Transport CO2 Emissions Research Articles

Beyond short-term impact of COVID-19 on transport decarbonization: a scenario analysis of passenger and freight transport by mode in China, 2020–2030

BackgroundThe processes of transport decarbonisation are complicated. In this paper, we adopt the Activity-Modal Share-Energy Intensity-Carbon Intensity of Fuel (ASIF) approach and propose a conceptual framework on the direct and indirect impact of COVID-19 on transport CO2 emissions. In the Chinese context, changes of carbon emissions associated with passenger and freight transport (including urban, rural, and inter-city transport) across different transport modes are estimated. Scenario analysis is then used to estimate the impact of COVID-19 on total transport carbon emissions up to 2030. Four scenarios, from minimal to significant behavioural changes and global recession associated with COVID-19, are generated.ResultsUnder the pandemic, the transport system in China was estimated to have produced 28% less CO2 emissions (1044.2 Mt) in 2020, when compared to 2019. Compared with the business-as-usual scenario, the estimated total transport carbon emissions in 2030 would drop by 6%, 15%, and 21% and 23% under the minimal-impact, low-impact, moderate-impact, and severe-impact scenarios, respectively.ConclusionsThe results suggest that the processes triggered by COVID-19 alone will not be sufficient to meet the ambitious transport decarbonisation targets. To meet China’s pledge under the United Nations Framework on Climate Change, the medium-term effects of COVID-19 must be combined with strong transport decarbonisation measures of modal shift and new energy applications. With these additional measures, it may be possible to advance the transport carbon peak before 2030. Lessons are relevant to other developing countries.

Vehicle To Building (V2b) Peak Load Shaving and Tariff Analysis

Abstract Commercial buildings are essentially important energy consumers for national electricity grid provider as it’s owned by businesses owners that directly keep up the national gross domestic product (GDP). Soaring load demand from commercial buildings usually occurs within few hours on peak business hour. Electricity grid infrastructure designed to support maximum demand of the system but underutilize most of the time outside the peak session. Growing number of EV penetration in local market can serve as mobile energy storage for Vehicle to Building (V2B) energy integration thus enable peak load shaving to minimize maximum demand during peak period. This study presents a feasible methodology approach on determining the suitable V2B tariff on several peak load shaving scenarios to provide attractive return to building owner and discounted off-peak tariff to accommodate lower EV owner’s charging cost. By having the combination of attractive V2B and off-peak tariff that benefits both sides, subsequently increase EV penetration for V2B. It may also charm EV markets as it minimizes the total cost of EV ownership and reduce maximum demand from electricity grid. Hence it reduces the transportation CO2 emissions and contribution toward optimization of grid network electrical infrastructure of transmission and distribution systems design with lower maximum design system requirement.

Urban transport emission prediction analysis through machine learning and deep learning techniques

About 6.6 million people die every year from air pollution diseases globally. Transportation industry is considered one of the leading contributors in air pollution. This research utilizes deep learning and machine learning techniques to predict China’s transport-related CO2 emissions and energy needs by utilizing variables like population, car kilometers, year and GDP per capita. The outcomes have been analyzed using six analytical measures: determination coefficient, RMSE, relative RMSE, mean absolute percentage error, mean bias error and mean absolute bias error. Findings indicate that yearly increase in transport-related CO2 emissions in China will be 3.66%, and transport energy consumption will increase by 3.8%. Energy consumption and transport CO2 emissions are projected to rise by roughly 3.5 times by 2050 as compared to current levels. Therefore, government should re-evaluate its energy investment plans for the future and institute new rules, and standards regarding transport-related energy consumption and pollution reduction.

Nonlinear relationship between urban form and transport CO2 emissions: Evidence from Chinese cities based on machine learning

Nonlinear relationship between urban form and transport CO2 emissions: Evidence from Chinese cities based on machine learning

Factor decomposition analysis of urban transport CO2 emissions in Chinese mega cities: case study of Beijing, Shanghai, Guangzhou and Shenzhen

Factor decomposition analysis of urban transport CO2 emissions in Chinese mega cities: case study of Beijing, Shanghai, Guangzhou and Shenzhen

Development Pathways and the Political Economy of Maladaptation: The Case of Bioenergy as a Climate Strategy in Brazil

