Ma Mingjuan, Wang Yumeng, Ke Shuifa
School of Agricultural Economics and Rural Development, Renmin University of China, Beijing, 100872, China.
School of Economics, North Minzu University, Yinchuan, 750030, Ningxia, China.
Sci Rep. 2024 Aug 24;14(1):19672. doi: 10.1038/s41598-024-70520-5.
Yellow River Basin (YRB) is a pivotal region for energy consumption and carbon emissions (CEs) in China, with cities emerging as the main sources of regional CEs. This highlights their critical role in achieving regional sustainable development and China's carbon neutrality. Consequently, there is a pressing need for a detailed exploration of the urban spillover effects and an in-depth analysis of the complex determinants influencing CEs within the YRB. Remote sensing data provide optimal conditions for conducting extensive studies across large geographical areas and extended time periods. This study integrates DMSP/OLS and NPP/VIIRS nighttime light datasets for a longitudinal analysis of urban CEs in the YRB. Using a harmonized dataset from DMSP/OLS and NPP/VIIRS nighttime light from 2007 to 2021, this study quantifies CEs of 58 prefecture-level cities in the YRB. By combining ESDA, STIRPAT model and spatial econometric model, this investigation further clarifies empirically the spatial spillover effects and driving factors of urban CEs. The analysis delineates a phase-wise augmentation in urban CEs, converging towards a distinct spatial distribution characterized by "lower reach > middle reach > upper reach". The spatial autocorrelation tests unravel a complex interplay between agglomeration and differentiation patterns within urban CEs, underscored by pronounced spatial lock-in phenomena. Significantly, this study demonstrates that urbanization, economic development, energy consumption structure, green coverage rate, industrial structure, population, technological progress, and FDI each exhibit varied direct and indirect effect on urban CEs. Furthermore, it elaborates on potential policy implications and future research directions, offering crucial insights for formulating CEs mitigation strategies to advance sustainable development.
黄河流域是中国能源消费和碳排放的关键地区，城市已成为区域碳排放的主要来源。这凸显了它们在实现区域可持续发展和中国碳中和方面的关键作用。因此，迫切需要详细探究城市溢出效应，并深入分析影响黄河流域碳排放的复杂决定因素。遥感数据为跨越大地理区域和较长时间段进行广泛研究提供了理想条件。本研究整合了国防气象卫星计划业务线扫描系统（DMSP/OLS）和国家极轨伙伴计划可见光红外成像辐射仪组（NPP/VIIRS）夜间灯光数据集，对黄河流域城市碳排放进行纵向分析。利用2007年至2021年DMSP/OLS和NPP/VIIRS夜间灯光的协调数据集，本研究对黄河流域58个地级市的碳排放进行了量化。通过结合探索性空间数据分析（ESDA）、随机影响的人口、富裕及技术对环境影响模型（STIRPAT）和空间计量模型，本调查进一步从实证角度阐明了城市碳排放的空间溢出效应和驱动因素。分析描绘了城市碳排放的阶段性增长，呈现出以“下游>中游>上游”为特征的独特空间分布。空间自相关检验揭示了城市碳排放中集聚与分化模式之间的复杂相互作用，突出表现为明显的空间锁定现象。值得注意的是，本研究表明城市化、经济发展、能源消费结构、绿色覆盖率、产业结构、人口、技术进步和外国直接投资（FDI）对城市碳排放均呈现出不同的直接和间接影响。此外，本研究阐述了潜在的政策含义和未来研究方向，为制定碳排放缓解策略以促进可持续发展提供了关键见解。
