1447 papers
This study reveals that despite high community awareness of wetland benefits, Okole Wetland in Lira City is severely degraded primarily due to alcohol production, necessitating strengthened community-led conservation efforts.
This paper presents the development and validation of a regional landslide forecast model for the Western Canada Sedimentary Basin, which integrates decades of infrastructure displacement data with hydroclimatic drivers to create an activity index capable of hindcasting past events and predicting future landslide risks for proactive infrastructure mitigation.
Using observational data and climate projections, this study demonstrates that global warming will significantly increase lightning density across Brazil by the end of the century—particularly in the Amazon—regardless of uncertainties in simulated cloud ice content, thereby heightening the risk of lightning-ignited wildfires.
This exploratory study of six textile facilities in Bangladesh's Chattogram Export Processing Zone reveals a decoupling between organic and heavy metal compliance, where frequent exceedances of BOD, COD, and colour limits contrast with universal adherence to lead and chromium regulations, suggesting distinct pollutant sources and treatment pathways.
This paper introduces the Congo Basin Catchment Information System (CB-CIS), a unified digital hydrological infrastructure that integrates diverse data sources and a nested catchment framework to address information gaps and enable systems-based, adaptive water resources management across the data-scarce Congo River Basin.
This study integrates stakeholder preferences identified from 902 households with spatiotemporal analysis and Bayesian belief network simulations to map trade-offs among priority cropland ecosystem services and delineate spatial optimization zones for sustainable land-use planning in the Yangtze River Delta.
This study reveals that wetland degradation exacerbates carbon and phosphorus co-limitation of soil microbial metabolism in the lower Yellow River wetlands, primarily driven by degradation-induced changes in plant community characteristics, soil electrical conductivity, and nutrient availability.
This study presents an integrated statistical and machine learning framework for Delhi that combines traditional frequency analysis, trend detection, and advanced predictive modeling to develop robust Intensity–Duration–Frequency (IDF) relationships and improve extreme rainfall estimation for urban flood risk management.
Using ARDL analysis on Nigerian data from 2002 to 2023, this study confirms a stable long-run cointegration where affluence and urbanization drive environmental degradation, while the effectiveness of technological innovation in mitigating these impacts depends critically on institutional quality and regulatory capacity.
This study demonstrates that technology modularity critically shapes latecomer cost convergence in renewables, revealing that emerging economies achieve faster cost reductions than developed nations in modular solar PV due to global knowledge transfer, but face slower progress in site-specific onshore wind, thereby necessitating technology-specific climate finance strategies rather than uniform global learning curves.