Low-carbon Transport System Research Articles

The impact of a low-carbon transport system policy on total factor carbon emission performance: Evidence from 283 cities in China

Reducing carbon emissions from the transport sector is crucial to achieve “carbon neutrality” and is an important component of a low-carbon policy system. In response to the uncertainty of evaluating current low-carbon transport policies, we provide an in-depth analysis and examination of low-carbon transport system (LCTS) policies implemented in China. Using data from 283 Chinese cities between 2006 and 2020, we provide evidence on the policy effects, potential mechanisms, and heterogeneity of LCTS policies on carbon emission performance. Results showed that LCTS policies significantly improved carbon emission performance in pilot regions. The main ways to achieve this policy's objective were to reduce energy consumption in pilot cities, to promote the development of green technologies, and to implement low-carbon transformation of public transport. Moreover, there was heterogeneity in the policy's effects, with the implementation of a LCTS being more effective in eastern and central regions of China and in larger and more compact cities. By analysing the policy's mechanisms and heterogeneity of effects, this article provides a reference for the formulation and selection of low-carbon transport policies.

Does green transportation affect carbon emission efficiency? Evidence from a quasi-experimental study in China

The establishment of a low-carbon transportation system (LCTS) holds prominent importance in advancing modern transportation development and achieving green growth. Employing the LCTS pilot policy as a shock to green transportation construction in China, we investigate its influence on carbon emission efficiency (CEE) using urban panel data from 2006 to 2021. Results based on difference-in-differences (DID) show that LCTS contributes to increasing CEE following such a pilot policy. This finding still holds after we perform a propensity score matching DID method, double machine learning, instrumental variable method, heterogeneous treatment effect, and other robustness checks. Meanwhile, the mediation analysis shows that the increased CEE can be accounted for by spurring green innovation activities. Moreover, the moderation outcomes reveal that FinTech exerts a positive moderating influence on the increased impacts of LCTS on CEE. Heterogeneity analysis shows that LCTS's impact on CEE varies by region and development level. Overall, our research stresses the importance of green transportation construction in shaping CEE, which has significant practical and theoretical implications for scholars and policymakers.

Assessing the impact of driving behaviors and traffic conflicts on vehicle emissions at non-signalized intersections using a trajectory-based computational framework

Vehicle emissions can rise due to traffic conflicts and aggressive driving behaviors, such as frequent acceleration and deceleration. This issue is particularly pronounced at non-signalized intersections with a high proportion of non-motorized vehicles. In this study, we propose a framework that integrates a microscopic vehicle emission model with trajectory data. By utilizing trajectory data collected from a non-signalized intersection in Shanghai, we analyzed vehicle emissions linked to driving behaviors and traffic conflicts. Our findings reveal that pre-braking at the entrance of non-signalized intersections can significantly reduce vehicle emissions, lowering them by nearly 80 % for straight maneuvers. However, this reduction is less substantial for turning maneuvers. Additionally, conflicts involving more than two types of targets lead to a significant increase in vehicle emissions. On average, stop-and-go emissions are 1.13 % higher than those resulting from traffic conflicts. Interestingly, when non-motorized vehicles constitute more than 80 % of the traffic volume, stop-and-go emissions fall below those generated by traffic conflicts. The results of this study provide valuable insights for optimizing eco-driving strategies and advancing towards a low-carbon transportation system.

Analyzing barriers to the adoption and development of electric vehicles: A roadmap towards sustainable transportation system

Electric vehicles (EVs) are positioned as an alternate green energy technology, which potentially can enable the efficient transition to sustainable low-carbon emission transportation systems. However, different barriers hamper the public adoption of EVs. The degree to which these barriers prevent the adoption of EVs and the potential alternatives to these barriers are mostly unknown. To fill this research gap, the current study contributes by systematically identifying those barriers based on their importance. Initially, several barriers were identified using existing literature, and then by employing the modified Delphi method, a total of twenty-five barriers were classified into five significant dimensions. The Analytical Hierarchical Process (AHP) determined the rankings of these barriers based on weight allocation. As a next step, the Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS) ranked alternative solutions to these barriers. The findings indicate that 'technological barrier' is the top-ranked barrier among all major dimensions. The overall ranking indicated that 'battery technology' is most influential among sub-dimensions. It is recommended that "investment in research and development" is the best alternative to overcome these barriers. In order to improve the renewable and sustainable urban transportation system, all stakeholders should cooperate in a consistent and coherent manner.

