Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions
2. Literature Review
2.1. Literature Review of the Influencing Factors of Carbon Emissions
2.2. Literature Review of Carbon Emissions Decoupling
2.3. The Deficiencies of the Existing Studies and the Innovations of this Article
- Decomposition methods of influencing factors of transportation carbon emissions are flawed. Most of the studies focused on index decomposition of the influencing factors of carbon emissions in the transportation sector use the Logarithmic Mean Divisia Index (LMDI) method. The method itself has some defects that cause inaccuracies in the decomposition result. Vaninsky  noted that there are two problems with the existing index decomposition methods, including LMDI. First, the methods decompose the carbon emissions into the product of various factors according to the Kaya identity. For example, carbon emissions are usually decomposed into the product of energy carbon emission intensity, energy intensity, GDP per capita and population, while at most one absolute factor (such as population) can be considered in the decomposition. When other absolute factors change, such as when energy consumption increases, while other factors remain unchanged, the model does not show an increase in carbon emissions; it simply reduces the energy carbon intensity and increases the energy intensity. This is contradictory. Second, due to the interdependence of various factors, when different factor decomposition models are used, the methods may lead to different decomposition results.
- The choice of influencing factors of transportation carbon emissions is not comprehensive enough. In analyzing the changes that affect the carbon emissions in the transportation sector, the limitations inherent in the selected decomposition method make the factors of the study less comprehensive. Some studies have expanded the basic decomposition model in order to analyze more selected factors. Although this can increase the research factors, the selection of factors is still subject to the Kaya identity; this cannot arbitrarily select the relative factor and the absolute factor, and at most, it can only study the impact of an absolute factor . This leads to typical factors, such as the energy consumption and the value added of the sector, not being taken into account, leaving the final conclusion insufficiently comprehensive.
- The causal chain model of decoupling in transportation is insufficient. Existing research mainly analyzes the decoupling situation of carbon emissions from economic growth in the transportation industry by constructing a causal chain. The Tapio model divides the decoupling into eight types according to the magnitude of the decoupling elasticity. This fine division means that the calculation results of the decoupling elasticity will be classified into different categories with little accuracy. In addition, although the existing research can analyze the reasons for the decoupling of carbon emissions from the energy-saving elasticity and emissions-reducing elasticity of transportation, the total amount of carbon emissions in the model is too macroscopic and does not take into account the differences among individuals. It also makes the calculation results of the decoupling elasticity not accurate enough and cannot respond sensitively to changes in the decoupling state, which may lead to deviation in the final analysis.
- In terms of research methods, this paper is the first to analyze the driving factors of changes in carbon emissions in China’s transportation industry using the Generalized Divisia Index Method (GDIM) proposed by Vaninsky . Based on the Kaya identity, GDIM constructs a decomposition model that contains multiple absolute and relative variables by deforming Kaya identities. There are three advantages to analyzing the driving factors of carbon emissions changes in the transportation industry based on GDIM: ① GDIM avoids the inherent flaws of other index decomposition methods and breaks through limitations when selecting factors. Furthermore, GDIM expands the analysis scope of Kaya’s identity, breaks the formal interdependence of various influencing factors, can reveal the impact of more than one absolute factor of change on carbon emissions in particular, and fully considers the factors that have an impact on carbon emissions that are implicit in the decomposition process; ② GDIM overcomes the contradictions in the existing index decomposition methods, doesn’t produce results that are inconsistent with economic common sense and breaks the current situation of interdependence when selecting factors. Moreover, GDIM avoids the factor decomposition results depending on the selection of influencing factors, and makes it impossible to produce paradoxical conclusions when using different factor decomposition forms; ③ Using GDIM can examine the implicit environmental impact factors, and its decomposition result can distinguish the correlation of all the factors without any double counting problem. Therefore, this paper examines a full range of eight key factors such as carbon intensity of added value, energy intensity and per capita carbon emissions in the transportation industry. In particular, it examines three absolute factors that the current literature fails to pay attention to at the same time, but cannot be ignored: population size, energy consumption, and added value of transportation industry. This paper uses GDIM to accurately and comprehensively quantify the actual contribution of the different factors in transportation carbon emissions, and draws more reasonable decomposition results.
