Elsevier

Journal of Cleaner Production

Volume 268, 20 September 2020, 121575
Journal of Cleaner Production

Revisiting the environmental Kuznets curve for city-level CO2 emissions: based on corrected NPP-VIIRS nighttime light data in China

https://doi.org/10.1016/j.jclepro.2020.121575Get rights and content

Highlights

  • NPP-VIIRS data is useful when exploring the CO2 emissions of cities in China.

  • Developed cities and cities dominated by industry and mining emitted more CO2 in 2015.

  • The EKC hypothesis is valid, with a turning point at around CNY 73,071.

  • Secondary industries have a positive effect on CO2 emissions.

  • Emissions are affected by Jan’s average temperature, expenditure ratio of science and technology, and road density.

Abstract

With the increasing trend of global warming, the Chinese government faces tremendous pressure to reduce CO2 emissions. The purpose of this study is to accurately measure CO2 emissions at the city scale in China and examine the environmental Kuznets curve, thereby providing a reference for decision-making. Corrected NPP-VIIRS nighttime light data were used to accurately estimate carbon dioxide emissions at the provincial and city scales in China. Then, based on the STRIPAT model, 291 cities in China were used to verify the environmental Kuznets curve. Our results show that on the provincial scale, the R2 between the estimated value and the statistical value of carbon dioxide reaches 0.85. Western cities in China emit more CO2, as do economically developed cities and industry- and mining-dominated cities. There are two CO2 emission hot spots in the north and one cold spot in the south. It was found that the environmental Kuznets curve on the city scale exists. This study has practical value in utilizing NPP-VIIRS data for the estimation of city CO2 emissions. The results also have academic value for determining factors that contribute to carbon dioxide emissions and can provide a reference for relevant decision makers. This study could be considered the first to simulate CO2 emissions at the provincial and city levels in China based on a NPP-VIIRS nighttime light model to explore the associated geographical distribution characteristics and potential influencing factors.

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].

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