Can low-carbon transportation construction improve urban green total factor productivity? A quasi-natural experiment based on 282 prefecture-level cities in China.

Total factor productivity in the green economy is a major step forward in fostering robust growth in the economy. Urban green total factor productivity (GTFP) may be increased and carbon emissions decreased through the low-carbon transformation of the transportation sector. To analyze how the pilot policy of low-carbon transportation system construction affects urban GTFP, this study builds a DID model and a spatial Durbin model using panel data from 282 Chinese cities between 2006 and 2021. The findings show that low-carbon transportation systems boost urban GTFP. In addition, the mechanism test shows that low-carbon transportation development increases urban GTFP by optimizing urban industrial structure, expediting economic agglomeration, and encouraging technological innovation. Lastly, the geographical correlation test shows that GTFP is geographically associated and that spatial spillover reduces the impact of low-carbon transportation growth on GTFP. The findings affirm the need to vigorously promote low-carbon transportation systems to aid in the achievement of the carbon neutrality target.

Consumer Adoption and Perception of Electric Vehicles

The ever-increasing need for environmentally friendly modes of transportation is the primary force behind the tremendous transition that the global automobile industry is currently undergoing. Electric cars, often known as EVs, have emerged as a potentially useful alternative to conventional automobiles powered by internal combustion engines. These vehicles offer a positive impact on the environment as well as technical developments. For policymakers, industry stakeholders, and researchers who are interested in accelerating the transition to a low-carbon transportation system, it is crucial to have a solid understanding of how consumers embrace electric vehicles and how they perceive them. A detailed study of consumer acceptance and perception of electric vehicles (EVs) is presented in this research article. The analysis is based on a review of relevant literature, empirical investigations, and case examples. Several key aspects that influence customer decision-making regarding the adoption of electric vehicles are investigated. These elements include environmental consciousness, government incentives, technical improvements, cost of ownership, availability of charging infrastructure, driving experience, and perception of the brand (brand perception).

Energy efficiency and electrochemical characteristic assessment of PEMFC-supercapacitor hybridization via railway profile

Electric trains equipped with efficient electric motors and powered by high-capacity hydrogen fuel cells are a choice of the global transportation industry to accelerate the global transition to a low-carbon transportation system. The operation of distinct vehicle types in varying geographical regions is characterized by unique features. Concurrently, the continually fluctuating demand for electricity necessitates the utilization of fuel cells in conjunction with energy storage devices. Thus, this study centers on examining the role of the PEMFC-supercapacitor direct hybridization system within the authentic driving profile of the subway in the Bangkok metropolitan region. The supercapacitor within the hybridization system serves as an energy management device, enhancing the durability of fuel cells when subjected to diverse load demands associated with train propulsion. In order to validate this hypothesis, an MRT (Mass Rapid Transit) protocol was formulated to emulate and serve as the load demand for the investigation, and then the PEMFC-supercapacitor direct hybridization system was operated using the created profile. Subsequently, the hybridization system undergoes electrochemical characterizations and fuel cell performance tests for diagnostic purposes. Empirical findings validated that the supercapacitor operates as a filter, generating a stable voltage response to varying load requirements, and possesses the capability to assimilate energy originating from regenerative braking. The integration of supercapacitors in stabilizing the operational parameters of proton exchange membrane fuel cells (PEMFCs) has markedly prolonged their operational lifespan. This assertion is substantiated by voltage signal analysis, which demonstrated a negligible increase in surface resistance of merely 0.23%, as depicted by Nyquist plots. Furthermore, cyclic voltammetry revealed that the system attained a peak electrochemical surface area (ECSA) of 0.0089 (106 cm2/mg of Pt). The information presented in this article constitutes data that has been minimally explored in existing research. Specifically, the study concentrates on the practical driving pattern of electric trains. Consequently, experimental results were deemed advantageous for the advancement of heavy-duty electric vehicles.

Using open-source tools to project bulk and critical material demand and assess implications for low-carbon energy and transport systems: Introducing Mat-dp model tools

Material demand is growing rapidly, increasing pressure on natural resources and greenhouse gas emissions. Accurate material demand projections require reliable estimates of material intensities- material amount required to produce a unit of output. However, there is a lack of comprehensive, up-to-date, and open-source material intensities. Studies that have estimated material demand usually focus on technology or location subsets, without reusable calculations and visualisations. To address these gaps, two open-source tools are introduced as part of the Material Demand Projections (Mat-dp) model: Mat-dp database, a comprehensive and open-source database of material intensities of energy and transport technologies; and Mat-dp pipeline, a tool to standardise, calculate and visualise results for given scenarios. The paper showcases the application of the tools and demonstrates material demand and supply considerations in planned and ongoing low-carbon transitions. The tools are, as far as the authors are aware, novel in their technology coverage and usable scenario sources.

