Analysis of carbon emissions from transportation in Beijing

At present, most of the literatures on transportation carbon emissions in China are to calculate the total amount of transportation carbon emissions. There are few literatures on carbon emission of specific transportation mode. There are few literatures on the calculation and analysis of urban traffic carbon emissions in Beijing. The historical data in these documents are very early, which can not effectively evaluate the current situation of urban transport in Beijing. Firstly, this paper analyzes the travel situation of Beijing residents in recent years. Then, based on the average daily passenger traffic volume and travel share rate of Beijing residents, a statistical model of urban transport vehicle energy consumption is established. Finally, the carbon emission of Beijing passenger transport is estimated.

Coping with the relation between the increase in carbon emissions and energy consumption in the transportation sector is a pressing issue today. Machine learning and deep neural networks were used in this study to explore the influential factors and trends in future transportation carbon emissions. First, the least absolute shrinkage and selection operator (LASSO) regression was adopted to screen out the key influencing factors in transportation carbon emissions. Second, the prediction performance of the long short-term memory (LSTM) network, generalized regress neural network, and back propagation (BP) network were compared, and an improved LSTM optimized by the sparrow search algorithm was proposed. Third, LASSO-SSA-LSTM was used to predict the transportation sector’s future carbon emissions trends under different scenarios. The results suggested that transportation carbon emissions in China presented a trend of ‘rapid increase—fluctuating decrease—continuous increase’ from 2010 to 2019. Although the main determinant in curbing the rising rate of carbon emissions effectively is the continuous development of renewable energy technology, the variation in transportation carbon emissions in China under eight scenarios showed significant differences. Generally, systemic changes and innovations are crucial to accommodate China’s future low-carbon and sustainable transportation development.

In alignment with China's "dual carbon" goals and its quest to build an ecological civilization, this study scrutinizes the carbon emissions derived from consumer lifestyles, with a particular focus on Beijing, a high-consumption urban metropolis. Utilizing the expanded STIRPAT model and ridge regression, factors such as permanent population, per capita consumption expenditure, energy intensity, energy structure, and consumption structure are examined to evaluate their impact on lifestyle-associated carbon emissions. A scenario analysis is also conducted to project future carbon emissions in Beijing. From 2010 to 2020, there was an overall upward trend in lifestyle-associated carbon emissions, up to a maximum of 87.8260 million tons. Indirect consumption-related carbon emissions, particularly those associated with residential and transportation-related consumption, constituted the primary sources. The most influential factors on carbon emissions were found to be the consumption structure. Notably, adopting a low-carbon consumption mindset and an optimized consumption structure could foster significant carbon reduction. Projections suggest that by 2035, carbon emissions due to residents' consumption could decline by 39.72% under a low-carbon consumption scenario and by 48.74% under a coordinated development scenario. Future efforts should prioritize promoting green, low-carbon living, refining consumption structure and practices, curbing excessive housing consumption, improving energy structure, and raising technological and energy efficiency standards.

Transportation carbon emission reduction has become an important driving point for China to achieve carbon peak and carbon neutrality. Based on the three-dimensional grey correlation analysis model, taking the five factors affecting transportation carbon emissions, namely, population, GDP, tertiary industry, energy structure and logistics scale, as the research object, the transportation carbon emissions of China’s low-carbon pilot and nonpilot provinces from 2010 to 2019 are calculated based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission accounting method. The time series grey correlation degree and regional grey correlation degree of each influencing factor and traffic carbon emission are obtained using the provincial data, so as to provide policy suggestions for China to achieve the goal of “carbon peak and carbon neutrality” in the field of transportation. The results show that the descending order of the five influencing factors on transportation carbon emissions is: energy structure, logistics scale, population, GDP and tertiary industry. From 2010 to 2019, the time series grey correlation degree between the five influencing factors and transportation carbon emissions shows a fluctuating downward trend, but the impact of demographic factors has become more and more obvious in the past two years; According to the difference of grey correlation degree in different regions, the traffic development of various provinces in China is different, so it is necessary to formulate relevant policies individually.

Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing

With the rapid proliferation of electric vehicles (EVs) in China, the landscape of transportation carbon emissions has undergone significant changes. However, research on the impact of the built environment on the carbon emissions of mixed traffic from gasoline and electric vehicles remains sparse. This paper focuses on urban traffic scenarios with a mix of gasoline and electric vehicles, analyzing the spatiotemporal distribution of carbon emissions from both types of vehicles and their nonlinear association with the built environment. Utilizing trajectory data from gasoline-powered and electric taxis in Chengdu, China, we establish segment-level carbon emission estimation models based on the vehicle-specific power of gasoline vehicles and the equivalent energy consumption of electric vehicles. Subsequently, we employ the XGBoost algorithm and SHapley Additive ExPlanation (SHAP) to analyze the nonlinear relationships between 13 built environment variables and vehicle carbon emissions. This paper reveals that most built environment variables exhibit nonlinear relationships with traffic carbon emissions, with five factors—population density, road density, residential density, metro accessibility, and the number of parking lots—having a significant impact on road carbon emissions. Finally, we discuss the carbon reduction benefits of EV adoption and propose policy recommendations for low-carbon initiatives in the transportation field.

Carbon peaking in the transportation sector has become an important step toward achieving the double carbon goals. Based on the causal relationship among the elements of the transportation carbon emission system, a system dynamics model was constructed from four aspects: economy, population, transportation, and carbon emission. In addition, baseline, single, low-carbon, and enhanced low-carbon scenarios were set up to analyze the change trend of transportation carbon emissions in China and the transportation carbon emission reduction potential from 2021 to 2035, and corresponding suggestions were presented. The results showed that: ① Under the baseline scenario, transportation carbon emissions increased rapidly, reaching 1.388 billion tons in 2035, with an average annual growth rate of 2.32%, and the peak of transportation carbon was not achieved. ② Under the single scenario, the enhanced freight- and industrial-structure scenarios had the best emission reduction effect, with the carbon emission reduction rate in 2035 reaching 124 and 67 million tons, respectively, and the emission reduction rate reached 8.93% and 4.85%, respectively. ③ Under the low-carbon scenario, the average annual growth rate of transportation carbon emissions from 2021 to 2030 was 1.77%, and the growth rate was expected to gradually slow down after 2030, with emission reduction measures beginning to show effects. By 2035, transportation carbon emissions were projected to reach 1.196 billion tons, but the peak in transportation carbon was still not reached. ④ Under the enhanced low-carbon scenario, through more powerful emission reduction measures such as optimizing the transportation structure and industrial structure and improving the level of transportation technology, transportation carbon emissions were projected to peak in 2033, with a peak value of 1.130 billion tons. By 2035, the emission reduction rate could reach 18.94%, among which the road carbon emission reduction rate would be 26.48% under the enhanced low-carbon scenario. This is an important breakthrough for transportation carbon emission reduction. Therefore, suggestions were presented to optimize the transportation structure, improve the transportation infrastructure, promote the upgrading of industrial structure, and strengthen the research and development of transportation equipment technology.

As a market-based instrument for transportation demand management, a transport fee-charging policy can not only effectively reduce traffic congestion, but also improve air quality. Considering the urgent need to improve urban transport fee-charging policies and reduce transport carbon emissions, the paper focuses on the role of the performance of fee-charging policies in reducing the carbon emissions of urban transport. In this study, we propose a methodological framework for the performance evaluation of urban traffic carbon emission fee-charging policies. First, we analyze the current situation of the implementation of fee-charging policies and their relationship with urban traffic congestion. Subsequently, we analyze changing trends of carbon emissions associated with transportation travel in Beijing in recent years, to identify the main sources of carbon emissions from transport. Finally, we used the DEA method to evaluate the performance of the fee policies for urban transport, which are meant to reduce carbon emissions, analyze their implementation efficiency, and then discuss the main factors affecting their efficiency. The results show that with the implementation of fee-charging policies, urban traffic congestion has eased. The overall carbon dioxide (CO2) emissions from transportation in Beijing grew rapidly. CO2 emissions generated by car travel are the main source of carbon emissions from transportation in Beijing. The average value of the overall technical efficiency (TE) of Beijing’s fee-charging policies to reduce transportation carbon emissions from 2006 to 2018 is 0.962, indicating that the overall implementation of Beijing’s fee-charging policies has been effective. Adjustments to the fee structure reduce motor vehicle travel to an extent, increase the proportion of green travel, and reduce the intensity of transportation carbon emissions. The technical non-efficiency in Beijing’s fee-charging policy is mainly due to non-efficiency of scale, followed by pure technical non-efficiency. Appropriately adjusting the fee structures imposed by different policies would help to improve the efficiency of policy implementation.

