Sector-Specific Carbon Emission Forecasting for Sustainable Urban Management: A Comparative Data-Driven Framework

Accurate, high-frequency carbon emission forecasting is crucial for urban climate mitigation and achieving sustainable development goals. However, generalized models often result in lower prediction accuracy by overlooking the unique “sector specificity” of urban emission systems, namely, the different temporal patterns driven by distinct physical and economic factors across sectors. This study establishes a decision-support framework to select optimal forecasting models for distinct sectors. Using daily multi-sector carbon emission and meteorological data from Hangzhou, we evaluated 12 models across statistical, machine learning, and deep learning classes. Our three-stage design identified the best model for each sector, quantified the contribution of meteorological drivers, and assessed multi-step forecasting stability. The results indicated the lack of universality in generalized models, as no single model performed best across all sectors. A hybrid CNN-LSTM model outperformed other candidates for ground transport (R 2 = 0.635), while LSTM showed better performance for industry (R 2 = 0.866) and residential (R 2 = 0.978) sectors. Integrating meteorological factors only improved accuracy in weather-sensitive sectors (e.g., residential) and acted as noise in others (e.g., aviation). We conclude that a sector-specific strategy is more robust than a one-size-fits-all approach for carbon emission forecasting. By resolving the specific driving mechanisms of each sector this decision-support framework provides the granular data foundation necessary for precise urban energy dispatch and targeted emission reduction policies.