“Robots taking over the world… fantastic!” Understanding social representations, familiarity and visions of experiments with autonomous public transportation

Autonomous public transport (APT) represents a potentially important innovation in the transition to a low-carbon transport system. Niche operators around the world are currently starting to experiment with forms of APT in urban environments. In these experiments visions and shared expectations are believed to play a pivotal role. However, such visions are not easily communicated to the local public, and experimenting can evoke and amplify different views. This study applies a novel combination of social representation theory and multi-level perspective (MLP) to explore the local social understandings of APT in an incumbent-led experiment in a Norwegian municipality. Based on a qualitative analysis, the study identifies three emerging social representations of the APT technology, where two oppose the niche vision. A general lack of information about the purpose and mission of the pilot evoked ambivalent and critical representations, drawing on pre-existing images and metaphors from a landscape level. We argue that social representations of technologies can help explain the complexity of the socially embedded reactions that emerge when niche experimentation takes place. We also discuss how such reactions may influence socio-technical transition processes.

Clean energy synergy with electric vehicles: Insights into carbon footprint

This study empirically examines the impact of Electric Vehicles (EVs) and clean energy adoption on carbon footprints. With growing concerns over climate change and the need to reduce greenhouse gas emissions, the transition to electric vehicles and clean energy sources has gained significant attention as potential solutions. This research investigates the relationship between the adoption of EVs and clean energy technologies and their subsequent effect on carbon footprints. The study applied the Generalised Method Moments (GMM) on the data sourced from the International Energy Agency (IEA) and the World Development Indicators (WDI) for the period 2011–2021 covering 27 countries. The findings reveal a strong correlation between the adoption of EVs and reductions in carbon footprints. It shows that a 1 % increase in renewable electricity output would result in a 0.5 % decline in carbon footprints. This implies that, as EV sales increase, the overall carbon emissions from the transportation sector decrease, primarily due to the lower emissions associated with electric vehicles compared to conventional internal combustion engine vehicles. Additionally, the study finds that the integration of clean energy sources into the electricity grid further contributes to carbon footprint reductions. The use of renewable energy in charging EVs minimizes the indirect emissions associated with electricity generation. The research concludes that the widespread adoption of EVs and clean energy sources has the potential to significantly reduce carbon footprints and mitigate the impact of climate change. However, it emphasizes the importance of continued technological advancements, increased availability of charging infrastructure, and supportive government policies to accelerate the transition towards a low-carbon transportation and energy system. Therefore, the study highlights the need for collaborative efforts among governments, industry stakeholders, and individuals to foster sustainable practices and achieve a more environmentally friendly future.

Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach

In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance of the classifiers, we identified that the Random Forest (RF) algorithm performed the best. Using the RF model, this study also explored the relative importance of BE factors in predicting commute mode choice, identified nonlinear relationships between the BE factors and mode choice, and examined the interaction effects of these factors on mode selection. Our results reveal that, compared to socio-demographic factors, the BE substantially influence commuter travel behavior. The BE characteristics have a specific nonlinear threshold limit at which they can have a notable impact on lowering private car use, and private car use does not display a constant return of scale with BE. Their interaction effects illustrate the potential optimal combination of BE interventions to lower private car use for commuting. These findings hold substantial implications for urban environmental policy, emphasizing the need for transit-oriented development, travel demand management, and integrated land-use transportation planning to foster low-carbon transportation systems in cities like Dhaka.

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

Towards a sustainable future: The decarbonization of transport

This themed edition of the Proceedings of the Nigerian Academy of Science delves into the pivotal topic of transport decarbonization, a critical component in the global strategy to combat climate change and promote environmental sustainability. It amalgamates a rich tapestry of research, case studies, and policy analyses focused on the multifaceted challenges and opportunities inherent in transitioning to a low-carbon transport system. Key areas of discussion include the complexities of electric vehicle (EV) adoption, the potential for invigorating local manufacturing sector through EV and e-bike production, the transformative impact of removing fuel subsidies on the uptake of solar power, and the intricate policy landscapes that underpin transport decarbonization efforts. By spotlighting Nigeria's unique context, from its policy strides to innovative local initiatives, this edition not only contributes to the academic discourse on sustainable transport but also aims to inspire actionable insights and collaborative efforts towards achieving a decarbonized transport future. This compilation seeks to foster a deeper understanding of the interplay between technological innovation, policy frameworks, and community engagement in driving the shift towards sustainable mobility solutions, with the hope of catalyzing further research and implementation of sustainable transport systems globally.

