Global Green Finance Index (GGFI)
The Global Green Finance Index (GGFI) is an initiative sponsored by the MAVA Foundation, and delivered by Finance Watch and Long Finance, that seeks to encourage financial centres to become greener and develop financial services in a way that enables society to live within planetary boundaries.
Finance Watch and Long Finance will be running events, seminars, webinars and workshops to raise awareness of the initiative, recruit community members, and refine the methodology.
The initial index will be published in Spring 2018.
Initial workshops and webinars are being held in November and December 2017 for sustainable finance experts and other interested parties to learn about and discuss the methodology; registration details can be found in the events section below.
Below you can:
Green Finance refers to any financial instrument or financial services activity – including insurance, equity, bonds, commodity and derivatives trading, analytical or risk management tools – which results in positive change for the environment and society over the long term (sustainability). The most basic “greenness” criterion of a company or project it that is contributes to reduce Green House Gases emissions.
Over the last two decades, the rise of new financial instruments, such as Green Bonds, and environmental markets, such as carbon, forestry, or water services, along with increasing analytical investigation, have increased attention on green finance.
Green finance is no longer seen as a fringe activity, but a profitable and desirable activity, which drives financial markets, serves society and enhances the status of financial centres which demonstrate expertise. Financial services are an essential component of a sustainable economy, which meets the needs of stakeholders, enhances quality of life, protects the environment and addresses global issues such as climate change.
The Global Green Finance Index (GGFI) gives a measure of how financial centres are responding to this challenge. We hope that enabling centres to compare their performance with their peers, will improve policy makers’ understanding of the drivers of green growth, and assist them in shaping the financial system to support sustainability goals.
GGFI aims to publish its initial index in Spring 2018. The index will be created by combining questionnaire assessments from financial services professionals, NGOs, regulators, and policy makers with instrumental factor analysis to produce rankings of green financial centres on a variety of indicators.
GGFI will assess ratings for financial centres calculated by a ‘factor assessment model’ that uses two distinct sets of input:
- Instrumental factors: objective evidence of both environmental credentials and green finance sought from a wide variety of comparable sources.
For example,; information on how a given financial centre’s activity contributes to lowering carbon dioxide/GHG emissions evidence of a financial centre’s commitments and achievements on ESG disclosure, and about the trading and regulatory environment of green finance, eg for instance volume trades in carbon or green bonds, as well as more global data such as the Ease of Doing Business Index (supplied by the World Bank), the Government Effectiveness rating (supplied by the World Bank) and the Corruption Perceptions Index (supplied by Transparency International).
Not all financial centres will be represented in all the external sources, and the statistical model will take account of these gaps.
- Green financial centre assessments: evidence of how a centre’s environmental market is viewed will be captured through an online questionnaire.
For the first edition of GGFI, analysis will be carried out on a range of financial centres across the world. Responses will be collected via an online questionnaire which will run continuously. A link to this questionnaire will be emailed to our growing list of respondents, which includes green finance professionals and experts, at regular intervals and other interested parties can fill this in by accessing these web pages;
In order to avoid home centre bias, the centre that a respondent is based in will be excluded from the assessment.
The financial centre assessments and instrumental factors will be used to build a predictive model of green financial centres using a support vector machine (SVM).
SVMs are based upon statistical techniques that classify and model complex historic data in order to make predictions on new data. SVMs work well on discrete, categorical data but also handle continuous numerical or time series data. The SVM that will be used for GGFI will provide information about the confidence with which each specific classification is made and the likelihood of other possible classifications.
A factor assessment model will be built using the centre assessments from responses to the online questionnaire. The model will then predict how respondents would have assessed centres with which they are unfamiliar by answering questions such as:
If a respondent gives Singapore and Sydney certain assessments then, based on the instrumental factors for Singapore, Sydney and Paris, how would that person assess Paris?
Predictions from the SVM are re-combined with actual financial centre assessments (except those from the respondents’ home centres) to produce the GGFI – a set of green financial centre ratings.
Over time, GGFI will be dynamically updated, either by updating and adding to the instrumental factors or through new green financial centre assessments. These updates will permit, for instance, a recently changed low carbon index to affect the rating of the centres.
The process of creating the GGFI is outlined diagrammatically below:
The strengths of this approach are:
- A wide range of indices can be used for each instrumental factor;
- A strong international community of respondents can be developed as the GGFI progresses;
- Sector-specific ratings are available using the business sectors represented by questionnaire respondents. This makes it possible to rate a centre as highly influential in carbon trading (for example) whilst less advanced in enhanced analytics (for instance).
The factor assessment model can be queried in a ‘what if’ mode - “how much would London carbon emissions costs need to increase in order to enhance London’s ranking against Singapore?”
Part of the process of building the GGFI will be sensitivity testing to changes in instrumental factors, the refining of the questionnaire, the development of the rating community and testing of the accuracy of predictions given by the SVM against actual assessments.
If you are a green economy sponsor or investor, or a green financial services professional, policy maker or represent an NGO with an interest in green finance we would love you to join the GGFI community.
E-mail us to register your interest and help us to develop our questionnaire, identify instrumental factors and receive notice about our workshops, webinars and seminars.
Initial workshops and webinars to discuss the methodology are being held in a variety of locations. Sustainable finance experts are invited to register as below:
22 November 2017 - Frankfurt register here
29 November 2017 - London register here
30 November 2017 - Paris register here
13 December 2017 - New York (details to be announced)
14 December 2017 - Shanghai (details to be announced)
Please bear with us, the questionnaire is under development and will live shortly!
In selecting instrumental factors to compile GGFI, a number of key properties are essential:
- Time series – where possible we will seek data that is contemporary (published within the last three years) is part of a series (collected as part of a programme rather than as a one off) and is on-going (there are plans to update it in the future).
- Locational relevance – where possible we will seek data at a financial centre level. Where this is not possible we will use appropriate apportionment rules and/or alternative data ie regional.
- Accessibility – data should be publically accessible, this enables transparency in the assessments.
Further details on the instrumental factors, please visit this page.