BACKGROUND AND OBJECTIVES: Carbon emission disclosure is an important mechanism for environmental accountability. However, disclosure quality in Asia remains relatively low despite the region’s large contribution to global carbon emissions. While extant literature has predominantly examined governance, regulation, and demographic characteristics, the critical role of organizational culture remains comparatively underexplored. The study objectives were to examine the association between collaborative culture and carbon emission disclosure quality in electricity companies operating in carbon-intensive Asian economies.METHODS: This study analyzed 210 firm-year observations from 45 publicly listed electricity companies in China, India, Indonesia, Japan, and South Korea during 2016–2023. Drawing upon the quality principles of the Global Reporting Initiative, a carbon emission disclosure quality index (CEDQI) was constructed and subsequently validated via expert review, yielding a robust Cohen’s kappa coefficient of 0.97. Collaborative culture was measured by sustainability collaboration activities. The analysis employed ordinary least squares, fixed-effects, cluster-robust, and two-stage least squares estimations.FINDINGS: Collaborative culture shows a positive and statistically significant association with carbon emission disclosure quality across all estimation models. The coefficient of collaborative culture reaches 3.749 (p < 0.01) under ordinary least squares, 1.723 (p < 0.05) under fixed effects model, and 4.107 (p < 0.01) under two-stage least squares estimation. Under the fixed-effects model, sustainability reporting experience exerted a positive and statistically significant effect (2.241, p < 0.05), highlighting the critical role of accumulated reporting capabilities within firms. Institutional ownership, firm age, and financial performance do not have significant effects. Furthermore, robustness checks employing an alternative metric for disclosure quality corroborate both the direction and statistical significance of the primary empirical findings.CONCLUSION: The findings indicate that collaborative culture contributes positively to carbon emission disclosure quality. This study contributes a context-specific disclosure quality index and extends empirical evidence on the role of organizational capital in enhancing environmental transparency. Higher disclosure quality, however, should not be interpreted as lower carbon emissions. Improving corporate reporting credibility and reducing greenwashing exposure necessitates regulatory intervention to strengthen disclosure quality, standardize science-based metrics, and formalize third-party assurance protocols.
The environmental sustainability of hospitals is an increasingly critical issue globally, as healthcare systems are expanding rapidly, together with the growing concerns about climate change and depletion of resources. With healthcare systems expanding, the environmental impact of hospitals, particularly their carbon footprint, has become a critical concern. The study aimed to examine a range of approaches aimed at minimizing the environmental impact of hospitals, with an emphasis on reducing carbon footprints to pinpoint effective strategies and underscore areas where research is lacking. Following the Preferred Reporting Items for systematic reviews and meta-analysis guidelines, a comprehensive search was conducted across PubMed, SCOPUS, and Embase databases from January 1, 2018, to December 31, 2024. The study included 17 studies highlighting effective strategies for reducing hospitals' carbon footprints. Key interventions included integrating renewable energy sources, energy-efficient technologies, and sustainable procurement practices. Hospitals implementing these strategies have noted considerable declines in carbon emissions, with certain facilities realizing reductions of as much as 40 percent by incorporating renewable energy sources. Waste reduction strategies, such as improved segregation, recycling programs, and reducing single-use plastics, contributed to lower indirect emissions. Despite these successes, challenges such as high initial investment costs, limited technical expertise, and resistance to change were noted, particularly in low- and middle-income countries. Key strategies such as renewable energy adoption, energy-efficient technologies, and sustainable procurement have effectively reduced carbon emissions and enhanced operational efficiency. However, high upfront costs, technical gaps, and resistance to change, especially in low-resource settings, hinder widespread implementation.
BACKGROUND AND OBJECTIVES: Eco-industrial parks integrate industrial ecology with spatial planning to improve resource efficiency and reduce environmental impacts in sustainable industrial development. The integration of standard subjective weighting methods, notably the analytic hierarchy process, into conventional geographical information system-based multi-criteria decision analysis for eco-industrial park selection introduces potential biases and limits the replicability of the findings. Additionally, suitability assessment and industrial symbiosis network design are often addressed separately, limiting their systematic integration. The study objectives were to develop an integrated geographical information system-based multi-criteria decision analysis and ecological network analysis framework for planning the East Baghdad Village Eco-Industrial Park in North Sinai Governorate, Egypt.METHODS: The framework comprises six sequential phases: 1) literature review and gap analysis; 2) geographical information system-based multi-criteria decision analysis of thirteen environmental, infrastructural, and socio-economic criteria; 3) entropy weight method objective weighting cross-checked using the criteria importance through intercriteria correlation method; 4) weighted overlay suitability mapping and ecological network analysis evaluation using total system throughput, cycling index, Finn cycling index, average path length, and network density; 5) industrial symbiosis network design; and 6) sensitivity analysis. Model robustness was verified via a ±20% criterion weight perturbation analysis using standard classification stability metrics.FINDINGS: Geographical information system-based multi-criteria decision analysis identified 847 hectares (23.4 percent) of the 3,619-ha study area as highly suitable for industrial development. Environmental sensitivity, flood risk, and transportation accessibility were the most influential criteria. Ecological network analysis yielded a total system throughput of 2.87 million tonnes per year, a cycling index of 0.34, a Finn cycling index of 0.41, and an average path length of 2.3, indicating circularity potential within the range reported for established eco-industrial parks. Compared to a conventional industrial configuration, the projected symbiotic exchanges demonstrate significant potential for minimizing both resource consumption and waste generation. Sensitivity analysis confirmed high classification stability (0.935; mean map-consistency coefficient = 0.875).CONCLUSION: This study introduces an integrated planning workflow that sequentially combines geographical information system-based multi-criteria decision analysis and ecological network analysis to evaluate both spatial suitability and industrial symbiosis workflow. The framework provides a replicable approach for supporting sustainable eco-industrial park planning in arid, data-scarce, and resource-constrained regions.
