Global Green Finance Index to Launch in 2018

Stockholm (NordSIP) – A Global Green Finance Index (GGFI) designed to encourage and develop financial services and centres to become greener will be published in the spring of 2018. The initiative, which is sponsored by the MAVA Foundation and developed by Finance Watch and Long Finance, will be a project-in-progress, recruiting members and running seminars to refine its methodology.

Green Finance denotes any financial instrument of financial services activity, including insurance, equity, bonds, commodity and derivatives trading, analytical risk or management tools that result in sustainability. On the premise that financial services are an essential component of a sustainable economy that also meets the needs of stakeholders while addressing issues such as climate change, the GGFI will seek to measure how financial centres are responding to the challenge. The objective is to help improve policy makers’ understanding of what drives green growth through comparison of the performance by financial centres on the measure.

The initial GGFI index is currently being assembled from questionnaire assessments from financial services professionals, NGOs, regulators and policy makers with instrumental factor analysis to produce rankings of green financial centres across a range of indicators. Instrumental factor analysis is the search for objective evidence of both environmental credentials and green finance sought from a range of comparable sources, e.g. information on how a financial centre’s activities contribute to lowering GHG emissions, evidence of commitments on ESG disclosure and achievements in the trading and regulatory environment of green finance.

Instrumental factor analysis is then supplemented by assessments of Green financial centres, or evidence of how their environmental markets are viewed objectively. These will then be used to build a predictive model of green financial centres using a ‘Support Vector Machine’, which is based on statistical techniques that classify and model complex historical data in order to make predictions on new data.

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