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Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets

Jincai Zhao, Guangxing Ji, YanLin Yue, Zhizhu Lai, Yulong Chen, Dongyang Yang, Xu Yang and Zheng Wang

Applied Energy, 2019, vol. 235, issue C, 612-624

Abstract: Increasing urban residential energy consumption and CO2 emissions pose a critical challenge for regional carbon reduction policy. This study integrated two nighttime light datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light images and the Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) composite data to improve on estimates of urban residential CO2 emissions from 2000 to 2015 at 1 km resolution. Then, the driving forces including socio-economic factors and climatic factors were discussed using spatial econometrics models based on panel data covering 288 prefecture-level cities. The results show that models built for the northern and southern regions separately performed better than those for the entire country in estimating urban residential CO2 emissions, which strongly suggests that climatic factors affect the behavior of urban residents and CO2 emissions. The spatio-temporal analysis revealed that rapid growth in emissions occurred in provincial capitals and was mainly concentrated in central China. Gross Domestic Product and energy utilization technology were associated with higher CO2 emission while GDP per capita and the number of employed workers had a negative effect. Measures based on daily mean temperatures had a substantial negative correlation with CO2 emissions. In contrast, the average of daily maximum air temperature in summer correlated with higher CO2 emissions. We conclude that extreme weather events and energy efficiency should be of particular concern for policy makers.

Keywords: Residential carbon emissions; Spatio-temporal dynamics; Integration of two nighttime light datasets; Spatial econometrics models (search for similar items in EconPapers)
Date: 2019
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