Evaluating the Impact of Green Bond Financing on Environmental and Social Outcomes in Saudi Arabia: An Empirical Analysis
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".