- In the selection of indicators, this paper adopts per capita carbon emissions in the transportation industry in the causal chain of decoupling elasticity for the first time. This paper doesn’t use the indicator of total carbon emissions in the decoupling causal chain model of the transportation industry, but instead innovatively decomposes per capita decoupling elasticity into per capita emissions reduction elasticity and energy-saving elasticity. This can further explore the key reasons for the decoupling of the development of the transportation industry from carbon emissions, fully take into account the differences among individuals, and reflect the individual’s role and value. In addition, it can more truly reflect the actual situation of carbon emissions in China’s transportation industry and the evolution trend of decoupling state over time than the previous decoupling causal chain model, avoid the misjudgment of the decoupling situation caused by the mistake of the calculation result, and make up for the deficiency of the existing decoupling model.
3. Estimation of Carbon Emissions in China’s Transportation Industry
4.1. Decomposition Model of Influencing Factors of Carbon Emissions in the Transportation Industry—GDIM
4.2. The Decoupling Model of Carbon Emissions from Transportation Development—Tapio
4.3. Model Summary
5. Analysis of Results
5.1. Analysis of the Factors Affecting Carbon Emissions in the Transportation Industry
5.1.1. Concrete Analysis of the Factors Affecting Carbon Emissions in the Transportation Industry
5.1.2. Cumulative Contribution Analysis of Factors Affecting Carbon Emissions in the Transportation Industry
5.2. Analysis of the Decoupling Elasticity between Transportation Development and Carbon Emissions
6. Discussion and Analysis
6.1. Discussion on the Comparison between the Results of This Paper and the Existing Research Results
6.2. The Reference Meaning of This Study
6.3. The Deficiency of This Research and Directions of Improvement
7. Conclusions and Suggestions
7.1. Main Conclusions
- The added value of transportation, energy consumption and per capita carbon emissions in the transportation industry are the major factors leading to the increase in carbon emissions. The energy carbon emission intensity is the key factor leading to a reduction in carbon emissions. Among other factors, population size has a positive effect on carbon emissions, while per capita added value of transportation, energy intensity and carbon intensity of added value have a decreasing effect on carbon emissions.
- The decreasing factors have a limited effect on the suppression of carbon emissions in the transportation industry, and this effect is far less than the contribution of the increasing factors to the increase in carbon emissions.
- The decoupling state between the development of China’s transportation industry and the carbon emission is poor, and it gradually shows a deteriorating trend after a short period of improvement.
- Per capita decoupling elasticity can reflect the decoupling status between transportation development and carbon emissions more accurately and subtly than the decoupling elasticity of total carbon emissions.
- Compared with the effect of per capita emissions reduction elasticity, energy-saving elasticity plays a more crucial role in decoupling the development of transportation from carbon emissions.
7.2. Policy Suggestions
- Optimize the traffic structure. From the analysis of Figure 2, Figure 3 and Figure 4, the added value of transportation industry is the main factor that promotes increases in carbon emissions, and the expansion of the economic scale also leads to deterioration of the decoupling state. The continuous economic growth has become the bottleneck of low-carbon development in China’s transportation industry, but at the same time it is also the goal of sustainable development of our country and the guarantee of people’s material well-being. Transportation carbon emissions reduction at the expense of economic development is not desirable, but the state can adjust the structure of the transportation industry, optimize the combination of various modes of transportation and the proportion of investment, gradually develop high-tech industries, and rationally plan the construction of low-carbon transportation infrastructure.
