national hotspots analysis to support science-based...

National hotspots analysis to support science-based national policy frameworks for

sustainable consumption and production

September 2019

Technical documentation of the Sustainable Consumption and Production Hotspots Analysis Tool (SCP-HAT)

Pablo Pintildeero1 Maartje Sevenster2 Stephan Lutter1 Stefan Giljum1 Jakob Gutschlhofer1 Daniel

Schmelz1

1Institute for Ecological Economics Vienna University of Economics and Business (WU)

Austria

2Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia

Please cite as Pintildeero P Sevenster M Lutter S Giljum S Gutschlhofer J Schmelz D

(2019) Technical documentation of the Sustainable Consumption and Production Hotspots

Analysis Tool (SCP-HAT) Commissioned by UN Life Cycle Initiative One Planet Network and

UN International Resource Panel Paris

Corresponding author stephanlutterwuacat

TABLE OF CONTENTS

ABBREVIATIONS 4

1 INTRODUCTION 5

2 METHOD DESCRIPTION 5

Technical foundations 5

Pressure and impact categories 8

Geographical and IndustryProduct coverage 9

3 DATA SOURCES 10

Multi-Regional Input-Output 10

GHG emissions and Climate Change (Short-Term and Long-Term) 10

Raw material use and mineral and fossil resource scarcity 12

Land use and potential species loss from land use 13

Air pollution and health impacts 14

Socio-economic data 15

4 FURTHER DEVELOPMENTS 15

Increasing sectorproduct coverage 15

Including national data providing default data 16

Automated data updates 16

Improving land use accounts 17

Improving approach on air pollutionDALY 17

Improving emission accounts and their linkages to the EE-MRIO framework 18

Including new category water 18

Include more socio-economic data 18

Technical set-up of SCP-HAT 18

Implement user guidance 19

5 ACKNOWLEDGEMENTS 20

Project management and coordination 20

Technical supports 20

Advisory team 20

Country experts 20

Funding 21

REFERENCES 22

ANNEX I MATHEMATICAL DESCRIPTION 27

ANNEX II GEOGRAPHICAL COVERAGE OF THE SCP-HAT 29

ANNEX III SECTOR CLASSIFICATION OF THE SCP-HAT 31

ANNEX IV IPCC CATEGORIES OF THE SCP-HAT AND SECTOR CORRESPONDENCE 32

ANNEX V IPCC CATEGORIES OF THE SCP-HAT AND SECTOR CORRESPONDENCE 33

ANNEX VI RAW MATERIAL CATEGORIES OF THE SCP-HAT AND SECTOR

CORRESPONDENCE 34

ANNEX VII LAND USE AND BIODIVERSITY LOSS CATEGORIES OF THE SCP-HAT AND

SECTOR CORRESPONDENCE 35

ANNEX VIII IPCC CATEGORIES OF THE SCP-HAT AND SECTOR CORRESPONDENCE

FOR AIR POLLUTANTS 36

ANNEX IX GLOSSARY OF SCP-HAT 36

ANNEX X CHANGES BETWEEN SCP-HAT V10 (RELEASE DECEMBER 2018) AND SCP-

HAT V11 (RELEASE OCTOBER 2019) 39

ANNEX XI LAND USE COMPARISONS 41


Pasture areas 41

Pasture and cropping allocation 45

Forestry 46

Forestry allocation 48

Trade flows 49

Characterization factors 52

General conclusions 52

Abbreviations

10YFP 10-year framework of programmes on sustainable consumption and

production patterns

AQUASTAT FAO Information System on Water and Agriculture

DALY Disability-Adjusted Life Years

EDGAR Emission Database for Global Atmospheric Research

EE-MRIO Environmentally-Extended Multi-Regional Input-Output

FAO UN Food and Agriculture Organisation

GDP Gross Domestic Product

GHG Greenhouse gas

GTP Global Temperature change Potential

GWP Global Warming Potential

HDI Human Development Index

IPCC Intergovernmental Panel on Climate Change

LCA Life Cycle Assessment

LCIA Life Cycle Impact Assessment

MRIO Multi-Regional Input-Output

OECD Organisation of Economic Co-operation Development

PRIMAP-hist Potsdam Realtime Integrated Model for Probabilistic Assessment of

Emissions Paths

RIVM Dutch National Institute for Public Health and the Environment

SCP Sustainable Consumption and Production

SCP-HAT Sustainable Consumption and Production Hotspots Analysis Tool

SDG Sustainable Development goals

UN United Nations

UN IRP UN International Resource Panel

UN LCI UN Life Cycle Initiative

UNEP UN Environmental Programme

UN-SEEA UN System of Environmental-Economic Accounting

1 Introduction

This document provides the technical documentation for the Sustainable Consumption and

Production Hotspots Analysis Tool (SCP-HAT) The SCP-HAT allows for analysing direct as well

as indirect ie trade-related impacts brought about by production and consumption activities

of national economies It is therefore able to identify hotspots related to domestic pressures and

impacts (production or territorial perspective) but also impacts occurring along the supply chains

of goods and services for final consumption in a given country The SCP-HAT provides the

possibility of analysing the performance of a large number of countries with regard to specific

environmental pressures and impacts These analyses can be conducted at national as well as

sectoral level Thereby the tool allows identifying the hotspot areas of unsustainable production

and consumption and based on this analysis supports the setting of priorities in national SCP

and climate policies for investment regulation and planning

The SCP-HAT is a web-based tool at the state of the art of web design It consists of three main

modules Module 1 ldquoCountry Profilerdquo provides the key information regarding the countryrsquos

environmental performance in the context of the most relevant SCP-related policy questions

The target users are policy makers NGOs and the general public Module 2 ldquoSCP Hotspotsrdquo

contains a wide range of SCP indicators to analyse hotspots of unsustainable consumption and

production at country and sector levels This module supports policy advisors and researchers

with expertise by means of detailed information Finally module 3 ldquoNational Data Systemrdquo

provides technical experts such as statisticians the option of inserting national data to receive

more tailored results and carry out crosschecks against the default data as contained in the

underlying model

2 Method description

Technical foundations

In its basic conception SCP-HAT is based upon an Environmentally-Extended Multi-Regional

Input-Output (EE-MRIO) model coupled with Life Cycle Assessment (LCA) for environmental

impact assessment EE-MRIO is an analytical tool supported by the UN System of Environmental-

Economic Accounting (UN-SEEA United Nations 2014) to attribute environmental pressures

and impacts to final demand categories (European Commission et al 2017) EE-MRIO adopts a

top-down approach where supply chain-wide (so-called ldquoindirectrdquo) environmental pressures and

impacts are accounted for at the macro level for broad product groups or industries This is done

by allocating domestic data on pressures (eg material extraction) and impacts (eg

deforestation) expressed in physical units (eg kg also called lsquosatellite accountsrsquo) to monetary

data on transactions among economic sectors and final consumers of different countries Hence

each monetary flow is associated with a physical equivalent (mathematical details in Annex I)

By that means double counting is avoided as the specific supply chains be they national or

international can be clearly identified form their start at resource extraction until the point of

final demand

This approach allows tracing all the pressures and impacts occurring at the different stages of even very complex supply chains and allocating them to the country of final consumption or sectoral production Hence domestic pressures (national environment) are linked to foreign consumption (countries A to F in

Figure 1Figure 1) and foreign pressures to domestic consumption This allows to analyse both

the domestic situation (ldquodomestic productionrdquo) with regard to prevailing pressures and impacts

and the role a country plays as global consumer (ldquoconsumption footprintrdquo) where the focus is

set on pressures and impacts occurring along the supply chains of consumed products

Figure 1 The basic concept of SCP-HAT

This type of analysis is used to identify hotspots of sustainable consumption and production and

opportunities for action (1) pressure or impact categories where immediate action is needed on

the national level and (2) sectors or consumption areas where the pressures or impacts caused

directly or indirectly are specifically high and action is required

In general the consumption footprint of a country is calculated by adding the pressures and

impacts related to imports to those occurring domestically and subtracting those related to the

exports It is important to note the differences between approaches based upon EE-MRIO and

those using coefficients to estimate the indirect trade flows of pressures and impacts and the

consumption footprint of a country

- Coefficient approaches calculate the total environmental pressures or impacts associated

with final consumption by accounting for physical in- and out-flows of a country and

considering the environmental intensity of the traded commodities along the whole

production chain They apply intensity coefficients ndash or ldquocradle-to-productrdquo coefficients ndash

to the specific traded products and activities Here the consumption footprint of a country

is calculated by adding the pressures and impacts related to all imports to those occurring

domestically and subtracting those related to all the exports

Coefficient approaches apply the concept of ldquoapparent consumptionrdquo ie they cannot

specify whether a certain environmental pressure or impact is associated with

intermediate production or consumed by the final consumer Accordingly trade balances

are calculated in gross terms ie considering both intermediate and final trade products

(see above) Though conceptually feasible in practice it is not possible to separate eg

private consumption from government consumption Finally often so-called ldquotruncation

errorsrdquo are produced as the indirect flows are not traced along the entire industrial supply

chains The most important advantage of coefficient-based approaches is the high level of

detail and transparency which can be applied in footprint-oriented indicator calculations

- As explained above input-output analysis allows tracing monetary flows and embodied

environmental factors from its origin (eg raw material extraction) to the final consumption

of the respective products The so-called ldquoLeontief inverserdquo a matrix generated from an

input-output table (see Annex I) shows for each commodity or industry represented in

the model all direct and indirect inputs and related environmental pressures and impacts

required along the complete supply chain Doing the calculation for all product groups all

environmental pressures and impacts needed to satisfy final demand of a country can be

quantified

A major advantage of input-output based approaches is that input-output tables

disaggregate a large number of different product groups and industries as well as final

demand categories (eg private consumption government consumption investments

etc) They allow calculating the footprints for all products and all sectors also those with

very complex supply chains and thus avoid truncation errors (see above) Finally the

countries of origin of a countryrsquos consumption footprint can be identified as well as the

countries of destination of the domestic environmental pressures and impacts

Consequently in contrast to coefficient-based approaches analyses of indirect trade flows

are done from the perspective of linking source countries with final demand in the

destination countries Hence trade analyses in Module 1 and 2 of SCP-HAT have to be

understood before this background import flows into a country of interest will only include

those pressures and impacts occurring abroad which contribute to the countryrsquos final

demand Export flows incorporate only the pressures and impacts which occur

domestically and contribute to final demand abroad Consequently flows which ldquopass

throughrdquo the country via imports and re-exports are not accounted for

When comparing results for a country of interest which stem from MRIO-based and

coefficient-based approaches conceptually the numbers for the consumption footprint can

be directly compared while those for trade flows cannot

EE-MRIO is complemented by impact assessment for the calculation of certain environmental

impact variables (using Life Cycle Impact Assessment (LCIA) characterization factors) While

environmental accounts used in EE-MRIO provide data on domestic resource use in the countries

around the world LCIA ldquotranslatesrdquo these amounts into environmental impacts such as

biodiversity loss from land use or resource depletion related to raw material use This

methodological step is realised in accordance with the LCIA guidelines set by the United Nations

Environment Programme (UNEP 2016)

Pressure and impact categories

Ideally the SCP-HAT tool would cover all indicators included in the 10-year framework of

programmes on sustainable consumption and production patterns (10YFP) Evaluation and

Monitoring Framework1 ie material use waste water use energy use GHG emissions air soil

and water pollutants biodiversity conservation and land use and human well-being indicators

related to gender decent work and health However for the development of the 2018 version

of the tool a first selection of highly relevant pressure and impact categories are implemented

