does not mean that the country framework will not changein the future. The countries should be able to decide for themselveswhich particular framework would be more appropriate to suit theirconditions and requirements.

Similarly, compendiums of environment statistics have beenprepared, utilizing administrative records as well as other existingdata in the countries. No country has made any attempt to collectadditional basic data for this purpose; the idea was to first organizeavailable data in a manner that will be useful to planners, decisionmakers, and other users. In the future, however, some environmentsurveys may have to be conducted to collect and compile specificenvironment data that are otherwise not easily available. Environmentsurveys can be expensive and will need specific expertise to conduct.The NSOs as well as concerned environment-related agencies should


examine the possibility of including some relevantenvironment-related questions in the schedules of various surveysthat they undertake on a regular basis. The compendiums are alsoexpected to provide major inputs for the SOERs, which the countriesproduce periodically. Therefore, it may be worthwhile to publish thecompendium at least once every two or three years.

A typical feature of environment statistics is that many datarefer to physical parameters (e.g., air quality, emissions to water, etc.)that the NSOs never measure directly but obtain from the concernedenvironment agencies, which have the equipment and the expertiseto measure and interpret the data collected. Further, as the NSOsare not qualified to use raw data, they rely on validated data appearingin technical reports. The typical task for them would be to select andaggregate data with a view to fitting them into the environmentstatistics framework. As the statisticians themselves are not in aposition to produce environment-related statistics, nor to scientificallyinterpret them, they will need technical support from theircounterparts in the environment agencies. Hence, strong and closecoordination will be required between NSOs and environmentagencies.

Though the subject of environment statistics is relatively newfor most DMCs, the latter can always learn from the experiences ofthe developed countries. Therefore, NSOs should exert maximumefforts to share the experiences and expertise of the developedcountries. They can also benefit by asking to be on mailing lists fortechnical reports and methodological documents prepared byconcerned agencies in developed countries. It will always beworthwhile to maintain close contact with such agencies and acquiretechnical reports and documents that they produce occasionally.

Methodologies for the collection and compilation ofenvironment statistics are not well-developed. As environmentstatistics represent a new field, statisticians often are not familiarwith the techniques and methodologies used to generate environmentstatistics. On the other hand, environment experts who are acquaintedwith these methodologies often are not quite familiar with statisticalsurveys and sampling techniques. Therefore, it is necessary to conductintensive training in environment statistics where both statisticiansand environment experts could be jointly trained in methodologiesfor environment statistics. The participating countries may think about


conducting different levels of training in environment statistics ona regular basis. They may also explore and avail themselves of trainingopportunities in environment statistics that are available at theregional and international levels.

Compared with socioeconomic and demographic statistics,environment statistics are relatively underdeveloped. But the methods,techniques, and choices of variables for environment statistics willimprove over time with the interaction of the data producers andusers. The country-specific FDESs identify the variables for thecollection of environment statistics, their sources, and the availabilityof such statistics. However, not enough technical documents andhandbooks are being produced, even in the developed countries andat the international level, to provide methodological guidance to staffinvolved in the collection and compilation of environment statistics.ESCAP is preparing a handbook on environment statistics, whichwill be useful to environment experts directly or indirectly involvedin the collection and compilation of environment statistics. When itbecomes available, the handbook is also expected to fill, to someextent, the general dearth of methodological publications concern-ing environment statistics. The scope and coverage of the ESCAPhandbook is, however, rather limited, and needs to be expanded andimproved. Therefore, countries participating in the RETA will be ina good position to contribute in this regard, since they have alreadydeveloped their own country-specific frameworks on environmentstatistics. If environment statistics have to develop at par withsocioeconomic and demographic statistics, all the countries willneed to make concerted efforts to prepare handbooks and technicalguidelines to assist staff in data collection and compilation.

In most developing countries, people who make policies anddecisions increasingly demand that the producers of environmentstatistics provide key environmental indicators. Certainly, indicatorsare an efficient way of measuring environmental issues as they area small, succinct set of data summarizing the key environmentalproblems in a country. The production of indicators is a veryworthwhile goal, but developing an agreed-upon set for a countryentails substantial cost. This work can build on the collection andpresentation of readily available data intended for the SOER. Asdeveloped countries and international agencies have alreadydeveloped key indicators on the environment and sustainable


development, there is no point in developing these indicators again.Instead, interested DMCs could use these indicators as a startingpoint to further develop indicators relevant to their conditions.

Finally, considerable resources are required to developenvironment statistics. Such resources are not likely to be obtainedin the near future as statistics is not a priority area in many developingcountries. Moreover, the recent financial crisis has impactednegatively on scarce government resources, part of which could havebeen available for statistics development. Maximum efforts will thushave to be made to convince government authorities of the need forsupport in the field of environment statistics. The participatingcountries have made a beginning in establishing a separate cell forenvironment statistics in their NSOs. Such cells will have to bestrengthened over time. Staff involved in the development ofenvironment statistics will also need further training. There istherefore a need to develop the capability of NSOs to providetraining in environment statistics to their personnel and otherinterested parties.

Key Environmental Indicators Identified by theAsian Development Bank for Inclusion in the SDBS

Indicators Unit

I. BY PHYSICAL ENVIRONMENT

A. Atmospheric Environment

Global Level - Emission of

1. Carbon dioxide (CO2) net CO2 kg/capnet CO2 kg/GDP

2. Methane (CH4) gross CH4 kg/capgross CH4 kg/GDP

3. Chlorofluorocarbons (CFCS) total CFC kg/captotal CFC kg/GDP

Regional Level (Transboundary Impacts) - Emission of

1. SOX total SOx kg/GDPtotal SOx kg/TOE3 of primaryenergy use

2. NOX total NOx kg/GDP total NOx kg/ TOE3 of primaryenergy use

Local Level – Concentration in two largest cities of

1. Carbon monoxide (CO) 8- hour, ppm2. Sulfur dioxide emissions, SO2 1-hour/µg/cu m3. Lead (Pb) 3-month average µg/cu m4. Suspended particulate matter

(SPM)5. Average number of times WHO

standards are exceeded in a year:a. short-term (1-hour average) frequency of exceedanceb. long-term (8-hour average) frequency of exceedance

