Position paper on corporate data inputs 20 September 2019

(1)

Position paper on corporate data inputs

20 September 2019

Produced by sub-group 3A as a part of the Aligning Biodiversity Measures for Business project

DRAFT FOR DISCUSSION

Chaired by Joshua Berger (CDC Biodiversité), with contributions from Annelisa Grigg (UNEP-WCMC), Katie Leach (UNEP-WCMC), Alexandra Marques (Joint Research Center JRC)

(2)

List of definitions

Definition 1: Characterisation factor ... 4

Definition 2: Midpoint ... 5

Definition 3: Endpoint ... 5

Definition 4: Input data ... 5

Definition 5: Characterisation factor ... 5

Definition 6: Input indicator ... 6

Definition 7: Nomenclature ... 6

Definition 8: User-collected data ... 15

Definition 9: Externally collected data ... 15

Definition 10: Inventory data ... 15

Definition 11: Pressure ... 15

Definition 12: Indicator ... 29

Definition 13: Measure ... 29

Definition 14: Metric ... 29

Definition 15: Unit ... 30

Introduction

Disclaimer

This position paper is based on the output of the “Biodiversity Accounting Approaches for Business”

workshop held in Brussels on 26-27 March 2019 and the working paper presented at the corporate data inputs sub-group (SG3A)’s webinar held on 8 July 2019.. It was prepared by the chair of SG3A sub- group taking into account the feedback provided during the webinar and the subsequent consultation of sub-group members. It is circulated as a draft for discussion with all SG3B members during the 12 September 2019 webinar. The final position paper will be provided as input to the workshop in Brazil 29-31 October 2019 and subsequent discussions.

Sub-group objectives and expected outputs

Objectives Outputs

#1: Map the data sets required by each methodology¹ as assessment inputs and briefly describe them (public or private, modelled or real data, geographic coverage, etc.). The focus is on

#1: Complete data mapping for each initiative to determine which data sets are used and what further data may be available now and in the future (a call has gone out from the European

1 The measurement approaches covered by this position paper are: Biodiversity Footprint Calculator (BF), Biodiversity Footprint for Financial Institutions (BFFI), Biodiversity Indicators for Extractives (BIE), Biodiversity Impact Metric (BIM) Global Biodiversity Score (GBS), LIFE Impact Index (LIFE Index), Product Biodiversity Footprint (PBF), Species Threat Abatement Reduction (STAR).

(4)

data used to assess the extent of the impacts, and not to attribute them among stakeholders.

B@B platform for information from which we will draw).

#2: Identify common input data sets and agree on a limited set of input indicators and formats (including granularity) which companies could collect to feed most measurement approaches.

#2: Common nomenclature for data used within measurement approaches, relating this to the ‘tiers’ of accuracy within the IPCC² and the the Natural Capital Protocol, and agreement on common data requests to companies.

#3: Determine links between site and corporate / portfolio level approaches and how data sets differ / are complementary or can reinforce each other.

#3: Exploration of linkages of approaches that rely on data estimates and proxies with approaches that rely on measured data through common ground nomenclature of data pressures, for example.

#4: Discussion and agreement to support other common ground principles identified previously.

Perimeter of the sub-group

Unlike climate change, biodiversity cannot be approached with global characterisation factors such as the impact of one ton of CO2 on global climate. Local characteristics and spatial differences need to be taken into account. In other words, while climate change assessments can use the total greenhouse gas emissions of a company to assess its impacts, without the need for break down by geographies, such a spatial breakdown is essential for biodiversity impact assessments. The data required for such assessments typically include spatially explicit data 1/ the state of biodiversity, 2/ pressures on biodiversity, resources and emissions, and 3/ an economic quantification of human activities³. Some measurement approaches do not make use of the third type of data.

Databases and models containing characterisation factors to translate pressures or economic data into biodiversity impacts are also required but are out of the scope of this subgroup. For instance, the GLOBIO or ReCiPe models link data on land uses or greenhouse gas emissions to biodiversity impacts.

These two models are used to move from “midpoint” to “endpoint” (impacts on biodiversity here) in Figure 1. They therefore fall outside the scope of this sub-group which is focused on input data.

Definitions are provided in the text whenever new technical terms appear. A number of other important definitions are listed in Annex 1: Definitions: indicator, measure, metric, unit.

Definition 1: Characterisation factor Cf. sub-group 3B.