- Optimize the energy structure of the transportation industry. From the analysis of Figure 2 and Figure 3, the energy carbon emission intensity is a key factor in reducing carbon emissions. Therefore, the government should focus on the following: optimizing the energy structure of the transportation industry in the future; reducing the consumption of traditional energy, such as petrol and diesel, by adjusting prices and taxes; phasing out the high-energy-consumption and high-emissions transportation vehicles; increasing the investment in the use of clean energy, such as solar energy; and encouraging and promoting the development of new energy sources for transportation.
- Improve the energy efficiency of the transportation industry. Through the analysis of Figure 2, Figure 3 and Figure 4, it can be seen that energy intensity has a decreasing effect on carbon emissions, and energy-saving elasticity plays an important role in decoupling the development of transportation from carbon emissions. Therefore, energy efficiency must be taken seriously. At present, the enhancement of energy efficiency in the transportation industry has a very limited effect on curbing carbon emissions and has not reached a satisfactory level. In the future, there will still be much room for improvement. Increasing energy efficiency plays an important role in decoupling the carbon emissions from the development of transportation and can achieve the effect of reducing carbon emissions in a relatively short period of time rather than optimizing the energy structure. In the future, the government should increase investment in and development of energy-saving technologies, actively develop and promote low-carbon transportation technologies, optimize the transportation system, and enhance the intelligence of transportation to reduce energy consumption.
- Increase public transportation system construction. Through the analysis of Figure 2, Figure 3 and Figure 4, it can be seen that energy consumption is the main factor that promotes carbon emissions, and the large increase in energy consumption also hinders the decoupling process of China’s transportation industry. With the advancing urbanization in China, the problems of the increase in the number of private cars, traffic congestion, slow driving and so on are becoming increasingly prominent. As a result, the demand and consumption of energy continues to increase, leading to more serious carbon emissions in the transportation sector. However, promoting public transportation construction is a fast and effective way to solve this problem. Therefore, the government should give priority to the development of public transportation, accelerate the construction of urban rail transit such as subways and skyrails, and rationally plan bus lanes and bus routes to facilitate citizens’ travel and transfer.
- Enhance citizens’ low-carbon traffic awareness. From Figure 2 and Figure 3, it can be seen that the population size has a positive effect on carbon emissions and the per capita carbon emissions is the main factor that lead to an increase in carbon emissions. Therefore, the population cannot be ignored in the carbon emissions reduction in transportation industry. Although China implements the family planning policy and controls the natural population growth rate, the huge population base still causes the population size to promote an increase in carbon emissions, making citizens’ choices of modes of transportation especially important for reducing carbon emissions. Therefore, the government should step up the publicity of low-carbon traffic, conduct lectures on the theme of low-carbon transportation, hold related art performances, carry out bicycle riding and other activities in schools, work units and communities, broadcast more of these public service advertisements and formulate corresponding incentive measures for citizens’ environmental protection behaviors in different degrees to raise citizens’ awareness of environmental protection and promote citizens’ green travel.
