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Findings of the sixth Global
Environment Outlook
Chapter
By:
Chapters 3 and 25Coordinating Lead Authors:
Florence Daguitan (Tebtebba, Indigenous Peoples’
International Centre for Policy Research and Education)
Charles Mwangi (Global Learning and Observations to
Benefit the Environment [GLOBE] Program)
Michelle Tan (ADEC Innovations)
Pali Lehohla (Pan African Institute for Evidence - PIE)
Joni Seager (Bentley University)
William Sonntag (Group on Earth Observation Secretariat)
Graeme Clark (University of New South Wales)
Lead Authors:
Daniel Cooper (University of Oxford)
James Donovan (ADEC Innovations)
Sheryl Gutierrez (ADEC Innovations)
Nina Kruglikova (University of Oxford)
GEO Fellow & Contributing Author:
Amit Patel (Planned Systems International, Inc.)
Chapter Coordinator:
Jillian CampbellYou may access the GEO6 Report at:
https://www.unenvironment.org/resources/global-environment-outlook-6
INTRODUCTION: ENVIRONMENTAL STATISTICS AND
DATA INTRODUCTION:BETTER DATA FOR
A HEALTHY PLANET WITH
HEALTHY PEOPLE
CURRENT STATE OF OUR DATA
AND KNOWLEDGE
FUTURE DATA AND KNOWLEDGE
NEEDS
CONCLUSION
CURRENT STATE AND FUTURE NEEDS OF DATA AND KNOWLEDGE
Social and Physical
Systems
REVIEW OF DATA NEEDS
Regular monitoring of data
Harmonization of data-collection approaches and methodologies
From the GEO 5 Report (2012)
DEMAND FOR ENVIRONMENTAL STATISTICS AND DATA
Biophysical
Earth Systems
Paradigm
shift
Establishment of the United NationsStatistical Commission(UNSC)
BrundtlandCommission leading to the Framework for the Development of Environment Statistics
UNSC started working on environmental economic accounting during the Earth Summit
Adoption of System of Environmental Economic Accounts (SEEA) as a statistical standard
Adoption of the Experimental Ecosystem Accounts
Resulted to increased
capacity of countries to
use statistics on social
development –
poverty, education,
health, gender,
environment and
governance
Focused on
environmental
statistical
disaggregation by
location, gender, age,
poverty and other
factors
HISTORY OF ENVIRONMENTAL STATISTICS
169
93ENVIRONMENT-RELATED
• Satellite Imagery• Water/Ocean
Observations• In Situ Monitoring• Air/Pollution• Ecosystems• Forest/
Agriculture• Climate• Land Use and
Cover• Cadaver/Parcels
• Citizen Science• Community
Programs• Crowd Sourcing• Research Data• Indigenous Local
Knowledge• Ground Truthing
• Population Demographics
• Poverty• Trade/Business
Environment• Labour/Economics• Agriculture
Disability/Gender CRVS
• Mobile Phones• Social Media• Automated
Devices• VGI• Web Analytics• Transactional
Data
BETTER DATA FOR A HEALTHY PLANET WITH HEALTHY PEOPLE
• Internationally established methodology.
• Data produced by 50% of countries.
• Conceptually clear indicators.• Data not regularly produced by
countries.
• No internationally established methodology or data collection.
PER ANNUM
Investment needed for lower-income countries to monitor the SDGs Sustainable Development Solutions Network 2017
BETTER DATA FOR A HEALTHY PLANET WITH HEALTHY PEOPLE
BETTER DATA FOR A HEALTHY PLANET WITH HEALTHY PEOPLE
• 68% of environment-related SDG indicators do not have enough data to assess global progress.
• Investment in data and statistics is essential.
• There is even less data availability that is disaggregated by vulnerable population or geospatially.
Towards achieving the environmental dimension of the SDGsNUMBER OF INDICATORS
POSITIVE
LITTLE CHANGE
NEGATIVE
NO DATA
BETTER DATA FOR A HEALTHY PLANET WITH HEALTHY PEOPLE
Behavior and Sustainable
Consumption and Production
Almost half of the SDG indicators
related to the environment measure
enabling mechanisms as opposed to
environmental conditions or factors.
Success in terms of the enabling
mechanisms identified may not
result in protecting our planet.
Environmental State or Trend
Mechanisms, enabling environment and
policy
People and the Environment
GEO-6 Major Data Gaps Organized by Respective Chapter
• Population • Urbanization
• Air Quality • Health Impact
• Water Consumption• Groundwater• Waste withdrawals• Wastewater
• Genomic data• Economic valuation
• Biofuels• Forest plantations• Soil degradation• Land use and ownership• Stockpiling and
pesticides
• Environmental disasters
• Planetary boundaries
• Survey systems
BETTER DATA FOR A HEALTHY PLANET WITH HEALTHY PEOPLE
and its social nexus and intersectionality
• Environmental relationships based on the
social construction of gender roles need
more gender-disaggregated information.
