Our analysis looks at how environmental finance tools connect to carbon-risk pricing in the energy sector. To capture different layers of evidence, we combine panel-data regressions with machine-learning techniques. Drawing on a panel of 10 countries (2010–2023), we examine whether carbon prices, energy-sector CO₂ emissions, and ESG scores account for variation in green bond issuance. Fixed- and random-effects regressions indicate that carbon pricing explains the largest share of variation, followed by emissions and ESG performance. Random Forest and XGBoost models are tested for robustness; both assign the highest importance to carbon pricing, though out-of-sample accuracy differs across models. Taken together, the evidence suggests that environmental indicators matter, yet country-specific institutions shape how green finance develops. We outline practical policy implications—clearer carbon-pricing frameworks, more transparent ESG disclosure, and country-tailored measures—to support a Paris-aligned transition.