WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

1WWF BIODIVERSITY RISK FILTERMETHODOLOGY DOCUMENTATION, JANUARY 2023

© ScotlandBigPicture.com/ WWF-UK

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
The WWF Biodiversity Risk Filter tool methodology
Authors:
Maria Walsh (WWF Germany), Rebekah Church (WWF Germany)
Development Team (the WWF Corporate Biodiversity Stewardship Core Team):
Amelia Meyer (WWF US), Camilla Välimaa (WWF Sweden), Colby Loucks (WWF US), Daniel Metzke(WWF Germany), Elisa Vacherand (WWF International), Fee Reinhart (WWF Switzerland), Gunnar
Heller (WWF Germany), Jaco du Toit (WWF Sweden), Leah Hupp (WWF Germany), Margareta
Renström Lindhe (WWF Sweden), Matthew Wilkinson (WWF International), Silke Düwel-Rieth (WWFGermany) and Wendy Elliott (WWF International). And with special thanks to Philipp Wagnitz andMario Vaupel.
Acknowledgements:
We are grateful to the WWF Corporate Biodiversity Stewardship Governance Committee for theirguidance throughout the process: Cristiane Close (WWF International), Elgin Brunner (WWFSwitzerland), Jeff Worden (WWF Regional Office for Africa), Jordi Surkin (WWF Bolivia), Margaret
Kinnaird (WWF International), Melissa Ho (WWF US), Rab Nawaz (WWF Pakistan) and Samantha Putt
del Pino (WWF International).
Warm thanks also to the WWF Water Stewardship Team for their guidance: Alexis Morgan (WWFGermany), Ariane Laporte-Bisquit (WWF Germany), Isabel Meza (WWF Germany) and Rafael
Camargo (WWF Germany).
We would also like to express our great appreciation to Dr. G. Areendran, and his team at WWF India,
including Dr. Krishna Raj, Abhijitha CS, Debojyoti Mallick for their support on data analysis.
We also sincerely thank the members of the WWF Biodiversity Stewardship Business Advisory Groupfor their support and feedback over several years in the development of this tool and approach.
Sponsors:
A special thanks goes to all the sponsors of the Biodiversity Risk Filter and Risk Filter Suite: AB InBev,
BII Plus, British International Investment’s Technical Assistance Facility, DEG gGmbH, EDEKA, H&M
Group and Tesco.
Partners:
This work has greatly benefited from our highly valued and ongoing collaborations with ENCORE, theIBAT Alliance, RepRisk and UNEP-WCMC.

WWF Biodiversity Risk Filter additional methodological guidance A-C
Authors:
Andrew McConnell (Climate & Company), Liyana Nayan (Climate & Company), Malte Hessenius(Climate & Company), Oliver Herrmann (Climate & Company), Tycho Tax (Climate & Company), Fee
Reinhart (WWF Switzerland), Florian Oeschger (WWF Switzerland)
Acknowledgements:
We are very grateful to Guido Riembauer, Hinrich Paulsen, Julia Haas, Markus Eichhorn and Momen
Mawad from mundialis for their valued input on interpreting and handling global spatial biodiversity
data and for the development of the front-end of a non-public pilot tool to gather important feedbackon the methodological guidance A-C from the project’s Investor Group. We also thank Amrei vonHase, Annelisa Grigg, Kerry ten Kate, Mathis Wackernagel and Sebastian Winkler from Earthmind for
helpful comments and stimulating discussions.
A special thanks goes to the members of the WWF Switzerland External Advisory Group and theInvestor Group for their valuable contributions and the fruitful discussions during the ad hoc sessions
over recent months on the additional methodological guidance for financial institutions.
A warm thank you also to the Climate & Company team for support and input, especially: David
Rusnok (Climate & Company), Ingmar Juergens (Climate & Company) and Mustafa Yahsi (former
Climate & Company).
Supporters:
The development of the WWF Biodiversity Risk Filter methodological guidance A-C and the nonpublic piloting tool for the financial institutions to test the guidance A-C was generously supported by
the Minerva foundation.
Citation:
WWF Biodiversity Risk Filter (2023). WWF Biodiversity Risk Filter Methodology Documentation,
January 2023.
Editor: Mark Nicholls, MRG CommsWWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
Disclaimer:
It should be noted that the views expressedin this report are the sole responsibility of theauthors and do not necessarily represent theviews of the external reviewers.

About WWFWWF is one of the world’s largest and mostexperienced independent conservation
organisations, with over 5 million supporters
and a global network active in more than
100 countries. WWF’s mission is to stop the
degradation of the planet’s natural environment
and to build a future in which humans live inharmony with nature, by conserving the world’s
biological diversity, ensuring that the use ofrenewable natural resources is sustainable,and promoting the reduction of pollution andwasteful consumption.

About Climate & Company© Andrew S. Wright / WWF-Canada

Climate & Company is one of Europe’s leading
sustainable finance think-tanks. We are agroup of mission-driven experts on climate andbiodiversity finance and policy from key EU
institutions, the academic world and the banking
and investment sectors. As a team, we makesustainable development a reality by acting as
bridge-builders between the private and public
sectors, supporting evidence-based policymaking, and creating international partnerships
and fora for international knowledge exchange.

CONTENTS© Shutterstock / Aleksander Bolbot / WWF

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONKEY TERMS
INTRODUCTION: THE WWF BIODIVERSITY RISK FILTER

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3

1. OBJECTIVES
2. COVERAGE AND ANALYTICAL FOCUS
3. HOW TO READ THIS DOCUMENT

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9STEP 0: SCOPING THE ASSESSMENT

12STEP 0A: IDENTIFYING INDUSTRY MATERIALITY (INFORM MODULE)
STEP 0B: EXPLORING BIODIVERSITY IMPORTANCE AND INTEGRITY (EXPLORE MODULE)

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14

STEP 1: COLLECTING LOCATION-SPECIFIC COMPANY AND SUPPLY CHAIN DATA15GUIDANCE A: COLLECTING LOCATION-SPECIFIC PROXY DATA ON PORTFOLIO COMPANIES’ OPERATIONAL SITES

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1. HYBRID APPROACH
2. ASSET-LEVEL DATA
3. CORPORATE STRUCTURE DATA
4. CITY OF HEADQUARTERS DATA
5. DISAGGREGATED REVENUE DATAGUIDANCE B: COLLECTING LOCATION-SPECIFIC PROXY DATA ON SUPPLY CHAINS

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20
22
24
25
26

1. COMPANY-SPECIFIC SUPPLY CHAIN DATA (APPROACH I)
2. INPUT-OUTPUT MODELS (APPROACH II)

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29

STEP 2: ASSESSING BIODIVERSITY-RELATED RISKS

34

STEP 3: AGGREGATING RISKS TO THE COMPANY AND PORTFOLIO LEVEL (GUIDANCE C)

37

STEP 3A: AGGREGATING TO THE COMPANY LEVEL (ACROSS COMPANY SITES)STEP 3B: AGGREGATING TO THE PORTFOLIO LEVEL (ACROSS COMPANIES)
STEP 3C: AGGREGATING SUPPLY CHAIN RISKS

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39
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LIMITATIONS OF THE WWF BRF
BIBLIOGRAPHY
APPENDIX STEP 0: SCOPING THE ASSESSMENT

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48

APPENDIX 0.1: WWF RISK FILTER INDUSTRY SECTOR CLASSIFICATIONAPPENDIX 0.2: INDUSTRY MATERIALITY MATRIXAPPENDIX 0.3: WWF BRF RISK HIERARCHYAPPENDIX 0.4: BIODIVERSITY IMPORTANCE AND INTEGRITY INDICATORS
1. PHYSICAL RISK INDICATORS
2. REPUTATIONAL RISK INDICATORS

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50
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68

APPENDIX STEP 1: COLLECTING LOCATION-SPECIFIC COMPANY AND SUPPLY CHAIN DATA77

APPENDIX GUIDANCE A: OVERVIEW OF DATA PROVIDERS
1. ASSET-LEVEL DATA
2. CORPORATE STRUCTURE DATA
3. COMPARISON OF COVERAGE
APPENDIX GUIDANCE A: COMPARING DIFFERENT DATA PROXIESAPPENDIX GUIDANCE A: CASE STUDY – THE IMPORTANCE OF INPUT DATAAPPENDIX GUIDANCE B: OVERVIEW OF IO MODELS

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APPENDIX STEP 2: ASSESSING BIODIVERSITY-RELATED RISKSAPPENDIX STEP 3: A REVIEW OF AGGREGATION METHODSAPPENDIX 4: LIST OF EXPERTS AND EXTERNAL CONTRIBUTORS

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONTABLE OF FIGURES
Figure 1: Biodiversity-related risk assessment as a combination of the locationof corporate activities and the importance and state of biodiversity integrity.............................................................................. 4
Figure 2: The four modules of the WWF BRF tool.......................................................................................................................... 5
Figure 3: The four components of the WWF BRF Methodology Documentation......................................................................... 6
Figure 4: WWF BRF risk hierarchy................................................................................................................................................... 7
Figure 5: Overview of implementation steps to run the WWF BRF or WRF Assess Modules.................................................... 10
Figure 6: How to use the WWF BRF Methodology Documentation per user group.................................................................. 11
Figure 7: Normalising raw data...................................................................................................................................................... 14
Figure 8: Required input data per user group (company vs. financial institution)and scope (own vs. supply chain sites)......................................................................................................................................... 17
Figure 9: Analysed data sources................................................................................................................................................... 19
Figure 10: Comparison and complementarities of Approach I and Approach II
in the collection of proxy supply chain data................................................................................................................................. 27
Figure 11: Schematic depiction of the IO model approach.......................................................................................................... 30
Figure 12: Illustrating prep work step............................................................................................................................................ 31
Figure 13: Illustrating aggregation steps....................................................................................................................................... 38
Figure 14: Top 1000 FactSet companies by number of sites....................................................................................................... 84
Figure 15: Overview of company sites identified by the SFI Cement database.......................................................................... 85
Figure 16: Top 50 highest physical risk scores from FactSet and the SFI cement databases.................................................. 87
Figure 17: Physical risk scores from FactSet and the SFI cement databases............................................................................ 87

TABLE OF TABLES
Table 1: Asset-level data excerpt (Source: Spatial Finance Initiative, Cement Data).................................................................21
Table 2: Excerpt corporate structure data (Source: FactSet).................................................................................................... 22
Table 3: Collecting data on a portfolio company’s supply chain sites...................................................................................... 28
Table 4: Scape risk calculation – example................................................................................................................................. 35
Table 5: Illustrating aggregation method “Percentile”.............................................................................................................. 36
Table 6: WWF Risk Filter industry sectors................................................................................................................................. 48
Table 7: Industry materiality matrix (industry-specific weightings)............................................................................................ 50
Table 8: Four-level hierarchy of the biodiversity risk assessment framework........................................................................... 53
Table 9: Comparing coverage (corporate structure data vs. disaggregated revenue data); Source: FactSet......................... 79
Table 10: Open-source asset-level data by WWF Risk Filter industry sector............................................................................81
Table 11: Commercial asset-level data by WWF Risk Filter industry sector..............................................................................81
Table 12: Comparing the four different data sources................................................................................................................. 82
Table 13: Top 10 number of sites from the FactSet and SFI cement data set........................................................................... 85
Table 14: Physical and reputational risk score distribution for the SFI and FactSet databases,
including % total cement portfolio in market cap...................................................................................................................... 86
Table 15: Overview of IO models................................................................................................................................................ 89
Table 16: Illustrating aggregation using percentiles....................................................................................................................91
Table 17: Summarising different aggregation methods............................................................................................................. 92
Table 18: Illustrating aggregation via the mean.......................................................................................................................... 92
Table 19: Illustrating aggregation via the median...................................................................................................................... 93
Table 20: List of consulted experts on the Biodiversity Risk Filter............................................................................................ 94
Table 21: WWF Switzerland External Advisory Group and Investor Group............................................................................... 96

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

LIST OF ABBREVIATIONSBIA-GBS		

Biodiversity Impact Assessment powered by the Global Biodiversity Score

BICS			

Bloomberg Industry Classification Standard

BNEF			

Bloomberg New Energy Finance

BRF			

Biodiversity Risk Filter

C4F			

Carbon 4 Finance

CGP			

China’s Global PowerENCORE		

Exploring Natural Capital Opportunities, Risk and Exposure

FAO			

Food and Agriculture Organization

GHG			

Greenhouse gas (emissions)

GLOBIO			Global Biodiversity Model for Policy Support

IBAT			

Integrated Biodiversity Assessment Tool

ISIC			

International Standard Industrial Classification

LEAP approach		

Locate, Evaluate, Assess, Prepare Approach

MRIO models		

Multi Region Input-output models

MSCI ACWI		Morgan Stanley Capital International All Country World Index

NACE			

Nomenclature of Economic Activities

NAICS			North American Industry Classification System

NUTS 3			

Nomenclature of Territorial Units for Statistics 3

RRTS			

Relative Realised Timber Services

SBTN			

Science Based Targets Network

SFI			

Spatial Finance Initiative

SME			

Small and medium-sized enterprises

TNFD			

Taskforce on Nature-related Financial Disclosures

WBCSD			World Business Council for Sustainable Development

WRF			

Water Risk Filter

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONKEY TERMS

1

Term

Definition

Abiotic

A non-living part of an ecosystem that shapes its environment. In a terrestrial ecosystem, examples
include temperature, light and water. In a marine ecosystem, abiotic factors include salinity andocean currents. Abiotic and biotic factors work together to create the overall ecosystem (National
Geographic, 2022).

Biodiversity

The variability among living organisms from all sources, including, among other things, terrestrial,
marine and other aquatic ecosystems and the ecological complexes of which they are part; thisincludes diversity within species, between species and of ecosystems (CBD, 1992). In other words,
biodiversity is the part of nature that is alive, and includes every living thing on earth (see also thedefinition of nature, below).

Biodiversity footprint

The impact of a commodity or company on global biodiversity, measured in terms of biodiversity
change as a result of production and consumption of particular goods and services.

Biodiversity loss

The reduction or disappearance of any aspect of biological diversity in a particular area through death(including extinction), destruction or manual removal. It can refer to many scales, from local population
declines to global extinctions, resulting in reduced total diversity at the same scale (IPBES, 2022a).

Biodiversity-related opportunities

Activities that create positive outcomes for organisations and biodiversity by avoiding or reducing
impacts on biodiversity or by contributing to its restoration. Biodiversity-related opportunities can
go beyond common sustainable business archetypes to include actions that companies can take toinfluence the threats and pressures driving biodiversity loss and degradation globally, both within theirvalue chains and in the places where they operate (WWF, 2022a).

Biodiversity-related risks

Potential threats posed to an organisation linked to its and other organisations’ impacts on biodiversityand dependencies on ecosystems. These can derive from physical, transition and systemic risks.

BioticA living organism that shapes its environment. In a freshwater ecosystem, examples include aquaticplants, fish, amphibians and algae. In a terrestrial ecosystem, examples include terrestrial plants,
fungi, insects, amphibians and mammals. Biotic and abiotic factors work together to create the overall
ecosystem (National Geographic, 2022).

Business importance of site

The economic importance of a specific company location (i.e., site) in relation to the overall company
performance. The business importance of a site can be determined on the basis of financial variables,such as revenues or sales, or on the basis of expert opinion.

Dependencies on biodiversity

Aspects of ecosystem services that an organisation or other actor relies on to function. An
organisation might be dependent upon an ecosystem’s regulation of water flow and quality, theresilience it provides against hazards like fires and floods, the pollination of crops it enables by
providing a suitable habitat for pollinators, or its provision of timber or fibres.1

Direct drivers of biodiversity andecosystem change

Drivers, both natural and human-induced, that unequivocally affect biodiversity, ecosystems andnature directly (also referred to as pressures). These drivers in turn affect the provision of ecosystemservices with consequences for people, the economy and society. The main direct drivers of
biodiversity and ecosystems loss are land, water and sea change, climate change, pollution, natural
resource use and exploitation and invasive species (IPBES, 2022b).

Ecosystem

A dynamic complex of plant, animal and microorganism communities and their non-living environment,interacting as a functional unit (CBD, 1992; IPBES, 2019a).

Ecosystem condition

The quality of an ecosystem measured by its abiotic and biotic characteristics. Condition is assessedby an ecosystem’s composition, structure and function which, in turn, underpins the ecologicalintegrity of the ecosystem and supports its capacity to supply ecosystem services (TNFD, 2022a).

Ecosystem function

The flow of energy and materials through the biotic and abiotic components of an ecosystem. This
includes processes such as biomass production, trophic transfer through plants and animals, nutrient
cycling, water dynamics and heat transfer (IPBES, 2019a).

Based on SBTN working definition, unpublished.

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

Term

Definition

Ecosystem (Biodiversity) integrity

The ability of an ecosystem to support and maintain ecological processes and a diverse communityof organisms. The ecological integrity of ecosystems, as it is also known, is measured as the degree
to which a diverse community of native organisms is maintained, and is used as a proxy for ecologicalresilience, or the capacity of an ecosystem to adapt in the face of stressors while maintaining its
functions and services of interest (IPBES, 2022a).
The contributions of ecosystems to the benefits that are used in economic and other human activity(UN , 2021). TNFD (2022b) defines ecosystem services as falling into one or more of the following
categories:

Ecosystem services•

Provisioning services represent the contributions to benefits that are extracted or harvested fromecosystems (e.g., timber and fuel wood from a forest, fresh water from a river).

•Regulating and maintenance services result from the ability of ecosystems to regulate biologicalprocesses and to influence climate, hydrological and biochemical cycles, and thereby maintainenvironmental conditions beneficial to individuals and society. Provisioning services are dependent
on these regulating and maintenance services (e.g., the provision of crops depends upon relatively
stable climate, hydrological and biochemical cycles).

•Cultural services are the experiential and intangible services related to the perceived or actual
qualities of ecosystems whose existence and functioning contributes to a range of cultural benefits(e.g., the recreational value of a forest or a coral reef for tourism).

Impacts on biodiversity

Changes in the state of nature which may result in changes to the capacity of nature to provide
social and economic functions. Impacts can be positive or negative. They can be the result of anorganisation’s or another party’s actions and can be direct, indirect or cumulative (TNFD, 2022a).

Materiality

A concept that defines why and how certain issues are important for a company or industry sector. A
material issue can have a major impact on the financial, economic, reputational or legal aspects of acompany, as well as on the system of internal and external stakeholders of that company. Although theconcept applies in a wide variety of contexts (e.g., accounting, reporting, etc.), in this report materiality
refers to biodiversity and water aspects affecting the financial performance of companies (“outsidein”) and how they and their activities impact biodiversity and nature (“inside-out”) (TNFD, 2022b; IPSF;
Climate & Company, 2021).

NatureThe natural world, with an emphasis on the diversity of living organisms (including people) and theirinteractions among themselves and with their environment (TNFD, 2022a). In other words, nature is
all life on Earth (i.e., biodiversity), together with the geology, water, climate and all other inanimate
components that comprise our planet (see also the definition of biodiversity, above).

Natural capital

The stock of renewable and non-renewable natural resources (e.g., plants, animals, air, water, soils
and minerals) that combine to yield a flow of benefits to people (Capitals Coalition, 2016).

Nature loss

The loss and/or decline of the state of nature. This includes, but is not limited to, the reduction of any
aspect of biological diversity, e.g., diversity at the genetic, species and ecosystem levels in a particulararea through death (including extinction), destruction or manual removal (TNFD, 2022a).

Nature-related opportunities

Activities that create positive outcomes for organisations and nature by avoiding or reducing impacts
on nature or by contributing to its restoration. Nature-related opportunities can occur i) whenorganisations mitigate the risk of natural capital and ecosystem service loss and ii) through strategictransformation of business models, products, services or investments that actively works to halt orreverse the loss of nature, including by the implementation of nature-based solutions (or support forthem through financing or insurance) (TNFD, 2022a).

Nature-related risks

Potential threats posed to an organisation linked to its and other organisations’ impacts anddependencies on nature. These can derive from physical, transition and systemic risks (TNFD, 2022a).

Scape risk

The term scape is used to refer collectively to landscapes, seascapes and river basins (freshwater
systems). Scape risk is informed by a company’s geographic location, it’s industry sector and theintegrity of biodiversity and ecosystems at the geographic location.

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONINTRODUCTION:
THE WWF BIODIVERSITY RISK FILTER© Ola Jennersten / WWF-Sweden

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

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Biodiversity-related risks arise from companies’ dependencies and impacts, in combination with
importance and local and global state of biodiversity integrity. As the importance and state of biodiversity
integrity can vary across the location of company sites, the assessment of biodiversity-related risks,
response options and progress need to be location-specific as well (see Figure 1). Understanding andaddressing biodiversity-related risks is vital for companies and financial institutions but is challenging as
location-specific data is necessary (SBTN, 2020a; TNFD, 2022b). Without location-specific biodiversity(i.e., data on the importance and integrity of biodiversity) and company data (i.e., location of company
sites, industry classification and business importance of the site), it is difficult for companies and financial
institutions to fully understand their biodiversity-related risks and prioritise areas for action. Such ananalysis requires a tool that can analyse the relevant and available spatially explicit biodiversity data and link
it to the company locations.
Responding to this need, WWF has launched the Biodiversity Risk Filter (BRF), building on WWF’s longstanding expertise and experience with the Water Risk Filter (WRF).
The following sections describe the objectives and deliverables, the scope of the
WWF BRF and the structure of this methodology documentation.

Figure 1: Biodiversity-related risk assessment as a combination of the location of
corporate activities and the importance and state of biodiversity integrity

IMPACTS AND DEPENDENCIES ON
BIODIVERSITY
Different corporate activities are
impacting biodiversity and depend on
ecosystem services to different degrees.

STATE OF BIODIVERSITY
Biodiversity is extremely location
specific. In order to assess biodiversity
risk for any location, one needs to assess
different aspects of biodiversity accrossthe globe.

BIODIVERSITY RISK RATINGS FOR COMPANY LOCATIONS
To assess biodiversity, information on impact/dependency on
biodiversity and the state/pressures biodiversity is facing at a specificlocation need to be combined.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

1. OBJECTIVES
The WWF Biodiversity Risk Filter currently consists of:1.

The WWF Biodiversity Risk Filter

2.

The WWF Biodiversity Risk Filter Methodology Documentation

The WWF Biodiversity Risk Filter
The WWF BRF is a free-of-charge, web-based, spatially explicit corporate- and portfolio-level screening andprioritisation tool for biodiversity-related risks. It allows companies to understand and assess the biodiversity-relatedrisks of their operational locations and their suppliers and to prepare an appropriate response plan. By the same
logic, financial institutions can assess biodiversity-related risks for all companies in a given portfolio.
The tool builds heavily on WWF’s experience with the WRF, launched in 2012 (WWF Water Risk Filter, 2021). In
essence, both tools are designed to be used by companies and financial institutions for company- and portfolio-levelscreening and prioritisation to identify risk hotspots across companies’ operational and supply chain sites. By using
spatially explicit data on biodiversity and freshwater at global scale, the tools provide location-specific and industryspecific assessments of biodiversity and water-related physical, regulatory2 and reputational risks. The tools aim to
help companies and financial institutions to better prioritise where and on what to focus contextual responses as wellas inform their biodiversity- and water-related stewardship strategies and target setting.
Both tools are available through the WWF Risk Filter Suite. This integrated platform has a common user database.
That means that users only need to enter the required location-specific company data once and can manage bothtools in one central location. Once the data is added, the users can assess their biodiversity- and water-related risks.
The current version of the WWF BRF tool consists of three key modules: the Inform Module, which provides an
overview of the industry-specific dependencies on ecosystem services and impacts on biodiversity; the Explore
Module, which is a collection of spatially explicit maps of the importance and local integrity of biodiversity; and theAssess Module, which contains a tailored physical and reputational risk assessment for which users need to input
location-specific company and/or supply chain data. A fourth module, the Respond Module, is currently underdevelopment. This will support users in identifying suitable actions to respond to the identified risks. In addition, it
will include guidance on where to get more specific information on biodiversity values in a particular identified highrisk site via complementary tools such as the Integrated Biodiversity Assessment Tool (IBAT). Figure 2 summarises
the key modules of the WWF BRF tool.
Figure 2: The four modules of the WWF BRF tool

2

INFORM MODULE

Industry materiality: Explore different industry sectors‘ dependencies on ecosystemservices and impacts on biodiversity using an interactive table that lets you select theindustries you are interested in.

EXPLORE MODULE

Maps on the importance and integrity of biodiversity: Explore maps of different
biodiversity aspects at different geographical scales. The maps show high-risk regions to
identify priority areas for action.

ASSESS MODULE

Assessment of company and supply chain locations: Upload your location-specificcompany and supply chain data for a tailored assessment of biodiversity-related physical andreputational risks of your operational sites, supply chain sites or your portfolio companies’sites respectively.

RESPOND MODULE

Under development: Draw up a suitable catalogue of response measures per site or acrosssites based on the individual risk assessment (i.e., the Assess Module).

Please note that the January 2023 version of the WWF BRF tool still needs to include the regulatory risk assessment. The regulatory risk assessment is underdevelopment and will be available in due course.

5WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

The WWF Biodiversity Risk Filter Methodology Documentation
This document describes the underlying methodology of the Inform, Explore and Assess Modules
that have already been integrated in the WWF BRF tool, including a description of the risk assessment
framework, underlying structure, data and limitations. In addition, WWF and Climate & Company
developed guidance that has not been included into the WWF BRF tool at this point: Guidance A, B
and C. Guidance A and B, on the one hand, provide support for companies and financial institutions oncollecting the required input data for the WWF BRF and WRF Assess Modules. Guidance A supports
financial institutions with collecting location-specific proxy data on companies’ operational sites.
Guidance B supports companies and financial institutions with collecting location-specific proxy data on
supply chain sites. On the other hand, Guidance C provides support for financial institutions on how thelocation-specific output data from the WWF BRF and WRF Assess Modules can be further processed
and aggregated to the company- and portfolio-level. Figure 3 summarises the four components of the
WWF BRF Methodology Documentation.
Figure 3: Overview of the components of the WWF BRF Methodology Documentation

BRF METHODOLOGYWWF BRF methodology: Details the underlying methodology of the WWF BRF tool,
including a description of the risk assessment framework, underlying structure and data andlimitations.

GUIDANCE A:
COMPANY DATA

Guidance on location-specific company data: Since location-specific company data isrequired to run the WWF BRF (and WRF) Assess Module, this guidance provides financial
institutions with an overview of existing proxy data for company locations and how to collectand prepare them at scale.

GUIDANCE B:
SUPPLY CHAIN DATA

Guidance on location-specific supply chain data: Since location-specific supply chain data
is required to run the WWF BRF (and WRF) Assess Module, this guidance provides companies
and financial institutions with an overview of existing approaches to collect and prepare
location-specific supply chain data at scale.

GUIDANCE C:
AGGREGATION

Guidance on WWF BRF and WRF tools output: Since the WWF BRF (and WRF) provide anassessment at the site level but not at the company or portfolio level, this guidance provides
financial institutions with a description of how the output of the tools can be further processed
and aggregated to the company and portfolio level.

To show how the WWF BRF tool can be applied to a representative investment portfolio using Guidance A andC, a case study was conducted on more than 600 companies listed in the MSCI All Country World Index (MSCIACWI)3 and operating across 24 WWF Risk Filter industry sectors (see WWF and Climate & Company, 2023).

3

The MSCI ACWI world index comprises 2,933 companies from 23 developed and 23 emerging markets.
For more information, see www.msci.com/our-solutions/indexes/acwi.

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

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2. COVERAGE AND ANALYTICAL FOCUS
Coverage of biodiversity-related risks
The current version of the WWF BRF covers physical and reputational
biodiversity-related risks that affect the locations of company or supply
chain sites:• Physical risks are driven by the ways in which a business and its supply
chains depend on and can be affected by both natural and human-induced
conditions of land- and seascapes, and how pressures might deteriorate
ecosystem services in the future. The global decline of ecosystem services,for example, could lead to reduced productivity (e.g., lack of fertile soils andpollination) or increased costs of inputs (e.g., scarcity of natural fibres or
harvest losses).
• Reputational risks can result from a company’s actual or perceived negative
impacts on biodiversity and people. Reputational risk represents stakeholders’and local communities’ perceptions of whether companies conduct businesssustainably or responsibly with respect to biodiversity and can ultimately
affect brand value and market share, among other factors. Adverse effects on
business could emerge from, for example, damages to the corporate brand andthus declining sales, or greater investor scrutiny and thus declining share price.

