The achievement of carbon peaking and carbon neutrality has been identified as a central strategic priority for sustainable development in China. As a key national energy base and ecological barrier, the Yellow River basin has been subjected to substantial pressure for carbon emission mitigation. To elucidate the underlying driving mechanisms of carbon emissions, a panel data-based grey relational analysis framework was constructed using data from nine provinces (autonomous regions) within the basin over the period 2010–2021. The correlation intensity between carbon emissions and five principal influencing factors—regional gross domestic product, energy mix, added value of the secondary industry, year-end permanent resident population, and urban population—was quantified from temporal, cross-sectional, and integrated perspectives. The results indicate that the comprehensive grey relational degree follows the order: energy mix (0.90) > year-end permanent resident population (0.86) > urban population (0.85) > added value of the secondary industry (0.82) > regional gross domestic product (0.77), with energy mix identified as the dominant driver of carbon emissions. From a temporal perspective, the correlation between regional gross domestic product and carbon emissions was observed to decline steadily from 0.87 in 2010 to 0.68 in 2021, suggesting a progressive decoupling of economic growth from carbon emissions. In contrast, the correlation associated with energy mix remained consistently above 0.88, indicating limited advancement in energy structure transformation. Cross-sectional analysis reveals pronounced regional heterogeneity, with emission-driving patterns categorized into three types: energy–industry dominated, population–urbanization driven, and multi-factor integrated. These findings provide robust empirical evidence for the formulation of differentiated carbon mitigation strategies and offer critical insights for advancing ecological protection and high-quality development within the Yellow River basin.
The poverty-reducing potential of agricultural employment, in conjunction with export-oriented technological innovation, was critically examined within the context of Bangladesh’s agrarian economy. Particular emphasis was placed on the extent to which agricultural research and development outputs—including technology transfer, patent generation, and research dissemination—contribute to gross domestic product per capita growth, a key proxy for economic development. It was demonstrated that export-oriented agricultural technologies were significantly associated with economic growth. In contrast, general government expenditure on agricultural research and development, when implemented without clear innovation-oriented objectives, was shown to exert a limited effect on economic growth. The findings suggest that the effectiveness of research and development investment is contingent not on scale alone, but on the capacity to generate scalable, market-oriented technological outputs. Moreover, structural dimensions of poverty were addressed by illustrating how a technology-driven and employment-intensive agricultural system functioned as a critical mechanism for inclusive development. It was further observed that poverty reduction was deeply embedded within broader institutional, economic, and policy frameworks, necessitating coordinated interventions that integrate technological advancement with equitable resource distribution. The analysis underscores the importance of targeted policy design aimed at fostering innovation ecosystems that prioritize export competitiveness, rural employment generation, and sustainable income growth. Such an approach is argued to facilitate a transition toward a more resilient, inclusive, and self-sustaining development trajectory in Bangladesh.
The long-term viability of fossil-based energy systems is closely linked to the net energy they deliver to society. However, many widely cited estimates of oil energy return on investment (EROI) rely on data that no longer reflect current production structures. In particular, the rapid expansion of shale oil in the US has fundamentally altered the relationship between production costs, market prices, and net energy yields. This study revisits recent EROI values for oil in the US and Russia by examining their relationship with production costs and observed price dynamics, together with projected trends in EROI decline. A complementary assessment based on monetary return on investment (MROI) is also conducted to capture the economic dimension of energy extraction. To place these findings in a broader context, the analysis considers long-term changes in the gold-to-oil price ratio and compares the energy content of crude oil with the mechanical output of human labor. The results indicate that effective net energy returns from oil continue to decline, with implications that extend beyond production economics. In the case of the US, the analysis points to an emerging net energy deficit when recent production structures are taken into account. These developments suggest increasing constraints on the capacity of oil to support sustained economic activity. The findings underline the need to reassess the role of oil within future energy systems, particularly in light of growing concerns regarding resource limits and long-term sustainability.
The impact of environmental, social, and governance practices on firm performance and firm value remains contested, particularly in emerging markets where sustainability adoption is still evolving. In this study, the relationship between environmental, social, and governance practices, firm performance, and firm value was empirically examined, with a comparative focus on consumer non-cyclical and consumer cyclical sectors in Indonesia. A total of 298 firms listed on the Indonesia Stock Exchange in 2023 were analyzed, comprising 132 consumer non-cyclical and 166 consumer cyclical firms. Environmental, social, and governance performance was operationalized using disclosure-based indicators derived from the Global Reporting Initiative standards. A structural equation modeling approach based on the generalized structured component analysis was employed. Measurement model evaluation was conducted and overall model fit and structural relationships were assessed. Following the removal of invalid indicators, all constructs satisfied validity and reliability requirements, and acceptable model fit was achieved across both sectors. However, the results indicate that environmental, social, and governance practices do not have a statistically significant effect on either firm performance or firm value in both sectors. These findings suggest that environmental, social, and governance implementation in Indonesian consumer sectors remains at an early stage, where disclosure practices have not yet translated into measurable economic outcomes. Furthermore, caution should be exercised by investors when interpreting environmental, social, and governance disclosures as indicators of short-term financial performance. This study contributes to the environmental, social, and governance literature by providing evidence from an emerging market context and highlights the need for more substantive and performance-oriented sustainability integration.
