Abstract

This study focuses on the environmental and social effects of energy projects in Saudi Arabia and how green bond financing contributes to reducing emissions of CO₂ and generating job opportunities aligned with Vision 2030. The study employs two-step empirical approach which combines pooled Ordinary Least Squares (OLS) regression and Propensity Score Matching (PSM), to address the issue of selection bias and enhance causal interpretation by utilizing the balanced panel dataset from 2017 to 2024 which consists of 12 energy projects that are financed through green bond and non-green bond. Diagnostic tests are used to confirm the presence of multicollinearity or heteroskedasticity and ensure the reliability of estimators. The findings of the OLS indicate that green bond financed projects perform significantly in terms of CO₂ reduction and job creation than non-greem projects, indicating their environmental and socioeconomic advantages. These findings are supported by PSM, which reveals that treated projects perform better than control projects even after considering the project level and macroeconomic factors. Overall, the findings demonstrate the potential of green bond financing to help Saudi Arabia reach its sustainability goals by supporting eco-friendly, capital-intensive, and employment-generating infrastructure. The study offers practical insights for policymakers and investors interested in expanding sustainable finance and tackling climate change and development issues.

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