Measures for Business

Aligning Biodiversity Measures for Business Sub-group 3A

Corporate data inputs

Webinar

20 September 2019

❑ Reminder of the objectives and context of the Aligning Biodiversity Measures for Business initiative

❑ Reminder of the objectives of the sub-group and of the webinar

❑ Presentation of the database on state, pressure, activity and response data sets

❑ Review of the SG3A position paper to finalize it for the Brazil workshop

▪

Output #1 - Data mapping – data used by each tool

▪

Output #2 - Agreement on common nomenclatures to request data from companies

▪

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

▪

Output #4 – Other common ground principle

Agenda

Reminder of the objectives and context of the

Aligning Biodiversity Measures for Business initiative

Reminder of the objectives of the sub-group and of

the webinar

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❑ What is this session about?

Mentimeter

1. Map the data sets required by each methodology as assessment inputs and briefly describe them (public or

private, modelled or real data, geographic coverage, etc.). The focus is on data used to assess the extent of the impacts, and not to attribute them among stakeholders.

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.

3. Determine links between site and corporate / portfolio level approaches and how data sets differ / are

complementary or can reinforce each other.

Objectives of the sub-group

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 based on a call for information from the European B@B platform for information.

2. Common nomenclature for data used within measurement

approaches, relating this to the ‘tiers’ of accuracy within the IPCC and the the Natural Capital Protocol, and agreement on common data requests to companies.

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.

Expected outputs of the sub-group

Linkage of the sub-group with sub-group 3B on metrics and characterisation factors

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’s data

② Fall back data sets

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

Input data Impacts on biodiversity

① Company’s data

② Fall 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)

1. Review the SG3A draft position paper and provide feedback to validate it as input of the sub-group to the Brazil workshop.

Objectives of the webinar

❑ Go to www.menti.com and use the code 76 29 81

❑ Questions? ➔ add them to the parking lot

Mentimeter

Review of the SG3A position paper to finalize it for

the Brazil workshop

❑ 20190917_ABMB_SG3A-datasets_position- paper_v2.docx

❑ Sent by Julie Dimitrijevic on 17 ^th September

SG3A position paper

❑ QUESTION #1: Should “attribution” data inputs be covered by the sub-group and how comprehensively?

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Remaining open questions

REVIEW - Output #1 - Data mapping – data used by

each tool

❑ QUESTION #2A: In Table 1, should pressure categories be classified by sub-pressure instead? For instance,

hydrological disturbance, etc.

❑ QUESTION #2B: Should data categories be mutually exclusive? Especially for data on biodiversity state.

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Remaining open questions

Data mapping – Figure 2

PAGE 16

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

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Data mapping – Figure 3

PAGE 17

Area of irrigated cropland Input indicator names

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 data sets originating from external databases(e.g. Production/Crops data set

originating from FAOSTAT database)

These 6 input indicators are all input data.

The input indicators within a category are expressed based on a

nomenclature. For instance input indicator ①and ②

are expressed with the GLOBIO’s land use nomenclature and ③and ④are

expressed with ReCiPe’s land use nomenclature

Area of intensive

cropland 5 ha

Area of monoculture

crops/weeds 10 ha

Area of intensive

crops/weeds 5 ha

⑤

⑥

④

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Data mapping – Figure 4

PAGE 18

Database (e.g. FAOSTAT)

Data set #1 (e.g.

Production/Crops) Input indicator #1.1 (e.g. Area harvested)

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

Input indicator #1.2 (e.g. Yield)

Input indicator #1.3 (e.g Production Quantity)

Input indicator #2.1 (e.g.

Production Quantity) Input indicator #2.2 (e.g.

Import Quantity) Etc.

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PAGE 19

Data mapping

❑ A database has been built by UNEP-WCMC and participants to the Gaining Consensus workshop in May 2019 in

Cambridge, UK

❑ It has been refined to map the data sets used by approaches followed by the ABMB initiative and can be found here:

https://www.dropbox.com/sh/ym0agydww9haz40/AABhLuktuX Ny3Ue8qfWv696Ca?dl=0

❑ The following slides list categories of data contained in this

database. The objective is NOT to have an exhaustive list

of categories but rather to categorize properly data already in

the database.

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Data mapping

❑ Each initiative can use the database to map the data inputs it uses as externally collected input data or as inputs to build characterisation factors.

❑ This mapping is led coherently with the EU B@B update on biodiversity accounting tools for business led by Johan

Lammerant: no need to do the work twice!

❑ The ID (#14 etc.) cited refer to the database here:

https://www.dropbox.com/sh/ym0agydww9haz40/AABhLuktuX

Ny3Ue8qfWv696Ca?dl=0

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Data mapping – Key messages

❑ Missing data for several approaches: Kering EP&L, Agrobiodiversity Index, STAR, Biodiversity Footprint calculator, BFFI.

