Working paper sub-group 3A on corporate data inputs 2 July 2019

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Working paper sub-group 3A on corporate data inputs

2 July 2019

Aligning Biodiversity Measures for Business project

DRAFT FOR DISCUSSION

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workshop held in Brussels on 26-27 March 2019. It was prepared by the chair of SG3A sub-group as a draft for discussion.on datasets. It is circulated as a draft for discussion with all SG3A members to be elaborated into a SG3A position paper as input for the workshop which will be held in Brazil on October 2019 and subsequent discussions.

Objectives of the sub-group

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

Objective 2 could allow data collected by one company to implement one approach to be fed into any other approach using the same indicators and formats (Figure 1) 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 1. 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).

LUC (common classification) Endangered

species

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The following outputs are expected 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 (a call has gone out from the European B@B platform for information from which we will draw).

2. Develop common nomenclature for data used within measurement approaches, relating this to the ‘tiers’ of accuracy within the IPCC¹. The Natural Capital Protocol also needs to be considered.

3. Discuss and agree to support common ground principles identified previously.

4. Explore linking 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.

Context

Perimeter of the sub-group

Unlike climate change, biodiversity cannot be approached with global impact factors such as the impact of one ton of CO2 on global climate. Local characteristics and spatial differences need to be taken into account. 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 include spatially explicit data on 1/ the state of biodiversity, 2/ pressures on biodiversity, and 3/ an economic quantification of human activities. In all cases, data reflects not only the “background situation” but also, and more importantly, the impacts of the actions and responses by businesses (the “response” in state, pressure, response frameworks).

Databases and models containing impact 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 model links 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 2. They therefore fall outside the scope of this sub-group which is focused on data inputs.

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

Figure 2 summarizes the perimeter of sub-group 3A and its connection with sub-group 3B.

Figure 2. Perimeter of sub-group 3A: the data inputs to the biodiversity impact assessment tools and approaches²

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

2 Fall back data are data used when company’s data are not available, see below.

Input data

Sub-group 3A

Biodiversity impact assessment Tools or approach

Impact factors Midpoints Endpoints

Sub-group 3B

① Company’sdata

②Fall back data sets

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Classification of input data and nomenclatures

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”³ and associated categories, themes and types. It 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).

These global data sets 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). The pressure themes are based on the IPBES Global Assessment direct driver categories (Díaz et al., 2019).

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).

3 “Input indicators” here mean specific data categories required to conduct 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).

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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)

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 areas Critical habitat screening layer

Pressure Land / sea use change (including in aquatic

Forest cover Ever-wet tropical forests layer

Infrastructure and roads Roads and railway from OpenStreetMap

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ecosystems, e.g.

hydrological disturbance) 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

Synthetic indicator of pressures

Multi-pressure Extractive Mineral Resources Online Spatial Data

Tourism

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Response Response Indigenous land Indigenous lands

Protected area World Database on Protected Areas (WDPA)

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 Popularity⁴

State Ecosystem Ecoregion

Functional richness Marine

Soil

Ecosystem service Provision - fish

Gene Genetic diversity

Habitat Wetland map

4 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.

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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

0⁵

Species distribution Species richness

Taxa Plant

Synthetic Biomass

Ecological integrity Priority areas Pressure Land / sea use change

(including in aquatic ecosystems, e.g.

hydrological disturbance)

Forest cover

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

5 But used by non-tool developers.

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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

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

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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

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Rationale for convergence on data input formats

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. Companies could access some data sets, as shown by recent indicative surveys reproduced below 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 because they lack a clear signal about what data is necessary to conduct biodiversity impact 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.

Common framework on input data

This section prepares output #2 of the sub-group, a “common nomenclature for data used within measurement approaches” and relates it “to the ‘tiers’ of accuracy within the IPCC”.

Accurate and precise data and impact factors have to be used to limit uncertainties in results.

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 3). 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 data accuracy, real and modelled data are distinguished. Furthermore, a quality tier system similar to the IPCC’s tier system to describe the quality of impact factors could be used and a proposal is described below (to be discussed with sub-group members). Tier 1 and modelled data are generally the least accurate.

