Abstract
Both the realization of the “double carbon” goal and the low-carbon economy development requires a focus on transportation CO2 emissions. Calculating Chinese transportation CO2 emissions and exploring its principles are essential for achieving high-quality development of the transportation industry. Firstly, we use a “top-down” method to assess carbon emissions from transportation operations from 2003 to 2019. Secondly, the study decomposes the influencing factors of transportation CO2 emissions in China using the log-average weight decomposition method. Thirdly, the Tapio decoupling model is applied to study the decoupling effect of transportation CO2 emissions in each province of China. The findings suggest that China’s transport carbon emissions are growing at an annual rate of roughly 16%. All GDP per capita, transportation energy intensity, and population size increase the growth of transportation CO2 emissions. Contrastly, energy use per unit of turnover and transportation intensity decrease the growth of transportation CO2 emissions. There is much variation in China’s carbon emission decoupling index from year to year. Policy recommendations are proposed in response to the study of the above findings and the differences in carbon reduction potential among provinces.
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Introduction
Climate warming is a common problem facing the world at present. Carbon emissions have caused a sharp increase in greenhouse gasses, causing irreversible losses to the ecology, society, and economy. Countries worldwide are reducing carbon emissions through international agreements and policies, and China has also set targets for reducing carbon emissions. The transportation sector is a key strategic industry in many countries’ economies and social-economic growth. It consumes around 60% of the world’s oil (IPCC 2013), emits 24.34% of total carbon emissions, and is also the industry with the highest growth in carbon emissions; as a result, it is a critical industry for meeting emission reduction targets (Cohan et al. 2016; IEA 2018; Shao and Wang 2021). According to the International Energy Agency, global transportation CO2 emissions will reach 50% in 2030 and 80% in 2050 (IEA 2009). Because of the small and mobile nature of transportation sources, reducing carbon emissions is more complex and challenging than in other industries. Therefore, it is of positive significance to focus on carbon reduction in transportation for the overall carbon reduction of cities and even air pollution control.
Accelerating the achievement of the goal of carbon neutrality has become a global consensus to address climate change. It is also China’s solemn commitment to the international community. In 2020, the Chinese government set the strategic goal of achieving carbon peaking by 2030 and carbon neutrality by 2060. This goal has been incorporated into the overall layout of ecological civilization construction. As the world’s top carbon emitter, China’s transportation sector is not only one of the major emitters of greenhouse gasses but also one of the sectors with the most rapid growth in carbon emissions. In recent years, China’s transportation CO2 emissions have increased. Transportation CO2 emissions are predicted to reach more than four times the 2000 level in 2030 (Li et al. 2021). With the vision of achieving the goal of carbon peaking and carbon neutrality in China, reasonable and practical emission reduction in the transportation sector has become a hot topic for scholars to study.
The analysis of the drivers of transportation CO2 emissions is mainly to verify the influence of demographic and economic factors on carbon emissions. Generally, we first analyze the possible factors that impact transportation CO2 emissions from a theoretical perspective and screen the characterization indicators. Then, quantitative analysis is used to statistically test the significance of the impact, identify the factors that play a significant influence, and determine the direction and affecting intensity. Many studies, summarized in Table 1, have adopted different methods to analyze the influencing factors of transportation CO2 emissions. These models include such as the LMDI, STIRPAT, VECM, geographically weighted regression model, quantile regression model, and GFI. We find scholars mainly analyzed the effects of factors such as per capita GDP, urbanization level, economic growth, transportation infrastructure, traffic intensity, and energy structure on transportation CO2 emissions. They show that per capita GDP, urbanization level, economic growth, and population size have positive effects on transportation CO2 emissions, while transportation infrastructure, traffic intensity, and energy structure negatively affect transportation CO2 emissions (Zhu and Gao 2019; Azlina et al. 2014; Zeng et al. 2020; Xu and Lin 2017; Xie et al. 2017; Wang et al. 2022; Song et al. 2022). Despite great research results analyzing the impact factors of transportation CO2 emissions, because transportation is a complex system with many influencing factors and interconnections, an overly comprehensive and targeted analysis of the impact factors often only yields broad and generalized conclusions. It is not difficult to find that the factors affecting transport carbon emissions involve economic, social, and industrial development. It is necessary to analyze the driving mechanism and factors of transportation CO2 emissions from the internal structure of transportation based on the existing studies. Based on the research of Papiez et al. (2021), Zeng et al. (2020), and Jaehun (2017), this paper fully considers the relationship between various factors and decomposes the traffic carbon emissions from GDP per capita, transportation energy intensity, population size, energy use per unit of turnover, and transportation intensity.