Eutrophication, defined as excessive nutrient enrichment in aquatic ecosystems, has been increasingly recognized as one of the most critical drivers of freshwater and coastal ecosystem degradation worldwide. A qualitative research framework based on systematic literature synthesis, statistical data interpretation, and comparative regional case analysis was employed to examine the systemic drivers and socio-ecological consequences of nutrient pollution. Case studies from India and the United States were comparatively analyzed in order to identify recurring patterns of nutrient loading and ecological outcomes. It is demonstrated that excessive nutrient inputs substantially increase the frequency and severity of harmful algal blooms, which subsequently contribute to hypoxic or anoxic conditions, biodiversity loss, and the structural destabilization of aquatic food webs. These ecological transformations are shown to generate cascading socio-economic impacts, particularly for fishing-dependent and agrarian communities whose livelihoods are directly linked to aquatic ecosystem services. The analysis further indicates that climate change amplifies nutrient cycling dynamics and accelerates eutrophication processes. To address these challenges, integrated mitigation strategies emphasizing watershed-scale nutrient management, improved wastewater treatment infrastructure, and strengthened environmental governance were critically evaluated. Community-based resource management and participatory water governance mechanisms were identified as essential components for enhancing ecological resilience and long-term sustainability. The findings highlight the necessity of systemic policy reforms that prioritize nutrient pollution control, sustainable agricultural practices, and coordinated water governance frameworks in order to mitigate the escalating environmental and socio-economic consequences of eutrophication.
The integration of digital technologies into circular economy systems has been increasingly promoted within the European Union as a pathway for accelerating climate change mitigation and resource efficiency. However, the environmental implications of large-scale digitalization remain insufficiently understood. In this study, the mitigation potential and systemic risks associated with digital technologies in circular economy transitions were systematically evaluated. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was conducted, through which 113 peer-reviewed articles and policy documents published between 2018 and 2025 were synthesized. The results indicate that certain digital technologies—particularly predictive maintenance systems, digital twins, and product life-cycle monitoring platforms—consistently generate net-positive mitigation outcomes by reducing material throughput. In contrast, the mitigation potential of highly data-intensive technologies, including artificial intelligence models and blockchain infrastructures, is frequently constrained by rebound dynamics and the substantial energy requirements of computational infrastructures. Evidence further suggests that efficiency gains achieved through digitally optimized industrial processes are often partially offset by the carbon footprint associated with large-scale model training and the growing electricity demand of digital infrastructures. By integrating technological, environmental, and governance perspectives, a governance-centered analytical framework was proposed to differentiate between net-positive digital circular economy interventions and those whose environmental performance remains conditional on regulatory oversight. The synthesis further reveals significant policy fragmentation between the European Union’s green transition agenda and its digital transformation strategies. These findings highlight the necessity of coordinated regulatory frameworks to ensure that digitalization contributes substantively to circular economy-driven decarbonization.
This literature review examines the multifaceted role of digital tools, such as mobile money and online banking, in expanding access to financial services for underserved populations globally. It synthesizes current research on the benefits, challenges, and policy implications of digital financial inclusion. The review highlights how digital financial services (DFS) contribute to poverty reduction, enhanced financial resilience, and economic empowerment, particularly for women, by overcoming traditional barriers like geographic distance and high transaction costs. However, it also critically assesses persistent challenges, including digital and financial literacy gaps, infrastructure limitations, trust deficits, and emerging risks such as fraud and over-indebtedness. The paper concludes by discussing the crucial role of responsive regulatory frameworks and targeted interventions in fostering a truly inclusive and sustainable digital financial ecosystem. It offers directions for future research and policy recommendations.