Additional biodiversity-related risks, such as regulatory (i.e., policy and legal) andmarket risks, as well as an assessment of biodiversity-related opportunities, areunder development and will be added in due course.
This classification of risks aligns with the risk classification of the Taskforce on
Nature-related Financial Disclosures (TNFD), according to which biodiversity-relatedrisks can be classified into physical, transition and systemic risks. Transition risks
are further divided into policy and legal, market, technology and reputational risks(TNFD, 2022b).
To assess the different aspects of physical and reputational biodiversity-relatedrisks, the WWF BRF follows a four-level risk hierarchy. It breaks down the physicaland reputational risk into 33 different indicators (see Table 8 in the Appendix),
covering aspects of biodiversity that may be (or may become) material risks from afinancial or environmental and social perspective.

The risk hierarchy consists of the following four levels (see also Figure 4):• LEVEL 4, Metrics, comprises the raw global data sets that measure different
aspects of biodiversity and ecosystems in a specific location that may lead tobiodiversity-related risks for companies and financial institutions. Currently, theWWF BRF tool contains 56 global biodiversity data sets;• LEVEL 3, Indicators, comprises information on the importance and local
integrity of biodiversity aspects, not as raw data but spatially (dis-)aggregatedto an assessment unit and translated to a risk score ranging from 1 to 5. The
56 metrics currently integrated in the WWF BRF have been grouped into 33
indicators (20 physical risk and 13 reputational risk indicators);• LEVEL 2, Risk categories, groups the indicators into higher-level risk clusters
with more direct relevance to companies and financial institutions. The 33
indicators have been grouped into eight different risk categories (five physicalrisk categories and three reputational risk categories); and• LEVEL 1, Risk types, combines the risk categories into the broader risk types(physical risks and reputational risks).
It should be noted that risk types, risk categories and indicators are visible in thetool, but metrics (i.e., the raw data sets) are not.

Figure 4: WWF BRF risk hierarchy

LEVEL 1: RISK TYPES

Physical Risk

Reputational Risk

LEVEL 2: RISK CATEGORIESLEVEL 3: INDICATORS

Provisioning Services

4 indicators

Regulating & Supporting Services - Enabling

5 indicators

Regulating Services - Mitigating

6 indicators

Cultural Services

1 indicator

Pressures on Biodiversity

4 indicators

Environmental Factors

5 indicators

Socioeconomic Factors

4 indicators

Additional Reputational Factors

4 indicators

LEVEL 4: METRICS

Over 50 different data layers are currently
integrated into the tool.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONThis structure was, on the one hand, put in place to construct a hierarchical framework that consists of not
only broad risk types, but more specific risk categories, as they provide more insights on the aspects therisks are comprised of. For example, biodiversity-related physical risks comprise very different aspectsof biodiversity, ecosystems and their services. In this case, the availability of “Provisioning Services”(such as wood or fibre) can be investigated separately from the availability of “Regulating and Supporting
Services” (such as pollination or soil condition). On the other hand, these broad risk types (i.e., physical andreputational risk) and the general structure of the WWF BRF risk hierarchy have already been successfully
used in the WWF WRF. This ensures consistency between water- and biodiversity-related risk assessments
and offers users a familiar approach that is still specific to the topics of water and biodiversity.
The WWF BRF provides an assessment of biodiversity-related risks taking into account industry sectors’
dependencies on ecosystem services and impacts on biodiversity. Still, it is not a biodiversity footprint
assessment that quantifies the negative impacts a company’s or supply chain’s operations might have on
biodiversity. It is also not a comprehensive model of biodiversity-related risks to nature and people. It isa user-oriented model of biodiversity-related physical and reputational risks to companies and financial
institutions. It supports them to prioritise what and where to act and better identify material biodiversityrelated risks.

Coverage of industry sectors
The WWF BRF provides an assessment of 25 industry sectors, encompassing the full spectrum ofcorporate activity. The 25 industry sectors were defined based on a harmonised list of different standard
industry classifications. Existing standard industry classifications were narrowed-down to this list of 25
WWF Risk Filter industry sectors since some broader Global Industry Classification Standard (GICS)
classifications (e.g., electric energy production) face diverse biodiversity-related risks and therefore are
better served through disaggregation (e.g. into electric energy production from solar/wind; hydropower;
combustion; etc.), while others facing similar biodiversity-related risk (e.g., professional services; software;etc.) need not be disaggregated and were therefore grouped into the same category. Table 6 in the
Appendix contains an overview of all corporate activities or processes that are associated with each WWF
Risk Filter industry sector.
Therefore, the WWF BRF can be applied by companies from all industry sectors and to a broad
portfolio of companies.

Coverage of asset classes
The WWF BRF tool offers a biodiversity-risk assessment based on the industry sector classification andthe importance and local state of biodiversity integrity. As biodiversity aspects are highly variable acrossgeographies, location-specific company data (i.e., location of company sites, industry classification andbusiness importance of the site) is required to run the WWF BRF Assess Module. As location-specificcompany data is not always readily available, especially for financial institutions and investors, Guidance
A and B provide an overview on existing data sources that can be used in the absence of companyreported location-specific information. By downloading the results from the Assess Module (available via adownloadable Excel function) and using the WWF and Climate & Company Guidance C, the results can befurther aggregated to the company- and portfolio-level. Against this background, all asset classes directly
related to companies (e.g., private and listed equity, corporate bonds, real estate or other tangible assets)
can be analysed.

3. HOW TO READ THIS DOCUMENT
The WWF BRF Methodology Documentation is structured according to the four implementation steps one
needs to follow to perform a risk assessment with the WWF BRF or WRF, presented in Figure 5.

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

10

Figure 5: Overview of implementation steps to run the WWF BRF or WRF Assess Modules

STEP 0: SCOPING THE ASSESSMENT
The WWF BRF and WRF tools’ Inform Module and Explore Modules can help focus the scope of the assessment onpriority industries and geographies as recommended by TNFD and SBTN.

BRF INFORM MODULE

BRF & WRF EXPLORE MODULE
Step 0B: Exploring biodiversity and water importance and integrity
The WWF BRF and WRF Explore Modules provide maps showing the levelof risk worldwide based on a total of 33 biodiversity risk indicators and 32
water risk indicators.

Step 0A: Identifying industry materiality
The WWF BRF Inform Module provides information about the level ofdependencies on ecosystem services and impacts on biodiversity fora total of 25 industry sector.

STEP 1: COLLECTING LOCATION-SPECIFIC COMPANY AND SUPPLY CHAIN DATA
After refining the scope of the assessment in Step 0, collect location-specific data on (portfolio) companies’ operational and supply
chain sites. The following input data is required to use the Assess Modules of the WWF BRF and WRF tools:
Geographic location of sites

Industry classification of sites

Business importance of sites(coordinates or address)(using WWF Risk Filter industry sector classification)(indicated through high, medium or low importance)

GUIDANCE A: COMPANY DATAGUIDANCE B: SUPPLY CHAIN DATA

For financial institutions

For companies and financial institutions

As a majority of financial institutions do not currently have easily
available location-specific company information, WWF and Climate& Company developed guidance on how to collect location-specificproxy data for portfolio companies at scale.

As a majority of companies and financial institutions do not currently
have easily available location-specific supply chain information, WWF
and Climate & Company developed guidance on how to collect locationspecific proxy data for supply chains at scale.

Data input

STEP 2: ASSESSING BIODIVERSITY-RELATED RISKS
The Assess Module combines the sites’ industry materiality rating (0A) and the local
biodiversity importance or integrity rating (0B) into a scape risk score for each company
location. This comprises 33 biodiversity indicators from “water scarcity” to “terrestrial
modification” following the BRF risk hierarchy.
Industry Materiality (0A)

BRF INFORM MODULE

STEP 2: ASSESSING WATER-RELATED RISKS
See WWF Water Risk Filter Methodology.

Biodiversity integrity (0B)

BRF EXPLORE MODULE

Biodiversity risk score (2)BRF ASSESS MODULE

Data Output

STEP 3: AGGREGATING BIODIVERSITY RISK TO THE COMPANY AND PORTFOLIO LEVELGUIDANCE C: AGGREGATION
For addressing the needs of financial institutions, WWF and Climate & Company developed guidance on how to aggregate scaperisk per indicator to the company and portfolio level using the site-specific WWF BRF data outputs. While this guidance focuses on
biodiversity risk, the same aggregation approach can be applied with the outputs of the WWF WRF data outputs.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

11

Since different user groups (e.g., companies, banks, asset managers, insurancecompanies, development banks) have different access to the required locationspecific input data and are interested in results on different levels (e.g., site-,company-, portfolio-level), not every step is equally relevant for the respective user.
Figure 6 provides an overview of the relevant implementation steps per user groupand broadly distinguishes between financial institutions and companies. By financial
institutions, we refer broadly to financial entities interested in assessing a wide rangeof (listed or unlisted) companies and their respective sites. Financial institutions
interested in assessing a single company or project should follow the suggested steps
for companies and not rely on proxy data.

Figure 6: How to use the WWF BRF Methodology Documentation per user group

STEP 0: SCOPING

FINANCIAL INSTITUTIONSCOMPANIES(i.e., assessing a broad range of portfolio companies)(i.e., where fewer data points are required)

SCOPING IS RELEVANT FOR BOTH USER GROUPSBRF INFORM MODULE
BRF & WRF EXPLORE MODULES

STEP 1 – COLLECTING DATACOMPANIES SHOULD COLLECT DATA ON THEIR OWN OPERATIONS THEMSELVES.

GUIDANCE A: COMPANY DATAGUIDANCE B: SUPPLY CHAIN DATASTEP 2 – ASSESSING RISKSBRF ASSESS MODULE

STEP 3 – AGGREGATIONGUIDANCE C: AGGREGATIONAGGREGATION IS PRIMARILY RELEVANT FOR FINANCIAL INSTITUTIONS TO COMPAREPORTFOLIO COMPANIES AND PORTFOLIOS.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONSTEP 0:SCOPING THE ASSESSMENT© Michel Gunther / WWF

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
Companies and financial institutions are recommended, especially if the topic of biodiversity is relatively
new to the organisation, to define a narrowed-down scope before starting with the biodiversity-related
risk assessment. The scoping aims to familiarise oneself with the approach of biodiversity-related risk
assessment, build a better understanding of it and reduce the complexity at the beginning. Once familiar withthe approach, organisations should extend the depth and breadth of the assessment, as a comprehensive
assessment of biodiversity-related risks requires the inclusion of the companies’ operational sites and(upstream and downstream) supply chain sites.
TNFD (2022b) and Science Based Targets Network (SBTN) (2020a), for example, provide companies andfinancial institutions with a specific set of questions that support them in narrowing down the scope of theassessment. For companies, these include considerations on what business operations can reasonably
be considered (e.g., direct operations, supply chain operations) based on internal and supply chain data
and what biodiversity aspects can and should be considered from a double materiality perspective (i.e.,
biodiversity aspects imposing a risk to the business and corporate activities imposing a threat to biodiversity).
For financial institutions, these include considerations on the type of business of the financial institution (e.g.,credit operations for banks; equity investment for asset managers; project finance for development banks;etc.), which industries and geographies capital is predominantly allocated to and where and how their financial
activities interact with biodiversity. An output of the scoping assessment could be an initial heat map of thepriority industries and geographies.
The WWF BRF Inform Module and Explore Module can support companies and financial institutions to focus
the scope of the assessment on priority industries and geographies:• The WWF BRF Inform Module helps to narrow the assessment from an industry perspective by
providing information about the level of dependencies on ecosystem services and impacts on biodiversity
for a total of 25 industry sectors encompassing the full spectrum of corporate activity. This helps to identify
the company activities or the industry sectors with the highest impact or dependency.
• The WWF BRF Explore Module helps to narrow the assessment from a geographical perspective
by providing maps on the importance and integrity of biodiversity, showing the level of biodiversity risk
worldwide based on a total of 33 biodiversity indicators. This helps to identify biodiversity-related
hotspot locations.
The following sections explain the WWF BRF Inform Module (Step 0A: Identifying industry materiality) andExplore Module (Step 0B: Exploring biodiversity importance and integrity) in more detail.

STEP 0A: IDENTIFYING INDUSTRY MATERIALITY(INFORM MODULE)
Since corporate activities impact biodiversity and depend on ecosystem services to different degrees,industry impact and dependency materiality ratings were developed for all 25 WWF Risk Filter industry
sectors, encompassing the full spectrum of corporate activity (see Table 6 in the Appendix). For each of the
25 industry sectors, a dependency and/or impact rating was assigned to each of the 33 indicators of the BRF
risk hierarchy, resulting in a total of 825 industry-indicator pairs. The rating was performed on a scale from 1
to 5: an industry materiality of 5 indicates a very high dependence or impact on the specific indicator, while
an industry materiality of 1 indicates a very low dependence or impact. If an industry has no dependence or
impact on a specific indicator, an industry materiality rating of 0 was assigned.
For example, the WWF Risk Filter industry sector ‘Agriculture (plant products)’ has a dependency rating of 5
for the physical risk indicator ‘Soil Condition’, indicating that agricultural plant production heavily depends on
healthy soils. It has a dependency rating of 0 for the physical risk indicator ‘Limited Marine Fish Availability’,as there is no direct relationship to that indicator, and it has an impact rating of 5 for the reputational risk
indicator ‘Protected/Conserved Areas’, indicating that agricultural activities may be very harmful, if they
overlap with protected/conserved areas (see Table 9 in the Appendix).
The definition of the industry materiality ratings for the assessment of the dependencies was based on work
conducted by ENCORE (2022), and for the assessment of impacts, it was based on the work by SBTN (2020a).
The industry materiality ratings were slightly adjusted following peer reviews with WWF experts, financial
institutions and companies. The ratings have been adjusted to enable a comparison between industry sectors
and allow for a meaningful aggregation of the indicator scores to risk categories and types.

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
That means that while the industry materiality ratings are meaningful within the context of the WWF BRF risk
assessment, they should not be taken out of the context of the WWF BRF. Table 7 in the Appendix contains an
overview of all 825 industry materiality ratings for the 25 WWF Risk Filter industry sectors. These can also be
seen, explored and downloaded in the WWF BRF Inform Module.
The industry materiality ratings provide a first overview of the level of the materiality of different
industries to biodiversity-related risks covered in the WWF BRF. It helps users understand how
different corporate activities might impact biodiversity or depend on ecosystem services to varying
degrees. It subsequently allows users to assess highly material issues or parts of the supply chain
representing highly impactful or dependent industry sectors.  

STEP 0B: EXPLORING BIODIVERSITY IMPORTANCE
AND INTEGRITY (EXPLORE MODULE)
The biodiversity importance and integrity assessment are based on the 56 global data sets that inform theWWF BRF’s indicators (LEVEL 3 of the WWF BRF risk hierarchy). Each data set represents an evaluation of
the importance or state of biodiversity integrity in a specific land- or seascape.
To produce indicators, it is necessary to adapt the raw data sets in two ways:• Alignment of spatial scales: the data sets use a variety of spatial scales (e.g., on a country level; 30m x
30m; etc.). So, the raw data first needed to be spatially aggregated or transposed to a common scale. For
terrestrial areas, the WWF BRF follows the WWF WRF assessment unit to the level of HydroSHED Level 7(Lehner & Grill, 2013) as this represents a degree of functional coherence at a biodiversity-level. For marine
areas, the WWF BRF uses Marine Ecoregions of the World (MEOW) for coastal areas and FAO Major
Fishing Areas for the high seas.
• Translation of raw data to risk score classes: The data sets provide data in a variety of different
units. So, the raw data was translated into the five risk score classes (i.e., given a value from 1 to 5). This
normalisation process (see Figure 7) allows for easy comparison between indicators and allows indicators
to be aggregated with others.
Figure 7: Normalising raw data

A risk score of 5 indicates very high risk, while a score of 1 indicates very low risk. If there was no data for
an indicator at a specific location, a risk score of 0 was assigned. Thresholds between risk score classes
were chosen individually for each data set. Details on the data source, adaptation steps and translation into
the five risk score classes can be found in Appendix 0.4. The resulting global maps for the indicators, risk
categories and risk types can be found in the WWF BRF Explore Module.
It should be noted that the WWF BRF is based on the best available data sets. While data sets are updated
on a regular basis, the WWF BRF is not intended to provide an assessment of real-time biodiversity-related
risk conditions.
The global maps of biodiversity importance and integrity provide a first overview of the level of
biodiversity risk aspects covered in the WWF BRF. They help the user to understand and identify
biodiversity-related hotspots and areas of key biodiversity importance and subsequently allow tofocus the assessment on high-risk geographies.  

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STEP 1
COLLECTING LOCATION-SPECIFIC
COMPANY AND SUPPLY CHAIN DATA© Peter Chadwick / WWF

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
Biodiversity-related dependencies and impacts represent sources of risk for companies and financial
institutions, as they affect business continuity, earnings and, ultimately, enterprise value. As the importanceand state of biodiversity integrity vary from location to location, the biodiversity-related dependencies
and impacts need to be assessed in a location-specific manner. Therefore, collecting data on where the(portfolio) company’s operational and supply chain sites are located is crucial to incorporate the importanceand integrity of biodiversity at a specific location into the analysis (TNFD, 2022b).

Required input data for WWF BRF Assess Module
Using the WWF BRF Assess Module to analyse biodiversity-related risks stemming from (portfolio)
companies’ operational or supply chain sites requires collecting the following input data:• Location of company operational sites and supply chain sites (latitude, longitude): A company
site designates a location where corporate activities are conducted. A portfolio of company sites
should capture a business model in its entire complexity. This can include, for example, the extractionof resources, the manufacturing of goods (factories), the storage of items (warehouses), the sale ofgoods (retail or wholesale stores) or other professional activities (offices). The location of the company
and supply chain sites helps to identify the interface of economic activity with biodiversity. Using the
WWF BRF Assess Module requires collecting the coordinates (latitude, longitude) or the address of thecompany and supply chain sites.
• Industry classification per site (using WWF Risk Filter industry sector classification): Industry
classifications place economic activities into industry groups based on similar production processes
or similar products or services. This helps to make use of well-structured industry materiality ratings tounderstand how a specific economic activity depends on or impacts biodiversity. Using the WWF BRF
Assess Module requires a classification of industries according to the WWF Risk Filter industry sector
classification (see Table 6 in the Appendix). Therefore, crosswalks for widely used industry classification
standards are required.4• Business importance of a site (indicated trough high, medium or low): The business importancedescribes the relative importance of a site to the overall company (e.g., a site with 5,000 units of output
per year might be more important than a site with only 1,000 units of output). Therefore, to better
understand organisation-wide implications, the business importance of each location-industry pair mustbe identified. This is conceptually challenging as the business importance can vary for many reasons(such as distinct spatial characteristics; the production of unique products; high importance for customer
relationships; etc.). Whenever possible, making use of expert opinion and industry knowledge issuggested to determine the importance of a site. Using the WWF BRF Assess Module requires the user
to select the importance according to three categories: high, medium or low importance.
Users can manually enter the required input data into the WWF BRF tool via a mask within the tool or
upload it via the provided Excel template on the WWF Risk Filter Suite Platform.

Required additional data for company- and portfolio-level aggregation(Guidance C)
For companies and financial institutions who would like to deploy Guidance C (i.e., aggregating biodiversity
risk to the company and portfolio level), the following additional data points are required:• Numeric weights for the business importance of each site: Guidance C for financial institutions
describes how to derive numerical values for the importance per site (summing up to 1 at the company
level). This is required for aggregating the risk scores at the company level (see Step 3a).
• Portfolio weights per company: For financial institutions, the weight per portfolio company serves as aweighting factor to derive portfolio-level aggregation.

4

We refer here to widely used industry classifications such as the North American Industry Classification System (NAICS), the Bloomberg Industry
Classification Standard (BICS), the Global Industry Classification Standard (GICS) developed by MSCI and Standard & Poor’s, the classification system of
the European Community, abbreviated as NACE, or the International Standard Industrial Classification of All Economic Activities (ISICs). If these are usedby the user internally, they need to be mapped to WWF Risk Filter industry sector classification. As this consist of only 25 larger macro sectors, creating acrosswalk is relatively simple.

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17

Another data point is needed to process and aggregate the analysis of supply chain sites (see Step 3C).
Conceptually speaking, the biodiversity-related supply chain risk score per risk type, r, of a Firm, A, is thesum of the first-order risk scores per risk type (physical or reputational) of all n suppliers, weighted by aweighting factor, w, per supplier, i, which denotes the importance of the supplier-customer relationship: 5nSupply Chain Risk ScoreA,r = ∑ wi x 1st order risk score r,i
i=1Aggregating the supply chain dimension, requires the following additional data point per (portfolio)
company’s supplier next to location-specific supplier data (i.e., location of supply chain sites, industry
classification and business importance of site):• Supplier-customer relationship: To distinguish between important and less important suppliers forthe company of interest, information on the supplier-customer relationship is needed. This could be, forexample, the revenue dependent on the supplier-customer pair.
This results in different data requirements subject to the user group and scope of the assessment(see Figure 8).
Figure 8: Required input data per user group (company vs. financial institution) and scope (own vs. supply chain sites)

COMPANIESFINANCIAL INSTITUTIONSOWN OPERATIONSPER PORTFOLIO COMPANY’S OPERATIONSLOCATION OF COMPANY SITES
INDUSTRY SECTOR PER SITE
BUSINESS IMPORTANCE PER SITEWEIGHT PER PORTFOLIO COMPANYPER SUPPLIERPER PORTFOLIO COMPANY’S SUPPLIER

WEIGHT OF THE SUPPLIER-CUSTOMER RELATIONSHIPSAME DATA POINTS AS ABOVE PER SUPPLIER

Availability of required input data
For individual companies, most of the required input data typically exist in their data infrastructure andcollection process. Thus, individual companies should collect and prepare the required location-specificdata for their operational sites themselves instead of relying on proxy data (since companies are data
issuers, compared to data users (TNFD, 2022c)). This data should be relatively quick for companies tocollect and prepare in a format acceptable to the WWF Risk Filter Suite tools. However, location-specificdata on the company’s suppliers are often not yet part of companies’ data infrastructure and collection
processes and must therefore be collected additionally. Guidance B illustrates how input-output (IO)
models can be of use in collecting these data. However, we strongly suggest that companies engage their
key suppliers on the location-specific data required before relying on IO models, as data received directlyfrom the supplier increases the accuracy of the assessment.

5This formula is largely a conceptual explanation and contains questionable assumptions (such as that risks are distributed linearly across suppliers).
The relevant subchapter, Guidance B goes into more depth on the limitations of the supply chain analysis presented.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
For financial institutions, the required location-specific data for a broad range of portfolio companies’
operational sites and supply chain sites is typically not part of their data infrastructure and collection
process and must therefore be collected additionally. As the required data are often not reported bycompanies, Guidance A explains how location-specific information on companies’ operational sites can becollected at scale using existing data solutions and proxies (e.g., asset-level data sets; corporate structure
data sets; etc.). However, we strongly suggest that financial institutions collect the required location-specificdata directly from their portfolio companies to increase the accuracy of the assessment. As for companies,
Guidance B provides support in assessing the supply chains of the portfolio companies.
Given the above mentioned difficulties in the data collection and preparation process, WWF and Climate &Company developed additional methodological guidance for companies and financial institutions which arepresented in the following subchapters:• Guidance A: Collecting location-specific proxy data on portfolio companies’ operational sites• Guidance B: Collecting location-specific proxy supply chain data

GUIDANCE A: COLLECTING LOCATION-SPECIFIC PROXYDATA ON PORTFOLIO COMPANIES’ OPERATIONAL SITES
Guidance A explains which available data sources can be used by financial institutions as proxies for therequired location-specific information on portfolio companies’ operational sites (i.e., location of company
sites, industry classification and business importance of the site). In total, four potential data sources wereidentified and analysed that could serve as proxies in the absence of corporate disclosure:• Corporate disclosure data refers to location-specific information reported by companies directly to thefinancial institution or publicly. Ideally, financial institutions obtain the required location-specific company
data from corporate disclosures, as these have the highest quality and accuracy. However, these data
points still need to be systematically disclosed.6• Asset-level data refers to data about physical assets, including attributes such as coordinates,
asset type, production capacity, productivity and age, tied to ownership information. Commercial
and open-source data providers typically offer this data (such as Asset Resolution or the
Spatial Finance Initiative (SFI)).
• Corporate structure data, also referred to as corporate hierarchy or corporate ownership structure
data, is typically a by-product of commercial data providers (e.g., Bloomberg or FactSet). It links theultimate parent company to its subsidiaries, affiliates and assets, including information on their industry
classification and location.
• City of headquarters data refers to location-specific information on a company’s headquarters (i.e.,
location and industry classification) and are available in commercial data sets. This implies that eachcompany is assessed on only one location.
• Disaggregated revenue data refers to revenue reporting by country (e.g., Firm A generates 20 per centof its revenue in country X) and by industry (e.g., Firm A generates 10 per cent of its revenue in industry
Y). This data is provided by commercial data providers (such as Refinitiv, Bloomberg or FactSet).
Next to the individual data sources, also a hybrid approach was investigated:• Hybrid approach refers to a combination of different data sources that have been identified as potential
proxies for company-reported location-specific information. As all data sources identified have different
advantages and disadvantages, combining them in a hybrid approach offers the possibility to use thestrengths and reduce or avoid the disadvantages of the individual data sources.

6

Corporate disclosure data is not investigated further, as this guidance focuses on proxy data in the absence of corporate disclosure on
location-specific company data.

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The following subchapters introduce the different proxy data sources and the hybrid approach in moredetail and discuss their coverage, advantages and disadvantages (see also Table 12 in the Appendix) andthe available data points to approximate the required WWF BRF (or WRF) input data. The subchapters also
contain workarounds in case typical data challenges, such as missing values, occur. The data sources arepresented in the order of suggested priority for data gathering (see also Figure 9).
Figure 9: Analysed data sources

BACKUP OPTIONSCITY OF HEADQUARTER(MANDATORY)CORPORATE DISCLOSUREHYBRID APPROACH(MERGING DIFFERENT SOURCES)ASSET-LEVEL DATADATA ON CORPORATE STRUCTURE
AND SUBSIDIARIESDISAGGREGATED REVENUE(EXCLUDED)HIGH QUALITY

LOW QUALITY

1. HYBRID APPROACH
While this is not an off-the-shelf data source, this approach suggests combining multiple data sources
rather than relying on only one to use each data source’s advantage and increase coverage of locationspecific company information (see Box 1 for an example).
Coverage: Subject to the number of combined data sources and sets, this approach has the mostextensive and granular coverage of industry sectors.
Advantages: Combining multiple data sources and sets addresses the various disadvantages of each data
source and leverages their advantages. In addition, industry sectors not covered in a particular data set canbe replaced by another. For example, combining different industry-specific asset-level data sets (focusing
on production facilities) with corporate structure data (containing other entities and subsidiaries linked tothe parent company) reduces the data-cleaning process and increases the depth and coverage of industry
sectors (see Box 1).
Disadvantage: Processing, cleaning and merging different data sources to build a comprehensive
location-specific data set can be time-consuming and costly, especially if the organisation does not yet
have access to the data sets. Determining the business importance of each site is also methodologicallymore challenging, as different data sources contain different data points that could be used as a proxy forbusiness importance.7
Recommendation: If capacity is available, we suggest merging different data sources. Apart from
implementation costs, this approach yields the most comprehensive list of location-specific data points as
it does not rely on only one data source and can thus fill in missing data points.

