Search Results (530)

The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO₂ emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO₂ emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO₂ emissions. This framework aids policymakers in making informed decisions and setting precise investments. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

To support carbon peaking and neutrality goals in the city transportation sector, this paper accounts for CO₂ emissions from the transport sector in Xuzhou City, North Jiangsu Province, from 1995 to 2023. This study explores the relationship between transport-related carbon emissions and economic growth, using the TAPIO decoupling index. Meanwhile, a carbon emission prediction model based on the STIRPAT framework is constructed, with scenario analysis applied to forecast future emissions. Results show three decoupling stages: the first, dominated by weak and expansive negative decoupling, reflects extensive economic growth; the second features weak decoupling with expansive coupling, indicating a more environmentally coordinated phase; the third transitions from expansive negative decoupling and weak decoupling to strong decoupling and expansive coupling, suggesting a shift in development patterns. Under the baseline, low-carbon, and enhanced low-carbon scenarios, by 2030, the CO₂ emissions of the transportation industry in Xuzhou will be 10,154,700 tons, 9,072,500 tons, and 8,835,000 tons, respectively, and the CO₂ emissions under the low-carbon scenario and the enhanced low-carbon scenario will be reduced by 10.66% and 13.00%, respectively. Based on these findings, the study proposes carbon reduction strategies for Xuzhou’s transport sector, focusing on policy regulation, energy use, and structural adjustments. Full article

Vehicle emissions, as a source of air pollution and greenhouse gases, have a significant impact on the environment and climate change. Battery electric vehicles (BEVs) have the potential to reduce air pollution and GHGs. However, BEVs often attract the criticism that their benefits are minimal as the power plant emissions compensate for emissions from the tailpipes of vehicles. This study compared two scenarios: scenario A considers all vehicles as internal combustion engine vehicles (ICEVs), and scenario B considers all vehicles as BEVs. The study used the City of San Antonio, Texas, as the study area. The study also focused on the seasonal and spatial variation in ICEV emissions. The results indicate that scenario A has a considerably higher volume of emissions than scenario B. For ICEVs, PM2.5 emissions were up to 50% higher in rural areas than urban areas, but 45% lower for unrestricted versus restricted conditions. CO₂ emissions were highly affected by seasonal variations, with a 51% decrease from winter to summer. The full adoption of BEVs could reduce CO₂ and N₂O emissions by 99% and 58% per km, especially for natural gas power resources. Therefore, BEVs play a significant role in reducing emissions from the transportation sector. Full article

The transportation sector significantly contributes to greenhouse gas (GHG) emissions and remains a key research focus on emission quantification and mitigation. Although numerous models exist for estimating vehicle-based emissions, most lack accuracy at regional scales, particularly in remote or underdeveloped areas, including backcountry national parks and mountainous regions lacking basic infrastructure. This study addresses that gap by developing and applying a terrain-adjusted, segment-based methodology to estimate GHG emissions from tourist vehicles in Altai Tavan Bogd National Park, one of Mongolia’s most remote protected areas. The proposed method uses Tier 1 IPCC emission factors but incorporates field-segmented route analysis, vehicle categorization, and terrain-based fuel adjustments to achieve a spatially disaggregated Tier 1 approach. Results show that carbon dioxide (CO₂) emissions increased from 118.7 tons in 2018 to 2239 tons in 2024. Tourist vehicle entries increased from 712 in 2018 to 13,192 in 2024, with 99.1% of entries occurring between May and October. Over the same period, cumulative methane (CH₄) and nitrous oxide (N₂O) emissions were estimated at 300.9 kg and 45.75 kg, respectively. This modular approach is especially suitable for high-altitude, infrastructure-limited regions where real-time emissions monitoring is not feasible. By integrating localized travel patterns with global frameworks such as the IPCC 2006 Guidelines, this model enables more precise and context-sensitive GHG estimates from vehicles in national parks and similar environments. Full article

