Impacts of city size change and industrial structure change on CO2 emissions in Chinese cities

Abstract. China is the world's largest energy consumer and CO2 emitter. Cities contribute 85 % of the total CO2 emissions in China and thus are considered the key areas for implementing policies designed for climate change adaption and CO2 emission mitigation. However, understanding the CO2 emission status of Chinese cities remains a challenge, mainly owing to the lack of systematic statistics and poor data quality. This study presents a method for constructing a CO2 emissions inventory for Chinese cities in terms of the definition provided by the IPCC territorial emission accounting approach. We apply this method to compile CO2 emissions inventories for 20 Chinese cities. Each inventory covers 47 socioeconomic sectors, 20 energy types and 9 primary industry products. We find that cities are large emissions sources because of their intensive industrial activities, such as electricity generation, production for cement and other construction materials. Additionally, coal and its related products are the primary energy source to power Chinese cities, providing an average of 70 % of the total CO2 emissions. Understanding the emissions sources in Chinese cities using a concrete and consistent methodology is the basis for implementing any climate policy and goal.

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The Regional Inequity of CO2 Emissions per Capita in China

International Journal of Economics and Finance ◽

10.5539/ijef.v9n7p228 ◽

2017 ◽

Vol 9 (7) ◽

pp. 228 ◽

Cited By ~ 2

Author(s):

Ting Liu ◽

Wenqing Pan

Keyword(s):

Co2 Emissions ◽

Emission Reduction ◽

Co2 Emission ◽

Energy Intensity ◽

Industrial Structure ◽

Theil Index ◽

Energy Structure ◽

Co2 Emission Reduction ◽

Index Method ◽

Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

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Spatio-Temporal Analysis of CO2 Emission Driving Force in Various Provinces in China Using the Extended STIRPAT-GWR Model

10.3233/faia210304 ◽

2021 ◽

Author(s):

Yulin Zhang

Keyword(s):

Co2 Emissions ◽

Co2 Emission ◽

Energy Intensity ◽

Industrial Structure ◽

Temporal Analysis ◽

Energy Structure ◽

High Effect ◽

Northern Regions ◽

Spatio Temporal ◽

Gwr Model

To fill the shortcomings of traditional research that ignores the driver’s own spatial characteristics and provide a theoretical support to formulate suitable emission reduction policies in different regions across China. In this pursuit, based on the panel data of provincial CO2 emission in 2007, 2012, and 2017, the present study employed the extended environmental impact assessment model (STIRPAT-GWR model) to study the effect of population, energy intensity, energy structure, urbanization and industrial structure on the CO2 emissions in 29 provinces across China. The empirical results show that the effect of drivers on the CO2 emissions exhibited significant variations among the different provinces. The effect of population in the southwest region was significantly lower than that of the central and eastern regions. Provinces with stronger energy intensity effects were concentrated in the central and western regions. The effect of energy structure in the eastern and northern regions was relatively strong, and gradually weakened towards the southeast region. The areas with high urbanization effect were concentrated in the central and the eastern regions. Furthermore, significant changes were observed in the high-effect regions of the industrial structure in 2017. The high-effect area showed a migration from the northwest and northeast regions in 2007 and 2012, respectively, to the southwest and southeast regions in 2017. Urbanization showed the strongest effect on the CO2 emissions, followed by population and energy intensity, and the weakest effect was exhibited by the energy and industrial structure. Thus, the effects of population and energy structure showed a downward trend, in contrary to the effect of urbanization on the CO2 emissions in China.

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What is the Price of Tea in China? Goods Prices and Availability in Chinese Cities

The Economic Journal ◽

10.1093/ej/ueaa066 ◽

2020 ◽

Vol 130 (632) ◽

pp. 2438-2467

Author(s):

Robert C Feenstra ◽

Mingzhi Xu ◽

Alexis Antoniades

Keyword(s):

United States ◽

Spatial Distribution ◽

Consumer Goods ◽

The United States ◽

City Size ◽

Competitive Effect ◽

Chinese Cities ◽

Price Indexes

Abstract We examine the price and variety of a sample of consumer goods at the barcode level in cities within China. Unlike the position in the United States, in China the prices of goods tend to be lower in larger cities. We explain that difference between the countries by the more uneven spatial distribution of manufacturers’ sales and retailers in China, and we confirm the pro-competitive effect of city size on reducing markups there. In both countries, there is a greater variety of goods in larger cities, but that effect is more pronounced in China. Combining the lower prices and greater variety, the price indexes in China for the goods we study fall with city size by around seven times more than in the United States.

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