Transport CO2 Emissions Research Articles

Environmental sustainability of urban expansion: Implications for transport emissions, air pollution, and city growth.

This study examines the environmental impacts of urban growth in Warsaw since 2006 and models the implications of future urban development for traffic pollutant emissions and pollution levels. Our findings demonstrate that, over the past two decades, urban sprawl has resulted in decreases in accessibility to public transport, social services, and natural areas. We analyse CO2 traffic emissions, NO2 concentrations, and population exposure across urban areas in future scenarios of further sprawling or alternative compacting land-use development. Results indicate that a compact future scenario reduces transport CO2 emissions and urban NO2 levels, though increases in population density raise exposure to air pollution. A sprawl future scenario increases CO2 and NOx emissions due to longer commutes and congestion, and NO2 levels increase up to 25% in parts of the city. Several traffic abatement strategies were simulated, and in all simulations a compact city consistently yields the largest reductions in CO2 emissions and NO2 levels, implying that the best abatement strategy for combating negative consequences of sprawl is to reduce sprawling. In both city layouts, network-wide improvements of public transport travel times gave significantly reduced emissions. Combined, our findings highlight the importance of co-beneficial urban planning strategies to balance CO2 emissions reduction, and air pollution exposure in expanding cities.

Passive Thermal Management System for Electric-Hybrid Vehicles - Hot Climate Condition

Abstract To achieve net-zero emissions targets, electric vehicles (EVs) and hybrid electric vehicles (HEVs) are prioritized for reducing CO₂ emissions in transportation. Among the available battery systems, lithium-based batteries are the most prominent due to their high energy storage density. The primary safety risk in lithium-ion batteries is thermal instability caused by overheating. Battery capacity drops significantly at operating temperatures above 45°C. At higher temperatures, the battery undergoes thermal decomposition, and once it reaches a critical temperature, it enters an irreversible state of thermal instability, which can lead to an explosion. In our previous study, we developed flexible phase-change material (PCM) packages for passive thermal energy storage (TES) of heat from lithium-ion batteries in HEVs/EVs, extending battery life and ensuring safety. In this paper, we present the results of applying these PCM packages under hot climate conditions. The test results show that both paraffin and composite PCMs can maintain lower temperatures compared to battery modules without PCM, ensuring safe operation. The PCM composite, which includes highly conductive materials, demonstrated better thermal performance than paraffin alone.

Hydrogen as an alternative energy source in railway vehicles

The potential of hydrogen related to the popularization of alternative energy sources is confirmed by the "Green Deal" implemented by the European Commission, which aims to reduce CO2 emissions in transport by 30% by 2030 and ultimately achieve climate neutrality by 2050. Additionally, the geopolitical situation in the world , characterized by increasingly difficult access to conventional energy sources, resulting in increasing costs of their purchase, confirms the validity of taking up the topic in the area of ​​hydrogen drives in railway vehicles. The article classifies fuels that power railway vehicles. Hydrogen fuel is presented as an alternative to powering railway vehicles. A review of railway vehicles and countries that currently have and are introducing hydrogen-powered vehicles. The advantages and threats of introducing hydrogen as a power source in railway vehicles are presented. Plans in Poland regarding the construction of hydrogen-powered railway vehicles and the planned construction of infrastructure accompanying the fuel in question were presented. The legal requirements accompanying the introduction of a railway vehicle powered by hydrogen fuel into operation in Poland are presented.

Climate Risks and Economic Consequences of Rising Global CO2 Emissions in Aviation, Shipping, and Heavy-Duty Transport

This study examines methods to lessen the environmental consequences of global CO2 emissions from the hard-to-abate transport sector. The paper analyzed historical and projected trends in global CO2 emissions from the hard-to-abate transport industry under two scenarios: the stated policy scenario (STEPS) and the announced pledged scenario (APS). The study covered the historical period from 2010 to 2022 and projected emissions up to 2050. The analysis revealed that the compound annual growth rate (CAAGR) of STEP exceeded that of APS in 2030 and 2050 for challenging emissions from heavy- duty vehicles, aircraft, and shipping when compared to the baseline year of 2022. The aviation industry has a higher CAAGR of 5.3% and 2.5% for 2030 and 2050, respectively, compared to heavy-duty vehicles at 1.6% and 0.9% for 2030 and 2050, respectively, and shipping at 0.7% and 0.9% for 2030 and 2050, respectively, under STEPS. Under the APS scenario, shipping showed a negative CAAGR of -0.8% and -2.8% for 2030 and 2050, respectively, and 0.4% and -1.8% for 2030 and 2050, respectively, for heavy-duty trucks. In comparison, the aviation industry had CAAGRs of 4.5% and 0.8% for 2030 and 2050, respectively. The data shows that the aviation industry is expected to see a far greater CAAGR in emissions than heavy-duty vehicles and shipping in both STEPS and APS scenarios. Targeted efforts are necessary to mitigate the environmental effects of air travel in the upcoming decades. The paper also examined 56 publicly traded international transportation companies and their corresponding carbon emission targets. Only seven companies, or 12.5%, have established goals for reducing emissions from 2023 to 2050; 10 companies, accounting for 17.9%, have committed to achieving carbon neutrality by 2040 to 2060; five corporations, representing 8.9%, have set targets for reducing emission intensity from 2025 to 2034; and 34 global corporations, making up 60.7%, have committed to achieving net zero emissions between 2040 and 2050. Despite some progress in setting emission reduction targets in the air travel industry, many companies still need to set carbon footprint reduction goals.

Decoupling transport-CO2 emissions: Mexico, Spain and The USA: A trend analysis

Existing methodologies to measure (de)coupling among economic activity, surface transport (road and rail), and CO2 emissions have not captured the complexity of decoupling. This study aims to contribute to the understanding of (de)coupling. Three groups of variables were formed (using 17 variables). By relating each variable to the others through a ratio, an indicator is obtained that corresponds to a coupling-decoupling category. In total, 36 indicators (by country) were analysed for the three countries. The results reveal that transport volume changes proportionately more than economic activity (materialization). Conversely, CO2 emissions change proportionately less than do economic activity and surface transport volume (decarbonization), although Mexico requires greater efforts. Additionally, decoupling is tangible at aggregated levels, while coupling is evident at disaggregated levels. Finally, the combination of these features contributes to the formulation of policies for achieving sustainable transport. One limitation of this study is the exclusion of air and maritime transport.

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.