Developing low-carbon transportation aims to decouple carbon emissions from economic growth. The key to building a low-carbon economy is to study the decoupling relationship between transportation CO2 emissions and economic growth. Using the Tapio decoupling model to study the relationship between macroeconomic activities and environmental pollution has become a focus of academic research thanks to in-depth and extensive investigation. Freitas and Kaneko (2011) and Engo (2018) used the Tapio decoupling model to study the decoupling index and characteristics of economic growth and environmental pressure in Brazil and Cameroon, respectively. Due to the continued evolution of the Tapio decoupling mode, many scholars are using the Tapio decoupling model to measure the relationship between industrial development and environmental pressure in industries like building, electrical power, equipment manufacture, and logistics (Xiao et al. 2013; Xie et al. 2019; Wan et al. 2016). Li et al. (2019) used the Tapio model to examine the relationship between China’s transportation industry development and carbon emissions. They found that compared with developed and coastal provinces, underdeveloped provinces are more prone to weak decoupling than others. Zhu and Li (2017) researched the rise of transportation CO2 emissions in the Beijing-Tianjin-Hebei region. They found that transportation CO2 emissions are not always synchronized with economic growth. Transportation economic development, carbon emissions, and government policies are all linked, according to Liu et al. (2021). Sikder et al. (2022) find that a 1% increase in energy use, economic growth, industrialization, and urbanization increases CO2 emissions by 0.23%, 0.17%, 0.54%, and 2.32%.
Many scholars have studied transportation CO2 emissions, and the results show that the influencing factors of transportation CO2 emissions are gradually increasing, and the decoupling effect is becoming more and more obvious (Jaehun 2017; Papiez et al. 2021). But compared with high-emission industries such as industry and power, there are some papers that focus on the drivers of transportation CO2 emissions in China. Due to the vast territory of China, the increasing frequency of travel between provinces will continue to release a large number of logistics and transportation demands, and the effect of the same carbon emission constraint policy varies for different regions.
Therefore, it is necessary to provide an in-depth analysis of the drivers and the decoupling trend of transportation CO2 emissions in each province of China. This paper is a supplement to the existing research on the change in carbon emissions from the transport industry in developing countries represented by China. Compared with existing studies, this paper mainly has the following innovations. Firstly, most current articles only use a few energy sources, such as coal, diesel, crude oil, and natural gas, to account for energy consumption. This paper expands the scope of accounting for transportation energy consumption. It measures transportation CO2 emissions using 16 types of energy consumer products such as raw coal, cleaned coal, other coal washing, briquette, coke, coke oven gas, crude, gasoline, kerosene, diesel fuel, fuel oil, lubricating oil, solvent oil, asphalt, liquefied petroleum gas, other petroleum products, natural gas, liquified natural gas and steam, and electricity. Secondly, the decomposition of the influencing factors in this paper includes transportation energy intensity, unit turnover energy consumption, transportation intensity, the GDP per capita of vehicles, and employees in the transportation industry. Compared with existing studies, the decomposition results of this study are more comprehensive. Thirdly, we analyze the spatial and temporal distribution characteristics of the decoupling trend of transportation CO2 emissions and analyze the dynamic relationship between transportation CO2 emissions and economic development at the provincial level by combining industry development characteristics. Thus, we can make reference suggestions for realizing China’s economic transformation and sustainable development.
The rest of the paper is organized as follows. “Methodology and data” section presents the methodologies and data definitions. “Results and discussion” section discusses the empirical results. “Conclusions and policy implications” section concludes the paper and puts forward some policy recommendations.
Methodology and data
Transportation carbon emission estimation method
The “top-down” input–output approach and the “bottom-up” process analysis approach are the most commonly used methods for measuring carbon emissions (Zhang et al. 2019a, b). Ideally, the bottom-up calculation method should consider real-time data for the transportation industry. These data did not include the current statistics system of China, which may lead to inaccurate results. In addition, there are also many differences in the types of transportation energy consumption in different provinces. This study uses the top-down method established by the Intergovernmental Panel on Climate Change (IPCC) to estimate carbon emissions from transportation energy consumption to sure the convenience of the evaluation and the accuracy of the results. The formula for this procedure is:
where \({E}_{i}\) is the physical energy consumption of the ith transportation type; \({K}_{i}\) is the coefficient for converting the ith energy into standard coal; and \({F}_{i}\) is the carbon emission coefficient of the ith power. Energy consumption in the transportation industry involves coal (such as raw coal, coke oven gas, coke, briquette, cleaned coal, and other washed coal), oil products (such as diesel, gasoline, crude oil, fuel oil, kerosene, petroleum pitch, solvent oil, liquefied petroleum, gas, lubricating oil, and other petroleum products), natural gas, liquefied natural gas, thermal energy, electricity, and other energy sources. This paper adopts the IPCC’s determination standard of carbon emission coefficients (Table 2).
IPAT equation and LMDI method
The IPAT equation takes into account both environmental and human influences. It effectively examines the interaction of three primary variables of population, wealth, and technology, as well as their impact on the environment. The equation can express as follows:
where I stand for the human impact. P, A, and T stand for population, affluence, and technology, respectively.
