Transport Carbon Emissions Research Articles

Allocation scheme selection for transportation carbon allowance -evidence from China's top ten economic regions

Establishing a scientific allocation scheme for transportation carbon allowance (TCA) is an inevitable requirement of China's sustainable development. Previous studies on the allocation problems of TCA considered the single goal, but this study constructs an allocation model of TCA under the framework of zero-sum gains data envelopment analysis and a spatial simultaneous panel. Furthermore, a profit maximization function model is used to measure the allocative efficiency and the differences in regional abatement costs of three different transport allocation mechanisms in China's top 10 economic regions from 1998 to 2019. Empirical results revealed four key results. (1) The efficient scheme is beneficial to regions with a high level of economic development and high-carbon emission efficiency, but it is easy to cause widening regional wealth disparities. (2) The equitable scheme is beneficial to regions with low transportation carbon emission efficiency, immature transportation structures, and green transportation technology, but it is easy to cause an unsuitable punishment effect. (3) Compared with the above two schemes, the comprehensive scheme is beneficial to more regions because it can both minimize the average marginal abatement costs and mitigate regional development inequalities. (4) With the gradual improvement of the national transportation carbon trading market, the comprehensive scheme is more sustainable and should be used more in the future. This study contributes to the formulation and implementation of energy allocation policies in the transportation sector.

Research on Transportation Carbon Emission Peak Prediction and Judgment System in China

The transportation sector is a major contributor to carbon emissions, and managing its carbon peak is essential for China to reach the 2030 carbon peak target. This paper uses the autoregressive integrated moving average model (ARIMA) to design baseline scenarios and “double carbon” scenarios (carbon peak and carbon neutrality) based on the accounting of transportation carbon emissions in 30 provinces and cities in China to facilitate regional differentiation and forecast the development trend of transportation carbon emissions. Using the fuzzy comprehensive evaluation method, a comprehensive transportation carbon emission research and judgment system has been developed based on the forecast results. The research indicates a substantial increase in carbon dioxide (CO2) emissions from transport in China over the past 15 years, with an average growth rate of 5.9%, from 387.42 mt in 2005 to 917.00 mt in 2019. In the scenario prediction analysis, the overall carbon emission of the “two-carbon” scenario exhibits varying levels of reduction compared with the baseline scenario. According to the comprehensive research and judgment system, when the comprehensive evaluation index corresponding to the turning point year of transportation carbon emissions is greater than 0.85, and the index remains above 0.85 after the turning point, it can be judged that a region has achieved the peak of transportation carbon dioxide emissions under 95% possibility. It shows that China’s policies and strategies for carbon and emission reduction have played a significant role in transportation, but the low-carbon transformation and development still face great challenges.

Spatiotemporal evolution trend and decoupling type identification of transport carbon emissions from economic development in China.

Carbon emissions are a major concern in China, and transportation is an important part of it. In this paper, data on China's 30 provinces' transport carbon emissions from 2005 to 2019 were selected to construct a spatial autocorrelation model and identified the decoupling types, which revealed the relationship between transport carbon emissions and economic development. This study suggests a regulation strategy for provincial transport carbon emissions in China based on the contribution rates of transport carbon emission variables. According to the findings, transport carbon emissions of China indicated a slow rise from 2005 to 2019, the annual growth rate has fluctuated downward, and petroleum products have been the most major source. The geographical correlation of transport carbon emissions has gradually improved, and the transport carbon emission intensity has become more significant. Differences in the transport carbon emission intensity slightly increased, which were significantly regionally correlated. There were seven forms of decoupling between yearly provincial transport carbon emissions and economic development, with weak decoupling accounting for the largest proportion, 45.24%. Decoupling was achieved in 83.33% of the provinces in the period of 2005-2019. As a consequence of factor decomposition, the energy intensity, transport intensity, and economic structure played an overall inhibitory role, while the carbon emission intensity, economic scale, and population played promoting roles. The economic scale was the most important influencing factor.