Environmental pressures

o Raw material use

o Land use (occupation only)

o Emissions of greenhouse gases

o Emissions of particulate matter and precursors

Environmental impacts

o Mineral resource scarcity

o Fossil resource scarcity

o Short-term climate change

o Long-term climate change

o Potential species loss from land use

o Damage to human health from particulate matter

The selection of a first set of pressures and impacts was guided by a set of criteria

Readiness of data availability

Relevance for SCP policy questions

Interrelatedness of pressures and impacts

Widely accepted impact characterization factors

Moreover the focus was set on an extensive coverage of related issues to be covered indirectly

with the selected domains For instance material flows include most energy carriers (and to a

large extend also cover energy) and allow for proxies for waste GHG emissions also have a large

overlap with energy use (more comprehensively when compared to material flows) They relate

1 httpwwwoneplanetnetworkorgresource10yfp-indicators-success

to two important policy areas of resource efficiency and climate mitigation identified as priorities

by many governments including the G7 and G20

Further categories to be integrated in future version of the SCP-HAT could include for example

Water use and water scarcity

Primary energy

Land transformation

Mercury emissions

Solid waste

Social life-cycle impacts (hazardous or child labour corruption etc)

Geographical and IndustryProduct coverage

The aim of SCP-HAT is to cover all countries in the world including those with relatively short

history of engaging more actively in policy making around policy areas such as SCP the SDGs

green economy etc The geographical coverage of SCP-HAT is based on the official list of UN

Member States2 but constrained by the country resolution of the databases utilised This choice

does not imply any political judgment All databases utilised in the SCP-HAT have their own

territorial definitions which do not always coincide especially regarding to former colonies

territories with independence partially recognized etc In total the SCP-HAT includes 171 UN

state members for which data are available in all databases There are some lsquospecial casesrsquo

Sudan includes South Sudan from 1990 to 2011 while Ethiopia includes Eritrea from 1990 to

1992 The full list is provided in Annex II The unified sector aggregation applied encompasses

26 economic sectorsproduct categories which is the level of detail of the underlying input-

output model (see below) A complete list can be found in the Annex III

3 Data sources

Multi-Regional Input-Output

There are a number of global input-output models available which allow for comprehensive EE-

MRIO modelling Depending on the political or scientific question to be answered they have their

strengths and weaknesses The input-output core applied in the SCP-HAT is the lsquoEorarsquo database

(Lenzen et al 2013 2012) Its major advantage in comparison to other available MRIO

databases is its geographical coverage of 188 single countries and thus the possibility to perform

nation-specific analysis for almost any country in the world Two Eora version are available the

2 httpwwwunorgenmember-states

Full Eora and the Eora26 The difference between them resides in the sectoral classification The

former uses the original sector disaggregation of the IO-tables as provided by the original source

Here the resolution varies between 26 and a few hundred sectors The latter applies a

harmonised 26sectors disaggregation for all countries For SCP-HAT we apply the Eora26 Even

though Full Eora can be considered more accurate using Eora26 avoids possible computational

problems since memory needs and server space would be significantly lower Time series are

available for 1990 to 2015 which is hence also the time span for the SCP-HAT

GHG emissions and Climate Change (Short-Term and Long-Term)

The UNEP LCI guidance for LCIA indicators makes an interim recommendation for considering

two impact categories for climate change short-term related to the rate of temperature change

and long-term related to the long-term temperature rise (UNEP 2016) The indicators

recommended for short-term and long-term respectively are Global Warming Potential 100

(GWP100) and Global Temperature change Potential (GTP100) at 100 years horizon The

corresponding characterization factors provided by UNEP are applied to GHG emissions data

with impact factors for 24 separate emissions including differentiation of fossil- and biogenic

methane (Full list of GWP and GTP factors in Annex IV) Near-term climate forcing gases are

excluded as the UNEP LCI guidance recommends those for sensitivity analysis only

Two sources are employed for compiling the SCP-HATrsquos GHG emissions input data The main

dataset was the PRIMAP-hist (PRIMAP ndash Potsdam Realtime Integrated Model for Probabilistic

Assessment of Emissions Paths) developed by the Potsdam Institute for Climate Impact Research

(Guumltschow et al 2019 2016) PRIMAP-hist was selected because of its coverage of long time

series and because it integrates other popular global GHG datasets (eg British Petroleum

Review of World Energy (BP 2014) Carbon Dioxide Information Analysis Center fossil fuel and

industrial CO2 emissions dataset (Boden et al 2015) the EDGAR dataset (Janssens-Maenhout

et al 2019) It includes emissions for the main Kyoto greenhouse gases carbon dioxide (CO2)

methane (CH4) nitrous oxide (N2O) hydrofluorocarbons (HFCs) perfluorocarbons

(PFCs)sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3) for the period of 1850 to 2016

The country detail is high including almost all countries considered in the SCP-HAT

The PRIMAP-hist sector categorization is based on the main Intergovernmental Panel on Climate

Change (IPCC) 2006 categories It has been analysed in the literature that too poor sector

resolution in the environmental extension of EE-MRIO models can cause aggregation errors

(Steen-Olsen et al 2014 Su et al 2010) and inclusion of external data for further

disaggregation is recommended (Lenzen 2011) To prevent this aggregation bias the PRIMAP-

hist data is further disaggregated using GHG emissions shares from the EDGAR database which

is maintained by the European Commission Joint Research Centre (JRC) and the Netherlands

Environmental Assessment Agency (PBL) (Janssens-Maenhout et al 2019 Olivier and Janssens-

Maenhout 2012) The shares of the different IPCC categories contributing to the total as reported

by EDGAR were applied to the totals published by PRIMAP-hist and used for SCP-HAT Three

specific EDGAR datasets are utilized the Edgar version 432 the EDGAR version 42 Fast Track

(FT) 2010 and the EDGAR version 42 For CO2 CH4 and N2O and the whole period Edgar

version 432 was used For SF6 the Edgar version 42 was used for the period 1990-1999 and

the Edgar version 42 FT for the period 2000-2010 Edgar shares from version 42 were adjusted

using the same approach as FAOSTAT for compiling their ldquoEmissions by sectorrdquo3 dataset For

HFCs and PFCs the EDGAR version 42 FT 2010 was used and shares from 2000 were assumed

as being equal for the period 1990-1999 For HFCs PFCs and SF6 shares for 2010 from Edgar

FT 2010 were applied for the disaggregation of the years 2011 to 2015 After the disaggregation

of the PRIMAP-hist data the IPCC 2006 categories were allocated to one or more sectors of the

SCP-HAT (ie Eora 26 sectors) The IPCC 2006 sector classification in the SCP-HAT and the

correspondence to the SCP-HAT sectors is provided in Annex V For those categories allocated

to more than one sector the emissions were disaggregated according to the relationship of the

total output of the sectors

Lastly emissions from road transportation from industries and households are recorded together

in PRIMAP-hits and EDGAR which need to be disaggregated for a finer analysis in the SCP-HAT

This was done applying the shares of different industries and households in the overall use of

the category lsquoPetroleum Chemical and Non-Metallic Mineral Productsrsquo as provided in Eora

Raw material use and mineral and fossil resource scarcity

For physical data for raw material extraction data from UN IRP Global Material Flows Database

(UN IRP 2017) are used It includes 13 aggregated raw material categories compiled following

lsquoeconomy-wide material flow accountingrsquo principles (Eurostat 2013 OECD 2008) for 192

countries including most of the countries of the SCP-HAT A full list of the raw material extraction

categories can be found in Annex VI National material extraction is allocated to the three

extractive sectors contained in the Eora26 sector classification ndash lsquoagriculturersquo lsquofishingrsquo and

lsquomining and quarryingrsquo While such a high aggregation of extractive sectors can result in a

distortion of the overall results (de Koning et al 2015 Pintildeero et al 2014) an improvement by

means for instance of allocating the extractions to intermediate users (Giljum et al 2017

Schaffartzik et al 2013 Wiebe et al 2012) ndash eg iron from mining to the steel industry ndash is

implemented in some cases The resulting allocation is sometimes referred as lsquouse extensionrsquo in

contrast to the most common lsquosupply extensionrsquo (further details in Annex I)

3 See httpwwwfaoorgfaostatendataEM

Raw material extraction for abiotic materials is translated into impacts by using the mineral and

fossil resource scarcity characterization factors from the Dutch National Institute for Public Health

and the Environment (RIVM) (Huijbregts et al 2016) The midpoint indicator (ie focussing on

intermediate stage along the impact pathway) for fossil resource scarcity is defined as the ratio

between the energy content of fossil resource x and the energy content of crude oil The unit of

this indicator is kg oil-equivalent and it simply reflects the energy content compared to a kilogram

of oil The characterization method for mineral resource scarcity is Surplus Ore Potential which

expresses the average extra amount of ore to be produced in the future due to the extraction of

1 kg of a mineral resource x In other words this reflects the decrease in ore grade of remaining

reserves The indicator is expressed relative to copper in units of kg Cu-equivalent The

ldquohierarchistrdquo version of this indicator is applied which means that future production is chosen to

be the ultimate recoverable resource For more details see RIVM (Huijbregts et al 2016) An

unweighted average characterization factor for the group of ldquoOther metalsrdquo was derived using

the 25 materials listed in that category in the Global Material Flows Database The resulting

factor was dominated by caesium which is probably higher than realistic In version 11 the

characterization factor for the group of ldquoOther metalsrdquo was updated by using a global-production-

weighted average (averaged over the years 2012-2014 as data for some metals are only

available after 2012) This resulted in a significant reduction of the factor with the main

contributors being mercury magnesium zirconium and hafnium

Land use and potential species loss from land use

The UNEP LCI guidance for LCIA indicators makes an interim recommendation (see UNEP 2016)

for characterization of biodiversity impacts of land use discerning occupation and

transformation based on the method developed by Chaudhary et al (2015) In this first version

of the SCP-HAT only land occupation is included and modelling of land transformation is left for

future tool developments To allow for the application of the recommended characterization

factors inventory data is required for land occupation (m2a) for six land use classes - Annual

crops Permanent crops Pasture Extensive forestry Intensive forestry Urban Annex VII shows

sector correspondence for the land use categories in the SCP-HAT Land use for crops and

pasture is allocated partly to the agriculture sector and partly to final demand The allocation to

final demand is made based on data on subsistence and low-input crop farming derived from the

Spatial Production Allocation Model version 2010 (SPAM You et al 2014) For details on the

allocation methodology see Annex XXI Land use for intensive forestry is allocated to the wood

and paper industry (ie allocation to first intermediate users see Annex I) Land use for

extensive forestry is allocated partly to the wood and paper industry and partly to final demand

based on domestic wood fuel production and consumption statistics For details on the allocation

methodology see Annex XXI

Land use for other industrial activities than agriculture or forestry (eg mining) is not covered

From version v11 onwards built-up land is allocated directly to final demand and estimated

using OECD land use statistics (OECD 2018)

The definition of the two forestry land use classes used for the impact characterization is i)

Intensive Forest forests with extractive use with either even-aged stands and clear-cut

patches or less than three naturally occurring species at plantingseeding and ii) Extensive

Forests forests with extractive use and associated disturbance like hunting and selective

logging where timber extraction is followed by re-growth including at least three naturally

occurring tree species For the latter alternative uses to wood and paper eg recreation

hunting etc are not considered

Land use data for Annual crops Permanent crops and Pasture are gathered from FAOSTATrsquos

databases for the analogous categories arable land permanent crops and permanent meadows

and pastures (Food and Agriculture Organization of the United Nations 2018) Table 1 shows

data sources and model description for Extensive and Intensive Forestry

Following UNEPrsquos recommendations country-level average characterization factors for global

species loss from Chaudhary et al (2015) are used in the SCP-HAT aggregated over five taxa

