Low-carbon Travel Research Articles

PUBLIC PARTICIPATION MECHANISM IN LOW-CARBON TRANSPORTATION FROM THE PERSPECTIVE OF CARBON NEUTRALITY

Under the goal of carbon neutrality, public participation in green travel is the fundamental premise of low-carbon transportation development. Based on the multiple mediation effect model, this paper explores the mechanism of social interaction on public participation in low-carbon transportation, and the hierarchical regression method is used to test the moderating effect of social network intensity and information network intensity. The results show that: (1) Both network media and social relations have a prominent positive effect on public participation, but the effect of network media is better than that of social relations. (2) Network media and social relations affect residents’ low-carbon travel behavior through three mediating variables, namely, traffic information, environmental protection knowledge and social norms. (3) The moderating effect of social network intensity on the relationship between network media and low-carbon travel behavior is prominently negative, while the moderating effect of information network intensity is significantly positive. Therefore, it is necessary to stimulate more public participation in low-carbon transport through real-time pushing of traffic information and strengthening the closeness of social relations.

Dual-Stage Analysis Combining Structural Equation Modeling and Machine Learning for Low-Carbon Travel Intention of Urban Residents

Guiding urban residents to travel with low carbon is an important measure to reduce carbon emissions and promote sustainable urban development. To explore the important factors affecting urban residents’ low-carbon travel intention, this study proposes a composite model of urban residents’ low-carbon travel intention based on the composite framework of the theory of planned behavior and value belief norm theory. A total of 398 valid pieces of data were collected in Qingdao, China. Structural equation modeling (SEM) was applied to empirically analyze the data to identify the predictors that have a significant effect on low-carbon travel intention. Then, two machine learning methods, artificial neural networks (ANN) and extreme gradient boosting (XGBoost), were used to conduct sensitivity analysis on the SEM results to identify the determinants that affect residents’ low-carbon travel intention. The results showed that personal norm, attitude, subjective norm, and perceived behavioral control have a significant direct effect on residents’ low-carbon travel intention. Policy factors can indirectly affect low-carbon travel intention, through mediating variables. Environmental awareness and travel time perception have a direct effect on both residents’ attitude and subjective norms. In addition, the explained variance (R2) of low-carbon travel intention by ANN and XGBoost is 0.75 and 0.77, respectively. The Root Square Mean Error (RMSE) in both models are small, which verifies the effectiveness of the two machine learning methods. The results can provide a reference basis for policymakers to prompt urban sustainable development.

Exploring the factors influencing low-carbon travel: Evidence from Taiwan

Exploring the factors influencing low-carbon travel: Evidence from Taiwan

Explore New Low-carbon Travel Routes for Beijing Residents

Explore New Low-carbon Travel Routes for Beijing Residents

No pain, no gain? Simulation of carbon reduction potential and socioeconomic effects of voluntary carbon trading in China during 2021–2060

Voluntary carbon market has become a key mechanism of stimulating carbon emission mitigation actions globally. In this study, we calculate the emission reduction potential of Carbon Generalized System of Preferences (CGSP), a dominated voluntary carbon trading mechanism in China, including on-going projects of forestry carbon sinks, waste clothes reuse, installation of distributed photovoltaic power generation system, and usage of energy-conserving devices, as well as other extended methods for their future mitigation potentials. We employ a computable general equilibrium model to delve into the effects of CGSP mechanism on carbon price, economic growth and household consumption, assessing its impact on the equitability of social welfares between urban and rural residents during 2021–2060. Our results indicate that employing the current methods for CGSP would lead to around 5.0 Mt CO2 reduction annually. since 2018, whereas adopting the extended methods would yield reductions of 157.1 Mt. Promoting forestry carbon sinks and energy-conserving appliances offers the most prominent emission reduction, while low-carbon travel shows the biggest potential going forward. CGSP will loosen carbon constraints on industries in the market and result in a lower carbon price, reducing the economic cost of carbon trading without compromising China's carbon peaking and carbon neutrality goals. Supplementing CGSP could reduce the GDP loss by up to 0.2 trillion Yuan by 2030 and 0.9 trillion Yuan by 2060, compared to the sole operation of compliance carbon trading market. Provided fiscal stimulus policies, CGSP will further reduce GDP loss by 1.1 trillion Yuan in 2060. Different fiscal stimulus scenarios have a strong implication on welfares of urban and rural residents, wherein monetary equivalent incentive will reduce urban-rural income gap by up to 718 Yuan/capita in 2060.

