Spatial–Temporal Characteristics and Influencing Factors of Carbon Emission Performance: A Comparative Analysis Between Provincial and Prefectural Levels from Global and Local Perspectives

To support China’s “3060” dual carbon targets, this study quantitatively evaluates the spatial–temporal characteristics and influencing factors of carbon emission performance (CEP) across administrative levels. While prior research has examined CEP patterns, a systematic comparison of factor contributions at different levels—particularly from global and local perspectives—is lacking. This study addresses this gap by analyzing CEP in 31 provinces and 333 prefecture-level cities (2003–2020) using a coupling coordination degree model to measure CEP, spatial autocorrelation indices (Moran’s I) to assess global/local dependence, static/dynamic Spatial Durbin model (SDM/DSDM) with two-way fixed effects to compare global impacts, and geographically and temporally weighted regression (GTWR) to quantify spatiotemporal heterogeneity. The results show the following: (1) CEP showed consistent growth at both levels with positive spatial autocorrelation, revealing significantly richer clustering patterns at the prefectural rather than provincial level. (2) From a global perspective, influencing factors’ contributions to CEP vary significantly between levels. Provincially, dominant factors rank as time-lagged CEP(CEP_lag)> proportion of built-up land(P_built) > spatial lag of CEP(W×CEP) > fractional vegetation coverage (lnFVC); while prefecturally, CEP_lag > spatial error coefficient(rho) > W×CEP > P_built, with the proportion of secondary industry in GDP (GDP2)/proportion of tertiary industry in GDP (GDP3) gaining greater significance. (3) Local regression results reveal significant spatiotemporal heterogeneity in CEP influencing factors. lnFVC and W×CEP show the most distinct differences between levels, while land-use factors like P_built and nighttime light index (NTL) exhibit unstable spatiotemporal effects. The study underscores the need for scale-specific policies addressing spatial spillovers and local heterogeneity, providing actionable insights for China’s carbon mitigation strategies.