The primary objective of this paper is to quantify a realistic pathway for Canada to reach net-zero emissions by 2050. This study analyzed greenhouse gas (GHG) emissions from the 10 provinces and 3 territories of Canada based on the emissions from their economic sectors. A time series analysis was performed to understand the trajectory of the emissions profile from 1990 to 2023. Using the 2023 emissions as the baseline, a linear reduction, based on the GHG proportions from each jurisdiction, was performed and projected to 2050 (except for Prince Edward Island (PEI), where net zero was targeted for 2040). Moreover, a machine learning technique (k-means unsupervised algorithm) was used to group all the jurisdictions into homogeneous regions for national strategic climate policy initiatives. The within-cluster sum of squares identified the following clusters: Cluster 1: Manitoba (MB), New Brunswick, Nova Scotia, and Newfoundland and Labrador; Cluster 2: Alberta (AB); Cluster 3: Quebec (QC) and Saskatchewan; Cluster 4: Ontario (ON); and Cluster 5: PEI, Northwest Territories, Nunavut, and Northwest Territories. Considering the maximum GHG reductions needed per cluster (Clusters 1–5), the results show that 0.309 Mt CO₂ eq/year, 5.447 Mt CO₂ eq/year, 1.293 Mt CO₂ eq/year, 2.217 Mt CO₂ eq/year, and 0.04 Mt CO₂ eq/year must be targeted from MB (transportation), AB (stationary combustion), QC (transportation), ON (stationary combustion) and PEI (transportation), respectively. The concept of climate stabilization wedges, which provides a practical framework for addressing the monumental challenge of mitigating climate change, was introduced to each derived region to cut GHG emissions in Canada through tangible, measurable actions that is specific to each sector/cluster. The clustering-based method breaks climate mitigation problems down into manageable pieces by grouping the jurisdictions into efficient regions that can be managed effectively by fostering collaboration across jurisdictions and economic sectors. Actionable and strategic recommendations were made within each province to reach the goal of net-zero. The implications of this study for policy and climate action include the fact that actionable strategies and tailored policies are applied to each cluster’s emission profile and economic sector, ensuring equitable and effective climate mitigation strategies in Canada. Full article

In Japan’s hilly and mountainous areas, which cover over 60% of the national land area, issues such as population outflow, aging, and regional decline are intensifying. This study explored sustainable decarbonization pathways by examining two representative regions (Maniwa City and Hidakagawa Town), while accounting for diverse regional characteristics. A bottom-up approach was adopted to calculate energy consumption and CO₂ emissions within residential and transportation sectors. Six future scenarios were developed to evaluate emission trends and countermeasure effectiveness in different regions. The key findings are as follows: (1) in the study areas, complex regional issues have resulted in relatively high current levels of CO₂ emissions in these sectors, and conditions may worsen without intervention; (2) if the current trends continue, per-capita CO₂ emissions in both regions are projected to decrease by only around 40% by 2050 compared to 2020 levels; (3) under enhanced countermeasure scenarios, CO₂ emissions could be reduced by >99%, indicating that regional decarbonization is achievable. This study provides reliable information for designing localized sustainability strategies in small-scale, under-researched areas, while highlighting the need for region-specific countermeasures. Furthermore, the findings contribute to the realization of multiple Sustainable Development Goals (SDGs), particularly goals 7, 11, and 13. Full article

This study aims to explore the factors and mechanisms influencing the low-carbon development of Green Port Collection and Distribution Systems (GPCDSs) and to identify effective pathways and policy approaches to promote such development. Given the limited prior research integrating low-carbon policies, energy structure, and transportation systems, this study combines these three dimensions into a unified analytical framework. A scenario-based system dynamics model of GPCDS low-carbon development is established, incorporating factors such as low-carbon policies, energy structure, and transportation structure. The control variable method is employed to examine system behavior under 13 scenarios. The results indicate that freight subsidy policies and the internalization of carbon emission costs make the most substantial contributions to low-carbon development in GPCDS, yielding CO₂ emission reductions of 14.3% and 15.7%, respectively. Additionally, improvements in port railway infrastructure contribute to a 6.4% reduction in CO₂ emissions. In contrast, carbon taxes and energy structure adjustments have relatively limited effects, likely due to the delayed responsiveness of fossil fuel-dependent transportation sectors to pricing signals and the inherent inertia in transitioning energy systems. Full article

