A Machine Learning Approach to Green Finance: Pathways for Enhancing Entrepreneurship, Employment, and the Energy Pillar of the Just Transition

Green finance is increasingly promoted as a lever for sustainable development, yet policy debates still lack clear causal evidence on whether it can deliver inclusive economic gains alongside environmental progress, especially in oil-dependent economies where transition pathways may differ across cities. This study investigates whether green finance intensity causally affects three interrelated dimensions of sustainable development—entrepreneurship, employment, and the energy pillar of the just transition (energy transition/decarbonization)—and how these effects vary across local contexts. We compile a balanced city–year panel for eight major Saudi cities over 2000–2024 and estimate both average and heterogeneous impacts using a causal machine-learning approach (Causal Forests) to recover context-dependent treatment effects in an observational setting. Results show that green finance is most consistently associated with improvements in the energy-transition dimension (average effect ≈ 0.81 in standardized units), whereas spillovers to entrepreneurship are smaller (≈0.36) and employment effects are more uneven and less precisely identified across cities. This heterogeneity reveals distinct local pathways, including “integrated” profiles where environmental and economic gains align and “eco-specialization” profiles where transition progress is not matched by comparable local economic diffusion, as illustrated by Hail. We further derive a policy-relevant city typology that helps diagnose where green finance is likely to generate broad-based benefits and where complementary interventions (e.g., skills development or SME support) may be required to translate transition gains into entrepreneurship and jobs. Overall, our findings highlight that green finance effectiveness is strongly context-dependent and support place-based strategies to convert energy-transition progress into inclusive development.