关键词: 夜光遥感, 碳排放, 长三角地区, 时空演变, 时空地理加权回归模型

Abstract: Objective　With the increasing concentration of population, industry, and innovation in Chinese cities, emission reduction has become progressively dependent on cross-city coordination rather than isolated municipal interventions. Under the highquality integrated development strategy of the Yangtze River Delta (YRD), clarifying the spatiotemporal characteristics of urban carbon emissions and their driving mechanisms in this region is essential for formulating scientifical and regionally coordinated carbon-mitigation action plans. Nevertheless, existing city-level evidence is still constrained by inconsistent statistical inventories and analytical frameworks that typically assume spatially and temporally stationary relationships. To address these limitations, this study aims to (i) estimate the city-level carbon emissions for 41 cities in the YRD from 2005 to 2021 using nighttime light remote sensing data, (ii) identify the spatial dependence, clustering patterns, and dynamic transitions patterns of urban carbon emissions, and (iii) quantify the spatially and temporally heterogeneous effects of key socioeconomic, structural, and governance-related factors, specifically economic development, industrial structure, urbanization, government policy, and scientific and technological progress, on urban carbon emissions across the YRD. Methods　Nighttime light remote sensing data from 2005 to 2021 were employed to estimate the carbon emissions of 41 cities in the YRD, and then a consistent spatiotemporal dataset was constructed to capture long-term changes in urban activity intensity. Specifically, spatial autocorrelation analysis and a spatial Markov chain model were applied to identify spatial clustering characteristics and transition dynamics of urban carbon emissions. A geographically and temporally weighted regression (GTWR) model was adopted to quantify the impacts of major driving factors while allowing regression coefficients to vary spatially and temporally. Explanatory variables included gross domestic product per capita (PGDP) as a proxy for economic development, the share of secondary-sector output in total GDP (IS) representing industrial structure, the urbanization rate (UR) reflecting urban development intensity, and indicators reflecting government policy and scientific and technological advancement. In addition, the coverage rate of urban green areas was also incorporated to examine the potential role of urban greening in regulating emissions. Results and Discussion　The results show that the overall carbon emissions in the YRD increased during the study period and exhibited a pronounced spatial agglomeration pattern. The effects of influencing factors demonstrate significant spatiotemporal heterogeneity, indicating that the magnitude and direction of driving factors vary across cities and years. PGDP and IS are the dominant contributors to the increase of urban carbon emissions, with mean regression coefficients of 0.57 and 0.61, respectively. UR is positively correlated with carbon emissions, and its estimated effect follows a non-linear pattern, initially increasing and subsequently declining. Furthermore, the urban green area coverage rate also shows heterogeneous impacts on carbon emissions, implying that the mitigation effectiveness of urban greening varies depending on the local stage of urban development. Conclusions　This study conducts a city-level spatiotemporal assessment of carbon emissions in the YRD from 2005 to 2021 using nighttime light remote sensing estimation, spatial autocorrelation diagnosis, spatial Markov transition analysis, and GTWR-based heterogeneous impact modeling. The empirical evidence shows that urban carbon emissions in the YRD have increased overall during the study period and exhibit significantly spatial clustering characteristics. The emission dynamics demonstrate neighborhood-conditioned sustainability, underscoring the importance of regional coordination in addressing high-emission agglomerations. Moreover, the determinants of emissions are distinctly spatiotemporally heterogeneous, manifested in the fact that PGDP and IS constitute the dominant driving forces of emission growth, whereas the effects of urbanization and urban greening are nonlinear and location-dependent, and the influences of government policy and scientific and technological progress vary by city and period. These findings suggest that effective mitigation measures for carbon emissions in the YRD should prioritize differentiated, city-specific policy packages and strengthened cross-city coordination mechanisms, especially for high-emission clusters, in order to promote industrial upgrading, enhance energy efficiency, and improve the overall effectiveness of low-carbon governance.

Key words: night light remote sensing, carbon emissions, Yangtze River Delta region, temporal and spatial evolution; geographically and temporally weighted regression model