7

Example: Production facilities retrieved via asset-level data may contain information on the facility’s size in terms of annual production capacity.
Subsidiaries retrieved from corporate structure data (not necessarily a production plant) may not include this information.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

BOX 1:A HYBRID APPROACH – COMBING ASSET-LEVEL DATA WITH CORPORATE STRUCTURE DATA
Data sources can also be combined for more comprehensive coverage and to reduce or avoid thedisadvantages of individual data sources. Below, an example of the hybrid approach is presented
using the example of a cement company called Company A.
Collecting location-specific company data via asset-level data sets
The SFI data set yielded 138 production facilities for Company A, containing information about thefacilities’ location, ownership, capacity, production process and age. Therefore, all required input data
for the assessment could be found clean and ready to use.
Collecting location-specific company data via corporate structure data
Downloading data from FactSet’s Data Management Solution provided a list of 990 entities related toCompany A. However, the quality of the location and industry sector information varied and required
data cleaning:• Location of company sites: 489 entities were dropped since no information on the location wasgiven. 501 entities remained. For subsidiaries with location information at the city level, a spatial
extrapolation approach was used to identify the respective coordinates.
• Industry classification: Regarding the industry classifications, Company A’s subsidiaries werelinked to a total of 63 distinct NACE codes (with around 20 per cent linked to finance). Only
subsidiaries with an industry attribute linked to the official corporate revenue reporting were kept toeliminate locations not relevant to the business model. This yielded a list of 130 location-industry
pairs in total.
Combining the two data sets
To merge both data sets, the asset-level data set (i.e., 138 production facilities) was combined withthe locations from the corporate structure data set, which were not part of the asset-level data set(i.e., subsidiaries fulfilling other functions). This improved the number of locations associated with
Company A and provided a more complete picture of Company A and its various economic activities.

2. ASSET-LEVEL DATA
Coverage. A range of asset-level data sets have been compiled by commercial and open-source data
providers, mostly covering economic activities related to the energy, transport (automotive and aviation)and manufacturing (steel, cement, etc.) industries. Commercial providers reach the largest asset coverageof assets. However, there are still apparent data gaps, particularly from a biodiversity perspective, as highimpact industry sectors such as agriculture and forestry are often not covered (see Table 10 and Table 11
in the Appendix for an overview of the industry sectors covered). Nevertheless, asset-level data sets are
expected to expand their scope to other industries, such as beef production and pulp and paper. Appendix
Guidance A contains a non-exhaustive but comprehensive overview of available asset-level data sets fromcommercial and open-source providers.
Advantages. To assess biodiversity-related risks, it is important to know where assets are located, what
sectoral attributes they have, and to whom they belong. This is precisely the information that asset-level
data sets contain. Asset-level data can be easily aggregated at the company or portfolio level, allowing an
objective, ‘bottom-up’ approach to measuring biodiversity-related risks. Furthermore, asset-level data sets
focus on production facilities, power plants and extractives (instead of, for example, offices) that have highrelevance for nature and biodiversity.
Disadvantages. A general limitation of asset-level data sets is the industry sector coverage, as describedabove. Further, open-source data sets might be less frequently updated and can provide a false sense ofcompleteness compared to commercial alternatives. However, commercial alternatives can be expensive.
Subject to data quality, incorporating selected asset-level data sets into a financial institution’s data
infrastructure can be time-consuming: For example, if a unique company identifier to the parent company is
missing, users must map companies via text-matching or do the work manually.

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Recommendation: First, asset-level data sets focus on production facilities, power plants and extractives(instead of, for example, offices) that have high relevance for nature and biodiversity. Second, the important
contextual attributes facilitate a bottom-up approach to measuring biodiversity-related risks. Subject tothe available (financial) capacity, we suggest exploring the range of open-source providers (such as theSpatial Finance Initiative) or commercial providers (such as Asset Resolution).
Example on how to use asset-level data. Using data from the SFI, for example, allows users to quickly
identify the required input data points for the WWF BRF (or WRF) Assess Module (see Table 1): The locationof company sites is required as well as the industry sector classification, which can be retrieved directlyfrom the SFI data set8; and the data on production capacity can be leveraged to define the business
importance of a site to the overall company. Box 2 highlights how production capacity data can be used
as a proxy for the business importance of sites and explains workarounds in the absence of production
capacity data.
Table 1: Asset-level data excerpt (Source: Spatial Finance Initiative, Cement Data)
Company

Cement company**

Country

Location of company sites(lat, long)1

Production capacity2(in millions of tonnes)

Belgium

3.44,50.78

0.90

Germany

12.03,49.21<missing>A

Bangladesh

90.52,23.71<missing>A

Germany

9.74,48.37

1.90

Egypt

32.21,29.77

4.20

Required input for the WWF BRF Assess Module; 1) Capacity data is useful to determine the business importance of each site to the overall company;
A) Work-around required. To maintain a large sample size, missing values could be replaced by the median (see Box 2).

BOX 2:SUGGESTED WORKAROUND TO RETRIEVE THE BUSINESS IMPORTANCE
OF A SITE FORM ASSET-LEVEL DATA SETS
To define the business importance of each site to the overall company, the following possibilities wereidentified, subject to the quality of the data received:
1. Use production capacity as a proxy. Asset-level data sets might contain data on the annual 		production capacity as a proxy for the size of the plant. The business importance, BI, per site, i,
can then be derived using the formula below:

		∑
ANCE OF SITE
BIi =ProductionCapacityin ProductionCapacityii=1with BIi = business importance of site i

2. Equal weighting. If the solution above is not applicable, and manual screening is not an option,an equal weighting approach can be followed:
BIi =

1

N

with BIi = the business importance of site i; and N = the number of company sites identified.
However, as mentioned above, the business importance can vary for many reasons (such as
distinct spatial characteristics; the production of unique products; a high importance for customer
relationships; etc.). Whenever possible, we suggest making use of expert opinion and industry
knowledge next to quantitative indicators such as production capacity.
8

Example – The Global Database of Cement Production Assets from the Spatial Finance Initiative: Cement production can be assigned to the WWF Risk
Filter industry sector “Construction Material”. The NACE equivalent would be 23.51 – Manufacture of Cement”.

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3. CORPORATE STRUCTURE DATA
Coverage. Many commercial providers provide data on companies’ corporate structure. These data sets
provide a list of subsidiaries, affiliates, bank branches, corporate assets and other entities for each parent
company. They are available for a broad universe of companies across all industries. For example, FactSet
provides corporate structure data on almost 50,000 publicly listed companies across all industries (given a
total universe of around 60,000 publicly listed companies in the FactSet database). Table 9 in the Appendix
compares the coverage of listed companies with corporate structure data. Other data providers, such as
ORBIS, also collect and provide company structure data for small and medium-sized enterprises (SMEs),
covering several million companies. Appendix Guidance A contains a non-exhaustive but comprehensive
overview of available corporate structure data providers.
Advantages. The main advantages are the availability of the required data points (location of sites, industry
classification and business importance) in a well-structured format, including company identifiers, allowing asmooth integration due to low implementation costs, and broad coverage of listed and non-listed companies.
In addition, many financial institutions already have access to such databases.
Disadvantages. Since the data product focuses on the corporate structure, production plants might only
be part of the data set if they belong to a separate legal entity (e.g., a subsidiary). Regarding coverage,
company structure data sets yield data points for a broad range of companies. However, the number ofsites per company can vary significantly (from several hundred sites to only one). This can give a false senseof completeness regarding the company sites covered. In addition, the quality of the data retrieved canvary. For example, location or industry classification data might be missing. Missing values require either
workarounds or removal of company site from the data set.
Recommendation: This proxy is a great starting point due to its broad company coverage and low
implementation costs. When using it, analysts should be aware of its limitations. Since the data productfocuses on the corporate hierarchy, production plants might not be part of it when they do not belong to a
separate legal entity (e.g., a subsidiary).
Example on how to use company structure data. Table 2 provides an example excerpt of corporate
structure data and highlights which data points can be used for the WWF BRF (or WRF) Assess Module.
Box 3 further provides guidance on workarounds in case of missing values for the location of sites, industry
classification and business importance of the sites. For a more detailed discussion on the coverage ofcompany structure data sets and a comparison of biodiversity risk analysis under asset-level and corporate
structure data sets, see Appendix Guidance A: Case study – the importance of input data.
Table 2: Excerpt corporate structure data (Source: FactSet)

Company

Cement company

Type

Location ofcompany
site

Industry classification (NACE) A

Corporate asset<missing>B

46.73 – Wholesale of construction
materials

Corporate asset

country

23.51 – Manufacture of cement

Public company

106.81, -6.22

23.69 – Manufacture of other articles
of concrete, plaster and cement

Subsidiary

CityB<missing> B

Subsidiary

24.69,59.41<missing> B

Subsidiary<missing>B<missing> BA) The retrieved industry classification needs to be mapped to the WWF Risk Filter industry sector classification; B) Work-around required. Replace value witha reasonable assumption or drop observation (see Box 3).

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BOX 3:SUGGESTED WORKAROUNDS TO RETRIEVE THE REQUIRED INPUT DATA (LOCATION OF COMPANY SITES, INDUSTRY
CLASSIFICATION, BUSINESS IMPORTANCE) FROM CORPORATE STRUCTURE DATA SETS
Location of company sites: The quality of data regarding the location of company sites varies. The location is sometimes
available in the form of precise coordinates, sometimes only for the city, region or country and sometimes not at all. We
suggest approximating the geographical location if address, city or NUTS3 (Nomenclature of Territorial Units for Statistics)
information is available. The European Central Bank has also followed this approach (ECB, 2021). To ensure the accuracy ofthe data, we suggest dropping observations for which the data provider specifies only the country, as this does not offer therequired spatial granularity to incorporate the importance and local integrity of biodiversity.
Industry classification of sites: There can be missing industry values per subsidiary in the data sets. For data retrieved
from FactSet’s Data Management Solution (containing corporate structure data), we retrieved the subsidiaries for all
publicly listed companies and for approximately 50 per cent of the subsidiaries no industry classification was given. In thiscase, the analyst needs to decide between ‘drop or replace’. A standard method is to replace the missing value with thesample’s mean or median (in this context: replacing the missing value of the subsidiary with the corporate’s primary industry
classification). Dropping an observation reduces the sample size, whereas replacing it might introduce a bias into theanalysis (e.g., the subsidiaries might be related to industries with higher or lower dependencies/impacts).
Business importance of sites: To derive the estimated business importance of each site, three approaches were identified:
1. ‘Size variables’ per entity. If possible, the user collects ‘size factors’ for each entity under scope to define thebusiness importance of the site, BIc,i , for company, c, and site, i, by dividing the size factor of the site, i,by the sum of N size factors:
BIc,i =

Size factori∑ N Size factori
i=1

Size factors could be variables such as total assets, costs of goods sold, production capacities or other indicators that help
to distinguish more material company sites from less material ones. However, we found very low coverage of ‘size factors’ forthe (small) entities linked to the parent company. Unless the data availability changes or significant manual work is carriedout, this solution remains impractical.
2. Conditional assignment of the business importance of each site based on reported disaggregated revenue
figures. One of the main issues with corporate structure data is that the list of subsidiaries can contain industry sectors that
do not show up in the corporate revenue reporting and are hence significantly less relevant for corporate business model.
In the case of Cement Company A (see Box 1), for example, all subsidiaries retrieved via FactSet are associated with 63
distinct NACE codes, with 100 of them being linked to financial services (i.e., financing activities and not Cement Company
A’s main business activity). As a workaround, one can assign revenue conditionally based on reported revenue figures, which
are available in most commercial data sets.
Example: Company A has 50 entities associated with industry sector X and 25 with industry sector Y. The revenue reporting
tells us that Company A generates 80 per cent in industry X and 20 per cent in industry Y. For each company site assigned toindustry X, we assign the value of 80 per cent/50. For each company site assigned to industry Y, we assign 20 per cent/25.
While this solution helps to filter out less relevant activities, revenue might not be the most accurate proxy since it focuses
on revenue generation instead of production volumes and thus environmental materiality. For example, an apparel company
might have its greatest environmental impact in the Global South, where the clothing is manufactured, rather than the Global
North, where its products are sold and where most of its revenue is generated). Other variables reported by companies indisaggregated format are: assets, operating expenditures and capital expenditures (with varying quality).
3. Equal weighting. As with asset-level data, equal weighting can be applied: the business importance of site, Bl,i,for company, c, and location, i, is equally weighted according to the N entities under scope of company, c:
BIc,i =

1

N

This approach is easy to implement but can be quite misleading without appropriate data cleaning. In the example of Cement
Company A above, 20 per cent of business importance would be assigned to subsidiaries linked to financial services.

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4. CITY OF HEADQUARTERS DATA
Coverage. While ‘city of headquarters’ is not a data product itself, it refers to the most basic company
data that can either be collected manually or retrieved at scale from commercial data providers. This data
is available for millions of listed and non-listed companies across the globe.9 Orbis10 could be a promisingsource, offering a database containing financial and business information on 116 million global public andprivate companies.
Advantages: The main advantage of such data is its availability and broad coverage of listed and nonlisted companies. In limited cases, headquarter data may serve as a reasonable proxy, particularly forsmaller firms with only one site.11
Disadvantages. It can provide a false sense of completeness if a company has several physical assets
spread across the globe. The accuracy of this proxy relies on the assumption that most production is linked
to the headquarter (putting aside the supply chain).
Recommendation: Only suggested as a backup option due to severe limitations. Even though theEuropean Central Bank has used this proxy to run a large-scale risk assessment on millions of portfolio
companies, the accuracy of the proxy relies on the assumption that 100% of the corporate production islinked to its headquarter.

BOX 4:SUGGESTED WORKAROUNDS TO RETRIEVE REQUIRED INPUT DATA (LOCATION OF SITE,
INDUSTRY CLASSIFICATION) FORM ‘CITY OF HEADQUARTERS’ DATA SETS
Location of company sites: Companies can be geolocated at the address level. If the precise
address is not available, a spatial exploration approach can be used to assign proxy coordinates
derived from companies’ postal codes or nomenclature of territorial units for statistics 3 (NUTS3). Thisapproach has been used by the European Central Bank in the context of its economy-wide climate
stress test, in which the authors assessed each non-financial corporation to which a bank is exposed
based on the entity’s address level (ECB, 2021).
Industry classification of site: The primary industry classification can be retrieved from any
commercial data provider.

9

For example, the European Central Bank conducted a climate stress test analysis on 4 million companies worldwide using information on their
headquarters (ECB, 2021).
		
10 https://www.bvdinfo.com/en-gb/our-products/data/international/orbis
11 Unfortunately, this data proxy’s (in)accuracy for smaller companies is unknown. Future research could investigate when smaller firms usually start
expanding and operating in additional locations.

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5. DISAGGREGATED REVENUE DATA
Coverage. Disaggregated revenue data sets are mostly available for listed companies and can be retrieved
from a number of commercial data providers. For example, FactSet provides disaggregated revenue data
for approximately half of the 60,000 listed companies it covers.12 Table 9 in the Appendix compares thecoverage of disaggregated revenue data sets with corporate structure data sets.
Advantages. Use of this data is common as the implementation costs are low due to its well-structured
format. The data source has been used in different biodiversity-related risk assessment contexts. For
example, biodiversity ‘footprinting’ tools, such as the Biodiversity Impact Analytics-Global Biodiversity
Score, the Biodiversity Footprint for Financial Institutions and the Corporate Biodiversity Footprint, userevenue splits by country and industry sector as the starting point to link companies to models such as
GLOBIO13 to calculate companies’ impact on biodiversity (WWF, 2021). For the assessment of upstream
dependencies, Banque de France (2021) relied on similar data provided by Carbon4 Finance which provides
the sectoral and geographical breakdown of revenue for each company (Carbon4 Finance, 2017).
Disadvantages. While disaggregated revenue data sets are relatively easily applied at scale, the mostsignificant limitation is their limited spatial granularity. The country is the most granular spatial unit provided
in these data sets which makes it difficult to accurately incorporate the importance and local integrity of
biodiversity indicators into the analysis. A potential workaround could be to assess the land area-weighted
average of an indicator at country level (for example, the average of biodiversity integrity in country X). Thisis also a (planned) functionality of the WWF BRF and WRF tools.14 This approach clearly would work betterwith smaller countries and could be extremally misleading for countries with larger territories and/or highland use heterogeneity. Therefore, it is very important that the world’s largest countries (at least Russia,
Canada, United Stated, China, Brazil, Australia, India, Argentina, Kazakhstan and Algeria) are sub-divided
into sub-national divisions (admin level 1) to avoid results being averaged across the climatic and land use
variability to one value. Another major limitation is the homogeneity assumption: while we know in whichcountries and industries revenue is generated, the precise distribution per country and industry is unknown(in this case a homogenous distribution must be assumed (Carbon4 Finance, 2017)). Furthermore, revenue
is a questionable proxy for physical activity (i.e., physical assets in a country): the revenue a company
generates in country X is not necessarily correlated with its physical assets.
Recommendation: Only suggested as a backup option due to severe limitations. First, the country is themost granular spatial unit of this data source, making it challenging to incorporate the local aspects of
biodiversity into the analysis.15 Second, to determine the precise revenue distribution across industries
and countries, it must be assumed that revenues are distributed homogeneously. Third, revenue is aquestionable proxy for physical assets in a country. However, due to low implementation costs, thisapproach may suffice as a first screening to determine risks at sectoral level (without considering theimportance and state of biodiversity integrity).

12 Refinitiv provides disaggregated revenue data using the Standard Industry Classification. Bloomberg makes use of its own BICS taxonomy.
		
FactSet Hiearchy uses its own Hierarchy classification, covering 7,000 industry sectors and product groups when fully expanded.
13 The GLOBIO4 model generates geospatial data sets with scenario results for land use and mean species abundance (MSA) for plants, warm-blooded
vertebrates (birds and mammals) and an overall MSA.
14 The WWF WRF includes land area-weighted averages for each indicator, risk category and risk type at country level. Risk scores are computed as thearea-weighted average values based on the global data set, using the average industry weighting. Ranking is a simple rank, where 1 represents thecountry or territory of least risk. While these are not part of the risk assessment, they can be used for to compare countries and territories regarding theirrisks and respective rankings to use in a first screening and scoping of sites. This functionality will be included soon in the WWF BRF as well.
		
15		This is only feasible by assessing the land area-weighted average of an indicator at country level (for example, the average of Ecosystem Intactness incountry X).

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GUIDANCE B: COLLECTING LOCATION-SPECIFICPROXY DATA ON SUPPLY CHAINS
In addition to the collection of location-specific data on portfolio companies’ operational sites (Guidance A),
it is important to also collect data on portfolio companies’ supply chain sites. Guidance B explains in moredetail which data sources and tools can be used by companies and financial institutions to approximate thebiodiversity-related risks within supply chains.
Assessing biodiversity-related risks throughout the supply chain is not very different to assessing firstorder risks conceptually nor in terms of required input data. The additional layer of information required is
the importance of the supplier-customer relationship. The formula below illustrates that relationship: thebiodiversity-related supply chain risk score per risk type, r, of a Firm A is the sum of the first-order risk score
per risk type (physical or reputational) of all n suppliers, weighted by a weighting factor, w, per supplier, i,which denotes the importance of the supplier-customer relationship:nSupply chain risk scoreA,r =∑ wi x 1st order risk score r,ii=1

Two approaches were identified and investigated as potential proxies forlocation-specific supply chain information.
1. Company-specific supply chain data (Approach I): This approach is similar to the data collection
process for direct operational sites. The only additional data point required is the weight of the suppliercustomer relationship. The approach looks as follows:1.

A list of suppliers per company is collected and a weighting per supplier-customer relationship isderived (ideally through company reporting or otherwise via third-parity data providers); and2. The required data points as described in Guidance A are collected per supplier (location of sites,industry classification, business importance).
Approach I is conceptually simple but suffers from poor data availability: it is difficult to obtain acomprehensive list of suppliers per company of interest (Climate & Company, 2021)16. Yet companies havean inherent interest to gather the most relevant supplier data in order to manage their supply chain risks.
Therefore, data collection through direct engagement with the supplier should be the first choice for real
economy companies. An example would be a clothing retailer finding out and documenting the locationsof their textile manufacturing sites, and then proceeding down the supply chain to find the location of their
manufacturer’s dye and textile providers and the locations of production of textile raw materials (e.g., cotton).
2. Input-output models (Approach II): This approach makes use of existing IO models and looks as follows:1.

The location-specific data of each company (location of sites, industry classification per site, 		business importance) are aggregated to the country-sector level to make it compatible 			with IO models;

2. The chosen IO model is applied to identify the upstream industries (and countries) associated withthe country-sector split (obtained under Step 1); and3. The importance and local integrity of biodiversity aspects is assessed for the upstream
industry-country pairs.

16 Looking at a sample of 1156 publicly listed companies headquartered in the EU, we found that 85% of the sample only disclosed less than 10 suppliers(based on FactSet Supply Chain Data). Reasons could be a low number of suppliers, an incomplete data extraction by FactSet, a low level of disclosure, ora combination of different factors. Given that the sample consists of listed firms in the EU, we conclude that retrieving comprehensive firm-specific supply
chain data from data providers is still difficult (Erdmann, Hessenius, & Yahsi, 2022).

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If a company or financial institution does not have access to supply chain data, they could use IO model
outputs as a proxy. Take for example a car manufacturer in Germany. A financial institution conducting ananalysis might not know where the manufacturer sources their steel and additionally where the ore used forthat steel comes from. IO tables can tell the user that X% of German steel imports come from China and
Y% of Chinese iron ore imports come from Australia. This gives a locational proxy for the whereabouts of
the German car manufacturer’s supply chain.
However, incorporating the importance and local integrity of biodiversity (Step 2) requires precise locations(coordinates), which is not given as IO models operate spatially at the country-level. Even though there arepotential workarounds,17 we currently suggest using them with extreme caution, since the workarounds
come with high uncertainties and have not been tested thoroughly. Nonetheless, the second sub-step canalready be leveraged to derive biodiversity-related upstream exposure scores for each company of interest(see Box 5).
Figure 10 visualises and compares the two approaches and their respective steps.
Figure 10: Comparison and complementarities of Approach I and Approach II in the collection of proxy supply chain data

FIRM A
APPROACH #1 (COMPANY DATA)APPROACH #2 (IO MODELS)

1: IDENTIFY UPSTREAM SUPPLIERS

1: PREPARE COUNTRY-SECTOR SPLITS (TO APPLY IO MODELS)

LIST

IMPORTANCE OF RELATIONSHIPCountry

Industry

SUPPLIER #1%/$

Country A

Food & Beverages

75

...

...

Country B

Paper & Forest

15

Country C%/$

Offices & Professional
Services

10

SUPPLIER #N

Business
Importance2: IDENTIFY UPSTREAM SECTORS

per supplier

2: COLLECT REQUIRED INPUT DATA PER SUPPLIER(RE-RUN ADDITIONAL GUIDANCE A ON DIRECT OPERATIONS)LOCATION

INDUSTRY

IMPORTANCE

SEE “GUIDANCE A”

SEE “GUIDANCE A”

SEE “GUIDANCE A”

PER COUNTRY-SECTOR SPLIT AN “INPUT” MATRIX AT
COUNTRY-SECTOR LEVEL IS RECEIVED

3: RESHAPE DATA TO RETRIEVE REQUIRED INPUT DATA FOR FIRM A

UPSTREAMLOCATION“COUNTRY” IS THE
MOST GRANULARSPATIAL UNIT

UPSTREAM
INDUSTRY

BUSINESSIMPORTANCEPART OF IO MODELS

THE “INPUT
MATRIX” CONTAINSA NUMERICWEIGHTING

17 One could, for example, extract a randomly chosen coordinate (or the centre) within a country and assess the importance and local integrity of biodiversity
indicators based on this coordinate. However, this introduces a severe bias to the analysis. Another option is to assess the average importance andintegrity of an indicator at the country level (for example the average integrity of surface water in country X). This might be accurate for smaller countries(such as Luxembourg) but could be completely misleading for larger countries (such as Russia).

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While Approach I, using firm-specific supply chain data, is the most accurate, financial institutions
and companies are not (yet) able to easily gather information at scale, as firm-specific supply chain datais patchy and largely incomplete (Climate & Company, 2021). Therefore, building on existing work (Banquede France, 2021), Approach II, using IO models, offers a second complementary, less accurate, butmore practical alternative, based on sector- and country-level averages. IO models are generally derived
from supply and use statistics from national account databases and help to show supply chains between
industries and countries.
The following subchapters introduce the two approaches identified in more detail and discuss their coverage,advantages and disadvantages as well as available data points to approximate the required WWF BRF input
data. The subchapters also contain workarounds to address typical data challenges, such as missing values.
It ends with an overview of the comparison and recommendations regarding the available approaches.

1. COMPANY-SPECIFIC SUPPLY CHAIN DATA (APPROACH I)
Table 3 below describes the required data structure to assess the supply chain-related risks faced by thecompany of interest.
Table 3: Collecting data on a portfolio company’s supply chain sites
1: Identify suppliersCompany nameCompany A

Relevance

2: Collect required input data per supplier (seeGuidance A)

Supplier

Importance of that
relationship

Location ofcompany sites

Industryclassification

Business
Importance

Supplier 1

0.5

Lat/long

Industry A%

Supplier 1

Lat/long

Industry A%

Supplier 1

Lat/long

Industry A%

Lat/long

Industry B%

Supplier 2

Lat/long

Industry B%

Supplier 2

Lat/long

Industry B%

Required to derive supply chain risks
for Firm A

Required as input data to the WWF BRF tool

Supplier 2

0.5

1. Identifying a company’s suppliers, including a weighting score persupplier-customer relationship
Identifying the list of suppliers
Ideally, the user receives a list of supply chain relationships for the company of interest (Firm A) alongwith a quantification of their (financial) importance. Different third-party data providers, such as FactSet,18
Bloomberg,19 or Refinitiv,20 provide supplier-customer relationship data in a structured format. Data iscollected by leveraging information based on the target company disclosing its suppliers.21 One way tomake the data set more complete is to add relationships based on reverse disclosures.22

18 Data product: FactSet Revere Supply Chain Relationships. FactSet data has been used for testing purpose. Therefore, data quality (coverage andaccuracy) might differ subject to the chosen provider.
19 Data product: Bloomberg Global Supply Chain Data.
20 Refinitiv provides more information here.
21 For example, Nestle decided to disclose a list of its suppliers, covering 95 per cent of the company’s annual sourcing of raw materials.
22 While Nestlé might only specify a limited number of suppliers, many more (often smaller) companies will disclose Nestlé as their customer. However, there
is no guarantee that the list of supplier relationships is complete and, in many cases, is likely far from it.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
Identifying the importance of each supplier
A logical measure of the financial importance of the customer-supplier relationship is the revenue
dependency of the company on each of its suppliers as a fraction of the total revenue of that company.
There are three potential ways to integrate this measure, in decreasing order of accuracy23:• Solution 1 – Disclosed data: Third-party providers collect financial figures of supplier-customer
relationships as part of their supply chain data products (example: Firm A is a customer of Firm B,
generating X% of Firm B’s revenue). However, this data is rarely disclosed: for a sample of 1461 listedcompanies headquartered in the EU, we only retrieved (incomplete) financial figures for 13% of thesample24.
• Solution 2 – Estimates: In the absence of disclosed data, the user could derive estimated weights
by determining the relative importance of the supplier-customer relationships25. FactSet, for example,
provides an ordinal relevance ranking to fill the gaps mentioned in Solution 1. Based on FactSet Supply
Chain Relationships data, a proprietary algorithm scores each relationship based on some hand-pickedfeatures (i.e., mutual disclosure, industry type, geography, etc). Users could also attempt to rank such
relationships themselves. They would then have to transform this ranking into a normalised list of weights
by making an assumption on how financial importance behaves as a function of the firm’s place in theordered list of suppliers. An example with four suppliers (S1, S2, S3 and S4) and an assumed linear
relationship (starting with a minimal financial importance of 0.1), would be to assign the weights 0.4, 0.3,
0.2 and 0.1.
• Solution 3 – Equal weighting: The user equally weights all identified suppliers of a specific company.

2. Collecting location-specific proxy data on a supplier’s
operational sites (see Guidance A)
Feeding the data on the portfolio companies’ supply chain sites into the WWF BRF (or WRF) Assess
Module, requires location-specific data on the portfolio companies’ suppliers. This proxy data can begenerated in a similar manner to that used for a company’s operational site. This is further explained in
Guidance A.

