Work

Explainable Artificial Intelligence for Designing Country-Specific Climate Mitigation and Sustainable Development Pathways

Year: 2026

Type: article

Abstract: This study compares six machine-learning models for predicting per-capita CO2 emissions in a panel of 118 countries over 2000–2020 and uses SHAP to examine the fitted XGBoost model. The test sample comprises 2019–2020. XGBoost achieved the lowest test RMSE (0.475) and highest R2 (0.324), indicating limited to moderate predictive fit. GDP per capita had the largest means absolute SHAP value (0.2821), followed by the country effect (0.0361). Renewable-energy associations differed across observations and should not be interpreted as a uniform emissions reduction. Country profiles and four descriptive clusters illustrate possible areas for policy review; SHAP contributions are predictive associations, not causal effects or evaluated policy impacts.

Source: Habitable Planet

DOI: 10.63335/j.hp.2027.0052

Authors: Shabnam Shehzadi, Hafiz Muddassir Majeed Butt, Shahzad Khan Durrani, Jiequn Guo

Institutions: Jiangxi University of Finance and Economics, Beijing Institute of Technology

Language: English

Topic: Energy, Environment, Economic Growth

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Climate action

Open Access status: diamond

OpenAlex ID: W7225565211