🌍 Understanding Clean Energy & Carbon Emissions in Chinese Cities

China’s cities show strong spatiotemporal heterogeneity in how different clean energy types—such as solar, wind, and hydropower—affect carbon dioxide emissions. By integrating multiscale geographically and temporally weighted regression (MGTWR), researchers capture how energy–emission relationships vary across space and time, revealing city-specific pathways toward low-carbon development 📊⚡.

🤖 Blending Advanced Regression with Machine Learning
Combining MGTWR with machine learning models enhances predictive accuracy and uncovers complex, nonlinear interactions between clean energy consumption and CO₂ emissions. Machine learning algorithms help identify hidden patterns, rank key influencing factors, and improve scenario forecasting, offering deeper insights beyond traditional statistical approaches 🧠📈.

🌱 Policy Insights for Urban Low-Carbon Transitions
This integrated framework supports evidence-based, location-specific policy design by highlighting which clean energy strategies work best in different cities and periods. Such insights enable policymakers to optimize energy structures, reduce emissions more effectively, and accelerate sustainable urban transitions aligned with China’s carbon neutrality goals 🏙️♻️.

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