GID Technical Overview

Impact assessment and monetisation consists of taking a selection of extensions and converting them into impact indicators that are expressed in monetary terms. Conversion from extensions to indicators requires impact factors and conversion to monetary units requires monetisation factors. These impact factors and monetisation factors are further explained below:

• Impact factors – factors that are multiplied by extensions to convert them into a standardised set of GID indicators e.g. kilograms of different green-house gas emission are converted into kilograms of CO2 equivalents.
• Monetisation factors – factors that are multiplied by GID indicators producing a comparable set of monetised GID impacts e.g. kilograms of CO2 equivalents and m3 of scarce water use are converted to comparable $ values.
• For more (general) information about impact measurement and valuation at organisational level refer to FIS and IAM Core.

Input-Output Analysis (IOA) is an economic technique that represents the interdependencies between different countries and sectors. It is a well-established method used for academic articles and research papers, that is now also being utilised for sustainability reporting by corporate and financial institutions. The Impact Institute has developed the pioneering ‘chains’ IOA to deliver a unique dataset allowing impact to be attributed, based on value added, across global value chains instantly. It includes upstream and downstream linkages.

IOA is a widely used method, a selection of academic articles and research papers where IOA is the main technique used for estimating environmental and social value chain impacts are listed below:

• Evaluating the environmental impacts of dietary recommendations. Behrens, Paul et al. PNAS. Web. December, 2017
• The Global Resource Footprint of Nations: Carbon, water, land and materials embodied in trade and final consumption calculated with EXIOBASE 2.1. Tukker, Arnold et al. ResearchGate. Web. June, 2014.
• Carbon Footprint of Nations: A Global, Trade-Linked Analysis. Hertwich, Edgar and Glen Peters. ACS Publications. Web. June 15, 2009.

The raw data used in GID is described below:

• Input Output (IO) data – trade data describing the interconnectedness of the global economy.
• Extensions – Datasets extending the IO trade data describing social, environmental and economic performance of sectors worldwide. These are provided by IO databases in the form of environmental and socio-economic extensions and derived from public sources such as Wageindicator, ILOStat, and OECD statistics.
• The main data sources used in GID include as GTAP, SHDB, Eora, and Exiobase.

This section explains how the GID works using the data elements and philosophies introduced in the sections above:

GID uses the principals of IO analysis with the monetised indicator data to produce estimates of what the impact of economic stimulation is throughout the economy, the concepts behind this approach are further described below.

Raw GID data has the unit impact per euro economic activity in a specific sector. However, if you source from a sector, you do not only stimulate economic activity in the sector where you source from, but also in sectors where that sector in turn sources from, etc. Similarly, if an organisation (e.g., a bank) stimulates economic activity through provision of loans, it does not only activate the economic sector it directly lends to, but also their value chain.

The principle of ‘value chain responsibility’ as laid down in FIS and IAM Core, state that an organisation should take (co-) responsibility of the impacts in their value chain (such as contribution to climate change), GID helps the user to do that. For every euro of sourcing, it traces what other sectors are stimulated, and what is the cumulative effect of all these sectors on the impacts (such as contribution to climate change). Similarly, for every euro of interest income from business lending, it traces how the loan has stimulated economic activity at the direct business client and beyond – and how much impact results from that.

The impact estimates are represented by impact indicators describing the impact of global value chains, see the section below for a list of impacts per capital. These indicators can be used to easily communicate impact using a variety of frameworks, such as:

• International Integrated Reporting Council’s (IIRC) Integrated Reporting (IR) framework capitals – further described below
• United Nation’s Sustainable Development Goals (SDGs)

A high level of data quality is ensured in GID through rigorous implementation of approaches developed over multiple GID development cycles, including outlier control, data gap filling and validation against third party datasets.

We have several data quality assurance processes and protocols in place, which can be evidenced by years of repeated use of our services by our clients, including leading global organisations and financial institutions.

GID development follows a pragmatic approach focused on the needs of the end user by following the methodical and comprehensive lifecycle development steps: requirements analysis, design, development, testing and finally release. The following versions of GID sector have already been released:

• v1.0.0 – First version (early 2019)
• v1.4.0 – Updated attribution method, updated extensions (mid 2019)
• v2.3.0 – Added outlier control, updated financial indicators, updated impact factors and monetization factors (early 2020)
• v2.4.4 – Added GenderWageGap and Forced Labour impacts (mid 2020)
• v3.0.0 – Increased sector granularity for select indicators (early 2021)
• v3.1.0 – Increased sector granularity for select indicators, updated disaggregation/aggregation method, updated monetization factors (end 2021)

We recommend use of the most up to date data. Where users want to directly compare results developed using previous data versions these can be provided.