The regional ecological environment is affected by the two significant factors of population and economy, and at the same time, is destroyed by a specific technology. The LMDI model, based on the IPAT model, establishes a transportation CO2 emission decomposition model by connecting the regional environment with various influencing variables in a chain product. Therefore, this paper adopts the LMDI method to complete the factor decomposition of China’s transportation CO2 emissions. The total carbon emissions C is calculated as follows:
where TT represents transportation turnover after conversion, and the conversion coefficients of passenger turnover of railways, highways, waterways, and aviation used to convert freight turnover are 1, 0.2, 0.3, and 0.083, respectively (Zhang et al. 2019a, b). C/E represents the transportation energy intensity, that is, the number of carbon emissions released per unit of transportation energy consumption. This ratio is closely related to the energy structure. E/TT indicates unit turnover energy consumption and is used to assess the technological level of vehicle energy conservation and emission reduction. TT/GDP represents the intensity of transportation, that is, the total transportation turnover per unit of GDP. This ratio reflects the level of transportation efficiency. GDP/P represents the GDP per capita of vehicles. P denotes the number of people employed in the transportation industry of some regions.
The formula is decomposed without a residual error in the LMDI approach proposed by Ang et al. (2015). The total effect of the difference in transport carbon emissions from the base period to the t period is expressed as \(\Delta C={C}^{t}-{C}^{0}\).
where \(\Delta {C}_{ce}^{t}\) denotes the traffic energy intensity effect, \(\Delta {C}_{et}^{t}\) denotes the energy consumption effect per unit turnover, \(\Delta {C}_{tg}^{t}\) denotes the transportation intensity effect, \(\Delta {C}_{gp}^{t}\) denotes the GDP per capita effect, and \(\Delta {C}_{p}^{t}\) denotes the population size effect. These five measures show the contributions of various factors to changes in transportation CO2 emissions.
Tapio decoupling model
“Decoupling” means economic growth brings less environmental pressure or consumes fewer resources. The Tapio decoupling model cannot only avoid uncertainty caused by base period selection but also judge multiple types of decoupling states based on the range of elastic value changes (Huang et al. 2021). At the same time, the LMDI model can well settle the residual and zero value problems and satisfy other conditions of “perfect decomposition approaches.” Then it has become one of the most extensively used methods in decomposing CO2 emission changes (Yu et al. 2015). This study uses incremental data in the Tapio decoupling model to investigate the relationship between transportation CO2 emissions and economic development over a long period, to completely characterize the volatility and accuracy of the decoupling condition of the transportation industry. Based on the connotations of decoupling theory, combined with existing transportation CO2 emission-related research, this paper uses the transportation industry’s GDP to characterize the industry’s economic development. It constructs a decoupling model of transportation CO2 emissions. The specific calculation formula is as follows:
where \(\varepsilon\) is the decoupling index; \(\Delta C\) represents the current carbon emissions minus the previous carbon emissions; \(C\) is the current period’s carbon emissions; \(\Delta E\) indicates the difference between the transportation industry’s GDP for the current period and the last period; and \(E\) represents the transportation industry’s GDP for the current period. According to Tapio’s division of the elastic value change range (Tapio 2005), we can decompose decoupling states into three types: decoupling, connection, and negative decoupling. According to the value of decoupling elasticity, they can be further classified into eight types, as shown in Table 3. The strong decoupling shows that the economic development of transportation is not based on resource consumption and environmental damage, and the negative decoupling and connection states are both non-ideal coupling states.
Data source
The data used in this article come primarily from China Statistical Yearbooks (2003–2019), China Transportation Yearbooks (2003–2019), China Energy Statistical Yearbooks (2003–2019), China Environment Yearbooks (2003–2019), and the National Bureau of Statistics website.Footnote 1 There are many missing data in Tibet, Hong Kong, Macau, and Taiwan, so these studies did not include the four provinces. We only study the remaining 30 provinces. The indicators and their sources are shown in Table 4. In addition, based on data from previous years, we apply interpolation and gray correlation forecasting algorithms to estimate some missing values of indicators.
Results and discussion
Measurement results and analysis of China’s transportation CO2 emissions
Using the data collected and the calculation formula for transportation CO2 emissions, we calculated CO2 emissions for the provinces of China from 2003 to 2019. The trend in transportation CO2 emissions is consistent with Guo et al. (2014) and Li et al. (2019). Furthermore, most research recognizes geographic closure as the primary requirement for regional grouping (Zhang et al. 2020), and many studies claim that China has regional technical heterogeneity (Yang et al. 2015; Wang et al. 2013). Therefore, to more clearly analyze the changing trend of China’s regional transportation CO2 emissions, we group the 30 provinces studied according to the classification of NBSC.Footnote 2 As illustrated in Fig. 1, this study splits China’s 30 provinces into four major regions: northeast, eastern, central, and western.