Analysis of transportation CO2 emission efficiency in the Yangtze River Economic Belt in China

ABSTRACT The transportation industry’s CO2 emissions and its main drivers are important for achieving sustainable development. With data from the Yangtze River Economic Belt (YEB) between 2004 and 2020, the efficiency of CO2 emissions is investigated by the super efficiency epsilon-based measurement method (super-EBM) and the Global Malmquist Luenberger Productivity Index (GML). The influencing factors on the different efficiency levels are explored by the panel quantile regression. This study shows that the overall CO2 emissions efficiency is not high, and the efficiency in different regions is largely consistent with the regional economy level. COVID-19 may reduce urban transport carbon emissions in the Yangtze River Economic Belt in 2020, as reflected by the reductions in average emissions and emissions efficiency. Findings also suggest that the influence of the number of private vehicles per 10,000 people on efficiency is positive at low and middle quantiles, and negative at high quantiles. Besides, the per-capita GDP, energy intensity, and transportation service structure negatively influence the efficiency at all quantiles, whereas urbanization and transportation intensity are positively related to the emissions efficiency.

Optimal vehicle fleet planning and collaboration under carbon neutrality: a game-theoretic perspective

This paper studies the optimal vehicle fleet planning and collaboration problem for a fuel vehicle (FV) transport service provider, a commercial electric vehicle (CEV) transport service provider, and a carbon emission treatment agency under carbon neutrality. The FV transport service provider pays a fixed fee or a portion of its sales revenue to a carbon emission treatment agency in exchange for technology to reduce its carbon emissions, and it can adopt three strategies (i.e., no emission reduction, purchasing technology for emission reduction, and entrusting a carbon emission treatment agency). We derive each party’s optimal fleet size, price, and profit in the three scenarios. Our results suggest that carbon emission reduction strategies may improve the market performance of the FV transport service provider. Then, we find no certain strategy is always preferable to another: the optimal cooperation strategy between the transport service provider and carbon emission treatment agency depends on the fixed technology fee, ratio of revenue sharing, government penalty, the transport service market potential, and consumer green preference, as well as the cost per CEV. This paper gives the transport service provider and carbon emission treatment agency a full picture of whether, when, and how to collaborate in green commerce.

Pathways to achieve low-carbon transition in the transportation sector under the constraints of carbon peak and carbon neutrality targets: A comprehensive analysis of intra- and extra-industry factors

Carbon emission mechanisms in the transportation sector are complex due to a linkage of intra- and extra-industry factors. The decomposition techniques of emission reduction factors are more inclined to analyze conventional factors and need to pay more attention to non-conventional factors. This paper proposes a new analysis framework and builds the LMDI@1 model based on the general analysis theory of index decomposition. In this direction, we calculate China's transportation carbon emissions (TCE) from 2005 to 2020 through direct and indirect sources. Within this model, extra-industry factors are linked into the system to analyze more clearly the degree of factors' contribution to TCE changes in China's regional perspective and clarify potential factors' interaction patterns. We find that factors' contribution has temporal and regional differences. Intra-industry and extra-industry factors form four closed-loop carbon reduction paths at economic development, resource allocation, industrial structure, and resource endowment levels. Therefore, the low-carbon transition policy of China's transportation sector should consider the interaction between external policies and the endogenous environment.

A review of transportation carbon emissions research using bibliometric analyses

A review of transportation carbon emissions research using bibliometric analyses

Low-Carbon Travel Behavior in Daily Residence and Tourism Destination: Based on TPB-ABC Integrated Model

Low-carbon travel is considered as one of the most important strategies to reduce transportation carbon emissions, and its success is decided by the active participation of residents. Based on the theory of planned behavior (TPB) and Attitude-Behavior-Context theory (ABC), this study explores the influencing factors and formation paths of individual low-carbon travel behavior, and analyzes low-carbon travel behavior regarding both daily commuting from residence and tourism destinations. This study collects a sample of 506 respondents and uses Mplus 8.0 to examine the hypotheses. Empirical research results indicate that: (1) A certain gap exists in the individuals’ low-carbon travel behavior between daily residence and tourism destination. Differences exist in direct effects, mediating effects and moderating effects. (2) Low-carbon travel behavioral intention plays a significant mediating role in both daily residence and tourism destination, especially the former. Regarding daily residence, individuals’ attitude, subjective norms and perceived behavioral control have a positive effect on behavior through behavioral intention. Regarding tourism destination, only the attitude-low-carbon travel behavioral intention-behavior path is significant. (3) Situational factors play a significant positive moderating effect on the relationship between low-carbon travel behavioral intention and behavior, especially in tourism destination. This study reveals the internal mechanism of individuals’ low-carbon travel behavior and the differences between travel in daily life and tourism, helping to deepen understanding of individuals’ low-carbon travel behavior and providing guidance for promoting individuals’ low-carbon travel.