B. Aquatic Environment

1. In three major riversa. Dissolved oxygen level mg/l

Appendix 1


b. Biological oxygen demand(5-day average) mg/l

c. Turbidity (suspended solids) mg/l2. Water resources withdrawal annual withdrawal as percent-

age of total water resources3. Annual per capita withdrawal

a. Total cu m/capb. Domestic cu m/capc. Industrial cu m/capd. Agricultural cu m/cap

C. Terrestrial Environment

Forest

1. Total forest area million hectares as percentageof total land area

2. Primary forest areaa. Annual deforestation million hectares as percentage

of total forest areab. Reforestation rate hectares per yearc. Annual timber harvest volume cu m/total forest area

- for export volume cu m/year- for domestic use volume cu m/year

d. Fuelwood use as percentage of total primaryenergy use (TOE units)

3. Speciesa. Endemic plants Numberb. Plants in Red Delta Book Numberc. Endemic animal species Numberd. World megadiversity country? Yes/No

4. Protected Areasa. Total coverage sq kmb. Marine sq kmc. Terrestrial sq km

- Terrestrial coverage as percentage of total landarea

- Sites in UN list number- Marine sites number- Terrestrial sites number

Indicators Unit

Appendix 1(continued)

5. Coastal Areasa. Population density person per sq km in coastal

areab. Total coastline sq kmc. Total coral coverage sq kmd. Total mangrove coverage sq kme. Annual rate of mangrove as percentage of the total

deforestation mangrovef. Total coverage of shrimp

ponds or aquaculture sq kmg. Unit value of fish $/kgh. Catch/unit effort kg/person-time

II. BY SECTOR

A. Agriculture

1. Agricultural land area million hectares as percentageof total land area

2. Fertilizer consumptiona. Nitrogen kg N/hab. Phosphorus kg P/ha

3. Pesticide consumption metric tons4. FAO-banned pesticides used number5. Soil erosion mm/year6. Livestock density

a. Cattle head/area of rangeland (ha)b. Sheep/goats head/area of rangeland (ha)c. Horses/mules head/area of rangeland (ha)d. Camels/dromedaries head/area of rangeland (ha)

7. Rangeland as percentage of total landarea

8. Cases of pesticide poisoning number

B. Industry

1. Nonhazardous, hazardous and tons/yeartoxic wastes generated

2. Industrial wastewater treated as percentage of industrialwastewater generated

Indicators Unit


Appendixes 167


3. Industrial pollution control per $ invested in pollution controlindustrial input (percentage) $ invested in total industrial

projects4. Industries generating hazardous

and toxic wastes- Treated number- Untreated number

5. Industries generating wastewater- Treated number- Untreated number

6. Industries with gaseous emissions- Treated number- Untreated number

7. Pb level in blood of children µg/l8. Accidents related to chemical

or oil spills number

C. Urban Environment

1. Urban pollution urban pollution/totalpopulation

2. Urban land use area as a percentage of total land area3. Green parks or open space area/capita4. Vehicles/caps

a. Total numberb. Diesel numberc. Gasoline number

5. Area served by sewer as a percentage of total land area6. Urban area with sanitary as a percentage of total land area

facilities7. Solid waste collected as a percentage of total waste

(percentage) generated8. Garbage (solid waste)

a. Generated Commercial-tons/capita/yearDomestic-tons/capita/year

b. Collected Commercial-tons/capita/yearDomestic-tons/capita/year

Indicators Unit


III. OTHER INDICATORS

A. Demographic and Economic Data

1. Population growth2. Population density

a. Grossb. Urban (in key cities)

3. GDP/cap growtha. Industry % GDP/capb. Agriculture % GDP/cap

4. GDP/cap in PPP terms

B. Government Budget/Expenditures

1. Annual budget of environment $ amountregulatory agency

2. Environmental expenditures $ amountas percentage of GNP

3. Annual budget of protected as percentage of total budgetareas for Department of Forest

4. Budget for pollutiona. Municipal wastewater $ amount

treatmentb. Solid waste management $ amount

C. Others

1. Comprehensive environment Yes/No (list year)policy

2. Environmental acts promulgateda. Water Yes/No (list year)b. Air Yes/No (list year)c. Protected area Yes/No (list year)

3. Formalized EIA requirement Yes/No (list year)

cap = capita, cu m = cubic meter, EIA = environmental impact assessment, GDP =gross domestic product, GNP = gross national product, ppm = parts per million,PPP = purchasing power parity, SDBS = statistical database system, TOE = tonof oil equivalent.

Indicators Unit


Appendixes 169


AIR AND WATER QUALITY INDEXES

Air Quality Index

I* W = AQI i1

3

=11∑

Where: AQI = air quality of the metropolitan areaI1 = estimated CO subindexI2 = estimated TSP subindexI3 = estimated SO2 subindexWi = weight assigned to pollution type I

MITRE Air Quality Index

I = MAQI 21

5

1=1

∑

Where: MAQI = MITRE Air Quality IndexIi = air quality subindex for pollutant i

Horton’s Water Quality Index

M M* Wi

I W = QI 21n

=1i

ii

n

=1i

∑

∑

Where: QI = water quality indexWi = weightIi = subindex calculated for pollution variable iM1 = 1 if water temperature is below a specific value; 0.5M1 = otherwiseM2 = 1 if there is no “obvious pollution,” 0.5 otherwise

“Obvious pollution” can include sludge deposits, oil, debris, foam, scum,odor, colored water, etc. M1 and M2 are designed to be tailored to fitindividual situations.

Appendix 2

ECOSYSTEM STRUCTURE INDICES

1. Niche Breadth Index

∑=

= S

i

PBthNicheBread

1

21

1)(

Where: Pi = frequency of utilization of the ith resource category,S = number of different resources.

2. Niche Overlap Index

∑ ∑∑=

)( 22ikij

ikijjk

PP

PPO

Where: Ojk = niche overlap between the species,Pij, Pik = niche breadth values for the species j and k.

3. Species Richness Indices (D)

1964(MenhinickN

SD =

1951(log

1Magalef

NS

D−

=

Where: S = number of species,N = total number of individuals.

Appendixes 171

Appendix 3

)1964

)1951


4. Universal Soil Loss Equation

A = R*K*L*S*C*P

Where: A = soil loss in the field,R = rainfall,K = soil erodibility,L = length of slope,S = slope steepness,C = cropping system, andP = support practice factor.