❑ Overlap on some input data:

▪ FAO data on area harvested, yield, production of crops

▪ EXIOBASE data on emissions and resource consumption

▪ IBAT data on presence of threatened species, protected

area proximity, etc.

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Data mapping – Biodiversity state – Table 3

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 GBS

Integration of abundance data (ecological surveys)

under consideration.

IBAT data for extinction risk

screening. NA

BIM NA Range rarity layer. NA

BIE

Company data on one or more species identified as a priority biodiversity feature or

area of priority habitat (as a proxy).

Not known NA

PBF

Sectoral and local ecological studies used to adjust characterisation factors.

NA IBAT data.

BFFI NA NA NA

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Data mapping – Biodiversity state – Table 3

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

STAR Not known Not known Not known

ABD Index Not known Not known Not known

BF NA NA NA

LIFE Index

Status of conservation of natural vegetation; Length

and width of biodiversity corridors; Stage of vegetal

dynamics.

Protected area categories;

#45 – Ecoregions; Biological Importance of the Area (national classifications);

threat status of species;

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Data mapping – Pressures, resources and emissions – Table 4

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

GBS

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; USGS data on

mines around the world;

EXIOBASE data on material consumption.

BIM Company data on land use

changes. NA #151 – FAO (crop) yield.

BIE

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

PBF

Company data on Energy use, Water use, Land

occupation, Land

transformation, Emissions to water, Emissions to soil,

Same data but from Life

Cycle Inventories. NA

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Data mapping – Pressures, resources and emissions – Table 4

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

GBS

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; USGS data on

mines around the world;

EXIOBASE data on material consumption.

BIM Company data on land use

changes. NA #151 – FAO (crop) yield.

BIE

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

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Data mapping – Pressures, resources and emissions – Table 4

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

PBF

Company data on Energy use, Water use, Land

occupation, Land

transformation, Emissions to water, Emissions to soil,

Emissions to air

Same data but from Life

Cycle Inventories. NA

BFFI 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.

-

Agrobiodiver sity Index (Biodiversity International)

Not known Not known

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Data mapping – Pressures, resources and emissions – Table 4

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 Biodiversity

Footprint Calculator

(Plansup)

Company data on LUC, GHG

emissions NA NA

LIFE Impact Index (LIFE

Institute)

Company data on land use change (including wetlands,

restored area, “area of occupation severity index”), GHG emissions, water usage,

waste generation, energy 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.

NA?

Country total consumptions (from governmental agencies)

for: water usage, waste generation, energy consumption, original natural

areas by ecoregion; water balance by hydrographic

region.

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Data mapping – Economic quantification of human activities – Table 5

Approach User-collected input data (company’s data)

Externally collected input data (e.g. global data sets)

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

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

BIM NA NA

BIE NA NA

PBF NA NA

BFFI NA Public financial reports, private

database on turnover

STAR Not known Not known

AI NA NA

BF NA NA

LIFE Index NA NA

Bioscope NA NA

❑ What is your general feedback on output #1 – Data mapping?

Data mapping

REVIEW - Output #2 - Agreement on common

nomenclatures to request data from companies

❑ 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. A precise assessment will for instance be

able to claim that the assessed value is “15.126” and not just “15”.

Accuracy and precision

Impact factor and data quality tiers to quickly assess data accuracy - Table 6

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 methodologies.

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.

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).

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.

Real 5 Direct measurements.

Feedback from BIE’s data quality tiers?

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Which data quality for which use?

Business applications (BAs) Desired and appropriate data quality tier

1.Assessment of current

biodiversity performance Depends on the final goal 2.Assessment of future biodiversity

performance

3. Tracking progress to targets Depends on the final goal and the target

4. Comparing options Depends on the final goal 5. Biodiversity Return on

Investment / Testing effectiveness

of reduction measures Depends on the final goal

6. Assessment / rating of biodiversity performance by third

parties, using external data Appropriate: 1 and

7. Certification by third parties Depends on the level of uncertainty allowed

8. Screening and assessment of

biodiversity risks and opportunities Appropriate: 1

The sub-group agrees/does not agree on:

❑ The use of 5 data quality tiers for characterisation factors

❑ The need to quantify as much as possible

uncertainties about the value of each measure.

▪ Uncertainties can be further broken down into different levels: inventory data, data in model, model

assumptions.

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Common ground reached in the sub-group

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❑ QUESTION #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.

