Simulation and Analysis of Transportation Carbon Emission Peaking Based on System Dynamics

Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon emissions in China’s transportation sector from 2000 to 2015 using the Generalized Divisia Index Method (GDIM). Based on this analysis, we use the improved Tapio model to estimate the decoupling elasticity between the development of China’s transportation industry and carbon emissions. The results show that: (1) the added value of transportation, energy consumption and per capita carbon emissions in transportation have always been major contributors to China’s carbon emissions from transportation. Energy carbon emission intensity is a key factor in reducing carbon emissions in transportation. The carbon intensity of the added value and the energy intensity have a continuous effect on carbon emissions in transportation; (2) compared with the increasing factors, the decreasing factors have a limited effect on inhibiting the increase in carbon emissions in China’s transportation industry; (3) compared with the total carbon emissions decoupling state, the per capita decoupling state can more accurately reflect the relationship between transportation and carbon emissions in China. The state of decoupling between the development of the transportation industry and carbon emissions in China is relatively poor, with a worsening trend after a short period of improvement; (4) the decoupling of transportation and carbon emissions has made energy-saving elasticity more important than the per capita emissions reduction elasticity effect. Based on the conclusions of this study, this paper puts forward some policy suggestions for reducing carbon emissions in the transportation industry.

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.

Regional transportation emissions reduction is the key to realizing deep emission reduction and the neutralization of transportation. Transportation development is accompanied by technological progress, and inter-regional transportation technological progress and carbon emission spillover effects are issues worthy of study. Based on the 2011–2020 provincial data of 30 provinces and cities in China, a spatial Durbin model was constructed to explore the impact of technological progress on regional spillovers of carbon emissions and the driving effect of emissions reduction. The conclusions show that the “community effect” causes direct interactions between transportation carbon emissions reduction practices in various provinces; the “acquired effect” and “leakage effect” drive technological progress between regions and cause indirect interactions between transportation carbon emissions reduction practices; transportation technology progress is more likely to occur between regions with similar transportation development. Finally, some suggestions are put forward in terms of establishing a mechanism for the coordinated reduction of regional carbon emissions, strengthening the interactions and economic connections between inter-regional transportation technologies, optimizing the spatial layout of transportation infrastructure, and building a low-carbon transportation system, so as to lay a solid foundation for the coordinated reduction of regional transportation carbon emissions.

Transportation carbon emission reduction potential and mitigation strategy in China

Transportation industry is the key industry of energy conservation and emission reduction in China. It is of great significance to study the measures of transportation carbon emission reduction. First of all, the “bottom-up” method was used to preliminarily estimate China's transport carbon emissions from 2008 to 2018. Second, on the method of combining qualitative and quantitative system dynamics, the transportation carbon emission system was divided into four subsystems, economy, transportation, energy and environment, and determined the internal structure of each subsystem and the feedback relationship between each subsystem.Then, on this basis, the system flow diagram of the transportation carbon emission system was established. Finally, based on the constructed SD model, a total of 9 scenarios were set to simulate the implementation effect of transportation emission reduction measures.The results shown that the transport and energy structure adjustment has the best emission reduction effect, followed by improving the efficiency of carbon pollution control and increasing environmental protection investment. The SD model of transportation carbon emission constructed in this paper can effectively simulate the implementation effect of different transportation carbon emission reduction policies, and has certain practical application value.

