ABSTRACT As a major source of national carbon emissions, the transportation sector plays a critical role in China’s decarbonization strategy. This study introduces a Koopman Grey Model (KGM) to analyze and forecast the development of China’s transportation industry. By embedding the Koopman operator into a state-space framework, the model captures multi-dimensional dynamics of complex systems. A flexible observation function integrates diverse accumulation methods and nonlinear fitting, while parameter estimation is optimized using the Coati Optimization Algorithm. These innovations enhance the adaptability of grey system modeling under small-sample and high-uncertainty conditions. Compared with traditional grey and statistical approaches, the KGM demonstrates superior predictive accuracy and robustness. Empirical results show that China’s transportation output will increase from 5.56 trillion RMB in 2024 to 8.39 trillion RMB by 2030, with an average annual growth rate of 6.6%. Energy consumption is projected to remain stable, while carbon emissions may rise moderately from 1.04 to 1.205 billion tons. This indicates a continuous decline in carbon intensity per unit of output, highlighting the sector’s transition toward greater sustainability and energy efficiency.

Transport accounts for about one-quarter of global energy-related carbon-dioxide (CO2) emissions. However, decarbonization policies only show incremental progress as most jurisdictions lack an integrated approach. Siloed thinking between the energy and transport sectors holds back the development of more effective policies for transport decarbonization, although previous work has only reported this anecdotally. We systematically identify diverging perspectives between energy and transport experts globally at all levels of government. Using thematic and frequency analysis of expert elicitation survey responses combined with machine learning, we find that (1) both sectors tend to have a narrow focus that neglects broader issues and has led to policy failures; (2) views differ on which energy sources are sustainable with somewhat less variation on how to prioritize decarbonization measures; (3) both sectors support increased communication and coordination for better outcomes and efficiency; and (4) most experts anticipate that transport decarbonization will be insufficient to achieve net zero by 2050, with views varying more based on affiliation type and region. These results provide a starting point for governments to bridge the divide between the sectors and to formulate adequate policies enabling transport decarbonization in line with global climate and sustainability goals.

Urban population growth is estimated to exceed 50% by 2050 in today's urban spaces. Therefore, the mobility patterns of people and objects become a fundamental element for planning, control, and decision-making in multimodal transportation. The use of an agnostic system that allows us to obtain the best combination of technologies and cognitive predictive inference models covering all areas of transportation (road, maritime, and air) without programming language limitations, supported by probability distribution functions on the entropic maximization theory of complex stochastic systems as the core model that could be incorporated into a machine learning logical architecture. It allows for selecting the most efficient, harmonious, and sustainable transportation trajectory. The methodology employed is exploratory-descriptive and theoretical, based on experiences implemented in other countries, and the incorporation from the coupling of Shannon theory with Gamma distribution functions in multivariate stochastic systems for the transportation sector as an innovative contribution of this work. A representative model of an intelligent agnostic logical architecture is presented, where the integration of the multivariate system is shown, nourishing the argument in the justification of the use, and could be taken as a proposal to be developed and implemented to reduce road congestion, reduce environmental pollution, and provide a sustainable alternative. The challenge is the understanding of this intelligent agnostic system by legislators in the transport area for the implementation of “IoT” devices in each transport unit and routes for connectivity to a "brain" that receives information from other areas of transport and walkers from their devices with high-speed technology in data navigation

Purpose Direct injection of methanol and diesel benefits over other fuel-air mixture preparation strategies. Design/methodology/approach This work investigates the effect of methanol and diesel injection pressure (IP) on combustion and emissions characteristics. CONVERGE CFD (computational fluid dynamics) was used for simulations, and the combustion model was validated with the experimental data. Findings The analysis found that 270 bar diesel IP and 230 bar methanol IP showed better engine performance. Practical implications Methanol can be an alternative fuel for internal combustion engines. Originality/value This work in original. Social implications Sustainable agriculture and transport sector development. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2025-0090/