(mammals reptiles birds amphibians and vascular plants) for each of the six land use

categories The unit of this indicator is PDFyear which stands for the Potentially Disappeared

Fraction of species for the duration of a year Land use impact modelling assumes that once an

activity (land use) stops the system will slowly return to the natural state The indicator

therefore does not reflect full extinction of species but a temporary decline in biodiversity

Table 1 Data sources and model description for Extensive and Intensive Forestry

Data sources Model description

FAOSTATrsquos Land Use database category

lsquoplanted forestrsquo (PLF)(Food and

Agriculture Organization of the United

Nations 2018)

Global Forest Resource Assessment

(Food and Agriculture Organization of the

United Nations 2015) for lsquoproduction

forestrsquo (PRF) table 22 and for lsquomultiple

use forest table 23 (MUF) For most

countries data is compiled for five years

period and missing years were

interpolated For some the data is even

scarcer and intraextrapolation is used

when needed

Intensive forestry if PRFgtPLF intensive

forestry is PRF If PRFltPLF intensive

forestry is PLF

Extensive forestry If PLFgtPRF extensive

forestry is MUF If PLFltPRF extensive

forestry is PRF+MUF-intensive forestry

Air pollution and health impacts

Many emissions contribute to air pollution via a variety of mechanisms SCP-HAT focuses on air

pollution via particulate matter (PM) which is caused by emissions of PM itself as well as by

emissions of NH3 NOx and SO2 which form so-called secondary PM via chemical reactions The

UNEP LCI guidance for LCIA indicators (UNEP 2016) recommends the use of characterization

factors at end-point reflecting damages to human health expressed in Disability-Adjusted Life

Years (DALY) The recommended approach for calculating DALY impact factors is via emission

source archetypes but this could not be implemented at the global highly aggregated scale of

emissions data used in SCP-HAT The tool therefore uses DALY impact factors from RIVM

(Huijbregts et al 2016) which reflect country-averaged DALY impacts per unit of emission for

each of the four substances (PM25 NH3 SO2 NOx) No differentiation is made by emission source

type at this stage A recent set of new effect factors (Fantke et al 2019) is still only available

at country level without additional distinction of emission source type

The emissions data are taken from the EDGAR database (v432) This database covers all

countries and years up to and including 2012 For emissions of primary PM25 fossil and biogenic

sources are distinguished There is no difference in impact (DALYkg emitted) between those

two categories The data are extrapolated to 2013 and 2015 using GHG emissions trends with a

fixed emission ratio based on 2012 data As shown in Crippa et al (2018) emission ratios of air

pollutants to carbon dioxide have steadily decreased since 1970 but have been relatively stable

since around 2010 especially for NOx and SO2 More specifically PM25 fossil NOx and SO2 are

projected using CO2 emissions trends while CH4 and N2O (in CO2 eq) are used for PM25 bio and

NH3 During data processing it was found that this approach is not satisfactory for NH3 emissions

from agriculture and results are of especially high uncertainty for this category Thus future

improvements should prioritize this specific flow

Socio-economic data

The SCP-HAT includes a set of basic socioeconomic data which mainly serves for the estimation

of relative performance indicators of economies and industries eg carbon per unit of economic

output In the following the data sources used are listed

Population The World Bank Group

GDP The World Bank Group

Value added Eora database

Output Eora database

Employment per sector International Labour Organization

Country groups The World Bank Group

Government and private final consumption Eora database

HDI United Nations Development Programme


Employment by sector and gender is compiled from the ILO (International Labour Organization

2018) The dataset on employment by gender and sector includes only 14 sectors

Disaggregation is done using compensation to employees in Eora as proxy (ie it is assumed

same retribution between sectors belonging to an ILO category)

4 Further developments

The SCP-HAT has been developed to support science-based national policy frameworks SCP-

HATv10 as finalised by the end of 2018 is a full-fledged online tool coming up to the overall

requirements of identifying for all countries in the world for the main policy topics those areas

(ie hotspots) where political action towards a more sustainable consumption and production is

needed However due to time and budget restrictions a number of methodological simplifications

had to been accepted which could be tackled in future developments of the SCP-HAT In the

following the main areas of future improvements are described ndash first identifying possible

shortcomings and then describing necessary development steps

Increasing sectorproduct coverage

Sectoral resolution in EE-MRIO can cause significant impacts in results and having a more

detailed classification would be in general preferable Expanding computing capacity would

allow use of more detailed databases eg the full Eora version with a twofold benefit

minimizing biases and providing wider analytical options Similarly the number of products

covered could be increased eg by providing more detail on primary commodity and

environmentally-intensive imports Following the concept of a lsquohybridrsquo calculation approach

these types of imports could be combined with detailed coefficients derived from LCA ie

coefficients expressing the environmental pressuresimpact per tonne of imported product

instead of per $ of imported product as done in the first version of SCP-HAT Product groups

such as primary products (ISIC Rev4 01 to 09) electricity and gas (ISIC Rev4 35) and waste

collection and materials recovery (ISIC Rev4 38) could be treated in this detailed way while for

manufactured goods with complex supply chains as well as for services the coefficients would

still be calculated using the monetary MRIO model (Pintildeero et al 2018)

Including national data providing default data

Another aspect of further development refers to the inclusion of additional national data For

example the default input-output table provided by the Eora database in the basic version could

be replaced by a national input-output table in order to improve the accuracy of allocating

domestic and imported environmental pressures and impacts to final demand Also the default

data on environmental indicators stem from international data sources which not always build

upon officially reported data In these cases data are reported by experts or have to be

homogenised before being could be replaced by data from official sources

Such an approach however would require a very well designed approach not only regarding

data collection but also data validation While currently Module 3 can be seen as a means to

allow users to cross-check their own data in comparison with the data used in the model the

Module could be re-designed to allow for data upload However the data could not be published

immediately as data quality check would be indispensable Experiences of wiki-type

collaborative work in virtual labs are the ideal starting point for implementing this tool

development (see Lenzen et al 2017) Finally users could be provided with the option of

downloading (sections of) the underlying data to enable them to carry out direct data

comparisons

Automated data updates

The SCP-HAT is developed to allow an easy and quick data updating of different data inputs and

app components This is an important issue considering the fast development of the databases

and models that are employed For instance it can be expected that the Eora database migrates

soon from old product and industry classifications to more updated ones (eg from CPC (Central

Product Classification) Ver1 to CPC Ver2) Also new methods and recommendations regarding

LCIA models can be expected for example the UN Environment Life Cycle Initiative is currently

working on an impact category lsquomineral primary resourcesrsquo that could be incorporated to the

SCP-HAT next versions

While some of these updates might be automated (ie by retrieving the new data automatically

from the original source) data manipulation and incorporation into the model will require

additional work

Improving land use accounts

Land transformation is known to be an important factor contributing to biodiversity loss

Including this next to the impact of land occupation in SCP-HAT would give visibility of a

significant environmental issue No international databases on land transformation exist but the

necessary data could be generated from annual changes in land use The challenge is that for

transformation both the pre- and the post-transformation state of land use need to be known

This requires analysis of likely scenarios of transformation for each country based on average

land cover Existing methodologies such as the one applied in the tool accompanying PAS2050-

12012 may be used as a basis for an approach suitable for SCP-HAT

The current land use (occupation) accounts in SCP-HAT are robust but improved characterization

factors (CFs) for biodiversity are available (Chaudhary and Brooks 2018) These CFs can be

implemented in SCP-HAT but this would require the land use accounts to be revised to

distinguish the necessary land use categories which are somewhat different from those in the

current version There is also a different biodiversity assessment framework (see Di Marco et al

2019) which may be further developed to give state-of-the-art biodiversity CFs Either approach

is likely to give a significant improvement in the biodiversity foot print compared to SCP-HAT 11

which follows the interim UNEP LCI recommended CFs (Chaudhary et al 2015) It should be

noted that the new CFs by Chaudhary and Brooks (2018) are adopted as being in line with the

UNEP LCI recommendation in the draft FAO LEAP guideline for biodiversity impact assessment

of livestock systems (FAO 2019) and as such might be adopted in SCP-HAT without creating

conflict with the UNEP LCI recommendations (UNEP 2016)

Improving approach on air pollutionDALY

In 2019 publication of improved characterization factors for air pollution by particulate matter

are foreseen (Peter Fantke private communication) These new factors would allow to

differentiate impacts by region (continental or subcontinental) as well as by emission source

archetype This would allow for more detailed assessment of hotspots acknowledging that

emissions from eg transport have higher impacts than the same emission from a power plant

Improving emission accounts and their linkages to the EE-MRIO

framework

GHG national inventories (and databases based on them as PRIMAP-hist) follow the territory

principle that is all emissions occurring within the boundaries of a country are accounted In

contrast input-output databases follow the residence principle ie output by the residence units

irrespective from the country where they occur are accounted Although in most cases a resident

unit operates on the domestic territory there are some exceptions such as air and maritime

transportation and combining both approaches with any prior adjustment can lead to some

inconsistencies (Usubiaga and Acosta-Fernaacutendez 2015) However reducing this gap would have

required extra data and assumptions which due to time limitations were out of scope of the

present project Existing approaches for converting GHG accounts from following the territory

principle to residence one could be applied in future versions

Including new category water

Water is an essential resource both for our ecosystems as well as for our societies SDG 6 (Clean

water and sanitation) is tackling the resource water directly while other SDGs are closely related

While the relevance of the topic is clear introducing a water section in Module 1 as well as the

related indicators in the other modules was not possible for different reasons (1) Data

availability ndash freely available datasets on national water abstraction consumption or pollution

are scarce The AQUASTAT dataset is very patchy and it is not always clear which concepts

have been used which makes it difficult to apply LCIA scarcity indicators such as AWARE (Boulay

et al 2018) More comprehensive datasets like results from the WaterGAP model (Floumlrke et al

2013) require more efforts in compilation than would have been possible for SCP-HAT v1 (2)

Time and budget restrictions ndash the decision was taken to prioritaise a section on pollution instead

However in future stages of tool development including an additional category on water is

indispensable

Include more socio-economic data

In order to allow for more comparisons of the ecological with the socio-economic dimension

further data and indicators are needed Among these would be financial investments financial

costs associated with environmental pressures impacts child labour associated with specific

sectors etc However it has to be investigated if such data are available from official sources

and if they cover all countries world-wide

Technical set-up of SCP-HAT

The app was coded to a large extent using R shiny (httpswwwrstudiocomproductsshiny)

a powerful web framework for building web R-language based applications which is the main

programming language used by the SCP-HAT developing team Once a module is started the

data are loaded and after a country is selected R shiny visualises the pre-calculated data

according to the (sub-) modules chosen In Module 3 inserted data are incorporated into the

underlying MRIO-model and the whole model runs real time calculations to produce the new

results to be visualised While in general the app performs satisfactorily depending on the

necessary calculation procedure some features show poor performance (eg first start-up of

modules computing time for plotting up to a minute for specific visualisations slow IO

calculations with domestic data) In future versions in-depth assessments should be carried out

towards increasing overall app operating capacity

A possibility to improve the performance without changing the code so much would be to move

the hosting of the RStudio Server from shinyappsio to a dedicated server with more capacity