Electric passenger vehicles are set to dominate the European car market, driven by EU climate policies and the 2035 ban on internal combustion engine production. This study assesses the sustainability of this transition, focusing on global warming potential and Critical Raw Material (CRM) extraction throughout its life cycle. The intensive use of CRMs raises environmental, economic, social, and geopolitical concerns. These materials are scarce and are concentrated in a few politically sensitive regions, leaving the EU highly dependent on external suppliers. The extraction, transport, and refining of CRMs and battery production are high-emission processes that contribute to climate change and pose risks to ecosystems and human health. A Life Cycle Assessment (LCA) was conducted, using OpenLCA software and the Ecoinvent 3.10 database, comparing a Peugeot 308 in its diesel and electric versions. This study adopts a cradle-to-grave approach, analyzing three phases: production, utilization, and end-of-life treatment. Key indicators included Global Warming Potential (GWP100) and Abiotic Resource Depletion Potential (ADP) to assess CO₂ emissions and mineral resource consumption. Technological advancements could mitigate mineral depletion concerns. Li-ion battery recycling is still underdeveloped, but has high recovery potential, with the sector expected to expand significantly. Moreover, repurposing used Li-ion batteries for stationary energy storage in renewable energy systems can extend their lifespan by over a decade, decreasing the demand for new batteries. Such innovations underscore the potential for a more sustainable electric vehicle industry. Full article

The promotion of new energy vehicles (NEVs) represents a critical strategy for mitigating carbon emissions and air pollution. To evaluate the CO₂ and air pollutant reduction potential of NEVs in the Beijing–Tianjin–Hebei region, this study developed an integrated framework combining gray correlation analysis (GRA) and bidirectional long short-term memory (BiLSTM), referred to as the GRA-BiLSTM model, to forecast the adoption trend of NEVs and calculate the CO₂ and air pollutant emission reduction. The GRA-BiLSTM model developed in this study shows optimal predictive performance. The results indicate that new energy vehicles (NEVs) have great potential for environmental collaborative emission reduction in the transportation sector: it is predicted that by 2035, the total number of NEVs will be nearly 11.88 million, with a cumulative reduction of 2.76 billion tons of carbon emissions and significant reductions in various key air pollutants. This study provides an important quantitative basis for formulating pollution reduction and carbon reduction policies in the transportation sector. Full article

Road transportation is a major contributor to greenhouse gas (GHG) emissions in Thailand. This study assesses the potential for GHG mitigation in the road transport sector from 2018 to 2030. Emission factors for various vehicle types and technologies were derived using the International Vehicle Emissions (IVE) model. Emissions were then estimated based on country-specific vehicle data. In the baseline year 2018, total emissions were estimated at 23,914.02 GgCO₂eq, primarily from pickups (24.38%), trucks (20.96%), passenger cars (19.48%), and buses (16.95%). Multiple mitigation scenarios were evaluated, including the adoption of electric vehicles (EVs), improvements in fuel efficiency, and a shift to renewable energy. Results indicate that transitioning all newly registered passenger cars (PCs) to EVs while phasing out older models could lead to a 16.42% reduction in total GHG emissions by 2030. The most effective integrated scenario, combining the expansion of electric vehicles with improvements in internal combustion engine efficiency, could achieve a 41.96% reduction, equivalent to 18,378.04 GgCO₂eq. These findings highlight the importance of clean technology deployment and fuel transition policies in meeting Thailand’s climate goals, while providing a valuable database to support strategic planning and implementation. Full article