AbstractAlthough it is well known that large-scale bioenergy expansion erodes different environmental, social, and economic dimensions of sustainable development, in countries like Brazil, bioenergy is institutionalized as a flagship climate strategy aimed to cut down CO2 emissions in transport. These trade-offs have serious implications for climate change governance and sustainable development; however, conventional approaches have not yet properly explained this seeming paradox. This article addresses this gap from a critical development pathways approach to bioenergy as a maladaptive strategy in Brazil. I propose an analytical framework to observe how different ideas, interests, and institutions interplay in the historical institutionalization of bioenergy as a climate strategy. The analysis shows that bioenergy institutionalization has been driven by the endemic economic crisis in the sugar sector and governmental interests associated with security and developmental imperatives. The unsustainable co-evolution of development pathways and bioenergy, marked by deforestation, land colonization, and agricultural expansion, has narrowed the adaptation space in agriculture, gearing current climate policy towards path-dependent maladaptive strategies like bioenergy. Paradoxically, framing bioenergy as a climate strategy has been useful to justify more expansive policies in favor of the sugarcane industry, and to greenwash the Brazilian climate policy in the international arena of climate governance.

Low-carbon efficiency analysis of rail-water multimodal transport based on cross efficiency network DEA approach

Multimodal transport is an effective method to reduce the energy consumption and carbon dioxide (CO2) emissions in transportation sector. Evaluating the low-carbon performance is a prerequisite for developing multimodal transport. This paper establishes the new cross-efficiency (CE) network data envelopment analysis (DEA) approach, and applies it to evaluate low-carbon efficiency of railway-waterway intermodal transportation (RIT) in China from 2017 to 2022. The proposed approach takes advantage of network DEA and considers the internal structure in the RIT system as well as the interconnections between various transportation modes. This article comprehensively analyzes the evaluation results in terms of the overall situation, geographic distribution characteristics, internal stages and links, external influencing factors, and efficiency prediction, and the results find that: (1) the low-carbon efficiency of China's overall RIT needs further improvement. (2) The geographical distribution shows a trend that the efficiency in the Northern Region is higher than that in the Southern Region. (3) The level of urban industry, external transportation conditions, and foreign trade development will all affect the performance of RIT. Finally, according to the empirical results, management strategies are proposed. The main contributions include: proposing a network-structured CE-DEA method focusing on internal states; The advantages of CE network DEA are utilized to study CO2 emissions of multimodal transport; The low-carbon efficiency of RIT in China is comprehensively analyzed. This study enriches the research on DEA methods, and the results can provide a basis for decision-making by the government and transport enterprises, help promote the development of low-carbon transport in China, and provide a reference for the optimization of multimodal transport in other countries.

Drivers of transportation CO2 emissions and their changing patterns: Empirical results from 18 countries

Transportation continues to be a significant contributor to CO2 emissions and may potentially be the final sector to reach its carbon peak in the future. Identifying the drivers of transportation CO2 emissions (TCE) and understanding their changing patterns is crucial to effectively control TCE. However, previous studies can only obtain fixed parameter values of TCE influencing factors throughout the study period, or although they can obtain the impacts of specific factors on TCE with accompanying changes over the years, they cannot conveniently clarify the changing patterns. Therefore, the key contribution of this study resides in providing a spatially explicit understanding of the heterogeneous primary drivers of TCE across countries, and in uncovering the temporal dynamics of these primary drivers' influences on TCE. The results show that at the country-group level (considering 18 selected countries as a group, collectively representing over 60% of global TCE), the drivers of TCE were GDP, energy intensity, and population in order of contribution. However, for developed countries, GDP and energy intensity contributed less to TCE than for developing countries. In addition, the influence of energy intensity on TCE declined faster than that of GDP, suggesting that decoupling TCE from economic growth should always be the top priority regardless of a country's development level. Policy-wise, for countries where GDP is the primary driver of TCE, measures to reduce transportation activities include industrial upgrading, coordinated planning, and accessibility promotion. For countries where energy intensity is the primary driver of TCE, measures to improve transportation efficiency consist of technology adoption, regulation/pricing, and habit improvement.