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|Research Topics||Study Authors||Research Content||Research Methods||Research Process||Insufficient Research|
|Carbon emission factor analysis||Timilsina et al. ||Factors affecting carbon emissions in the transportation sector in some countries||LMDI decomposition method||Studying the main influencing factors of carbon emissions in Latin America and the Caribbean countries, such as population growth, energy intensity, and energy structure and so on.||Taking some developing countries as their research subjects cannot completely explain the impact of urbanization and other factors on the transportation industry and has little reference to other countries.|
|Wu et al. ||Evolution of carbon emissions in China and its driving factors||Three complete decomposition methods||A study is conducted on China’s energy-related changes in carbon emissions from 1996–1999 and their influencing factors. The results show that energy intensity, the number of motor vehicles and the average distance traveled are the driving forces behind the growth of carbon emissions in transportation.||Consideration of the impact factors of transportation carbon emissions is relatively shallow, does not explain the impact of population growth and economic growth, and cannot truly reflect the drivers of transportation carbon emissions.|
|M’raihi et al.||The driving factors of carbon emissions changes from road freight transportation in Tunisia||LMDI decomposition method||This paper decomposes the carbon emissions changes from road freight transportation in Tunisia into five kinds of factors from 1990 to 2006. The results show that economic growth and average petroleum emissions are the main driving factors.||Due to the inherent flaw in the LMDI decomposition method used, it is limited in the selection of factors and only includes the absolute GDP, which leads to some important factors not being considered.|
|The decoupling relationship between carbon emissions and economic development||Guo Shiyi ||The decoupling of carbon emissions from industrial economic growth||IGT decoupling index model||Based on the IGT decoupling index, the paper studies the relationship between industrial economic growth and carbon emissions in China from 2000 to 2014. The results show that China’s industry is in a period of transition from “relative decoupling” to “absolute decoupling”.||Although the unique advantage of the IGT model is the ability to quantitatively analyze the relationship between economic growth and environmental loading, the Tapio decoupling model provides a more detailed breakdown of decoupling types and is more appropriate for the current context of decoupling carbon emissions from industrial economies.|
|Tang et al. ||Decoupling indicators of carbon emissions from the tourism industry in China||Tapio decoupling model||This paper explores the decoupling effects between tourism-related carbon emissions and the tourism economy from 1990 to 2012. The results show that the decoupling state is very complicated and alternates between negative decoupling and weak decoupling.||The decoupling elasticity is not further decomposed, leading to a shallow analysis of the decoupling situation, and the study fails to analyze in more detail the specific reasons for the decoupling of tourism carbon emissions.|
|Zhou Yinxiang ||The decoupling and coupling relationship between traffic carbon emissions and industrial economic growth||Tapio decoupling model||This paper measures the decoupling relationship between China’s transportation carbon emissions and industrial economic growth from 1990 to 2013 and the trends of their evolution, and it constructs the causal chain to further analyze the influencing factors and mechanism of action. The results show that the decoupling status is not ideal and that the decoupling depends mainly on the traffic energy-saving elasticity.||In the decoupling model, the total amount of carbon emissions in the transportation industry is taken into account without taking into account the differences among individuals, making the analysis not meticulous.|
|Structural decomposition method||It is based on an input-output model, which has higher requirements on data, and the decomposition result can only be an additive form. So it’s not convenient to analyze in practice .|
|Laspeyres decomposition method||When the number of influencing factors exceeds 3, the calculation process becomes complicated.|
|AMDI method||The model contains residuals; it cannot be used when there are zero values in the data.|
|LMDI method||The result of decomposition depends on the interdependence of factors and the choice of factors is limited.|
|GDIM||It breaks the formal interdependence of various influencing factors and makes the results more comprehensive and accurate.|
|Decoupling State||Per Capita Decoupling Elasticity||Meaning|
