Periodic Reporting for period 4 - GREENFIN (Effective green financial policies for the low-carbon transition)
Reporting period: 2025-07-01 to 2025-12-31
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.
Technology Finance for the Low-Carbon Transition (WP1): This part of the project studied technology-specific financing needs for Europe and the related investment shifts required to move towards a net-zero pathway. We performed an extensive meta-analysis including both model-based information and industry studies. Results were published in the journal Nature Climate Change and received a lot of media attention. More theoretical work included understanding how financing needs depend on technology-inherent characteristics, studying the cost of capital for new low-carbon technologies, and understanding how technological maturity affects financing patterns.
Green financial policy options and effectiveness (WP2): Concerning policies in general, we first developed a definition of “green financial policies” and analyzed the policy output in OECD countries, with the dataset and analysis widely cited. Further analyses concerned two specific policy instruments. First, we assess the role of Green State Investment Banks as a way to mobilize private finance and to absorb investment risks related to new technologies. We provided an empirical contribution by assessing the predictors of SIB involvement in renewable energy deals in OECD countries, and a theoretical contribution via an analytical model evaluating the role of SIBs vis-à-vis other policies such as carbon pricing. Second, we were interested in climate-related disclosure requirements and used machine learning methods to automatically identify disclosures of five different types of climate risks.
Green Financial Policy Mix (WP3): To synthesize the ex-post evidence concerning the effectiveness of green financial policy interventions, we conducted several meta-analysis. Further, we worked on improving ex-ante analyses, mainly through improving how integrated assessment models and energy system models to represent financial aspects. Focusing on the cost of capital of different novel low-carbon technologies, several articles in Nature Energy and Joule discuss how financial policies can affect deployment.