Forecasting Thailand’s Transportation CO2 Emissions: A Comparison among Artificial Intelligent Models

Forecasting Thailand’s Transportation CO2 Emissions: A Comparison among Artificial Intelligent Models

Defining provincial responsibility for controlling transportation CO2 emissions in China: As fairness perspective

Transportation CO2 emissions (TCE) in China account for a high proportion and are growing rapidly, making this sector likely one of the last to achieve carbon peaking nationally. However, directly comparing absolute TCE values to measure the responsibility of TCE control targets for each province in future years would be unfair due to substantial disparities between provinces in population, affluence, and technology characteristics. To address this issue, this study establishes an equitable and efficient framework to help policymakers determine province-specific and year-specific TCE benchmark and policy values. The results show that the less developed regions face greater challenges in controlling TCE per unit of GDP, and they should focus more on the use of shifting and improvement measures to reduce TCE. In contrast, the developed provinces bear a higher responsibility for TCE control in the near future; thereby, they need to implement an appropriate combination of measures.

Do patents, renewable energies and energy taxes in the transport sector reduce transportation carbon emissions in the European Union?

ABSTRACT Transportation infrastructure plays a significant role in exacerbating climate change as it contributes to a substantial increase in carbon dioxide (CO2) emissions. The sole economic sector in which European countries have not succeeded in reducing greenhouse gas (GHG)emissions is the transportation industry. Therefore, reducing transport-related emissions is a primary focus of emphasis in the European Union’s efforts to meet its climate targets. Accordingly, this study analyzes the impact of renewable energy use, patent development, and energy taxes in the transport sector on the three different modes of transport-related emissions (aviation, road, and rail) in the 10 highest-income countries of the European Union over the period 2008–2020. The study uses the novel half-panel jackknife estimator for this analysis. The results of the estimation show that patents contribute to the reduction of CO2 emissions in aviation and rail transportation, while renewable energies are only effective in rail transportation. In the context of road transportation, energy taxes are effective in mitigating CO2 emissions. Based on these findings, it is recommended that European Union policymakers promote the use of electric vehicles and eco- friendly means of transportation in road transport through energy taxes, increase the use of renewable energy sources such as biodiesel in rail transport, and reduce CO2 emissions by supporting patents that promote green innovations in the aviation sector.

Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model

The transportation sector plays a pivotal role in China’s efforts to achieve CO2 reduction targets. As the capital of China, Beijing has the responsibility to lead the era’s demand for low-carbon development and provide replicable and scalable low-carbon transportation development experience and knowledge for other cities in China. This study calculates the CO2 emissions of the transportation sector in Beijing from 1999 to 2019, constructs an extended STIRPAT model (population, affluence, technology, and efficiency), employs ridge regression to mitigate the effects of multicollinearity among the eight indicators, reveals the extent and direction of influence exerted by different indicators on CO2 emissions, and predicts the development trends, peak times, and quantities of transportation CO2 emissions in nine scenarios for Beijing from 2021 to 2035. Finally, adaptive low-carbon planning strategies are proposed for Beijing pertaining to population size and structure, industrial layout optimization, urban functional reorganization and adjustment, transportation infrastructure allocation, technological research and promotion, energy transition planning, and regional collaborative development. The results are as follows: (1) The total amount of CO2 emissions from Beijing’s transportation sector exhibits a trend of gradually stabilizing in terms of growth, with a corresponding gradual deceleration in the rate of increase. Kerosene, gasoline, and diesel are the main sources of transportation CO2 emissions in Beijing, with an annual average proportion of 95.78%. (2) The degree of influence of the indicators on transportation CO2 emissions, in descending order, is energy intensity, per capita GDP, population size, GDP by transportation sector, total transportation turnover, public transportation efficiency, possession of private vehicles, and clean energy structure. Among them, the proportion of clean energy structure and public transportation efficiency are negatively correlated with transportation CO2 emissions, while the remaining indicators are positively correlated. (3) In the nine predicted scenarios, all scenarios, except scenario 2 and scenario 4, can achieve CO2 emission peaks by 2030, while scenarios 7 and 9 can reach the peak as early as 2025. (4) The significant advancement and application of green carbon reduction technologies have profound implications, as they can effectively offset the impacts of population, economy, and efficiency indicators under extensive development. Effective population control, sustainable economic development, and transportation efficiency improvement are viable means to help achieve carbon peaking and peak value in the transportation sector.