2. INPUT-OUTPUT MODELS (APPROACH II)

In the absence of comprehensive, firm-specific supply chain data, a well-established way to gain insights
into the supply chain links between different industries of the economy is to use IO models (see Figure 11).
This has already been applied by some actors in this field, including Banque de France and the World
Economic Forum (WEF) 26 and could be used as a bridging solution (until supply chain data is collected or
becomes available).
Input-output tables are generally derived from supply and use statistics from national account databases,
and thereby rely on sectoral averages at state level rather than relying on more granular firm-specific data.
In more recent state-of-the-art IO models, international databases as well as trade statistics arealso incorporated.

23 These methods also to apply to other potential metrics such as % of cost of goods or amount paid for purchased goods.
24 To get an idea on the order of magnitude, we assessed a sample of 1461 listed companies headquartered in the EU. For 87% of the sample (1261
companies) we retrieved no financial figures on supplier-customer relationships at all. For the remaining 13% at least some financial figures were stated by
the source companies (Source: based on data from FactSet’s Supply Chain Relationship database in 2022; reverse disclosures were not considered).
25 Example: a steel company is likely to be a more important supplier for an automotive company than, say, a supplier producing office materials.
26 While an extensive overview is beyond the scope of this chapter, it is informative to look at some relevant existing approaches. Firstly, the WEF and PwC
Report Nature Risk Rising (WEF, 2020) compares direct and indirect dependencies across industry sectors using a preliminary version of ENCORE’s
dependency ratings. The authors used a global Multi-Region Input Output (MRIO) model to identify the sectors in the supply chain which were thenweighted according to high, medium and low dependency. Secondly, the paper by Svartzman et al (2021): A ‘Silent Spring’ for the Financial System?
(Banque de France, 2021) explores biodiversity-related financial risks for the French financial system (including transition risks as well as direct andindirect/upstream dependencies) using a sectoral approach which, in turn, was inspired by the pioneering study by van Toor et al. (2020). The study findsthat 42 per cent of the value of securities held by French institutions are highly or very highly dependent on at least one ecosystem service. Finally, the
Science Based Targets Network’s business guide also contains an upstream/downstream dependency/impact rating, which builds on linking ENCORE
with a specific MRIO called EXIOBASE (SBTN, 2020b).

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30

Figure 11: Schematic depiction of the IO model approach(GEOLOCATED) COMPANY-LEVEL DATARESULT OF GUIDANCE A
1: PREP WORK -- DERIVE A LIST OF
SECTOR-REGION PAIRS FOR EACH
COMPANY

COUNTRY-SECTOR PAIRS PER COMPANY OF INTEREST
SectorCountry

Importance

X

Y

%

...

...

...

Y

Z

%

2:IDENTIFY THE UPSTREAM VALUECHAIN AT COUNTRY- AND SECTORLEVEL (BY APPLYING IO MODELS)

APPLY CHOSEN IO MODELCOUNTRY-SECTOR INPUT VECTOR PER PAIRCountry I...

Country N

Sector I...

CAN BE USED FOR FURTHER ANALYSIS

Sector N

The resulting multi-regional tables describe the sectoral dependencies between countries (or sets ofcountries, depending on the spatial granularity). In other words, the supply relationships among the different
industries can be shown (Value Balancing Alliance, 2021). Of particular importance is the table containing
the input-output coefficients (known as the A matrix). The input coefficients, aij, can be interpreted as theshare of costs in monetary terms for intermediate inputs of industry, i, to create one unit (e.g., one dollar) ofindustry, j. There are various multi-region input-output models (MRIOs) that could be used. For an overview,
see Table 15 in the Appendix.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

31

Limitations• Inherent assumptions of IO models: The estimated relationships of IO models between the (sector,
region) pairs are obviously not perfect, and depend on assumptions inherent to IO models. A discussionof inherent IO model limitations is beyond the scope of this paper, but we refer the interested reader toany standard text on the issue.27• Based on averages: IO models are based on averages at the country and industry level. This means
that, assuming the IO accurately represents on average the share of costs in monetary units of anintermediary industry on some other industry (e.g., of German car manufacturing on Polish steel
production), it could still provide a very bad estimate for any specific company, especially if thatcompany does things in a way that is out of the ordinary. So, the second obvious limitation is that the
specific supply chains for each company are not taken into account and are instead assumed to beequal to the average way that supply chain is structured in the wider economy. Furthermore, using
country aggregates does not allow investors to identify and address specific high-risk locations acompany might have in its supply chain.
• Static snapshot: The IO models are a static snapshot in time, as the inter-industry linkages are derived
for a certain year. The 10th version of the IO database derived from the Global Trade Analysis Project,for example, would illustrate the inter-industry and inter-country relationships for 2004, 2007, 2011 and2014 (Aguiar, Chepeliev, Corong, McDougall, & van der Mensbrugghe, 2019).
• Mapping of different classifications: mappings between the industry and region classifications of
the initial (sector, region) pairs for each company are not always perfect. The severity of the mismatch
depends on the exact standard or method used to determine the former, as well as the IO model
chosen. For instance, mapping the NACE industry standard (which comprises 615 industries at themost granular level) to the industries specified by the IO model EXIOBASE3 (with 163 industries, see
Table 15) will result in a loss of information due to the lower granularity of the latter. Also, the problem of‘one-to-many’ mappings can occur (e.g., if one NACE industry sector can be assigned to two or moreindustry sectors specified by EXIOBASE3) which requires further assumptions.

1: Prep work – Derive a list of sector-region pairs for each company
IO models work on a country-sector logic: for every monetary unit generated in, say, Germany (country)
and car manufacturing (sector/industry), the IO models provide an extensive matrix from which countries
and industries the monetary input is derived (say, 5 per cent from Polish steel manufacturing etc.).
Therefore, for each company that is being assessed, the importance of each sector-country split isneeded to map the company activities to an IO model. This is obtained by building on the proxy datacollection in Guidance A (which includes a list of location-industry-importance triplets per company).
Figure 12: Illustrating prep work stepCompany
Name

Location of company
site (Step 1)25

Lat, Long (Country A)

20
Food and beverage
production

15

Lat, Long (Country A)

10Lat, Long (Country A)

5Lat, Long (Country B)

Paper & Forest ProductProduction

Lat, Long (Country B)
Lat, Long (Country C)
Lat, Long (Country C)

Company
Name

Company A

Business importance percountry-industry split

Country

Industry

Country A

Food and beverage
production

75

Country B

Paper & Forest
Product Production

15

Country C

Offices &Professional
services

105Lat, Long (Country B)

27

Business importanceof site % (Step 1)

Lat, Long (Country A)

Lat, Long (Country A)

Company A

Industry  classificationof site (Step 1)

5
5

Offices &Professional services

One particularly good introductory text is Miernyk (1965).

55MAPPING TO COUNTRY AND INDUSTRY CLASSIFICATIONOF IO MODEL REQUIREDWWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONIn practice, the data collection from Guidance A needs to be transformed from ‘location-industry splits’
into ‘country-industry splits’ (see Figure 12), which need to be mapped to the corresponding country andindustry classification of the selected IO model. An alternative solution that also works at scale (due to broad
coverage) would be to define country-sector splits using the data source on disaggregated revenue splits(see Guidance A - 5): extracting disaggregated revenue splits at country and industry level from commercial
data providers and applying the homogeneity assumption yields a comprehensive list of country-sector splits
per company of interest. This has been used by Svartzman et al. (2021) and Lepousez et al. (2017).

2: Identify the upstream supply chain at the country and industry level byapplying IO models
As mentioned above, the A coefficient matrix provides the quantitative basis for determining the relationships
between different industries and regions. For each country-industry split, the IO model yields an A matrixof size (Nindustries x Nregions ) where Nindustries is the number of industries distinguished by the IO model,
and Nregions the number of regions. The input coefficients, aij, can be interpreted as the share of costs inmonetary terms for intermediate inputs of industry, i, to create one unit (e.g., US$ 1) of industry, j.
Therefore, the IO model (almost) yields the required data points per supplier:• The location of company “sites” is identified at the country-level (instead of the precise
longitude/latitude information);• The industry classification is derived from the IO model’s industry classification; and• The business importance of each location-industry pair is identified through the input coefficients, aij,
(instead of the importance per location-industry pair of own sites, it is the importance of the upstream
industry-country pair).
This information can be leveraged to generate a helpful interim result about the supply chain-related risks
per company of interest (see Box 5). To continue, the user needs to prepare the required data structure
for Step 2, which requires precise coordinates. This is currently problematic and requires complex
workarounds, discussed in the next paragraph.

3: Preparing data input (to include the location-specific dimension)
The fundamental difference to Approach 1 (firm-specific supply chain data) is that precise longitude andlatitude information cannot be provided since the most granular spatial unit of IO models is at countrylevel. A potential workaround could be to assess the land area-weighted average of an indicator at country
level (for example, the average of Ecosystem Intactness in country X). This clearly would work better withsmaller countries and could be extremely misleading for countries with larger territories and/or high land
use heterogeneity. Therefore, it is very important that the world’s largest (at least Russia, Canada, UnitedStated, China, Brazil, Australia, India, Argentina, Kazakhstan and Algeria) are sub-divided into sub-national
divisions (admin level 1) to avoid results being averaged across the climatic and land use variability to one
value. The WWF WRF includes land area-weighted averages for each indicator, risk category and risk
type at country level. Risk scores are computed as the area-weighted average values based on the global
data set, using the average industry weighting. Ranking is a simple rank, where 1 represents the country
or territory of least risk. While these are not part of the risk assessment, they can be used for to compare
countries and territories regarding their risks and respective rankings to use in a first screening and scopingof sites. This functionality will be included soon in the WWF BRF as well.
To the best of our knowledge, using IO models to assess supply chain risks has not been combined withcountry-level importance and integrity values of biodiversity. This is time consuming and builds on thequestionable assumption that country level importance and integrity values are an accurate proxy forlocation-specific importance and integrity values. However, there is work in progress to make IO models
more spatially granular.28

28 See, for example: Croft, S. A., West, C. D., & Green, J. M. (2018).

32

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION33

BOX 5 :DERIVING UPSTREAM EXPOSURE SCORES PER COMPANY WITHOUTINCORPORATING THE LOCATION-SPECIFIC DIMENSION
The output of IO models can be used to compute the upstream exposure score per sector-country split (and ultimately percompany). After splitting corporate location-level data into sector-region pairs and applying an IO model, the user produces acountry-sector input matrix per country-sector split.

Company
Name

Company A

Country

Industry

Sector 1

Business
importanceCountry A

Food and beverage production

75

Country B

Paper & Forest Product Production

15

Country C

Offices & Professional services

10(IO model)...

Sector N

Country 1...
Country N
Sum per sector

∑

∑

∑

Concordance table to WWF BRF
sector classification

Sum per sector

∑

∑

∑

Multiplied by BRF industry weighting perrisk indicator

Sector 1...

Sector N

Risk Indicator1

(0-5)

(0-5)

(0-5)

...

(0-5)

(0-5)

(0-5)

Risk Indicator N

(0-5)

(0-5)

(0-5)

After mapping the industry classification of the IO model to the WWF Risk Filter industry classification, the user can calculate
the importance of each BRF upstream industry to the main company, which can be interpreted as the weight per industry, s,
ws (i.e., the value of industry s’s production, integrated into the value produced by the main industry). To retrieve the overall
exposure score per upstream industry sector, the user derives the direct exposure score DEs, per upstream industry, s, by
taking the mean across all n industry materiality ratings per risk indicator, RI, resulting in a value between 0 and 5 (the scale of
the BRF industry materiality ratings).
N RIi
DEs = ∑ i=1 nInterim result (example): One upstream industry of company A’s country-sector split “Country A – Food and beverage” isUpstream Industry 1. For Upstream Industry 1, the generic industry materiality ratings are known. The average of all industry
materiality ratings for Sector 1 makes the exposure comparable across biodiversity indicators and ecosystem services.
To retrieve the aggregated, upstream supply chain exposure score per country-sector split, UE, (for example country A, Foodand beverage), one weights, DEs ,by its weight, ws.
N

UE = ∑ ws x DEs
S=1

This yields an upstream score per country-sector split, which can be aggregated to the company-level using the weight ofeach country-sector split (yielding a score between 1 and 5, where 5 stands for high potential exposure to biodiversity-relatedrisks in the upstream supply chain). The results could also be disentangled and presented by risk indicator.
Sources: Adapted from Banque de France (Banque de France, 2021), partly building on the BIA-GBS methodology (Biodiversity Impact Analytics powered by
the Global Biodiversity Score; developed by Carbone4 and CDC

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONSTEP 2: ASSESSING
BIODIVERSITY-RELATED RISKS© WWF - US / Irene Magafan34

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

35

After scoping the assessment (Step 0) and collecting location-specific company and supply chain data
(Step 1), the required input data is fed into the WWF BRF (or WRF) Assess Module which calculates
biodiversity-related (or water-related) physical and reputational risk scores for each provided site.
Biodiversity-related risks arise from companies’ dependencies and impacts, in combination with theimportance and local and global state of biodiversity integrity. Subsequently, the WWF BRF Assess Module
combines the sites’ industry materiality rating (0A) and the biodiversity importance or integrity rating basedon sites’ geographic location (0B) into a scape risk score for each of the 33 different biodiversity importanceand integrity indicators (see ‘Scape risk calculation’ below for more detail).
To capture the complexity of the topic, a comprehensive structure is needed to organise and structure thebiodiversity indicators to ease interpretation and translate the assessment to business relevant terms. The
WWF BRF employs a risk hierarchy to group indicators in thematically relevant risk categories and risk
types that align with major frameworks, such as TNFD or SBTN (see ‘Grouping indicators using biodiversity
risk hierarchy’ below for more detail).

Step 2A: Calculating scape risk
Scape risk is a risk score, on a scale of 0 to 5, assessing a specific aspect of biodiversity at a specific site
for a specific industry (see Table 4). It is determined by two factors: (a) industry materiality (e.g., paper mills
are very highly dependent (5) on timber availability); and (b) the local state of biodiversity aspects (e.g.,indicator ‘Limited Timber Availability’ is rated as being at very high (5) if there is no timber available). For
each site, all 33 indicators of physical and reputational risk are calculated by the WWF BRF tool.
Mathematically, this can be done by taking the arithmetic mean of the industry materiality (impact/
dependency) rating, IM, for indicator, i, and industry sector, s, and the indicator assessment, IA, forindicator, i, at site (location), l:
Scape risk i,s =IMi,s + IA i,l2

Combining both via the arithmetic mean captures two assumptions: 1) the higher the industry materiality
rating, the higher the potential risk exposure, and 2) the higher an indicator’s risk score, the higher the
potential threat. This means that the scape risk for each indicator (‘Limited Timber Availability’ in theexample below) will vary depending on the location and the industry classification of a company location,
as illustrated in Table 4.
Table 4: Scape risk calculation – example
Location ofcompany site

Integrity rating for
Limited Timber
Availability

Industry-specificweightings of Limited
Timber Availability

Scape risk

Nashville, TN, USA

1 - Very low risk

Offices andprofessional
services

1 - Very low dependency

1

Nashville, TN, USA

1 - Very low risk

Paper and
forest productproduction

5 - Very high dependency

3

Reggane, Algeria

5 - Very high risk

Offices andprofessional
services

Not applicable for this industry

0Reggane, Algeria

5 - Very high risk

Paper and
forest productproduction

5 – Very high dependency

5

Industry

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

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Step 2B: Calculating site-level risk
In a biodiversity risk analysis, a large number of biodiversity indicators are required to capture the system’s
complexity. However, using many indicators poses problems for aggregating risk over a single company
site, as outliers and indicators of minor business importance can influence the final aggregation and lead tounder- or overstatement of risk. It is therefore important to employ a risk hierarchy that groups indicators inin thematically relevant risk categories and risk types and an aggregation scheme that will highlight aspectsof biodiversity risk that are business important without being diluted by outliers or indicators of minor
business importance.
The current WWF BRF tool employs a risk hierarchy that groups indicators (LEVEL 3) in thematically
relevant risk categories (LEVEL 2), and only then to risk type (LEVEL 1) (see Figure 4). This has a numberof advantages. First, when using a large number of different indicators to assess biodiversity risk, theinfluence of each single indicator is reduced. That means that if all indicators were aggregated into one
single number, a few high-risk indicators could be averaged out with a large number of low-risk indicators.
Grouping the indicators into thematically relevant categories reduces this averaging risk, because even one
high-risk indicator within a risk category will influence and therefore be visible in the risk category score.
Secondly, thematically relevant risk scores and risk types can give users a better overview of why a site
might experience high risk, without having to understand the risk assessment at the indicator level. Third,such grouping is aligned with other reporting standards (e.g., TNFD), which do not require reporting at theindicator level, but only, for example, for physical risk.
The 75th percentile method is used both in the aggregation of indicators to risk categories and fromrisk categories to risk types. Percentiles as a measure takes the value of the nth percentage numberin the distribution.29
Using the 75th percentile emphasises high-risk scores. As shown in Table 5, this method emphasises theright tail of risk distributions (higher risks). Although the mean (average) of indicators for Company A islower than that of Company B (a mean of 2.2 compared to a mean of 3), the high scape risk of Indicator 4
and Indicator 5 are emphasised. This method helps to inform companies that certain sites might be highly
exposed to biodiversity-related risks that could be integral to business operations. This is important,because even a single high-risk issue could result in considerable damage to a business or its supply chain.
The omission of a high-risk score should therefore be avoided. For example, a water utilities site might bebelow average risk for most indicators, such as soil condition and invasive species, but their core business
relies on indicators such as water availability and water condition. Though the calculation of scape risktakes this importance into account, these extremely relevant risks would be lost in mean aggregation. Using
the 75th percentile method helps to emphasise the important exposures, such as water availability and watercondition in the case of a water utility site location.
Once the risk assessment has been conducted, a company can identify the areas of operations andgeographical areas in which it has the highest risk. Those operational and geographic areas should thenbe subject to ‘deep dives’ to understand the specific local context better and identify response actions to
reduce those risks (e.g., by conserving and/or restoring biodiversity in those specific geographies).
Table 5: Illustrating aggregation method “Percentile”
Aggregated scape risk per indicator in risk category
Indicator 1

Indicator 2

Indicator 3

Indicator 4

Indicator 5

75thPercentile

Company A

1

1

1

4

5

4.5

2.2

Company B

3

4

2

3

3

3.5

3

29

For example, the 75th percentile of a distribution with eight observations would be the value of the sixth observation.

Mean

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONSTEP 3: AGGREGATING RISKS TO
THE COMPANY AND PORTFOLIOLEVEL (GUIDANCE C)© Vicki Sahanatien / WWF

37

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION38

It is important to note that not only does the WWF BRF Assess Module provide a scape-level assessment,
it also offers overview assessments per company or group.30 It does not, however, create an aggregate
risk score per company, group or portfolio. Instead, it shows the number of sites that fall within the fiverisk-score classes. WWF and Climate & Company, therefore, developed further guidance on aggregating
the results to the company and portfolio level.
This section explains in more detail how the output data from the WWF BRF Assess Module can be
aggregated to serve different use cases and user groups. It predominantly targets financial institutions, as
the aggregation is particularly relevant to compare portfolios and portfolio companies at scale. Physicaland reputational risk scores are aggregated separately, as they form distinct categories of risk: Physicalrisk is pertinent to the supply-side dimension of a company’s production while reputational risk is foundmore on the demand side.31 The suggested aggregation steps are outlined in Figure 13.
Figure 13: Illustrating aggregation steps
Physical Risk
Risk
Indicator 1

Risk
Indicator 2...

Reputational Risk
Risk
Indicator n

Risk
Indicator 1

Risk
Indicator 2

...

Risk
Indicator n

Step 2A: Calculating Scape Risk
Location Risk 1

Location Risk n

Location Risk 1

Location Risk n

Industry Risk 1

Industry Risk n

Industry Risk 1

Industry Risk nCompany 1

Reputational risk
score of site nCompany nPhysical risk
score of site n

STEP 3A: AGGREGATING TO THE COMPANY LEVEL (ACROSS COMPANY SITES)
Step 2A and 2B yield a risk aggregate score for each company site, per LEVEL 2 ‘risk categories’
and Level 1 ‘risk types’.
Every location-specific risk contributes to a company’s total potential biodiversity-related risk. One canmeasure the business importance of a site as a percentage. This can be done following Guidance A:
Collecting location-specific proxy data on portfolio companies’ operational sites. A final company risk score
can then be computed by weighing each site’s grouped scape risk by its business importance. The sum of acompany’s business importance should equal 1. The calculation is shown in the formula below.
An aggregated company risk indicator, RI, per company, c, and per LEVEL 1 risk type, r, can becomputed by multiplying the business importance, BI, per site, i, with the aggregated/grouped site
risk of site, i, per risk type, r.
nRIc,r = ∑Site Riski,r x BIi’iwith N= total numberof sites per company

30 The WWF BRF allows users to assign sites to groups to analyse a subset of sites. The groups can be freely defined by the user. Examples include:
groupings by supply chain management (classes; groupings by geography; groupings by commodity.
31 Physical risks might increase production costs or disrupt the production process (supply side). Reputational risks are rather demand-driven as
consumers or investors might stop purchasing goods or stop investing in the company.

Step 3B: Aggregating to the portfolio level

Reputational risk
score of site 1

Portfolio

Step 3A:
Aggregating to thecompany level

Step 2B: Calculating site-level risk
Physical risk
score of site 1

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

39

As companies can operate globally, each one of their sites could be exposed to considerably different
biodiversity-related risks. Additionally, each site has a different importance to the functioning of thecompany’s operations. By aggregating each site risk score weighted by its business importance, one candetermine a company’s overall physical and reputational risk score. For example, a telecommunications
company has three sites spread across two different countries. Each site has a distinct physical (andreputational) biodiversity risk score based on its location and industry-related dependency or impact,
for example, a physical risk score of 5, 4 and 1, respectively. Each site also has a distinct business
importance, for instance, because a certain site is where an important radio tower node or headquarter
is located. The business importance is quantified per site as a percentage that together over all sites
sums up to 1, for example, 0.6, 0.2, 0.2. By multiplying and adding the business importance and the sitespecific risk scores, an overall physical risk score for the company can be determined: a physical risk
score of 4 (i.e., 5x0.6 + 4x0.2 + 1x0.2 = 4)

STEP 3B: AGGREGATING TO THE PORTFOLIO LEVEL (ACROSS COMPANIES)
Within a portfolio, the weight of an individual asset or company is derived from the percentage of its value
compared with the total portfolio value. An aggregated risk score for a portfolio can be calculated bymultiplying individual companies’ risk scores by their portfolio weight (see below).
An aggregated portfolio risk score, P, for each LEVEL 1 risk type, and portfolio, p, can be computed bymultiplying the portfolio weight of a company, Wc, to the aggregated company risk score, RI, for company,c, and per risk type, r.
n

Pp,r = ∑ RIc,r x Wccwith N= total number of companies in the portfolio
In practical terms this aggregation could also be applied to a portfolio of assets. Imagine a portfolio thatconsist of a mix of equities and bonds. Each equity and bond can be viewed as a company and is thefinancial realisation to an assortment of different site-specific locations. Using the aggregation methods
described in the previous step allows us to find asset specific risks. Each one of these assets has a
specific market value that all together form the portfolio’s total value. To find the portfolio’s total risk thesum is taken of each asset’s risk is weighted by their value as a percentage of the portfolio’s total value.

STEP 3C: AGGREGATING SUPPLY CHAIN RISKS
While the subchapters above have focused on deriving first-order risks at the site, company andportfolio level (i.e., risks stemming from portfolio companies’ own operations), it is also possible toaggregate upstream supply chain risks across companies and portfolios (i.e., risks stemming from theportfolio companies’ suppliers). With the additional data point on the supplier-customer relationships,
the assessment of biodiversity-related risks throughout the supply chain is conceptually not different toassessing first-order risks. The additional layer of information is the importance of each supplier-customer
relationship. The biodiversity-related supply chain risk score for risk type, r, of a Firm A is the sum of thefirst-order risk score for each risk type (physical or reputational) of all n suppliers, weighted by a weighting
factor, W, for supplier, i, which denotes the importance of the supplier-customer relationship:nSupply chain risk scoreA,r = ∑i=1

Wi x 1st order risk score r, iWWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

LIMITATIONS OF THE WWF BRF© Bento Viana / WWF Brazil

40

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONThis section describes the current cross-cutting limitations of the methodology and suggests
avenues for future research.
Potential risk instead of actual risks
The WWF BRF assessment is currently, in a nutshell, based on the location of company sites, their industry
classification and corresponding industry materiality rating, and the local state of biodiversity. For biodiversityrelated risks to become material to a business depends on three factors: 1) the likelihood of threats emerging(i.e., the local state of various aspects of biodiversity), 2) the degree of a company’s exposure to these threats(i.e., the industry materiality) and the vulnerability of the company to the threat (i.e., the company’s preparedness
and response) (WWF, 2022a). The current BRF assessment covers the first two factors, but not the last.
Incorporating company preparedness and response is crucial to move from the potential to the actual risks thata company faces. The resulting physical and reputational risk scores should therefore be carefully interpreted
as potential risks rather than actual risks. The planned inclusion of an operational assessment will better allow
companies and financial institutions to fine-tune industry materiality ratings based on a detailed questionnaire.
Risk score rather than a monetary valuation of risks
Financial risk refers to the risk inherent to an investment, where there is a quantifiable amount of money that
could be lost. The current assessment does not produce a financial risk metric (such as Value at Risk). This will
require further work, in particular on damage functions that translate the local state of biodiversity into negative
impacts on a company’s business model.
Assessment of biodiversity-related opportunities not yet included
The WWF BRF currently provides an assessment of biodiversity-related physical and reputational risks.
However, there are also biodiversity-related business opportunities that arise from conservation and restoringand mitigating existing damage. Future iterations of the WWF BRF will also include this opportunity perspective.
Other planned developments of the tool include: the addition of other risk types (e.g., regulatory and marketrisk); the creation of a Respond Module, which will allow users to start tackling risks across sites through risk
reduction and shifting towards a more nature-positive business model.
Limited data for supply chain assessment
The BRF Methodology Documentation provides guidance for companies and financial institutions on how toapproximate biodiversity-related risks in supply chains. Two approaches were presented in the Guidance B:
Approach I on company-specific supply chain data and Approach II on IO models. Both approaches have theirown inherent limitations. The first approach suffers from the limited availability of company-specific supply chain
data. The IO model approach is based on averages at the country and industry level (and might therefore be
misleading for companies operating outside the norm); and the inter-industry relationships are a static snapshot.
Limitations inherent to our modelling principles• Applying this methodology at scale requires workarounds. For financial institutions in particular,
the WWF BRF Methodology documentation describes different proxies and data sources to circumvent
data gaps (for example using third-party data on the corporate hierarchy instead of reported data). The
workarounds make the application feasible but may offer a false sense of accuracy or completeness. The
case study presented in Appendix Guidance A: Overview of data providers shows that different data inputs
lead to different results.
• Point-in-time assessment. As of writing in January 2023, the assessment is a point-in-time evaluation andis only as up to date as the data set of the underlying biodiversity indicator itself (e.g., Ecosystem Intactness& Connectivity is based in part on 2016 data.). The data will be updated regularly to incorporate the mostrecent data sets. It should be noted that at this point in time the WWF BRF does not include information on
how these risks will potentially evolve following different climate and socio-economic scenarios.
• Point location as site input. Currently, the WWF BRF tool only allows for point location input (address or
longitude and latitude) to identify the location of a company’s site. Incorporating a polygon (such as arable
farming) or linear infrastructure (such as railway lines) is currently only possible by manual workarounds (e.g.,by extracting the centroid of the polygon or by extracting multiple sites along a linear infrastructure).
• Spatial granularity of assessment units differs. Since biodiversity loss is a spatially explicit problem, it isimportant to consider the local variations of biodiversity in any assessment of corporate biodiversity-related
risk. The global biodiversity data in the WWF BRF varies in scale between fine-scale raster data (30m x 30m)
to country-level data. To impose a common scale, the raw data is aggregated or transposed to HydroSHED
Level 7, as it represents a degree of functional coherence for measuring biodiversity. Some spatial granularity
is lost through the aggregation.