Real or modelled

Data quality

tier

Description Example for impact factors

Modelled 1 Simple linear approach. Tier 1 impact factors are international defaults.

Average agricultural yield of wheat across the world.

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

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2

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

methodologies⁶.

Average agricultural yield of wheat in Brazil.

3

Impact 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- response relationships through simple

attribution rules.

Impact 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

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

biodiversity

Impact 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, impact factors for each of the 13 habitat

types used in GLOBIO.

Real 5 Direct measurements.

The quality tiers apply to impact factors, but, by extension, can be used to describe the quality of data sets based on the quality of the best impact factors which can be used with these data sets. For instance, if a dataset contains changes from impervious to permeable land uses (and vice-versa), at best, only tier 3 impact 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 impact factor could be used.

6 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 around its mean.

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Figure 3. Use of data quality tiers to describe data accuracy

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.

Inputs directly based on company data are called primary data, while data derived from global data sets are called secondary data (as illustrated by Figure 2). Secondary 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.

A number of definitions are agreed upon and listed in Annex 1: Definitions. They include: indicator, measure, metric, unit.

Data mapping

Data sets used by different measurement approaches had common elements, but also some key differences. Site based approaches focused more on state and response than pressure, while footprinting approaches heavily emphasized 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 4). Three input indicators came out as more widely used and easier to collect: water consumption withdrawal, yearly greenhouse gas emissions and land occupation.

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Figure 4. 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 data input from primary sources (company data) or from secondary sources (e.g. global data sets) each approach uses to assess corporate impacts. They broadly follow the State Pressure Response framework.

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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

Primary input data (company’s data) Secondary input data (e.g. global data sets)

Global Biodiversity Score (CDC B)

Integration of abundance data (ecological surveys) under consideration.

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 (STAR)⁷

Not known To update

7 Previously called Biodiversity Return on Investment (BRIM).

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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

Bioscope (Platform BEE)

Not known Not known

Pressures

Pressure data include data relative to pressures on biodiversity (climate change, land use change, etc.), companies’ contribution to these pressures (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 caused by the company (negative or positive) on biodiversity

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

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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

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 (STAR)

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.

International)

Not known Not known

Company data on LUC, GHG emissions 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

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Not known Not known

Activity

Activity data includes financial data describing companies’ activity (turnover, industries and countries of operation, market capitalisation, etc.).

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

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

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

NA NA

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

oekom)

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Species Threat Abatement and Recovery (STAR)

Not known Not known

International)

NA NA

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Common primary data requests to companies

Rationale behind converging on common input data sets

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.

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 4). 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 5. 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 indicators should (i) distinguish between land use categories (e.g. from GLC2000) and (ii) distinguish between different

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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

❖ 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.

Greenhouse gas

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

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

Perfluorocarbons (PFCs)

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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.

Common ground principles

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

• 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

Discussion questions

The following questions need to be tackled to improve this working paper into a position paper of sub- group 3A:

1. What data sets are used by each methodology and how can they be used by others?

2. What common data types are shared by several methodologies? This probably includes GHG emissions, land use conversion and occupation, nitrogen and phosphorous concentrations, wetland conversion and pesticide concentration, among others.

3. What data types could be required from companies for use by multiple methodologies? What should be the format (or unit) of data? (e.g. GHG expressed in CO2 eq.)

4. What are the implications of using different data sets for different measurement approaches for decision making and uptake?

5. What Common Ground principles for corporate biodiversity measurement could promote alignment between different approaches?

6. Should “inventory” data be distinguished from “pressure” data? For instance, water consumption (m³) or waste generation (t) are not pressure but can be used as proxies to assess pressures.

Currently associated input indicators are listed in the pressure tables.

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

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

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Annex 1: Definitions

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”⁸. 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?’⁹

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.

The term “indicator” can also be used to describe specific data required by tools to conduct biodiversity impact assessments. Such “input indicators” could include for instance yearly corporate turnover by industry or region (EUR), area of natural forest converted into intensive agriculture every year (ha), etc.

Measure

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

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.

Unit

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

8 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

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

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Annex 2: Data set mapping

Cf. the attached Excel spreadsheet. 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

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Working paper sub-group 3A on corporate data inputs 2 July 2019