Energy Carbon Emission Reduction Based on Spatiotemporal Heterogeneity: A County-Level Empirical Analysis in Guangdong, Fujian, and Zhejiang

Guangdong, Fujian, and Zhejiang (GFZ), located on China’s southeast coast, have long been economically active and rapidly growing provinces in China. However, the rising energy consumption in these provinces poses a major challenge to their carbon emissions reduction. Due to the spatial variation in the natural environment and socio-economic activities, energy carbon emissions (ECEs) and their reduction may vary among counties. The matter of scientifically formulating localized carbon reduction paths has therefore become a critical issue. This study proposed a novel path analysis framework based on exploring spatiotemporal heterogeneity using a spatiotemporal statistic model (i.e., spatiotemporal weighted regression). The path’s learning procedure was based on linking the changes in the amount of ECEs to the shifts in dominant factors, which were detected through local significance tests on the coefficients of STWR. To verify its effectiveness, we conducted a county-level empirical study considering four drivers (i.e., population (P), impervious surfaces (I), the proportion of secondary industry (manufacturing, M), and the proportion of tertiary industry (services, S)) in GFZ from 2014 to 2021. The ECEs show two different trends that may be affected by the COVID-19 pandemic and economic recession; hence, we divided them into two periods: an active period (2014–2018) and a stable period (2018–2021). Many interpretable paths and their occurrences were derived from our results, including the following: (1) P and S showed higher sensitivity to the changes in ECEs compared with I and M. Most counties (more than 50%) were dominated by P, but the dominator P may shift to I, M, and S during the active period. Many S-dominated counties reverted to being P-dominated ones during the stable period. (2) For the active period, the two most significant paths, M + → S − and M + → P + (+/− denotes positive or negative impacts of dominated driver), reduced ECEs by about 7.747 × 10 5 tons and 3.145 × 10 5 tons, respectively. Meanwhile, the worst path, S + → P + , increased ECEs by nearly 1.186 × 10 6 tons. (3) For the stable period, the best path (S + → I + ) significantly reduced ECEs by 1.122 × 10 6 tons, while the worst two paths, M − → P + and I + → P + , increased ECEs by 1.978 × 10 6 tons and 4.107 ×10 5 tons, respectively. These findings verify the effectiveness of our framework and further highlight the need for tailored, region-specific policies to achieve carbon reduction goals.