2 IPCC. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Retrieved from https://www.ipcc- nggip.iges.or.jp/public/2006gl/

3

(5)

Definition 2: Midpoint Cf. sub-group 3B.

Definition 3: Endpoint Cf. sub-group 3B.

Definition 4: Input data Cf. sub-group 3B.

“Midpoint characterisation factors”, i.e. factors such as the global warming potentials (in kg CO2-eq/kg) which are used in intermediate calculations, are dealt with in sub-group #3B.

Definition 5: Characterisation factor Cf. sub-group 3B.

Figure 1 summarizes the perimeter of sub-group 3A and its connection with sub-group 3B. The figure distinguishes between approaches using the quantification of pressures and economic activities to assess biodiversity impacts and approaches which directly evaluate biodiversity impacts based on measures of biodiversity state provided by the company assessed or fall back data sets.

Input data

Sub-group 3A

Impacts on biodiversity

(endpoint) Tools or approach

Secondary inventory data CF

& midpoints CF

Endpoints CF

Sub-group 3B (characterisation factors)

① Company’sdata

②Fall back data sets

Sub-group 3B (rationale of the different metrics) Modeling of biodiversity impacts based on pressures and economic activity

(6)

Figure 1. Perimeter of sub-group 3A: the data inputs to the biodiversity impact assessment tools and approaches⁴

Remaining open questions and discussions

1. Should “attribution” data inputs be covered by the sub-group and how comprehensively?

2. In Table 1, should pressure categories be classified by sub-pressure instead? For instance, hydrological disturbance, etc.

3. Do we agree to expand the definition of data quality tier to input data associated to characterisation factors? The IPCC uses it for characterisation factors.

Context

In order to map the data sets currently used by measurement approach, it is valuable to have a common typology of data sets and a list of existing data sets. Table 1 provides such a list of “input indicators”

and associated categories, themes and types.

Definition 6: Input indicator

Specific data required to conduct (biodiversity impact) assessments, for instance an input indicator for habitat change could be “Area of natural forest” and it would be associated with a unit (e.g. hectare) or “Yearly corporate turnover by industry”

(EUR).

Definition 7: Nomenclature

A system of names or terms, or the rules for forming these terms in a particular field of arts or sciences. In other words, a typology. For instance the 22 land cover classes of GLC2000 forms a nomenclature of land covers.

Table 1 is an extract of a broader database listed in annex 2 which lists the global data sets which exist and can be used as fallback proxies when businesses are unable to provide their own data (e.g. land uses from a global database can be used to assess the situation in the area a business operates can be used if the business does not have its land use data). The terms “categories”, “themes” and “types”

all come from this broader database and do not have specific meaning, beyond the need to have three hierarchical levels of grouping (they could be replaced by “level 1 group”, “level 2 group” and “level 3

4

Input data Impacts on biodiversity

① Company’sdata

②Fall back data sets

Direct evaluation of biodiversity impacts based on data on biodiversity state

Sub-group 3A

Sub-group 3B (rationale of the different metrics)

(7)

group”). The global data sets thus listed can also be combined with corporate data to refine assessments (e.g. water scarcity data from Aqueduct can be combined to water withdrawal corporate data).

Table 2 provides for each topic the nomenclatures which already exist and can be used as models by businesses to express their data into the categories of relevant input indicators.

These two tables are the foundations on which to build output #1 of the sub-group (the data mapping).

In both tables, data are broken down into three types. Figure 2 illustrates the boundaries of these three types and how they relate to the vocabulary used in sub-group 3B. Economic quantification of human activities is limited to the quantification in monetary terms of human activites (which require specific tools to be used to assess impacts on biodiversity). Biodiversity state includes all the data on biodiversity itself. And pressures, resources and emissions include all the remaining data, ranging from water consumed (m³) to global mean temperature increase (°C).

Figure 2: Mapping of language between sub-group 3A and sub-group 3B

Figure 3 illustrates how the different terms defined above relate to each other. Input indicators are for instance a sub-set of input data, which is a much more generic term. Figure 4 further clarifies the hierarchical relationship between databases, which include data sets which themselves contain input indicators. The same example of the FAOSTAT’s Production/Crops data set is used in both figures to facilitate understanding⁵.

5 http://www.fao.org/faostat/en/#data/FO This data set is item #1 in the data set mapping of Annex 2.