Based on the Tapio decoupling model, this paper calculates the decoupling indexes of the transportation output and the transportation carbon emissions of China's 30 provinces and municipalities from 2006 to 2015. The research period (2006-2015) is divided into the 11th Five-Year Plan period (FYP) (2006-2010) and the 12th FYP period (2011-2015). On this basis, we conduct a comparative analysis to describe the spatial-temporal evolution of the decoupling states of transportation output and transportation carbon emissions. Furthermore, in order to deeply analyze the reasons for the evolution of the decoupling states during the 12th FYP period compared with the 11th FYP period, the LMDI decomposition method is used to decompose and compare the factors affecting the transportation carbon emissions in the two periods. The results show the following: (1) from the national point of view, the decoupling relationship between transportation output and transportation carbon emissions improved gradually, with small fluctuations from 2006 to 2015; (2) from the provincial point of view, their decoupling states mainly were expansive negative decoupling and weak decoupling, and the spatial evolution of the two decoupling states is significantly different; (3) the reductions in the transportation energy intensity and transportation intensity were the main factors inhibiting the increase of transportation carbon emissions in the 11th and 12th FYP periods, respectively. The growth of per-capita wealth was the decisive factor driving the increase in transportation carbon emissions in the two periods; (4) in contrast to the causes of decreases in the carbon emission variations in the 11th FYP period, in the 12th FYP period, the significant reduction in transportation intensity is the main reason causing the significant decrease of carbon emission variations. However, the transportation energy intensity and the transportation intensity fail to reduce simultaneously in the two periods.

Analysis of transportation carbon emissions and its potential for reduction in China

A scientific carbon accounting system can help enterprises reduce carbon emissions. This study took an enterprise in the Yangtze River basin as a case study. The accounting classification of carbon emissions in the life cycle of lime production was assessed, and the composition of the sources of carbon emission was analyzed, covering mining explosives, fuel (diesel, coal), electricity and high-temperature limestone decomposition. Using the IPCC emission factor method, a carbon life cycle emission accounting model for lime production was established. We determined that the carbon dioxide equivalent from producing one ton of quicklime ranged from 1096.68 kg CO2 equiv. to 1176.96 kg CO2 equiv. from 2019 to 2021 in the studied case. The decomposition of limestone at a high temperature was the largest carbon emission source, accounting for 64% of the total carbon emission. Coal combustion was the second major source of carbon emissions, accounting for 31% of total carbon emissions. Based upon the main sources of carbon emission for lime production, carbon emission reduction should focus on CO2 capture technology and fuel optimization. Based on the error transfer method, we calculated that the overall uncertainty of the life cycle carbon emissions of quicklime from 2019 to 2021 are 2.13%, 2.07% and 2.09%, respectively. Using our analysis of carbon emissions, the carbon emission factor of producing one unit of quicklime in the lime enterprise in the Yangtze River basin was determined. Furthermore, this research into carbon emission reduction for lime production can provide a point of reference for the promotion of carbon neutrality in the same industry.

The city’s industrial transformation leads to a large amount of carbon emissions, which poses a thorny problem for the allocation of carbon responsibilities. This study established a multi-dimension long-term carbon emission analysis model to explore the characteristic of Beijing’s embodied carbon emissions, which could calculate the production-based, consumption-based and income-based carbon emissions. Then, structural decomposition analysis was adopted to quantify the contribution of socioeconomic factors in local and imported carbon emissions. In addition, emission linkage analysis was used for revealing the long-term evolutionary trajectories of sectors. The key discovery can be summarized as follows: 1) the fluctuation trend of production-side and income-side carbon emissions in Beijing is stable and decreased by 3.53% from 2002 to 2017, while consumption-side carbon emissions increased rapidly by 795.45%. 2) The energy, transportation and other services sectors from the supply, production and consumption perspectives. 3)Per capita consumption, production structure and consumption structure are the major contributors of carbon emissions. The study is expected to provide decision support for policymakers to reasonably formulate carbon mitigation policies and allocate carbon mitigation responsibilities from multiple perspectives, and promote the realization of the “carbon peak and carbon neutrality” strategy.

Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting.

Assessing carbon emissions from road transport through traffic flow estimators

Social network analysis of regional transport carbon emissions in China: Based on motif analysis and exponential random graph model