Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Abstract

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

Industrial Coagglomeration, Green Innovation, and Manufacturing Carbon Emissions: Coagglomeration’s Dynamic Evolution Perspective

“…Nevertheless, it shares some common defects with the IPAT model. For example, when analyzing the problem by changing one influencing factor while keeping other factors fixed, the result obtained is the proportional effect of the changing influencing factor on dependent variables, which is inconsistent with the actual situation [ 39 ]. To overcome that drawback, Dietz and Rosa expressed the IPAT model as a random form and developed a STIRPAT model, which can examine the non-proportional effect of the impact factors on the environment; thus more effectively identifying the intricate connections between variables [ 40 ].…”

“…Scholars have widely employed this model to test the driving factors of the environmental impact incorporating carbon emissions [ 41 ]. Referring to previous studies [ 39 , 40 , 41 , 42 ], we introduced the variable of industrial coagglomeration and developed a new model, which can be expressed as follows: where Coagg is the industrial coagglomeration level of manufacturing and producer service industries. represents the elasticity of industrial coagglomeration on environmental impact.…”

An Empirical Study on the Impact of Energy Poverty on Carbon Intensity of the Construction Industry: Moderating Role of Technological Innovation

“…Wei (2010) analyzed the influencing factors of China's interprovincial carbon emissions from 1997 to 2007 and proposed that technological progress played a significant role in promoting China's carbon emissions, showing obvious regional differences (Wei and Yang, 2010). Li (2019) uses the IPCC calculation method to calculate China's carbon emissions, and the STIRPAT model to analyze the impact of factors such as total population, technological level, and industrial structure on carbon emissions, and finds that improving the technological level can effectively control carbon emissions (Li et al, 2019). constructed the super-era total factor carbon emission performance index of the construction industry in 30 provinces, and concluded that the growth of NMTCPI was mainly caused by technological progress, but the regional technological gap gradually widened after 2011 (Li et al, 2020a).…”

Carbon Emission Reduction—Carbon Tax, Carbon Trading, and Carbon Offset

“…Li, Li, and Shao [5] used the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and used it as a dependent variable to analyze the influencing factors. Their results showed that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level.…”