Furthermore all data is now loaded on start-up (see above) which slows down the operation ndash

an option here is to move the data to a database that enables faster start-up and a more

lightweight application instance This is a labour-intensive process also requiring additional

technical infrastructure to be set up which is why the current set-up has been chosen to stay

within the time and budget constraints

In addition the website the app is embedded in is based on an adapted Wordpress install with

an open source theme that contains an advanced visual editor This means that smaller content

changes can be done quite easily while larger adaptations should only be done using a broader

web-design process that focuses on a clean and efficient user experience Setting up such a web-

design process should be foreseen for the next development phase of the tool

Implement user guidance

The app as well as the website tries to keep the interfaces and functionalities self-explanatory

by using common tried-and-tested usability and interaction design practices Where additional

information is required - such as definitions of expert technical vocabulary - it is placed context-

dependent where needed (A short glossary is also provided in Annex XIX) In addition a

document is provided containing non-technical guidance on how to use the SCP-HAT and how to

interpret results

To increase user guidance user friendliness and applicability in policy processes a state-of-the

art option is to include for each module short explanatory videos which introduce the main

features and explain the main options of use An additional option is to record a series of

webinars where possible results are discussed and options for policy application of the different

analyses are shown

5 Acknowledgements

Project management and coordination

10YFP Secretariat One Planet network

Fabienne Pierre Programme Management Officer with the support of Listya Kusumawati

Consultant under the supervision of Charles Arden-Clarke Head of the 10YFP Secretariat

Life Cycle Initiative

Llorenccedil Milagrave I Canals Head and Kristina Bowers Programme Management Officer Secretariat

of the Life Cycle Initiative

International Resource Panel

Mariacutea Joseacute Baptista Economic Affairs Officer Secretariat of the International Resource Panel

Technical supports

Content

Stephan Lutter Stefan Giljum Pablo Pintildeero Maartje Sevenster Heinz Schandl

Data management and visualisation

Pablo Pintildeero Maartje Sevenster and Jakob Gutschlhofer

Web design

Daniel Schmelz and Jakob Gutschlhofer

Advisory team

The tool development was reviewed and advised by a team of renown experts in the field

including members of the International Resource Panel (IRP) and Life Cycle Initiative (LCI) Hans

Bruyninckx (European Environmental Agency) Jillian Campbel (UN Environment) Edgar

Hertwich (Yale University) Manfred Lenzen (The University of Sydney) Stephan Pfister (ETH

Zuumlrich) Sungwon Suh (University of California Santa Barbara) Sonia Valdivia (World Resources

Forum) and Ester van der Voet (Leiden University)

Country experts

This tool was developed with the active participation of the Secretariat of Environment and

Sustainable Development of Argentina Ministry of Environment and Sustainable Development

of Ivory Coast and Ministry of Energy of the Republic of Kazakhstan While piloting the

methodology and tool they provided valuable feedback regarding policy relevance and

application Maria Celeste Pintildeera Prem Zalzman Javier Nahem Mariano Fernandez (Argentina)

Alain Serges Kouadio (Ivory Coast) Aliya Shalabekova Saule Sabieva Olga Menik (Kazakhstan)

Funding

The project also benefited from the generous financing support of the European Commission and

Norway

References

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from Earth observation data International Journal of Remote Sensing 26 1959-1977

Boden T Marland G Andres R 2015 Global Regional and National Fossil-Fuel CO2

emissions

Boulay A-M Bare J Benini L Berger M Lathuilliegravere MJ Manzardo A Margni M

Motoshita M Nuacutentildeez M Pastor AV 2018 The WULCA consensus characterization

model for water scarcity footprints assessing impacts of water consumption based on

available water remaining (AWARE) The International Journal of Life Cycle Assessment

23 368-378

BP 2014 BP Statistical Review of Energy 2014 London

Cadarso M Aacute Monsalve F amp Arce G (2018) Emissions burden shifting in global value

chains ndash winners and losers under multi-regional versus bilateral accounting Economic

Systems Research 5314 1ndash23 httpsdoiorg1010800953531420181431768

Chaudhary A Brooks TM 2018 Land Use Intensity-Specific Global Characterization Factors

to Assess Product Biodiversity Footprints Environ Sci Technol 52 5094-5104

Chaudhary A Verones F De Baan L Hellweg S 2015 Quantifying Land Use Impacts on

Biodiversity Combining Species-Area Models and Vulnerability Indicators Environ Sci

Technol 49 9987ndash9995 doi101021acsest5b02507

Commonwealth of Australia (2018) National Inventory Report 2016 Volume 1 Canberra

Crippa M Guizzardi D Muntean M Schaaf E Dentener F van Aardenne JA Monni S

Doering U Olivier JGJ Pagliari V Janssens-Maenhout G 2018 Gridded emissions

of air pollutants for the period 1970ndash2012 within EDGAR v432 Earth System Science

Data 10 1987-2013

Di Marco M S Ferrier T D Harwood A J Hoskins and J E M Watson (2019) Wilderness

areas halve the extinction risk of terrestrial biodiversity Nature 573(7775) 582-585

European Commission Food and Agriculture Organization of the United Nations Organisation

for Economic Co-operation and Development United Nations World Bank 2017 System

of Environmental-Economic Accounting 2012 Applications and Extensions

Eurostat 2013 Economy-wide Material Flow Accounts (EW-MFA) Compilation Guide 2013

Fantke P McKone TE Tainio M Jolliet O Apte JS Stylianou KS Illner N Marshall

JD Choma EF Evans JS 2019 Global Effect Factors for Exposure to Fine Particulate

Matter Environ Sci Technol 53 6855-6868

Floumlrke M Kynast E Baumlrlund I Eisner S Wimmer F Alcamo J 2013 Domestic and

industrial water uses of the past 60 years as a mirror of socio-economic development A

global simulation study Global Environmental Change 23 144-156

Food and Agriculture Organization of the United Nations 2019 Biodiversity and the livestock

sector ndash Guidelines for quantitative assessment (Draft for public review) Livestock

Environmental Assessment and Performance (LEAP) Partnership FAO Rome Italy

Food and Agriculture Organization of the United Nations 2018 FAOSTAT URL

httpwwwfaoorgfaostatendata

Food and Agriculture Organization of the United Nations 2015 Global Forest Resources

Assessment 2015 - Desk reference

Frischknecht R NC Alig M Stolz P Tschuumlmperlin L Hellmuumlller P 2018 Environmental

Footprints of Switzerland Developments from 1996 to 2015 Extended summary Federal

Office for the Environment State of the environment n 1811

Furukawa T Kayo C Kadoya T Kastner T Hondo H Matsuda H Kaneko N 2015

Forest harvest index Accounting for global gross forest cover loss of wood production and

an application of trade analysis Glob Ecol Conserv 4 150ndash159

httpsdoiorg101016jgecco201506011

Giljum S Lutter S Bruckner M Wieland H Eisenmenger N Wiedenhofer D Schandl

H 2017 Empirical assessment of the OECD Inter-Country Input-Output database to

calculate demand-based material flows Paris

Guumltschow J Jeffery L Gieseke R Gebel R 2019 The PRIMAP-hist national historical

emissions time series (1850-2016) V 20 doihttpsdoiorg105880PIK2019001


emissions time series (1850-2014) V 11 doihttpdoiorg105880PIK2017001

Guumltschow J Jeffery ML Gieseke R Gebel R Stevens D Krapp M Rocha M 2016

The PRIMAP-hist national historical emissions time series Earth Syst Sci Data 8 571ndash

603 doi105194essd-8-571-2016

Hoskins AJ Bush A Gilmore J Harwood T Hudson LN Ware C Williams KJ

Ferrier S 2016 Downscaling land-use data to provide global 30 estimates of five land-

use classes Ecol Evol 6 3040-3055

Huijbregts MAJ Steinmann ZJN Elshout PMF Stam G Verones F Vieira MDM

Hollander A Zijp M Zelm R van 2016 ReCiPe 2016 v1 A harmonized life cycle

impact assessment method at midpoint and endpoint level Report I Characterization

RIVM Report 2016-0104a

International Labour Organization 2018 ILOSTAT (Employment by sex and economic activity)

Package ldquoRilostatrdquo [WWW Document]

International Monetary Fund 2019 World Economic Outlook DatabasemdashWEO Groups and

Aggregates Information April 2019 Consulted August 2019

httpswwwimforgexternalpubsftweo201901weodatagroupshtm

Janssens-Maenhout G Crippa M Guizzardi D Muntean M Schaaf E Dentener F

Bergamaschi P Pagliari V Olivier J Peters J van Aardenne J Monni S Doering

U Petrescu R Solazzo E Oreggioni G 2019 EDGAR v432 Global Atlas of the three

major Greenhouse Gas Emissions for the period 1970-2012 Earth Syst Sci Data Discuss

1ndash52 httpsdoiorg105194essd-2018-164

Kanemoto K Lenzen M Peters G P Moran D D amp Geschke A (2012) Frameworks for

comparing emissions associated with production consumption and international trade

Environmental Science and Technology 46(1) 172ndash179

httpsdoiorg101021es202239t

Lenzen M 2011 Aggregation Versus Disaggregation in InputndashOutput Analysis of the

Environment Econ Syst Res 23 73ndash89 doi101080095353142010548793

Lenzen M Geschke A Abd Rahman MD Xiao Y Fry J Reyes R Dietzenbacher E

Inomata S Kanemoto K Los B Moran D Schulte in den Baumlumen H Tukker A

Walmsley T Wiedmann T Wood R Yamano N 2017 The Global MRIO Labndashcharting

the world economy Econ Syst Res 29 doi1010800953531420171301887

Lenzen M Kanemoto K Moran D Geschke A 2012 Mapping the structure of the world

economy Environ Sci Technol 46 8374ndash8381 doi101021es300171x

Lenzen M Moran D Kanemoto K Geschke A 2013 Building Eora a Global Multi-Region

InputndashOutput Database At High Country and Sector Resolution Econ Syst Res 25 20ndash

49 doi101080095353142013769938

Lutter S Giljum S Bruckner M 2016 A review and comparative assessment of existing

approaches to calculate material footprints Ecological Economics 127 1-10

Marques A Martins IS Kastner T Plutzar C Theurl MC Eisenmenger N Huijbregts

MAJ Wood R Stadler K Bruckner M Canelas J Hilbers JP Tukker A Erb K

Pereira HM 2019 Increasing impacts of land use on biodiversity and carbon

sequestration driven by population and economic growth Nat Ecol Evol 3 628-637

MLA 2019 Cattle numbers as at June 2018 MLA market information

Monitoring and Evaluation Task Force 10-Year Framework of Programmes on Sustainable

Consumption and Production (10YFP) UNEP 2017 Indicators of Success Demonstrating

the shift to Sustainable Consumption and Production ndash Principles process and

methodology

Nachtergaele F Biancalani R 2013 Land Degradation Assessment in Drylands Mapping land

use systems at global and regional scales for land degradation assessment analysis FAO

Rome

Olivier J Janssens-Maenhout G 2012 Part III Greenhouse gas emissions in CO2

Emissions from Fuel Combustion 2012 OECD Publishing Paris

Organisation for Economic Co-operation and Development (OECD) 2018 OECDStat Built-up

area and built-up area change in countries and regions URL

httpsstatsoecdorgIndexaspxDataSetCode=LAND_USE

Organisation for Economic Co-operation and Development (OECD) 2008 Measuring material

flow and resource productivity Volume I The OECD guide

Pintildeero P Cazcarro I Arto I Maumlenpaumlauml I Juutinen A Pongraacutecz E 2018 Accounting for

Raw Material Embodied in Imports by Multi-regional Input-Output Modelling and Life Cycle

Assessment Using Finland as a Study Case Ecol Econ 152

doi101016jecolecon201802021

Ramankutty N Evan AT Monfreda C Foley JA 2008 Farming the planet 1

Geographic distribution of global agricultural lands in the year 2000 Global

Biogeochemical Cycles 22 1-19

Schaffartzik A Eisenmenger N Krausmann F Weisz H 2013 Consumption-based

material flow accounting  Austrian trade and consumption in raw material equivalents