What are the stimulants on transportation carbon dioxide emissions?: A nation-level analysis

The transportation sector is the second-largest contributor to CO2 emissions globally. To develop a low-carbon country, identifying factors influencing transportation CO2 emissions on a country-by-country basis is necessary due to differences in travel behaviors and transportation infrastructures. This study identifies the CO2 emission stimulants across 145 countries. The first analysis uses stepwise and robust regressions to provide a holistic perspective. The second analysis employs the Least Absolute Shrinkage and Selection Operator (LASSO) regression after categorizing 145 countries based on their economic levels and geographical regions to obtain more detailed results. An additional LASSO regression involves grouping countries based on their OECD memberships and economic levels. The findings suggest that socio-demographics, travel characteristics, and logistics have distinct impacts on CO2 emissions at different economic levels of countries. This study reveals strong correlations between the increased share of cycling and decreased CO2 emissions, especially in all developed countries and Asian developing countries. In most countries, higher gasoline prices are associated with lower CO2 emissions. Moreover, the rise in electric vehicle usage is contributing to lower CO2 emissions in developed countries. These findings contribute to the global efforts to address transportation-related carbon emissions at the national level.

Sustainable urban farming using a two‐phase multi‐objective and multi‐criteria decision‐making approach

AbstractFor several reasons, recent attention has focused on urban agriculture in the context of sustainable and smart agriculture. Most of the global population has relocated from rural to urban areas. The ecological impact of agriculture is a rising concern. Furthermore, food insecurity, particularly food availability, remains a significant issue. Satisfying rising food demand with minimal environmental impact is a significant barrier to more sustainable food production. In this article, we study a sustainable location for urban farming that optimally balances economic and environmental objectives. We formulate the problem as a multi‐objective linear program that considers maximizing the ecological benefit and crop production yield and minimizing transportation cost, sensor cost, and CO2 emissions. The proposed mathematical model is then solved using a two‐phase method. The first phase uses several multi‐objective optimization (MOO) methods (weighted sum, epsilon‐constraint, augmented epsilon) to generate a pool of compromise solutions. The second phase uses multi‐criteria decision‐making (MCDM) methods (MARCOS, VIKOR, and Possibility Degree) to rank compromise solutions. We performed a sensitivity analysis first by studying the effect of different criteria weights (balanced, environmentally, and economically oriented) and then by investigating the rank reversal. Furthermore, we developed a thorough validity test that examines the impact of the dynamic elements of rank reversal by changing the importance of the model's input parameters. To provide a fusion ranking of the MCDM rankings, we devised an aggregated ranking approach using the half‐quadratic (HQ) method.

Energy price reform to mitigate transportation carbon emissions in oil-rich economies

This study examines the impact of domestic fuel prices, population, and economic activity on transport CO2 emissions, employing Saudi Arabia as a case study. The research uncovers statistically significant long-term associations between these variables. Despite transport CO2 emissions demonstrating slight responsiveness to fuel price alterations, with estimated elasticity values between – 0.1 and – 0.15, the study affirms the relevance and timeliness of the Saudi government's strategy to curtail fuel incentives. Projections for a 2030 scenario, encompassing heightened economic activity aspirations and further escalations in domestic fuel prices to mirror true market costs, revealed a 1.8 percent annual reduction in transport CO2 emissions from 2021 to 2030 compared to a scenario with unchanging fuel prices. The insights from this study bear significance not only for Saudi Arabia but also for other oil-rich nations striving to pave the way toward a sustainable transportation future.

Assessment of Implementing Green Logistics Principles in Railway Transport: The Case of Lithuania

Today, green transport is a growing trend in terms of transport costs, CO2 emissions and satisfaction with logistics services. Green logistics is an environmentally friendly and sustainable logistics system that encompasses activities affecting logistics functions and processes, promoting the environment and the development of a circular economy. Rail transport is considered to be one of the least polluting modes of transport, but the fact that only electrified rail represents the concept of green logistics has been underemphasised, as the fleet consisting of diesel trains is a major challenge. This article aims to investigate the implementation of green logistics in railway transport in the case of Lithuania. The scientific literature, SWOT analysis, expert evaluation and parameter correlation methods were used to achieve this goal. An analysis of the internal and external factors of Lithuanian Railways in terms of green logistics has identified the main strengths of rail transport. These are environmental friendliness, electric trains reducing CO2 emissions, a strong focus on circular economy and others. The main weaknesses include the more extensive use of diesel trains, competition, etc. The external factors of the company show that the main opportunities are the reduction in net CO2 emissions to zero, the acquisition of more electric locomotives and others, while the main threats include an increase in energy costs or the complexity of implementing new technologies. An analysis of these factors has led to the construction of a SWOT matrix, which shows that the company’s strategy in the field of green logistics is quite strong, with more strengths than weaknesses. The results made it possible to identify the links between the individual implementation principles of green logistics in railway transport and provide strategic guidelines for the successful integration of green logistic principles into railway transport in Lithuania.