Inventory data

Pressures State

Resources

&

emissions Economic

quantification of human activities

① Economic quantification of human activities

② Pressures, resources and

emissions

③ State

SG3B

SG3A

(8)

Figure 3: Relationships between input data, input indicators and nomenclatures

Figure 4: Relationships between database, data set and input indicator

Data input

Area of natural forest Example of input indicators

Water consummed Methane emissions

①

②

③

Values 10

100 1 567

Units ha m³ kg Categories

Land use Water resources Greenhouse gas

emissions

Coming from company data (user-collected data) or from external databases (e.g. FAO STAT) or data sets within the

database(e.g. Crops) The input indicators within a category are expressed based on a nomenclature(for instance GLOBIO’s nomenclature lists 16 land

uses and can thus be associated to 16 input indicators)

Database (e.g. FAOSTAT)

Data set #1 (e.g.

Production/Crops) Input indicator #1 (e.g. Area harvested)

Data set #2 (e.g. Forestry Production and Trade)

Input indicator #2 (e.g. Yield)

Input indicator #3 (e.g Production Quantity)

Input indicator #4 (e.g.

Production Quantity) Input indicator #5 (e.g.

Import Quantity)

Etc.

(9)

Table 1. List of key categories of data required for biodiversity impact assessments (non-exhaustive, for a more comprehensive version, please refer to Annex 2). The pressure themes are based on the IPBES Global Assessment direct driver categories (Díaz et al., 2019).

Type Theme Category Indicator

State Ecosystem Ecoregion WWF G200 Ecoregions

Functional richness

Marine Ocean Health

Soil

Ecosystem service Provision - fish Global Coral Reef Fisheries

Gene Genetic diversity

Habitat Wetland map Global Lakes and Wetlands Database

Other habitats

Species Risk of extinction Distribution map of species from the IUCN Red List of Threatened Species Species distribution

Species richness

Taxa Plant

Other Biomass GlobBiomass

Ecological integrity Mean Species Abundance (MSA) across the world

Priority areas⁶ Critical habitat screening layer

6 “Priority areas” is understood here as all datasets used to prioritize areas for protection.

(10)

Pressure Land / sea use change (including in aquatic ecosystems, e.g.

hydrological disturbance)

Forest cover Ever-wet tropical forests layer

Infrastructure and roads Roads and railway from OpenStreetMap Global Human Settlement Layer

Land cover Current agricultural area under different crops (ha) Land cover CCI

Land cover change Land use (cover + intensity)

Land tenure and value Value of agricultural land

Water resources Water stress

Direct exploitation Invasive alien species

Pollution Air pollution Air pollution emissions accounts (under SEEA)

Nitrogen and phosphorous Pesticides

Climate change Greenhouse gas emission Greenhouse Gas Emissions on Croplands

Other Indirect driver Gridded Population of the World GPWv4

Natural disaster

Soil erosion Soil erosion around the world

(11)

Global Soil Erosion Synthetic indicator of pressures

Multi-pressure Extractive Mineral Resources Online Spatial Data

Tourism

Response Response Indigenous land Indigenous lands

Protected area World Database on Protected Areas (WDPA) [database]

Digital Observatory For Protected Areas (DOPA) [database]

Restoration Economic

quantification of human activities

Activity Company turnover

Company purchase

Table 2. Existing nomenclatures which could be used as models for businesses to provide data

Type Theme Category Nomenclature Popularity⁷

State Ecosystem Ecoregion

Functional richness

7 Frequency of use among tool developers, based on Figure 2 of the Public minutes to the 26-27 March 2019 workshop. See also Annex 2 for more details. The more

“+”, the more popular.

(12)

Marine Soil

Ecosystem service Provision - fish

Gene Genetic diversity

Habitat Wetland map

Other habitats

Species Risk of extinction Global Reporting Initiative (GRI)’s Disclosure 304-1 Operational sites owned, leased, managed in, or adjacent to, protected areas and areas of high biodiversity value outside protected areas

GRI’s Disclosure 304-4 IUCN Red List species and national conservation list species with habitats in areas affected by operations

0⁸

Species distribution Species richness

Taxa Plant

Synthetic Biomass

Ecological integrity Priority areas

Pressure Forest cover

8

(13)

Land / sea use change (including in aquatic ecosystems, e.g.

hydrological disturbance)