The Hu-Bao-O-Yu urban agglomeration is an important energy exporting and high-end chemical base in China, and is an important source of carbon emissions in China. The early achievement of peak carbon emissions in this region is particularly crucial to achieving the national carbon emission reduction targets. However, there is a lack of multi-factor system dynamics analysis of resource-dependent urban agglomerations in Northwest China, as most studies have focused on single or static aspects of developed urban agglomerations. This paper analyses the relationship between carbon emissions and their influencing factors, constructs a carbon emission system dynamics model for the Hu-Bao-O-Yu urban agglomeration, and sets up different single regulation and comprehensive regulation scenarios to simulate and predict the carbon peak time, peak value, and emission reduction potential of each city and urban agglomeration under different scenarios. The results show that: (1) Hohhot and Baotou are expected to reach peak carbon by 2033 and 2031 respectively, under the baseline scenario, while other regions and the urban agglomeration will not be able to reach peak carbon by 2035. (2) Under single regulation scenarios, the effect of factors other than the energy consumption varies across cities, but the energy consumption and environmental protection input are the main factors affecting carbon emissions in the urban agglomeration. (3) A combination of the economic growth, industrial structure, energy policy, environmental protection, and technology investment is the best measure to achieve carbon peaking and enhance the carbon emission reduction in each region as soon as possible. In the future, we need to coordinate the economic development, energy structure optimisation and transformation, low-carbon transformation of industry, strengthen research on carbon sequestration technology, and further increase the investment in environmental protection to make the Hu-Bao-O-Yu urban agglomeration a resource-saving urban agglomeration with an optimal emission reduction.

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road.

Current status, future prediction and offset potential of fossil fuel CO2 emissions in China

Impacts of the sea-rail intermodal transport policy on carbon emission reduction: The China case study

Aggregates are the second-most consumed product in the world after water. This geological resource is used as building and construction material, and its production in quarries and delivery to customers generates several environmental problems. Their transport from quarries to consumption points, almost entirely done by truck, also generates impacts such as an increase in traffic and noise and the emission of greenhouse gases and other pollutants. Transportation and storage of goods account for 15% of greenhouse gas emissions in Europe and will increase significantly by 2050. To mitigate this, the European Union suggested shifting 30% of long-distance road freight to cleaner alternatives, such as rail or waterborne transport. This approach neglects the enormous volume of short-distance freight movement and its impact on achieving the goal of reducing greenhouse gas emissions. In this study, the hypothesis to test is whether the use of an intermodal rail/road transport mode, instead of just roads, for the transport of some products can help reduce global CO2 emissions even for short distances. To test this, this study investigates the carbon emissions (and transport cost reduction) generated by rail/road intermodal aggregate transport for short distances in the Madrid region (Spain), rather than the currently used direct truck transport. An analysis of variables, such as aggregate supply, demand locations and amounts, and road and rail networks, using a geographical information system provides the associated carbon emissions of the different transport alternatives. To obtain a reduction in CO2 emissions, this study proposes the establishment of intermodal transfer facilities near consumption centers, where materials are primarily transported by rail, with road transport limited to the final delivery to consumption areas. The results anticipate a notable decrease in carbon emissions in aggregate transport and allow the establishment of more efficient and environmentally friendly rail/road intermodal transport that would help to meet the goals of reducing climate change while making the use of aggregates more environmentally friendly.

With the development of economy, the service industry has become a new field of energy saving and emission reduction needed to explore. As the most major component of service industry, the energy saving and emission in transportation industry are paid more and more attention. The carbon emission in Beijing from 2005 to 2012 is accounted in this paper. With 2005 as the baseline year, the complete decomposition method is used to analyze Beijing’s transportation energy consumption and carbon emission. The conclusions are as follows: The overall transportation energy consumption and carbon emission show an upward trend in Beijing, and scale factor and energy consumption intensity factor are playing the catalytic role in transportation energy consumption and carbon emission. Among them, the industrial scale factor plays a major role on the growth of carbon emission in transportation industry. Energy consumption structure factor plays a smaller role on carbon emission in transportation industry, and it almost has no effect on energy consumption. This paper also proposes recommendations for future low-carbon development in Beijing transportation industry.