Energy consumption structure model considering urban green and low-carbon transportation

<p class="Abstract">The importance of energy conservation and emission reduction has become the consensus of the international community, and Iraq is also actively improving the urban public transportation system to control carbon emissions. This paper collects panel data of Tikrit city in Iraq in the past 3 years, constructs a random effect variable coefficient model, and studies the impact of the development of urban low-carbon transportation system on the energy consumption structure. The study finds that the government can use public transportation pricing strategies to influence consumers. In order to realize the optimization of energy consumption structure, the impact of electric vehicles on energy consumption structure will decrease with the increase of urban development. The transportation sector can increase the purchase and travel costs of traditional cars by restricting travel, purchases, and charging parking fees, which affects the number of private cars and reduces the obstacles to optimizing the energy consumption structure. The government should increase financial subsidies, improve rail transit and reasonable bus (electric) vehicle operation systems, increase investment in new energy vehicle research and development, and encourage high energy density and low power consumption technologies. development, increase residents’ demand for new energy passenger vehicles, and optimize the energy consumption structure.</p>

How do energy supply and energy use link to environmental degradation in China?

China's energy supply and energy use are closely linked to environmental degradation. The country's heavy reliance on coal, oil, and natural gas, as well as its rapidly growing demand for energy, have contributed to air and water pollution, soil erosion, and other environmental problems. To address these issues, China must transition to cleaner and more sustainable forms of energy and implement policies to reduce energy demand and promote conservation. This paper explores the impacts of total primary energy supply and energy use on environmental degradation in China, using international trade and economic growth as moderating variables from 1971 to 2019. The results indicate that the total primary energy supply is significant at a 1% significance level, which suggests that it strongly impacts the ecological footprint. Energy use and international trade are significant at a 5% level, indicating that they also influence ecological footprint, albeit to a lesser extent. Economic growth is significant at a 1% significance level, suggesting a strong positive association with the ecological footprint. Energy supply improves the environment in China, whereas energy use degrades the environment. Policymakers should focus on promoting energy efficiency in the industrial and transportation sectors. This can be achieved through the implementation of energy-saving technologies, the promotion of public transportation, and the development of low-carbon transportation systems.

An Evolutionary Game Analysis of Shared Private Charging Pile Behavior in Low-Carbon Urban Traffic

Choosing new energy vehicles for travel, especially electric vehicles, is an important component of building a low-carbon urban transportation system. However, the charging need of electric vehicle users is still constrained by the unreasonable layout and insufficient supply of public charging piles in cities. Private charging pile sharing, as an alternative policy tool, can play a beneficial role in solving this problem. However, it needs decision-makers in urban transportation to take corresponding measures to promote. This paper constructs an evolutionary game model to study the decision behavior of participants in a private pile-sharing platform. Through numerical simulation analysis, it is found that under most parameter conditions, the government tends to establish a shared charging pile platform based on public interests. Private charging pile owners are influenced by the relationship between the cost of supply modification and revenue, and they tend to join the shared platform when they expect to recover the modification cost. The research conclusions of this paper will provide support for exploring how participants make decisions to maximize overall benefits in the development of low-carbon urban transportation.

Assessment of competitiveness of a Russian electric car as a substantiation of the need to stimulate electric cars market in Russia

