Revisiting the environmental Kuznets curve for city-level CO2 emissions: based on corrected NPP-VIIRS nighttime light data in China
Introduction
Issues related to climate change have attracted much attention worldwide and have become an increasingly pressing problem facing society (Fan et al., 2016; Nicholson-Cole, 2005). Global warming is one of the most serious current events, which has affected human beings socially, politically, and economically in recent decades (Ang, 2009; Lu et al., 2007). Reports from the Intergovernmental Panel on Climate Change (IPCC) and other studies show that carbon dioxide (CO2) emissions are the most important contributor (Griggs and Noguer, 2010; Karl and Trenberth, 2003) to global warming. Human activities have greatly exacerbated the process of climate warming and caused many adverse effects on the natural ecological environment on the earth’s surface. The Paris Agreement proposes that in the 21st century, the world should strive to control the increase of the global average temperature to below 2 °C higher than that in the pre-industrial era, preferably reaching below 1.5 °C. As the temperature increase is triggered by the accumulation of greenhouse gases (GHGs), this global problem requires the cooperation of all countries and groups. As of 2016, a total of 178 parties had signed the Paris Agreement. Most countries have made commitments to curb global warming caused by harmful emissions from burning coal, oil and natural gas. China became the 23rd party to complete the ratification agreement. As the world’s second largest economy and the largest greenhouse gas emitter (Guan et al., 2014; Mu et al., 2013), China needs to take appropriate responsibility for reducing its CO2 emissions to curb the trend of accelerated global warming. In 2016, China announced that its total emissions will peak by the year 2030 (Li et al., 2018).
However, the annual average growth rate of CO2 emissions in China has reached approximately 10% since 2000 (Liu et al., 2010). Additionally, China contributes 27.3% to the world’s energy-related CO2 emissions (Jiang et al., 2018). According to the Global Carbon Budget Report (2017), China’s CO2 emissions were nearly twice those of the United States in 2016. Facing huge pressure to reduce CO2 emissions, the Chinese government had to implement a crucial national emissions reduction strategy in its 12th five-year plan, committing to reducing its intensity of CO2 emissions (calculated as CO2 per unit of GDP) by 17% between 2011 and 2015. In addition, the Chinese government promised in a joint statement with the United States in 2014 that its national CO2 emissions would peak by 2030 and then decline (Wang and Liu, 2017). To reach the CO2 emissions reduction target as soon as possible, the Chinese government needs detailed CO2 emissions data to accurately implement different countermeasures for different cities. The statistical scale of CO2 emissions from energy consumption is limited to the provincial level at present. Measuring CO2 emissions at a finer scale is a key step in achieving emissions reduction targets. China has experienced rapid urbanization, with an increase from 16.4% of the population living in urban areas in 1949 to 58.52% in 2017 (Zhou et al., 2020). The 35 largest cities contribute 40% of China’s CO2 emissions (Ouyang and Lin, 2017). Therefore, a current problem facing academia and politicians involves measuring the level of CO2 emissions in various cities accurately and efficiently. In recent years, nighttime light (NTL) data have become a powerful tool for measuring carbon dioxide emissions. Many scholars have performed studies on carbon dioxide emissions using nighttime light data, including the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) NTL data and the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data (Lv et al., 2020).
In this study, we apply corrected nighttime light data to estimate the CO2 emissions of China at the city level. This corrected method was first proposed by Ghosh et al. (2010), and Ou et al. (2015) used it to map CO2 emissions worldwide, applying scatter plots to fit the statistics and simulations using the 50 U.S. states. The resulting R2 of above 0.8 showed that NPP-VIIRS can be a powerful tool for use in studying CO2 distributions. However, previous authors did not consider the practicability of this method in China, and there is a lack of in-depth analysis of the potential socio-economic factors affecting CO2 emissions at the city level.