BACKGROUND: Due to the rapid growth of the electrified transport sector, one of the key issues in designing electrified vehicles is determining the optimal parameters of hybrid energy systems. This work investigates an extra-large class urban passenger vehicle equipped with a hybrid energy system consisting of a rechargeable electrical energy storage system and an electrochemical generator. A method is proposed to synthesize optimal parameters of the hybrid energy system that accounts for the main design requirements and operational features of the studied vehicle. AIM: To determine the optimal parameters of a hybrid energy system for a vehicle, taking into account technical and operational parameters and the actual operating modes of an extra-large class urban passenger vehicle. METHODS: Optimization of the hybrid energy system parameters is performed using a global search optimization algorithm included in the GlobalToolbox package of MATLAB. Simulation modeling methods in Simulink are used to calculate the optimization criterion. RESULTS: The paper presents a formulation of the optimization problem for the hybrid energy system parameters, a description of the simulation mathematical model in Simulink, verification of the mathematical model against experimental data, and the results of synthesizing optimal parameters for different cell chemistries of the rechargeable electrical energy storage system. CONCLUSION: The practical value of this work is the possibility of using the proposed methodology for determining optimal parameters of hybrid energy systems in the design of commercial vehicles, in particular extra-large class passenger vehicles with a hybrid energy system based on an electrochemical generator and a rechargeable electrical energy storage system.

The primary objective of this study is to utilize the K-Nearest Neighbor Algorithm (KNN) to investigate the relationship between energy intensity and energy consumption across the Residential, Commercial, Industrial, Transportation, and electric power sectors. The paper approved the KNN Algorithm as more accurate than the remaining algorithms. The most influential factors affecting energy intensity are the Power Sector (61.89%), the Industrial Sector (24.62%), the Transportation Sector (8.5%), the Residential Sector (3.6%), and the Commercial Sector (1.3%). Consequently, the Industrial, Electric Power, and industrial Sectors have the most significant influence on energy intensity. Thus, enhancing the energy performance of these sectors can reduce energy intensity and maximize efficiency, leading to improved environmental sustainability.

The freight transport sector in Thailand’s Eastern Economic Corridor (EEC) is a major source of greenhouse gas (GHG) emissions due to its heavy dependence on diesel trucks. This study offers a scenario-based analysis of battery-electric truck (BET) deployment as a way to reduce emissions in the freight sector by 2030. Using official vehicle registration data, projected growth rates, and emission factors based on IPCC guidelines, three future scenarios are analyzed: A business-as-usual (BAU) case, a likely case assuming 30% BET adoption, and an extreme case assuming 50% adoption. Results show that emissions are expected to increase from 4.36 MtCO₂e to 5.17 MtCO₂e under the BAU scenario, while the probable and extreme BET scenarios could cut emissions to 3.62 and 2.58 MtCO₂e, respectively. The study also provides policy recommendations for each scenario, including financial incentives, investment in fast-charging infrastructure, zero-emission vehicle mandates, and grid integration strategies. These findings offer data-driven insights to support Thailand’s transition to low-carbon freight and highlight the EEC’s potential to serve as a model for sustainable logistics development across Southeast Asia.

Purpose The quest to lower global carbon emissions has urged scholars to explore the factors behind the rising carbon emissions. However, there is a dearth of empirical studies on how renewable energy and technical grants moderate the impact of manufacturing on carbon emissions. In this study, the effects of manufacturing, renewable energy and technical grants on per capita carbon emissions, emissions from the transport sector and emissions from the industrial sector are assessed for the economy of Kenya. Design/methodology/approach The study adopted the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used time series data from 1990 to 2023. Regression analyses were performed using the Fully Modified Ordinary Least Squares and Canonical Cointegrating Regression methods. Findings It was found that manufacturing growth increases all indicators of carbon footprint. The direct effect of renewable energy and technical grants is negative for per capita carbon emissions. They were also found to moderate the positive impact of manufacturing on carbon emissions. Research limitations/implications Support for renewable energy and the technical and managerial skills development of citizens are essential for reducing carbon emissions. Hence, Kenya should strengthen its partnerships with developed countries and global funding agencies through well-defined cooperation mechanisms, including bilateral and multilateral technical cooperation agreements that focus on clean energy and the exchange of knowledge. Originality/value The study’s value lies in the fact that it explores the role of technical grants in the fight against carbon emissions. It also focuses on the moderating role of renewable energy and technical cooperation grants in the carbon emission effects of the manufacturing sector.