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Remaining open questions

PAGE 36

Agreement on common nomenclatures to request

data from companies

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Top priority for convergence: land uses

❑ The tool developers within the sub-group agree/do not agree to use the following nomenclature to

request data to companies. They retain the possibility to further break-down the indicators as long as it is clear for companies this is the minimum data required.

❑ Yearly land occupation

▪ Forest

• Forest – Natural

• Forest – Used

▪ Grassland

• Natural grassland

• Pasture - moderately to intensively used

• Pasture - man-made

▪ Cropland

• Extensive cropland

• Intensive cropland

• Monoculture cropland

▪ Natural bare and ice

▪ Urban area

❑ Yearly wetland conversions

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Other potential area of convergence

❑ Yearly greenhouse gas (GHG) emissions

▪ Yearly emissions to air, water and land

▪ By GHG and expressed in kg

▪ IPCC nomenclature

Greenhouse gas

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

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

Perfluorocarbons (PFCs)

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Other potential area of convergence

❑ Yearly water withdrawals and consumptions

▪ Expressed in m

▪ Water withdrawal: “[water pumped out] of e.g. a

groundwater body or diverted from a river.” Also called

“water abstraction” or “water use”.”

▪ 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 other words, the

“water consumption” is the abstraction minus the return flows. It is also called “consumptive use”.

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❑ What is your general feedback on output #2 – Agreement on common nomenclatures to request data from

companies

Agreement on common nomenclatures to request

data from companies

REVIEW - Output #3 – Link between between inventories of

species and habitat and aggregated metrics approaches

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Link between between inventories of species and habitat and aggregated metrics approaches

❑ Approaches using aggregated metrics could push companies to acquire user-collected (direct measurements on their sites) and externally collected (e.g. by using IBAT) data on taxa and habitats

▪ Could satisfy screening and “environmental safeguards” phases of their assessment process and feed approaches focused on taxa and habitats with data.

❑ Approaches focusing on taxa and habitats could push companies to acquire input data useful for approaches using pressure and

economic activities (e.g. land use in ha, water consumption, etc.).

▪ In particular, Yearly land occupation in the nomenclature described in #2 should be collected.

LUC (common classification) Endangered

species

❑ What is your general feedback on output #3 – Link

between between inventories of species and habitat and aggregated metrics approaches

The sub-group agrees/does not agree on:

❑ Cross-collecting data on taxa and habitat, and yearly land occupation.

Agreement on common nomenclatures to request data from companies

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REVIEW - Output #4 – Other common

ground principles

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Generic common ground

❑ 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

Additional material

PAGE 47

Definitions

❑ #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).

❑ #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.

❑ #8 - User-collected data: Inputs based directly on

measurements conducted by the company assessed . 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.

❑ #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).

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Definitions

❑ #12 - Indicator: “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”.

❑ Key Performance Indicators (KPI): indicators 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.

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Definitions

❑ 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?’

❑ 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?’.

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Definitions

❑ #13 - Measure: an assessment of the amount, extent or

condition, usually expressed in physical terms. Can be either qualitative or quantitative.

❑ #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.

❑ #15 - Unit: a standard measure that is used to express amounts. For instance MSA.m

or PDF.yr.m

are units.

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Definitions

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Data categories - State

Type Theme Category

State Ecosystem Ecoregion

Functional richness Marine

Soil

Ecosystem service Provision - fish

Gene Genetic diversity

Habitat Wetland map

Other habitats Species Risk of extinction

Species distribution Species richness

Taxa Plant

Other Biomass

Ecological integrity

Priority areas

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Data categories - Pressure

Type Theme Category

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

Forest cover

Infrastructure and roads Land cover

Land cover change

Land use (cover + intensity) Land tenure and value

Water resources Direct exploitation

Invasive alien species

Pollution Air pollution

Nitrogen and phosphorous Pesticides

Climate change Greenhouse gas emission

Other Indirect driver

Natural disaster Soil erosion

Synthetic indicator of pressures

Multi-pressure Extractive

Tourism

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Data categories - Response

Type Theme Category

Response Response Indigenous land

Protected area

Restoration

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Data categories - Economic quantification of human activities

Type Theme Category

Economic quantification of human activities

Activity Company turnover

Company purchase

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Output #2 - Other potential area of convergence with no progress

❑ Ecological survey data

▪ No proposal made?

❑ Nitrogen and phosphorous concentrations in water

❑ Pesticides

Contacts

Aligning Biodiversity Measures for Business

Annelisa Grigg, UN Environment World Conservation Monitoring Centre

Tel: +44 (0)1223 277314 Email: annelisa.grigg@unep- wcmc.org

Sub-group 3A chair

Joshua Berger, CDC Biodiversité Tel: +33 (0)1 80 40 15 41

Email: joshua.berger@cdc- biodiversite.fr