The contribution of this research is mainly embodied on the following aspects. In terms of research content, we used the corrected NPP-VIIRS NTL data to estimate carbon dioxide emissions from energy consumption in 30 provinces and 291 cities in China for the first time. In addition, we carried out the visualization and hot spot analysis of urban per capita CO2 emissions, and high-emission zones and hot and cold regions are presented on a map. Based on these data, the environmental Kuznets curve was tested based on the STRIPT model. In terms of maintaining regional sustainable development, energy conservation and emissions reduction, the inflection point of the Kuznets curve was predicted. Through further analysis of the influencing factors of city development such as the per capita GDP, FAI, urbanization rate, etc., development recommendations and countermeasures are proposed for certain cities order to control the total CO2 emissions and reduce per capita CO2 emissions.
Section snippets
Literature review
In recent decades, many studies have sought to measure China’s carbon emissions at different geographical scales and by different methods. The province and the city are often used as the basic study unit (Shan et al., 2016). The various methods used to measure carbon emissions include constructing carbon inventories (Shan et al., 2018), modeling the carbon footprint (Brown et al., 2009), and input-output analysis (Shao et al., 2016). In addition, some scholars use data from unofficial sectors
Study area
The study area is mainland China, and considering the availability of data, 30 provinces and 291 cities comprise the research sample.
Data collection
The main data comprise 2015 and 2016 NPP-VIIRS nighttime composites, a China population spatial distribution kilometer grid dataset, energy consumption statistical data, data on socioeconomic indicators (including the GDP per capita, industrial structure, urbanization rate, January temperature, investment in fixed assets, expenditure on science and technology, and
Fitting accuracy using statistical data of CO2 emissions at the provincial level
Using the energy consumption statistics and the estimation method described in 2.3.1, the carbon emissions are obtained for each province. Table 1 shows the CO2 emissions data, population, GDP, and carbon intensity data of 27 provinces and the 4 municipalities directly under the central government (i.e., Beijing, Shanghai, Tianjin, and Chongqing). Shandong, Hebei, Shanxi, Jiangsu, and Inner Mongolia are the five highest total carbon emitters. Shandong and Jiangsu Provinces are also ranked at
Conclusions
This study uses corrected nighttime light data, which combine the original NPP-VIIRS data and population data to estimate the amount of 2015 CO2 emissions produced by various provinces in China. The CO2 emissions statistics obtained from the energy consumption data are then employed to verify the estimated values. The process and results demonstrate that this estimation method is feasible for studying China’s CO2 emissions. The linear relationship obtained at the provincial scale by applying
Credit author statement
H.C. and X.Z. designed the research; H.C. and R.W. performed the research; X.Z. analyzed the data; H.C. drafted the manuscript, which was revised by X.Z. and T.C.; All authors have read and approved the final manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) [grant number: XDA20040400]; the National Natural Science Foundation of China [grant numbers: 71834005, 71303203, 71673232]; the Research Grant Council of Hong Kong, China [grant number: CityU 11271716]; and the CityU Internal Funds [grant numbers: 9680195, 9610386].
References (87)
CO2 emissions, research and technology transfer in China
Ecol. Econ.
(2009)- et al.
Simple diagnostic tests for spatial dependence
Reg. Sci. Urban Econ.
(1996) - et al.
The geography of metropolitan carbon footprints
Policy and Society
(2009) - et al.
Local strategies for China’s carbon mitigation: an investigation of Chinese city-level CO2 emissions
J. Clean. Prod.
(2018) - et al.
China high resolution emission database (CHRED) with point emission sources, gridded emission data, and supplementary socioeconomic data
Resour. Conserv. Recycl.
(2018) - et al.
The implications of China’s investment-driven economy on its energy consumption and carbon emissions
Energy Convers. Manag.
(2014) - et al.
The relationship between CO2 emissions, economic scale, technology, income and population in China
Proc. Environ. Sci.
(2011) - et al.
Estimating nitrogen oxides emissions at city scale in China with a nightlight remote sensing model
Sci. Total Environ.
