An accurate modeling of urban CO₂ emissions is important for understanding the dynamics of carbon cycle and for designing low-carbon policies. We develop an improved nightlight-based method to model urban CO₂ emissions and investigate their spatiotemporal patterns. Differing from the previous methods, in processing the pre-modeling data, we bring forward the existing CO₂ inventories from national and provincial levels to city level, and correct the saturation and blooming problems of nightlight. In modeling the correlation between nightlight and statistically accounted CO₂ emissions, we highlight a panel-data regression analysis that considers the spatiotemporal heterogeneity across cities and over time simultaneously. Eleven cities in Yangtze River Delta of China were selected for a case study testing our method. The internal and external validations have proven the predominance of our proposed method for capturing the nightlight-CO₂ correlation, and for describing the spatial distribution and heterogeneity of urban CO₂ emissions.

准确的城市CO ₂排放模型对于理解碳循环的动力学和设计低碳政策非常重要。我们开发了一种改进的基于夜光的方法来模拟城市CO ₂排放并调查其时空模式。与以前的方法不同，在处理预建模数据时，我们将现有的CO ₂清单从国家和省级提升到了城市级，并纠正了夜灯的饱和度和开花问题。在建模夜灯和统计计算出的CO ₂之间的相关性时排放方面，我们重点介绍了面板数据回归分析，该分析考虑了城市之间以及同时随时间变化的时空异质性。我们选择了长江三角洲的11个城市作为案例研究来检验我们的方法。内部和外部验证已经证明了我们提出的捕获夜灯与CO ₂相关性以及描述城市CO ₂排放的空间分布和异质性的方法的优势。