|Strong decoupling||-||+||(−∞,0)||Economic growth, decreasing carbon emissions|
|Weak decoupling||+||+||[0,0.8)||Economic growth, increased carbon emissions, carbon emissions increased less than the rate of economic growth|
|Recessive decoupling||-||-||(1.2, +∞)||Economic recession, carbon emissions decreasing, but the decrease in carbon emissions is greater than the economic recession|
|Strong negative decoupling||+||-||(−∞,0)||Economic recession, carbon emissions increase|
|Weak negative decoupling||-||-||[0,0.8)||Economic recession, carbon emissions decrease, but the decrease in carbon emissions is less than the magnitude of economic recession|
|Expansive negative decoupling||+||+||(1.2, +∞)||Economic growth, increased carbon emissions, carbon emissions increased more than economic growth|
|Expansive coupling||+||+||[0.8,1.2]||Economic growth, increased carbon emissions, the increase in carbon emissions is greater than or equal to the economic growth rate|
|Recessive coupling||-||-||[0.8,1.2]||Economic recession, carbon emissions decrease, carbon emissions are less than or equal to the magnitude of economic recession|
|Years||Energy Carbon Emission Intensity Effect||Energy Intensity Effect||Population Size Effect|
|Comparative Literature||This Article||Comparative Literature||This Article||Comparative Literature||This Article|
|Comparative Literature||This Article|
|2001||Weak decoupling||Weak decoupling|
|2002||Expansive coupling||Expansive coupling|
|2003||Expansive negative decoupling||Expansive negative decoupling|
|2004||Expansive negative decoupling||Expansive negative decoupling|
|2005||Expansive coupling||Weak decoupling|
|2006||Expansive coupling||Expansive coupling|
|2007||Weak decoupling||Weak decoupling|
|2008||Weak decoupling||Weak decoupling|
|2009||Weak decoupling||Weak decoupling|
|2010||Expansive coupling||Expansive coupling|
|2011||Weak decoupling||Expansive coupling|
|2012||Expansive negative decoupling||Expansive negative decoupling|
|2013||Expansive negative decoupling||Expansive coupling|
|Research Topics||Factors||Contribution to Carbon Emissions (104 tons)||Cumulative Contribution to Carbon Emissions (104 tons)||Character|
|Decomposition of influencing factors—GDIM||Added value of transportation industry||4588.32||6122.68||6633.09||Increasing from 6,137,100 tons in 2001 to 161,496,400 tons in 2015||The primary increasing factor|
|Carbon intensity of added value||487.99||−895.65||46.00||Reducing a total of 3.5895 million tons of carbon emissions in 2000-2015||The more volatile decreasing factor|
|Energy consumption||4786.97||4803.62||6738.01||Increasing from 2,336,700 tons in 2001 to 162,401,800 tons in 2015||The primary increasing factor|
|Energy carbon emission intensity||−8.02||−261.17||−376.08||The decreasing effect growing rapidly from 783,600 tons in 2008 to 6.3401 million tons in 2015||The main decreasing factors|
|Population size||249.18||312.57||436.35||Cumulative resulting in 10,892,500 tons of carbon emissions by 2015||The relatively weak increasing factor|
|Per capita carbon emissions in the transportation industry||5090.32||4747.34||6370.25||Maintaining its rapid growth at an average annual rate of 39.78%||The important increasing factor|
|Per capita added value of transportation||−1088.61||−1216.04||−1070.08||The decreasing effect increasing from 200,100 tons in 2001 to 6.4745 million tons in 2015||The main decreasing factors|
|Energy intensity||−46.74||18.69||−22.96||Reducing 934,300 tons of carbon emissions cumulatively by 2015||The small decreasing factor|
|Decoupling elasticity—Improved Tapio causal Chain||Situation|
|Decoupling state||The years 2001-2004 were a period of deterioration; 2005-2009 was a period of improvement, and 2010-2015 was a period of slight deterioration.|
|Per capita decoupling elasticity||It increased sharply from 0.21 in 2001 to 2.22 in 2003. After a period of fluctuation, it reached 1.00 in 2015.|
|Per capita emissions reduction elasticity||It fluctuated relatively more in 2008-2010, but it tended to be stable in general.|
|Energy-saving elasticity||Its volatility is relatively large, the trends of it and per capita decoupling elasticity are almost the same, and it plays a more crucial role in decoupling the development of transportation from carbon emissions.|
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Wang, Y.; Zhou, Y.; Zhu, L.; Zhang, F.; Zhang, Y. Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions. Energies 2018, 11, 1157. https://doi.org/10.3390/en11051157
Wang Y, Zhou Y, Zhu L, Zhang F, Zhang Y. Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions. Energies. 2018; 11(5):1157. https://doi.org/10.3390/en11051157Chicago/Turabian Style
Wang, Yong, Yu Zhou, Lin Zhu, Fei Zhang, and Yingchun Zhang. 2018. "Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions" Energies 11, no. 5: 1157. https://doi.org/10.3390/en11051157