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The expert contributors explore how decisions are made at different stages in mega-projects and the multi-actor relationships between public and private partners. They evaluate the perspectives and pitfalls in determining the costs and benefits of a mega-project ex ante, and examine the wider impacts of mega-projects, including issues such as regional growth, energy transition and climate change. Although the focus is on the advanced economies of North America, Europe, and Australia, much of the material is useful for other parts of the world where large transport infrastructure projects are currently underway or will be developed in the coming years. Providing crucial background information for those who want to understand decision-making processes on large transport infrastructure projects, this fascinating Handbook will prove an important source of information for academics, researchers and students in the fields of transport, infrastructure, project management, management science, economic analysis (cost-benefit analysis), public policy, environmental policy and ethics. Practitioners, politicians and policymakers involved in large transport infrastructure projects will also find this book to be an invaluable reference tool. -- Publisher description.

Energy efficiency gains are advocated to be a plausible strategy to mitigate rising carbon emissions in the Indian transportation sector. This study, thus, estimates the energy efficiency across transportation modes in India for 2000–2014, employing the panel stochastic frontier approach. Further, the long-run effect of energy efficiency gains on carbon emissions is also examined by employing the panel fully modified least square (FMOLS) and panel dynamic ordinary least square (DOLS) estimators. The empirical findings indicate an inverted U-shaped trend in energy efficiency for land transportation and a substantial rise for air transportation with higher volatility. However, the trend in energy efficiency for water transportation only shows a minor uptick with nearly stable movement. The long-run effect reveals that a 1% increase in energy efficiency will reduce carbon emissions in the transportation sector by more than 1%, between 1.343 (FMOLS) and 1.665% (DOLS). Based on such findings, a few implications are discussed to achieve a low-carbon energy system.

PurposeThis study aims to examine the roles of board gender and cultural diversities in driving social sustainability practices through the moderating effect of board structure policies in the logistics and transportation sector.Design/methodology/approachThe authors conducted fixed-effects regression with 2005–2019 data from Thomson Reuters Eikon.FindingsThe results showed that female directors are significant predictors of social sustainability across the four dimensions of human rights, workforce, product responsibility and community development. Additionally, directors with different cultural backgrounds (but not the workforce) are significant determinants of community development, human rights and product responsibility. Furthermore, although board structure policies positively moderate the relationship between board gender diversity and social sustainability, they fail to moderate the relationship between board cultural diversity and social sustainability.Originality/valueThe findings have crucial implications for the logistics and transportation sector's social sustainability and may help the sector align with employees' and society's expectations. The incorporation of board gender and cultural diversities into the research design was a response to calls by the European Union (EU) and the United Nations (UN) to address board configuration and stakeholders' concerns.

The transportation sector accounts for more than 70% of Nigeria’s energy consumption. This sector has been the major consumer of fossil fuels in the past 20 years. In this study, the technical and economic feasibility of an electrical vehicle (EV) charging scheme is investigated based on the availability of renewable energy (RE) sources in six sites representing diverse geographic and climatic conditions in Nigeria. The HOMER Pro® microgrid software with the grid-search and proprietary derivative-free optimization techniques is used to assess the viability of the proposed EV charging scheme. The PV/WT/battery charging station with a quantity of two WT, 174 kW of PV panels, a quantity of 380 batteries storage, and a converter of 109 kW located in Sokoto provide the best economic metrics with the lowest NPC, electricity cost, and initial costs of USD547,717, USD0.211/kWh, and USD449,134, respectively. The optimal charging scheme is able to reliably satisfy most of the EV charging demand as it presents a small percentage of the unmet load, which is the lowest when compared with the corresponding values of the other charging stations. Moreover, the optimal charging system in all six locations is able to sufficiently meet the EV charge requirement with maximum uptime. A sensitivity analysis was conducted to check the robustness of the optimum charging scheme. This sensitivity analysis reveals that the technical and economic performance indicators of the optimum charging station are sensitive to the changes in the sensitivity variables. Furthermore, the outcomes ensure that the hybrid system of RE sources and EVs can minimize carbon and other pollutant emissions. The results and findings in this study can be implemented by all relevant parties involved to accelerate the development of EVs not only in Nigeria but also in other parts of the African continent and the rest of the world.

China’s transport carbon emissions are increasing quickly and the issue of emission reduction is urgent. This article aims to calculate and decompose China’s transport carbon emissions during 2001–2019. It first calculates the China’s transport carbon emissions by IPCC carbon emission factor method, and then applies the Logarithmic Mean Divisia Index (LMDI) model for decomposition analysis. The conclusion indicates that: (1) Diesel, gasoline, kerosene, and fuel oil are the major energy sources used in China’s transport sector, with the combined consumption of diesel and gasoline exceeding 70%, and the annual growth rate of energy consumption reached 8.92% during 2000–2019. Among them, natural gas and liquefied petroleum gas (LPG) have the fastest growth rate, while the only one showing a downward trend is raw coal, indicating that China’s transportation energy structure is being optimized. (2) Although China’s transport carbon emissions have been increasing, the growth rate has declined since 2013. The proportion of carbon emissions from kerosene, diesel, natural gas, and LPG increased from 2000 to 2019, while that of raw coal, gasoline, and fuel oil decreased. This suggests that the use of clean energy, air transportation, and large-scale transportation is increasing, while the use of heavily polluting fuels and small-scale road transportation is decreasing. (3) Per capita GDP is the driving factor that has the most influence on the increase of China’s transport carbon emissions. Population positively influences transportation carbon emissions too, but issues such as aging, low fertility rates, and insufficient labor force may change the direction of the impact in the next 30 years. (4) The negative effect of energy intensity on transport carbon emissions is the greatest, followed by industrial structure and energy structure. The development of highways, new energy vehicles, railway electrification, multimodal transportation, third-party logistics, and logistics information technology in China has improved the energy structure, reduced energy intensity, and brought China’s transport sector into an important stage of innovation driven and pursuit of coordinated development with the environment.