Figure 2 depicts the CO2 emissions from transportation in China’s various regions from 2003 to 2019. It can be seen that all four areas have an increasing trend in vehicle CO2 emissions. Especially from 2003 to 2012, China’s transportation CO2 emissions showed a rapid growth trend, mainly due to China’s increased investment in the construction of transportation networks. Although China’s transportation CO2 emissions appeared to decline in 2012–2013, they rebounded in the following years. This is mainly due to the particular survey on economic statistics in China’s transportation industry in 2013. The statistical scope of road, waterway, passenger, and freight transportation data was adjusted, which led to a significant decrease in transportation CO2 emissions. Transportation CO2 emissions in the eastern, central, western, and northeastern regions grew by 5477 × 104 t, 3521 × 104 t, 1954 × 104 t, and 1559 × 104 t, respectively, from 2003 to 2019. As the east part develops rapidly, the transportation of CO2 emissions in the east region becomes the primary source of CO2 emissions. As of 2019, the transportation CO2 emissions of Shanghai, Shandong, and Guangdong provinces in the eastern region exceeded the 1000 × 104 t mark, ranking among the top three in the country. The main reason for the small total carbon emissions in the northeast is that the region includes only three provinces and has a small economic scale. This brings about a low dynamic in the transportation industry and low transportation CO2 emissions. However, in terms of per capita transportation CO2 emissions, the northeast is much higher than the central and western regions. In addition, carbon dioxide emissions from transport in the central region continued their slow but steady upward trend.
Figure 3 shows that from 2003 to 2019, the transportation CO2 emissions in all studied provinces of China increased vastly. Most eastern provinces generate more CO2 emissions than other provinces. In 2019, the three eastern provinces of Guangdong, Shandong, and Shanghai posted the highest carbon dioxide emissions. Guangdong’s CO2 emissions reached 13.04 million tons in 2019, around 20 times that of Ningxia. Furthermore, coastal provinces’ carbon emissions are much higher than inland provinces. This may be because water transportation in coastal provinces is a massive part of the transportation industry. By the same, fuel oil pollution and carbon emissions from water transportation are higher than from road transportation. During the study period, China’s transportation CO2 emissions increased by nearly 16% on average. In terms of the average yearly rise rate of emissions in each province, Henan Province had the highest rate (35.69%), followed by Heilongjiang Province (30.14%). On the other hand, Ningxia had the slowest growth rate (3.22%).
Analysis of driving factors of China’s transportation CO2 emissions
The above analysis shows significant spatial and temporal, and regional differences in transportation CO2 emissions among the 30 Chinese provinces. So, what factors play a crucial role in transportation CO2 emissions? This paper uses the LMDI model from the traffic energy intensity effect (\(\Delta {C}_{ce}^{t}\)), energy consumption effect per unit turnover (\(\Delta {C}_{et}^{t}\)), transportation intensity effect (\(\Delta {C}_{tg}^{t}\)), GDP per capita effect (\(\Delta {C}_{gp}^{t}\)), and the population size effect (\(\Delta {C}_{p}^{t}\)) to decompose the drivers of transportation CO2 emissions in 30 provinces. Equations (4) to (8) calculate the decomposition results, as shown in Table 5.
Table 5 shows that the growth in GDP per capita from 2003 to 2019 resulted in 112,705,700 tons of carbon emissions from transportation, with an average annual increase of about 7 million tons. The overall rate of GDP per capita contribution to carbon emissions shows a rising and then dropping trend. It demonstrates that the transportation industry’s ongoing GDP expansion has resulted in rapid development and a significant increase in energy consumption, putting additional pressure on carbon emissions.
Transportation CO2 emissions increased by 8,983,500 tons due to population changes from 2003 to 2019, with an average annual growth of roughly 561,469 tons. An inverted U-shape showed the overall contribution rate of population size to carbon emissions and its promotion effect.
Another aspect influencing transportation CO2 emission rise is transportation energy intensity. The increased energy intensity of transportation has resulted in a total increase of 2.3 million tons of carbon emissions. It may be because of continuous improvement in the structure of transportation energy consumption, and the increase in the use of new energy vehicles, which means the transportation energy intensity does not promote carbon emissions.
Energy consumption per unit of turnover has achieved cumulative reductions of 28,192,800 tons, and an average annual reduction of 1,762,100 tons. This is mainly because, in recent years, China has used energy conservation and reducing emissions as its main goal to adjust its transport structure and improve the efficiency of continuous transport. This has, to some extent, effectively slowed the trend of rising carbon emissions.
Transportation intensity has resulted in a total reduction of 20,441,700 tons of carbon emissions, with an average annual decrease of 1,277,600 tons. It is mainly because China’s transportation structure has been relatively stable for 20 years. Then reducing transportation CO2 emissions through continuous improvement of transportation intensity has become an essential measure of China’s low-carbon transportation development.