Infrastructure and roads

Land cover GLC2000’s nomenclature

Corine Land Cover’s nomenclature

Climate Change Initiative (CCI)’s nomenclature Copernicus’s nomenclature

+ ++

+

? Land cover change

Land use (cover + intensity) GLOBIO’s land use ReCiPe’s land use

+++

+ Land tenure and value

Water resources Direct exploitation

Invasive alien species

Pollution Air pollution

Nitrogen and phosphorous Pesticides

Climate change Greenhouse gas emission IPCC’s classification of GHG +++

Other Indirect driver

Natural disaster Soil erosion

(14)

Synthetic indicator of pressures GRI’s Disclosure 304-2 Significant impacts of activities, products, and services on biodiversity

0

Multi-pressure Extractive Tourism

Response Response Indigenous land

Protected area IUCN’s typology of protected areas

Restoration GRI’s Disclosure 304-3 Habitats protected or restored 0

Economic quantification of human activities

Activity Company turnover GICS’s sector nomenclature

NACE (European Union) ’s sector nomenclature EXIOBASE’s sector nomenclature

+ 0 ++

Company purchase

(15)

Output #1 - Data mapping

Definition 8: User-collected data

Inputs based directly on measurements conducted by the company assessed⁹. These measurements can relate to biodiversity state but also to pressures or inventory data. User-collected data on inventories can thus be associated to modelling of biodiversity state.

Definition 9: Externally collected data

Data derived from external (sometimes global) data sets and not from direct measurements by the company assessed. Externally collected data can nonetheless include biodiversity state data, e.g. based on species distribution maps from the IUCN (or from the Integrated Biodiversity Assessment Tool or IBAT).

Definition 10: Inventory data Cf. SG3B.

Definition 11: Pressure Cf. SG3B

Externally collected data are usually not responsive to actions taken by businesses: those actions may not show up in global data sets, especially when these data sets were developed prior to the implementation of the actions.

Data sets used by different measurement approaches have common elements, but also some key differences. Site based approaches focus more on state and response than pressure, while approaches assessing biodiversity state based on pressures and economic activity heavily emphasize pressure data sets. Different data will be important depending on the business application and aims.

During the 26-27 March 2019 workshop, participants were asked to indicate the level of use of data against the ease of collection of several input indicators related to pressures (Figure 5). Three input indicators came out as more widely used and easier to collect: water consumption withdrawal, yearly greenhouse gas emissions and land occupation.

9 Defined as “primary data” in the Life Cycle Inventory/Assessment world: emission and resource data that are collected directly from the lifecycle actor that operates a process.

(16)

Figure 5. Use and perception of the ease of collecting pressure data of participants to the 26-27 March workshop

The tables in the following sections briefly list the user-collected data (company data) or externally collected data (e.g. global data sets) each approach uses to assess corporate impacts on biodiversity.

Externally collected data often requires the use of user-collected data in order to be used in assessments. For instance EXIOBASE’s environmental extensions provide data on wheat production per million euros in different parts of the world. This data can be used only associated to turnover figures, which are usually sourced from companies themselves (user-collected data). This is reflected in the tables by distinguishing between data which can be used as direct inputs by the approach or tool, and data used to build characterisation factors, such as “impacts per million euros”, etc.

User-collected data (almost) always fall within the first category and can be used as direct inputs.

Externally collected data can be used as direct inputs (e.g. data on the state of biodiversity from the Integrated Biodiversity Assessment Tool or IBAT) or to build characterisation factors.

It should be noted that currently no “super tool” able to use all the data listed as part of output #1. The goal of the mapping is not to seek to feed such an hypothetic “super tool” but rather to map what data existing approaches use.

(17)

Biodiversity state

Table 3 shows how each methodology can use biodiversity state data (usually from field ecological surveys) to adjust its assessment of biodiversity impacts. The table is not about the underlying biodiversity data used to determine pressure-impact relationships.

Table 3: Integration of field biodiversity state measurements into biodiversity impacts assessment

Measurement approach

User-collected input data (company’s data) Externally collected input data (e.g. global data sets) Used as direct inputs Used to build characterisation factors Global Biodiversity

Score (CDC B)

Integration of abundance data (ecological surveys) under consideration.

To update To update

Biodiversity Impact Metric (CISL)

Not known Not known

Biodiversity Indicators for Extractives (UNEP- WCMC)

Company data on one or more species identified as a priority biodiversity feature or area of priority habitat (as a proxy).