Multimodal transportation and city carbon emissions over space and time: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China

The effectiveness of climate policies will determine CO2 emissions and goal achievement. However, the coupled integration among driving factors on emissions is not given enough attention and analysis in most quantitative modeling. To clarify the effectiveness of de-carbonization pathways, here we identify coupled factors documented across a range of studies and connect them in a coupled system framework. The empirical analysis reveals the de-carbonization significance and potential of coupled variables in the GBA. These potentials can be decisive for determining policies and emission outcomes. After in-depth spatial and time-fixed analysis, spatial spillover effects of driving factors are identified. And the driving effect of the independent variable is further decomposed into direct effect and indirect effect to show a complex coupled correlation. The novelty of this study is that it explores the coupled correlation among driving factors and quantitatively verified these relationships. Research findings have important reference value for policymakers in assisting de-carbonization strategies design.

Optimizing crop residues collection patterns in rural areas to reduce transportation costs and carbon emissions

Cost optimization of biomass collection is indispensable for its increased utilization, especially in rural areas where crop residues (CRs)-based biomass is in abundance and energy is usually in short supply. However, due to the discrete distribution of rural settlements, the increasing economic and environmental costs associated with CRs collection have become key factors limiting its application. Therefore, this study proposes a method for optimizing the spatial pattern of CRs collection in rural areas while also considering transportation costs and carbon emissions reduction. Based on a multi-scenario location–allocation model, a CRs collection pattern was constructed for biomass resource points and energy facilities. An empirical study in Fujin County found that as CRs collection distance threshold (CDT) increased, transportation costs and carbon emissions reduction potential tended to increase and gradually converge. When the CDT was taken for 15 km, the spatial pattern of CRs collection maximized the overall benefits of transportation costs and carbon emissions reduction, with a transportation cost of 898,104 yuan and a carbon emissions reduction potential of 501,627.5 t. The optimal spatial pattern for collection achieved the collection-utilization spatial matching of 647 biomass feedstock resource points and 196 villages in the study area, resulting in the exploitation of 84% of CRs in total. 25% of villages in the study area can independently meet their heating needs through CRs, and 75% need to consider mixed energy sources for heating. This method can support planning and decision-making for the distributed development and sustainable utilization of biomass resources in rural areas.

Research on the contribution of metro-based freight to reducing urban transportation exhaust emissions

Most carbon emissions come from cities, including mainly human activities in the fields of the urban economy, urban construction, and urban transportation. In terms of urban transportation, a truck produces far more carbon emissions than does a car. From the perspective of reducing the freight amount of urban road transport, underground logistics systems are a feasible solution in the future. Currently, metro-based freight is easier to implement, and the redundant transport capacity of the urban metro during off-peak hours can be put to good use. In addition, the pressure of the ground freight can be alleviated. Against the background of the coordinated operation mode of metro-based freight and ground freight, this study aims to measure the contribution of metro-based freight to reducing urban transportation carbon emissions by using the optimization method of metro passenger–freight coordinated transportation and traffic flow theory. Taking the Beijing Metro Batong Line as an example, the results of experiments show that the coordinated operation mode can significantly reduce urban transportation exhaust emissions.