Analysis of decoupling characteristics of China’s transportation CO2 emissions
Temporal changes of the decoupling characteristics
Using the Tapio decoupling model and Eq. (10), we calculate China’s transportation industry’s carbon emission decoupling index from 2003 to 2019 and assess the decoupling features time series from 2003 to 2019. Table 6 shows that during the study period, the relationship between transportation CO2 emissions and the value added of the transportation sector in the 30 provinces experienced a trend from strong negative decoupling to weak decoupling. The transportation sector’s reliance on fossil fuels has gradually diminished, and energy use efficiency has improved. This paper divides the sample interval into two phases (2003–2007 and 2007–2019). Then, the decoupling relationship between transportation CO2 emissions and the economic growth of the transportation sector in each province is analyzed.
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(a)
From 2003 to 2007, transportation CO2 emissions showed a fluctuating decline, whereas transportation GDP showed fluctuating growth. However, transportation CO2 emissions, on the other hand, grew at a far faster rate than transportation GDP in 2003 and 2004. During the sample period, carbon emission decoupling alternates between strong negative decoupling, growth negative decoupling, weak decoupling, and growth connection.
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(b)
From 2008 to 2019, China’s transportation CO2 emissions and GDP increased, although carbon emissions grew faster than the economy. Carbon emission decoupling takes the form of weak decoupling throughout this period. China’s transportation CO2 emissions and GDP growth rate fluctuated in a “low–high-low” pattern.
The decoupling index of transportation carbon emission in China fluctuates negatively. Despite promising results in reducing transportation CO2 emissions, the transportation economy and carbon emissions are rising. Neither has reached the environmental Kuznets curve’s inverted U-shaped inflection point. China’s weak decoupling of transportation CO2 emissions is expected to continue, and it is consistent with the scientific rule of thumb after the carbon peak. Therefore, it is especially critical to implement more muscular transportation energy efficiency and emission reduction policies.
Regional differences in decoupling characteristics
It is considering that each province in China’s economic development level, transportation infrastructure, and energy consumption structure have significant differences. It is essential to explore regional differences in transportation CO2 emissions by province. Figure 4 shows the decoupling characteristics of transportation CO2 emissions in the periods 2003–2006, 2006–2009, 2009–2012, 2012–2015, 2015–2019, and the entire sample period 2003–2019.
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(a)
From 2003 to 2006, Beijing, Heilongjiang, Guangdong, Yunnan, and Qinghai showed strong negative decoupling. In contrast, Gansu showed strong decoupling, Jiangxi showed weak decoupling, Sichuan showed growth connection, and the remaining 22 provinces all showed negative growth decoupling. Yunnan had the highest degree of decoupling, with a decoupling elasticity value of − 254.7539. It may be because Yunnan has seized the strategic development opportunity of China’s western development and continuously increased its transportation infrastructure investment. While promoting the value added to the transportation industry, it also boosts energy consumption, which drives the growth of carbon emissions. The lowest degree of decoupling is in Gansu Province, which has a decoupling elasticity value of − 0.0799 and has reached the ideal coupling state of transportation economic development and carbon emissions. During this period, the decoupling state of transportation CO2 emissions in various provinces in China was diversified, and the degree of decoupling was relatively high.
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(b)
From 2006 to 2009, Shanghai showed a solid negative decoupling state, and its decoupling elasticity was − 4.044. Heilongjiang and Ningxia Hui Autonomous Region showed a strong decoupling state, and their decoupling elasticity was between − 0.2 and 0. Chongqing was in a state of growth connection, and its decoupling elasticity was 0.9129. Beijing, Shanxi, Anhui, Fujian, Henan, Hainan, Sichuan, Yunnan, Shaanxi, and Qinghai all showed negative growth decoupling, whereas the remaining 16 provinces showed weak decoupling. Generally speaking, the decoupling of transportation CO2 emissions in different provinces in China was diversified at this stage. The relationship between transportation CO2 emissions and industry value shifts gradually from a negative decoupling to a weak decoupling relationship. It is consistent with the pre-peak characteristics of the EKC curve.
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(c)
From 2009 to 2012, Inner Mongolia, Jilin, Heilongjiang, Anhui, Henan, Hubei, Chongqing, Guizhou, and Yunnan showed negative growth decoupling, and the remaining 21 provinces showed weak decoupling. Overall, at this stage, the state of decoupling and the degree of decoupling of China’s provincial transportation CO2 emissions became consistent. The relationship between transportation development and carbon emissions is at a reasonable stage.