41

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION• Level of abstraction. Each data set that is included in the WWF BRF has been translated to a risk
score of 1-5. This reduces the complexity of the data to single numbers, which allows for comparison
between the different indicators. This, however, also reduces the complexity of biological and ecologicalcontext and introduces artificial classifications to fit every global data set into. As the tool is meant to bea global prioritization tool, these abstract scores should only be used to give the user a first overview of
biodiversity risk within the supply chain. When high risks have been detected, it will always be helpful
to consult not only the source data, but also further leading resources and local data sets to investigate
specific risk of a site.
• Robustness of data. To ensure the robustness of the data the utmost care has been taken to select only
the most up-to-date, reputable, global and mostly freely available data sets to assess each indicator.
However, due to data availability of global data sets, some proxy data had to be included. Furthermore,users are advised on how the data should be used and interpreted and the WWF BRF team offers
support in data interpretation. The WWF BRF team will continuously work on improving the robustness
through testing and consultation.
• Error of omission. The WWF BRF is based on 56 different data sets covering a wide varietyof biodiversity risk aspects. This was done to avoid an approach in which the complex topic of
biodiversity is reduced to only one or few indicators. The current set of indicators have been carefully
selected to try to achieve a balance between completeness, usefulness for the user and data
availability. However, there are more aspects to biodiversity risk that could be included in the tool.
The explanations of the different indicators contain disclaimers if other data sets were considered but
were not available or are planned to be included in future iterations of the tool. As new data becomes
available and the inclusion of more data becomes necessary, indicators/data sets may be added or
removed.
• Gap in trade-offs. Interrelations, trade-offs and feedback loops between different ecosystems and theservices they provide are currently not considered. This is relevant due to the potential trade-offs thatcan occur between different services, where the provisioning of one service can decrease a different
service.
• Industry materiality and the importance and integrity of biodiversity as key drivers of results.
Results are sensitive to a) the industry materiality rating linked to companies’ industry classification; andb) the thresholds of converging spatial biodiversity data into a score of 1 to 5. Both elements are key
drivers of the results. At this point in time, the WWF BRF does not consider potential actions that havealready been taken to reduce or mitigate biodiversity risk but is only a reflection of potential risk due tolocation and industry-associated activities.
• Natural resources are sourced from surrounding areas. For provisioning services, such as timber
availability, it is assumed that natural resources are sourced directly from the surrounding area. This may
or may not be the case.
While there are several limitations to the WWF BRF assessment, it nonetheless provides a helpful starting
point for companies and financial institutions to understand and address biodiversity-related risks within
their portfolios. The portfolio screening and prioritisation enables users to better understand, assess
and respond to biodiversity-related risks. The WWF BRF also addresses certain gaps in the biodiversity
risk assessment landscape such as spatially explicit assessment and the consideration of supply chain
elements.32 A few avenues of improvement are already planned, including:• Incorporating corporate response elements, where users receive a list of suitable response actions
for and across company sites (i.e., the Respond Module),• Additional risk types, such as regulatory risk and market risk;• Additional refinement on sector granularity, e.g. by incorporating an operational risk assessment;• Incorporating more local data sets to allow assessment at a smaller scale• Incorporating the aggregation to the company and portfolio-level to tailor the WWF BRF and WRF
Assess Modules output more to the needs of financial institutions

32

For more information, please refer to the report ‘Tackling Biodiversity Risk’ (WWF, Climate & Company, 2023).

42

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION© Michel Gunther / WWF

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

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data-and-methods

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONAPPENDIX STEP 0: SCOPING THE ASSESSMENT
APPENDIX 0.1: WWF RISK FILTER INDUSTRY SECTOR CLASSIFICATION
Table 6: WWF Risk Filter industry sectors
WWF Risk Filter Industry Sector
Agriculture (animal products)

Associated Activity Guidelines
Large-scale livestock (beef and dairy)
Small-scale livestock (beef and dairy)
Large-scale irrigated arable crops

Agriculture (plant products)

Large-scale rainfed arable crops
Small-scale irrigated arable crops
Small-scale rainfed arable crops

Appliances and general goods manufacturing
Automotive, electrical equipment and machinery
production

Manufacture of machinery, parts and equipment
Houseware and specialities production
Manufacture of machinery, parts and equipment
Tyre and rubber production
Catalytic cracking, fractional distillation and crystallization
Incomplete combustion
Polymerisation
Vulcanisation
Synthetic fertilizer production

Chemicals and other materials production

Cryogenic air separation
Gas adsorption
Membrane technology
Natural gas combustion
Recovery and separation of carbon dioxide
Solids processing

Construction materials

Glass making
Construction materials production
Infrastructure holdings

Electric energy production – combustion(biomass, coal, gas, nuclear, oil), geothermalenergyElectric/nuclear power transmission and distribution
Nuclear and thermal power stations
Biomass energy production
Geothermal energy production
Infrastructure holdings

Electric energy production – hydropower

Hydropower production
Electric/nuclear power transmission and distribution
Infrastructure holdings

Electric energy production – solar, wind

Solar energy provision
Wind energy provision
Electric/nuclear power transmission and distribution

Electronics and semiconductor manufacturing

Electronics and hardware production
Manufacture of semiconductor equipment
Aquaculture

Fishing and aquaculture

Freshwater wild-caught fish
Saltwater wild-caught fish

Food and beverage production

Alcoholic fermentation and distilling
Processed food and drink production

Food retailing

Infrastructure holdings

General or speciality retailing

Infrastructure holdings
Infrastructure holdingsLife science, pharma and biotech manufacture

Health care, pharmaceuticals and biotechnology

Life science, pharma and biotech tools and services
Provision of health care
Managed health care48

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONWWF Risk Filter Industry Sector

Associated Activity Guidelines
Cruise line provision

Hospitality services

Hotels and resorts provision
Restaurant provision

Land development and construction

Construction
Infrastructure builds
Alumina refining
Mining

Metals and mining

Iron extraction
Iron metal production
Metal processing
Steel production
Infrastructure maintenance contracts
Infrastructure holdings

Offices and professional services

Financial services
Leisure facility provision
Real estate activities
Environmental and facilities services
MiningOil and gas drilling
Manufacture of machinery, parts and equipment
Oil and gas services

Oil, gas and consumable fuels

Oil and gas exploration surveys
Oil and gas refining
Oil and gas storage
Oil and gas transportation
Gas distribution
Gas retail
Large-scale forestry
Production of forest and wood-based products

Paper and forest product production

Small-scale forestry
Paper packaging production
Production of paper products
Cable and satellite installations on land

Telecommunication services (including wireless)Fibre-optic cable installation (marine)
Telecommunication and wireless services
Jewellery production
Natural fibre production

Textiles, apparel and luxury good production

Synthetic fibre production
Footwear production
Production of leisure or personal products
Tobacco production
Infrastructure maintenance contracts
Distribution
Airport services

Transportation services

Marine transportation
Marine ports and services
Railway transportation
Construction

Water utilities and water service providers

Water services (e.g. waste water, treatment and distribution)

49

51

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONAPPENDIX 0.2: INDUSTRY MATERIALITY MATRIXTable 7: Industry materiality matrix (industry-specific weightings)

Other (Average of all sectors)

4

5

2

5

4

5

4

4

0

4

5

2

5

2

0

1

1.3

Limited Wild Flora & Fauna Availability

Dependency

1

1

0

3

1

1

0

0

0

0

3

3

0

0

3

2

2

0

0

0

3

0

2

0

0

1

1.4

Limited Marine Fish Availability

Dependency

0

0

0

0

0

0

0

0

0

0

5

0

0

0

0

2

0

0

0

0

0

0

0

0

0

1

2.1

Soil Condition

Dependency

4

5

0

0

0

0

0

0

0

0

1

0

0

0

0

1

1

0

0

0

5

0

0

0

1

1

2.2

Water Condition

Dependency

5

4

2

2

3

2

2

3

2

2

5

4

2

2

3

4

2

2

2

2

4

2

2

2

4

3

2.3

Air Condition

Dependency

3

3

2

2

2

2

2

1

2

2

3

2

1

1

1

3

3

3

3

3

3

0

2

2

2

2

2.4

Ecosystem Condition

Dependency

2

2

0

0

0

0

0

0

0

0

5

0

0

0

0

0

0

0

0

0

4

0

0

0

0

1

2.5

Pollination

Dependency

2

4

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

4

0

0

0

0

1

Transportation Services

2

4

Textiles, Apparel & Luxury
Good Production

52

Telecommunication services(including wireless)30Paper & Forest ProductProduction

40Oil, Gas & Consumable Fuels

40

Metals & Mining

2

0Land Development &Construction

20

Hospitality Services

50Food Retailing

50Food & Beverage Production

40

Fishing and aquaculture

34Electronics & Semiconductor
Manufacturing

5

4Electric Energy Production Solar, Wind

5

0

Electric Energy Production –Hydropower

50Construction Materials

40

Chemicals & Other Materials
Production

4

0

Automotive, Electrical
Equipment & Machinery
Production

4

0Appliances & General Goods
Manufacturing5

Dependency

Agriculture (plant products)

5

Forest Productivity and Distance to Markets

Agriculture (animal products)

Dependency

1.2

BRF Indicators

Impact/ Dependency

Water utilities / Water Service
Providers

Offices & Professional Services

Health Care, Pharmaceuticals
and Biotechnology

General or Speciality RetailingElectric Energy Production Combustion (Biomass, Coal,
Gas, Nuclear, Oil), Geothermal
EnergyIndustry

Physical Risk
1.1

Water Scarcity

3.1

Landslides

Dependency

5

5

4

4

4

4

4

4

4

4

5

4

4

4

4

4

4

4

4

4

5

4

4

5

5

4

3.2

Wildfire Hazard

Dependency

4

4

3

3

3

3

3

3

3

3

4

3

3

3

3

3

3

3

3

3

5

3

3

4

4

3

3.3

Plant/Forest/Aquatic Pests and Diseases

Dependency

4

4

0

0

0

0

0

0

0

0

4

4

0

0

4

1

0

0

0

0

4

0

0

0

0

1

3.4

Herbicide Resistance

Dependency

4

4

0

0

0

0

0

0

0

0

3

0

0

0

0

1

1

0

0

0

4

0

0

0

2

1

3.5

Extreme Heat

Dependency

5

5

3

3

3

3

4

3

3

3

5

3

3

3

3

4

4

4

4

4

5

3

3

4

4

4

3.6

Tropical Cyclones

Dependency

5

5

4

4

4

4

4

4

4

4

5

4

4

4

4

4

4

4

4

4

5

4

4

5

5

4

4.1

Tourism Attractiveness

Dependency

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

5

1

0

0

0

0

0

0

0

0

1

5.1

Land, Freshwater and Sea Use Change

Impact

5

5

1

1

1

1

1

5

3

1

5

1

1

1

1

1

5

5

1

5

5

3

1

5

1

3

5.2

Tree Cover Loss

Impact

5

5

1

1

1

5

4

4

1

1

1

1

1

1

1

3

5

5

1

5

5

5

1

5

1

3

5.3

Invasives

Impact

3

3

0

0

0

2

0

2

0

0

3

2

2

2

0

3

2

2

0

2

3

2

0

3

3

2

5.4

Pollution

Impact

5

5

5

5

5

5

5

4

4

5

5

4

4

4

5

2

5

5

2

5

4

2

5

4

2

1

Reputational Risk
6.1

Protected/Conserved Areas

Impact

5

5

3

3

3

3

5

4

4

3

3

3

1

1

3

1

5

5

1

5

5

3

3

5

3

3

6.2

Key Biodiversity Areas

Impact

4

4

2

2

2

2

4

3

3

2

2

2

1

1

2

1

4

4

1

4

4

2

2

4

2

3

6.3

Other Important Delineated Areas

Impact

4

4

2

2

2

2

4

4

2

2

2

2

1

1

2

1

4

4

1

4

4

2

2

4

2

3

6.4

Ecosystem Condition

Impact

4

4

2

2

2

2

4

4

2

2

2

2

1

1

2

1

4

4

1

4

4

2

2

4

2

3

6.5

Range Rarity

Impact

3

3

1

1

1

1

3

3

3

1

3

1

0

0

1

2

3

3

0

3

3

2

1

2

2

2

7.1

Indigenous Peoples (IPs); Local Communities (LCs) Lands andTerritories

Impact

5

5

3

3

3

3

3

5

3

3

3

3

1

1

3

1

5

5

1

5

5

3

3

5

3

3

7.2

Resource Scarcity: Food - Water - Air

Impact

3

3

1

1

1

1

1

1

0

1

2

2

1

0

1

3

1

2

0

2

0

0

2

1

1

1

7.3

Labor/Human Rights

Impact

4

4

2

2

2

2

2

2

2

2

4

2

2

2

2

4

4

4

2

4

2

2

4

2

2

3

7.4

Financial Inequality

Impact

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

8.1

Media Scrutiny

Dependency

5

5

3

1

4

4

2

2

2

1

5

5

3

3

1

3

4

5

3

4

1

1

3

1

5

3

8.2

Political Situation

Dependency

3

3

2

2

2

2

3

3

2

2

3

3

1

1

2

3

3

3

1

3

3

2

2

3

2

2

8.3

Sites of International Interest

Dependency

3

3

2

2

2

2

3

3

2

2

3

2

0

0

2

3

3

3

0

3

3

2

2

3

2

2

8.4

Risk Preparation

Dependency

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION© Andrew S. Wright / WWF-Canada

52

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION53

APPENDIX 0.3: WWF BRF RISK HIERARCHY
Table 8: Four-level hierarchy of the biodiversity risk assessment framework
Risk type

Risk category

Risk indicator

Metrics
Water Scarcity

Provisioning
Services

Limited Wild Flora & Fauna Availability
Limited Marine Fish Availability
Soil Condition

Water Condition
Regulating &Supporting Services- Enabling

Water Scarcity

Forest Productivity and Distance to Markets Realisable Hard and Soft Commercial Timber

Air Condition

Global Centers of Unsustainable Commercial Harvesting of Species
Stock Status
Soil Organic Carbon
Freshwater Quality
Marine Water Quality
PM2.5 Concentrations
Ecosystem Intactness & Connectivity (Terrestrial)

Ecosystem Condition

Ecosystem Connectivity (Freshwater)

Physical Risk

Ecosystem Intactness (Marine)

Regulating Services– Mitigating

Pollination

Crop Pollination

Landslides

Landslide Hazard

Wildfire Hazard

Wildfire Hazard

Plant/Forest/Aquatic Pests and Diseases
Herbicide Resistance
Extreme Heat
Tropical Cyclones

Cultural Services

Tourism AttractivenessFrequency of Plant/Forest/Aquatic Pests and Diseases
Antimicrobial and Agrochemical Resistances
Extreme Heat Hazard
Tropical Cyclonic Wind and Storm Surge Hazard
Tourism Demand Drivers (Natural and Cultural)
Cropland Expansion (Terrestrial)

Land, Freshwater and Sea Use Change

Fragmentation of Rivers (Freshwater)
Direct Human Impact & Fishing (Marine)

Tree Cover Loss
Pressures on
Biodiversity

Invasives

Tree Cover Loss
Presence of Invasives
Terrestrial Nutrient Pollution
Terrestrial Pesticide Pollution

PollutionFreshwater Nutrient Pollution
Marine Nutrient Pollution
Marine Pesticide Pollution
Air Pollution

Protected/Conserved Areas
Key Biodiversity Areas

Protected Areas
Key Biodiversity Areas
Intact Forest Landscapes

Environmental
Factors

Other Important Delineated Areas

WWF’s Global 200
Ecologically or Biologically Significant Marine Areas
Vulnerable Marine Ecosystems
Ecosystem Intactness & Connectivity (Terrestrial)

Ecosystem Condition

Ecosystem Connectivity (Freshwater)
Ecosystem Intactness (Marine)

Reputational Risk

Range Rarity
Indigenous Peoples (IPs); Local Communities(LCs) Lands and Territories

Range Rarity
No Data
Food Security

Socioeconomic
Factors

Resource Scarcity: Food - Water - Air

Water Scarcity
Air Condition

Labor/Human Rights
Financial Inequality
Media Scrutiny

Ratified International Human Rights Instruments
Labor Rights Violations
Financial Inequality
Media Scrutiny (Ecological Topics)
Media Scrutiny (Social Topics)
Violence Against Land and Environmental Defenders

Additional
Reputational Factors

Political Situation

Freedom
GovernanceCorruption

Sites of International Interest
Risk Preparation

Natural World Heritage Sites
RAMSAR Sites
Index of Risk Preparation

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONAPPENDIX 0.4: BIODIVERSITY IMPORTANCE
AND INTEGRITY INDICATORSThe 56 data sets that were used to create importance and integrity scores for the 33 different indicators
are described in detail below, including information on the rationale, the thresholds for the risk-scoreclassification and data sources.

1. PHYSICAL RISK INDICATORS
Physical risk is driven by the ways in which a company depends on nature and can be affected by bothnatural and human-induced changes to the condition of land- and seascapes. It comprises the following
risk categories: 1) Provisioning Services; 2) Regulating & Supporting Services - Enabling; 3) Regulating
Services - Mitigating; 4) Cultural Services; and 5) Pressures on Biodiversity. Therefore, physical risks
account for the status of the ecosystem services that companies, or their suppliers, rely on.
1 – Provisioning Services
Many industries or companies rely directly on the provisioning of natural inputs for their operations or
production. As such, declines due to ecosystem service degradation in the quantity or quality of direct
inputs for feed, raw materials, genetic materials, etc. can result in increased costs or disruption of
production. This risk category identifies the main categories of natural resources needed for production.
This risk category within the BRF comprises the risk indicators: 1) Water Scarcity, 2) Forest Productivity andDistance to Markets, 3) Limited Wild Flora & Fauna Availability, and 4) Limited Marine Fish Availability.
1.1 – Water Scarcity

1.1.1 – Water Scarcity
Water scarcity refers to the physical abundance or lack of freshwater resources, which can significantly
impact a company through production or supply chain disruption, higher operating costs and growthconstraints. Water scarcity is human-driven and can be aggravated by natural conditions (e.g., aridity,
drought). It is generally calculated as a function of the volume of water used relative to the volume of water
available in a given area.
This indicator has already been calculated in the WWF WRF and has been integrated into the WWF BRF
without changes. The WWF WRF water scarcity risk category is a comprehensive and robust metric as it
integrates a total of seven best available and peer-reviewed data sets covering different aspects of scarcity,
as well as different modelling approaches: aridity index, water depletion, baseline water stress, blue
water scarcity, available water remaining, drought frequency probability and projected change in drought
occurrence. For more information, please consult the Water Risk Filter Methodology or visit the Water Risk
Filter directly.
1.2 – Forest Productivity and Distance to Markets

1.2.1 – Realisable Hard and Soft Commercial Timber
Realised hard and soft commercial timber value. Model results from the costing nature version 3 policy
support system (Mulligan M. , 2021) has been used as the basis to estimate the availability and commercial
accessibility of wood-based fibres and timber. Please note that this is a global indicator and may not beapplicable in certain conditions, e.g. in sparsely populated areas such as some boreal regions and forplantations with connection to infrastructure that is independent of population centers.
Timber provision is critical for activities such as house building, furniture manufacture, food storage
and water and agricultural infrastructure. At low extraction rates, it is sustainable and can continue to beprovided at the rates consumed. At high extraction rates, it is consumptive of the ecosystem and may
damage co-benefits for other services provided by forests. Realised timber services thus need to beconsidered carefully in this regard. Timber is separated into commercial timber and domestic timber.
Commercial timber contributes value to national beneficiaries, whereas domestic timber contributes value
to local beneficiaries. Only the former has been considered in this analysis.
For commercial timber, the first step is to estimate the potential mass of timber (the potential service)from the above-ground carbon stock map (Ruesch & Gibbs, 2008; Saatchi, et al., 2011). The proportion ofcarbon coming from trees in a pixel is calculated as the product of carbon stock and fractional tree cover(Copernicus, 2015) for rural areas only (Schneider, Friedl, & Potere, 2009), as urban trees are considered not
to be usable for timber. The sustainable harvest is considered to be the reciprocal of the number of years
taken to develop the stock at the annual sequestration rate, according to the dry matter productivity data of(Mulligan M. , 2018), based on a 10-year climatology of SPOT VGT data.

54

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

55

Softwood and hardwood are also considered since these have differing economic values. The spatial
distribution of hardwood and softwood is calculated according to the work of Box and Fujiwara (Box &Kazue, 2013), in which mean annual temperature and a cold index are calculated from mean monthly
temperatures (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). The cold index is the sum of temperature
for months where temperatures are less than 5°C. Hardwood distributions are calculated as those with amean annual temperature above 20°C and a cold index above -10°C. Softwood areas are calculated as allother areas.
To calculate the relative realised timber services indexes (RRTS), the realised service (the timber accessible)
is calculated as potential timber within six hours travel time of a population centre of greater 50,000
people and on slope gradients below 31.5 degrees (70 per cent) (Lehner, Verdin, & Jarvis, 2008), which areconsidered to be workable for logging (Greulich, Hanley, McNell, & Baumgartner, 1999). This accessibility
requirement represents the availability of transport infrastructure for timber. Timber mass defined asaccessible is constrained by slope to reflect the higher cost of removal and increased wastage on steeper
slopes, using a linear decrease in timber availability (from the potential availability to zero) as slope
increases from 0 to 90 degrees. The product of the potential services and these accessibility constraints is
the realised timber service in tonnes.
To produce the WWF BRF indicator, the raw data was processed as follows: 1) the categorical raster data
was aggregated to the HydroBASINS level 7 using the mean value; 2) it was then classified into the five riskscore classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Relative realised timber services indices (RRTS)

1 - Very low risk

>0.18-1

2 - Low risk

>0.066-0.18

3 - Moderate risk

>0.0053-0.066

4 - High risk

>0-0.0053

5 - Very high risk

0

1.3 – Limited Wild Flora & Fauna Availability

1.3.1 – Global Centers of Unsustainable Commercial Harvesting of Species
Non-timber wild plants are used in many applications, including for medicinal, cosmetic, aromatic andgenetic purposes. They are used globally as feed, fibre (e.g., for clothing, building materials, etc.), fuel,medicines and food ingredients (Jenkins, Timoshyna, & Cornthwaite, 2018). Overexploitation is one of themain threats to nature, but the intensity of this threat varies geographically. To estimate availability of wild
flora and fauna, De Minin E. et al.’s (2019) global centres of unsustainable commercial harvest paper has
been used. The paper identified global concentrations, on land and at sea, of 4,543 species threatened byunsustainable commercial harvesting, to identify regions under threat.
To produce the WWF BRF indicator, the raw data was processed as follows: 1) the categorical raster data
was aggregated to the assessment unit level using the max value; 2) and it was then classified into the fiverisk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Intensity of unsustainable commercial harvesting

1 - Very low risk
2 - Low risk
3 - Moderate risk

Unknown intensity of unsustainable commercial harvesting

4 - High risk
5 - Very high risk

High intensity of unsustainable commercial harvestingWWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

56

1.4 – Limited Marine Fish Availability
1.4.1 – Stock Status

As the largest traded food commodity in the world, seafood provides sustenance to billions of people
worldwide (WWF, 2022b). Nowadays, more than 85 per cent of the world’s fisheries have been pushed toor beyond their biological limits. Overfishing occurs in areas that have been exploited at levels that exceed
the capacity for replacement by reproduction and growth of the exploited species. Species that are being
overfished are producing catches that are below the level that could be sustainably derived. As a resultof intense exploitation, most fisheries generally follow sequential stages of development: undeveloped,
developing, fully exploited, overfished and collapsed. The indicator measures the percentage of stocks
categorized as rebuilding/collapsed/over-exploited/exploited as opposed to stocks that are categorized as
developing (Sea Around Us, 2020).
To produce the WWF BRF indicator, the raw data was processed as follows: 1) the percentage ofstocks categorized as rebuilding/collapsed/over-exploited/exploited per Exclusive Economic Zone were
aggregated to Marine Ecoregions of the World using the mean value; 2) it was then classified into the fiverisk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Percentage of stocks of categorized as rebuilding/collapsed/
over-exploited/exploited

1 - Very low risk

<25%

2 - Low risk>25-50%3 - Moderate risk>50-804 - High risk

>80-955 - Very high risk

>95%

2 – Regulating and supporting services – Enabling
Many companies rely on regulating and supporting ecosystem services that enable production processes,
including the cultivation of crops or breeding of animals. Declines in enabling ecosystem services such as
soil health, water quality and quantity and habitat provision can result in increased costs of production or
an inability to operate. This risk category within the BRF comprises the risk indicators: 1) Soil Condition, 2)
Water Condition, 3) Air Condition, 4) Ecosystem Condition and 5) Pollination.
2.1 – Soil Condition

2.1.1 – Soil Organic Carbon
Soil organic carbon (SOC) is the main component of soil organic matter (SOM) and is a prerequisite forfood production, mitigation and adaptation to climate change and the achievement of the Sustainable
Development Goals (SDGs). SOC affects most of the processes relevant to soil functions and foodproduction. A high SOM and therefore SOC content provides plants with the nutrients and water they needby increasing soil fertility and water availability, which in turn improves food productivity. SOC has also
long been used as an indicator of soil health, due to its capacity to improve soil structural stability, which
affects porosity, aeration and water filtration capacities to supply clean water. However, SOC mineralisation
can be an important source of greenhouse gas emissions. This means that changing SOM (and henceSOC) not only changes the provision of ecosystem services required for crop production, but also affects
the capacity of soils to buffer against environmental changes, as it regulates the resilience of agricultural
systems to climate change (FAO; ITPS, 2018).
GSOCmap (FAO, 2019) is the first global SOC map, produced through a consultative and participatory
process involving Global Soil Partnership member countries, which makes this map unique. The map was
prepared by member countries under the guidance of the Intergovernmental Technical Panel on Soils andthe Global Soil Partnership Secretariat. Countries agreed on the methodology used to produce the map andwere trained on modern tools and methodologies to develop national maps. The Global Soil Partnershipthen gathered all national maps to produce the final product, ensuring a thorough harmonisation process.
		WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION57

To produce the WWF BRF indicator, the raw data was processed as follows: 1) the categorical raster data
was aggregated to the HydroBASIN level 7 using the mean value; 2) it was then classified into the five riskscore classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Average SOC tonnes/ha

1 - Very low risk

>90

2 - Low risk

>70-903 - Moderate risk>50-704 - High risk>30-505 - Very high risk<=30

2.2 – Water Condition
Water quality indicates whether water resources are fit for use by humans and ecosystems alike. Poor water
quality – water pollution – can impact a company indirectly by destabilising ecosystems or by causing
serious health issues, as well as directly through increased operating costs and a reduction in production or
growth.
The water condition indicator has been calculated separately for freshwater and marine areas.
2.2.1 – Freshwater Quality
This indicator has already been calculated in the Water Risk Filter and has been integrated here withoutchanges. The Water Risk Filter risk category water quality considers parameters with well-documented
direct and indirect negative effects on water security for both humans and freshwater nature, which
are aligned to SDG 6.3.2: biological oxygen demand (BOD) as a widely used umbrella proxy for overallwater quality; electrical conductivity (EC) as a proxy for salinity balance and pH alteration; and nitrogen,to capture nutrient loading in water bodies. For more information, please consult the Water Risk Filter
Methodology or visit the Water Risk Filter website directly.
2.2.2 – Marine Water Quality
Marine water quality was estimated using three data sets: Ocean Health Index nutrient pollution data
(Halpern, et al., 2012); ocean acidification data sets (Halpern, et al., 2012); and the WRI’s Eutrophication andHypoxia data set (Diaz, Selman, & Chique, 2011).
To produce the BRF indicator, the raw data was processed as follows: 1) the hypoxia, nutrient pollution andacidification data was aggregated to the FAO Statistical Fishing Areas and Marine Ecoregions of the World
using the mean value; 2) it was then classified into the five risk-score classes, following the classification
as in the tables below. Finally, the risk score for marine water quality was derived from the mean of thehypoxia, nutrient pollution and acidification risk scores.
Biodiversity Risk Filter risk score# of hypoxia events

1 - Very low risk

0

2 - Low risk

1

3 - Moderate risk

2-3

4 - High risk

4-10

5 - Very high risk

>10

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

58

Biodiversity Risk Filter risk score

Nutrient pollution (0-1)

1 - Very low risk

0

2 - Low risk

>0.000001-0.001758

3 - Moderate risk

>0.001759-0.007936

4 - High risk

>0.007937-0.026952

5 - Very high risk

> 0.026953

Biodiversity Risk Filter risk score

Acidification as the difference of the aragonite saturation
state (Ωarag) in the pre-industrial era and modern times

1 - Very low risk

0.144533-0.192124

2 - Low risk

0.192125-0.201112

3 - Moderate risk

0.201113-0.224868

4 - High risk

0.224869-0.290256

5 - Very high risk

>0.290257

2.3 – Air Condition

2.3.1 – PM2.5 Concentrations(Hammer, 2022) measured the annual global surface of concentrations (micrograms per cubic metre) of
all composition ground-level fine particulate matter of 2.5 micrometres or smaller (PM2.5) for large-scale
health and environmental research by combining Aerosol Optical Depth retrievals from multiple satellite
algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS
C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementationof Atmospheric Correction Collection 6 (MAIAC C6) and the Sea-Viewing Wide Field-of-View Sensor(SeaWiFS) Deep Blue Version 4. The data set is used as a proxy for air quality, as exposure to high average
concentrations of PM2.5 over time has been a reliable predictor of heightened mortality (Health Effects
Institue, 2020).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: the categoricalraster data was aggregated to the HydroBASIN level 7 using the mean value; 2) it was then classified into
the five risk-score classes, following the classification as in the table below.

