Assessing the Economic Impact of Large-Scale Renewable Energy Adoption: A Multivariate Causal Inference Approach
DOI:
https://doi.org/10.51976/p1c4ra12Keywords:
Renewable energy, regional economics, causal inference, Synthetic Control Method, Propensity Score Matching, Vector Auto regression, panel data, policy analysisAbstract
Such changes provide both possibilities and difficulties in terms of economics. There is still little empirical data on regional economic repercussions brought forth by these developments. This article analyses the financial effects of implementing renewable energy into the regional economy using a complete framework for multivariate causal inference. We use six sophisticated statistical tools: Synthetic Control Method (SCM), Difference-in-Differences (DiD), Instrumental Variables (IV), Propensity Score Matching (PSM), Vector Autoregression (VAR), Panel Data Regression to handle the complexity and dynamic character of the transition. This allows us to assess impacts of the adoption of RE on important economic indicators: GDP, employment, and sectoral production by utilising the data of areas in Europe and North America where quite large projects for renewable energy were carried out. The results suggest that while having less direct impact on regional GDP, renewable energy boosts employment in the green industry. It goes beyond and offers policy suggestions based on strong empirical research in addition to clarifying the underlying processes of economic transformation
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