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(d)
From 2012 to 2015, Gansu showed a solid negative decoupling state, and its decoupling elasticity was − 3.8515. Tianjin, Liaoning, Henan, Hubei, Hainan, Sichuan, and Shaanxi showed strong decoupling. The two provinces of Inner Mongolia and Shandong showed decoupling. Hebei, Shanxi, Jilin, Heilongjiang, Jiangsu, Anhui, Jiangxi, Hunan, Qinghai, Ningxia, and Xinjiang all showed negative growth, whereas the remaining nine provinces showed weak decoupling. In general, the decoupling state of China’s provincial transportation CO2 emissions is diversified at this stage, and the editorial contribution of transportation development to the growth of carbon emissions is gradually weakening.
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(e)
From 2015 to 2019, Liaoning, Fujian, and Guizhou showed strong negative decoupling. Inner Mongolia, Jilin, Zhejiang, Chongqing, and Qinghai were in solid decoupling. This decoupling process is consistent with the EKC curve. Heilongjiang showed weak negative decoupling, and its decoupling elasticity was 0.788. Ningxia showed weak connecting, and its decoupling elasticity was 0.8754. Beijing, Tianjin, Shanxi, Jiangsu, Hunan, Guangdong, and Sichuan showed negative growth decoupling, while the remaining 13 provinces showed weak decoupling. The four provinces of Shaanxi, Henan, Hubei, and Hainan, strongly decoupled in the previous period (2012–2015), all returned to a weakly decoupled state. It is in line with the scientific rule that there is a more extended plateau period after carbon peaking.
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(f)
From 2003 to 2019, Beijing, Hebei, Shanxi, Liaoning, Jilin, Heilongjiang, Shanghai, Fujian, Henan, Hunan, Guangdong, Chongqing, and Qinghai all showed negative growth decoupling, and the relationship between transportation economy development and carbon emissions did not reach the ideal state. The remaining 17 provinces showed weak decoupling. It shows that most provinces have departed from the traditional development model of resource consumption and environmental destruction and have begun to engage in green development, but this transformation must be further strengthened.
Discussion
This paper finds that the overall transportation CO2 emissions of each province in China show a rising trend, and it is regionally characterized as high in the east, followed by the mid-west, and low in the northeast. The decoupling effects in each province of China showed a trend from strong negative decoupling to weak decoupling. Both the trend in transportation CO2 emissions and its decoupling characteristics are consistent with Guo et al. (2014), Li et al. (2019), and Song et al. (2022). However, this paper uses 16 kinds of fossil fuels to account for transportation CO2 emissions, and the estimated results are more accurate than other papers. However, this paper selects GDP per capita, population size, transportation energy intensity, energy use per unit of turnover, and traffic intensity to decompose the influencing factors of transportation CO2 emissions, which increases the innovation and operability of transportation carbon emission research. This paper found that GDP per capita and population size contribute to the growth of transportation CO2 emissions. But energy use per unit of turnover and transportation intensity decrease transportation CO2 emissions. These results are consistent with Zhu and Gao (2019), Zhang and Su (2020), Xu and Lin (2017), and Song et al. (2022). However, this paper found transportation energy intensity increases transportation CO2 emissions. This result is inconsistent with Zhang and Su (2020). It may be because Zhang and Su (2020) take the Yellow River Basin in China as the study target, and the continuous increase in the proportion of clean energy shown in the Yellow River Basin after 2012 has slowed down the trend of carbon emission growth to some extent. However, this study has several limitations. On the one hand, we use the top-down approach advocated by the IPCC to measure carbon emissions, which may lead to biased estimates of the results; on the other hand, due to the vast size of China, transportation CO2 emissions and regional economic development vary greatly. In the follow-up study, we must consider the changes in energy use technologies in different regions of China, narrow the scope of the study, and conduct more detailed research at the regional or provincial level.
Conclusions and policy implications
Conclusions
To establish energy conservation and emission reduction regulations in the transportation industry, it is critical to research transportation emissions in China and the elements that drive them. The Tapio decoupling model analyzes the dynamic decoupling effect of transportation CO2 emissions in China’s provinces from 2003 to 2019. It first estimated China’s transportation CO2 emissions using the “top-down” method from 2003 to 2019. Finally, the LMDI approach examines the factors influencing China’s transportation CO2 emissions. The study’s main findings are listed below.
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(1)
China’s transportation CO2 emissions are increasing by around 16% each year. Transportation CO2 emissions in the eastern region are much greater than in the central and western regions, while carbon emissions in coastal provinces are significantly higher than in inland provinces. Guangdong, Shandong, Shanghai, and Liaoning have always had higher transportation CO2 emissions than other provinces, whereas Ningxia and Qinghai have the lowest. All Chinese provinces confront significant energy-saving and emission-reduction challenges in the transportation business.
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(2)
Over time, China’s transportation industry’s carbon emission decoupling index has shifted dramatically. For a long time, the type of decoupling has evolved from strong negative decoupling to weak decoupling. Although China’s green transportation development has yielded some achievements, it has yet to reach the inverted U-shaped inflection point of the environmental Kuznets curve. There is a significant gap between the strong decoupling of low-carbon transportation’s leading indicators. The decoupling index of transportation CO2 emissions has also varied throughout China’s provinces. Anhui, Henan, and Yunnan provinces fared well, but Anhui, Henan, and Yunnan provinces did not.