Information Management of Low-Carbon Travel Behavior

Low-carbon travel is widely recognized as an important strategy for reducing energy consumption, mitigating pollution emissions, and alleviating traffic congestion. This study utilizes a sample of 2167 residents from four Chinese cities and employs the Theory of Planned Behavior (TPB) in conjunction with Structural Equation Modeling (SEM) to obtain more information about the determinants of Low-carbon travel behavior (LTB). Key findings include: (1) The extended TPB proved to be highly applicable to the analysis of LTB, with perceived behavioral control (PBC) exhibiting the most influential factor, and the relationship between PBC and LTB is partially mediated. (2) Gender, education, and commuting distance positively affect LTB, while income, private car ownership, and possessing a driver's license demonstrate significant negative effects. (3) Concern for environmental quality significantly enhances LTB. In contrast, perceived traffic congestion significantly reduces LTB. Based on the empirical results, targeted and implementable policy recommendations are proposed.

Optimal design of bus schedule timing based on PID system

With more and more attention paid to the concept of low-carbon travel, public transport has gradually become the mainstream mode of travel. Nowadays many people will choose bus as a means of transportation. But how to make a reasonable bus departure schedule is a very complicated problem. Sometimes the departure interval is long, which leads to passengers having to wait for a long time, and sometimes there are not many passengers or even empty cars, resulting in a waste of resources. The purpose of this paper is to create a Proportional Integral Derivative (PID) controlled automated control system, which can adjust the bus departure schedule by the number of passengers on the bus in real time, to make a more reasonable bus schedule. Shorten the departure interval when the number of people is large and extend the departure interval when the number of people is small. This can greatly reduce the waiting time for passengers and reduce the waste of public resources when there are fewer people.

Electromagnetic Exposure Levels of Electric Vehicle Drive Motors to Passenger Wearing Cardiac Pacemakers.

The number of individuals wearing cardiac pacemakers is gradually increasing as the population ages and cardiovascular disease becomes highly prevalent. The safety of pacemaker wearers is of significant concern because they must ensure that the device properly functions in various life scenarios. Electric vehicles have become one of the most frequently used travel tools due to the gradual promotion of low-carbon travel policies in various countries. The electromagnetic environment inside the vehicle is highly complex during driving due to the integration of numerous high-power electrical devices inside the vehicle. In order to ensure the safety of this group, the paper takes passengers wearing cardiac pacemakers as the object and the electric vehicle drive motors as the exposure source. Calculation models, with the vehicle body, human body, heart, and cardiac pacemaker, are built. The induced electric field, specific absorption rate, and temperature changes in the passenger's body and heart are calculated by using the finite element method. Results show that the maximum value of the induced electric field of the passenger occurs at the ankle of the body, which is 60.3 mV/m. The value of the induced electric field of the heart is greater than that of the human trunk, and the maximum value (283 mV/m) is around the pacemaker electrode. The maximum specific absorption rate of the human body is 1.08 × 10-6 W/kg, and that of heart positioned near the electrode is 2.76 × 10-5 W/kg. In addition, the maximum temperature increases of the human torso, heart, and pacemaker are 0.16 × 10-5 °C, 0.4 × 10-6 °C, and 0.44 × 10-6 °C within 30 min, respectively. Accordingly, the induced electric field, specific absorption rate, and temperature rise in the human body and heart are less than the safety limits specified in the ICNIRP. The electric field intensity at the pacemaker electrode and the temperature rise of the pacemaker meet the requirements of the medical device standards of ICNIRP and ISO 14708-2. Consequently, the electromagnetic radiation from the motor operation in the electric vehicle does not pose a safety risk to the health of passengers wearing cardiac pacemakers in this paper. This study also contributes to advancing research on the electromagnetic environment of electric vehicles and provides guidance for ensuring the safe travel of individuals wearing cardiac pacemakers.

