Measures for Business Sub-group 3A Corporate data inputs Webinar - - PowerPoint PPT Presentation

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

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

Modeling of biodiversity impacts based on pressures and economic activity

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 17th September

SG3A position paper

❑ QUESTION #1: Should “attribution” data inputs be covered by the sub-group and how comprehensively? ❑ Go to www.menti.com and use the code 76 29 81

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

Remaining open questions

Data mapping – Figure 2

PAGE 16

Inventory data Pressures State Resources & emissions Economic quantification

• f 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

• riginating from external databases (e.g. Production/Crops data set
• riginating 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 ① and ② are expressed with the GLOBIO’s land use nomenclature and ③ and ④ are 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

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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. www.menti.com Code 76 29 81

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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
• f 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

• f 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

• f 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

• ccupation, 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

• f 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

• f 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

• ccupation, 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

• ccupation 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

• f

commodities, services

• r

refined products inventories (only GBS?) Public financial reports, private database on turnover (e.g. ISS-

• ekom)

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? www.menti.com Code 76 29 81

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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 5 impossible so 4 at best

• 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

• f reduction measures

Depends on the final goal

• 6. Assessment / rating of

biodiversity performance by third parties, using external data Appropriate: 1 and 2

• 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 www.menti.com Code 76 29 81

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

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 m3 ▪ 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

• n 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 www.menti.com Code 76 29 81

REVIEW - Output #4 – Other common ground principles

PAGE 45

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’

• r ‘output’ indicators, these are used to monitor the completion
• f 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.m2 or PDF.yr.m2 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

• f 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