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(3)
GDP per capita is essential for raising China’s transportation CO2 emissions, whereas energy usage per unit turnover is the most critical factor for cutting emissions. Transportation energy intensity and population size have contributed to the transportation industry’s carbon emissions, while transportation intensity has also helped reduce emissions.
Policy implications
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(1)
Optimizing the energy structure and improving low-carbon travel for residents are vital initiatives to control transportation CO2 emissions. All provinces in China should further optimize their energy structure and reduce the proportion of oil consumption as much as possible. At the same time, they should vigorously develop low-carbon energy sources such as natural gas and electricity and strengthen efforts to promote new energy vehicles. The government can encourage the use of new clean energy, such as natural gas and solar power, through financial subsidies and carbon trading policies and thus increase the share of clean energy in the transportation sector. In addition, we need to pay attention to demographic changes, raise people’s awareness of environmental protection through various channels, and encourage the public to use public transportation.
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(2)
Reducing energy consumption per unit turnover and increasing the intensity of transportation are the most important methods for solving the current transportation CO2 emission problem. The rapid development of China’s economy will inevitably lead to a gradual increase in total energy consumption, so we must pay great attention to improving energy efficiency. On the one hand, the Chinese government should further encourage cooperation between industry, academia, and research in developing and promoting related technologies. Vigorously research, develop and promote energy-efficient means of transport, and promote green transportation development by reducing the energy efficiency level of transport. On the other hand, promoting and strengthening transportation intelligence is critical. To increase transportation efficiency, the Chinese government should actively construct intelligent systems such as logistics platform information services and multimodal transportation starting at the provincial level.
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(3)
Each province should adopt differentiated carbon emission reduction initiatives to achieve the transformation of the transportation industry. In promoting the “double carbon” goal, the Chinese government should fully consider the differences in transportation carbon reduction potential among provinces and the decoupling effect of transportation CO2 emissions and economic development, and then implement differentiated transportation carbon reduction measures and policies. The economic growth and technological innovation ability in the eastern provinces are relatively high; they should take advantage of their low-carbon technology to promote low-carbon transportation transformation. The central provinces have larger populations and high pressure on transportation trips. These provinces should strengthen the integration of public transportation to increase the proportion of public transportation in the population’s travel and reduce transportation energy consumption. The northeastern and western provinces face a mismatch between the development and consumption of resources. These provinces should promote the use of new energy vehicles and subsequently improve the energy consumption structure.
Data availability
All data used in this paper are from field research, then they are reliable and available.
References
Ang BW, Xu X, Su B (2015) Multi-country comparisons of energy performance: the index decomposition analysis approach[J]. Energy Econ 47:68–76
Azlina AA, Law SH, Mustapha NHN (2014) Dynamic linkages among transport energy consumption, income and CO2 emission in Malaysia [J]. Energy Policy 73(10):598–606
Cohan DS, Sengupta et al (2016) Net greenhouse gas emissions savings from natural gas substitutions in vehicles, furnaces, and power plants[J]. Int J Global Warming 14:2405
Engo J (2018) Decomposing the decoupling of CO2 emissions from economic growth in Cameroon[J]. Environ Sci Pollut Res 25(35):35451–35463
Freitas L, Kaneko S (2011) Ecol Econ 70(8):1459–1469
Guo B, Geng Y, Franke B et al (2014) Uncovering China’s transport CO2 emission patterns at the regional level[J]. Energy Policy 74:134–146
Huang G, Wang Z, Shi P, Zhou Y et al (2021) Measurement and spatial heterogeneity of tourism carbon emission and its decoupling effects: a case study of the Yellow River Basin in China. China Soft Science 4:82–93. (in Chinese)
IEA (2009) Transport, energy and CO2: moving toward sustainability[M]. IEA Paris
IEA (2018) CO2 emissions from fuel combustion [R]. IEA, Paris
IPCC (2013) Climate change 2013: the physical science basis. Contribution of working group to the fifth assessment report of the Intergovernmental Panel on Climate Change[M]. Cambridge University Press, Cambridge
Jaehun S (2017) The influence of new carbon emission abatement goals on the truck-freight transportation sector in South Korea[J]. J Clean Prod 164:153–162