Optimization of Urban Road Green Belts under the Background of Carbon Peak Policy

To explore the reasonable width of urban road green belts under the background of carbon peak policy and provide quantitative design guidance for urban green-belt planning, a closed-loop design that integrates urban traffic, carbon emissions, urban greening, and carbon absorption was established at the road network level. First, the factors affecting urban transport carbon emissions were analyzed from the aspects of environment, economy, population, and technology. A carbon emission prediction model was constructed based on the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The path of the carbon peak in the transportation sector was simulated. A scenario under the carbon peak target was designed to determine the development trend of each factor. The layout forms and design requirements of urban road greening were then summarized. The annual carbon sequestration amounts of different plant types were calculated. An objective optimization model was constructed with the lowest cost of greening as the objective function. The constraint conditions specify that the carbon absorption be greater than the carbon emissions, in addition to specifying the basic greening design requirements. Finally, an empirical study was conducted on a road network area in Xi’an. According to the results, the traffic carbon emissions of Xi’an City will be 2.71 Mt in 2025, with road traffic accounting for 1.94 Mt. Without considering the road-measurement green-width constraint, the proportions of the road green belt for six road classes and the red-line width under the carbon peak target are 0.31, 0.33, 0.40, 0.22, 0.21, and 0.23. These research results provide a quantitative and reliable basis for designing the width of an urban green belt under the background of carbon peak policy. Under proportion restrictions, road greening yields better performance by considering both aesthetics and road characteristics.

Research on carbon footprint measurement and emissions reduction optimization of the beer supply chain in China.

The carbon emissions of the beer industry have always been a concern. To reduce environmental pollution, this paper systematically calculates the carbon emissions of the beer supply chain and studies the optimization of carbon emissions reduction through scenario simulation. In this study, the boundary of the beer supply chain carbon emissions system is constructed. The scenario simulation and simulation analysis are carried out based on five factors: water recycling utilization rate, power generation mode, weight-volume ratio, recovery ratio, and transportation mode. To obtain the optimal scheme of economic cost and emissions reduction, the cost of the beer supply chain is analyzed under the condition of carbon trading. The results show that the proportion of carbon emissions in each link of the beer supply chain is production, packaging, transportation, and storage, which are 59.58%, 22.27%, 17.67%, and 0.5%, respectively. The improvement of the water recycling utilization rate of manufacturing enterprises can reduce the overall carbon emissions of the supply chain, and the effect is obvious. The highest proportion of carbon emissions in the packaging link is the bottle consumption stage (22.43%). Raw materials transportation accounts for the highest proportion of total carbon emissions in transportation (63.73%). The waterway/railway ratio will be increased to 0.6/0.4, and the carbon emissions in the supply chain will be reduced by 5.7%. In the context of carbon trading, when the carbon trading price is higher than 49.16 yuan/ton, enterprises can achieve a win-win situation in reducing emissions and maintaining positive economic growth by the measures given in the paper.

Cross efficiency model of network DEA and its application on low carbon efficiency evaluation of multimodal transport

The transportation industry is the main source of carbon dioxide (CO2) emissions, and the development of multimodal transport is conducive to reducing transportation carbon emissions. In order to evaluate the low carbon efficiency of multimodal transport, this article proposes a cross efficiency data envelopment analysis (DEA) method for complex network structures from the perspective of fairness, and takes China's rail-water intermodal transport as an example for research. The proposed method includes two secondary goal models that reduce the unreasonable differences between self-evaluation and peer-evaluation and treat all stages within the network system equally. Then, the improved entropy weight method is used to aggregate the cross efficiency. In addition, a numerical example is given to verify the effectiveness of the proposed method. The results of the case study show that the efficiency of Tangshan Port and Rizhao Port is relatively high, and others should learn from the successful experiences and develop improvement strategies based on their own characteristics. The conclusions can provide reference for transportation enterprise management and policy formulation, and the proposed method can be widely applied to the performance analysis of enterprise network, industrial chain, and supply chain.