To update

Product Biodiversity Footprint (I Care + Sayari)

Not known Not known

Biodiversity Footprint for Financial Institutions (ASN Bank)

NA NA

Species Threat Abatement and Recovery (IUCN)¹⁰

Not known To update

10 Previously called Biodiversity Return on Investment (BRIM).

(18)

User-collected input data (company’s data) Externally collected input data (e.g. global data sets) Used as direct inputs Used to build characterisation factors Agrobiodiversity Index

(Biodiversity International)

Not known Not known

Biodiversity Footprint Calculator (Plansup)

NA NA

LIFE Impact Index (LIFE Institute)

Ecological surveys (used in the management recommendations but not directly in the Index metric)

Not known

Pressures, resources and emissions

As mentioned in Figure 2, Pressures, resources and emissions data include data relative to pressures on biodiversity (climate change, land use change, etc.), companies’ contribution to these pressures through resource consumptions and emissions (e.g. raw material consumptions, GHG emissions) and companies’ efforts to reduce their impact (e.g. management strategies).

Table 4. Integration of input data on pressures, resources and emissions

User-collected input data (company’s data) Externally collected input data (e.g. global data sets)

Used as direct inputs Used to build characterisation factors

Global Biodiversity Score (CDC B)

Company data on land use change (LUC, including wetlands), GHG emissions, water consumption, N & P concentration (and in the future pollutant emissions).

- GLOBIO scenarios as proxy of

current pressures FAO data on yields

Aqueduct data on water consumption by watershed

(19)

USGS data on mines around the world

EXIOBASE data on material consumption

Company data on land use changes. Not known

Company data for emissions to water and air, water abstraction, habitat destruction/degradation, disturbance and invasive species, assessed qualitatively based on timing of pressure, proportion of population affected and severity of pressure.

National or global averages of the same data if primary data unavailable

NA

Company data on quantities and yields of agricultural products produced.

Not known

NA - EXIOBASE data on resource

(land occupation) and material consumption

Species Threat Abatement and Recovery (IUCN)

Global pressure maps on climate change & severe weather, transportation & service corridor based on global data sets and combined to the threat assessment from the IUCN Red List.

Combined to qualitative assessments of how threats would evolve due to actions implemented by the business assessed.

-

Agrobiodiversity Index (Biodiversity

International)

Not known Not known

(20)

Company data on LUC, GHG emissions NA NA

Company data on land use change (LUC, including wetlands), GHG emissions, water consumption, pesticide use (used only for the management recommendations, not in the Index).

Company data on the energy source used and waste generated are also collected.

Not known

Economic quantification of human activities

Economic quantification of human activities data includes financial data describing companies’ activity (turnover, industries and countries of operation, market capitalisation, etc.). Such data is always used as direct inputs and not to build characterisation factors.

Table 5. Integration of economic quantification of human activities (€) input data

Global Biodiversity Score (CDC B)

Consumption of commodities, services or refined products inventories (only GBS?)

Public financial reports, private database on turnover (e.g. ISS- oekom)

NA NA

(21)

NA NA

NA Public financial reports, private database on turnover (e.g. ISS-

oekom)

Species Threat Abatement and Recovery (IUCN)

Not known Not known

Agrobiodiversity Index (Biodiversity

International)

NA NA

(22)

Output #2 – Common nomenclature for data used and common data requests from companies

Data quality tiers

This section introduces the concept of “‘tiers’ of accuracy” borrowed, among others, from the IPCC, as part of output #2.

Accurate and precise data and characterisation factors have to be used to limit uncertainties in assessments of biodiversity state (and thus impacts). Accuracy refers to how close an assessed value is to the actual (true) value. Precision refers to how close the assessed values are to each other (Figure 6). A precise assessment will for instance be able to claim that the assessed value is “15.126” and not just “15”.

In order to quickly estimate the accuracy of the assessment of biodiversity state, real and modelled data are distinguished. Furthermore, sub-group 3B suggests to use a quality tier system similar to the IPCC’s tier system to describe the quality of characterisation factors. Tier 1 and modelled data are generally the least accurate.

Table 6: Suggested data quality tiers

Real or modelled

Data quality

tier

Description Example for characterisation factors

Modelled 1

Simple linear approach. Tier 1 characterisation factors are international

defaults.

Average agricultural yield of wheat across the world.

2

Region (country)-specific linear factors or more refined empirical estimation

methodologies¹¹.

Average agricultural yield of wheat in Brazil.

