Mitigating dangerous climate change requires a fundamental transition of global economies. This transition calls for an annual investment of 2–4 trillion USD, which is a multiple of current climate investment. Accordingly, the EU and many countries are enacting green financial policies, intervening in the financial sector to improve financing conditions for low-carbon technologies. Examples include green state investment banks, green finance taxonomies, and instruments to lower the cost of capital for low-carbon technologies. However, the effect of these financial policies on investments in non-financial sectors such as energy or transport was largely unknown, and it remained unclear how to make best use of such policies to fill the climate finance gap.
Against this backdrop, the project GREENFIN combined theory from technology innovation studies and financial economics to derive how different low-carbon technologies require different types of finance, as a starting point for targeted policy interventions. Empirically, novel climate finance datasets were being used, exploiting both structured financial data and unstructured information, drawing on recent advances in machine learning methods (e.g. natural language processing). The project delivered specific recommendations for designing more effective green financial policies in the EU and beyond.