1995-2007

Stadler K Wood R Bulavskaya T Soumldersten C-J Simas M Schmidt S Usubiaga A

Acosta-Fernaacutendez J Kuenen J Bruckner M Giljum S Lutter S Merciai S

Schmidt JH Theurl MC Plutzar C Kastner T Eisenmenger N Erb K-H de

Koning A Tukker A 2018 EXIOBASE 3 Developing a Time Series of Detailed

Environmentally Extended Multi-Regional Input-Output Tables Journal of Industrial

Ecology 22 502-515

Steen-Olsen K Owen A Hertwich EG Lenzen M 2014 Effects of Sector Aggregation on

Co2 Multipliers in Multiregional InputndashOutput Analyses Econ Syst Res 26 284ndash302

doi101080095353142014934325

Su B Huang HC Ang BW Zhou P 2010 Inputndashoutput analysis of CO2 emissions

embodied in trade The effects of sector aggregation Energy Econ 32 166ndash175

doi101016jeneco200907010

UNEP 2016 Global guidance for life cycle impact assessment indicators Volume 1

doi101146annurevnutr22120501134539

UN IRP 2017 Global Material Flows Database Version 2017 in International Resource Panel

(Ed) Paris Available at httpwwwresourcepanelorgglobal-material-flows-database

Usubiaga A Acosta-Fernaacutendez J 2015 Carbon emission accounting in mrio models the

territory vs the residence principle Econ Syst Res 27 458ndash477

doi1010800953531420151049126

Wiebe KS Bruckner M Giljum S Lutz C Polzin C 2012 Carbon and materials

embodied in the international trade of emerging economies A multiregional input-output

assessment of trends between 1995 and 2005 J Ind Ecol 16 636ndash646

doi101111j1530-9290201200504x

You L U Wood-Sichra S Fritz Z Guo L See and J Koo 2014 Spatial Production

Allocation Model (SPAM) 2005 v20 Available from httpmapspaminfo

Annex I Mathematical description

In this description the nomenclature introduced in the System of Environmental-Economic

Accounting (SEEA) 2012 (European Commission et al 2017) is followed and the reader is

referred to that document for further method details Table 1 shows the main components of a

two countries EE-MRIO model Using matrix notation 119885 records intermediate deliveries

between industries of each country 119910 is the final demand of products and 119907 is value added in

production (or payments to suppliers of primary inputs) Sub-indexes A and B denote the

country of origin or the country of origin and destination (ie 119910119860119861 records final demand of

products from country A consume by country B) In the input-output system total output must

be equal to total input per sector Total output 119909 equals intermediate consumption plus final

demand that is 119909 = 119885119894 + 119910 whereas total input 119909prime equals all inter-industry purchases plus value

added 119909prime = 119894prime119885 + 119907 In the EE-MRIO the monetary framework is expanded to include exchanges

between the natural and socioeconomic systems measured in physical units This is

represented by 119903 which accounts for physical flows by industry (inputs to the system such as

raw material extracted in tons or outputs eg CO2 emissions in kg) Capital and minor letters

denote matrix and column-vector respectively and 119894 is a vector of ones The general

expression of the EE-MRIO model is presented in equation 1

120567 = 120575prime(119868 minus 119860)minus1119910 = 120575prime119871119910 = 120572prime119910 (1)

where 120567 is the consumption-based or footprint for a given final demand 119910 The allocation to

final demand is calculated using the Multipliers 120572 obtained multiplying the Leontief inverse 119871 =

(119868 minus 119860)minus1 whose elements indicates total input requirements per unit of final demand of

products and the environmental pressure intensity 120575prime which expresses physical flows per unit

of industry output and calculated following 120575prime = 119903prime119902minus1 119868 is the identity matrix and 119860 = 119885119909minus1 is the

direct input coefficients matrix

The equation 1 shows the generic expression for EE-MRIO models and it corresponds with the

lsquosupply extensionrsquo that is environmental intensities refer to the industry supplying products

whose production causes environmental pressure directly (eg sand and gravel extraction is

allocated to the mining and quarrying sector) However when the sectoral resolution of the EE-

MRIO model is low allocating the environmental pressures to intermediate consumers (ie

using a lsquouse extensionrsquo) can be an acceptable solution for preventing aggregation errors

(Giljum et al 2017) (eg sand and gravel extracted is allocated to the construction sector)

This can be performed using a supply-to-use conversion matrix 119872 whose elements are zeros

and ones appropriately placed so the general expression is slightly modified

120567 = 120575prime119872119871119910 (2)

In practice it may be also convenient to combine both logics in a mixed approach Accordingly

which perspective provides more satisfactory results need to be assessed in a case by case

basis

Further equation 1 can be further developed for applying LCIA characterization factors 120573

which convert physical flows (ie environmental pressures) to environmental impacts for

example from km2 land use to number of species loss This expansion is shown in equation 3

120567 = 120573prime120575119871119910 (3)

Lastly in EE-MRIO trade and international dependencies in terms of natural resources and

environmental impacts can also be assessed for each countryrsquos final demand bundle 119910

However since in EE-MRIO trade of intermediates is endogenized EE-MRIO trade balances

refer exclusively to indirect and direct pressures and impacts of final products (Cadarso et al

2018 Kanemoto et al 2015) As a consequence EE-MRIO trade balances arenrsquot directly

comparable to conventional bilateral trade balances

Annex II Geographical coverage of the SCP-HAT

n Country ISO_A3 n Country ISO_A3

1 Afghanistan AFG 57 Gabon GAB

2 Albania ALB 58 Gambia GMB

3 Algeria DZA 59 Georgia GEO

4 Angola AGO 60 Germany DEU

5 Antigua ATG 61 Ghana GHA

6 Argentina ARG 62 Greece GRC

7 Armenia ARM 63 Guatemala GTM

8 Australia AUS 64 Guinea GIN

9 Austria AUT 65 Guyana GUY

10 Azerbaijan AZE 66 Haiti HTI

11 Bahamas BHS 67 Honduras HND

12 Bahrain BHR 68 Hungary HUN

13 Bangladesh BGD 69 Iceland ISL

14 Barbados BRB 70 India IND

15 Belarus BLR 71 Indonesia IDN

16 Belgium BEL 72 Iran IRN

17 Belize BLZ 73 Iraq IRQ

18 Benin BEN 74 Ireland IRL

19 Bhutan BTN 75 Israel ISR

20 Bolivia BOL 76 Italy ITA

21 Bosnia and Herzegovina BIH 77 Jamaica JAM

22 Botswana BWA 78 Japan JPN

23 Brazil BRA 79 Jordan JOR

24 Brunei BRN 80 Kazakhstan KAZ

25 Bulgaria BGR 81 Kenya KEN

26 Burkina Faso BFA 82 Kuwait KWT

27 Burundi BDI 83 Kyrgyzstan KGZ

28 Cambodia KHM 84 Laos LAO

29 Cameroon CMR 85 Latvia LVA

30 Canada CAN 86 Lebanon LBN

31 Cape Verde CPV 87 Lesotho LSO

32 Central African Republic CAF 88 Liberia LBR

33 Chad TCD 89 Libya LBY

34 Chile CHL 90 Lithuania LTU

35 China CHN 91 Luxembourg LUX

36 Colombia COL 92 Madagascar MDG

37 Costa Rica CRI 93 Malawi MWI

38 Croatia HRV 94 Malaysia MYS

39 Cuba CUB 95 Maldives MDV

40 Cyprus CYP 96 Mali MLI

41 Czech Republic CZE 97 Malta MLT

42 Cote dIvoire CIV 98 Mauritania MRT

43 North Korea PRK 99 Mauritius MUS

44 DR Congo COD 100 Mexico MEX

45 Denmark DNK 101 Mongolia MNG

46 Djibouti DJI 102 Montenegro MNE

47 Dominican Republic DOM 103 Morocco MAR

48 Ecuador ECU 105 Mozambique MOZ

49 Egypt EGY 106 Myanmar MMR

50 El Salvador SLV 107 Namibia NAM

51 Eritrea ERI 108 Nepal NPL

52 Estonia EST 109 Netherlands NLD

53 Ethiopia ETH 110 New Zealand NZL

54 Fiji FJI 111 Nicaragua NIC

55 Finland FIN 112 Niger NER

56 France FRA 113 Nigeria NGA

114 Oman OMN 143 Sri Lanka LKA

115 Pakistan PAK 144 Sudan SDN

116 Panama PAN 145 Suriname SUR

117 Papua New Guinea PNG 146 Swaziland SWZ

118 Paraguay PRY 147 Sweden SWE

119 Peru PER 148 Switzerland CHE

120 Philippines PHL 149 Syria SYR

121 Poland POL 150 Tajikistan TJK

122 Portugal PRT 151 Thailand THA

123 Qatar QAT 152 TFYR Macedonia MKD

124 South Korea KOR 153 Togo TGO

125 Moldova MDA 154 Trinidad and Tobago TTO

126 Romania ROU 155 Tunisia TUN

127 Russia RUS 156 Turkey TUR

128 Rwanda RWA 157 Turkmenistan TKM

129 Samoa WSM 158 Uganda UGA

130 Sao Tome and Principe STP 159 Ukraine UKR

131 Saudi Arabia SAU 160 UAE ARE

132 Senegal SEN 161 UK GBR

133 Serbia SRB 162 Tanzania TZA

134 Seychelles SYC 163 USA USA

135 Sierra Leone SLE 164 Uruguay URY

136 Singapore SGP 165 Uzbekistan UZB

137 Slovakia SVK 166 Vanuatu VUT

138 Slovenia SVN 167 Venezuela VEN

139 Somalia SOM 168 Viet Nam VNM

140 South Africa ZAF 169 Yemen YEM

141 South Sudan SSD 170 Zambia ZMB

142 Spain ESP 171 Zimbabwe ZWE

Source httpwwwunorgenmember-states

Annex III Sector classification of the SCP-HAT

The following table provides a list of the 26 sectors of the SCP-HAT

Sector Name ISIC Rev3

correspondence

Agriculture 1 2

Fishing 5

Mining and Quarrying 10 11 12 13 14

Food amp Beverages 15 16

Textiles and Wearing Apparel 17 18 19

Wood and Paper 20 21 22

Petroleum Chemical and Non-Metallic Mineral Products 23 24 25 26

Metal Products 27 28

Electrical and Machinery 29 30 31 32 33

Transport Equipment 34 35

Other Manufacturing 36

Recycling 37

Electricity Gas and Water 40 41

Construction 45

Maintenance and Repair 50

Wholesale Trade 51

Retail Trade 52

Hotels and Restaurants 55

Transport 60 61 62 63

Post and Telecommunications 64

Financial Intermediation and Business Activities 65 66 67 70 71 72 73 74

Public Administration 75

Education Health and Other Services 80 85 90 91 92 93

Private Households 95

Others 99

Source Eora 26 (httpwwwworldmriocom)

Annex IV IPCC categories of the SCP-HAT and sector

correspondence

Full list substances

kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]

Methane (IPCC Cat 1235) 36 13

Methane (IPCC Cat 4 and 6) 34 11

Carbon dioxide 1 1

Dinitrogen Oxide 298 297

Sulphur hexafluoride 26087 33631

Nitrogen trifluoride 17885 21852

HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)