• Environmental economics accounting
provides the value of ‘nature’s
contributions to people’ (value) and cost
of residuals, which needs a diverse
methodology of ecology, economics, social,
and cultural dynamics.
• Social environment has a strong
influence on health and well-being,
with health-environment nexus
defining the ‘exposure’ and ‘outcome’
of well-being.
• Vital to addressing equity issues is a
knowledge base that reflects
human-environment interactions
and taps disaggregated data.
EXISTING DATA SYSTEMS
Government or publicly-published environmental data, statistics and knowledge - foundations of successful environmental assessments.
CURRENT STATE OF OFFICIAL STATISTICS• Need for environment statistics in the local context and methodological
guidance on society-environment interactions.• Technological tools for measurement has been changing the data landscape.
IMPERATIVE FOR STATISTICS AND DATA
• Geospatial statistical approach provides a transformative infrastructure that
improves the information necessary to ‘leave no one behind”.
MEASURING THE ENVIRONMENT IN THE CONTEXT OF THE SDGS
• Innovation has encouraged low-cost standardized forms of data collection.
• Disaggregated & location-based information, essential to ‘leaving no one behind’.
SDG INDICATOR FRAMEWORK• Strategic prioritization and sequencing of data, statistics, and indicators.• United Nations Secretary General has recognized the need for clear coordination
among national agencies for data and statistics.
• Providing major data gaps• Integration of data• Accessibility of data• Visibility of citizen science data
• Combining information from multiple systems to generate statistics and indicators remains a major challenge.
• Open Earth observations, citizen science, traditional knowledge, social media, and digital platform or big data transform data into an inclusive, social, and robust knowledge for decision-making.
• Evidence-based modelling of social and biophysical aspects.
• Exposure to environmental contamination and degradation.
• Georeferencing of data and information and aggregation of data from different/multiple sources
• UNSC InterAgency Expert Working Group (IAEG-SDG) and the United Nations custodial agencies incorporate earth observation and geospatial data for support of the SDGs.
Information or data regarding a location relative to the earth which
includes surface observations surface observations (in situ) and
data generated from satellites and other space missions.
New data sources provide additional opportunities for environmental monitoring and assessment.
EMERGING TOOLS FOR ENVIRONMENTAL ASSESSMENT
“knowledge, know-how, skills and practices that are developed, sustained
and passed on from generation to generation within a community, often
forming part of its cultural or spiritual identity”.
- World Intellectual Property Organization (WIPO) (2010)
22%of the world’s land surface
80% of the world’s biodiversity
UN FAO 2017
Lands and territories of indigenous peoples are the base of their knowledge.
UNCCD Decision 20/COP.12:
“improvement of knowledge
dissemination including traditional
knowledge, best practices, and
success stories”.
Harness scientific knowledge, technology, and traditional knowledge to solve issues related to sustainable natural resource management and biodiversity conservation.
• Loss lands
• Misappropriating traditional knowledge
• Modern medicine and agriculture
• Unequal co-production of knowledge
• Rapid erosion of linguistic diversity
• Lapse in implementation of free prior and informed consent
Co-creation between indigenous and Western systems:
• Community-based monitoring and information systems
• Multiple evidence-base system
Holistic, including metaphysical and spiritual
Context and community-based using quantitative data
Practical and applied goals using data for sustainability
Fragmented using separate and distinct disciplines
Controlled experimentation using quantitative data
Goal to find universal truths using hypothesis testing
Explain complex systems
Seek to understand the physical world
Based on observation
Bodies of knowledge that
change over time
Verify through repetition
Policies based on “further interdisciplinary action research that brings together indigenous knowledge holders and scientists, both natural and social, to build mutual understanding and reinforce dialogue
In forests in the Amazon, ecosystems improve when
indigenous peoples inhabit them.
In mountains, indigenous peoples have designed agricultural systems
that protect the soil, reduce erosion, conserve water and reverse the risk of disasters
In range lands, indigenous pastoralist communities manage
cattle grazing and cropping in sustainable ways that allow the
preservation of rangeland diversity.
Indigenous peoples as stewards of the environment
In rivers, indigenous knowledge is blended with simulation modelling
to develop a modern fishery management in the Coastal British
Columbia, Canada.
• Technology revolution has introduced multiple ways of collecting, archiving, analyzing, transmitting, and processing huge volumes of data.