(2016) - et al.
Investigating factors affecting carbon emission in China and the USA: a perspective of stratified heterogeneity
J. Clean. Prod.
(2018) - et al.
A top-bottom method for city-scale energy-related CO2 emissions estimation: a case study of 41 Chinese cities
J. Clean. Prod.
(2018)
Environmental Kuznets curve for CO2 emissions in China: a spatial panel data approach
Ecol. Indicat.
Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis
Int. J. Appl. Earth Obs. Geoinf.
Spatial econometric panel data model specification: a Bayesian approach
Spatial Statistics
Can China achieve its CO2 emissions peak by 2030?
Ecol. Indicat.
Situation and determinants of household carbon emissions in Northwest China
Habitat Int.
How does foreign direct investment influence energy intensity convergence in China? Evidence from prefecture-level data
J. Clean. Prod.
Multiscale analysis on spatiotemporal dynamics of energy consumption CO2 emissions in China: utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets
Sci. Total Environ.
Carbon emissions from energy consumption in China: its measurement and driving factors
Sci. Total Environ.
Environmental Kuznets curves: a spatial econometric approach
J. Environ. Econ. Manag.
Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program’s Operational Linescan System) nighttime light imagery: methodological challenges and a case study for China
Energy
Analysis of China’s carbon dioxide flow for 2008
Energy Pol.
Representing climate change futures: a critique on the use of images for visual communication
Comput. Environ. Urban Syst.
Determinants of environmental sustainability: evidence from Saudi arabia
Sci. Total Environ.
Carbon dioxide (CO2) emissions during urbanization: a comparative study between China and Japan
J. Clean. Prod.
Economic growth, energy, and environmental Kuznets curve
Renew. Sustain. Energy Rev.
Decoupling CO2 emission and economic growth in China: is there consistency in estimation results in analyzing environmental Kuznets curve?
J. Clean. Prod.
New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors
Appl. Energy
Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis
Appl. Energy
Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China
Appl. Energy
Urbanisation, energy consumption, and carbon dioxide emissions in China: a panel data analysis of China’s provinces
Appl. Energy
China’s city-level energy-related CO2 emissions: spatiotemporal patterns and driving forces
Appl. Energy
Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities
Appl. Energy
Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China
Environ. Plann.: Economy and Space
Re-examining environmental Kuznets curve for China’s city-level carbon dioxide (CO2) emissions
Spatial Statistics
Urbanization impact on carbon emissions in the Pearl River Delta region: Kuznets curve relationships
J. Clean. Prod.
Driving forces of CO2 emissions in the G20 countries: an index decomposition analysis from 1971 to 2010
Ecol. Inf.
Carbon emissions, energy consumption and economic growth: evidence from the agricultural sector of China’s main grain-producing areas
Sci. Total Environ.
Spatial distribution pattern of the headquarters of listed firms in China
Sustainability
Understanding the relation between urbanization and the eco-environment in China’s Yangtze River Delta using an improved EKC model and coupling analysis
Sci. Total Environ.
Examining the determinants and the spatial nexus of city-level CO2 emissions in China: a dynamic spatial panel analysis of China’s cities
J. Clean. Prod.
Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input–output model
Energy Pol.
Investigating the non-linear relationship between urbanization and CO2 emissions: an empirical analysis
Air Qual. Atmos. Health
Geographically weighted regression: a method for exploring spatial nonstationarity
Geogr. Anal.
Cited by (72)
Renewable energy and CO<inf>2</inf> emissions: Does human capital matter?
2024, Energy ReportsImpact of economic development on soil trace metal(loid)s pollution: A case study of China
2024, Environmental PollutionRasterizing CO<inf>2</inf> emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013–2019
2023, Science of the Total EnvironmentCorrelation modelling between land surface temperatures and urban carbon emissions using multi-source remote sensing data: A case study
2023, Physics and Chemistry of the Earth