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Glossary of Environment Terms

Abatement: Reduction in degree or intensity of pollution.

Absorbed dose: The energy imparted to a unit mass of matter byionizing radiation. The unit of absorbed dose is the rad. One radequals 100 ergs per gram.

Absorption: The penetration of one substance into or through another.

Acclimatization: The adaptation over several generations of a speciesto a marked change in the environment.

Acute respiratory disease: Respiratory infection, characterized byrapid onset and short duration.

Acute toxicity: Any poisonous effect produced within a short periodof time following exposure, usually within 24-96 hours, resulting insevere biological harm and, often, death.

Aerosol: A particle of solid or liquid matter that can remainsuspended in the air because of its small size. Particulates under1 micron in diameter are generally called aerosols.

Agricultural solid waste: The solid waste that is generated by therearing of animals, and the production and harvesting of cropsor trees.

Air: The so-called pure air is a mixture of gases containing about78 percent nitrogen; 21 percent oxygen; less than 1 percent ofcarbon dioxide, argon, and other inert gases; and varying amountsof water vapor.

Air contaminant: Any substance either man-made or of natural originin the ambient air, such as dust, fly ash, gas fumes, mist (other thanH20), smoke, radiation, heat, noise, etc.


Air pollutant: (i) Dust, fumes, mist, smoke, and other particulatematter, vapor, gas, odorous substances, or any combination thereof.(ii) Any air pollution agent or combination of such agents, includingany physical, chemical, biological, radioactive (including sourcematerial, and by-product material) substance or matter that is emittedinto or otherwise enters the ambient air.

Air quality criteria: The levels of pollution and lengths of exposureabove which adverse effects may occur on health and welfare.

Air quality standards: The level of pollutants prescribed by law orregulation that cannot be exceeded during a specified time in adefined area.

Airborne pathogen: A disease-causing microorganism that travelsin the air or on particles in the air.

Aldehydes: A class of fast-reacting organic compounds containingoxygen, hydrogen, and carbon.

Algae: Simple rootless plants that grow in bodies of water in relativeproportion to the amounts of nutrients available. Algal blooms, orsudden growth spurts can affect water quality adversely.

Alkalinity: The measurable ability of solutions or aqueoussuspensions to neutralize an acid.

Ambient air: The portion of the atmosphere, external to buildings,to which the general public has access.

Ambient water criterion: That concentration of a toxic pollutant innavigable water that, based upon available data, will not result inadverse impact on important aquatic life, or on consumers of such aquaticlife, after exposure of that aquatic life for periods of time exceeding96 hours and continuing at least through one reproductive cycle;and will not result in significant risk of adverse health effects in a largehuman population, according to available information such as mamma-lian laboratory toxicity data, epidemiological studies of human occupa-tional exposures or human exposure data, or any other relevant data.

Aquatic flora: Plant life associated with the aquatic ecosystemincluding, but not limited to, algae and higher plants.

Aquifer: (i) An underground bed or layer of earth, gravel, or porousstone that contains water; (ii) A geological formation, group offormations, or part of a formation that is capable of yielding asignificant amount of water to a well or spring.

Area source: Any small residential, governmental, institutional,commercial, or industrial fuel combustion operations that contributeair pollutants to the ambient air: on-site solid waste disposal facility;motor vehicles, aircraft, vessels, or other transportation facilities; orother miscellaneous sources.

Ash: Inorganic residue remaining after ignition of combustiblesubstances determined by definite prescribed methods.

Ash pit: A pit or hopper located below a furnace in which residueis accumulated and from which it is removed.

Assimilative capacity: The capacity of a natural body of water toreceive (i) waste-waters, without deleterious effects; (ii) toxicmaterials, without damage to aquatic life or humans who consumethe water; (iii) Biological Oxygen Demand (BOD), within prescribeddissolved oxygen limits.

Backfill: The material used to refill a ditch or other excavation, orthe process of doing so.

Background level: With respect to air pollution, amounts of pollutantspresent in the ambient air due to natural sources.

Bagasse: An agricultural waste material consisting of the dry pulpresidue that remains after juice is extracted from sugarcane or sugarbeets. The residue is used in the manufacture of pulp and paper.

Basin: Includes, but is not limited to, rivers and their tributaries,streams, coastal waters, sounds, estuaries, bays, lakes, and portionsthereof, as well as the lands drained by them.

Glossary of Environment Terms 179


Bioassay: Using living organisms to measure the effect of asubstance, factor, or condition.

Biochemical oxygen demand (BOD): The dissolved oxygen requiredto decompose organic matter in water. It is a measure of pollutionbecause heavy waste loads have a high demand for oxygen.

Biodegradable: Descriptive of any substance that decomposesthrough the action of microorganisms.

Biological agents: Microbiological cultures, enzymes, or nutrientadditives that are deliberately introduced into an oil or hazardoussubstance spill for the purpose of encouraging biodegradation tomitigate the effects of the spill.

Biological monitoring: The determination of the effects of pollutantson aquatic life by techniques and procedures, including samplingof organisms representative of levels of the food chain appropriateto the volume and the physical, chemical, and biological character-istics of the effluent; and at appropriate frequencies and locations.

Biomonitoring: The use of living organisms to test water quality ata discharge site or downstream.

Biota: All living organisms that exist in an area.

Body burden: The amount of radioactive material present in the bodyof a man or an animal.

Carbonaceous matter: Pure carbon or carbon compounds presentin the fuel or residue of a combustion process.

Carrying capacity: (i) In recreation, the amount of use a recreationarea can sustain without deterioration of its quality. (ii) In wildlife,the maximum number of animals an area can support during a givenperiod of the year.

Chemical oxygen demand (COD): A measure of the oxygen requiredto oxidize all compounds in water, organic and inorganic.

Chlorinated hydrocarbons: A class of persistent, broad-spectruminsecticides, notably DDT, that linger in the environment andaccumulate in the food chain. Other examples are aldrin, dieldrin,heptachlor, chlordane, lindane, endrin, mirex, benzene, hexachloride,and toxaphene.

Clean water standards: Any enforceable limitation, control,condition, prohibition, standard, or other requirement, which ispromulgated.

Coliform index: A rating of the purity of water based on a count offecal bacteria.