Objective: to assess the competitiveness of a Russian electric car and an ICE car, taking into account the external costs associated with emissions of the main greenhouse gas – CO2.Methods: the article uses comparative and quantitative methods, the method of analysis and synthesis, the method of estimating the total cost of ownership (TCO), justifies the use of the avoidance cost approach as an optimal approach to assessing the external costs associated with climate change.Results: modern efficient economic development is inextricably linked with the organization of a low-carbon transport system. The green transition to electric vehicles makes it possible to solve the problem of externalities associated with the increased public costs due to greenhouse gas emissions. The paper examines the prospects for the transition to electric cars from the viewpoint of reducing the external costs of cars. The article assesses the current state and key barriers to the development of electric transport production. A comparative assessment of the competitiveness of Russian electric vehicles and ICE cars is given, taking into account the external costs associated with carbon emissions. The comparison was carried out based on an estimate of the total cost of ownership, taking into account the external costs associated with greenhouse gas emissions. As a result of the comparative analysis, it was determined that the total cost of five-year ownership of the Evolute i-Pro car, taking into account support measures, is even lower than that of the Lada Vesta Sport ICE sedan, which is close to it in terms of technical characteristics, by 342.7 thousand rubles. Without subsidies, Evolute i-Pro does not yet achieve parity in the cost of ownership with an ICE car, despite low operating costs, free parking and exemption from paying transport tax. Based on the conducted research, conclusions are made about the measures necessary to increase the gap between the total costs of ownership in ICE cars and electric cars to increase the competitiveness of the latter.Scientific novelty: the author’s approach was used to compare the competitiveness of the new Russian electric car Evolute i-Pro and Lada Vesta Sport ICE car, which, in addition to support measures, includes external costs associated with carbon emissions.Practical significance: the results obtained may be useful to developers of state support measures in assessing the effectiveness of the current subsidy mechanism and making decisions on further stimulating the development of the electric cars market in Russia. The results of the study will also be especially useful to residents of Moscow who are thinking of buying a Russian electric car.

Battery valuation and management for battery swapping station

Battery swapping station (BSS), a business model of battery energy storage (BES), has great potential in future integrated low-carbon energy and transportation systems. However, frequent battery swapping will inevitably accelerate battery degradation and shorten the battery life accordingly. To model the tradeoff of BSS use between energy and transportation applications, we develop a life-cycle decision model that coordinates battery charging and swapping in which the sensitivity of demand to price for battery swapping is considered and the optimal marginal degradation cost (MDC) of BSS is determined to maximize the BSS benefit across time and application. The proposed model is applied to manage a BSS that simultaneously provides battery swapping services to electric vehicle customers and provides flexibility service to the power grid, including energy arbitrage and reserve. The case study shows that while the end of the battery physical life occurs faster with battery swapping, the economic life becomes considerably longer. The results also reveal that the optimal MDC depends on the battery values in each application, and we analyze how the battery swapping price affects the optimal MDC and battery life. The proposed model can also provide decision support for on-demand BSS service, such as battery trading and renting.

A hybrid approach for low-carbon transportation system analysis: integrating CRITIC-DEMATEL and deep learning features.

As supply chains, logistics, and transportation activities continue to play a significant role in China's economic and social developments, concerns around energy consumption and carbon emissions are becoming increasingly prevalent. In light of sustainable development goals and the trend toward sustainable or green transportation, there is a need to minimize the environmental impact of these activities. To address this need, the government of China has made efforts to promote low-carbon transportation systems. This study aims to assess the development of low-carbon transportation systems in a case study in China using a hybrid approach based on the Criteria Importance Through Intercriteria Correlation (CRITIC), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and deep learning features. The proposed method provides an accurate quantitative assessment of low-carbon transportation development levels, identifies the key influencing factors, and sorts out the inner connection among the factors. The CRITIC weight matrix is used to obtain the weight ratio, reducing the subjective color of the DEMATEL method. The weighting results are then corrected using an artificial neural network to make the weighting more accurate and objective. To validate our hybrid method, a numerical example in China is applied, and sensitivity analysis is conducted to show the impact of our main parameters and analyze the efficiency of our hybrid method. Overall, the proposed approach offers a novel method for assessing low-carbon transportation development and identifying key factors in China. The results of this study can be used to inform policy and decision-making to promote sustainable transportation systems in China and beyond.

A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems.

With the increasing severity of environmental problems, low-carbon development has become an inevitable choice. Nowadays, low-carbon green sustainable development is influenced by a variety of factors such as social, environmental, technological, and economic development levels, making its development complex, which in turn imposes challenges on decision-makers. In this context, the application of multi-criteria decision-making (MCDM) in different areas of sustainable development engineering has become a hot topic. Although many reviews of MCDM techniques already exist, there is a lack of holistic review efforts on MCDM in the field of low-carbon transport and green logistics. Considering these shortcomings in the state of the art, this paper systematically reviews more than 190 papers from 2010 to 2022, constructs a general structure of MCDM techniques for this research topic, provides a comprehensive review and analysis of it, and clarifies the current practices. Furthermore, future directions for the development of MCDM techniques for green logistics and low-carbon transportation systems are presented as well.