Diverse experiences by active travel for carbon neutrality: A longitudinal study of residential context, daily travel and experience types

Two key goals for sustainable spatial planning are to promote low-carbon travel in daily life and to enhance human wellbeing through diverse human-environment interactions. Yet, the integration of these goals has been underexplored. This study investigates the potential for experiential diversity via active travel in different residential contexts within the Gävle city-region, Sweden. Over 15 months, we collected spatiotemporal data from 165 participants, analyzing 4,362 reported experiences and 13,192 GPS-derived travel trajectories. Our analysis uncovered a significant spatial discrepancy: while the travelled distances to locations of positive experiences typically ranged from 1.5 km to 5 km, active travel predominated only within 1.5 km. This discrepancy persisted across urban, suburban, and peripheral contexts. Although residents in different contexts reported the same types of experiences, urban dwellers travelled about 50 % farther for nature experiences compared with other positive experiences, whereas peripheral dwellers travelled twice the distance for urbanicity experiences compared with other positive experiences. Consequently, urban residents mostly relied on active travel for urbanicity experiences and motorised travel for nature experiences, with the reverse trend observed among peripheral dwellers. These results illustrate the importance of spatial scale for promoting diverse positive experiences via active travel, regardless of residential context. Effective planning strategies may include enhancing environmental diversity near homes and developing infrastructure that favours active over motorised travel for short to moderate distances.

Shifting air travel demand: a case study on enabling experimentation in surface travel

This paper presents the first empirical evaluation of Climate Perks, an employee benefit scheme designed to reduce air travel demand by offering additional paid annual leave to those travelling without flying for vacation purposes. In-depth interviews and surveys were undertaken with employers that offer Climate Perks and travellers who have claimed journey days. The findings show the value of positive action to enable sustainable consumption in contributing to catalysing and mainstreaming low-carbon travel, drawing out key themes around recruiting travellers to surface travel, sharing knowledge and know-how, recrafting temporal experiences and learning-by doing to overcome infrastructural complexities of surface travel in a mobility system presently dominated by air travel. The paper also discusses how the boundaries around personal action on climate change are shifting as employers recognise the contribution of work to the formation of personal routines. This paper thereby contributes to understanding air travel demand and explores the potential for reducing this emissions intensive practice.

Can China's carbon generalized system of preferences reduce urban residents' carbon emissions? Evidence from a quasi-natural experiment

The carbon generalized system of preferences (CGSP) is an innovative incentive mechanism implemented by the Chinese government, which has also become an important part of carbon emission reduction at the living end, and it is of great significance to study whether the Pilot Policy can reduce the carbon emissions of residents. This study firstly accounts for the total carbon emissions and per capita carbon emissions of the residents of 284 cities in China, and on this basis, adopts the SCM method to quantitatively study and analyze the overall and local implementation effects of CGSP in China by taking the first batch of CGSP pilots in China as an example, and further applies the mediation effect model to test the pathways of the role of CGSP. The main findings of the study are as follows: (1) During the period of 2010–2020, the total carbon emissions from urban residents' living in China showed a yearly growth trend, from 36,623.98 ×10−2Mt in 2010–85,241.20 ×10−2Mt in 2020, an increase of 8.83%. Total carbon emissions present a structural difference of "electricity consumption > central heating > private transport > gas (oil, natural gas) consumption". (2) Overall, the implementation of the CGSP had a robust positive impact on the overall carbon emission reduction in the pilot cities, with an average annual emission reduction effect value of 36.53 ×10−2Mt. Locally, the annual net policy effect values of Dongguan, Zhongshan, Heyuan, and Guangzhou are 6169.79 ×10−2, 26,600.17 ×10−2, 17,081.34 ×10−2 and 9393.36 ×10−2Mt respectively. (3) CGSP has a good carbon emission reduction effect by suppressing the impact on residents' carbon emissions through enhancing the city's innovation capacity and promoting electricity saving and consumption reduction, while the mediating effect played by the promotion of green and low-carbon travel in the pilot policy is not significant. Finally, based on the research findings, relevant suggestions are targeted.

