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Journal of Geographical Sciences    2016, Vol. 26 Issue (7) : 855-870     DOI: 10.1007/s11442-016-1302-3
Orginal Article |
Review on carbon emissions, energy consumption and low-carbon economy in China from a perspective of global climate change
SHEN Lei1(),*SUN Yanzhi1,2()
1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
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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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*SUN Yanzhi
Cite this article:   
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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Figure 1  The cognitive evolution of climate change
Methods Topic word High frequency keywords The number of documents Type of journal
Co-word Analysis
‘energy consumption’, ‘carbon emission’, ‘low-carbon economy’ LMDI model,
economy growth,
climate change,
carbon emission
reduction, decomposition analysis, industrial
carbon emission permits
480 Acta Geographica Sinica, Journal of Natural Resources, Economic Geography, Resources Science,
China Population, Resources and
Ecological Economy and others
Table 1  Related methods and information of this paper
Carbon emission Low-
carbon economy
Energy consumption LMDI model Economic growth Climate change Carbon reduction Decomposition analysis Industrial structure Low-
Carbon emission 0 34 59 35 30 18 9 19 13 4
Low-carbon economy 34 0 18 9 6 11 3 4 5 0
Energy consumption 59 18 0 10 13 6 0 5 5 1
LMDI model 35 9 10 0 7 1 3 2 0 0
Economy growth 30 6 13 7 0 2 1 1 1 0
Climate change 18 11 6 1 2 0 5 0 1 1
Carbon reduction 9 3 0 3 1 5 0 0 0 0
Decomposition analysis 19 4 5 2 1 0 0 0 2 0
Industrial structure 13 5 5 0 1 1 0 2 0 2
Low-carbon 4 0 1 0 0 1 0 0 2 0
Table 2  Co-word matrix of high frequency keywords (partial)
Authors Methods Time Research
Decomposition of impact factors
Impact factors Promoting factors Inhibitory
Zhu and Zhang, 2011 IDA 1995-2008 Carbon
Energy intensity, energy structure, industrial structure Energy intensity Energy structure, industrial structure
Zhao et al., 2014 Spatial Panel Data Analysis 1991-2010 Carbon emission intensity of energy consumption Per capita GDP, population density, energy consumption structure, transportation, energy price Per capita GDP, population density Energy
Liu and Liu, 2009 LMDI 1992-2005 Carbon
emissions of
industrial combustion
Energy consumption, energy structure, technical factors, intermediate inputs, industrial structure, industrial output Energy
consumption, energy structure, industrial output
Song and Lu, 2009 LMDI 1990-2005 Carbon
emissions of energy
Output scale, energy structure, emission intensity, energy intensity Output scale Energy
Li et al., 2011 Kaya 1993-2008 Agricultural carbon
Economic development, efficiency factor, structure factor, labor force scale Economic development Efficiency
factor, structure factor, labor force scale
Yang and Liu, 2012 STIRPAT, EKC 1995-2009 Carbon
Per capita GDP, population size, energy intensity, energy structure, industrial structure, urbanization rate, trade openness, foreign direct investment Per capita GDP, energy intensity
Guo, 2010 LMDI 1995-2007 Carbon
Economic aggregate, economic structure, energy use efficiency, energy consumption structure, carbon emission coefficient Economic aggregate Energy use
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
Energy demand (100 million tons of standard coal) Proportion (%)
1990 2008 2020 2030 2035 2008 2035
Coal 7.78 20.55 30.60 35.22 37.43 66 61
Petroleum 1.66 5.37 8.25 10.15 10.98 17 18
Natural gas 0.19 1.03 2.60 3.93 4.74 3 8
New energy 0.00 0.26 1.80 2.53 2.75 1 4
Hydrogen 0.16 0.73 1.34 1.54 1.63 2 3
Biomass energy 2.91 2.95 2.78 2.68 2.85 10 5
Renewable energy 0.00 0.10 0.47 0.79 0.92 0 1
Total 12.68 30.99 47.82 56.82 61.30 100 100
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=(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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