Analysis on influence factors of China's CO2 emissions based on Path–STIRPAT model

Issues concerning which factors that influence carbon dioxide emission, and which administrative measures should be imposed to reduce carbon emission in Chinese cities, have been on the agenda in cities' policy-making. Yet little literature has studied this topic from the city level. This paper first measures CO 2 emission of 73 Chinese cities. We find heterogeneity embedded in the cross-city distribution of CO 2 emission per capita and a nonlinear structure in the relationship between carbon emission and GDP per capita. To describe such multimodality and examine the determinants of CO 2 emission in these cities, this article applies a linear mixed effect model covering the quadratic term of GDP per capita to extend the stochastic impact by regression on population, affluence, and technology (STIRPAT) model. The empirical results demonstrate that population size, secondary industry proportion, energy consumption structure, urbanization level and economic level have generally shown a positive influence on CO 2 emissions in Chinese cities. However, the urbanization level is of no significance. The phenomenon of the environmental Kuznets curve varies across Chinese cities, according to which three city groups are formed. Specific policy recommendations are given to each city group in light of their unique influencing modes on carbon emissions.

Section: Model Selectionmentioning

confidence: 99%

Examining Determinants of CO2 Emissions in 73 Cities in China

Zheng

Guan

et al. 2016

“…For example, in both Meng's [42] and Li's [43] research, the driving factors of China's CO2 emissions are researched, but because of the different factors selected to reflect the technologies, with Meng [42] selecting CO2 intensity of GDP and Li [43] selecting energy intensity, the elasticities of both population and affluence are quite different in these two studies. The results of Meng [42] show that a 1% increase in population and affluence will result in a 1.81% and 1.91% increase in CO2 emissions, respectively, while in Li's [43] research, these two figures are 1.12% and 1.31%. This is quite confusing, and in order to conquer this obstacle in using the STIRPAT model, this work developed a double-layer STIRPAT model, the basic framework of which is illustrated by Figure 3.…”

Section: A Double-layers Stirpat Modelmentioning

confidence: 99%

The Effects of Coal Switching and Improvements in Electricity Production Efficiency and Consumption on CO2 Mitigation Goals in China

Wang

2015

Although the average CO2 emission for a person in China is only about 1/4 that of a person in the US, the government of China still made a commitment to ensure that CO2 emissions will reach their peak in 2030 because of the ever-increasing pressure of global warming. In this work, we examined the effects of coal switching, efficiency improvements in thermal power generation and the electricity consumption of economic activities on realizing this goal. An improved STIRPAT model was developed to create the scenarios. In order to make the estimated elasticities more consistent with different variables selected to construct the formulation, a double-layer STIRPAT model was constructed, and by integrating the two equations obtained by regressing the series in each layer, we finally got the equation to describe the long-run relationship among CO2 emissions (Ic), the share of coal in overall energy consumption (FMC), coal intensity of thermal power generation (CIp) and electricity intensity of GDP (EIelec). The long term elasticities represented by the equation show that the growth of CO2 emissions in China is quite sensitive to FMC, CIp and EIelec. After that, five scenarios were developed in order to examine the effects of China's possible different CO2 emission reduction policies, focusing on improving FMC, CIp and EIelec respectively. Through a rigorous analysis, we found that in order to realize the committed CO2 emissions mitigating goal, China should obviously accelerate the pace in switching from coal to low carbon fuels, coupled with a consistent improvement in electricity OPEN ACCESS Sustainability 2015, 7 9541 efficiency of economic activities and a slightly slower improvement in the coal efficiency of thermal power generation.

“…Wang, Zhaohua et al [20] combined the improved STIRPAT model to study the impact of urbanization level, the proportion of the third industry, energy intensity and R&D output on carbon emissions. Li Huanan et al [21] explored the driving force of the impact of China's carbon emissions based on Path-STIRPAT model. Brantley Liddle [22] investigated a stochastic IPAT model and STIRPAT to examine the impact of population and affluence on carbon emissions from transportation and household electricity consumption in developed and developing countries.…”

Section: Factor Analysis Of Carbon Emission Difference Among Regionsmentioning

confidence: 99%

Regional-Level Allocation of CO2 Emission Permits in China: Evidence from the Boltzmann Distribution Method

et al. 2018

To achieve the commitment of carbon emission reduction in 2030 at the climate conference in Paris, it is an important task for China to decompose the carbon emission target among regions. In this paper, entropy maximization is brought to inter-provincial carbon emissions allocation via the Boltzmann distribution method, which provides guidelines for allocating carbon emissions permits among provinces. The research is mainly divided into three parts: (1) We develop the CO 2 influence factor, including per capita GDP, per capita carbon emissions, carbon emission intensity and carbon emissions of per unit industrial added value; the proportion of the second industry; and the urbanization rate, to optimize the Boltzmann distribution model. (2) The probability of carbon emission reduction allocation in each province was calculated by the Boltzmann distribution model, and then the absolute emission reduction target was allocated among different provinces. (3) Comparing the distribution results with the actual carbon emission data in 2015, we then put forward the targeted development strategies for different provinces. Finally, suggestions were provided for CO 2 emission permits allocation to optimize the national carbon emissions trading market in China.