3

Characterisation factors derived from the use of relationships (equations) linking

the impact source (for instance a land use change) to biodiversity impacts, with

inputs requiring a translation into the appropriate typology. For instance, this

covers cases where inputs are

“impervious areas” and “permeable areas” and the relationships to biodiversity used does not include

“permeable areas”. In such a case,

“impervious areas” and “permeable areas” need to be translated into one of

the habitat types used in the dose-

Characterisation factors for data in formats requiring transformation to be fed to

dynamic bio-geophysical simulation models using multi-

year time series and context- specific parameterization (such

as GLOBIO).

11 Data quality tier 1 and 2 are actually associated with similar accuracy (they are both linear factors) but data quality tier 2 displays a higher precision. For instance, the (data quality tier 1) global yield of rice has a wide distribution around its average, whereas the yield of rice in a specific rice paddy has a narrower distribution

(23)

response relationships through simple attribution rules.

4

Characterisation factors derived from the use of direct relationships (equations) to

biodiversity

Characterisation factors for data which can be directly fed to

dynamic bio-geophysical simulation models using multi-

year time series and context- specific parameterization (such

as GLOBIO). For instance, characterisation factors for each

of the 13 habitat types used in GLOBIO.

Real 5 Direct measurements.

The quality tiers apply to characterisation factors, but, by extension, can be used to describe the quality of data sets and data inputs based on the quality of the best characterisation factors which can be used with these data sets or data inputs. For instance, if a dataset contains changes from impervious to permeable land uses, at best, only tier 3 characterisation factors can be used by approximating impervious and permeable land uses with habitats among the types used by the model used.

Conversely, if the data sets contained directly land use changes from the dose-response relationships, a tier 4 characterisation factor could be used. Another example would be a company providing a data input consisting of its wheat production and no spatial information (on the origin of the wheat): an average global yield would need to be used in order to assess the biodiversity impacts (to assess the land use for instance). Since a yield is a linear factor and since the yield is global (and not regional), it is associated to a tier 1 quality: the average wheat production data input is thus also associated to a tier 1 quality.

It should however be noted that it is the accuracy of characterisation factors and thus of the assessed (or measured) biodiversity state which is evaluated, not the accuracy of the data input itself. For instance, the tier 4 (high accuracy of the assessed biodiversity state) awarded to the land use changes from the previous paragraph does not necessarily mean that the way these land use changes have been measured is very accurate. Such data may have been measured through very accurate satellite monitoring or broadly mapped manually after an habitat mapping on the ground, which may not be very accurate. Uncertainties on the value of the data input should thus be appropriately recorded on top of the data quality tier system.

Figure 6. Use of data quality tiers to describe data accuracy

It should be noted that the concept of data quality tier is different from the distinction between user- collected data and externally collected data. Data quality tier focuses on the expected accuracy of

(24)

the assessment of the biodiversity impact, while the user-collected data and externally collected data focus on the source of data.

A company can for instance measure the yield of the crops it harvests and provide it to assess its biodiversity impacts: this will be a user-collected tier 2 data. Conversely, satellite monitoring data from Copernicus to track land use changes would be externally collected tier 3 or tier 4 data. User-collected data does not always has a better data quality tier.

To further improve the quality of the measurement approaches on top of such a real/modelled or data quality tier system, uncertainties about the value of each measure should be quantified as much as possible. Uncertainties can be further broken down into different levels: inventory data, data in model, model assumptions.

Rationale for convergence on data input formats

The second objective of the sub-group is to “Identify common input data sets and agree on a limited set of input indicators and formats (including granularity) which companies could collect to feed most measurement approaches”. The reasoning behind this objective is that if the project can converge on a set of common input indicators, businesses would be more likely to actually collect data on this set.

Businesses would be reassured about the robustness of the set and would know that the data collected can be used with any tool, allowing them to switch from one tool to the next without obstacle.

Conversely, if the impact assessment community fails to converge on input indicators and formats, businesses will drag their feet to collect the necessary data and all the measurement approaches will suffer as a consequence as their uptake will consequently be very limited or their accuracy (and thus relevance) severely restrained.

Existing measurement approaches use different data sets and require different data inputs to conduct assessments. Some data sets are public, others have restricted access. Before the Aligning Biodiversity Measures for Business project, the data sets used had not been mapped so some initiatives might have missed out on higher quality data because they ignored their existence.

In order to assess the impacts of company policies and actions, it is necessary for companies to access their own data. Anecdotal evidence show companies can access some data sets (Figure 7).