Coliform organisms: Organisms found in the intestinal tract ofhumans and animals. Their presence in water indicates pollutionand potentially dangerous bacterial contamination.

Compost: A relatively stable mixture of decomposed organic wastematerials, generally used to fertilize and condition the soil.

Confined aquifer: An aquifer bounded above and below byimpermeable beds or by beds of distinctly lower permeability thanthat of the aquifer itself; an aquifer containing confined groundwater.

Contaminant: Any biological, chemical, physical, or radiologicalsubstance or matter in water, air, or soil.

Decibel (dB): The unit of measurement of sound level calculated bytaking ten times the common logarithm of the ratio of the magnitudeof the particular sound pressure to the standard reference soundpressure of 20 micropascals and its derivatives. It is abbreviated as dB.

Density: (i) The mass of a unit volume; its numerical expressionvaries with the units selected. (ii) The mass of a unit volume of liquid,expressed as grams per cubic centimeter, kilograms per liter, or poundsper gallon, at a specified temperature.

Detergent: Synthetic washing agent that helps water to remove dirtand oil. Most contain large amounts of phosphorus compounds which



may kill useful bacteria and encourage algae growth in the receivingwater.

Disposal: (i) The planned release or placement of waste in a mannerthat precludes recovery. (ii) The discharge, deposit, injection,dumping, spilling, leaking, or placing of any solid waste or hazardouswaste into or on any land or water so that such solid waste orhazardous waste or any of its constituent may enter the environmentor be emitted into the air or discharged into any waters, includinggroundwaters.

Dissolved oxygen (DO): A measure of the amount of oxygen availablefor biochemical activity in a given amount of water. Adequate levelsof DO are needed to support aquatic life. Low DO concentrationscan result from inadequate waste treatment.

Dissolved solids: The total of disintegrated organic and inorganicmaterial contained in water. Excesses can make water unfit to drinkor use in industrial processes.

Dose: A general term denoting the quality of radiation or energyabsorbed. For special purposes it must be appropriately qualified. Ifunqualified, it refers to absorbed dose.

Dose equivalent: The product of the absorbed dose from ionizingradiation and such factors as account for differences in biologicaleffectiveness due to the type of radiation and its distribution in thebody as specified by the International Commission on RadiologicalUnits and Measurements (ICRU).

Dose rate: Absorbed dose delivered per unit time.

Dosimeter: An instrument that measures exposure to radiation.

Dust loading: The amount of dust in a gas, usually expressed ingrains per cubic foot or pounds per thousand pounds of gas.

Ecosystem: The interacting system of a biological community andits nonliving surroundings.

Effluent: Waste material discharged into the environment, treatedor untreated. Generally refers to water pollution.

Emission factor: The relationship between the amount of pollutionproduced and the amount of raw material processed. For example,an emission factor for a blast furnace making iron would be thenumber of pounds of particulates per ton of raw materials.

Emission standard: The maximum amount of a pollutant that ispermitted to be discharged from a single polluting source; e.g., thenumber of pounds of fly ash per cubic foot of air that may be emittedfrom a coal-fired boiler.

Environment: Water, air, and land and the interrelationship that existsamong and between water, air, and land and all living things.

Epidemiology: The study of diseases as they affect populations ratherthan individuals. It includes the distribution and incidence of disease;mortality and morbidity rates; and the relationship of climate, age,sex, race, and other factors.

Episode (pollution): An air pollution incident in a given area causedby a concentration of atmospheric pollutants reacting withmeteorological conditions that may result in a significant increasein illnesses or deaths.

Erosion: The wearing away of the land surface by wind or water.Erosion occurs naturally from weather or runoff, but can be intensifiedby land clearing practices.

Estuaries: Areas where freshwater meets saltwater (bays, mouthsof rivers, salt marshes, lagoons). These brackish water ecosystemsshelter and feed marine life, birds, and wildlife.

Eutrophication: The slow aging process of a lake evolving into amarsh and eventually disappearing. During eutrophication, the lakeis choked by abundant plant life. Human activities that add nutrientsto a water body can speed up this action.



Exhaust emissions: Substances emitted to the atmosphere fromany opening downstream from the exhaust port of a motor vehicleengine.

Exposure: A measure of the ionization produced in air by x or gammaradiation. It is the sum of the electrical charges on all ions of onesign produced in air when all electrons liberated by photons in avolume element of air are completely stopped in air, divided by themass of the air in the volume element. The special unit of exposureis the roentgen.

Fecal coliform bacteria: Organisms associated with the intestinesof warm-blooded animals and commonly used to indicate the presenceof fecal material and the potential presence of organisms capable ofcausing human disease.

Flue: Any passage designed to carry combustion gases and entrainedparticulates.

Fly ash: (i) The component of coal that results from the combustionof coal, and is the finely divided mineral residue typically collectedfrom boiler stack gases by electrostatic precipitators or mechanicalcollection devices; (ii) The ash that is carried out of the furnace bythe gas stream and collected by mechanical precipitators, electrostaticprecipitators, and/or fabric filters. Economizer ash is included whenit is collected with fly ash.

Fog: Suspended liquid particles formed by condensation of vapor.

Fossil fuel: Natural gas, petroleum, coal, and any form of solid, liquid,or gaseous fuel derived from such materials for the purpose of creatinguseful heat.

Fugitive dust: Particulate matter composed of soil that is uncontami-nated by pollutants resulting from industrial activity. Fugitive dustmay include emissions from haul roads, surfaces and soil storagepiles, and other activities in which soil is either removed, stored,transported, or redistributed; also dust emitted from any source otherthan through a stack.

Fugitive emissions: (i) Emissions that could not reasonably passthrough a stack, chimney, vent, or other functionally equivalentopening; (ii) Any air pollutants emitted to the atmosphere other thanfrom a stack.

Garbage: Waste materials that are likely to decompose or putrefy.

Green belts: Certain areas restricted from being used for buildingand houses; they often serve as separating buffers between pollutionsources and concentrations of population.

Greenhouse effect: The warming of the earth’s atmosphere causedby the buildup of carbon dioxide, which allows light from the sun’srays to heat the earth but prevents loss of the heat.

Groundwater infiltration: Water that enters the treatment facilityas a result of the interception of natural springs, aquifers, or runoff,which percolates into the ground and seeps into the treatmentfacility’s tailings pond or wastewater holding facility and that cannotbe diverted by ditching or grouting the tailings pond or wastewaterholding facility.