PurposeRecent trends in total quality management (TQM) argue in favor of incorporating environmental concerns into TQM and considering external stakeholders. The aim of this study is to bring environmental consciousness in the soft TQM dimension of human resource management (HRM) and assess its interrelationship with stakeholder integration towards achieving a competitive advantage.Design/methodology/approachAn empirical study was conducted in the transportation sector, specifically targeting managers in Greek shipping companies involved in global cargo transport and vessel operations. A structured questionnaire was administered, yielding 109 responses. The collected data were analyzed using partial least squares structural equation modeling.FindingsThe findings reveal the positive effect of both green HRM (GHRM) and stakeholder integration on the innovation differentiation advantage and market differentiation advantage of shipping companies. Results confirm the complementary (partial) mediating effect of GHRM in the relationship between stakeholder integration and both types of competitive advantage.Research limitations/implicationsThe primary limitation resides in data collection exclusively from shipping companies in Greece. A longitudinal approach would be beneficial for examining how the relationship between variables changes over time.Practical implicationsThe findings of the study could assist shipping managers in their decisions to allocate resources for developing GHRM practices and for involving stakeholders in organizational practices to overcome competition.Originality/valueThis study contributes to the discourse on TQM by empirically investigating the combined impact of GHRM and stakeholder integration on competitive advantage – an aspect that has been relatively overlooked in existing literature.

The concentration of carbon dioxide (CO2) in Earth's atmosphere is increasing due to human activities and the resulting effects on the global climate system have initiated several policy‐driven approaches to reduce emissions of this greenhouse gas. Quantifying the effectiveness of such policies requires both bottom‐up and top‐down approaches to estimate CO2 emissions. This work investigates, for the first time, the potential of using Snapshot Area Map observations from NASA's OCO‐3 instrument to disaggregate sector‐specific emissions from instrument observations. Optimized sector‐specific timeseries were produced using Bayesian inversion techniques and compared to proxy activity data from transportation, commercial maritime, and industrial sectors in the Los Angeles Basin. Results demonstrate that dense space‐based observations of atmospheric CO2 are capable of disentangling sector‐specific CO2 fluxes, paving the way for accurate monitoring of the effects of carbon‐reduction policies and operational carbon monitoring systems. Plain Language Summary Carbon dioxide (CO2) is a key greenhouse gas and several local‐to‐international policies are in place to reduce the amount being emitted by human activities. This work investigates the amount of CO2 emitted within the Los Angeles Basin during the period between January 2020 and December 2021 using NASA's Orbiting Carbon Observatory‐3. The observed CO2 is used to assess contributions from specific sectors (on‐road transportation, industrial sources, commercial maritime activity, etc.). The results of this work demonstrate that urban CO2 emissions observed from space‐based instrumentation can be disaggregated to several socioeconomic sectors to study trends that may be present in each one. Notable detected features include the sudden reduction of on‐road CO2 emissions due to the COVID‐19 lockdown period and the steady increase in off‐shore emissions due to ship idling and delays. The effectiveness of current and future policies regarding sector‐specific reductions have the potential to be observed over time using the framework presented here. Key Points NASA's OCO‐3 instrument provides the densest spatial coverage of urban XCO2 from space, which includes information on spatially variant surface fluxes. We show this spatial coverage makes it possible to disaggregate sectoral emissions information from observations Using OCO‐3 and the Los Angeles Basin as a case study, three emission sectors from an emission inventory are optimized to include effects from COVID‐19 lockdowns. In two contributing sectors, On‐road Transportation and Industry, optimized CO2 flux decreased considerably around the time COVID‐19 lockdowns were implemented in the Los Angeles area. In the third sector, Maritime Transportation, optimized CO2 flux steadily increased over time The timeseries of optimized fluxes followed sector‐specific proxy data

Along with the development of urbanization and informationization, an increasing attention has been attracted to CO 2 emissions of China’s transportation sector and its influencing factors. Such researches mainly utilize single indicator or two indicators to represent technology process. This research aims to verify the influence of technology-environmental innovation indicator system on CO 2 emissions of China’s transportation sector by decoupling elasticity and econometric model. We firstly recognize the decoupling status of CO 2 emissions of China’s transportation sector from social economic development and aggregate China’s 30 provinces into two groups according to the varied decoupling status, namely expansive coupling and weak decoupling groups. Then, we develop a relatively comprehensive technology-environmental innovation indicator system to measure technology process. Finally, the multi-region comparison of emission drivers is studied among overall China and the two groups. The result shows that the decoupling elasticity of China’s transportation has experienced an evolution process trending to desired development status and all the provinces have experienced expansive coupling and weak decoupling from 2001 to 2016, except Qinghai. Innovation performance indicators exert most important influence on the CO 2 emissions of transportation sector. Finally, the influences of technology-environmental innovation indicators are similar across groups with different magnitude, suggesting that common but differentiated strategies should be provided when mitigating CO 2 emissions with technology process. Graphical abstract

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.