Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

Yu Sun,Uwe Schlink,Sheng Zheng,Ramesh P Singh,Yuzhe Wu

doi:10.3390/rs12182916

Abstract

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making efforts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two different paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed.

Highlights

China is the largest carbon emitter in the world, contributing 30% of global carbon emissions [1], and is one of the most polluted countries
Compared to the overall country (Figure S2, Supplementary Materials) and provincial level (Figure 3), we see that there is an overall decline in carbon emissions rather than just being evening away from the largest cities
This study introduced nighttime light data as a proxy variable to estimate city-level carbon emissions based on the assumption that nighttime light data has a constant linear relationship with carbon emissions within a specific province

Summary

Introduction

China is the largest carbon emitter in the world, contributing 30% of global carbon emissions [1], and is one of the most polluted countries. Under the pressure of achieving its carbon emissions reduction goals, China has put forward clear strategies for energy-saving and sustainable energy development. Before instituting these strategies, accurate and timely analysis of spatiotemporal variations of carbon emissions is of great importance. Our detailed spatiotemporal analysis provides a basis for developing strategies for low-carbon emissions in different regions in China. There has been an ever-increasing interest in utilizing various methods to evaluate spatiotemporal variations in carbon emissions, such as atmospheric modeling, the geographically weighted regression (GWR), kernel density estimation, the center of gravity method, etc. Spatial autocorrelations of carbon emissions are significantly positive in China [6,9], while the degrees of spatial autocorrelation of carbon emissions are different on different scales of analysis [10,11]

Methods

Results

Conclusion