BACKGROUND AND OBJECTIVES: This study examines the determinants of environmental sustainability within 27 European Union countries from 2014 to 2024, capturing the impact of critical structural and policy changes. This study systematically examines five dimensions of sustainability. It first determines the influence of energy transition variables on per capita carbon emissions, subsequently examines the effect of circular economy strategies on resource productivity and finally evaluates the the non-linear link between economic growth and carbon dioxide emissions is identified using the Environmental Kuznets Curve framework. The study objectives were to explore five empirical models concerning the European Unions-27 countries and the impacts of the energy transition on per capita carbon emissions and role of circular economy in enhancing resource efficiency. Additionally, it tests the validity of the Environmental Kuznets Curve theory and income turning point.METHODS: A complete panel data set that comprises 297 country year observations is used. The empirical methodology is based on robust fixed effects regression models. Principal component analysis is used on six standardized variables. Principal component analysis serves as a robust statistical framework for constructing environmental sustainability index. The socio-economic factors affecting environmental sustainability index are further analyzed. Validation of the model through diagnostic tests is performed. FINDING: Renewable energy consumption mitigates carbon dioxide emissions, while energy intensity and environmental tax revenues emerge as critical determinants of sustainability. The presence of strong empirical evidence in support of the inverted-U shape of the Environmental Kuznets Curve theory is confirmed. These findings indicate that most European Unions’ member have progressed to the declining phase of the curve. According to the composite index, Sweden, Finland, and Austria belong to the best performers. Simultaneously, the lowest performance ratings are observed in Malta, Romania, and Cyprus.CONCLUSION: Offering critical empirical insights for European policymakers, this evidence underscores the need to prioritize reductions in energy intensity while supporting the strategic utilization of fiscal environmental mechanisms. Implementation of targeted green transition frameworks is highly recommended.
BACKGROUND AND OBJECTIVES: Land use and land cover change is a major driver of hydrological change, particularly in mountainous basins where hydropower production depends on reliable streamflow. The Suki Kinari Hydropower Project on Pakistan’s Kunhar River is a run-of-river facility that is highly sensitive to such variations. The study objectives were to evaluate the impact of past and projected land use and land cover change on streamflow to provide insights for watershed management and sustainable hydropower operations.METHODS: The soil and water assessment tool were employed to simulate streamflow dynamics under future land use scenarios. The model was calibrated for the period 2000–2011 and validated for 2012–2016 against monthly discharge data from the Garhi Habibullah gauging station. Future land use and land cover projections for 2052 and 2082 were generated using a cellular Automata-Markov chain model based on historical transition probabilities from 1992, 2002, and 2012 satellite observations.FINDINGS: The soil and water assessment tool model demonstrated acceptable performance for monthly simulations (Calibration: R² = 0.64, Nash–Sutcliffe Efficiency = 0.62; Validation: R² = 0.61, Nash–Sutcliffe Efficiency = 0.52). land use and land cover projections indicate a continuous expansion of urban areas (+400 percent by 2082), agricultural land (+72 percent), and barren land (+3.8 percent), driven by deforestation (-38 percent) and grassland degradation. Under these projected changes, mean monthly streamflow is predicted to increase by 2.64 percent in 2052 and 7.54 percent in 2082, with peak runoffs shifting earlier in the year.CONCLUSION: The study reveals that future land use and land cover changes will significantly increase streamflow and runoff in the Kunhar River Basin, posing potential risks of sedimentation and flooding for the Suki Kinari Hydropower Project. The findings underscore the critical need for a dense hydro-meteorological monitoring network in the upper catchment and the integration of scenario-based modeling into water resources planning. Sustainable land management is essential to mitigate adverse hydrological impacts in mountainous basins.