Figure 7: Resuls of a survey conducted on a small sample of companies at the Business & Biodiversity Forum organised by the French Biodiversity Agency in December 2018

Similarly, the Natural Capital Coalition data information flow project identified a demand for biodiversity data amongst companies applying the Protocol but challenges in accessing appropriate data for decision making. Companies however hesitate to launch expensive new data collection initiatives

Land use change Greenhouse gas emissions Nitrogen/phosphorous pollutions Wetland conversion

Impossible Easy

Same question for your suppliers What is your level of access to data on the pressures on

biodiversity in your company?

Land use change Greenhouse gas emissions

Nitrogen/phosphorous pollutions Wetland conversion

Impossible Easy

(25)

assessments and what standards it should meet. There is thus a clear need for convergence in terms of content and format between the impact assessment initiatives in order to require the same data from businesses.

The guiding principles to build such common input data sets are that most of the measurement approaches should benefit from the data collected, the impacts which can be assessed if data are collected are material (and, as much as possible can be acted upon), and an existing, widely used and robust nomenclature can be linked to the data sets.

During the 26-27 March 2019 workshop, land cover data was considered a priority for alignment between measurement initiatives (Figure 5). GHG emissions and ecological survey data, and to a lesser extent water use data, were also considered important for convergence. In addition to the data outlined in Figure 4, more ‘positive’ data such as habitat restoration or carbon capture were suggested as important. Waste, energy consumption and attribution data were considered missing from the above and pesticides were considered lacking in broader applicability.

Figure 8. View on which data should be a priority for alignment between measurement approaches from the 26-27 March 2019 workshop

Pressures probably offer the greatest potential for convergence as all the initiatives rely on pressure data at some point and pressure data can be collected by business at reasonable costs (unlike biodiversity state data). Based on the results of the March 2019 workshop, the following provides a preliminary list of input indicator and nomenclatures which could be agreed upon by the SG3A and then the Aligning Biodiversity Measures for Business project.

Top priority input indicator – Land use

1. Yearly land occupation

For input indicators relative to habitat change, two requirements need to be met. The input indicators should (i) distinguish between land use categories (e.g. from GLC2000) and (ii) distinguish between different land use intensities (since the impact on biodiversity of intensive agriculture is very different from extensive agriculture). Data should also (iii) reflect annual changes, since it is the artificialisation and intensification of land uses which cause additional biodiversity losses. A first proposal of 10 land uses to meet these three requirements while converging between methodologies is presented below for feedback (it is based on a comparison of the GLC2000, GLOBIO and ReCiPe categories).

❖ Forest

o Forest – Natural o Forest – Used

(26)

❖ Grassland

o Natural grassland

o Pasture - moderately to intensively used o Pasture - man-made

❖ Cropland

o Extensive cropland o Intensive cropland o Monoculture cropland

❖ Natural bare and ice

❖ Urban area

2. Yearly wetland conversions

Ideally, this nomenclature would further account for a 11^th land use: wetlands. This would allow to assess directly yearly wetland conversions.

Other priority input indicators

1. Yearly greenhouse gas (GHG) emissions

In order to be able to compute the impact with the desired time horizon (e.g. 20 years of 100 years), data on GHG emissions should be split by GHG.

The data input could be Yearly emissions to air, water and land, by GHG and expressed in kg, in the following nomenclature:

2. Ecological survey data

Though there is agreement on the need to converge on how businesses should collect ecological survey data, a widely agreed methodology and nomenclature is currently lacking. The work of the Biodiversity Indicators for Extractive on biodiversity state data is probably the most advanced in this area. It focuses on a list of key biodiversity features and rates them qualitatively.

Other input indicators with a potential for convergence

1. Yearly water withdrawals and consumptions a. Expressed in m³.

b. Water withdrawal: “[water pumped out] of e.g. a groundwater body or diverted from a river. The pressure exerted consists in the lowering of the water table and the reduced availability of water in the water body for the ecosystem to maintain its functioning. In accounting terms water abstraction is called “water use”.”

c. Water consumption: “share of the water originally abstracted [incorporated] into the product or lost to the ecosystem it was taken from (e.g. water evapotranspirated throughout a production process)”. “In many cases, water is abstracted from a water body, used in different production processes and then returned to the same water body,

Greenhouse gas

Carbon dioxide (CO₂) Fossil and biogenic methane (CH₄)

Nitrous oxide (N₂O) Sulphur hexafluoride (SF₆) Hydrofluorocarbons (HFCs)

Perfluorocarbons (PFCs)

(27)

or at least to the same ecosystem. The status of the water might have changed (it might be polluted or warmer) […]. In literature, this “water consumption” (abstraction minus return flows) is also called “consumptive use”."