SVD-based adaptive fuzzy for generalized transportation

This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems.

COMBUSTIBLES DE AVIACIÓN SOSTENIBLES

The commercial aviation sector emitted 915 million tonnes of CO2 in 2019, 2% of human activity emissions and 12% of transport CO2 emissions. The sector is thus committed to mitigating its impact on climate change.

Modelling the impact of sublingual immunotherapy versus subcutaneous immunotherapy on patient travel time and CO2 emissions in Sweden

In Sweden, allergy immunotherapy (AIT) is available as either subcutaneous immunotherapy (SCIT) injections or sublingual immunotherapy (SLIT) tablets and is used to treat moderate-severe allergic rhinitis (AR). This study sought to determine treatment-related CO2 emissions and travel times in Swedish patients receiving either SCIT or SLIT-tablets. A list of specialized Swedish AR clinics that administer AIT was determined, and respective co-ordinates retrieved. Swedish municipality population data were obtained from a national database. The mean distance from each Swedish municipality to the nearest AR clinic was calculated, adjusted using a detour index, and weighted by estimated patient population size. Transport modality data were obtained from a Swedish urban transport study and CO2 emissions were obtained from Government sources. The mean number of annual SLIT-tablets and SCIT doses required were calculated based on product labels and clinical expert input. The annual number of healthcare professional interactions were layered into the model to estimate changes in mean patient travel time, distance, and travel-related CO2 emissions associated with using SCIT versus SLIT-tablets. Mean annual travel-related CO2 emissions were 410 tonnes (to two significant figures [s.f.]; standard deviation [SD] 90) with SLIT-tablets, versus 1700 tonnes (SD 380) for SCIT, resulting in mean annual savings of approximately 1300 tonnes (SD 290) of CO2 if all AIT patients were to receive SLIT-tablets instead of SCIT, over 380 times greater than 2021 average Swedish CO2 emissions per capita. Approximate mean annual travel times for patients taking SLIT-tablets were 66,500 h (three s.f.; SD 14,400), and 278,000 h (SD 60,200) for SCIT, resulting in mean annual savings of 211,000 h (SD 45,800) if all AIT patients were to receive SLIT-tablets instead of SCIT. Compared with SCIT injections, SLIT-tablets led to substantial reductions in treatment-related CO2 emissions and travel times for Swedish patients.

Scenario simulation and synergistic effect analysis of CO2 and atmospheric pollutant emission reduction in urban transport sector: A case study of Chengdu, China

Realizing coordinated control of CO2 and atmospheric pollutants in comprehensive transportation in large cities is an important way to promote sustainable urban development. Based on the LEAP framework, this study established a transport CO2 and atmospheric pollutant emission analysis model for Chengdu from 2021 to 2040, set up three main scenarios for the study of carbon peaking trend and 12 sub-scenarios for the study of the synergistic effect of various emission reduction measures. In addition, the synergistic effects were analyzed by radar charts, grading charts of synergistic effects and the synergetic equivalence index. The results show that under the Green Scenario (GS) and the Strengthen Green Scenario (SGS), the CO2 emissions from comprehensive transportation in Chengdu will peak at 26.774 × 106 t and 23.507 × 106 t in 2032 and 2029, respectively. The emissions of other atmospheric pollutants show a general downward trend. Under SGS, The measures that play the greatest role in reducing CO2, CO, VOCs, and NOx emissions by 2040 are NEV (New energy conversion of vehicles), Among them, the CO2 emission reduction rate relative to BS scenario is 20.7%. While the measures that play the greatest role in reducing PM and SO2 emissions are IES (Improvement of emission standards) and SRT (Strengthen railway transportation), respectively. Compared with other measures, the synergistic effect of SRT is the most highest. The emission reduction effect of NEV is most significant, with a synergistic effect level above level II. Under the joint regulation of multiple measures by GS and SGS, the synergistic equivalence index based on air quality standards has been effectively controlled, showing a downward trend. Overall, Chengdu faces challenges due to a large number of motor vehicles and air passenger throughput. Therefore, vigorously developing hydrogen vehicles and electric vehicles, strengthening railway construction in Chengdu, transferring the pressure of civil aviation and road transportation to railway, can make significant contributions to the pollution reduction and carbon reduction of Chengdu's transportation sector.