Habitat: The sum of environmental conditions in a specific placethat is occupied by an organism, population, or community.

Hard water: Alkaline water containing dissolved mineral salts thatinterfere with some industrial processes and prevent soap fromlathering.

Hazard: A probability that a given pesticide (or other pollutants)will have an adverse effect on man or the environment in a givensituation, the relative likelihood of danger or ill effect beingdependent on a number of interrelated factors present at any giventime.

Heavy metals: Metallic elements like mercury, chromium, cadmium,arsenic, and lead, with high molecular weights. At low concen-trations they can damage living things and tend to accumulate inthe food chain.



Humus: The dark brown or black residue found in soil resulting fromthe decomposition of organic matter. Residues in well-digestedsludges and activated sludge are similar to humus in appearanceand behavior.

Hydrocarbon: Any of a vast family of compounds containing carbonand hydrogen in various combinations and found especially in fossilfuels. Some hydrocarbon compounds are major air pollutants; theymay be carcinogenic or active participants in the photochemicalprocess.

Hydrology: The science dealing with the properties, distribution,and circulation of water.

Incineration: The controlled process in which combustible solid,liquid, or gaseous wastes are burned and changed into noncombus-tible gases.

Inorganic matter: Chemical substances of mineral origin, notcontaining carbon-to-carbon bonding. Generally structured throughionic bonding.

Inorganic refuse: Solid waste composed of matter other than plant,animal, and certain carbon compounds (e.g., metals and glass).

Insecticides: All substances or mixtures of substances intended forpreventing or inhibiting the establishment, reproduction,development, growth of, or destroying or repelling any member ofthe class Insecta or other allied classes in phylum Arthropoda,declared to be pests.

Lagoon: A shallow pond where sunlight, bacterial action, and oxygenwork to purify wastewater.

Landfill: A disposal facility or part of a facility where hazardous wasteis placed in or on land and which is not a land treatment facility, asurface impoundment, or an injection well.

LC50 (lethal concentration): The concentration of material that islethal to one half of the test population of aquatic animals uponcontinuous exposure for 96 hours or less.

LD50 (lethal dose): Generally, the quantity of a substance that isfatal to 50 percent of the population on which it is tested. With largetest subjects, LD50 is often given as a quantity per unit of body weight.

Leach: To undergo the process by which materials in the soil aremoved into a lower layer of soil or are dissolved and carried throughsoil by water.

Leachate: Any liquid, including any suspended components in theliquid, that has percolated through or drained from hazardous waste.

Marsh: Wet, soft, low-lying land that provides a habitat for manyplants and animals. It can be destroyed by dredging and filling.

Material balance: An accounting of the weights of materials enteringor leaving a processing unit, such as an incinerator, usually on anhourly basis.

Mine: An active mining area, including all land and property placedunder or above the surface of such land, used in or resulting fromthe work of extracting metal ore or minerals from their natural depositsby any means or method, including secondary recovery of metal orefrom refuse or other storage piles, wastes, or rock dumps and milltailings derived from the mining, cleaning, or concentration of metalores.

Mining wastes: Residues that result from the extraction of rawmaterials from the earth.

Mixing depth: The expanse in which air rises from the earth andmixes with the air above it until it meets air that is equal or warmerin temperature.

Mobile source: A moving producer of air pollution, mainly forms oftransportation such as cars, motorcycles, planes.



Monitoring: Periodic or continuous sampling to determine the levelof pollution or radioactivity.

Municipal incinerator: A privately or publicly owned incineratorprimarily designed and used to burn residential and commercial solidwaste within a community.

Municipal sanitary landfill: The disposal site for residential andcommercial solid waste generated, collected, and processed withina community.

Mutagen: Any substance that causes changes in the genetic structurein subsequent generations.

National ambient air quality standard: A federally promulgatedmaximum level of an air pollutant that can exist in the ambient airwithout producing an adverse effect on humans (primary standard)or the public welfare (secondary standard).

Nitrogen oxides: Gases formed in great part from atmosphericnitrogen and oxygen when combustion takes place under conditionsof high temperature and pressure. Nitrogen oxides include nitric oxide(NO) and nitrogen dioxide (NO2). Can be harmful themselves andare precursors of photochemical oxidant.

Nitrogenous wastes: Animal or plant residues that contain largeamounts of nitrogen.

Nonattainment area: For any air pollutant, an area that is shownby monitored data or which is calculated by air quality modeling (orother methods determined by the administrator to be reliable) toexceed any national ambient air quality standard for such pollutant.

Nonpoint source: Cause of water pollution that is not associatedwith point sources. Examples include (i) agriculturally relatednonpoint sources of pollution including runoff from manure disposalareas and from land used for livestock and crop production;(ii) silviculturally related nonpoint sources of pollution; (iii) mine-related sources of pollution including new, current, and abandoned

surface and underground mine runoff; (iv) construction-activity-related sources of pollution; (v) sources of pollution from wastedisposal on land, in wells, or in subsurface excavations that affectgroundwater and surface water quality; (vi) saltwater intrusion intorivers, lakes, estuaries, and groundwater resulting from reduction offresh water flow from any cause, including irrigation, obstruction,groundwater extraction, and diversion; and (vii) sources of pollutionrelated to hydrologic modifications, including those caused bychanges in the movement, flow, or circulation of any navigable watersor groundwaters due to construction and operation of dams, levees,channels, or flow diversion facilities.

Nutrients: Elements or compounds (e.g., carbon, oxygen, nitrogen,potassium, phosphorus) essential to the growth and development ofliving things.

Oil spill: Accidental discharge into bodies of water.

Open dump: Any facility or site, but not a sanitary landfill, wheresolid waste is disposed of.

Organic content: The ratio of carbon compounds, whether from livingorganisms or not, to the total chemical composition of a substance.

Organic materials: Chemical compounds of carbon excluding car-bon monoxide, carbon dioxide, carbonic acid, metallic carbides,metallic carbonates, and ammonium carbonate.

Organic refuse: Solid waste composed of carbon compounds andgenerally, but not exclusively, by-products of plant and animal lifeprocesses (e.g., paper, wood, excreta, yard trimmings).

