Transport Carbon Emissions Research Articles

Spatial-Temporal Evolution of Sales Volume of New Energy Vehicles in China and Analysis of Influencing Factors

China’s transportation carbon emissions account for 10% of the total, with nearly 90% originating from road transport. Additionally, China is the world’s largest automotive demand market. Therefore, in the context of achieving the “dual carbon” goals, the promotion and application of new energy vehicles (NEVs) are particularly crucial. However, the current situation regarding the promotion trends and driving mechanisms of NEVs in China remains unclear. Therefore, this study, based on panel data, explores the spatial-temporal evolution of NEV sales in China from 2016 to 2022 through spatial analysis. Simultaneously, based on correlation analysis and geographical detectors, this study qualitatively and quantitatively investigates the driving factors of NEV sales in China. The results show that: (1) China’s NEV sales will increase by 5.7 million units in the seven years from 2016 to 2022, which is an extremely fast growth rate; (2) There are significant spatial-temporal heterogeneities in the sales of NEVs in China. Sales in the eastern region constitute the largest share among the four major economic regions, accounting for 61% by 2022. The northeastern region has the lowest sales, representing only 2.9% of the national total. (3) Among different provinces, the sales in coastal provinces such as Guangdong, ZheJiang, and Jiangsu are much higher than in inland provinces like Tibet and QingHai. (4) The contribution rates of driving factors vary across regions. Overall, however, the order of influence factors is as follows: road length (0.49) > proportion of the tertiary industry (0.48) > road area (0.40). Therefore, infrastructure is identified as the primary influencing factor for the promotion of NEV. This study has revealed the spatial-temporal evolution of NEV sales and their driving mechanisms, aiming to provide theoretical support for the promotion of NEVs in China.

An analysis of the decomposition and driving force of carbon emissions in transport sector in China

China’s transport carbon emissions are increasing quickly and the issue of emission reduction is urgent. This article aims to calculate and decompose China’s transport carbon emissions during 2001–2019. It first calculates the China’s transport carbon emissions by IPCC carbon emission factor method, and then applies the Logarithmic Mean Divisia Index (LMDI) model for decomposition analysis. The conclusion indicates that: (1) Diesel, gasoline, kerosene, and fuel oil are the major energy sources used in China’s transport sector, with the combined consumption of diesel and gasoline exceeding 70%, and the annual growth rate of energy consumption reached 8.92% during 2000–2019. Among them, natural gas and liquefied petroleum gas (LPG) have the fastest growth rate, while the only one showing a downward trend is raw coal, indicating that China’s transportation energy structure is being optimized. (2) Although China’s transport carbon emissions have been increasing, the growth rate has declined since 2013. The proportion of carbon emissions from kerosene, diesel, natural gas, and LPG increased from 2000 to 2019, while that of raw coal, gasoline, and fuel oil decreased. This suggests that the use of clean energy, air transportation, and large-scale transportation is increasing, while the use of heavily polluting fuels and small-scale road transportation is decreasing. (3) Per capita GDP is the driving factor that has the most influence on the increase of China’s transport carbon emissions. Population positively influences transportation carbon emissions too, but issues such as aging, low fertility rates, and insufficient labor force may change the direction of the impact in the next 30 years. (4) The negative effect of energy intensity on transport carbon emissions is the greatest, followed by industrial structure and energy structure. The development of highways, new energy vehicles, railway electrification, multimodal transportation, third-party logistics, and logistics information technology in China has improved the energy structure, reduced energy intensity, and brought China’s transport sector into an important stage of innovation driven and pursuit of coordinated development with the environment.