• Citizen science can be used to sensitize and engage the community on issues related to their natural environment
.
Features user-configurable cause-effect
(DPSIR) indicator dashboards.• Individual Citizen• Governments• Communities• Scientists and Researchers
• Top-down - Scientists train volunteers• Bottom-up – Community-driven research
• Collaborative knowledge (e.g. Wikipedia, OpenStreetMap)
• Volunteer computing (e.g. Citizen Grid, climateprediction.net)
• Pattern classification (e.g. Galaxy Zoo, eyewire)• Community collection of observations
(e.g. bird counting, air sensor toolbox)
ENGAGEMENT OF VOLUNTEERS IN SCIENCE AND RESEARCH
• Collection of data at lower cost.
• Increased scientific literacy
• Citizen engagement
• Cost-effective measure
• Improved environmental monitoring
• Exposure to scientific expertise and indigenous knowledge
Citizen scientists collecting environmental data
• Automated Organizational issues• Data-collection issues• Data-use
• Encapsulating Use of local knowledge• Timely data from dispersed sources• Capability to address large knowledge and funding deficits• Ability to educate the public about environmental policy issues• Enhance participatory democracy
• Capitalizing on Training and support for citizen science project• Design and quality assurance methods
• Aggregates information, video and blogs about citizenscience projects
www.scistarter.com
• UN open access platform of global, regional and national environmental data
https://environmentlive.unep.org
Global Citizen-science projects and websites
• Provides a framework to access data from multiple data sources (including citizen science data)
www.dataone.org
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DATA ARE ONE OF THE WORLD’S VALUABLE RESOURCES, SHIFTING THE LANDSCAPE OF ENVIRONMENTAL ASSESSMENT
INNOVATION FOR PUBLIC GOODDigital Big data used for common good and decision-making.
DATA COLLABORATIVESGovernments, private sector, and non-governmental organizations develop solutions through collaborative learning.
OPEN DATA ACCESSOpen access to valuable and timely data provides
ENVIRONMENTAL ASSESSMENTS AND EVALUATIONBig data analytics enable illustrations of trends and predictive analysis.
Datasets whose size is beyond the ability
of typical database (Manyika et al. 2011).
BIG DATA
Create patterns from intricate data sets
and find correlations using algorithms,
programming, and mechanical and
statistical methods.
SCIENCE OF DATA ANALYTICS
Source: Adapted and recreated the infographics of IBM, with information from World Bank (2016a), IBM (2017); IDC (2012); Harvard Business Review (2016).
Achieving greater value through insights from superior analytics.
• Gaps in the collection, monitoring, analysis and interpretation of data
• Accessibility, quality and sparsity of data for varying scales, contexts and time series.
• Challenges on scope, privacy and potential for misinterpretation
of data
• Limited capacities of developing countries to generate acceptable large volumes of data
• Increased government and global initiatives exploring the benefits of Big Data for transparency, market analysis, research, education and environmental protection.
• International environmental agreements provide guidelines to members states on how to make environmental data publicly accessible.
• Collaborations are needed where both parties benefit without sacrificing the economic value of data, and at the same time, maintaining fair competition among businesses.
• Leadership and data governance
• Partnerships among governments, institutions and the private sector
• Institutionalizing legal frameworks with safeguards on information
• Accessibility
• Quality
• Sparsity
Coping with the shift in the data
landscape will need new information technology skills and a holistic approach to utilize emerging and existing data and knowledge tools
Tracking the progress on SDGs requires exploring other data streams
– citizen science, Big Data and
traditional knowledge.
Technology has crucial assets which are founded in their development in compliance with standards.
Advancing the SDGs requires integrating knowledge and technology between communities –from local, through national, to global levels.
Open data and reproducibility of research
encourage the accessibility of data and
knowledge for equity, transparency
and advancement of science.
Open and accessible of digital data must be aligned with recognized standards, practices and open source community-driven testing for methodology and data quality.
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OFFICIAL STATISTICS
CITIZEN SCIENCE
GEOSPATIAL INFORMATION
TRADITIONAL KNOWLEDGE
BIG DATA
Bringing together existing and emerging data tools.
Sound data-governance programme, including legal frameworks.
Data assets management, data semantics and data-governance
specialist.
Integration, sharing and reuse of data, commitment to common global goods, and intergovernmental arrangements.
Working value chain – from data collection to use, environmental education at all levels.
Co-creation through people-first partnerships - collaborations that involve
civil society.
Rights to indigenous knowledge, documentation of traditional knowledge and network building partnerships.
Conventional methods combined with emerging forms of data and knowledge, and with the social data,