Annex V IPCC categories of the SCP-HAT and sector

correspondence

Main IPCC

category IPCC category in SCP-HAT Sector name Entity

1 ENERGY

1A1a Public electricity and heat production Electricity Gas and Water CH4 CO2

N2O

1A2 Manufacturing Industries and Construction Mining and Quarrying Manufacturing

and Construction CH4 CO2

N2O

1A3a Domestic aviation Transport CH4 CO2

N2O

1A3b Road transportation TransportHouseholds CH4 CO2

N2O

1A3c Rail transportation Transport CH4 CO2

N2O

1A3d Inland transportation Transport CH4 CO2

N2O

1A3e Other transportation Transport CH4 CO2

N2O

1A4 Residential and other sectors Households CH4 CO2

N2O

1A5 Other energy industries Petroleum Chemical and Non-Metallic

Mineral Products CH4 CO2

N2O

1B1 Fugitive emissions from fuels Petroleum Chemical and Non-Metallic


N2O

1B2 Oil and Natural gas Petroleum Chemical and Non-Metallic


N2O

1B3 Other emissions from Energy Production Petroleum Chemical and Non-Metallic


N2O

2 INDUSTRIAL

PROCESSES

2A Mineral industry Petroleum Chemical and Non-Metallic

Mineral Products CO2

2B Chemical industries Petroleum Chemical and Non-Metallic


N2O

2C Metal production Metal Products CH4 CO2

N2O SF6

NF3 PFCs 2D Non-Energy Products from Fuels and Solvent

Use

Petroleum Chemical and Non-Metallic

Mineral Products CH4 CO2N2O

2E Production of halocarbons and sulphur

hexafluoride Petroleum Chemical and Non-Metallic

Mineral Products SF6 NF3

HFCs 2F Consumption of halocarbons and sulphur

hexafluoride Electrical and Machinery

SF6 NF3

HFCs PFCs

2G Other Product Manufacture and Use All sectors (exc Petroleum [hellip] and

Metal Products) CH4 CO2

N2O

2H Other All sectors (exc Petroleum [hellip] and


N2O

3AGRICULTURE 3A Livestock Agriculture CH4 N2O

MAGELV Agriculture excluding Livestock Agriculture CH4 CO2

N2O

4 WASTE 4 Waste RecyclingEducation Health and Other

Services CH4 CO2

N2O

5 OTHER 5 Other All sectors CH4 CO2

N2O

Source Primap-hist (httpdataservicesgfz-

potsdamdepikshowshortphpid=escidoc3842934) and Edgar

(httpedgarjrceceuropaeubackgroundphp)

Annex VI Raw material categories of the SCP-HAT and

sector correspondence

The following table provides a list of the 13 raw material categories of the SCP-HAT

Sector Name Category Sector

(Production-based)

Sector

(Use extension ndash SCP-HAT)

Crops Biomass Agriculture Agriculture

Crop residues Biomass Agriculture Agriculture

Grazed biomass and fodder crops Biomass Agriculture Agriculture

Wood Biomass Agriculture Wood and Paper

Wild catch and harvest Biomass Fishing Fishing

Ferrous ores Metals Mining and quarrying Mining and quarrying

Non-ferrous ores Metals Mining and quarrying Mining and quarrying

Non-metallic minerals - construction

dominant Construction minerals Mining and quarrying Construction

Non-metallic minerals - industrial or

agricultural dominant Industrial minerals Mining and quarrying Mining and quarrying

Coal Fossil fuels Mining and quarrying Mining and quarrying

Petroleum Fossil fuels Mining and quarrying Mining and quarrying

Natural Gas Fossil fuels Mining and quarrying Mining and quarrying

Oil shale and tar sands Fossil fuels Mining and quarrying Mining and quarrying

Source UN IRP Global Material Flows Database (httpwwwresourcepanelorgglobal-

material-flows-database)

Annex VII Land use and biodiversity loss categories of the

SCP-HAT and sector correspondence

The following table provides a list of the six land usebiodiversity loss categories of the SCP-

HAT

Category Sector

(Production-based)

Sector


Annual crops Agriculturehouseholds Agriculturehouseholds

Permanent crops Agriculturehouseholds Agriculturehouseholds

Pasture Agriculturehouseholds Agriculturehouseholds

Extensive forestry Agriculturehouseholds Wood and Paperhouseholds

Intensive forestry Agriculture Wood and Paper

Urban All sectorsHouseholds Households

Source UNEP LCI guidance for LCIA indicators (UNEP 2016)

Annex VIII IPCC categories of the SCP-HAT and sector

correspondence for air pollutants

Main IPCC


1 ENERGY

1A1a Public electricity and heat production Electricity Gas and Water NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A1bc Other Energy Industries Petroleum Chemical and Non-

Metallic Mineral Products NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A2 Manufacturing Industries and Construction Mining and Quarrying

Manufacturing Construction NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A3a Domestic aviation Transport NOx PM25 (fossil) SO2

1A3b Road transportation TransportHouseholds NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A3c Rail transportation Transport NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A3d Inland navigation Transport NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A3e Other transportation Transport NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A4 Residential and other sectors Households NH3 NOx PM25 (bio)

PM25 (fossil) SO2

1A5 Other Energy Industries Petroleum Chemical and Non-

Metallic Mineral Products NH3 NOx PM25 (fossil)

SO2

1B1 Fugitive emissions from solid fuels Petroleum Chemical and Non-


PM25 (fossil)

1B2 Fugitive emissions from oil and gas Petroleum Chemical and Non-

Metallic Mineral Products NOx PM25 (fossil) SO2

2 INDUSTRIAL

PROCESSES

2A1 Cement production Petroleum Chemical and Non-

Metallic Mineral Products PM25 (fossil) SO2

2A2 Lime production Petroleum Chemical and Non-


2A4 Soda ash production and use Petroleum Chemical and Non-

Metallic Mineral Products NH3 PM25 (fossil) SO2

2A7 Production of other minerals Petroleum Chemical and Non-

Metallic Mineral Products PM25 (fossil)

2B Production of chemicals Petroleum Chemical and Non-


SO2 2C Production of metals

Metal Products NOx PM25 (fossil) SO2

2D Production of pulppaperfooddrink Food amp Beverages Wood and

Paper NOx PM25 (fossil) SO2

3 SOLVENT AND

OTHER

PRODUCT USE

3B Solvent and other product use degrease Textiles and Wearing Apparel PM25 (fossil)

3D Solvent and other product use other Petroleum Chemical and Non-


4AGRICULTURE 4 Agriculture Agriculture NH3 NOx PM25 (bio)

PM25 (fossil) SO2

6 WASTE 6 Waste RecyclingEducation Health

and Other Services NH3 NOx PM25 (bio)

PM25 (fossil) SO2

7 OTHER 7A fossil fuel fires Petroleum Chemical and Non-


Source Edgar (httpedgarjrceceuropaeubackgroundphp)

Annex IX Glossary of SCP-HAT

Indicator this term refers to the environmental and socioeconomic variables which are

included in the SCP-HAT Indicators available in the SCP-HAT are

Environmental indicators

o Raw material use








Socio-economic indicators

o Final demand

o Government final consumption

o Private final consumption

o Employment (total)

o Employment (women)

o Employment (men)

o Output

o Value added

Perspective This term refers to the accounting principles guiding allocation of environmental

pressures and impacts SCP-HAT comprises two perspectives

- Domestic production This perspective equivalent to the so-called ldquoterritorialrdquo

approach allocates environmental pressures and impacts to the nation where

those pressure and impacts physically occur irrespectively where goods and

services are finally consumed Therefore in the frame of SCP this perspective

could be employed for sustainability assessment of certain production

technologies In this approach no allocation to trade products take place

- Consumption footprint This perspective applying EE-IO technique allocates

environmental pressures and impacts to the nation where final consumers

reside irrespectively to where those pressure and impacts physically occur

Therefore in the frame of SCP this perspective could be utilized for

sustainability assessment of consumer lifestyles This approach allows for

defining a Trade balance between importers and exporters of environmental

pressures and impacts

Unit analyses can be performed using different measurement units which depend on the

perspective on focus

Consumption footprint in absolute terms per consumer (ie per capita) per

unit of GDP (Productivity) per unit of area (km2) or per unit of final demand

Domestic production in absolute terms per capita per unit of GDP

(Productivity) per unit of area (km2) per sectoral worker or per unit of sectoral

output

Annex X Changes between SCP-HAT v10 (release

December 2018) and SCP-HAT v11 (release October

2019)

Climate Change

Data Underlying data were updated using PRIMAP-hist version 20 instead of version 11

(Guumltschow et al 2017) and employing Edgar 432 for CO2 CH4 and N2O for splitting

emissions among sectors (see section 3 Data sources) As a result of updating to PRIMAP-

hist version 20 the categories Land Use Land Use Change and Forestry emissions are

now excluded

Allocation In v10 IPCC-1A2 GHG emissions were not allocated to Mining and Quarrying

while in v11 a share of these emissions is assigned to Mining and Quarrying using total

sectoral output

Air pollution

Data GHG emissions are used for extrapolating air pollutants for 2013-2015 (previously

for 2013 and 2014 and 2015 were linearly extrapolated) and thus updates described for

Climate Change (ie update to PRIMAP-hist v20 and Edgar 432) also applied for air

pollution

Bug fixes emissions assigned to final consumption in ldquodomestic productionrdquo were not

included in v10

Materials

Impacts characterization factor for Other metals using weighted average instead of

unweighted average in v10


Allocation allocation to households implemented for land use for annual crops permanent

crops pasture and extensive forestry

Bug fixes intensive and extensive forestry labels flipped in v10 for ldquoconsumption

footprintrdquo approach and environmental trends graphs

Country classification

Approach Homogenization of the approach for states that entered in existence or

disappeared within the time period considered in SCP-HAT this refers to ex-soviets

countries former Yugoslavia former Czechoslovakia Ethiopia-Eritrea and Sudan and

South Sudan Only currently existing countries are displayed

User interface

Bug fixes Graphs didnrsquot re-scale when a environmental sub-category is isolated (eg CH4

emissions) was selected in v10 (in ldquoEnvironmental Trendsrdquo and ldquoTrends by sectorrdquo) in

Module 2 Hotspots Identification Further simultaneous data filtering and plotting were not

supported in v10 Module 3 National Data System

Annex XI Land use comparisons


SCP-HAT uses EORA as a MRIO model FAO Land Use and Forest Research Assessment data as

land use inventory (pressure) data and characterization factors (CF) for potential species loss

(see Chapter 3) The land use inventory data can be compared to the data used in the

environmentally extended MRIO EXIOBASE v3 (Stadler et al 2018) which has also been used

for evaluation of potential species loss by (Marques et al 2019) Cropland areas are very similar

in both data sets Forest areas deviate for many countries and are typically higher in the

EXIOBASE studies (Figure 2) Pasture areas also deviate for many countries and are typically

higher in SCP-HAT Because pasture has a very prominent contribution to the total biodiversity

footprints (production and consumption) in SCP-HAT this is investigated further

Figure 2 Comparison of land use areas for year 2010 for selected large countries and regions showing ratio of area in EXIOBASE studies to area in SCP-HAT Source Stadler et al (2018) and SCP-HAT

Pasture areas

For EXIOBASE permanent pasture areas for livestock production (input into economy) are

derived from satellite data for the year 2000 such as Global Land Cover 2000 (Bartholomeacute and

Belward 2007) This resulted in a considerable reduction in global area compared to FAO land

use data The rationale for deviating from the FAO land use data is that there is inconsistency

in reporting between countries

00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures

In a subsequent step areas with a productivity (NPP) below 20gCmsup2yr were also excluded in

EXIOBASE as these areas are not considered suitable for permanent land use (Stadler et al