Unraveling the relation between carbon emission and carbon footprint: A literature review and framework for sustainable transportation

Transportation decarbonization has drawn enormous attention globally, and two concepts play vital roles: transportation carbon emission (TCE) and transportation carbon footprint (TCF). However, their overlapping definitions and mixed uses in relevant literature have caused ambiguities and misunderstandings, resulting in a pressing need to clarify their subtle relationships in a more introspective manner. Therefore, we performed an in-depth literature review to investigate their essential commonalities and differences, with a focus on two aspects: understanding the scientific progress in terms of research trends, foci, and clusters to capture their connotations and use cases; and diagnosing how they are inherently estimated and how misuses can be alleviated. Accordingly, we developed an integrated life cycle analytical framework to relate TCE and TCF in an interdisciplinary landscape. Implications regarding systematic and explicit quantification standards were identified and discussed. Finally, we proposed three research gaps and four possible directions for future sustainable transportation studies.

Optimization of carbon peaking achieving paths in China's transportation sector under digital feature clustering

Digital development disparities, technological diversity, and regional imbalances have led to a shortage of tailored carbon reduction strategies for the transportation sector. Understanding the mechanisms and future trajectories of transportation carbon emission (TCE) drivers across varying digitization levels is crucial. To address this issue, we propose a strategy that classifies Chinese provinces into four distinct digitization regions based on their digital development characteristics. First, we extend the STIRPAT-PLS framework to analyze the historical impacts of external governance and internal development factors on TCE. Second, we use scenario settings and Monte Carlo simulation techniques to simulate future trends of TCE. Third, we conduct a dynamic sensitivity analysis to assess the contributions of factors, aiming to optimize the TCE peaking path. Our results indicate that the digitization context cannot fully explain the differences in TCE; even in regions with similar levels of digitization, the contributions of influencing factors vary significantly. Under the BAU scenario, existing policies and technologies may be insufficient to achieve TCE peaking before 2030. This work guides achieving carbon peaking in China's transportation sector. It emphasizes that we should build upon the digital development context and harness the positive effects of different influences to promote context-specific policy implementation.

Multiobjective Route Optimization for Multimodal Cold Chain Networks Considering Carbon Emissions and Food Waste

The cold chain logistics industry faces significant challenges in terms of transportation costs and carbon emissions. It is imperative to plan multimodal transportation routes efficiently to address these issues, minimize food waste, and reduce carbon emissions. This paper focuses on four key optimization objectives for multimodal cold chain transport: minimizing total transportation time, costs, carbon emissions, and food waste. To tackle these objectives, we propose a high-dimensional multiobjective route optimization model for multimodal cold chain networks. Our approach involves the development of a multiobjective evolutionary algorithm, utilizing Monte Carlo simulation and a one-by-one selection strategy. We evaluate the proposed algorithm’s performance by analyzing various convergence and distribution indicators. The average values for the minimum total transportation time, transportation cost, carbon emission cost, and cargo loss rate derived from the proposed algorithm ultimately converge to 6721.7, 5184.4, 301.5, and 0.21, respectively, demonstrating the effectiveness of the algorithmic solution. Additionally, we benchmark our algorithm against the existing literature to showcase its efficiency in solving high-dimensional multi-objective route optimization problems. Furthermore, we investigate the impact of different parameters, such as carbon tax rates, temperature, and cargo activation energy, on carbon emissions, and food waste. Moreover, we conduct a real-world case study to apply our approach to solving a practical business problem related to multimodal cold chain transportation. The insights gained from this research offer valuable decision-making support for multimodal carriers in developing low-carbon and environmentally friendly transportation strategies to efficiently transport perishable goods.

Decomposition and scenario analysis on carbon emissions of road transportation: case study of the rural areas of Tianjin, China

Decomposition and scenario analysis on carbon emissions of road transportation: case study of the rural areas of Tianjin, China

An Interval Fuzzy Programming Approach to Solve a Green Intermodal Routing Problem for Timber Transportation Under Uncertain Information

