Zhang Binbin, Liang Zongzheng, Guo Wenru, Cui Zhanyou, Li Deguang
College of Sciences, Shihezi University, Shihezi, 832003, China.
Academy of Regional and Global Governance, Beijing Foreign Studies University, Beijing 100089, China.
Heliyon. 2025 Jan 8;11(2):e41806. doi: 10.1016/j.heliyon.2025.e41806. eCollection 2025 Jan 30.
Carbon emissions have increasingly been the focus of all governments as countries throughout the world choose carbon neutrality as a national development strategy. The analysis of the spatiotemporal dynamics of CO emission has emerged as a significant research topic considering the dual-carbon goal. In this research, we explore the spatiotemporal changes of CO emission at different scales based on nighttime light data. The Chinese Academy of Science's Earth Luminous Dataset, CO emission data from Carbon Emission Accounts and Datasets, and basic national geographical data are used for analysis. A linear regression model between nighttime light data and CO emission is constructed. Thereafter, the global Moran's I index of exploratory spatial data analysis is used to verify the spatial parameters of all provinces. The trend value method is utilized to analyze the changing trend of CO emission at multiscale levels, covering the Chinese mainland, three major economic regions, and six largest agglomerations from 2012 to 2019. Experimental results show a significant positive correlation between the CO emission and nighttime light data from 2012 to 2019. The nighttime light data could be used to effectively estimate the total CO emission at the provincial and municipal levels in China. The growth rate of CO emissions in China is stable and has decreased in 2015. Furthermore, the spatiotemporal dynamics of CO emission in different agglomerations vary. Our work provides a scientific basis for the different provinces and cities to develop feasible emission reduction policies.
随着世界各国将碳中和作为国家发展战略，碳排放日益成为各国政府关注的焦点。考虑到双碳目标，对碳排放时空动态的分析已成为一个重要的研究课题。在本研究中，我们基于夜间灯光数据探索不同尺度下碳排放的时空变化。使用了中国科学院的地球夜光数据集、碳排放账户和数据集的碳排放数据以及国家基础地理数据进行分析。构建了夜间灯光数据与碳排放之间的线性回归模型。此后，利用探索性空间数据分析的全局莫兰指数来验证所有省份的空间参数。采用趋势值法分析了2012年至2019年中国大陆、三大经济区域和六大城市群等多尺度层面碳排放的变化趋势。实验结果表明，2012年至2019年碳排放与夜间灯光数据之间存在显著正相关。夜间灯光数据可有效估算中国省级和市级层面的碳排放总量。中国碳排放的增长率稳定，且在2015年有所下降。此外，不同城市群的碳排放时空动态各不相同。我们的工作为不同省市制定可行的减排政策提供了科学依据。
