Assessing the Economic Impact of Large-Scale Renewable Energy Adoption: A Multivariate Causal Inference Approach

The economic impact of large-scale renewable energy adoption is profound, driving sustainable growth, job creation, and sectoral shifts. By replacing fossil fuels, renewable energy projects stimulate economic development through investments in infrastructure, technology, and green manufacturing. Regions with significant renewable energy capacity, such as Germany and the USA, see higher economic growth and increased employment, particularly in the energy, manufacturing, and agricultural sectors. This paper investigates the economic impact of large-scale renewable energy adoption in Europe and North America, focusing on its influence on regional GDP, employment, and sectoral shifts. Using advanced statistical methods such as Permutation Deep Learning (PDL), Synthetic Control Method (SCM), Difference-in-Difference (DiD), and Propensity Score Matching (PSM), the study assesses the causal effects of renewable energy adoption on economic growth. The results reveal that districts with higher renewable energy capacities, such as Anantapur (4,200 MW) and Kurnool (3,750 MW), exhibit higher average Gross State Domestic Product (GSDP) growth of 6.8% and 7.1%, respectively. In contrast, regions like Nellore and Srikakulam, with lower renewable energy capacities (980 MW and 500 MW), show GSDP growth rates of 5.0% and 4.7%, respectively. The Permutation Importance Scores indicate that renewable energy capacity is the most influential factor, with a score of 0.093, followed by industrial output and population growth. Additionally, the study highlights the sectoral shifts towards green manufacturing and energy technologies, resulting in new job creation in the renewable energy sector, with significant employment increases in districts like Anantapur and Kurnool. The findings underscore the importance of renewable energy in driving sustainable economic growth, creating jobs, and promoting regional economic restructuring in Europe and North America . 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