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

“An island on the edge of Europe”: A study on academic air travel in Finland through a combined model of practice

Although evidence about the environmental harm of air travel is growing, the goal of internationalization remains firmly built into academia. The aim of this article is to study this dilemma from both the institutional and individual levels. We seek to determine the elements of academic air travel from an online survey conducted at a Finnish research university. We utilize a combined model of practice, which merges the three-element model of practice theory and the levels of society approach. We also reflect upon the travel policies of all 14 Finnish research universities against the needs and wants of the individual academic.Our findings highlight that the core elements of academic air travel in the institution we studied are physical events, social interaction, technologies, and environmental values. Although virtual alternatives and slow travel are gaining momentum, more support is needed to replace the informal interactions associated with physical presence. In addition, the external pressure to travel, e.g., to undertake fieldwork or attend conferences, is limiting efforts toward low-carbon travel. We hypothesize that change is particularly difficult to induce with linkages between various levels of society. Our combined model of practice provides a tool to identify the elements of practice that can be influenced by the individual or require changes at the institutional or even the national levels. Finally, we note a mismatch between some forms of policy support between individuals and institutions, showing a need for more practical rather than theoretical support, especially with lowering academic expectations on the need to travel.

Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network

With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.

The inequality of household carbon footprint in China: A city-level analysis

Climate change mitigation and carbon inequality reduction are of great significance to the sustainable development. Most of the existing studies on China's household carbon footprint inequality are at provincial level and lack in-depth analysis of cross-dimension of consumption categories. We calculated the carbon footprints in 309 city-level areas in China based on the multi-regional input-output table, and further explored the inequality of household carbon footprints from perspective of cross-dimension with Gini coefficient and Lorentz curve based on the China Family Panel Studies dataset. We found that although the carbon footprint inequality of different areas, income groups and family size were relatively high on the whole, the level of that in different consumption categories showed significant different characters. For instance, affluent demographics possess the requisite financial capacity to acquire luxury apparel, whereas economically disadvantaged cohorts predominantly procure garments at standard price points. This dynamic may engender heightened carbon disparity within the realm of clothing, with a proclivity for greater imbalances observed in high-income strata as opposed to their low-income counterparts. Specific measures for guiding residents to rationally consume luxury food and allocate educational resources, and advocate low-carbon travel to eliminate household carbon footprint inequality in China are proposed.

Impact of social-psychological factors on low-carbon travel intention: Merging theory of planned behavior and value-belief-norm theory

Low-carbon travel assumes paramount significance in energy conservation and the establishment of an eco-friendly transportation ecosystem. This paper endeavors to explore the relationship between low-carbon travel intention, latent psychological variables, and sociodemographic attributes, drawing insights from responses of 602 residents in Hangzhou, China by structural equation model and multi-group model. In particular, we synthesize the theory of planned behavior, value-belief-norm theory, and view of incentive, a reflection of the public support for incentive policies. Results reveals that the primary determinants influencing the low-carbon travel intention encompass the view of incentive, attitude, and subjective norms. Individuals with diverse sociodemographic attributes manifest varying sensitivities, with males and elders exhibiting heightened responsiveness to incentive, while the presence of children decrease the attraction of incentive. These findings demonstrate that low-carbon travel intention can be increased by three ways, one is by the strong attraction of incentive especially tailor incentive policy, another is by making family-friendly policies to facilitate travel for groups with children, and the last is by improving the quality of low-carbon travel services thus increasing the attitude and other determinants.