This study investigates an intermodal routing problem for transporting wood from a storage yard of the timber harvest area to a timber mill, in which the transfer nodes in the intermodal transportation network have multiple service time windows. To improve the environmental sustainability of timber transportation, a carbon tax policy is employed in the routing to reduce the carbon emissions. Uncertain information on the capacities and carbon emission factors of the transportation activities in the intermodal transportation network is modeled using interval fuzzy numbers to enhance the feasibility of the routing optimization in the actual timber transportation. Based on the above consideration, an interval fuzzy nonlinear optimization model is established to handle the specific routing problem. Model defuzzification and linearization are then conducted to obtain an equivalent formulation that is crisp and linear to make the global optimum solution attainable. A numerical experiment is conducted to verify the feasibility of the proposed model, and it reveals the influence of the optimization level and service time windows on the routing optimization, and it confirms that intermodal transportation is suitable for timber transportation. This experiment also analyzes the feasibility of a carbon tax policy in reducing the carbon emissions of timber transportation, and it finds that the performance of this policy is determined by the optimization level given by the timber mill and is not always feasible in all cases. For the case where a carbon tax policy is infeasible, this study proposes a bi-objective optimization that can use Pareto solutions to balance the economic and environmental objectives as an alternative. The bi-objective optimization further shows the relationship between lowering the transportation costs, reducing the carbon emissions, and enhancing the reliability on capacity and budget by improving the optimization level. The conclusions provide managerial insights that can help the timber mill and intermodal transportation operator organize cost-efficient, low-carbon, and reliable intermodal transportation for timber distribution, and support sustainable forest logistics.

Structural Characteristics of Expressway Carbon Emission Correlation Network and Its Influencing Factors: A Case Study in Guangdong Province

Understanding the spatial correlation of transportation carbon emissions and their influencing factors is significant in achieving an overall regional carbon emission reduction. This study analyzed the structure characteristics of the expressway carbon emission correlation network in Guangdong Province and examined its influencing factors with intercity expressway traffic flow data using social network analysis (SNA). The findings indicate that the correlation network of expressway carbon emissions in Guangdong Province exhibited a “core-edge” spatial pattern. The overall network demonstrated strong cohesion and stability, and a significant difference existed between the passenger vehicle and freight vehicle carbon emission networks. The positions and roles of different cities varied within the carbon emission network, with the Pearl River Delta (PRD) cities being in a dominant position in the carbon network. Cities such as Guangzhou, Foshan, and Dongguan play the role of “bridges” in the carbon network. The expansion of differences in GDP per capita, industrial structure, technological level, and transportation intensity facilitates the formation of a carbon emission network. At the same time, geographical distance between cities and policy factors inhibit them. This study provides references for developing regional collaborative carbon emission governance programs.

Potential Reduction in Carbon Emissions in the Transport of Aggregates by Switching from Road-Only Transport to an Intermodal Rail/Road System

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.

Use of Distributed Energy Resources Integrated with the Electric Grid in the Amazon: A Case Study of the Universidade Federal do Pará Poraquê Electric Boat Using a Digital Twin

Electric mobility is a global trend and necessity, with electric and solar boats offering a promising alternative for transportation electrification and carbon emission reduction, especially in the Amazon region. This study analyzes the system of a solar boat from an electric mobility project—to be implemented at Universidade Federal do Pará (UFPA)—using MATLAB software for modeling. The Simulink tool was utilized to model the system, focusing on operational parameters such as module voltage, converter voltage, and speed. The results indicate that the solar boat’s operational cost is significantly lower compared to a similar internal combustion model, considering diesel’s high consumption and cost. The environmental impact is also reduced, with nearly 72 tons of CO2 emissions avoided annually, thanks to Brazil’s renewable energy matrix. Simulations confirmed the project’s parameters, demonstrating the efficiency of digital-twin technology in monitoring and predicting system performance. The study underscores the importance of digital twins and renewable energy in promoting sustainable transportation solutions, advocating for the replication of such projects globally. Future research should focus on further advancing digital-twin applications in electric mobility to enhance predictive maintenance and operational efficiency.

