An improved nightlight-based method for modeling urban CO2 emissions. (September 2018)

• Record Type: Journal Article
• Authors: Han, Ji; Meng, Xing; Liang, Hanwei; Cao, Zhi; Dong, Liang; Huang, Cheng
• Is Part Of: Environmental modelling & software. Volume 107(2018)
• Page Start: 307
• Page End: 320
• Publication Date: 2018-09
• Subjects: Urban CO2 emissions -- Nighttime light -- Panel-data regression -- Yangtze river delta; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.05.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: An improved method was developed to model urban CO2 emissions. Spatiotemporal heterogeneity were considered in modeling the nightlight-CO2 correlation. The internal and external validations prove the method's predominance. The uncertainties and limitations of the method were evaluated.