Conventional ammonia production uses fossil-based hydrogen, resulting in high greenhouse gas emissions. Given the growing demand for sustainable solutions, it is essential to replace fossil hydrogen with renewable alternatives. This study assessed the technical, economic, and environmental viability of renewable ammonia production in Minas Gerais. To this end, an optimization model based on mixed integer linear programming (MILP) was developed and implemented in LINGO 20^® software. The model incorporated investment costs; raw materials; transportation; emissions; and indicators such as NPV, payback, and minimum sale price. Hydrogen production routes integrated into the Haber–Bosch process were analyzed: biomass gasification (GS_WGS), anaerobic digestion of vinasse (Vinasse_BD_SMR), ethanol reforming (Ethanol_ESR), and electrolysis (PEM_electrolysis). Vinasse_BD_SMR showed the lowest costs and the greatest economic viability, with a payback of just 2 years, due to the use of vinasse waste as a raw material. In contrast, the electrolysis-based route had the longest payback time (8 years), mainly due to the high cost of the electrolyzers. The substitution of conventional hydrogen made it possible to avoid 580,000 t CO₂ eq/year for a plant capacity of 200,000 t NH₃/year, which represents 13% of the Brazilian emissions from the nitrogenated fertilizer sector. It can be concluded that the viability of renewable ammonia depends on the choice of hydrogen source and logistical optimization and is essential for reducing emissions at large scale. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article

Electric vehicles (EVs) play a critical role in the transition to transportation electrification and are important for achieving carbon neutrality in this sector. China currently leads the world in EV ownership; however, the energy regulation potential of in-use batteries remains largely untapped in the context of an increasingly saturated EV stock. This study systematically evaluates the long-term benefits of vehicle-to-everything (V2X) applications based on EV sales projections and advancements in battery technology. The results indicate that, without compromising daily travel requirements, V2X applications could enable 109.50–422.37 TWh of annual electricity dispatch by 2030, achieving an estimated economic benefit of 198.92–767.25 billion CNY, and reducing carbon dioxide (CO₂) emissions by 45.01–173.60 Mt. By 2060, these figures are projected to increase significantly, with annual dispatchable electricity reaching 4217.39–21,689.43 TWh, generating an economic value of 10.82–55.66 trillion CNY, and reducing CO₂ emissions by 118.09–607.30 Mt. Furthermore, V2X applications could substantially contribute to achieving the emission reduction targets outlined in China’s Nationally Determined Contributions (NDCs). These findings highlight that V2X applications, as a transformative solution that promotes deep integration between the transportation and power sectors, enhance cross-sectoral emission reduction synergies and support the realization of carbon neutrality goals. Full article

The growing interest in alternative fuels stems from the need to reduce greenhouse gas emissions and promote sustainable development. Despite the dominance of fossil fuels in aviation, pulsejet engines offer a promising platform for testing new fuels due to their simple design and fuel versatility. This study presents a multi-criteria analysis of alternative fuels for use in pulsejet engines, emphasizing environmental impacts. Both gaseous (biogas, ethyne, LPG, and natural gas) and liquid fuels (methanol, ethanol, biodiesel, Jet A-1, and SAF) were examined. Exhaust emissions (CO₂, H₂O, CO) were simulated in Ansys 2025 based on literature data and chemical calculations. Additional factors analyzed included calorific value, production cost, thermal expansion, density, life cycle emissions (LCA), CO₂ emissions per fuel mass, and renewable energy content. Using the zero-unitization method, results were normalized into a single aggregate variable for each fuel. The highest values were recorded for biogas and methanol, respectively, indicating their potential as alternative fuels. The findings support further development of sustainable fuels for pulsejet engines. Future research should address combustion optimization and noise reduction, enhancing viability in aviation and other transport sectors. Integration with the current fuel infrastructure is also recommended to facilitate broader implementation. Full article