The multidisciplinary nature of City Logistics (CL) and evolving technological and organizational models make the state of the art fragmented. This work proposes a Systematic Literature Review (SLR) to uncover trends and gaps in CL initiatives focusing on impacts. A predefined set of keywords is used to search Scopus, considering papers published before 2024, and backward and forward snowballing are carried out. The resulting corpus is analysed to identify major CL initiatives and their impacts, while the initiatives are classified according to the Avoid-Shift-Improve (ASI) framework to clarify their intervention approaches. Results show that economic impacts are less studied than operational ones, partly because of difficulties in collecting and sharing sensitive economic information. In addition, there is a need to understand the economic and social potential of recent initiatives, such as drones and autonomous vehicles, which will become clearer with wider adoption. Logistics packaging innovations require further research due to increasing waste generated by e-commerce and the potential of initiatives like the Physical Internet and delivery robots. By explicitly considering trade-offs among impacts, the analysis moves beyond validating individual effects and supports the anticipation of recurrent tensions associated with different initiatives. This work encourages further research on CL initiatives by highlighting mature and underexplored solutions, considering the evolving nature of urban logistics driven by changing habits and sustainability awareness. Moreover, it offers stakeholders a comprehensive review of the existing initiatives to guide future investments and highlights the need for fully informed CL processes based on digital technologies.
Life Cycle Assessment (LCA) represents a robust framework for evaluating the environmental performance of recycling systems; however, its application to closed-loop strategies remains methodologically challenging, and standardized guidelines are still scarce. This article presents a systematic review of 196 recent papers (2020–2025) to analyze current methodological trends, identify key analytical choices across all industrial sectors, and provide recommendations for future assessments. The findings highlight a growing research interest, concentrated primarily in EU countries. However, substantial discrepancies emerge in how studies address technical modeling choices. Notably, there is a strong dominance of attributional over consequential approaches, frequent reporting omissions regarding allocation and inventory choices, and a widespread neglect of uncertainty analyses despite their importance in innovative contexts. Furthermore, scenario analyses predominantly compare different recycling pathways while neglecting alternative end-of-life options like incineration or landfilling, and rarely account for recycled material downcycling or technical substitutability. The study concludes that no clear consensus on a unified methodology exists across various industrial sectors. To enhance transparency, comparability, and decision-making utility of LCA in recycling systems, this paper outlines strategic recommendations and emphasizes the urgent need for harmonized, sector-specific guidelines.
This study examines whether the relationship between governance capacity and Sustainable Development Goal (SDG) performance differs across the Gulf Cooperation Council (GCC), non-GCC Middle East and North Africa (MENA), and the rest of the world. It also identifies which dimensions of governance are most strongly associated with SDG progress within the MENA region and whether these relationships vary across individual SDGs. The analysis uses an unbalanced panel of up to 165 countries covering 2016–2023. Governance is measured using the World Bank’s Worldwide Governance Indicators and is disaggregated through principal component analysis into two theoretically distinct dimensions: administrative capacity, comprising government effectiveness, regulatory quality, rule of law, and control of corruption; and political process, comprising voice and accountability and political stability. Two-way fixed-effects models with country and year effects are estimated, controlling for GDP per capita, foreign aid, and natural-resource rents. The results identify a “Gulf premium”: within-country improvements in administrative capacity are associated with stronger gains in aggregate SDG performance in GCC countries than in non-GCC MENA countries and the rest of the world. The differential is positive throughout but is not significant at the 5% level when 2023 is excluded or when the aggregate index is recomputed from the goal-level scores, so its magnitude remains imprecisely estimated. Within the MENA sample, control of corruption emerges as the strongest positive governance correlate of SDG performance, whereas political stability shows a negative association after accounting for the other governance dimensions. Results for individual SDGs further reveal that governance effects differ considerably across development domains. These findings demonstrate that the governance–SDG relationship is context dependent and that administrative capability can support rapid SDG implementation in high-capacity states. However, administrative strength should be complemented by participatory, accountable, and adaptive institutions to promote more inclusive and durable sustainability transitions.
For petrostates, achieving sustainable energy security presents a critical dimension for national security, where entrenched hydrocarbon dependence conflicts with long-term sustainability and resilience objectives. While technical pathways are often clear, the governance barriers to transition remain poorly theorized, particularly in petrostates. Therefore, this study examines how governance-related and structural factors within Iran’s energy system condition or constrain the achievement of sustainable energy security, using MICMAC structural analysis and Random Forest Regression. Moving beyond sectoral analyses, the study first uses MICMAC to identify the influence and dependence relationships among the key drivers of Iran’s energy-security system and to determine the position of governance-related factors within that system. Second, a Random Forest Regression is employed to assess the predictive importance of selected energy and economic factors and to provide an exploratory scenario-based projection of energy intensity toward 2030. The results show that energy policy, energy production and consumption, and climate change function as relay variables within Iran’s energy-security system, while economic growth and energy efficiency are identified as the influential variables. The Random Forest projection indicates that energy intensity is expected to reach 2.1791 MJ/$GDP by the end of 2030, with solar, wind, and hydro variables showing the highest predictive importance. Furthermore, the findings demonstrate that technical transition pathways alone are insufficient unless the structural and governance conditions shaping Iran’s energy system are addressed to support sustainable energy security.
This study examines factors associated with business students’ self-reported use of Meta AI-supported learning tools in academic tasks. A quantitative cross-sectional survey was conducted with 400 business students from higher education institutions in Bangladesh who had prior academic experience using Meta AI-supported assistance. The study applied a TAM-informed adoption framework and tested the effects of ethical AI practices, personalization, trust in technology, and perceived value on technology use behavior using partial least squares structural equation modeling. The results show that all four predictors were positively associated with technology use behavior under one-tailed directional testing. Perceived value had the strongest relationship with technology use behavior, followed by personalization, ethical AI practices, and trust in technology. Effect size results further showed that perceived value made the largest contribution to students’ reported use. The findings suggest that students’ use of Meta AI-supported learning tools is primarily shaped by perceived academic benefit, while personalization, ethical AI practices, and trust provide additional adoption conditions. The study contributes adoption-level evidence on AI-supported learning in business education. The findings should be interpreted within the limits of a cross-sectional, self-reported design and do not establish effects on learning outcomes, academic performance, or educational equity.