Biodiversity Risk Filter risk score

Concentrations (micrograms per cubic metre) of allcomposition ground-level fine particulate matter of 2.5
micrometres or smaller (PM2.5)

1 – Very low risk

<7

2 – Low risk

>7-12

3 – Moderate risk

>12-22

4 – High risk>22-505 – Very high risk

>50

2.4 – Ecosystem Condition
Natural habitats provide a wide array of ecosystem services that are important to companies, people andcommunities, such as climate and streamflow regulation, water purification, species habitat maintenance,
regulation/buffering of pests and diseases, pollination, maintenance of soil structure and fertility, nutrient
cycling and hydrological services and indigenous cultural practices, among many others (Beyer, Venter,
Grantham, & Watson, 2020). The degradation of natural habitats can therefore result in restricted access tothe enablers on which companies and people rely.
The preservation and restoration of terrestrial, freshwater and marine habitats is a key component inaddressing biodiversity risk and in the achievement of the SDGs.

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2.4.1 – Ecosystem Intactness & Connectivity (Terrestrial)
To calculate terrestrial ecosystem intactness, the Biodiversity Intactness Index (BII) (Natural History
Museum, 2016) was used in combination with the Functional Connectivity of the World’s Protected Areas(Brennan, et.al. 2022).
1) The BII is the modelled average abundance of originally present species, relative to their abundance in
an intact ecosystem. It functions as a global estimate of how pressures have affected the numbers ofspecies and individuals found in samples from local terrestrial ecological assemblages.
2) Functional Connectivity of the World’s Protected Areas (Brennan, et al., 2022) maps the functional
connectivity (how mammals move through landscapes) of the world’s terrestrial protected areas. Thisdata set is the first of its kind to describe connectivity currents globally. It is used as a proxy for changes
in the spatial configuration of the landscape.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the BII and
Connectivity data were aggregated to the HydroBASINS level 7 using the mean value; 2) it was thenclassified into the five risk-score classes, following the classification as in the tables below. 3) Finally,a risk score was created for terrestrial intactness & connectivity by calculating the mean of the BII andconnectivity risk scores.
Biodiversity Risk Filter risk score2005 Biodiversity Intactness (%)

1 - Very low risk

>97.5

2 - Low risk

>90-97.5

3 - Moderate risk

>80-904 - High risk>70-805 - Very high risk<=70

Biodiversity Risk Filter risk score

Mammal movement probability (MMP)

1 - Very low risk

>2,070 (Very high connectivity)

2 - Low risk

>1,240-2070 (High connectivity)

3 - Moderate risk>825-1,240 (Medium connectivity)

4 - High risk

>415-825 (Low connectivity)

5 - Very high risk

0-415 (Very low connectivity)

2.4.2 - Connectivity (Freshwater)This indicator has already been calculated in the Water Risk Filter (4.1. Fragmentation Status of Rivers) andhas been integrated into the Biodiversity Risk Filter without changes. For more information, please consult
the Water Risk Filter methodology or visit the Water Risk Filter directly.
2.4.2 – Ecosystem Intactness (Marine)
To calculate marine habitat conditions, Ocean Health Index habitat condition data was considered forsix marine ecosystems: coral, mangrove, sea ice, sea grass, salt marsh and softbottom habitat(Halpern, et al., 2012).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) an averagevalue was calculated for marine habitat condition from the habitat conditions of coral, mangrove, sea ice,sea grass, salt marsh and softbottom; 2) the resulting habitat condition average was aggregated to the
FAO Statistical Fishing Areas and Marine Ecoregions of the World using the mean value; 3) this was thenclassified into the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Habitat condition (%)

1 – Very low risk

100

2 – Low risk

>92-993 – Moderate risk

>86-92

4 – High risk

>82.2-86

5 – Very high risk<=86

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2.5 – Pollination

2.5.1 – Crop Pollination
Up to two-thirds of all crops require some degree of animal pollination to reach their maximum yields, andnatural habitat around farmlands can support healthy populations of wild pollinators by providing them withforaging and nesting resources.
As part of the mapping of the planet’s critical natural assets for people (NCP) (Chaplin Kramer, et al., 2020),
the crop pollination data set models the potential contribution of wild pollinators to nutrition production,
based on pollination sufficiency of habitat surrounding farmland and the pollination dependency ofcrops. NCP for crop pollination is expressed in terms of the average equivalent number of people fed bypollination-dependent crops, attributed to nearby ecosystems based on the area of pollinator habitat within
pollinator flight distance of crops. It measures how much nutrition is produced on fields that are dependent
on the surrounding natural habitat to sustain pollination.33
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the categoricalraster data was aggregated to the HydroBASINS level 7 using the mean value; 2) it was then classified into
the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Equivalent people fed

1 - Very low risk

0-0.0004

2 - Low risk

>0.0004-0.008

3 - Moderate risk

>0.008-1.5

4 - High risk

>1.5-2.7

5 - Very high risk

>2.7

3. Regulating Services – Mitigating
The occurrence of natural hazards such as landslides, fires and storms can disturb or disrupt projects,
operations, or entire value chains, and in some cases can result in severe damage to or loss of assets.
Intact ecosystems can help to mitigate the impact of some natural hazards. This risk category within the
BRF comprises the risk indicators: 1) Landslides, 2) Forest Canopy Loss, 3) Invasives and 4) Pollution.
3.1 – Landslides

3.1.1 – Landslide Hazard
Landslides impose significant risks to human lives and economic activities. Landslides have become moreprevalent because of anthropogenic disturbances, such as land-cover changes, land degradation andexpansion of infrastructure. These are further exacerbated by more extreme precipitation due to climatechange, which is predicted to trigger more landslides and threaten sustainable development in vulnerable
regions (Binbin, Clinton, Xu, & Weihua, 2021).
The Global Landslide Hazard Map has been used as the basis for this indicator. It presents a qualitative
representation of global landslide hazard at a global scale. It is a combination of the Global Landslide
Hazard Map: Median Annual Rainfall-Triggered Landslide Hazard (1980-2018) and the Global Landslide
Hazard Map: Earthquake-Triggered Landslide Hazard, which has then been simplified to four categories,
ranging from very low to high landslide hazard (GFDRR, 2020).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the categoricalraster data was aggregated to the HydroBASINS level 7 using the mean value; 2) it was then classified into
the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Landslide hazard categories

1 - Very low risk

1

2 - Low risk

2

3 - Moderate risk
4 - High risk

3

5 - Very high risk

4

33 As an example, consider two fields of equal size, one surrounded by one acre of natural habitat, the other by 10 acres of natural habitat. The first field will
have more ‘equivalent people fed’ per acre of natural habitat. However, the first field is under more risk, because if the acre of natural habitat is destroyed,
the field will have no pollinators: higher equivalent people fed means higher dependency and therefore higher risk.

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3.2 – Wildfire Hazard

3.2.1 – Global Wildfire Hazard
This indicator is based on the Global Facility for Disaster Reduction and Recovery’s (GFDRR) global wildfire
hazard levels (GFDRR, 2017c). The approach to classifying wildfire hazard levels used is based solely on
fire weather index climatology. Fire weather indexes are used in many countries to assess both the onset of
conditions that will enable fires to spread, as well as the likelihood of fire at any point in the landscape. The
method presented uses statistical modelling (extreme value analysis) of a 30-year fire weather climatology toassess the predicted fire weather intensity for a 10-year return period interval. These intensities are classified
based on thresholds using conventions to provide hazard classes that correspond to conditions that can
support problematic fire spread in the landscape, if an ignition and sufficient fuel were to be present.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the categoricalraster data was aggregated to the HydroBASINS level 7 using the mean value; 2) it was then classified into
the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Maximum predicted fire weather intensity for a 10-year return
period

1 - Very low risk

>0-15

2 - Low risk

>15-303 - Moderate risk>30-604 - High risk

>60-120

5 - Very high risk

>120

3.3 – Plant/Forest/Aquatic Pests and Diseases

3.3.1 – Frequency of Plant/Forest/Aquatic Pests and Diseases
As genetic and species diversity is lost and ecosystems are degraded, the complexity of the overall system
can be compromised, making it more vulnerable and potentially creating new opportunities for disease
emergence and poor health outcomes in humans, livestock and wildlife (World Health Organization, 2022)
This increases risks to economic activity, as well as risks of epidemics and pandemics. Emerging diseases
include transboundary animal and plant pests and diseases, including forest/timber pests and aquatic
animal diseases. Food safety threats can have a large impact on food security, human health, livelihoods
and trade (FAO, 2020b).

.

To estimate the frequency of zoonotic, vector-borne and water-borne diseases, data from the FAO’s
Food Chain Crisis Early Warning Bulletin (2018-2020) was used. The purpose of the bulletin is to inform of
forecasted threats to animal and plant health and food safety that may have a significant impact on foodand nutrition security for the three months ahead. Please note that the source data for this indicator is only
available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the total numberof forecasted threats was counted between 2018 and 2020 per country, 2) country data was aggregated to
the HydroBASINS level 7 using the majority value; 3) it was then classified into the five risk-score classes,
following the classification as in the table below.
Biodiversity Risk Filter risks score

Number of forecasted transboundary animal and plant pests
and diseases

1 - Very low risk

0-1

2 - Low risk

2-8

3 - Moderate risk

9-21

4 - High risk

22-42

5 - Very high risk

>42

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3.4 – Herbicide Resistance3.4.1 – Antimicrobial and Agrochemical Resistances
Herbicide resistance is the ability of a weed to survive a herbicide application that had been used to containthat population. As unwanted plants compete with crops, issues of crop loss and contamination arise.
To estimate antimicrobial and agrochemical resistance, data from the Weed resistance database(International Survey of Herbicide Resistant Weeds) was used (Heap, 2021).
The International Survey of Herbicide Resistant Weeds is a collaborative effort between weed scientists
in over 80 countries with the aim of maintaining scientific accuracy in the reporting of herbicide resistantweeds globally. Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the number ofoccurrences of herbicide resistant weeds was tallied from the weed resistance database; 2) the count by
country was aggregated to the HydroBASIN level 7 using the majority value; 3) it was then classified into thefive risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Number of occurrences of herbicide resistant weed

1 - Very low risk

0

2 - Low risk

1-15

3 - Moderate risk

16-28

4 - High risk

29-45

5 - Very high risk

>45

3.5 – Extreme Heat

3.5.1 – Extreme Heat Hazard
Extreme heat has an obvious impact on human health, but it is also relevant to all kinds of projects andsectors, including the built environment, as heat stress affects people using buildings and infrastructure,
therefore influencing their design.
GFDRR’s extreme heat hazard is classified based on an existing and widely accepted heat-stress indicator,
the wet bulb globe temperature (WBGT, in °C) – more specifically the daily maximum WGBT. A short return
period (five years) reflects more frequent extreme heat events (GFDRR, 2017b).
With climate change, the frequency and the intensity of abnormal weather and extreme temperaturepatterns have dramatically increased, and the shift to warmer temperatures, driven by climate change, will
only exacerbate this phenomenon (Betrand & Parnaudeau, 2017).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the categoricalraster data was aggregated to the HydroBASINS level 7 using the mean value; 2) it was then classified into
the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Daily maximum wet bulb globe temperature (WBGT, in °C) –5yr return period

1 - Very low risk

<24

2 - Low risk

24-28

3 - Moderate risk

28-30

4 - High risk

30-32

5 - Very high risk

>32

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3.6 Tropical Cyclones

3.6.1 – Tropical Cyclonic Wind and Storm Surge Hazard
Storms can impact companies and value chains through a variety of ways, including building and property
damage, flooding or power outages, which may lead to temporary or permanent company closures andloss of revenue.
This indicator is based on GFDRR’s tropical cyclonic strong wind and storm surge model, using informationfrom 2,594 historical tropical cyclones, topography, terrain roughness and bathymetry. The historical tropical
cyclones used in the GAR15 cyclone wind and storm surge model are from five different oceanic basins:Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic, with the
tracks of the storms obtained from the IBTrACS database (Knapp, Kruk, Levinson, Diamond, & Neumann,
2010). This database is the most up-to-date repository of information associated with tropical cyclones.
Topography information was taken from NASA’s Shuttle Radar Topography Mission (SRTM), which
provides terrain elevation grids at a 90-metre resolution, delivered by quadrants around the world.
To account for surface roughness, polygons of urban areas worldwide were obtained from theSocioeconomic Data and Applications Centre, SEDAC (2011). This was considered a good proxy for the
spatial variation of surface roughness.
A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the
GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data
Centre (GEBCO, 2009). Bathymetry relates to information about the ocean floor, which has direct influenceon the formation of storm surges. More information about the cyclone wind and storm surge hazard can befound in CIMNE (CIMNE-INGENIAR, 2015).
Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler(Cardona, et al., 2014). The vulnerability models used in the risk calculation for GAR correlate loss to windspeed for three-second gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform, which is arisk modelling tool within the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by
CIMNE and Ingeniar, to produce AAL and PML values for cyclone risk (GFDRR, 2017a).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the categoricalraster data was aggregated to the HydroBASINS level 7 using the mean value; 2) it was then classified into
the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Predicted maximum wind speed (mph)– 50-year return period

1 - Very low risk

0

2 - Low risk

<60

3 - Moderate risk>60-804 - High risk

>80-120

5 - Very high risk

>120

4 – Cultural Services

4.1 – Tourism Attractiveness4.1.1 – Tourism Demand Drivers (Natural + Cultural)
Some industries, such as tourism, real estate and education, can depend highly on the presence of
culturally valuable land or seascapes or specific sites. Tourism is an engine for jobs and investment. The
degradation or loss of key attractive features in an area can negatively impact companies that rely on them.
Nature-based tourism (NBT) is a sub-sector of the tourism industry that includes wildlife-based tourism,
such as viewing, photographing, feeding and hunting (World Bank, 2018). The loss of key species uponwhich wildlife-based tourism is dependent would be catastrophic to NBT.
The Travel and Tourism Demand Drivers subindex of WEF’s Travel & Tourism Development Index 2021
Edition captures the principal “reasons to travel” (WEF, 2021). For the BRF analysis, natural resource
indicators and cultural resource indicators were included.
The natural resources pillar measures the available natural capital as well as the development of outdoor
tourism activities. Countries with natural assets may be better positioned to attract tourists.

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WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONIn this pillar, we include several attractiveness measures, including the number of UN Educational, Culturaland Scientific Organisation (UNESCO) natural World Heritage sites, the richness of fauna and biodiversity
in the country and the scope of protected areas, which indicates the extent of national parks and nature
reserves. Digital demand for nature and relevant activities is also measured as an illustration of how wellknown and effectively marketed a country’s natural assets are.
The cultural resource pillar measures the availability of cultural resources such as archaeological sites andentertainment facilities. To an extent, this pillar captures how cultural resources are protected, developed
and promoted. Included here are the number of UNESCO cultural World Heritage Sites, the number oflarge stadiums that can host significant sport or entertainment events and a measure of Digital Demand
for a country’s cultural sites and entertainment. Also included are the number of UNESCO Creative Cities,
representing efforts to protect and develop cultural and creative activities and industries in urban centres.
Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator the raw data was processed as follows: 1) Create amean score of both pillars (natural and cultural resource scores) 2) aggregate the country data to theHydroBASINS level 7 as well as the Marine Ecoregions of the World associated with each country’s
Exclusive Economic Zones using the mean value; 3) classify it into the 5 risk score classes, following theclassification, as in the table below.
Biodiversity Risk Filter Risk Score

Mean Natural and Cultural Resource Score

1 - Very Low Risk

>6.2

2 - Low Risk<=6.2

3 - Moderate Risk<=5.2

4 - High Risk<=4.5

5 - Very High Risk<=3.4

5 – Pressures on Biodiversity
Direct drivers or pressures are drivers that unequivocally influence biodiversity and ecosystem processes(IPBES, 2017). This risk category within the BRF comprises the risk indicators: 1) Land, Freshwater and Sea
Use Change, 2) Forest Canopy Loss, 3) Invasives and 4) Pollution.
5.1 – Land, Freshwater and Sea Use Change
Land-use change is the major human influence on habitats and can include the conversion of land cover(e.g. expansion of cropland), changes in the spatial configuration of the landscape (e.g. fragmentationof habitats) or changes in the management of the ecosystem or agro-ecosystem (e.g. through theintensification of agricultural management or forest harvesting) (IPBES, 2017). Here, we only include
metrics for the first two, as there is currently no available global data set for changes in the management of
ecosystems or agro-ecosystems.
5.1.1 – Cropland Expansion (Terrestrial)
To calculate this indicator Potapov’s Global maps of cropland extent gain were used to assess conversionof land cover (Potapov, Turubanova, & Hansen, 2022). Please note that forest canopy loss (another important
aspect of land-use change) is covered in indicator 5.2.2 although we recognize that a significant amountof forest loss is caused by cropland expansion.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the Global
maps of cropland extent were aggregated to the HydroBASINS level 7 using the mean value; 2) it was thenclassified into the five risk-score classes, following the classification as in the tables below. 3) Finally, a risk
score was created for terrestrial modification by calculating the mean of the change in cropland extent.
Biodiversity Risk Filter risk score

Percentage of cropland expansion between 2000 and 2019

1 - Very low risk

0-1

2 - Low risk

>1-3

3 - Moderate risk

>3-6

4 - High risk

>6-12

5 - Very high risk

>12

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5.1.2 – Fragmentation of Rivers (Freshwater)This indicator has already been calculated in the Water Risk Filter (4.1. Fragmentation Status of Rivers) andhas been integrated into the Biodiversity Risk Filter without changes. For more information, please consult
the Water Risk Filter methodology or visit the Water Risk Filter directly.
5.1.3 – Direct Human Impact & Fishing (Marine)
Halpern et.al. (2019) produced a shipping and direct human impact score as part of their analysis on humanimpact on the world’s oceans (Halpern, et al., 2019). Fishing is included here as many fishing techniques(e.g., demersal fishing) have the potential to alter the sea floor and the natural marine environment. In a
future iteration of the tool, this may also be included as a pressure on biodiversity through overexploitationof marine fish.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the shipping
impact score and the direct human impact score were aggregated to the FAO Statistical Fishing Areas andMarine Ecoregions of the World using the mean value; 2) it was then classified into the five risk-score classes,
following the classification as in the tables below. 3) Finally, the higher of the two scores was chosen.
Biodiversity Risk Filter risk score

Shipping impact score

1 - Very low risk

0-0.056608

2 - Low risk

0.056609-0.138555

3 - Moderate risk

0.138556-0.251314

4 - High risk

0.251315-0.398667

5 - Very high risk

0.398668-0.606808

Biodiversity Risk Filter risk score

Direct human impact score

1 - Very low risk

0-0.002793

2 - Low risk

0.002794-0.007965

3 - Moderate risk

0.007966-0.014956

4 - High risk

0.014957-0.032081

5 - Very high risk

0.032082-0.156244

5.2 – Tree Cover Loss

5.2.1 – Tree Cover Loss

Hansen et al. examined global Landsat data at a 30-metre spatial resolution to characterise tree coverextent, loss and gain from 2000 to 2021 (Hansen, et al., 2021). Tree cover loss was defined as a standreplacement disturbance or the complete removal of tree cover at the Landsat pixel scale (30m). Recently

harvestedareas using clear cutting practices are thus included. For this indicator, only tree cover loss

since 2020 was taken into account. To produce the Biodiversity Risk Filter indicator, the raw data wasprocessed as follows: 1) the tree cover loss maps were aggregated to the HydroBASINS level 7 using
the mean value; 2) it was then classified into the five risk-score classes, following the classification, as in
the tables below.

Biodiversity Risk Filter risk score

Average tree cover loss in %1 - Very low risk

0

2 - Low risk

>0 - 1%

3 - Moderate risk

>1-3%

4 - High risk

>3- 8%

5 - Very high risk

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5.3 – Invasives
Invasive species may be indigenous and/or exotic or alien. They occur mostly in terrestrial and aquatic
ecosystems, both marine and freshwater, and can disrupt the ecological functioning of natural systems.
Invasive species can out-compete local and indigenous species for natural resources, with negative
implications for biodiversity. Invasive and alien species have been reported around the world, resulting inloss of biodiversity at local and regional scales and causing significant economic damage (IPBES, 2017).
5.3.1 – Presence of Invasives
The basis for this indicator is the Invasive Species Specialist Group’s Global Invasive Species Database,which lists 100 of the world’s worst invasive alien species as well as in which countries they are consideredinvasive. Species were selected for the list according to two criteria: their serious impact on biologicaldiversity and/or human activities and their illustration of important issues surrounding biological invasion. To
ensure the inclusion of a wide variety of examples, only one species from each genus was selected. Absencefrom the list does not imply that a species poses a lesser threat (Global Invasive Species Database, 2022).
Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the number ofthe world’s worst invasive alien species per country/marine unit was aggregated to the HydroBASINS level/marine assessment units, respectively; 2) it was then classified into the five risk-score classes, following theclassification as in the tables below.
Terrestrial Biodiversity Risk Filter risk score# of world’s worst invasive alien species

1 - Very low risk<=5

2 - Low risk

>5-15

3 - Moderate risk

>15-254 - High risk

>25-455 - Very high risk

>45

Marine Biodiversity Risk Filter risk score# of world’s worst invasive alien species

1 - Very low risk

0

2 - Low risk

1

3 - Moderate risk

2

4 - High risk

3

5 - Very high risk

>4

5.4 – Pollution
Pollution is an important driver of biodiversity and ecosystem change throughout all biomes, withparticularly devastating direct effects on freshwater and marine habitats (IPBES, 2017). The BRF only
focuses on nutrient, pesticide and air pollution at this point. Further inclusions could be made, e.g. plastic
and light and noise pollution, which can have a significant impact on biodiversity.
5.4.1 – Terrestrial Nutrient Pollution
At a global level, the atmospheric deposition of nitrogen has been recognised as one of the most important
threats to the integrity of global biodiversity. Once nitrogen is deposited on terrestrial ecosystems, acascade of effects can occur that often leads to overall declines in biodiversity. Within terrestrial biomes,
nitrogen deposition through fossil fuels and fertiliser use has been found to impede decomposition and slow
microbial growth, with a number of implications for terrestrial biodiversity (IPBES, 2017).
FAO provides data on total nitrogen per country as well as the total area of cropland per country, fromwhich total nitrogen per hectare of cropland can be inferred (FAO, 2020a). Please note that the source data
for this indicator is only available on a country level.

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To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the nitrogenuse per country was aggregated to the HydroBASINS level; 2) it was then classified into the five risk-scoreclasses, following the classification as in the tables below.
Biodiversity Risk Filter risk score

Total nitrogen used (kg/ha)

1 - Very low risk

<14

2 - Low risk

>14-533 - Moderate risk

>53-724 - High risk>72-77

5 - Very high risk

>77

5.4.2 – Terrestrial Pesticide Pollution
FAO provides county-level data on total pesticide use per country, as well as total area of cropland per
country, from which total pesticide per hectare of cropland can be inferred (FAO, 2020c). Please note thatthe source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the pesticide
use per country was aggregated to the HydroBASINS level 7; 2) it was then classified into the five risk-scoreclasses, following the classification as in the tables below.
Biodiversity Risk Filter risk score

Total pesticides used (kg/ha)

1 - Very low risk

<0.73

2 - Low risk

0.73-4.2

3 - Moderate risk

>4.2-5.6

4 - High risk

>5.6-5.9

5 - Very high risk

>5.9

5.4.3 – Freshwater Nutrient Pollution
While terrestrial ecosystems have been affected by nitrogen-phosphorous fertilisers, these have had a far
more pernicious effect on the biodiversity of freshwater and marine habitats, leading to eutrophication andhypoxic or ‘dead’ zones that support no aquatic life. Eutrophication and acidification occur when nitrogenand phosphorous are introduced, allowing algal blooms to proliferate which deplete the water of oxygen
and which are frequently toxic (IPBES, 2017).
Using predictive models, McDowell et.al. projected median concentrations for total nitrogen concentrations
during the growing season for catchments across the globe (McDowell, Noble, & Pletnvakov, 2020).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the total nitrogenconcentration per HydroBASIN level 4 was aggregated to the HydroBASINS level 7; 2) it was then classified
into the five risk-score classes, following the classification as in the tables below.
Biodiversity Risk Filter risk score

Total N concentration (mg/L)

1 - Very low risk<=0.4

2 - Low risk

>0.4-0.8

3 - Moderate risk

>0.8-1.6

4 - High risk

>1.6-2.6

5 - Very high risk

>2.6

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5.4.4 – Marine Nutrient Pollution
As with freshwater ecosystems, nitrogen and phosphorous pollution has led to eutrophication and hypoxicor ‘dead’ zones, caused by algal blooms (IPBES, 2017). Halpern et.al (2019) produced an impact score fornutrient pollution (from fertilizer runoff) as part of their analysis on human impact on the world’s oceans(Halpern, et al., 2019).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the nutrient
pollution impact score was aggregated to the FAO Statistical Fishing Areas and Marine Ecoregions of
the World using the mean value; 2) it was then classified into the five risk-score classes, following theclassification as in the tables below.
Biodiversity Risk Filter risk score

Nutrient pollution impact score

1 - Very low risk

0.000001-0.015

2 - Low risk

>0.015-0.042

3 - Moderate risk

>0.042-0.102

4 - High risk

>0.102-0.156

5 - Very high risk

>0.1565.4.5 – Marine Pesticide Pollution
Halpern et.al (2019) produced an impact score for organic chemical pollution (from pesticide runoff) as partof their analysis on human impact on the world’s oceans (Halpern, et al., 2019).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the organic
chemical impact score was aggregated to the FAO Statistical Fishing Areas and Marine Ecoregions of
the World using the mean value; 2) it was then classified into the five risk-score classes, following theclassification as in the tables below.
Biodiversity Risk Filter risk score

Organic chemical pollution impact score

1 - Very low risk

<0.0162 - Low risk

>0.016-0.049

3 - Moderate risk

>0.049-0.09

4 - High risk

>0.09-0.156

5 - Very high risk

>0.156

5.4.6 – Air Pollution
Please see indicator 2.3.1 – PM2.5 Concentrations

2. REPUTATIONAL RISK INDICATORS
Reputational risk can result from a company’s actual or perceived impacts on nature and people.
Reputational risk represents stakeholders’ and local communities’ perceptions of whether companies
conduct business sustainably and responsibly with respect to biodiversity, and can ultimately affect brandvalue and market share, among other factors. Reputational risk is influenced both by operational factors(i.e., what a company does) and scape-based factors (i.e., the conditions of the places in which those
operations occur). It comprises three risk categories: 1) Environmental Factors; 2) Socioeconomic Factors
and 3) Additional Reputational Factors.
6 – Environmental Factors
Reputational risk can be driven by negative impacts on local environmental assets and the local prevalenceof biodiversity-related issues. This risk category within the BRF comprises the risk indicators: 1) Protected/
Conserved Areas, 2) Key Biodiversity Areas, 3) Other Important Delineated Areas, 4) Ecosystem Condition
and 5) Range Rarity.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
6.1 – Protected/Conserved Areas
Protected and conserved areas have long been considered the cornerstones of biodiversity conservation(IPBES, 2019), and have an important role to play in achieving many of the Aichi global biodiversity
targets and the SDGs (CBD, 2020) and in safeguarding the health of people and planet for generations
to come (UNEP-WCMC and IUCN, 2021a). Currently the BRF only includes data on protected areas, as
the global database on conserved areas (Other Area Based Effective Conservation Measures) is not yet
globally representative.
6.1.1 – Protected Areas
For this indicator, UNEP-WCMC’s World Database of Protected Areas (WDPA) (UNEP-WCMC and IUCN,
2021a) was used. It is the most authoritative source of data on protected areas globally.
We were generously given permission to utilise the WDPA data in the BRF by the Integrated Biodiversity
Assessment Tool (IBAT) partners (IBAT, 2022).
To produce the BRF indicator, the raw data was processed as follows: 1) the per cent coverage was foundfor each assessment unit (terrestrial and marine) by protected area IUCN Category I – IV, as well as notcategorized protected areas; 2) the per cent coverage was found for each assessment unit (terrestrial andmarine) by all other categories of protected area; 3) each assessment unit was classified into the five riskscore classes, following the classification as in the table on teh following page.