Li M, Huang Y, Zhu W et al (2021) Research on the carbon emission total factor productivity of the transportation industry in China based on the global malmquist-luenberger index[J]. Science and Technology Management Research 2021(9):203–211
Li Y, Du Q, Lu X et al (2019) Relationship between the development and CO2 emissions of transport sector in China[J]. Transp Res 74:1–14
Liu Y, Wu M, Mu R (2021) Carbon emission measurement, factor decomposition and low carbonization strategies of transportation industry in Tibet. J Tibet Univ 36(1):126–133 ((in Chinese))
Papiez M, Smiech S, Frodvma K (2021) The role of energy policy on the decoupling processes in the European Union countries[J]. J Clean Prod 318:128484
Shao H, Wang Z (2021) 2021 Spatial network structure of transportation carbon emission efficiency in China and its influencing factors[J]. China Popul Resour Environ 31(4):32–41 ((in Chinese))
Sikder M, Wang C, Yao X et al (2022) The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: evidence from the panel ARDL approach[J]. Sci Total Environ 837:155795
Song D, Song Q, Zhang Q (2022) An analysis of the driving factors of China’s transportation carbon emissions: based on decoupling theory and generalized fisher index decomposition[J]. Sci Technol Manag Res 42(11):216–228 ((in Chinese))
Tapio P (2005) Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001[J]. Transp Policy 12(2):137–151
Wan L, Wang ZL, Jhony N (2016) Measurement research on the decoupling effect of industries’ carbon emission: based on the equipment manufacturing industry in China[J]. Energies 9(11):921–938
Wang C, Wood J, Wang Y et al (2020a) CO2 emission in transportation sector across 51 countries along the Belt and Road from 2000 to 2014[J]. J Clean Prod 266:122000
Wang C, Zhao Y, Wang Y et al (2020b) Transportation CO2 emission decoupling: an assessment of the Eurasian logistics corridor[J]. Transp Res D Transp Environ 86:102486
Wang C, Yao X, Sinha PN et al (2022) Why do government policy and environmental awareness matter in predicting NEVs purchase intention[J]? Moderating role of education level 131:103904
Wang Q, Zhao Z, Zhou P et al (2013) Energy efficiency and production technology heterogeneity in China: a meta-frontier DEA approach. [J]. Economic Modelling (35):283–289
Xiao H, Yi D, Zhou M (2013) Research on the decoupling relationship between China’s industrial economic development and carbon emission[J]. Fujian Tribune 3:57–63. (in Chinese)
Xie P, Gao S, Sun F (2019) An analysis of the decoupling relationship between CO2 emission in power industry and GDP in China based on LMDI method[J]. J Clean Prod 211(20):598–606
Xie R, Fang J, Liu C (2017) The effects of transportation infrastructure on urban carbon emission[J]. Appl Energy 196(15):199–207
Xu B, Lin B (2017) Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: evidence from a quantile regression model[J]. J Clean Prod 2017(175):109–122
Yang W, Li T, Gao X (2015) Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: a panel data analysis of China’s provinces[J]. Habitat International 49:212–220
Yu J, Da Y, Ou Y (2015) Analysis of carbon emission changes in China’s transportation industry based on LMDI decomposition method [J]. China J Highw Transp 28(10):112–119
Zeng X, Qiu R, Lin D, Hou X, Zhang L, Hu X et al (2020) Spatio-temporal heterogeneity of transportation carbon emission and its influencing factors in China. China Environ Sci 40(10):4304–4313 ((in Chinese))
Zhang G, Su Z (2020) Analysis of influencing factors and scenario prediction of transportation carbon emission in the Yellow River Basin. Manag Rev 32(12):283–294 ((in Chinese))
Zhang K, Liu X, Guo P, Fang J, Yao J et al (2019a) Spatial-temporal differences of CO2 emissions of transport sector in Beijing-Tianjin-Hebei region. Syst Eng 37(5):12–20 ((in Chinese))
Zhang L, Long R, Chen H et al (2019b) A review of China’s road traffic carbon emission[J]. J Clean Prod 207:569–581
Zhang YJ, Jiang L, Shi W (2020) Exploring the growth-adjusted energy-emission efficiency of transportation industry in China[J]. Energy Econ 90:104873
Zhu C, Gao D (2019) A research on the factors influencing carbon emission of transportation industry in “the Belt and Road Initiative” countries based on panel data[J]. Energies 12:2405
Zhu X, Li R (2017) An analysis of decoupling and influencing factors of carbon emission from the transportation department in the Beijing-Tianjin-Hebei area, China[J]. Sustainability 9(5):1–19
Funding
This research was funded by Shaanxi Provincial Social Science Foundation (Grant No. 2020R026), Shaanxi Province Natural Science Foundation (Grant No. 2021JQ-294 and 2020JQ-400), Key Scientific Research Project of Shaanxi Provincial Department of Education (Grant No. 21JT001), and the Fundamental Research Funds for the Central Universities (CHD 300102112604 and 300102112501).
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Cai, J., Ma, S., Ji, H. et al. Spatial–temporal characteristics and decoupling effects of China’s transportation CO2 emissions. Environ Sci Pollut Res 30, 32614–32627 (2023). https://doi.org/10.1007/s11356-022-24470-y
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DOI: https://doi.org/10.1007/s11356-022-24470-y