Low-carbon route optimization model for multimodal freight transport considering value and time attributes

As the international community increasingly focuses on climate change, optimizing low-carbon transportation routes in the multimodal freight transport system has become a pressing issue. However, due to the variability in cargo properties and the influence of various factors on transportation route decisions, formulating a low-carbon and economical multimodal freight transport plan remains a significant challenge. To address the issue, this study considered cargoes with different attributes in terms of both value and time attributes. Triangular fuzzy numbers were employed to represent the uncertain demand for cargo, with confidence levels introduced for clarification. A low-carbon route decision optimization model for multimodal freight transport was established to minimize the combined transportation carbon emission and time costs. The catastrophe adaptive genetic algorithm, based on Monte Carlo sampling, was employed to solve the model using arithmetic examples. Finally, parameter sensitivity analysis revealed that adjustments to carbon tax values and changes in the proportion of electric trucks and green electricity supply had the most significant impact on the low-carbon route decision-making plan for multimodal freight transport. For low value-added and timeliness-strong cargo, a 60 % increase in carbon tax value shifted the mode of transportation from road to railway. When the carbon tax increased by more than 140 %, the transportation mode shifted from railway to waterway. Additionally, when the proportion of electric trucks and green electricity supply both exceeded 80 %, the transportation mode between some city nodes shifted from railway to road. When these proportions increased beyond 90 %, road transportation became the predominant mode.

Mechanism of Transportation Capacity Influence on Carbon Emission of Transportation Sector at China Provincial Scale from a Spatial Perspectiv

Transportation carbon emissions （TCE） are a major contributor to emissions, and their emission reduction pathway can provide recommendations for the overall carbon emission reduction decision-making. Therefore, identifying the impact mechanism of transportation capacity on TCE through scientific methods has become an important foundation to respond to the national "dual-carbon" strategy. Based on the panel data of 30 provinces in China from 2012 to 2021, a dynamic spatial dubin model （SDM） was included to empirically analyze the dynamic spatial spillover effects of transportation capacity on carbon emissions from China's transportation sector, taking correlation and heterogeneity into account. The results were as follows： ① There was regional spatial correlation in carbon emissions from China's transportation sector, and this situation was becoming increasingly evident in time series. ② Transportation capacity had a positive impact on the carbon emission reduction of the local transport sector, which fluctuated, but the negative value was not less than -1.212%. There may have been an inverted U-shaped EKC relationship between the transportation capacity and the carbon emissions of the transport sector in a region. ③ Transportation capacity reduced TCE through economic level with a coefficient of 0.204%, but the opposite result was observed for the level of investment in the transportation sector. ④ The effect of transportation capacity on carbon emissions varied widely in different regions of China. Those of North and Central were significantly negative, and that of Central was consistent with the national level. The research results can provide reference to formulate differentiated policies for different regions and achieve the carbon peaking and carbon neutrality goals.

Can polycentric urban morphology improve transportation carbon emission efficiency? Evidence from 285 Chinese cities, 2005–2020

Can polycentric urban morphology improve transportation carbon emission efficiency? Evidence from 285 Chinese cities, 2005–2020

Impact of Urban Form in the Yangtze River Delta of China on the Spatiotemporal Evolution of Carbon Emissions from Transportation

The impact of urban form on carbon emissions has become a crucial issue for sustainable socioeconomic development and the advancement of low-carbon cities. Transportation is a significant source of urban carbon emissions, highlighting the need for comprehensive research to aid China in achieving its carbon peak and neutrality goals. Currently, there is a lack of quantitative studies exploring the effects of urban form on transportation-related carbon emissions. This paper seeks to quantify the effect of urban form on the spatial and temporal patterns of transportation carbon emissions, utilizing panel data from 27 cities in the Yangtze River Delta (YRD) region of China, covering the years 2000 to 2020. First, CO2 emissions from transportation are estimated following IPCC guidelines, with Moran’s I utilized to analyze spatial autocorrelation. Next, urban form indicators are quantified based on landscape ecology theory. Finally, econometric models are employed for regression analysis of the panel data. The findings reveal that urban complexity, compactness, and expansion influence transportation carbon emissions to varying degrees, with urban expansion and complexity associated with increased emissions, while compactness contributes to their reduction. This study offers theoretical support and a scientific basis for low-carbon urban spatial planning and development, underscoring the importance of urban form in emissions reduction strategies.

Spatio-temporal heterogeneity and scenario prediction of influencing factors of transportation carbon emissions in the Yangtze River Economic Belt, China

Abstract 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.