Biodiversity Risk Filter risk score

Based on % of assessment units overlapping with protected areas(PAs)

1 - Very low risk

0% overlap

2 - Low risk

>0-5% overlap with any PA

3 - Moderate risk

>5% overlap with any PA

4 - High risk

>5-30% overlap with PA I-IV + not categorzied

5 - Very high risk

>30% overlap with PA I-IV + not categorized

6.2 – Key Biodiversity Areas

6.2.1 – Key Biodiversity Areas
For this indicator, BirdLife International’s World Database of Key Biodiversity Areas has been used (BirdLife
International, 2022). Key biodiversity areas (KBAs) are the most important places in the world for species
and their habitats. The KBA Programme supports the identification, mapping, monitoring and conservationof KBAs to help safeguard the most critical sites for nature on our planet – from rainforests to reefs,
mountains to marshes, deserts to grasslands and to the deepest parts of the oceans (KBA, 2022).
We were generously given permission to utilise the KBA data in the BRF by the Integrated Biodiversity
Assessment Tool (IBAT) partners (IBAT, 2022).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) a 15km buffer
around all global KBAs was created to account for any border discrepancies; 2) the per cent coverageof each assessment unit (terrestrial and marine) was found for all KBAs; 3) each assessment unit wasclassified into the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Based on % of assessment units overlapping with global Key
Biodiversity Areas (KBA)

1 - Very low risk

no overlap with 15km buffer around KBA; No overlap with KBA

2 - Low risk

overlap with 15km buffer around KBA; No overlap with KBA

3 - Moderate risk

>0%-10% overlap with KBA

4 - High risk

>10%-50% overlap with KBA

5 - Very high risk

>50% overlap with KBA

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6.3 – Other Important Delineated Areas
This sub-category is based on areas other than protected and conserved areas (PA) and Key Biodiversity
Areas (KBA), which have been delineated due to their contribution to different aspects of biodiversity.
Reputational risk will be influenced heavily by proximity to PAs and KBAs, particularly as these two
designations are used in corporate and financial safeguards (e.g., IFC PS6). However, the delineation ofthese areas is not yet complete, being restricted by administrative, logistical and funding constraints.
For example, KBAs must be identified ‘bottom up’ by local experts, using detailed local data, meaningthat many sites exist that may meet the KBA criteria but have not yet been formally identified. In addition,
while KBAs represent the most significant sites for the global persistence of biodiversity, other areas ofimportance are highly important regionally or nationally, and for particular biomes such as marine or forests.
This indicator therefore includes a range of other designations of importance. A detailed description of all
source data is provided in the following subsections.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) assessment units(terrestrial and marine) were found which overlap with any of the areas detailed in the following subsections,and 2) each assessment unit was classified into the five risk-score classes, following the classification as in
the table below.
Biodiversity Risk Filter risk score

Based on other important areas overlapping with terrestrial
assessment units

1 - Very low risk

No overlap

2 - Low risk
3 - Moderate risk

Overlap with WWF Global 200

4 - High risk
5 - Very high risk

Overlap with intact forest landscapes

Biodiversity Risk Filter risk scorebased on other important areas overlapping with marine
assessment units (FAO, MEOW)

1 - Very low risk

No overlap

2 - Low risk
3 - Moderate risk

Overlap with WWF Global 200 or Ecologically or BiologicallySignificant Marine Areas

4 - High risk
5 - Very high risk

Overlap with vulnerable marine ecosystems

6.3.1 – Intact Forest Landscapes
An intact forest landscape (IFL) is a seamless mosaic of forest and naturally treeless ecosystems within
the zone of current forest extent, which exhibits no remotely detected signs of human activity or habitat
fragmentation and is large enough to maintain all native biological diversity, including viable populations ofwide-ranging species. IFLs have high conservation value and are critical for stabilising terrestrial carbon
storage, harbouring biodiversity, regulating hydrological regimes and providing other ecosystem functions(Potapov, et al., 2017).
6.3.2 – WWF’s Global 200
WWF’s Global 200 project analysed global patterns of biodiversity to identify a set of the Earth’s
terrestrial, freshwater and marine ecoregions that harbour exceptional biodiversity and are representativeof its ecosystems.
Each of the Earth’s ecoregions was placed within a system of 30 biomes and biogeographic realms tofacilitate a representative analysis. Biodiversity features were compared among ecoregions to assess theirirreplaceability or distinctiveness. These features included species richness, endemic species, unusual
higher taxa, unusual ecological or evolutionary phenomena and the global rarity of habitats.
This process yielded 238 ecoregions – the Global 200 – comprised of 142 terrestrial, 53 freshwater and43 marine priority ecoregions (Olson & Dinerstein, 2012).

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6.3.3 – Ecologically or Biologically Significant Marine Areas
Ecologically or biologically significant marine areas (EBSA) are special areas in the ocean that serve
important purposes, including the support of the healthy functioning of oceans and the many services
that they provide. They are selected via the Convention on Biological Diversity, based on one or more of
the following scientific criteria: uniqueness or rarity; special importance for life history stages of species;
importance for threatened, endangered or declining species and/or habitats; vulnerability, fragility,
sensitivity or slow recovery; biological productivity; biological diversity; and naturalness (CBD, 2021).
6.3.4 – Vulnerable Marine Ecosystems
Vulnerable marine ecosystems (VMEs) are areas within the deep sea that are characterised by their highbiodiversity as well as by their high vulnerability to disturbances and that have been delineated following
the FAO’s International Guidelines for the Management of Deep-sea Fisheries in the High Seas (FAO, 2009).
Examples include seamounts, hydrothermal vents, cold water corals and sponge fields.
6.4 – Ecosystem Condition
Reputational risk is likely to be higher in scapes that are still intact or connected, etc., as the impact ofcorporate activities will be more significant, and of higher profile, as the social/cultural response and critiquewill be greater. This indicator aims to evaluate intactness and connectivity of ecosystems as a proxy forecosystem condition, independent of any legal or administrative delineation. As ecosystem condition was
already evaluated for indicator 2.4, the same data and risk levels were applied here. However, while in 2.4
low physical risk was associated with intact and connected ecosystems, here high reputational risk is
associated with intact and connected ecosystems. Thus the inverse of the 2.4 indicator has been used here.
6.5 – Range Rarity
Reputation risk will likely be highest where corporate actions cause or contribute significantly to a species
extinction. Range-size rarity is a measure of species endemism – a state of a species being found in asingle and/or restricted geographic range. This indicator specifies those areas where impact on a species
might more easily cause or contribute to an extinction.
It is calculated from the area of the pixel divided by the area of the range for each species, i.e. theproportion of the species’ range contained within the given pixel. These values are summed across all
species to show the aggregate importance of each pixel to the species occurring there (IUCN , 2022).
For this indicator, permission was given to utilise the IUCN Red List of Threatened Species data in the BRF
by the IBAT partners (IBAT, 2022).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the raster data
was aggregated data to the assessment units using the max value; 2) it was then classified into the five riskscore classes, following the classification as in the tables below.
Biodiversity Risk Filter risk score

Max range rarity score per assessment unit

1 - Very low risk<=0,00005

2 - Low risk<=0,0002

3 - Moderate risk<=0,0005

4 - High risk<=0,0008

5 - Very high risk

>0,0008

7 – Socioeconomic Factors
Reputational risk can be driven by negative impacts on local socioeconomic conditions and the local
prevalence of socioeconomic issues. This risk category within the BRF comprises the risk indicators: 1)
Indigenous Peoples (IPs); Local Communities (LCs) Lands and Territories, 2) Resource Scarcity: Food Water - Air, 3) Labor/Human Rights and 4) Financial Inequality.
7.1 – Indigenous Peoples (IPs); Local Communities (LCs) Lands and Territories
Whilst global data on IPs and LCs lands and territories exists, this indicator has not yet been included in themap visualisation and the risk assessment. Potential inclusion of this data will be explored with IPs and LCs
a priority for the next phase.

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7.2 – Resource Scarcity: Food - Water - Air
Sometimes named ‘the Big Three’, air, water and food are essential for human survival. This trifecta of
indicators was included in the BRF to measure where the most basic conditions were at risk, which cancompromise working conditions and could potentially reflect badly on companies operating in regions
where these conditions might not be met.
To produce the Biodiversity Risk Filter indicator, the highest risk score of the following three raw data sets
was used.
7.2.1 – Food Security
The FAO publishes statistics on the prevalence of moderate or severe food insecurity in the total population
on a country-by-country basis (FAO, 2021), which has been used as a basis for this indicator. Please notethat the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the country data
was aggregated to the HydroBASINS level 7 using the mean value 2) it was then classified into the five riskscore classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Percentage of moderate or severe food insecurity in the
total population

1 - Very low risk<=8.9

2 - Low risk

>8.9-19.7

3 - Moderate risk

>19.7-37.4

4 - High risk

>37.4-57.7

5 - Very high risk

>57.7

7.2.2 – Water Scarcity
Please see indicator 1.1.1 – Water Scarcity
7.2.3 – Air Condition
Please see indicator 2.3 – Air condition
7.3 – Labor/Human Rights
Labour and human rights are at the basis of just working conditions for employees and the treatment oflocal stakeholders. The objective of labour and human rights risk management is to prevent, mitigate or end
negative impacts of business activity on people (Global Compact Network Germany, 2021). This indicatoraims to give a first insight into regional discrepancies in labour and human rights situations.
To create this indicator, the average of the scores for the following two metrics was created.
7.3.1 – Ratified International Human Rights Instruments
As part of their Human Rights Indicators Guide, the UN Human Rights Office of the High Commissioner
developed a database of the number of international human rights instruments ratified per country (UNHuman Rights Office of the High Commissioner , 2012). This has been used as the basis of this indicator.
Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the country
data was aggregated to the HydroBASINS level 7; 2) it was then classified into the five risk-score classes,
following the classification as in the table below.
Biodiversity Risk Filter risk score

Number of international human rights instruments ratified

1 - Very low risk<=18

2 - Low risk<=16

3 - Moderate risk<=13

4 - High risk<=11

5 - Very high risk<=8WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
7.3.2 – Labor Rights Violations
The International Trade Union Congress (ITUC) Global Rights Index depicts the world’s worst countries forworkers by rating countries on a scale from 1 to 5+ on their degree of respect for workers’ rights. Violations
are recorded each year from April to March. Detailed information exposing violations of workers’ rights
in each country is published in the ITUC Survey, found at survey.ituc-csi.org (ITUC, 2020). This has beenused as the basis of this indicator. Please note that the source data for this indicator is only available on acountry level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the country data
was aggregated to the HydroBASINS level 7; 2) it was then classified into the five risk-score classes, following
the classification as in the table below.
Biodiversity Risk Filter risk score

ITUC Global Rights Index

1 - Very low risk

1

2 - Low risk

2

3 - Moderate risk

3

4 - High risk

45 - Very high risk

5 and 5+

7.4 – Financial Inequality

7.4.1 – Financial Inequality
For companies, systemic financial inequality is a great source of risk. It threatens operations and has thepotential to destabilise supply chains, trigger political instability and jeopardise their social licence tooperate (KPMG, 2022).
The Gini index is a measure of financial inequality and is the basis of this BRF indicator. It measures theextent to which the distribution of income (or, in some cases, consumption expenditure) among individuals
or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the
cumulative percentages of total income received against the cumulative number of recipients, starting withthe poorest individual or household. The Gini index measures the area between the Lorenz curve and ahypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus,a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality (World Bank,2021). Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) the country
data was aggregated to the HydroBASINS level 7; 2) it was then classified into the five risk-score classes,
following the classification as in the table below.
Biodiversity Risk Filter risk score

Gini Index

1 – Very low risk

<30.8

2 – Low risk

>30.8-36.1

3 – Moderate risk

>36.1-41.6

4 – High risk

>41.6-49.8

5 – Very high risk

>49.8

8 – Additional Reputational Factors
Reputational risk can be driven by the actual or perceived importance or value of ecological assets andsocioeconomic conditions and the level of public scrutiny of companies operating in a given geography.
Additional reputational factors within the BRF comprises the risk sub-categories: 1) media scrutiny, 2)
political situation, 3) sites of international interest; and 4) risk preparation.
8.1 – Media Scrutiny
Media scrutiny indicates whether there has been documented negative news (e.g., incidents, criticism orcontroversies) related to environmental and social issues that can affect a company’s reputational risk.
To create this indicator, the higher of the scores for the following two metrics was used.

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8.1.1 – Media Scrutiny (Ecological Topics)
For this indicator, RepRisk’s (2021) country-specific, weighted score of negative news for all ecological tags
was used. Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) For eachcountry, the number of incidents related to ecological topics were multiplied by their severity score; 2) the
country’s weighted score was transposed to the HydroBASINS level 7 using the majority value; 3) it wasthen classified into the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Sum of incidents*severity per country

1 - Very low risk

0

2 - Low risk

>0-1003 - Moderate risk

>100-250

4 - High risk

>250-750

5 - Very high risk

>750

8.1.2 – Media Scrutiny (Social Topics)
For this indicator, RepRisk’s (2021) country-specific, weighted score of negative news for all social tags wasused. Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) For eachcountry, the number of incidents related to social topics was multiplied by their severity score; 2) the
country’s weighted score was transposed to the HydroBASINS level 7 using the majority value; 3) it wasthen classified into the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Sum of incidents*severity per country

1 - Very low risk

0

2 - Low risk

>0-1003 - Moderate risk

>100-250

4 - High risk

>250-750

5 - Very high risk

>750

8.2 – Political Situation
Unstable and ineffective institutions and governance can potentially undermine business viability andincrease potential for reputational risks.
This indicator is informed by four datasets: violence against land and environmental defenders; theFreedom in the World index; the World Bank’s Governance index (The World Bank, 2022); and the
Corruption Perceptions index.
8.2.1 – Violence Against Land and Environmental Defenders
For this indicator, Global Witness’s record of total number of killings per country was used (Global Witness,2019). Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) The country’s
percentile rank was aggregated to the HydroBASINS level 7 using the majority value; 2) it was thenclassified into the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Total number of killings per country

1 - Very low risk

0

2 - Low risk
3 - Moderate risk
4 - High risk

1-5

5 - Very high risk

>5WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
8.2.2 – Freedom
This risk indicator is based on the latest data from Freedom House: the Freedom in the World 2021
(Freedom House, 2021), an annual global report on political rights and civil liberties, composed of numericalratings and descriptive texts for each country and a select group of territories. The 2021 edition involved
more than 125 analysts and nearly 40 advisers with global, regional and issue-based expertise to coverdevelopments in 195 countries and 15 territories from 1 January to 31 December 2020. Please note that thesource data for this indicator is only available on a country level.
This indicator has already been calculated in the Water Risk Filter and has been integrated into the
Biodiversity Risk Filter without changes. For more information, please consult the Water Risk Filter
Methodology or visit the Water Risk Filter directly.
8.2.3 – GovernanceFor this indicator, the World Bank’s worldwide ‘government effectiveness in percentile’ governance indicator(World Bank, 2010) was used. Government effectiveness captures perceptions of the quality of public
services, the quality of the civil service and the degree of its independence from political pressures, thequality of policy formulation and implementation, and the credibility of the government’s commitment tosuch policies. Percentile rank indicates the country’s rank among all countries covered by the aggregate
indicator, with 0 corresponding to the lowest rank and 100 to the highest. Percentile ranks have beenadjusted to correct for changes over time in the composition of the countries covered by the indicator.34
Please note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) The country’s
percentile rank was aggregated to the HydroBASINS level 7 using the majority value; 2) it was thenclassified into the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Government effectiveness – percentile rank

1 - Very low risk

>80-100

2 - Low risk>60-803 - Moderate risk>40-604 - High risk

>20-405 - Very high risk

>0-20

8.2.4 – Corruption
This risk indicator is based on the latest data from Transparency International: The Corruption Perceptions
Index 2020 (Transparency International, 2021). This index aggregates data from different sources that tracks
perceptions of business and country experts on the level of corruption in the public sector.
This indicator has already been calculated in the Water Risk Filter and has been integrated into the
Biodiversity Risk Filter without changes. For more information, please consult the Water Risk Filter
Methodology or visit the Water Risk Filter directly.
8.3 – Sites of International Interest
The sites of international interest comprise RAMSAR and World Heritage sites. Wetlands are among themost diverse and productive ecosystems. They provide essential services and supply all our fresh water.
RAMSAR sites highlight important wetlands and encourage their wise use (Ramsar, 2020).35 World Heritage
sites are a collection of unique and diverse places that encourage nature conservation and the preservationof cultural properties (UNESCO-WHC, 2022).36 Both RAMSAR and World Heritage sites are adopted byintergovernmental processes (the RAMSAR Convention and the World Heritage Convention). To give both
RAMSAR and World Heritage sites additional weight, they are considered both as part of the protectedareas indicator, and as a unique indicator in their own right.

34

See https://databank.worldbank.org/source/worldwide-governance-indicators#

35

See https://www.ramsar.org/

36

See https://whc.unesco.org/

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8.3.1 – World Heritage and RAMSAR sites
For this indicator, UNEP-WCMC’s World Database of Protected Areas (WDPA) (UNEP-WCMC and IUCN,
2021a) was used. It is the most authoritative global source of data on protected areas and features Natural
World Heritage Sites and RAMSAR sites in its collection.
We were generously given permission to utilise the WDPA data in the BRF by the Integrated Biodiversity
Assessment Tool (IBAT) partners (IBAT, 2022).
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) Assessment
units (terrestrial and marine) were found which overlap with World Heritage Sites, RAMSAR sites, or both;2) each assessment unit was classified into the five risk-score classes, following the classification as in thetable below.
Biodiversity Risk Filter risk score

Based on overlap of natural world heritage sites and RAMSAR sites
with assessment units

1 - Very low risk

No overlap with either RAMSAR or natural world hertiage site

2 - Low riskx

3 - Moderate riskx

4 - High riskx

5 - Very high risk

Overlap with either RAMSAR or natural world heritage site, or both

8.4 – Risk Preparation
The level of risk preparation has implications for the kind of coping response needed to the realisation of
biodiversity risks which, in turn, can contribute to vicious or virtuous circles in risk management. Wheneffective preparation limits the damages from adverse shocks, coping can be minimal, leaving moreresources available for further investments in risk management, reducing vulnerability to future shocks andso on (World Bank, 2014).
8.4.1 – Index of Risk Preparation
For this indicator, the World Bank’s Index of Risk Preparation was used (World Bank, 2014). Preparation
for risk at the country level includes actions by and contributions from all social and economic groups andinstitutions, including the state. The index, developed for the World Development Report 2014, comprises
measures of assets and services across four important categories – human capital, physical and financial
assets, social support and state support – that influence preparation for risk (World Bank, 2014). Please
note that the source data for this indicator is only available on a country level.
To produce the Biodiversity Risk Filter indicator, the raw data was processed as follows: 1) The country’s
percentile rank was aggregated to the HydroBASINS level 7 using the majority value; 2) it was thenclassified into the five risk-score classes, following the classification as in the table below.
Biodiversity Risk Filter risk score

Index of Risk Preparation

1 - Very low risk

1 (most prepared quintile)

2 - Low risk

2

3 - Moderate risk

3

4 - High risk

45 - Very high risk

5 (least prepared quintile)76

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77

APPENDIX STEP 1: COLLECTING LOCATION-SPECIFIC
COMPANY AND SUPPLY CHAIN DATAAPPENDIX GUIDANCE A: OVERVIEW OF DATA PROVIDERS
1. ASSET-LEVEL DATA

Below is a non-exhaustive list of different asset-level data sets.
Open-source data sets
• Spatial Finance Initiative: The Geoasset databases of
the Spatial Finance Initiative provide information on global
cement, iron and steel production assets. The Spatial
Finance Initiative is also working on databases covering
aluminium, petrochemicals, pulp and paper, waste
management and beef production.
• World Resources Institute (WRI): The WRI’s Global Power
Plant database provides information on thermal plants (e.g.,coal, gas, oil, nuclear, biomass, waste and geothermal)
and renewables (e.g., hydro, wind and solar). Each power
plant is geolocated and entries contain information on plant
capacity, generation, ownership and fuel type.
• Global Tailings Portal: Grid-Arenal’s free, searchable
Global Tailings Storage Facilities database contains
detailed information on more than 1,800 mine tailings dams
around the world, based on mining companies’ disclosure.
• Boston University Global Development Policy Centre:
The BU Global Development Policy Centre’s China’s Global
Power (CGP) database provides information on power
plants outside China financed by Chinese foreign direct
investment (FDI) and/or China’s two policy banks.
• Global Energy Monitor (GEM): GEM catalogues
global fossil fuel and renewables projects in databases
and provides trackers covering coal-fired power, fossil
infrastructure, coal mines, gas plants, nuclear power andrenewables (solar, wind, hydro, geothermal and bioenergy).
• Leadership Group for Industry Transition: Its Green
Steel Tracker collates public announcements of low-carbon
investments in the steel industry at asset level. A Green
Cement Technology Tracker will be available soon.
• WikiRate: WikiRate is an open, collaborative platform toanswer questions about corporate impacts. The Fashion
Checker: Factories Data is the result of the Clean Clothes
Campaign project to investigate wages in apparel supply
chains. The data set covers mostly social aspects but also
contains information on the factories’ province and city.
Table 10 presents the availability of open-source asset-level
data by WWF Risk filter industry classif.
Commercial data sets
Commercial data sets produced by third-party providers
are widely available.

• Asset Resolution combines sectoral data from individual
providers and provides them in a ready-to-use format• Four Twenty Seven, now part of Moody’s, is a good source ofclimate risk data.
• S&P Global provides industry specific asset-level covering
energy, financial institutions, fintech, maritime and trade,metals and mining, real estate, telecommunications, retail aswell as construction.
• The report by the 2 degrees investing initiative (2017) contains
an excellent overview of climate-related asset-level data.
Regulatory data sets
In theory, regulatory data sets can be a great source of
information as they mandate disclosure via regulation. In
practice, the data is sometimes difficult to extract.
• EU Emissions Trading System (EU Transaction Log):
the EU ETS covers plants producing around 40 per centof the EU’s greenhouse gas emissions and is updated
annually. Extracting the data is relatively cumbersome.
The private website euets.info processes the data andmakes it available.
• US EPA Greenhouse Gas Reporting Program (Datasets): the GHGRP contains facility-level GHG emissions
data from large emitting facilities in the USA (incl.
information on location of sites).

2. CORPORATE STRUCTURE DATA
Below is a non-exhaustive list of data sets illustratingcompany hierarchies:• FactSet (Data Management Solutions)• Bloomberg (Global Corporate Structure Data)• Refinitiv (Ownership, Insiders and Institutional Profile)• ORBIS (Corporate Structures and Hierarchies).

3. COMPARISON OF COVERAGE
Based on FactSet data, we checked the availability ofdata points linked to disaggregated revenue data (FactSet
Georev) for the universe of approximately 60,000 publicly
listed companies and compared it with the coverageof corporate structure data (FactSet Data Management
solution). Table 9 shows that leveraging disaggregatedrevenue data would be available for around 30,000
publicly listed companies. Corporate structure data yields
information on almost 50,000 companies.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

79

Table 9: Comparing coverage (corporate structure data vs. disaggregated revenue data); Source: FactSet

Industry Sector(following NACE)

Frequency (in sample)

Data on corporate structure and subsidiaries

Data on sectoral and geographical revenue distribution#% (sample percentage)

#

%

#

%

A - Agriculture and forestry

915

1.51

623

1.26

451

1.42

B - Mining and quarrying

4,366

7.2

3,636

7.36

1,229

3.86

C - Manufacturing

23,17938.22

16,901

34.21

11,33835.6

D - Electricity

1,292

2.13

847

1.71

747

2.35

E - Water

396

0.65

330

0.67

209

0.66

F - Construction

1,756

2.9

1,320

2.67

1,054

3.31

G - Wholesale, repair of vehicles

3,900

6.43

3,766

7.62

2,432

7.64

H - Transportation and storage

1,474

2.43

1,094

2.21

881

2.77

I - Accommodation and food service

915

1.51

786

1.59

654

2.05

J - Information and communications

6,191

10.21

5,463

11.06

3,533

11.09

K - Financial and insurance

8,495

14.01

7,713

15.61

4,442

13.95

L - Real estate

3,093

5.1

2,206

4.47

2,290

7.19

Other1

4672

7.7

4,713

9.54

2,584

8.11

60,644100

49,398100

31,844100

Interpretation: There are around 60,000 publicly listed companies, 915 of them belong to NACE industry sector A (agriculture, forestry, fisheries). For around 600 of them, data on the corporate structure (i.e. the ultimate parent company and its subsidiaries, including location data) is available. For around
450 of them, we have at least disaggregated revenue reporting.
1) ‘Other’ comprises the NACE macro sectors M-U (covering administration, research and entertainment, amongst others).

Electric Energy (Combustion)
Electric Energy (Hydro)
Electric Energy (Solar,
Wind)

Asset Resolution
x
x
x
x
x

Global Data
x
x
x
x
x

BNEF
x
x

World Steel

WardsAuto/AutoForecastxMarklinesxxx

Pulp and paper (P)Waste management (P)Oil and gas

x

WoodMackenzie Upstream Data Tool

Global Cement DirectoryxInternational Cement Review
x

WBCSD GNRx

Enerdataxxx

Green Steel Tracker

Fashion Checker: Factories Data

x
x
x
x

x

PlantFacts

x
x
x

x
x
x
x

x
x
x
x

Rystad Energy Ucube
x

x

x

x
x

x

x

x
x
x

x

x

x
x

CAPA Fleetsx

FlightsGlobalx

Rightship GHG Ratingx

shippingefficiency.org BetterFleetx

Clarksonxx

Water Utilities/
Water Serv.
Providers(P)

Transportation
Servicesx

Aluminium (Planned)

Textiles, Apparel& Luxury Good
Prod.

Iron

Telecommunication ServicesxPaper & Forest
Product Production

x

Steel

Oil, Gas & Consumable Fuels

CementOffices & Professional Services

Steel

Metals & MiningxxLand Development & Construction

China’s Global Power Databasex

Hospitality Services

Global Tailingsx

Health Care,
Pharmaceuticals

Othersx

General orSpecialty
Retailing

EnergyFood Retailingx

Food & Beverage Production

Renewables

Fishing and
Aquaculture

Thermal plants

Electronics &Semiconductors

Construction
Materials

Petrochemicals (P)Chemicals &Other Materials

EU – ETS

Automotives& Machinery

Appliances

Beef production (P)Agriculture(plant products)

Agriculture(animal products)

Water utilities andwater services
providers

Transportation
services

Textiles, apparel andluxury goods Prod.