2018) This step affected Australia primarily reducing the pasture area included in EE-MRIO

from 408 Mha (FAO) to 188 Mha for the year 2000 (Stadler et al 2018) The NPP cut-off used

by EXIOBASE equates to about 0001 dmthaday of grazing pressure assuming no net

increase or decrease of biomass and 50 carbon content of dry matter The National

Inventory Report for Australia (Commonwealth of Australia 2018 Fig 623 therein) shows that

more than 80 of land has a grazing pressure below 0002 dmthaday suggesting some of

this land area might have been below the cut-off applied for EXIOBASE Cattle number maps

(MLA 2019) however show that in these areas at least 5 million head of cattle are being

grazed (this is a conservative estimate)

Taking the map from Ramankutty and colleagues (2008) (Figure 3) before the NPP cut off is

applied the entirety of the Northern Territory is shown as 0 pasture In reality however

cattle numbers in that state are over 2 million head Further evidence is provided by comparing

Figure 3 with Figure 4 which reveals that large areas of extensive and medium intensity

livestock grazing in Australia are not reflected as land use in the Ramankutty map even before

the cut-off is applied

Figure 3 Pasture mapped for year 2000 by Ramankutty et al (2008) which is the basis for EXIOBASE (before NPP cut-off applied by Stadler et al 2018)

ABARES4 national land use summary statistics list 419 Mha for ldquograzing native vegetationrdquo in

year 2000 in Australia and an additional 23 Mha for grazing of ldquomodified pasturesrdquo Clearly

the EXIOBASE approach applied leads to a significant underestimate of grazing area in the case

4 ABARES 2018 National Land Use Summary Statistics 2000-2001 version 2018-04-12

httpsdatagovaudatadatasetland-use-of-australia-version-3-28093-20012002

of Australia where grazing can be very extensive compared to most countries Even if the

entire lsquoOther Landrsquo category (Stadler et al 2018) is assumed to be grazing land which may

well be the case for Australia given any ldquoOther Landrdquo with tree cover lower than 5 is

attributed to grazing the total still falls well short of the values reported by ABARES and by

FAO (Note that the lsquoOther Landrsquo of EXIOBASE is different from lsquoOther Landrsquo reported by FAO

Note also that assuming the characterization model used in eg (Marques et al 2019) is

adapted to the land use inventory the total species loss results may still be correct) The FAO

land use data for permanent pastures in 2000 is 408 Mha which is also slightly less than the

bottom-up national land use statistics by ABARES but much closer It is clear that Australia

reports ldquograzing native vegetationrdquo as part of the FAO category Permanent Pasture

In SCP-HAT excluding the low productivity grazing land would not be valid First the footprints

for land use are shown as well as the resulting species loss footprints Second the Chaudhary

species loss CF (Chaudhary et al 2015) have been developed using a combination of the

spatial LADA dataset (Nachtergaele 2013) (also based on GLC 2000 but combined with

several other databases see Figure 4) and FAO land use data and the Forest Resource

Assessment for country-level proportions of subcategories of land use While it is hard to

establish how exactly the land use inventory was developed by Chaudhary it looks like there is

a major difference between Figure 3 and Figure 4 regarding the extensive livestock area While

these land areas would be subject to very extensive grazing by most standards the

biodiversity impacts are still considerable

Two ecoregions AA0701 (Arnhem Land) and AA0706 (Kimberley) in Northern Australia have

CFs for pasture land use that are higher than the Australian average and both are mapped

with grazing pressure below 0002 dmthaday The stocking rates and therefore livestock

product output in these areas will be very low but this should be properly reflected in the

national average CF as established by Chaudhary and colleagues (2015) Excluding these areas

from the inventory would result in an underestimate of the total impact

Figure 4 Pasture mapping for year 2005 LADA (Nachtergaele 2013) basis for

characterization factors of Chaudhary et al (2015) as used for SCP-HAT

Hoskins and colleagues (2016) have calculated new high-resolution global land use maps for

the year 2005 These maps are currently considered the best available (eg Chaudhary and

Brooks 2018) The pasture areas derived from these maps align well with those used in SCP-

HAT with typically less than 10 deviation (Figure 5) For large grazing countries such as

Brazil Argentina China Australia and the USA the deviation is even less than 5

Figure 5 Areas in 1000 km2 of permanent pastures for selected countries year 2005

comparison between SCP-HAT and Hoskins et al (2016)

Summarizing the pasture land use data applied in EXIOBASE excludes extensive grazing areas

and therefore does not align with the characterization factors of Chaudhary (Chaudhary et al

2015) To what extent the absolute areas of productive land use underlying the Chaudhary

factors deviate from SCP-HAT land use areas in general is hard to establish The new high-

resolution maps (Hoskins et al 2016) agree well with FAO land use data for pasture areas For

Australia as a test case the absolute areas used in SCP-HAT are more in line with data

reported by other statistical agencies and the meat and livestock industry than those used in

EXIOBASE Hence pasture land use data should not be changed in the current land

usebiodiversity module

Pasture and cropping allocation

In SCP-HAT 10 all pasture and cropping land use was allocated to the agriculture sector This

led to an overestimate of land use associated with trade A correction was made by allocating

subsistence farming and part of low-input farming to final demand Percentages of cropping

land use areas classified as subsistence and low-input crop farming were derived from the

Spatial Production Allocation Model version 2010 (SPAM You et al 2014) at country level

Allocation to final demand should include true subsistence farming as well as farming that

supplies very local markets only Low input farming in certain regions is likely to supply local

markets whereas in other regions it can be fully commercial and feed into export markets For

pasture (livestock) the methodology is particularly complex because no suitable primary data

were found and contexts vary enormously between regions Highly pastoral systems in

0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km

2Hoskins pasture SCPHAT pasture

countries such as Mongolia should be considered fully commercial whereas in countries such

as Mali they are almost entirely subsistence

It was assumed that in Europe Asia-Pacific and North America any (semi-) subsistence

livestock farming is likely to be in mixed systems and therefore already covered by the ldquoannual

croprdquo approach For the other countries the assumption is that (semi-) subsistence farming is

a reflection of economic context and therefore likely to be similar for annual crops and livestock

systems This is obviously a rough approximation but an improvement compared to assuming

zero allocation to final demand

The allocation factors were determined as follows

- Annual crops

Advanced economies Latin America former Soviet states and Other

Emerging countries (ie Eastern Europe and Turkey) the percentage of

subsistence farming is allocated to final demand

All other countries the percentage of subsistence and low input farming

is allocated to final demand

- Perennial crops percentage of subsistence and low input farming is allocated

to final demand for all countries

- Pasture

Sub-Saharan Africa Middle East North Africa Latin America allocation

to final demand is assumed to equal the allocation factor for annual crops

Other countries zero allocation to final demand

The choices were made after careful analysis of the data and yield credible results except for a

small number of countries that deviate in level of economic development compared to their

continental peers Examples are Bolivia (low GDP PPP) and Botswana (high GDP PPP) A

finetuning using actual GDP PPP values could be considered The factors as described above

are applied in SCP-HAT 11

Forestry

Land use for forestry (total area) diverges significantly for some countries between EXIOBASE

studies and SCP-HAT (Figure 2) A more comprehensive comparison is given in Figure 6 that

also shows the comparison to FAO land use categories

Figure 6 Comparison of forest land use areas in SCP-HAT Exiobase and FAO Vertical axis has

been cut off at 30 (Mexico Switzerland)

A detailed comparison for forestry is complex because the definitions of extensive and

intensive forestry as required for application of the CF for potential species loss are specific to

SCP-HAT The Exiobase classification of ldquoForest area ndash Forestryrdquo and ldquoOther land ndash Wood Fuelrdquo

only has limited similarity to this (Stadler et al 2018) The FAO land use database aims to

classify forest area by type rather than by use It distinguishes Forest Land Primary Forest

Other naturally regenerated forest and Planted Forest with the last being the only clear use

category Therefore one-on-one correspondence is not expected but at the same time the

comparison gives interesting insights Note that the areas for Exiobase ldquoOther land ndash Wood

Fuelrdquo are not available for comparison

The fact that Exiobase forest land use is close to FAO ldquonon primaryrdquo land use for some

countries such as Brazil Mexico Slovenia and Switzerland suggests that their method picks

up a lot of the area of ldquoOther naturally regenerated forestrdquo It is likely that some of this area

would be reported as ldquoMultiple Use Forestrdquo Global Forest Resource Assessment (GFRA) (FAO

2015) and as such also feed into the SCP-HAT category of Extensive Forestry but not all of it

For instance for Switzerland the Exiobase ldquoForest area ndash Forestryrdquo area is somewhat larger

even than FAO total Forest Land The Swiss country study (Frischknecht R 2018) also uses an

(intensive) forestry area of 12273 km2 for 2015 which is close to FAO total Forest Land This

suggests that both Exiobase and Frischknecht and colleagues allocate even Primary Forest to

the production footprint which may be valid if all forest area is considered to contribute to eg

tourism (possibly in Frischknecht R 2018) but it seems an overestimate for allocation to

Forestry products as in Exiobase Applying the Chaudhary characterization factor (Chaudhary

et al 2015) for intensive forestry to all forest land (possibly in Frischknecht et al 2018) also

seems inappropriate The GFRA reports 3830 km2 of ldquoProduction Forestrdquo for Switzerland

(2015) and zero multiple-use forest FAO Planted Forest area is 1720 km2 (2015)

00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)

Countries ranked by SCP-HAT forest area

Comparison of Forest land data

SCPHAT (intensive+extensive) EXIOBASE (Forest land forestry) FAO (All non-primary) FAO2 (Planted forest)

For many countries the SCP-HAT and Exiobase values are close In the current land use

system in SCP-HAT forestry contributes 20 globally to biodiversity footprints A comparison

between SCP-HAT total forestry and the ldquosecondary habitatrdquo category of Hoskins and

colleagues (Hoskins et al 2016) has not been made yet due to resource constraints The

secondary habitat land use category includes areas that are not used for production and

therefore would be expected to give similar to higher values per country than extensive plus

intensive forestry in SCP-HAT

Forestry allocation

In SCP-HAT all extensive and intensive forest land use is allocated to the Wood and Paper

sector (Annex VII) In many countries however some to almost all of the extensive forest

land use may link to wood fuel collection for household use and should therefore be allocated

to final demand Without this allocation to final demand results for land use trade balances

may be flawed because impacts of exports are equal to impacts of domestic production (by

value)

Forest land use may be split into roundwood and fuelwood by using the Forest Harvest Index

(Furukawa et al 2015) This index was developed to calculate forest cover loss but the ratio

between roundwood and fuelwood area is the same for total land use Roundwood is assumed

to link to intensive forestry first assuming that intensive forestry products will all flow into the

formal economy so no allocation directly to households is expected The remainder is linked to

extensive forestry and then the remainder of extensive forestry is assumed to be for fuelwood

sourcing To establish the fraction of fuelwood forestry land use to be allocated to households

UN data on wood fuel production and consumption are used (The latter step is also applied in

Exiobase except they apply it to their satellite ldquoother landrdquo data) The Energy Statistics

Database (dataunorg) gives data for fuel wood production and consumption by households (in

cubic meters) for most countries The ratio of consumption to production is used as the

allocation factor of the remaining extensive forestry area to final demand for countries other

than those listed as Advanced Economies in the World Economic Outlook (IMF 2019) The

assumption underlying this choice is that subsistence-type use of forest land is not included in

the FAO land use database for advanced economies and that most fuel wood consumption by

households will be sourced via commercial channels

The approach yields allocation factors of extensive forest land use to final demand up to 99