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.

How Does Digital Economy Influence Green Mobility for Sustainable Development? Moderating Effect of Policy Instruments

The role of green mobility as a low-carbon lifestyle in carbon reduction and sustainable development cannot be ignored. The digital economy effectively promotes green mobility for sustainable energy use in the broader setting of the significant data era and sustainable development. This study utilizes the panel data of 264 cities in China from 2011 to 2021 to construct a two-way fixed-effects regression model to analyze the impact of the digital economy on residents’ green mobility and the indirect impact mechanism of the two policy tools, a low-carbon transportation pilot and carbon emissions trading, from theoretical and empirical aspects. The results show that digital economic development helps promote residents’ green mobility. In addition, the implementation of low-carbon transportation pilots and carbon trading policies has strengthened the role of the digital economy in promoting green mobility. The findings remain after introducing robustness tests such as “smart city” pilots as exogenous shock policies. A heterogeneity study suggests that the effect of the digital economy on green mobility for residents is more significant in economically developed and human capital-rich areas. This study contributes to the literature by providing empirical evidence on the role of the digital economy in promoting sustainable urban transportation and by demonstrating the moderating effects of policy instruments, thereby offering practical insights for policymakers aiming to reduce urban pollution and enhance sustainable development.

A Stochastic Approach to the Power Requirements of the Electric Vehicle Charging Infrastructure: The Case of Spain

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total.

Examining the Causal and Heterogeneous Influence of Three-Dimensional Urban Forms on CO2 Emissions in 285 Chinese Cities

Despite the efforts to examine the influence of urban forms on CO2 emissions, most studies have mainly measured urban forms from a two-dimensional perspective, with relatively little attention given to three-dimensional urban forms and their causal relationships. Utilizing the built-up area dataset from the Global Human Settlement Layer (GHSL) project and the carbon emission dataset from the China City Greenhouse Gas Working Group (CCG), we examine a causal and heterogeneous effect of three-dimensional urban forms on CO2 emissions—specifically urban height, density, and intensity—in 285 Chinese cities. The empirical results reveal a robust and positive causal effect of 3D urban forms on carbon emissions. Even when incorporating the spatial spillover effect, the positive effect of 3D urban forms remains. Moreover, GDP per capita and total population have a greater impact on urban CO2 emissions. Additionally, we find that the influence of 3D urban forms on CO2 emissions is U-shaped, with total population serving as a moderating factor in this effect. Importantly, there is significant geographic and sectoral heterogeneity in the influence of 3D urban forms on CO2 emissions. Specifically, the influence of 3D urban forms is greater in eastern cities than in non-eastern cities. Furthermore, 3D urban forms primarily influence household carbon emissions rather than industrial and transportation carbon emissions. Therefore, in response to the growing challenges of global climate change and environmental issues, urban governments should adopt various strategies to develop more rational three-dimensional urban forms to reduce CO2 emissions.

Measurement methods and influencing factors of carbon emissions from residents' travel

Residents' travel is a significant source of transport carbon emissions. Revealing the mechanisms behind travel-related carbon emissions can assist transportation departments in developing effective emission reduction policies. However, current research often focuses on specific aspects such as measurement accuracy or the impact of certain factors, lacking a comprehensive review and discussion. This study addresses the research gap by conducting a systematic literature review on measurement methods and influencing factors of carbon emissions from residents' travel. Through evaluating each method's applicability and limitations, we establish a systematic framework for measuring carbon emissions from residents' travel. By selecting appropriate measurement methods, detailed quantitative analysis of influencing factors is enabled, providing crucial data for subsequent emission reduction strategies. Influencing factors are categorized into four groups, including public infrastructure, road traffic facilities, land use and population, and traveler's socio-economic attributes, with a total of nine sub-factors across these categories. The relationship between each factor and carbon emissions is examined through statistical and qualitative analyses, establishing a robust quantitative analysis methodology for future research. Finally, a roadmap for reducing carbon emissions in residents' travel is presented.