Oxidant: A substance containing oxygen that reacts chemically inair to produce a new substance; primary source of photochemical smog.

Oxide: A compound of two elements, one of which is oxygen.

Ozone (O3): A pungent, colorless, toxic gas that contributes tophotochemical smog.



Parameter: A quantitative or characteristic element that describesphysical, chemical, or biological conditions of water.

Particulate: A particle of solid or liquid matter.

Particulate matter: Any material, except water in uncombined form,that is or has been airborne and exists as a liquid or a solid at standardconditions.

Particulates: Fine liquid or solid particles such as dust smoke, mist,fumes, or smog, found in the air or emissions.

Parts per million (ppm): A volume unit of measurement; the numberof parts of a given pollutant in a million parts of air.

Pathogen: Any virus, microorganism, or other substance causingdisease.

Pathogenic waste: Discarded materials that contain organismscapable of causing disease.

PH: The logarithm of the reciprocal of hydrogen ion concentration.

Photochemical oxidants: Air pollutants formed by the action ofsunlight on oxides of nitrogen and hydrocarbons.

Photochemical smog: Air pollution caused by not one pollutant butby chemical reactions of various pollutants emitted from differentsources.

Phytotoxic: Poisonous to plants.

Point source: A stationary location where pollutants are discharged,usually from an industry. Any discernible, confined, and discreteconveyance, including but not limited to any pipe, ditch, channel,tunnel, conduit, well, discrete fissure, container, rolling stock,concentrated animal feeding operation, vessel, or other floating craft,from which pollutants are or may be discharged. This term does notinclude return flows from irrigated agriculture.

ppb: Parts per billion.

ppm: Parts per million.

Precursor: A pollutant that takes part in a chemical reaction resultingin the formation of one or more new pollutants.

Putrescible: Can rot quickly enough to cause odors and attract flies.

Radioactive: Substances that emit rays either naturally or as a resultof scientific manipulation.

Receiving waters: Any body of water where untreated wastes aredumped.

Recharge zone: The area through which water is added to an aquifer.

Recycling: Converting solid waste into new products by using theresources contained in discarded materials.

Region: Usually a rural area of reasonable homogeneous geographyextending from tens to hundreds of kilometers.

Residual wastes: Those solid, liquid, or sludge substances from man’sactivities in the urban, agricultural, mining, and industrialenvironments remaining after collection and necessary treatment.

Runoff: (i) That portion of precipitation that flows over the groundsurface and returns to streams. It can collect pollutants from air orland and carry them to the receiving waters; (ii) Any rainwater,leachate, or other liquid that drains over land from any part of afacility.

Salinity: The degree of salt in water.

Sanitary landfill: A facility for the disposal of solid waste; a landdisposal site employing an engineered method of disposing of solidwastes on land in a manner that minimizes environmental hazardsby spreading the solid wastes in thin layers, compacting the solid



wastes to the smallest practical volume, and applying and compactingcover material at the end of each operating day.

Sanitation: Control of physical factors in the human environmentthat can harm development, health, or survival.

Secondary standard: A standard that establishes an ambientconcentration of the pollutant that, with an adequate margin of safety,will protect the public welfare (i.e., all parts of the environment otherthan human health) from adverse impacts.

Sedimentation: The process of letting solids settle out of wastewaterby gravity during wastewater treatment.

Seepage: The movement of liquids or gases through soil withoutthe formation of definite channels.

Sewage: Human body wastes and the wastes from toilets and otherreceptacles intended to receive or retain body wastes.

Sewer: A channel that carries wastewater and storm water runofffrom the source to a treatment plant or receiving stream., Sanitarysewers carry household and commercial waste. Storm sewers carryrunoff from rain or snow. Combined sewers are used for both purposes.

Silviculture: Management of forestland for timber. Silviculturesometimes contributes to water pollution, as in clear-cutting.

Smog: (i) The irritating haze resulting from the sun’s effect on certainpollutants in the air, notably those from automobile exhaust. (ii) Alsoa mixture of fog and smoke.

Smoke: (i) Solid or liquid particles less than 1 micron in diameter.(ii) Particles suspended in air after incomplete combustion of materialscontaining carbon. (iii) The matter in exhaust emissions that obscuresthe transmission of light.

Soil pH: The value obtained by sampling the soil to the depth ofcultivation or solid waste placement, whichever is greater, andanalyzing by the electrometric method.

Solid waste: Any garbage, refuse, sludge from a waste treatmentplant, water supply treatment plant, or air pollution control facility;and other discarded material, including solid, liquid, semisolid, orcontained gaseous material resulting from industrial, commercial,mining, and agricultural operations, and from community activities.Does not include solid or dissolved material in domestic sewage, orsolid or dissolved materials in irrigation return flows or industrialdischarges.

Soot: Carbon dust formed by incomplete combustion.

Source/receptor area: Source area is that area in which contami-nants are discharged and a receptor area is that area in which thecontaminants accumulate and are measured.

Stationary source: Any building, structure, facility, or installationthat emits or may emit any air pollutant.

Subsidence: The lowering of the natural land surface in responseto earth movements; lowering of fluid pressure; removal of under-lying supporting material by mining or solution of solids, eitherartificially or from natural causes; compaction due to wetting oxi-dation of organic matter in soils; or added load on the land surface.

Subsoil: The layer of earth beneath the topsoil, and usually lackingin appreciable quantities of organic matter.

Suspended solids: Tiny particles of solids dispersed but undissolvedin a solid, liquid, or gas. Suspended solids in sewage cloud the waterand require special treatment to remove.

Tailings: Residue of raw materials or waste separated out duringthe processing of crops or mineral ores.



Tidal marsh: Low, flat marshlands traversed by interlaced channelsand tidal sloughs and subject to tidal inundation; normally, the onlyvegetation present is salt-tolerant bushes and grasses.

Topography: The physical features of a surface area including rela-tive elevations and the position of natural and man-made features.

Topsoil: The surface layer of soil; it contains humus and is capableof supporting good plant growth.

Total solids: The sum of dissolved and undissolved constituents inwastewater, usually stated in milligrams per liter.

Toxic substance: A chemical or a mixture that may present an un-reasonable risk of injury to health or the environment.