2. Nitrogen and phosphorous concentrations in water a. Average yearly concentrations?

b. Useful mainly in cases where nitrogen and phosphorous concentrations are caused only by the companies (and not by many stakeholders, making attribution complex).

c. The focus should be on the “additional” concentration caused by the business.

3. Pesticides

a. Concentrations expressed in kg 1,4-dichlorobenzene equivalents (1,4DCB-eq)?

b. Same comments as for nitrogen and phosphorous concentrations.

Output #3 – Link between between inventories of species and habitat and aggregated metrics approaches

Objective #2 could allow data collected by one company to implement one approach to be fed into any other approach using the same inputs and formats (Figure 9) which would limit the efforts required from companies in terms of data collection and management, thus maximising the chances that they indeed collect data and do so properly. This could allow measurement approaches developed from global data sets to be verified by and consistent with those based on site level data. It could also contribute to ensuring assessments conducted by different industries or by financial investors or customers are consistent with each other.

Figure 9. Possible data transfers between tools: data on land use changes collected by site-level tools would feed corporate- level tools, which could conversely transfer data provided by companies on endangered species. Adapted from CDC Biodiversité (2019).

Output #4 – Other common ground principles

The following principles are suggested for review and discussion that are relevant to data issues.

LUC (common classification) Endangered

species

(28)

• Responsive to change. The measure should be susceptible to changes in the management activity.

• Rigor. The information, data and methods used should be technically robust or clearly stated as to the levels of accuracy it confers

• Compatibility. High compatibility between impact assessment measurement approaches should be maintained such that similar data sets are used

(29)

Annex 1: Definitions

Definition 12: Indicator

The main use of the term indicator by sub-group 3A is to deal with input indicators, see Definition 6.

In other contexts, indicator can also cover other meanings which are important to keep in mind to distinguish indicators from metrics or units. Those meaning are described below.

“A quantitative or qualitative factor or variable that provides a simple and reliable means to measure achievement, to reflect changes connected to an intervention, or to help assess the performance of a development actor”¹². There are two main types of indicators:

1.Impact indicators: sometimes known as ‘performance’ or ‘outcome’ indicators.

These provide information on actual impacts of actions taken to address biodiversity or drivers of change. They help to answer the question, ‘how are our activities affecting biodiversity?’¹³

2.Implementation indicators: sometimes known as ‘process’ or ‘output’ indicators, these are used to monitor the completion of actions that enable conservation to be achieved: e.g. whether a Biodiversity Action Plan has been developed and implemented or not (but not to track the actual impacts on biodiversity of the Biodiversity Action Plan). They help to answer the question, ‘did we do what we said we would, when we said we would?’.

Indicators can further be built into Key Performance Indicators (KPI) against which to measure corporate performance. Such a KPI could for instance be the total biodiversity impact of a business, and it could for example be associated to a reduction target by 2030.

Definition 13: Measure

An assessment of the amount, extent or condition, usually expressed in physical terms. Can be either qualitative or quantitative.

Definition 14: Metric

“A system or standard of measurement”. A combination of measures or modelled elements. The Mean Species Abundance (MSA) and the Potentially Disappeared Fraction (PDF) are for instance metrics expressed as a percentage.

12 OECD/DAC 2002 Development Results: An overview of results measurements and management. Available at https://www.oecd.org/dac/peer-reviews/Development-Results-Note.pdf

13 Bubb, P., Brooks, S., and Chenery, A. (2014). Incorporating Indicators into NBSAPs- Guidance for Practitioners. UNEP- WCMC, Cambridge, UK

(30)

Definition 15: Unit

A standard measure that is used to express amounts. For instance MSA.m² or PDF.yr.m² are units.

(31)

Annex 2: Data set mapping

Cf. the spreadsheet here:

https://www.dropbox.com/sh/ym0agydww9haz40/AABhLuktuXNy3Ue8qfWv696Ca?dl=0. The spreadsheet is based on the significant work of the Gaining consensus on spatial and temporal biodiversity metrics for informed decision-making workshop held on 20-24 May 2019, in Cambridge, UK. It will be fed back into the “Gaining Consensus” database.

The following improvements need to be brought to it:

- complete with missing data sets - add a “unit” column

- distinguish between input indicators and whole databases (containing several input indicators) - reclassify data sets in appropriate types, themes and categories

-

Position paper on corporate data inputs 20 September 2019