A hybrid intelligent predicting model for exploring household CO₂ emissions mitigation strategies derived from...

Area of Science:

• Environmental Science
• Climate Change Research
• Energy Policy

Background:

• Rising global temperatures necessitate urgent climate change mitigation strategies.
• Improving living standards have led to increased residential energy consumption and CO2 emissions, particularly in regions like the Yangtze River Delta (YRD).
• Accurate estimation and prediction of household CO2 emissions are crucial for developing effective reduction policies.

Purpose of the Study:

• To propose and validate a novel intelligent model for predicting residential energy-related CO2 emissions in the YRD region.
• To explore the driving forces behind regional discrepancies in household CO2 emissions.
• To provide data-driven recommendations for policymakers to reduce residential CO2 emissions.

Main Methods:

• Estimation of residential energy-related CO2 emissions.
• Bivariate correlation analysis to identify key emission factors across 13 indicators.
• Kernel Principal Component Analysis (KPCA) for feature extraction.
• Enhanced Butterfly Optimization Algorithm (BOA) for optimizing Least Square Support Vector Machine (LSSVM) parameters, creating the EBOA-LSSVM model.

Main Results:

• The proposed EBOA-LSSVM model demonstrated superior performance in predicting residential CO2 emissions compared to other models.
• The study identified key factors influencing regional variations in household CO2 emissions within the YRD.
• Accurate emission predictions were achieved, highlighting the model's practical applicability.

Conclusions:

• The EBOA-LSSVM model offers a robust and accurate approach for forecasting residential CO2 emissions.
• Understanding regional emission drivers is essential for targeted mitigation efforts.
• The findings provide valuable insights for policymakers aiming to reduce household carbon footprints in the YRD and similar regions.