Trophic condition: A relative description of a lake’s biological pro-ductivity based on the availability of plant nutrients. The range oftrophic conditions is characterized by the terms oligotrophic for theleast biologically productive, to eutrophic for the most biologicallyproductive.

Troposphere: The portion of the atmosphere between seven and tenmiles from the earth’s surface, where clouds form.

Vapor: The gaseous phase of substances that are liquid or solid atatmospheric temperature and pressure, such as steam.

Vector: An organism, often an insect, that carries disease.

Virgin material: A raw material, including previously unused cop-per, aluminum, lead, zinc, iron, or other metal or metal ore, anyundeveloped resource that is - or with new technology will become- a source of raw materials.

Volatile organic compound: Any compound containing carbon andhydrogen or containing carbon and hydrogen in combination withany other element which has a vapor pressure of 1.5 pounds per squareinch absolute (77.6 mm. Hg) or greater under actual storage conditions.

Wastewater: Water carrying dissolved or suspended solids fromhomes, farms, businesses, and industries.

Water quality criteria: Levels of pollutants in bodies of water thatare consistent with various uses of water, i.e., drinking water, sportfishing, industrial use.

Watershed: The land area that drains into a stream.

Wetlands: Areas that are inundated or saturated by surface wateror groundwater at a frequency and duration sufficient to support -and that under normal circumstances do support - a prevalence ofvegetation typically adapted for life in saturated soil conditions. Wet-lands generally include swamps, marshes, bogs, and similar areas.

Zone of saturation: That part of the earth’s crust in which all voidsare filled with water.

Zooplankton: Tiny aquatic animals that fish feed on.

Source: Frick G W. 1984. Environmental Glossary. Rockville: Government Institutes.325 pp.



Environment Statisticsand Indicators

Index to the bibliography

I. Frameworks for Environment Statistics and Basic Issues

1. Sheram (1993)2. UN (1984)3. UN (1987)4. UN (1988a)5. UN (1988b)6. UN (1991)7. UN (1993a)8. UN (1997c)9. UNECE (1995)

10. WHO (1982)

II. Glossaries and Dictionaries

1. Crump (1993)2. Gilpin (1976)3. IULA (1991)4. UN (1997a)5. UNECE (1995)6. UNEP (1990)7. UNEP (1997b)8. USEPA (1994)

III. International/Regional Data Tables

1. ADB (1997)2. GESAMP (1990)3. Murray and Lopez (1996)4. OECD (1996)

5. OECD (1997d)6. OECD (1997e)7. UN (1995)8. UN (1997d)9. UNDP (1998)

10. UNEP (1994)11. UNEP (1997a)12. UNESCAP (1992)13. The World Bank (1992)14. The World Bank(1998a)15. Worldwatch Institute (1996)16. Worldwatch Institute (1998)17. WRI (1998)

IV. State of Environment Reporting

1. Comolet (1991)2. Parker and Hope (1992)3. UN (1996b)4. UNEP/RIVM (1995)5. UNEP & UNDP (1994)6. UNEP (1997a)7. UNESCAP (1992)8. Worldwatch Institute (1996)9. Worldwatch Institute (1998)

10. WRI (1995)

V. Indicators and Indices

1. Adriaanse (1993)2. Adriaanse et al (1989)3. Albert and Parke (1991)4. Alfsen and Hans (1993)5. Briggs et al (1996)6. Bringezu and Schmidt - Bleek (1992)7. Duinker and Ronald (1994)8. Dumanski et al (1998)9. Environment Canada (1991)

10. Frankenberger and Goldstein (1990)

Environment Statistics and Indicators 197


11. Gosselin et al (1993)12. Hajer (1992)13. Hall and Kerr (1991)14. Hammond et al (1995)15. Hardi and Laszlo (1994)16. Henderson (1990)17. Hope and Parker (1991)18. IISD (1993)19. Inhaber (1976)20. International Energy Agency (1997)21. Kjellstrom and Corvalan (1995)22. Kolsky and Blumenthal (1995)23. Kuik and Verbruggen (1991)24. Leitmann (1993)25. Leitmann (1994)26. Lohani and Todino (19984)27. Lohani (1980)28. Moldan, et al (1997)29. OECD (1993a)30. OECD (1993b)31. OECD (1993c)32. OECD (1995)33. OECD (1996)34. OECD (1997a)35. OECD (1997b)36. OECD (1997c)37. OECD (1998)38. Ott (1978)39. Petersen (1997)40. Pieri et al (1995)41. Pinter (1994)42. Rogers et al (1997)43. Rothwell, et al (1991)44. Schoerers (1983)45. Scott, et al (1996)46. Smjyth and Dumanski (1995)47. Syers, et al (1995)48. The World Bank (1992)

49. The World Bank (1995)50. The World Bank (1996)51. The World Bank (1998a)52. The World Bank (1998b)53. Thomas (1972)54. Tschirley (1992)55. UN (1994b)56. UN (1996a)57. UNDP (1998)58. UNEP/RIVM (1994)59. UNEP/RIVM (1995)60. Victor (1991)61. Washington (1984)62. Wills and Briggs (1995)63. WWF International (1998)

VI. Natural Resources Accounting and EnvironmentalEconomics

1. Alfieri and Bartelmus (1998)2. Costanza and Lisa (1991)3. Henderson (1990)4. Kunte et al (1998)5. Peskin (1998)6. Rogers, et al (1997)7. The World Bank (1998b)8. UN (1993b)9. UN (1997b)

10. Victor (1991)

VII. Environmental Information Systems

1. Adriaanse et al (1988)2. Adriaanse et al (1989)3. Briggs (1995)4. Drasson et al (1987)5. Gunther (1998)6. Michener et al (1994)



VIII. Environmental Monitoring

1. Chapman (1992)2. Drasson et al (1987)3. Gilbert (1987)4. Hortensius and Nortcliff (1991)5. Keith (1991)6. Kovacs (1992)7. UNEP/WHO (1993)8. UNEP/WHO (1995)9. WHO (1980)

IX. Statistical Theory

1. Barnett and Turkman (1994)2. Cairns et al (1979)3. Gilbert (1987)4. Nychka and Cox (1998)5. Ott (1995)6. Patil and Rao (1994)7. Walford (1995)

X. Environmental Health

1. Briggs et al (1996)2. Gosselin et al (1993)3. Kjellstrom and Corvalan (1995)4. Kolsky and Blumenthal (1995)5. Murray and Lopez (1996)6. Rothwell et al (1991)7. Wills and Briggs (1995)

