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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3

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6

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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2

3

4

5

6

7

8

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,