(eg Bhutan) The factors have been derived using land use data and fuel wood production and

consumption data for the year 2010 The Forest Harvest Index is only available as an average

over years 2000-2004 This may lead to overestimates of the allocation to final demand for

countries that have been rapidly developing over the period 1990-2015

It should also be noted that countries that only report intensive forestry or no final

consumption of wood fuel will still have all land use allocated to the lsquoWood and Paperrsquo sector

Trade flows

A country-specific study of land use and biodiversity footprints is available for Switzerland

(Frischknecht R 2018) The approach used in that study links physical trade data with life

cycle inventory (LCI) data that feed into the calculation of a land use footprint for imported

goods For domestic production national land use statistics are used This leads to reasonable

agreement between the Swiss country study and SCP-HAT on the biodiversity impacts

associated with domestic production (Figure 7 versus Figure 8) with the main discrepancy in

the forest category (see discussion above)

Figure 7 Biodiversity impact by land use category domestic production Switzerland from Frischknecht et al (2018) Vertical axis unit and land use colour coding same as Figure 8

Figure 8 Biodiversity impact by land use category domestic production Switzerland from SCP-HAT with urban land use added

However when comparing the consumption footprints (Figure 9 versus Figure 10) the values

from SCP-HAT are significantly higher than those from (Frischknecht R 2018) with the

deviation mainly due to the contribution of foreign land use (ie imports)

0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops

Figure 9 Biodiversity consumption footprint in picoPDFyr per capita distinguishing contribution from domestic (light blue) and foreign (dark blue) production from Frischknecht et al (2018)

Figure 10 Biodiversity consumption footprint in picoPDFyr per capita distinguishing contribution from domestic and foreign production from SCP-HAT

This discrepancy is as yet unexplained While it is not unusual that a life cycle assessment

approach (ldquobottom uprdquo) reports lower values than an input-output (ldquotop downrdquo) approach as

the former are not able to cover the complete supply chain of all goods (Lutter et al 2016)

the difference is not expected to be this large In the SCP-HAT results for Switzerland the

contribution of pasture is about 50 which cannot be easily explained when looking at direct

product imports into the country Similarly there is a very large contribution from Madagascar

which cannot be easily explained either when looking at direct product imports from

Madagascar to Switzerland

It is likely that the high sectoral aggregation of EORA which does not distinguish livestock

products from other agricultural products leads to a distortion of the land use profile of

agricultural exports Essentially the land use profile of exports is identical to the land use

profile of domestic production which is not realistic At the global scale this averages out but

for countries that rely heavily on imports of agricultural products this possibly results in too

high values for consumption footprint

Characterization factors

The characterization factors (CF) used in SCP-HAT (as well as in Frischknecht et al 2018) are

recommended to assess biodiversity loss in the UNEP LCIA guidelines However Chaudhary

and Brooks (2018) have published an updated set of CFs for potential species loss using the

maps byn Hoskins and colleagues (2016) Within each land use category the new CFs

differentiate between low medium and high intensity use According to Chaudhary (private

communication) these values for land use and species loss are superior to the (previous) ones

that are recommended by the UNEP LCIA guidelines Given the good agreement between FAO

land use data and the Hoskins maps it would be feasible to change land use and impact

characterization to this newer method if information on low medium and high intensity use is

available for all countries as well as the required data to split up the category ldquosecondary

habitatrdquo into a range of forestry use types The new CFs for pasture and cropping are about a

factor 100 higher than the previous ones CFs for plantation forestry are almost identical to

those for cropping in the new set compared to a factor of 2 or 3 lower in the existing set as

used by SCP-HAT (those recommended in UNEP 2016) Not only would the absolute impacts

increase dramatically but the contribution of forestry would likely be higher The relative

profiles of countries (ie comparison) might not be influenced much

General conclusions

There is good agreement on cropping land use areas between the different studies (Figure 2

and Figure 11)

Figure 11 Areas in 1000 km2 of crop land (arable land) for selected countries year 2005


There is more discrepancy between different databases regarding pasture land use but as

demonstrated above the data used in SCP-HAT is robust and matches the characterization

factors leading to a consistent approach The contribution of pasture land in trade flows is

probably due to the limited sectoral differentiation of EORA (see Chapter 2) This is an intrinsic

limitation in SCP-HAT that needs to be monitored

There is also discrepancy regarding forest land use which would require additional resources to

investigate but note that this category does not typically have a major contribution to country

production or consumption footprints in the current approach For some countries the allocation

of forest land use to the Wood and Paper sector may result in an overestimate of trade balances

(especially export of extensive forestry) which is recommended to address

A more systematic update may be considered for the land use module using the land use map

from (Hoskins et al 2016) in combination with FAO land use data to improve land use data and

combine those with the new characterization factors by (Chaudhary and Brooks 2018) This

needs to be explored in more detail

0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km

2Hoskins cropping SCPHAT cropping (all)

TABLE OF CONTENTS

ABBREVIATIONS 4

1 INTRODUCTION 5

2 METHOD DESCRIPTION 5

Technical foundations 5

Pressure and impact categories 8

Geographical and IndustryProduct coverage 9

3 DATA SOURCES 10

Multi-Regional Input-Output 10

GHG emissions and Climate Change (Short-Term and Long-Term) 10

Raw material use and mineral and fossil resource scarcity 12


Air pollution and health impacts 14

Socio-economic data 15

4 FURTHER DEVELOPMENTS 15

Increasing sectorproduct coverage 15

Including national data providing default data 16

Automated data updates 16

Improving land use accounts 17

Improving approach on air pollutionDALY 17

Improving emission accounts and their linkages to the EE-MRIO framework 18

Including new category water 18

Include more socio-economic data 18

Technical set-up of SCP-HAT 18

Implement user guidance 19

5 ACKNOWLEDGEMENTS 20

Project management and coordination 20

Technical supports 20

Advisory team 20

Country experts 20

Funding 21

REFERENCES 22

ANNEX I MATHEMATICAL DESCRIPTION 27

ANNEX II GEOGRAPHICAL COVERAGE OF THE SCP-HAT 29

ANNEX III SECTOR CLASSIFICATION OF THE SCP-HAT 31

ANNEX IV IPCC CATEGORIES OF THE SCP-HAT AND SECTOR CORRESPONDENCE 32

ANNEX V IPCC CATEGORIES OF THE SCP-HAT AND SECTOR CORRESPONDENCE 33

ANNEX VI RAW MATERIAL CATEGORIES OF THE SCP-HAT AND SECTOR

CORRESPONDENCE 34










Pasture areas 41


Forestry 46


Trade flows 49



Abbreviations


production patterns







GHG Greenhouse gas










Emissions Paths





UN United Nations





1 Introduction





















underlying model
















final demand







































quantified


































o Raw material use

























Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km











Pasture areas 41


Forestry 46


Trade flows 49



Abbreviations


production patterns







GHG Greenhouse gas










Emissions Paths





UN United Nations





1 Introduction





















underlying model
















final demand







































quantified


































o Raw material use

























Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km


Abbreviations


production patterns







GHG Greenhouse gas










Emissions Paths





UN United Nations





1 Introduction





















underlying model
















final demand







































quantified


































o Raw material use

























Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km


1 Introduction





















underlying model
















final demand







































quantified


































o Raw material use

























Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km






final demand







































quantified


































o Raw material use

























Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km



































quantified


































o Raw material use

























Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km













o Raw material use

























Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km






Primary energy

Land transformation

Mercury emissions

Solid waste















3 Data sources












































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km





































































































































Nations 2018)










when needed



forestry is PLF




























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km


























Socio-economic data
























































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km















































comparisons












additional work





























framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km





















framework























indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km









indispensable





































interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km













interpret results





analyses are shown

5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km


5 Acknowledgements










Technical supports

Content




Web design


Advisory team







Country experts







Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km






Funding


Norway

References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km


References




emissions





23 368-378














Data 10 1987-2013




































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km































603 doi105194essd-8-571-2016
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km
































49 doi101080095353142013769938











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km











methodology



Rome

















1995-2007






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km






Ecology 22 502-515



doi101080095353142014934325



doi101016jeneco200907010







doi1010800953531420151049126




doi101111j1530-9290201200504x




































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km



































120567 = 120575prime119872119871119910 (2)



basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km




basis




120567 = 120573prime120575119871119910 (3)


































































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km





























































































correspondence

Agriculture 1 2

Fishing 5










Recycling 37


Construction 45


Wholesale Trade 51

Retail Trade 52








Others 99



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km



correspondence


kg CO2-eqkg

[GWP100]

kg CO2-eqkg

[GTP100]



Carbon dioxide 1 1




HFC-23 13856 15622

HFC-134a 1549 530

HFC-125 3691 1766

HFC-32 817 265

HFC-43-10mee 1952 698

HFC-143a 5508 3693

HFC-152a 167 54

HFC-227ea 3860 2294

HFC-236fa 8998 10267

HFC-245fa 1032 337

HFC-365mfc 966 317

PFC-116 12340 16016

PFC-14 7349 9560

PFC-218 9878 12705

PFC-318 10592 13655

PFC-31-10 10213 13137

PFC-41-12 9484 12253

PFC-51-14 8780 11316

PFC-61-16 8681 11183

Source UNEP (2016)


correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km



correspondence

Main IPCC


1 ENERGY


N2O



N2O


N2O


N2O


N2O


N2O


N2O


N2O



N2O



N2O



N2O



N2O

2 INDUSTRIAL

PROCESSES





N2O


N2O SF6


Use








SF6 NF3

HFCs PFCs



N2O



N2O



N2O


Services CH4 CO2

N2O


N2O








(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km






(Production-based)

Sector






















HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km





HAT

Category Sector

(Production-based)

Sector











Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km




Main IPCC


1 ENERGY


PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2



PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2


PM25 (fossil) SO2



SO2



PM25 (fossil)



2 INDUSTRIAL

PROCESSES















3 SOLVENT AND

OTHER

PRODUCT USE





PM25 (fossil) SO2



PM25 (fossil) SO2








o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km



o Raw material use









o Final demand





o Employment (men)

o Output

o Value added






















output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km








output



2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km




2019)

Climate Change





now excluded



sectoral output

Air pollution




pollution


included in v10

Materials













User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km




User interface

















Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km














Pasture areas






00

05

10

15

20

25

30

Rat

io (d

imen

sio

nles

s)

Cropland

Forests

Pastures















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km
















































































0

500

1000

1500

2000

2500

3000

3500

4000

4500

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km











- Annual crops








- Pasture









Forestry






























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km




























00

05

10

15

20

25

30

Ru

ssia

Can

ada

Uni

ted

Sta

tes

Ch

ina

Bra

zil

Ind

one

sia

Aus

tral

ia

Ind

ia

Fin

lan

d

Jap

an

Swed

en

Ger

man

y

Fran

ce

Spai

n

No

rway

Me

xico

Pol

and

Turk

ey

Sou

th A

fric

a

Ro

man

ia

Sou

th K

ore

a

Ital

y

Gre

ece

Cze

ch R

epu

blic

Bu

lgar

ia

Por

tuga

l

Uni

ted

Kin

gdom

Latv

ia

Aus

tria

Slo

vaki

a

Esto

nia

Cro

atia

Lith

uan

ia

Hu

nga

ry

Bel

giu

m

Irel

and

Net

herl

and

s

Slo

ven

ia

De

nmar

k

Swit

zerl

and

Cyp

rus

Luxe

mbo

urg

Mal

ta

Rat

io (S

CPH

AT=

1)











Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km









Forestry allocation






value)
























Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km




Trade flows













0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km







0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

mic

ro P

DF

yr Urban

Forestry

Permanent crops

Pasture

Annual crops


































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km



































General conclusions


and Figure 11)

















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km


















0

200

400

600

800

1000

1200

1400

1600

1800

2000

10

00

km


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