Telecommunication
services

Paper and forest
Product Production

Oil, gas andconsumable fuels

Offices andprofessional services

Metals and mining

Land development
and construction

Hospitality services

Health care,pharmaceuticals

General or specialty
retailing

Food retailing

Food and beverage
production

Fishing andaquaculture

Electronics andsemiconductors

Electric energy (solar,
wind)Electric energy (hydro)

Electric energy(combustion)

Construction materials

Chemicals and othermaterials

Automotives andmachinery

Appliances

Agriculture (plant
products)

Agriculture (animal
products)

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Table 10: Open-source asset-level data by WWF Risk Filter industry sector

Spatial Finance Initiative

(P)
(P)

(P)
(P)

World Resources Institutex

Global Energy Monitorx

Coal (plants, terminals, projects)

x
x

x
x
x

Table 11: Commercial asset-level data by WWF Risk Filter industry sector

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

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APPENDIX GUIDANCE A: COMPARING DIFFERENT DATA PROXIESTable 12: Comparing the four different data sources

Asset-level data

Coverage

Advantages

Disadvantages

Company structure data

City of headquarters

Disaggregated revenue

Available for a broad range of industries. The largest asset
coverage is reached by commercial providers. However,availability of open-source data is limited. See Appendix
Guidance A: Overview of data providers, for a morecomprehensive overview.

Having checked FactSet data, this data can be retrieved foralmost 50,000 publicly listed companies (from a universeof around 60,000). However, the number of retrieved sites
per company varies a lot (for 5,457 companies we retrieved
more than 10 sites, but for around 45,000 companies,
fewer than 10).

Data points that are available in (all) commercial data sets.
The coordinates of the headquarters can be used to run theassessment. This data is available for millions of listed andnon-listed companies across the globe.

Broad coverage, in particular for publicly listed companies.
FactSet, for example, provides this information for around
30,000 companies (see Table 9).

- Focus on corporate production facilities (which have thehighest biodiversity relevance compared to, for example,
real estate or office buildings).

- Available in a well-structured format, including industry
classifications and company identifiers, which allows forsmooth integration.

- Important attributes available.

- Regarding coverage, Bloomberg, FactSet and otherdata providers provide this data for a broad universe ofcompanies.

Data points on the location of headquarters are, in
principle, available from any third-party data provider.
Also used and tested by the European Central Bank to
explore physical climate-related risks of banks.

Broadly available and low implementation costs.
Disaggregated revenue by industry sector and by country,
combined with the homogeneity assumption, has beenfrequently applied.

- (Open-source) data sets can provide a false senseof completeness (since the number of sites percompany can differ between commercial andopen-source data sets).

- Relevance of the sites is not entirely clear since many
subsidiaries are not associated with the company’s main
business line.

It can provide a false sense of completeness if a company
has in fact several physical assets spread across the globe.
The accuracy of this proxy relies on the assumption thatmost production is linked to the headquarter (putting aside
the supply chain).

- The spatial granularity is only at the country level whichmakes it difficult to accurately incorporate the importanceand local integrity of biodiversity indicators into theanalysis.

- Commercial data sets are costly.
- Incorporation can be time consuming if company
identifiers (such as ISIN or LEI) are missing. (For example:
The LEI (Legal Entity Identifier) is missing
for 50 per cent of the cases in SFI’s cement andsteel database.)-Since the data product focuses on the corporate
hierarchy, production plants might not be part of it when
they do not belong to a separate legal entity (e.g., asubsidiary).
- Missing location (e.g., coordinates) and industry
classification values require a work-around (described in
the step-by-step guidance) or to drop observations.

- The precise revenue distribution per industry and country
is not known and analysts must assume that revenue
is homogenously distributed across industries andcountries.
- Revenue is a questionable proxy for physical assets in acountry.

- Available only for selected industries.

Assessment

Recommended to use whenever available due togeoreferenced, contextualised site-level data. (First, assetlevel data focuses on biodiversity-relevant production
facilities. Second, the important contextual attributes
facilitate a bottom-up risk measurement.)

This proxy is a great starting point due to broad company
coverage and low implementation costs. When using it,
analysts should be aware of its limitations. Since the data
product focuses on the corporate hierarchy, production
plants might not be part of it when they do not belong to a
separate legal entity (e.g., a subsidiary).

Only a backup option. Even though this proxy has beenused by the ECB to run a risk assessment on millions of
portfolio companies, the accuracy of the proxy relies on theassumption that 100% of the corporate production is linked
to its headquarter.

Only a backup option due to severe limitations (see cell
above) However, due to low implementation costs, thisapproach may suffice as a first screening tool.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONAPPENDIX GUIDANCE A:
CASE STUDY – THE IMPORTANCE OF INPUT DATA
Asset-level data vs corporate structure data
As seen in Table 12, there are different data sources that can be used to build a comprehensive locationspecific database. While asset-level data stands out due to its contextualised attributes, corporate
structure data can offer a promising solution due to its broad and accessible coverage. To investigate if thecontextualised attributes of asset-level data provide additional insight regarding companies’ biodiversity
risk, we have used the WWF BRF tool to analyse a collection of cement companies using asset-level data(from the SFI database) and corporate-level data (from FactSet).
Although corporate hierarchy data benefits from its broad scale (the FactSet database has around 50,000
data points, see Table 12) the number of sites per company varies widely. Figure 14 below shows thelobsided distribution of the top 1,000 companies in terms of number of sites.37
This distribution is partly because of a small concentration of very large global companies, but it also
reflects the possible poor data quality that does not register all companies’ locations.
If all of a company’s sites are not represented in a biodiversity risk analysis, it is likely that risk might not beadequately represented. Could asset-level data lend more accuracy to a biodiversity risk analysis?
Figure 14: Top 1000 FactSet companies by number of sites.

37 The distribution does not reflect the raw data from FactSet’s database but is already the result of some data cleaning, as suggested by the workarounds
presented in Box 3. Downloading data from FactSet’s Data Management Solutions sometimes yields lists of several hundred locations for bigger
companies. However, the quality of the location and sector information varies. We delete subsidiaries for which only the country is known and use aspatial extrapolation approach for subsidiaries with location information at city level. As for the sectoral information, some subsidiaries are linked toindustry classifications that are different to the company’s main business model (for example, some 20 per cent of the subsidiaries of a big cementcompany are linked to financial services). In a second step, we keep only subsidiaries with a industry classification that is linked to the official corporate
revenue reporting.

84WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION85

To explore how the results can change, we compared a sample of 122 companies from the cement industry
for which we 1) collected location data from SFI’s cement data set; and 2) retrieved location-specificcompany data from FactSet’s Data Management Solutions.
Description of the sample• SFI cement data set: global distribution of 122 companies across 1,339 locations (see Figure 15)• Cement companies from FactSet’s Data Management Solutions: global distribution of 122 companies
across 1,130 locations
Despite the SFI data set containing the company with the highest number of sites (Holcim Ltd, with 193),
the average number of sites for both data sets is very similar, at 10 sites per company (see Table 13).
Additionally, the mean difference per company between the number of company sites was only 0.05,
indicating that the composition of the two data sets is fairly similar.
Figure 15: Overview of company sites identified by the SFI Cement database

Table 13: Top 10 number of sites from the FactSet and SFI cement data set
Company Name

SFI

FactSetDifference

Holcim Ltd

193

121

72

HeidelbergCement AG

138

130

8

Anhui Conch Cement Co., Ltd

104

52

52

CEMEX SAB de CV

61

41

20

CRH Plc

58

128

-70

Huaxin Cement Co., Ltd

53

20

33

Buzzi Unicem SpA

42

16

26

China Tianrui Group Cement Co., Ltd

30

6

24

Grupo Argos SA

23

24

-1

Vicat SA

21

21

0

…
TPI Polene Public Co. Ltd
Mean

…

…

…

1

2

-1

9.9

10

-0.05

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION86

For the case study, we analysed both SFI and FactSet cement data sets separately, using the November
2022 version of the WWF BRF tool.38 Following the methodology stated above the scape risk was aggregatedto the site level using binning and the 75th percentile method (see Step 2). Site-level risk was further
aggregated to company-level risk by weighing each location based on its business importance (see Step 3a).
The results of the analysis show that using asset-level data (the SFI data set) results in slightly higher risk
scores on average compared with using corporate-level data. A breakdown of the physical and reputationalrisk score distribution in Table 14 shows that, while the risk scores of both data sets mostly lie within the
3.0 to 4.0 range, the SFI data set has a higher risk on average. Additionally, a higher percentage of the data
set’s market capitalisation is exposed to higher risk using the SFI’s data set. A possible explanation is that
the SFI focuses on corporate production facilities which have a higher risk profile than the administrative
operating facilities that are more prevalent in company structure data (which results in slightly different
industry sector classifications and hence different industry materiality ratings).
Table 14: Physical and reputational risk score distribution for the SFI and FactSet databases, including % total cement
portfolio in market cap.
Physical risk

Reputational risk

SFI
Score

% of
companies

FactSet% of data
set market
cap

% of
companies

SFI% of portfolio
market cap% ofcompanies

FactSet% of data set
market cap% ofcompanies% of portfolio
market cap

0.0 – 3.0

0

0

0

0

0

0

0

0

3.1 – 3.4

12.5

17.2

25

41.4

36.4

60

54

92

3.5 – 3.9

87.5

82.8

75

58.6

54.54

36

37

4

4.0 – 5.0

0

0

0

0

9.1

4

9

4

Despite the table above, on a company-by-company analysis there is an insignificant difference between
the use of the two data sets. Figure 16 and Figure 17 show a comparison of the physical risk scores fromboth the FactSet and SFI data sets. Although some variation exists, the vast majorities have a similar levelof risk under both data sets. As shown in Table 13, each company had a similar number of sites and, assuch, each company would have a similar amount of detail for each analysis. We can observe that the
number of sites is particularly important to the risk analysis. The top 50 riskiest companies in the data setsare presented in Figure 16, in which we see consistent divergence between the risk produced by each data
set at the top end. This seems to be driven by a difference in locational data, as the number of sites percompany for these companies (top 30) differs between data sets (see Table 13). This result is unsurprising,
given that the industry classification for both data sets is similar.39 As scape risk is an equal combinationof the importance and local integrity of biodiversity and industry materiality (see Step 3), given similarindustry classifications, much of the difference in risk is explained by locational attributes. A differencein the number of site locations between data sets will then likely have a noticeable effect on risk result.
However, we observe a convergence of risk between the two data sets for those companies with a similar
number of sites. This might be an indication that corporate-level data could act as a reasonable proxy for
asset-level data when the latter is either not available or the fiduciary resources are not present. As more
SFI data becomes available, we will repeat the exercise to validate whether the results presented above
remain consistent.

38 The case study is based on WWF BRF data from November 2022. As the underlying data is continuously improved, changes may occur that are notreflected in the case study.
39 All companies in the SFI data set are given the industry classification of “Construction Materials” and, although there is more heterogenous industry
classification in the FactSet data set, 78 per cent of companies are still classified as being in that industry sector.

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION
Figure 16: Top 50 highest physical risk scores from FactSet and the SFI cement databases

Figure 17: Physical risk scores from FactSet and the SFI cement
databases

87WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATIONAPPENDIX GUIDANCE B: OVERVIEW OF IO MODELSAs outlined in our methodology, input-output (IO) models provide a pragmatic and frequently used workaround. This raises the question of which IO model should be used. At least one comparative study has
been performed between different state-of-the-art IO models, which found that the carbon footprint formost major economies disagree by less than 10 per cent between the most used MRIOs. A detailed
discussion on inherent IO model assumptions and limitations is beyond the scope of this paper, but we refer
the interested reader to any standard text on the issue.40
Furthermore, Table 15 provides a non-exhaustive overview of existing IO models. The decision which IO
model to use should depend on the following parameters:• Available time series data: to incorporate relatively up-to-date data into the analysis, the data should
not be outdated;• Coverage of countries and regions: some IO models group countries into different blocks, for example‘Rest of the World’, which limits usability;• Industry sector classification used, as the industry classification has to be mapped to industry
materiality ratings that might be linked to another industry sector taxonomy. For example, if a user wants
to merge EXIOBASE with ENCORE, they first have to map the EXIOBASE industry classification to
ENCORE’s taxonomy of production processes.
Practical considerations on applying IO models
In practice, the user receives a large Nsector x Ncountries matrix from the IO table per country-sector pair, which
is mapped to Nrisk indicators (using the BRF industry materiality, for example). Practically, this computation
takes a long time when done for a large number of firms on the fly. However, since there is a limited total
number of permutations, subject to the number of distinct country-sector pairs of the financial portfolio, theuser can pre-compute the upstream risk score for each specific (sector, region) pair to each biodiversity
importance or integrity indicator (see Table 8 for the full list of indicators). When doing a computation forany particular company, the user then simply has to compute a weighted average over the relevant (sector,
region, biodiversity indicator) triplets.

40 See, for example, Miernyk, (W.H, 1965). The Elements of Input-Output Analysis. Reprint. Edited by Randall Jackson. WVU Research Repository, 2020,
for a good introductory text. Also the (Value Balancing Alliance, 2021), contains an excellent introduction including basic descriptions as well as acomprehensive overview.

88WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

89

Table 15: Overview of IO models
NameCountries orregions

Industries andproducts

Time series

Satellite accounts

Licensing

Other comments and use
cases

GLORIA - Global
Resource
Input-Output
Assessment

164 countries

97 industry
sectors

1990-2020

GHG emissions,materials, energy,
air pollution, landuse, water use,biodiversity, skills
and employment

US$20,000 forcommercial use

Used, for example, for the
Sustainable Consumption
and Production Hotspots
Analysis Tool (SCP-HAT)(link)
Technical documentation
here

EXIOBASE (link)

44 countries(28 EU,
16 majoreconomies)
and five RoW
regions

200 products
in 163 industry
sector1995 to2011, withpredictions formore recent
years

Resources,
emissions, waste
flows, packaging,materials and crop
residues

Requirespermission forcommercial use

Used, for example, by
Banque de France (link)

OECD Global InterCountry IO (ICIO)

66 countries(all OECD
countries, all
G20, all EU,
all ASEAN), 17
region groups,
RoW

45 industry
sectors (ISIC
Revision 4)

1995-2018

Value added andtaxes, demandbased material flows

Data can be
downloaded forfree in zipped .
csv and .R
data formats(here or here)

ICIO provides
decompositions of
aggregate, bilateral andsectoral exports andimports according to thesource and destination of
their value-added content

Eora Global Supply
Chain Database(link)

189 countries

EORA 26:
26 industry
sectors
Full EORA:
15,90941

1990-2015

GHG emissions,labour inputs, air
pollution, energy use,water requirements,
land occupation, N
and P emissions,primary inputs toagriculture from
FAOSTAT and Human
Appropriation of Net
Primary Productivity

Commercial
licence starts
from US$10,000
link

There are two versions of
EORA.
- Full Eora
- EORA 26: a simplified
model in a harmonised
26-sector classification.
Technical documentation
here

World InputOutput Database(WIOD)
link

43 countries(28 EU
countries and
15 other majorcountries)

56 industry
sectors and 59
products

2000-2014

Socio-economic:
Capital and labour(HS, MS, LS) in
physical inputs andfactor incomes
Environmental
accounts (emissions,energy use andresource use)

Available for freein Excel format(link )

Applications- Socioeconomic analysis(factor content of trade,
effects of outsourcing on
labour markets, trade invalue added, etc.)- Environmental analysis- Modelling (CGE modelling,
dynamic IO-basedmodelling)

SEI’s Input-Output
Trade Analysis Tool
(IOTA) (link)

236 regions

150 agriculturalcommodities

2005-2017

Various resource
inputs orenvironmental
extensions, including
land area; water(green, blue andgrey water), carbon
dioxide emissions
and nitrogen use

Freely available
for use (link)

SEI’s IOTA model is anenvironmental footprinting
tool that links physical dataon commodity production
in different countries witha detailed financial matrix
that traces inter-industry
buying and selling acrossthe world.

Global Trade
Analysis Project(GTAP) (link)

121 countries
and 20aggregatedregions (link )2004, 2007,2011 and 2014

Food, resources
and manufactures,
services

The model canbe downloaded
from here (link)

The most agriculturallybased MRIO in the world,which makes it also highly
relevant for a biodiversityfocused assessment.
Technical documentation
here.

Tables (link)

41

57 industry
sectors

65 industry
sectors (link)

Preserving sectoral classifications from each data providerWWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION© Arttu Timonen / WWF

90

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91

APPENDIX STEP 2: ASSESSING
BIODIVERSITY-RELATED RISKS
Percentile

Table 16: Illustrating aggregation using percentiles
Aggregated scape risk per indicator in risk category
Indicator 1

Indicator 2

Indicator 3

Indicator 4

Indicator 5

75th Percentile

Company A

1

1

1

4

5

4.5

Company B

3

4

2

3

3

3.5

Description: Percentiles as a measure takes the value of the nth percentage number in the distribution.42
In the case of biodiversity risk analysis, this measure would take the nth percentage observation of all
scape risk indicators. A suggested starting point would be the 75th percentile, although any percentile isapplicable, given sufficient observations.
Advantages: Using the 75th percentile emphasises high-risk scores. As show in Table 16, this method
has the property of emphasising the right tail of risk distributions (higher risks). Although the mean (50thpercentile) of indicators for company A (a mean of 2.2) is lower than that of company B (a mean of 3), thehigh levels of indicators 4 and 5 are emphasised. This method helps to inform companies that certain sites
might be highly exposed to biodiversity-related risks that could be integral to business operations. This isimportant, because even a single issue could result in considerable damage to a business and/or its value
chain. The omission of a high-risk score should therefore be avoided.
Disadvantages: There are two disadvantages to the method.
• First, it does not consider the full range of risks, and the scope of its effectiveness remains arbitrary in
the percentile chosen. Despite having a higher mean risk across all indicators, company B has a lower
risk under the 75th percentile measure than company A. Not incorporating the full distribution of risk into
the aggregation measure equals information loss.
• Secondly, how effective the percentile method is at picking up outlier risks is arbitrary to the number
given to the percentile. If the analysis in Table 16 had been increased to include ten indicators with theadditional indicators having a risk value of 1, then the 75th percentile measure would be 1 and hence
would not incorporate the tail risks.

42

For example, the 75th percentile of a distribution with eight observations would be the value of the sixth observation.

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APPENDIX STEP 3: A REVIEW OF
AGGREGATION METHODS
Several methods can be used to aggregate risks across biodiversity indicators, sites, companies andportfolios, subject to the different level 2 and level 1 risk categories (see Figure 4). Each method has
advantages and disadvantages across different dimensions and consideration should be given to various
factors, including the statistical literacy of the audience, information loss and feasibility of implementation. If
a measure of aggregation is not interpretable, then the severity of risk posed to an asset or company could
be underestimated and result in inadequate action. When presenting examples of the different methods, weaggregate across risk indicators per site; however, the methods are applicable to all dimensions
Table 17: Summarising different aggregation methods
Description

Mean

Mean across scape risk
indicators.

Pros

Cons

Broadly understood.

Distribution of risks is not captured. An increased
number of observations will reduce the prominenceof outliers and diminish the importance of tail risks.
This could lead to the understatement of biodiversity
risk (which makes it difficult to convey that addressing
biodiversity is of importance).

MedianCaptures 50 per cent of the
distribution.

Robust to outlier values.

Robustness to outliers is also a disadvantage, as
the distribution of risks is not captured. Using manydifferent indicators, the final score will be drawn
towards the median, which makes it extremely hard toconvey that addressing biodiversity is of importance.

Percentile

The nth percentage observationof risk indicators.

Robust to outlier values. High
percentile values help toemphasise fat right-tail risks thatcan be critical to a company orsite’s function.

Determining the nth percentage is arbitrary. Loss of
information as (1-n) per cent of observations are lost.

Moments

In particular, standard deviationand skewness.

Allows for the full visualisation of
the risk distribution, giving theanalyst a precise understanding of
the risk present.

Cannot be presented as a single indicator. Requires
statistical literary.

Mean

Table 18: Illustrating aggregation via the meanAggregated scape risk per indicator in risk category
Indicator 1

Indicator 2

Indicator 3

Indicator 4

Mean

Company A

1

5

1

5

3

Company B

3

3

3

3

3

Description: The mean method provides the simplest aggregation measure and is derived by taking
the mean across risk indicators within one aggregation cluster (for example, by taking the mean of all
indicators under the level 2 risk category provisioning services, and then taking the mean of all risk
categories to the level 1 risk type).
Advantages: The mean (or normalised sum) is broadly understood and interpretable.
Disadvantages: Despite being easily understood, considerable information about the distribution andnature of risks to a company can be lost. As shown in Table 18, two companies can have the same meanrisk, although company A is considerably more exposed to biodiversity risk through indicators 2 and 3.
This cancelling of risk exposure in the mean calculation is especially pertinent to biodiversity risk analysis,
as some indicators work in opposition to one another (e.g., drought and flood risk).

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MedianTable 19: Illustrating aggregation via the median
Aggregated scape risk per indicator in risk category
Indicator 1

Indicator 2

Indicator 3

Indicator 4MedianCompany A

1

1

1

5

1

Company B

2

2

2

1

2

Description: The median also serves as a simple measure by which to quantify the aggregate scape risk
indicators. The N/2th number represents 50 percent of the distribution and it is equivalent to the meanunder a typical normal distribution.
Advantages: The median is robust to outlier values. Unlike the mean, the median is not heavily influenced
by outlier values that might be an anomaly within the distribution of values.
Disadvantages: That the median is robust to outlier values can also work as a disadvantage in risk
analysis as it does not incorporate skewness and fat tails that could pose high levels of risk to acompany. In Table 19, we can see that company A has a median lower than that of company B despite
being considerably more exposed to indicator 4. Abnormally high levels of risk that could be dangerous if
left addressed are not picked up in the median.

Moments
Description: Rather than solely using the first moment (mean) of a distribution of measurements, thismethod emphasises the use of the first four moments (mean, standard deviation, skew and kurtosis), toprovide a description of the aggregation. The addition of a probability distribution could help statisticallylimited users visualise what the moments convey.
Advantages: By providing more information, this method helps users understand the details of the
distribution of scape risks of a company or site. Because each additional moment describes the distributionof risk, it allows the user to infer the importance of the risk to their own application themselves.
Disadvantages: Statistical literacy is required. Furthermore, more than one figure is presented (forexample mean and standard deviation) which increases complexity and defies the initial purpose ofcreating a single score.

Benchmarking explained
Description: Benchmarking is the process by which a company or asset is compared against a groupof companies or an index. The comparison allows for a measure of over- or underperformance. To
establish a benchmark, a group of companies must first be analysed and all their risks aggregated,which poses its own complications. An example of this would be to compute the risk for all companies
in the MSCI World index (following a certain aggregation method) and use this as the benchmark value
for biodiversity risk.
Advantages: The method is extremely intuitive and is common practice across the financial world.
Because of this, it can be easily understood by all levels of analyst and allows users to understandhow a company is operating relative to the market average. Additionally, the inclusion of different
benchmarks (sector and region specific) can help analysts understand different landscapes inincreased detail.
Disadvantages: The method in essence extends the problem of aggregation, as to benchmark one
must first determine a method of company-specific risk aggregation and then a method of aggregatinggroup risks. Additionally, in terms of biodiversity and scape risk, large quantities of data are required toestablish a benchmark, as they usually comprise hundreds or thousands of large-scale companies.

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APPENDIX 4: LIST OF EXPERTS AND
EXTERNAL CONTRIBUTORSTable 20: List of consulted experts on the Biodiversity Risk Filte
Name

Affiliation

Area of expertise

Alexis Morgan

WWF Germany

Freshwater biodiversity/Water Risk Filter

Alison Midgley

WWF UK

Agriculture and foodAnastasiya Timoshyna

Traffic

Sustainable trade

Ariane Laporte-Biscuit

WWF Germany

Freshwater biodiversity/Water Risk Filter

Axel Krumsiek

WWF Germany

OceansBrent Loken

WWF International

Food

Camilla Välimaa

WWF Sweden

Sustainability and business

Carly Cowell

KEW

Invasive species

Carolina Soto-Navarro

UNEP-WCMC, WWF Vietnam

Multidimensional Biodiversity Index

Chris WeberWWF

Climate and energy

Christine Scholl

WWF Germany

Agriculture

Colman O’Criodain

WWF International

Invasive species

Craig Beatty

WWF US

Forests and GIS

Daniel Brizuela

WWF US

GIS, supply chain risk analysis

Daniel Metzke

PIK

Planetary boundaries

David J. Patterson

WWF UK

Conservation Intelligence/WWF SIGHT

David Olson

WWF Hong Kong

Ecosystem type metrics

Deon Nel

WWF Netherlands

Conservation

Elaine Geyer-Allely

WWF International

Governance

Elisa Vacherand

WWF International

Sustainable finance

Florian Titze

WWF Germany

Governance and regulation

Franck Hollander

WWF Germany

OceansFrançois Gardin

Polygones E.R.I., advisor WWF DK

Oceans, investment, risk analysis

Ghislaine Llewelynn

WWF International

OceansGregory Gigoi

BCG

Data analysis

Guido Broekhoven

WWF International

Policy and regulation

Isabel MezaWWF Germany

Risk analysis/Water Risk Filter

Jaco Du Toit

WWF Sweden

Biodiversity and policy

Karen LuzWWF International

Food

Karen MoWWF US

Forests

Karin Bilo

WWF Germany

Oceans

Kathy Hughes

WWF UK

Freshwater biodiversity

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

95

Name

Affiliation

Area of expertise

Laura Prill

WWF Germany

Forests

Lin LiWWF International

Policy and regulation

Luca Chinotti

WWF International

Policy and regulation

Margaret Kinnaird

WWF International

Invasive species

Maja-Catrin Riecher

WWF Germany

Agriculture

Malou van Kempen

WWF Netherlands

OceansMargareta Renström

WWF Sweden

Business and biodiversity

Mario Vaupel

BCG

Risk hierarchy

Mark Heuer

WWF Germany

OceansMartha Stevenson

WWF US

Forests

Matthew Wilkinson

WWF International

Corporate transformation and sustainability

Melissa de Kock

WWF Norway

Climate models

Michele Thieme

WWF US

Freshwater biodiversity

Nicolas Poolen

WWF Netherlands

Green finance

Pablo Pacheco Balanza

WWF US

Forests and GIS

Philip Leonard

WWF International

Freshwater biodiversity

Philipp Kanstinger

WWF Germany

OceansPhilipp Wagnitz

WWF Germany

Ecosystems and natural resources

Pierre-Yves Hardy

WWF France

OceansRafael Camargo

WWF Germany

Water Risk Filter

Sandeep Chamling

WWF International

Climate models

Shaun Martin

WWF USClimate models

Stefano Esposito

WWF Norway

Agriculture and foodSusanne Schmidt

WWF UK

Agriculture and food, WWF SIGHT

Tanja Draeger de TeranWWF Germany

Agriculture

Vanessa Pérez

WWF International

Climate models

Vishaish Uppal

WWF India

Governance and regulation

Wendy Elliot

WWF International

Wildlife conservation

Review by relevant groups:
WWF Global Science +Biodiversity Stewardship Governance Committee
Business Advisory GroupIBAT Scientific Committee

WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION

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Table 21: WWF Switzerland External Advisory Group and Investor Group
Name

Organization

Anne Schoenauer

2° Investing Initiative

Benjamin Gränicher

Consultant

Chiara Colesanti Senni

Council on Economic Policies

Christoph Biehl

Credit Suisse Asset Management

Corli Pretorius

UNEP-WCMC

Doris Hauser

Forma Futura

Farah Nadiah Fadzil

Khazanah Nasional Berhad

Gautier Desme

S&P Global

Grant Rudgley

Cambridge Institute for Sustainability Leadership (CISL)

Harriet Wildgoose

Fidelity

Jakub Červenka

2° Investing Initiative

Jessica Smith

UNEP FI

Judson Berkey

UBS

Karianne Lancee

UBS

Liudmila Strakodonskaya

AXA Investment Management

Mark van Oorschot

PBL Netherlands

Michal Kulak

RobecoMieke Siebers

Foundation for Sustainable Development

Naasir Roomanay

Ninety One

Nico Frey

Radicant

Philipp Staudacher

Radicant

Robert-Alexandre Poujade

BNP Paribas Asset Management

Romain Svartzman

Banque de France

Sam Anthony

Ninety One

Sebastian Becker

UNEP-WCMC

Sebastian Wiesel

J. Safra Sarasin

Susanne Schmitt

Consultant

Therese Niklasson

Newton Investment Management

Wendy FrancesconiInternational Center for Tropical Agriculture (CIAT)WWF BIODIVERSITY RISK FILTER: METHODOLOGY DOCUMENTATION97

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