The transportation sector's reliance on fossil fuels necessitates a transition towards sustainable alternatives like electric vehicles (EVs). While lithium-ion (Li-ion) batteries currently dominate the EV market, their limitations in charging time, thermal management, and resource sustainability motivate the exploration of advanced battery technologies. This research investigates the potential of graphene-enhanced batteries as a viable alternative for Li-ion batteries in EVs, focusing on enhancing charging efficiency and thermal management. A comparative analysis is conducted using a MATLAB-based simulation framework, modelling a graphene-enhanced battery system against a conventional Li-ion system based on considered reference of Tata Nexon EV Prime specifications. The simulations evaluate performance across various discharge rates (0.2 to 3C), analysing charging time, temperature profiles, charging efficiency, and temperature coefficients. The results demonstrate that graphene-enhanced batteries exhibit significantly faster charging times (22% - 27%), maintain lower operating temperatures (0.1to 5°C lower), and also offer the potential for substantial weight reduction i.e. 53% in the modelled simulation). These advancements, stemming from graphene's exceptional electrical and thermal conductivity, indicate a promising route toward the development of more efficient, safer, and higher-performing electric vehicles. This study provides quantitative insights into the benefits of graphene integration in EV battery technology, highlighting its potential to address key limitations of Li-ion batteries and contribute to a more sustainable transportation future.

Exposure to fine particulate matter (PM2.5) is a leading global health risk factor. Effective mitigation demands a multidimensional understanding that integrates chemical, source and region-level differences in PM2.5 toxicities relevant to human health. We present a standardized in vitro cellular assay dataset characterizing PM2.5 toxic potencies across emission sources, chemical constituents and atmospheric environments. Real-world PM2.5 samples from 23 major anthropogenic sources, covering industrial, transportation and residential sectors, were evaluated for cytotoxicity and oxidative stress potency, identifying key sources driving PM2.5-induced health risks. Toxic potency-adjusted concentrations of bioactive PM2.5 components, including polycyclic aromatic hydrocarbons, elemental carbon, metals, and non-metal species, were quantified to attribute overall PM2.5 toxicity to specific chemicals. Furthermore, the toxic potencies of ambient PM2.5 collected from selected urban and rural areas in China were identified, enabling the development of evaluation metrics for quantifying regional inequalities in PM2.5 health risks. This dataset establishes a universal toxicity benchmark for standardized comparisons of PM2.5 health impacts, providing a valuable resource for exposure assessment, source prioritization, and air quality risk evaluation.

Infrastructure and transportation companies experienced a decline in stock prices a few years ago. This can be triggered by inflation, interest rates, and the company's dividend policy. Inflation and interest rates that change every year and dividend policy which is a measurement of profits earned by companies. The purpose of this study was to look at how inflation, interest rates, and dividend policies affect a company's share price. This research involves all companies in the field of infrastructure and transportation, the sampling criteria are as follows: companies listed in the foam securities from 2020-2024, companies uploading financial statements in 2020-2024, and companies that regularly distribute dividends in 2020-2024. 12 companies were selected through purposive sampling. This study was descriptive and quantitative and analyzed using several tests: normality, multicollinearity, heteroscedasticity, autocorrelation, linear regression, and T (Partial) test. The data was processed with the SPSS 24 analysis tool. The results showed that if inflation does not affect stock prices as indicated by the t-test, then the inflation value is 0.561 > 0.05. The interest rate has no effect on the stock price, as evidenced by the T-test obtained an interest rate value of 0.278 > 0.05. Dividend policy affects stock price, as seen in the T-test of the SIG value of dividend policy of 0.004 < 0.05.

