Quantifying Socio-Regional Variability via Factor Analysis over China: Optimizing Residential Sector Emission Reduction Pathways

Policy synergy, the evidence-based coordination of public policies, can aid in more rapidly achieving air pollutant and carbon dioxide (CO2) emission reduction targets. Using logarithmic mean Divisia index (LMDI) decomposition, coupling coordination degree (CCD), and geographically and temporally weighted regression (GTWR) models, we analyzed the emission characteristics, drivers, and reduction pathways of residential air pollution across 30 Chinese provinces from 2001 to 2020. The southern provinces produced more air pollution than the northern provinces, with the gap widening after 2015. In the residential sector, energy emission factors (LMDI decomposition result, 686,681.9) and population size (14,331) had greater impacts on air pollutant emissions than the energy structure, energy intensity, synergies, or GDP per capita. The GTWR analysis of the CCD mechanism indicated that hydroelectricity and urbanization enhanced coupling coordination in the southeast. Meanwhile, in the west, coupling coordination was improved by R&D investment, government spending on industrial pollution control, electricity consumption, per capita cropland, temperature, and urbanization. This analysis provides a valuable reference for optimizing emission reduction strategies.