Food security is a critical issue that not only pertains to public health but also affects productivity and well-being, particularly among tourism sector workers who face demanding work patterns and limited access to nutritious food. This study aims to examine the demographic characteristics of tourism workers in Ubud, analyze the relationship between socioeconomic factors and their food safety awareness, risk perception, and trust, and assess the association between socioeconomic factors and their food purchasing and food handling behaviors. Furthermore, it seeks to identify the main Food Choice Questionnaire (FCQ) indicators that influence workers’ food choice motives. The research employed a quantitative survey method using a modified 29-item FCQ, administered to workers in the accommodation and culinary sectors through purposive and snowball sampling techniques. Data were analyzed using descriptive statistics, Spearman’s rank correlation, and Principal Component Analysis (PCA). The findings indicate that the tourism workforce in Ubud is predominantly composed of young workers with secondary-level education and low to middle-income levels. Socioeconomic factors, particularly education and the number of household dependents, are significantly associated with food safety awareness and risk perception, but show weak relationships with trust in the food safety system. Practical considerations, especially price and convenience, primarily drive food purchasing behavior, while the number of dependents and monthly expenditure are associated with food handling and processing practices. PCA identifies five principal dimensions of food choice motives: food awareness, practicality and price, nutritional components, trust in food sources, and consumption culture.
In emerging markets, logistics systems play a critical role in shaping economic integration, the attractiveness of investment, and the potential of development. Differences in logistics performance across countries often reflect in-depth structural conditions related to institutional quality, business environment, and infrastructural capacity, which in turn create distinct development-related opportunities and challenges. This study aims to comparatively assess the logistics performance of emerging markets, in order to identify such structural conditions and their implications for development pathways. To achieve this objective, an integrated “CRiteria Importance Through Intercriteria Correlation Opportunity Losses‐Based Polar Coordinate Distance” (CRITIC–OPLO-POCOD) Multi-Criteria Decision-Making (MCDM) framework was applied to evaluate the logistics performance of 49 emerging markets with four indicators derived from the Agility Emerging Markets Logistics Index (AEMLI). The empirical results indicated that business fundamentals were the most influential determinant of logistics performance. The importance of regulatory stability, governance effectiveness, and investment climate has been highlighted. Contrasting structural opportunities and constraints were reflected by the fact that China emerged as the highest-performing country whereas Venezuela consistently ranked lowest. Robustness analysis confirmed a high degree of consistency between the proposed approach and several established decision-making methods, thus supporting the reliability of the findings. Overall, the study provided evidence-based insights into how logistics performance affected the opportunities and challenges in the development of the emerging markets, in order to offer practical implications for policy prioritization and strategic planning.
This study investigates the pathways through which data factor agglomeration (DFA) facilitates the green development of traditional firms in the digital economy. First, we construct a micro-theoretical framework to systematically analyze the mechanisms by which data factor agglomeration influences firms’ green and sustainable development. Second, exploiting the establishment of China’s National Big Data Comprehensive Pilot Zones as a quasi-natural experiment, we employ a difference-in-differences (DID) approach using a panel of A-share listed traditional manufacturing firms from 2011 to 2022. The empirical results indicate that data factor agglomeration significantly promotes green development in traditional firms by accelerating IT and improving capacity utilization (CU) and energy efficiency. These findings remain robust after a battery of robustness checks, including double machine learning (DML) and instrumental-variable approaches. Heterogeneity analyses reveal that the positive effects of data factor agglomeration are more pronounced for state-owned enterprises, firms led by technologically skilled executives, heavily polluting industries, and firms located in regions with stronger government support and stricter environmental regulation. Further analysis uncovers substantial spatial heterogeneity: while the direct effect of data factor agglomeration on local firms’ green development is significantly positive, it generates a “siphoning effect” on geographically adjacent regions, whereas no significant spillover effects are observed among economically similar regions. Overall, this study elucidates the mechanisms and key determinants of green development for traditional firms in the digital era, providing important theoretical and practical implications for global economic growth and sustainable development.
Regulating Services (RS) is one of the four ecosystem services (ES) derived from the diverse ecosystems in the Lake Hawassa Basin (LHB). These services are crucial for the local community, but ongoing anthropogenic activities are exerting negative pressure on these ecosystems, diminishing their capacity to provide RS. This study aimed to assess and map RS at the basin level and propose development options for decision-makers considering the study years of 2007, 2016 and 2024. The study utilized primary and secondary data collection and analysis, stakeholder consultations involving 60 participants, site visits, and tools such as land use land cover (LULC) classification using ArcGIS v10.1 and expert judgment matrix (EJM). The study prioritized 4 out of 11 RSs, created spatial pattern maps at the basin scale, and suggested development options to integrate into decision-making processes and sectoral policies. These recommendations aim to benefit current and future planning and management of development activities within the LHB. The study's methodology and results are vital for addressing biophysical and socioeconomic environmental problems, ensuring sustainable management of natural resources, and enhancing the well-being of the local community. The user-friendly methodology can be adopted globally, with future improvements suggested through additional methods like modeling and valuation of prioritized RS.
Submit to OCS
Review for OCS
Propose a Special Issue 
Open Access
Journal Metrics
Journal Overview
Associated Journals