Optimum Route and Transport Mode Selection of Multimodal Transport with Time Window under Uncertain Conditions

Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified by the fuzzy expectation value method and fuzzy chance-constrained planning method. Combining the game theory method with the weighted sum method, a cooperative game theory-based multi-objective optimization method is proposed. Finally, the effectiveness of the algorithm is verified in a real intermodal network. The experimental results show that the proposed method can effectively improve the performance of the weighted sum method and obtain the optimal multimodal transport path that satisfies the time window requirement, and the path optimization results are better than MOPSO and NSGA-II, effectively reducing transportation costs and carbon emissions. Meanwhile, the influence of uncertainty factors on the multimodal transport route planning results is analyzed. The results show that the uncertain factors will significantly increase the transportation cost and carbon emissions and affect the choice of route and transportation mode. Considering uncertainty factors can increase the reliability of route planning results and provide a more robust and effective solution for multimodal transportation.

Promoting “NEVs Pilot Policy” as an Effective Way for Reducing Urban Transport Carbon Emissions: Empirical Evidence from China

To reduce urban transport carbon emissions, the Chinese government issued the NEVs Pilot Policy in 2009 to promote NEVs in both the public and private transport sectors. Using panel data from 53 prefecture-level cities in China spanning from 2006 to 2020, this study evaluates the effectiveness of the NEVs Pilot Policy on urban transport carbon emissions based on the difference-in-differences (DID) model. Additionally, it analyzes the influencing mechanism of this policy, as well as the varying policy effect among heterogeneous cities. The empirical results show that the NEVs Pilot Policy has effectively reduced urban transport emissions by an annual average of 29.3%. Annual per capita emissions were lowered by an average of 0.31 t, and the annual emission intensity was also reduced by an average of 2.04 t per unit GDP. We also found that its dynamic effectiveness has lagged but cumulatively increased over time. Furthermore, the mechanism analysis indicates that the policy effect is mainly achieved by adjusting the vehicle structure (VS) and lowering the energy intensity (EI). The heterogeneity analysis also reveals that the effectiveness of NEVs Pilot Policy varies significantly among different cities. The economic level, the political status, and the urban transport development are found to be the key factors that determine its effectiveness. Based on these findings, this study proposes some targeted policy suggestions to promote NEVs in different cities.

Public perception of electric vehicles on Reddit and Twitter: A cross-platform analysis

Electrified mobility such as electric vehicles (EVs) is a promising solution to reduce carbon emissions in transportation and mitigate global warming. Understanding public perception of EVs can help better support their adoption. A previous study shows that online social networks (OSNs) such as Reddit can be a valuable source for studying public perceptions of EVs and provide different perspectives from traditional methods that leverage surveys, questionnaires, or interviews (Ruan and Lv, 2022). Our work aims to investigate this direction further through the following research question: Given the distinct mechanisms of various OSNs, can we obtain a more comprehensive picture of public perception of EVs by integrating the analysis from different platforms? Specifically, our study is based on EV-related discussions on two popular OSN platforms: Twitter and Reddit. We have collected 3,437,917 Reddit posts (including 274,979 submissions and 3,162,938 comments) and 7,383,327 Tweets between January 2011 and December 2020 and analyzed them from several perspectives. Our analysis shows that users have had different topic and sentiment patterns in EV-related discussions on the two platforms over the past decade. We also leverage the verified account information on Twitter to reveal that the most influential users are politicians and news media; however, the general public has very different conversation patterns with the two types of accounts — politicians seem to be increasingly (over) optimistic about EVs while the public may think differently.

Transport carbon emissions reduction efficiency and economic growth: a perspective from nighttime lights remote sensing