Collaborative mechanism and simulation of low-carbon travel for residents in community-built environment based on evolutionary game

Given the worsening environmental pollution and China's carbon peak and carbon neutrality goals, it is imperative to study the low-carbon travel behavior of urban residents. Previous research has mainly focused on objective environmental factors, subjective behavioral factors, and low-carbon travel behavior. However, the evolving decision-making of local governments, community residents, and individuals within the low-carbon travel system still needs to be explored. Tripartite evolutionary game theory is an effective method for studying the dynamic changes in strategies among limited rationality multiple actors in long-term repeated games, making it applicable to this study. Therefore, this paper simulates participants' behavior using evolutionary game theory, conducts stability analysis of equilibrium points of the replicator dynamic system using the Lyapunov first method, derives evolutionarily stable strategy combinations under different conditions, and finally performs simulation analysis using Matlab. The simulation results indicate: (1) By implementing reasonable incentive mechanisms, Local governments can encourage citizens to adopt low-carbon travel and community transformation. They should pay attention to their expenditures and subsidies, manage their spending to reduce consumption, maximize efficiency, and strive to achieve carbon emission reduction goals. (2) The likelihood of residents choosing low-carbon travel is mainly influenced by direct advantages such as health and economy in the initial stages, and later, it is more affected by the indirect benefits brought about by subsequent objective environmental changes. (3) The cost of renovation significantly affects the built environment of communities, leading to a reduction in renovation costs and the adoption of uniform standards and, consequently, stimulating enthusiasm for community transformation. The research findings suggest that the government can collaborate with the public to reduce carbon emissions by implementing specific environmental changes and providing incentives to encourage citizens to participate in low-carbon transportation. Simultaneously, the government can cooperate with the public, considering the quality of environmentally friendly transportation for citizens, formulating uniform regulations, reducing renovation costs, and achieving the highest low-carbon goals with minimal financial expenditure.

Intention-to-use low-carbon travel modes - An investigation integrating Maslow's hierarchy of (driving) needs and the theory of planned behavior

The increasingly serious traffic congestion and damages on environment due to excess driving have brought considerable negative influences on the sustainable development of cities and urban residents’ well-being. The promotion of low-carbon travel modes is now necessarily required due to the environmentally-friendly and high-capacity features. This study attempts to investigate the mechanism underlying intention-to-use low-carbon travel modes based on an integration of Maslow's hierarchy of (driving) needs and the theory of planned behavior. A four-step analysis method, i.e., Exploratory factor analysis – Regression model – Bayesian network – Structural equation model, is proposed to (1) extract TPB constructs, (2) examine the hierarchical nature of perceived driving attributes, (3) identify the dependencies between driving needs, and (4) investigate influences on intention-to-use low-carbon travel modes. The final model results reflect only perceived social status at the top-level of driving hierarchy and perceived public responsibility to reduce driving at the bottom-level directly influence intention-to-use low-carbon travel modes, while other attributes have indirect effects through attitude, perceived behavioral control and subjective norm towards low-carbon travel modes. The results are expected to provide valuable implications on increasing the popularity of low-carbon travel modes by virtue of both “pull” and “push” measures.

The Impact of Environmental Policy Tools on Public Low-Carbon Travel in Xi'an

The Impact of Environmental Policy Tools on Public Low-Carbon Travel in Xi'an

New Energy Commuting Optimization under Low-Carbon Orientation: A Case Study of Xi’an Metropolitan Area

Low-carbon travel is an important part of low-carbon cities and low-carbon transportation, and low-carbon transportation is an inevitable choice to slow down the growth of carbon emissions in China. All countries in the world are actively promoting new energy vehicles and attach great importance to the application of the new energy industry in urban transportation. Commuting is an important part of urban life, and the choice of travel behavior has an important impact on traffic and environmental protection. Taking the Xi’an metropolitan area as an example, this paper expounds on the integrated development path of the industrial chain of new energy + travel in the metropolitan area and clarifies the energy transformation model of the integrated development of low-carbon transportation and energy. From the perspective of green and low-carbon, 1000 commuters were interviewed using a questionnaire survey, and the cumulative prospect model was used to verify the internal mechanism affecting commuters in metropolitan areas to choose new energy commuting. The results of the study show that new energy transportation modes play an important role in the low-carbon economy, and under different scenarios and assumptions, there are significant differences in the cumulative prospect values of the subway, new energy buses and fuel private cars, and corresponding optimization measures are proposed to increase the proportion of new energy commuting trips. The results will help further promote the development of a low-carbon economy and energy integration in the field of transportation and provide a reference for the sustainable development of public transportation.