XI. Information Technology (Databases, GIS, Internet, etc.)

1. Gunther (1998)2. Katz and Thornton (1997)3. Michael (1991)4. Oppenheimer et al (1976)5. Schumann (1995)

XII. Ecology

1. Bringezu and Schmidt-Bleek (1992)2. Hastings and Boonralasa (1990)3. Schoerers (1983)4. Washington (1984)5. WWF International (1998)

XIII. Land Use

1. Dumanski et al (1998)2. Pieri et al (1995)3. Smjyth and Dumanski (1995)4. Syers et al (1995)5. WHO (1982)

XIV. Human Settlements

1. UN (1988b)2. UN (1995)

XV. Urban Environment

1. Kreisel (1984)2. Leitmann (1993)3. Leitmann (1994)4. OECD (1993c)5. OECD (1997c)

XVI. Agriculture

1. Frankenberger and Goldstein (1990)2. OECD (1997b)3. Tschirley (1992)

XVII. Forestry

1. Duinker and Ronald (1994)2. FAO (1997)



3. USDA (1986)4. WWF International (1998)

XVIII. Biodiversity

1. Harper and Hawksworth (1994)2. UNEP (1995)

XIX. Water

1. Barnett and Turkman (1994)2. Chapman (1992)3. Lohani and Todino (1984)4. WHO (1982)5. WWF International (1998)

XX. Air

1. Lohani (1980)2. UNEP/WHO (1993)3. UNEP/WHO (1995)4. WHO (1980)5. WHO (1982)6. WHO and UNEP (1992)

XXI. Energy

1. International Energy Agency (1997)2. UN (1987)3. UN (1997d)4. OECD (1993b)

XXII. Marine Life

1. GESAMP (1990)

XXIII. Soil

1. Hortensius and Nortcliff (1991)

XXIV. Hazardous Wastes

1. OECD (1997e)

XXV. Tourism

1. IISD (1993)2. UN (1994a)

XXVI. Business and Industry

1. Azzone and Raffaella (1994)

XXVII. Global Warming

1. Houghton (1996a)2. Houghton (1996b)3. Houghton (1996c)4. WWF International (1998)



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Useful Web Sites

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Environment Canadahttp://www1.ec.gc.ca/~soer/defaulte.htm

European Environment AgencyEurope’s Environment: The Dobris Assessment: Statistical Compendiumhttp://www.eea.dk/Document/3-yearly/Dobris/StatComp/statcomp.htm

European Environment AgencyEuropean Reference Centre of Environmental Informationhttp://www.eea.dk/frdb.htm

Food and Agriculture OrganizationGlobal Terrestrial Observing Systemhttp://www.fao.org/GTOS/

International Institute for Sustainable DevelopmentSustainable Development Indicators...Selected Sourceshttp://iisd1.iisd.ca/ic/info/ss9504.htm

International Institute for Sustainable DevelopmentCompendium of Sustainable Development Indicator Initiativeshttp://iisd1.iisd.ca/measure/compindex.asp

Natural Environment Research Councilhttp://www.nerc.ac.uk/environmental-data/

Organization for Economic Co-operation and DevelopmentState of the environment, environmental data and information,

environmental indicators, environmental performance of membercountries

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Organization for Economic Co-operation and DevelopmentEnvironmental Data and Informationhttp://www.oecd.org/env/soe/data.htm#Environmental Data

United NationsSystem-wide Earthwatchhttp://www.unep.ch/earthw.html

United NationsIndicators of Sustainable Developmenthttp://www.un.org/esa/sustdev/isd.htm

United Nations Centre for Human Settlements (Habitat)Urban Indicators Programmehttp://www.unhabitat.org/guo/uip.htm

United Nations Development ProgrammeDevelopment Watch Indicatorshttp://www.undp.org/devwatch/

United Nations Economic Commission for EuropeEnvironment statisticshttp://www.unece.org/stats/env/env.htm

United Nations Environment ProgrammeEnvironmental and natural resource accountinghttp://www.unchs.unon.org/unep/products/eeu/ecoserie/ecos3/

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United Nations Environment ProgrammeState of environment reportshttp://www.unep.org/unep/soe.htm

United Nations Environment ProgrammeGlobal Resource Information Databasehttp://www.unep.org/unep/eia/ein/grid/web/document/grid.htm

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United Nations Environment ProgrammeDivision of Environmental Information and Assessmenthttp://www.unep.org/unep/deiamenu.htm

United Nations Environment ProgrammeUNEPnethttp://www.unep.org/unep/eia/eis/unepnet/

United Nations Environment ProgrammeThe Major Environmental Assessmentshttp://www.unep.ch/earthw/assess.htm

United Nations Environment ProgrammeA Survey of Environmental Monitoring and Information Management

Programmes of International Organizationshttp://www.gsf.de/UNEP/blue1.html

United Nations Environment ProgrammeIndicatorshttp://www.unep.ch/earthw/indicat.htm

United Nations Statistics Divisionhttp://www.un.org/Depts/unsd/

United States Environment Protection AgencyCenter for Environmental Information and Statisticshttp://www.epa.gov/ceis/

United States Environment Protection AgencyEnvirofacts Warehouse Websitehttp://www.epa.gov/enviro/index_java.html

United States Environment Protection AgencyEnvironmental Data and Tools for Scientific Inquiryhttp://www.epa.gov/epahome/scidata.htm

United States Environment Protection AgencyEnvironmental Indicatorshttp://www.epa.gov/indicators/

University of California BerkeleyDigital Library Projecthttp://elib.cs.berkeley.edu/

World BankPerformance Monitoring Indicatorshttp://www.worldbank.org/html/opr/pmi/contents.html

World BankEnvironmental Economics and Indicatorshttp://www-esd.worldbank.org/eei/

World Health OrganizationHealth statisticshttp://www.who.int/whosis/

World Meteorological OrganizationGlobal Climate Observing Systemhttp://www.wmo.ch/web/gcos/gcoshome.html

World Resources InstituteSustainable Development Information Servicehttp://www.wri.org/sdis/

Worldwide Fund for NatureLiving Planet Indexhttp://panda.org/livingplanet/lpr/index.htm

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