Effects of urbanization on freight transport carbon emissions in China: Common characteristics and regional disparity

Reducing carbon emissions in the transportation sector is a crucial aspect of China achieving its carbon peak and carbon neutrality goals. This study investigates the spatiotemporal differentiation characteristics of carbon emissions from transportation in the Yangtze River Economic Belt. Using the Geographically and Temporally Weighted Regression(GTWR) model to reveal the spatio-temporal heterogeneity of factors influencing transportation carbon emissions. Additionally, the Support Vector Regression(SVR) is trained to predict the carbon emissions reduction potential of transportation under different scenarios. The results showed that: From 2000 to 2021, the transportation emissions of the Yangtze River economic belt showed an overall upward trend. The high carbon emission regions are Jiangsu Province, Shanghai, Zhejiang Province and Hubei Province, and the emission center is located in Hubei Province. The total population, urbanization rate, per capita GDP, carbon emission intensity, passenger turnover volume, and civilian vehicle ownership all have a positive effect on transportation carbon emissions, while energy structure has a negative impact. Moreover, the influence of each factor exhibits significant spatial heterogeneity. Under three scenarios: baseline, low-carbon scenario I, and low-carbon scenario II, transportation carbon emissions in the Yangtze River Economic Belt are projected to peak by 2030. With the application of clean energy and a reduction in population size, low carbon scenario II demonstrates greater potential for carbon emission reduction, with a projected value of 88.552 million tons by 2032.

Reducing agricultural carbon emissions is key to promoting the sustainable development of agriculture. Carbon sources play a significant role in the carbon emissions of China’s planting industry. Researching the principles of evolutionary trends of carbon sources regarding carbon emissions in China’s planting industry helps formulate scientific policies to control such emissions in the industry. This paper adopted an emission factor approach from the IPCC to estimate the CO2 emissions of all kinds of carbon sources in China’s planting industry from 1997 to 2017. On the basis of the data, the principles of dynamic evolution in China’s planting industry and six carbon sources were analyzed by the kernel density estimation approach. Notably, the study discovered that carbon emissions peaked in 2015. In terms of the contributions of various carbon sources to the carbon emissions of the planting industry, sorted by chemical fertilizers, agricultural diesel oil, agricultural films, pesticides, agricultural irrigation, and seeding, their contribution rates were 60.82%, 13.95%, 12.88%, 9.83%, 1.88%, and 0.64%. At the same time, the kernel density results show that there was an increasing trend in carbon emissions across the whole of China’s planting industry and six kinds of carbon sources nationwide, with apparent “multipolarization”. From the perspective of various regions, the carbon emissions of chemical fertilizers, diesel oil, films, and pesticides in China’s planting industry had an evolutionary trend of multipolarization in central regions, while there was an evolutionary trend of monopolarization in eastern and western regions. The carbon emissions of seeding and irrigation had a similarly evolutionary trend in eastern, central, and western regions. Basically, they all had a double increase pattern in carbon emissions and regional differences. Therefore, China’s government needs a target to set up long-term mechanisms to ensure a stable and orderly reduction in carbon emissions in the planting industry, leading its development from the traditional planting industry to a climate-smart planting industry.

In the context of the "dual carbon" strategy, the transportation industry actively seeks a new development path of low-carbon transformation, which is one of the hot spots in China's carbon emission reduction. Based on the Tapio decoupling and logarithmic mean Divisia index （LMDI） models, this study analyzed the carbon emission characteristics, decoupling status, and driving factors of China's provincial transportation industry from multiple perspectives, such as overall, time period, and regional decomposition from 2012 to 2021. The results showed that China's total carbon emissions from transport sector exhibited an increasing trend with passing years overall, but growth rate showed decreasing trend. The spatial distribution pattern of carbon emissions from transportation industry was higher in the southeast and lower in northwest. During the past decade, 40.0% of the provinces achieved absolute decoupling between carbon emissions from transportation industry and economic development, and 53.3% of the provinces achieved relative decoupling. From the perspective of the transportation industry, economic growth, population size, and carbon emission coefficient promoted an increase in carbon emissions, while transportation energy intensity and industry scale inhibited the increase in carbon emissions. Therefore, during the process of realizing the "dual carbon" goal in China, each province should formulate differentiated reduction policies in regional carbon emissions according to local conditions, actively assume emission reduction responsibilities, increase efforts to promote the decoupling process, and promote the green and low-carbon transformation of the transportation industry.