Review on carbon emissions, energy consumption and low-carbon economy in China from a perspective of global climate change

doi:10.1007/s11442-016-1302-3

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Abstract

Accompanying the rapid growth of China’s population and economy, energy consumption and carbon emission increased significantly from 1978 to 2012. China is now the largest energy consumer and CO₂ emitter of the world, leading to much interest in researches on the nexus between energy consumption, carbon emissions and low-carbon economy. This article presents the domestic Chinese studies on this hotpot issue, and we obtain the following findings. First, most research fields involve geography, ecology and resource economics, and research contents contained some analysis of current situation, factors decomposition, predictive analysis and the introduction of methods and models. Second, there exists an inverted “U-shaped” curve connection between carbon emission, energy consumption and economic development. Energy consumption in China will be in a low-speed growth after 2035 and it is expected to peak between 6.19-12.13 billion TCE in 2050. China’s carbon emissions are expected to peak in 2035, or during 2020 to 2045, and the optimal range of carbon emissions is between 2.4-3.3 PgC/year (1 PgC=1 billion tons C) in 2050. Third, future research should be focused on global carbon trading, regional carbon flows, reforming the current energy structure, reducing energy consumption and innovating the low-carbon economic theory, as well as establishing a comprehensive theoretical system of energy consumption, carbon emissions and low-carbon economy.

Keywords carbon emissions energy consumption low-carbon economy global climate change

Fund:National Natural Science Foundation of China, No.41271547;Strategic Priority Research Program - Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences, No.XDA05010400;National Natural Science Foundation of China, No.41401644

Issue Date: 29 July 2016

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	SHEN Lei
	*SUN Yanzhi

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SHEN Lei,*SUN Yanzhi. Review on carbon emissions, energy consumption and low-carbon economy in China from a perspective of global climate change[J]. Journal of Geographical Sciences, 2016, 26(7): 855-870.

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http://www.geogsci.com/EN/10.1007/s11442-016-1302-3 OR http://www.geogsci.com/EN/Y2016/V26/I7/855

Figure 1 The cognitive evolution of climate change

Table 1 Related methods and information of this paper

Table 2 Co-word matrix of high frequency keywords (partial)

Table 3 Studies of contributors to the changes of CO₂ emissions in China

Figure 2 The trend of energy consumption from 1953 to 2013 in China

Table 4 The forecast for energy demand in China

Figure 3 The linkage between energy consumption and carbon emission from 1980-2012 in China

Figure 4 The tendency of linkage between carbon emission, energy consumption and economic development

Figure 5 Transition from theory to application about study on low-carbon economy

Method and model	Basic algorithm	Application description
IPCC greenhouse gas emission inventory preparation method (Cheng, 2014)	c=a×f	C indicates carbon emission, a is the activity level, f is the emission factor. It provides a unified algorithm and reference standard for the estimation of carbon emission.
LMDI model	C=P×(Y/P)×(E/Y)× (C/E)	C indicates carbon emission, P is the population, Y/P is per capita GDP, E/Y represents the energy consumption intensity, C/E is the energy structure intensity; the method is widely used in carbon emissions calculation and its effect decomposition.
IPAT model	I=P×A×T	I depicts the impact of evaluation, P is the population, A expresses the wealthy degree, T represents the scientific and technological progress; the model is originally used for environmental impact assessment, after being improved for carbon dioxide impact factor analysis.
STIRPAT model (Song, 2012)		The model is an extension of IPAT model, and a new factor is introduced in the model.
Econometric model		y indicates carbon emission, x is the influencing factor of carbon emissions, α is the intercept, β is the coefficient, i is the number of cross section, t is the time, M represents the number of influencing factors of carbon emission; compared to the traditional time-series and cross-sectional data model, this model expands the amount of information, with dynamic reliability analysis, which is helpful to reflect the system structure.
Granger causality tests (Wu et al., 2013)	If X helps to predict Y, then X is the granger cause of Y	This model can only be used for the test of smooth sequence, and the information contained in the past X can improve the forecast of Y.
EKC model	The relationship between economic development and environmental factors in inverted U-shaped curve	Environmental Kuznets curve is used to illustrate the relationship between economic growth and carbon emissions, energy consumption.
Kaya identical equation		GHG indicates the greenhouse gas emissions, TOE represents the energy consumption, GDP is the gross domestic product, POP is the population, f is the energy structure intensity, e is the energy consumption intensity, g is the per capita GDP, p is the population; the model is mainly used for analysis of the driving factors of carbon dioxide emissions.
Hierarchical Analysis	Target-Criterion-Scheme	The model is a kind of weight decision analysis method, which is mainly used to construct the evaluation system of low carbon economy. It is the basic step for the qualitative and quantitative analysis.
Input-output Analysis (Wang et al., 2015)	c=f(I-A)^-1×Y	c denotes the energy carbon emissions vector, f is the direct carbon emission vector of the department, I is the intensity matrix, A is the input coefficient matrix, Y is the final demand matrix, (I-A)^-1 is the Leontief inverse matrix; this model is used to analyze the carbon emissions of intermediate products in the economic operation process.

Table 5 The main research methods and models

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