The carbon emissions are essential for climate change and 26% of the world's carbon emissions are related to transport. But focusing only on fewer carbon emissions might be biased at times. In order to keep a balance be-tween economic growth and carbon emissions reduction, this paper evaluated the performance of carbon control by considering the input factors and output factors together, which is more comprehensive and reliable. Firstly, this paper has calculated the transport carbon emissions reduction efficiency (TCERE) based on the model of super SBM with undesirable outputs. The input factors include capital stock, labor force and fossil energy con-sumption. And the output factors include gross domestic product and carbon dioxide emissions. Then the influenc-ing factors of TCERE were analyzed using econometric models. The economic growth, transport structure, tech-nology level and population density were posited as influencing factors. This paper creatively proposed the per capita nighttime lights brightness as a new indicator for economic growth. An empirical study was conducted in East China from 2013 to 2017, and this study has found that the relationship between TCERE and economic growth shows an U-shape. Besides, transport structure and technology level both show a positive impact on TCERE. The implications of our findings are that: (a) The TCERE declines slower in East China, giving us reason to believe that the improvement of TCERE is predictable; (b) When economic growth exceeds the turning point, economic growth is conducive to the improvement of TCERE. We could develop the economy boldly and confident-ly; (c) Increased investment in railway and waterway transportation infrastructure projects is needed to strengthen the structure of the railway and waterway transportation systems. Furthermore, the general public and businesses should be encouraged to prefer rail or river transportation; (d) Investment in scientific and technological innova-tion should be enhanced in order to produce more efficient energy-use methods.

Optimization of China's freight transportation structure based on adaptive genetic algorithm under the background of carbon peak.

Transportation is the key carbon emission source after energy supply and industrial production. Under the vision of carbon peak and carbon neutralization, the pressure of reducing carbon emissions in transportation will be greater in the future. This paper constructs a model that takes transportation carbon emission as the main target and freight transportation utility value as the auxiliary target. The constructed model satisfies the constraints of freight turnover in the whole society, freight economic and social benefits, and the ecological constraints of the freight system. With MATLAB, the freight turnover of roadways, railways, and waterways (excluding ocean transportation) in 2030 is solved by using the adaptive genetic algorithm. The results indicate that (I) compared with the current freight structure of China, the roadway freight sharing rate in 2030 will decrease by 8.07%, and the railway freight sharing rate and the waterway freight sharing rate (excluding ocean transportation) will increase by 0.93% and 7.13%, respectively. (II) After optimization, the energy consumption and carbon emission are reduced by 42,471,500 tons (10.3%) and 91,379,400 tons (10.2%) of standard coal, respectively. (III) The adaptive genetic algorithm outperforms the traditional genetic algorithm in terms of convergence speed and accuracy. (IV) As the weight coefficient of carbon emission increases, the utility value of freight transportation consistently decreases, and the sensitivity increases. Meanwhile, as the carbon emission weight coefficient increases, carbon emission keeps decreasing, and the sensitivity decreases.

Comparative analysis of the USA's Washington Ferries and road transport carbon emissions using the Trozzi and Vaccaro and Greatest Integer functions.

Countries' sectors are currently under great scrutiny for their response to the greenhouse gas (GHG) emission profile and the general effect of the sectoral activities on the environment. As in the agenda of all sectors, environmental concerns and investigations are of high importance in shipping and maritime transport. Amidst the rising forms of globalization, the need for sustainable transportation is constantly increasing. However, the machines that are the cornerstone of transportation largely depend on fossil fuels, thus resulting in environmental degradation. Notably, environmental-related degradation has continued to account for global warming, climate change, and ocean acidification. Shipping is considered the most environmentally friendly mode of transportation in terms of carbon dioxide (CO2) emissions per ton per mile of transported unit load when compared against road transportation. In this study, six ferry lines (FLs) of Washington State Ferries were calculated to compare ship-generated carbon dioxide (CO2) emissions with those from road transportation as if the carried vehicles had used the highway instead of transport by FL. While making these calculations, the Greatest Integer function (GIF) and Trozzi and Vaccaro function (TVF) were utilized. From the examined three scenarios, i.e., all passengers travel by car instead of ferry as scenario 1, all ferries carry both cars and passengers as scenario 2, and all car-free passengers travel by bus instead of ferry as scenario 3, the outlined results are as follows: (i) none of the cars were carried by the ferry, and car-free passengers preferred traveling by their own cars as observed in scenario 1; (ii) hypothetical scenarios (1 to 3) in which the road vehicles carried on FLs had instead used the highway, and the total potential CO2 emissions of these road vehicles were calculated as 2,638,858.138, 704,958.2998, and 1,394,148.577 tonnes per year, respectively. Policy-wise, this study revealed the management strategies for CO2 emissions reduction for two transport modes, shipping and road transportation, under current conditions.