Achieving "carbon neutrality" in Japan is complicated by fragmented institutional responsibilities and the absence of municipal-scale energy data. Decarbonization is led by the Ministry of the Environment, reflecting a segmented administrative framework. Moreover, sectoral energy consumption data are not officially published at the municipal level, hindering evidence-based local policy development. To overcome these challenges, we have developed the Japan Energy Database, a comprehensive dataset of energy supply and demand estimates for all 1,741 municipalities. This open-access dataset enables: (1) locally grounded energy strategies and implementation, (2) assessment of regional renewable energy potential and utilization, and (3) evidence based support for resilient, sustainable social transitions. It integrates national statistics, spatial information, and engineering assumptions to estimate final energy consumption in transportation, industrial, residential, and commercial sectors. We detail the data-construction methodology and validate its internal consistency and policy relevance. The dataset provides a foundation for decentralized, data-driven energy planning and region-specific decarbonization pathways in Japan.

Economic assessment of biodiesel pathways for carbon neutrality: Scenario analysis of China’s transportation sector

Tax avoidance is an effort to reduce tax burdens in order to maximize profits in accordance with tax regulations. The purpose of this study is to confirm and collect empirical data on the impact of executive characteristics and thin capitalization on tax avoidance in the transportation sector listed on the IDX between 2020 and 2024. This study uses quantitative data. Through purposive sampling, a total of 275 samples were obtained from 55 transportation sector companies listed on the IDX between 2020 and 2024. This study uses secondary data as its data type. Using multiple linear regression analysis, the research findings show that thin capitalization, company size, and profitability have a significant positive effect on tax avoidance in transportation sector companies listed on the IDX for the years 2020-2024. Meanwhile, executive characteristics and sales growth do not significantly influence tax avoidance in transportation sector companies listed on the IDX for the years 2020-2024.

Against the backdrop of global climate change and carbon neutrality goals, the transportation sector has become a focal point in urban carbon emission research. This study develops a Spatiotemporal Geographically Weighted Regression (SGTWR) model that integrates spatial, temporal, and attribute similarity dimensions to identify the main driving factors of urban transportation carbon emissions (TCE) across 287 Chinese cities from 2000 to 2019. The model incorporates climatic and geographical variables to capture the spatiotemporal heterogeneity of emission patterns. The results indicate that population density, private vehicle ownership, and heating degree days have positive effects on TCE, while terrain elevation exhibits a mitigating effect. The SGTWR model demonstrates superior explanatory power and accuracy (adjusted R2 = 0.900) compared with traditional models, revealing significant spatial patterns and temporal trends in emission drivers. Based on coefficient clustering, six types of cities are identified, highlighting regional disparities in emission mechanisms. These findings provide methodological and theoretical support for formulating differentiated low-carbon transportation policies tailored to regional geographic and socio-economic contexts.

Efficiently supporting decision-making in a multi-pollutant environment.

Decarbonizing Pakistan’s transport sector: insights from water-energy nexus simulation by LEAP-WEAP integrated model

A diffusion-model-based approach for forecasting energy demand in New Zealand’s transport sector

Assessing carbon emissions and decarbonization potential of urban cold chain transport sector: A case study in Hefei city

Interplanetary interest in solar PV systems in automobiles has grown as renewable energy, especially in transportation subsystems, is used more widely. Emphasizing innovative control strategies to increase power conversion efficiency, reliability, and flexibility, this paper identifies and assesses solar photovoltaic integrated vehicle drive systems. In Simulink, several researchers replicate power systems, solar PV systems, vehicle propulsion systems, and power conversion technologies. To imitate real-world settings, researchers evaluate the efficiency of the device at many solar light and load values. High-level control techniques suitable in such unpredictable conditions are MPPT and dynamic load control. These controls are definitely required to ensure the correct functioning of the plant system, independent of natural variables, like irradiation and temperature. After that, the performance of the suggested control strategies is investigated under the main success criteria: energy analysis, system efficiency, and operational stability. This implies that solar PV integrated systems for automobiles could gain from ideal performance and durability, hence improving the off-grid operation of cars. These findings offered latent promise for use in the developing transportation sector and advancement of solar PV technology.