Recently, membrane-based separation gains significant attention among available water treatment technologies. Among the advanced techniques for wastewater treatment, emulsion liquid membranes (ELM) stand out, since it is environmentally friendly. In the present work, flupentixol (FL), an emergent micropollutant and antidepressant, was selected as the model pharmaceutical pollutant for removal using ELM system containing jujube oil (JJO-ELM). The primary effects and interactions of key independent variables, surfactant concentration (2–3 v/v
The transition to renewable energy and the implementation of adequate environmental instruments are crucial measures to prevent carbon lock-in. To this end, this analysis scrutinizes the impacts of renewable energies, GDP per capita, and environmental policy, in particular market-based and non-market-based environmental strategies, on GHG emissions in selected European Union countries. The Instrumental Variable Method of Moments Quantile Regression is used to control for endogeneity. In addition, new techniques, such as quantile slope homogeneity, quantile unit root, quantile cross-sectional dependence, and quantile causality tests, are used. The main findings reveal that the negative effect of renewable energies persists across the distribution of GHG emissions. However, this effect is weaker at high emission levels than at low emission levels. Concerning market-based instruments, they reduce GHG emissions at low and medium levels. Furthermore, the magnitude of this effect progressively diminishes as GHG emissions increase. When considering non-market-based instruments, their impact is negative at medium and high levels of GHG emissions. In addition, their effect is increasing with the level of GHG emissions. Finally, the quantile causality test and robustness checks using Smoothed Instrumental Variables Quantile Regression and Quantile Regression with Common Shocks confirm the baseline results. These results emphasize the necessity to adapt environmental instruments to the level of GHG emissions to attain optimal results. This is not applicable.
Rapid urbanization and climate change are intensifying heat exposure in cities, turning extreme heat into a persistent and unevenly distributed public health risk. Conventional heat risk assessments often rely on temperature thresholds or surface heat mapping, but these approaches may overlook the biological sensitivity, socio-spatial inequality, urban form, health-system capacity, and governance conditions that determine who is harmed, where, and why. This paper presents a framework-oriented integrative synthesis that develops the Urban Heat–Exposure–Health (UHEH) axis as a conceptual and translational structure for organizing heat-health risk in rapidly urbanizing cities. Drawing on evidence from urban climate science, environmental epidemiology, public health, spatial analytics, heat vulnerability research, and urban governance, the framework links urban form, thermal exposure, demographic sensitivity, social vulnerability, health outcomes, and institutional response pathways. Rather than presenting a fully implemented predictive model or empirical validation study, the paper clarifies the data streams, analytical relationships, equity indicators, and validation requirements needed for future operational heat-health modeling. The UHEH framework identifies how satellite, in situ, and modeled thermal data can be combined with socio-spatial vulnerability indicators, urban morphology metrics, and health outcome proxies such as emergency department visits, hospital admissions, ambulance calls, and mortality records. It also maps key mechanistic pathways, including cardiovascular strain, dehydration, renal stress, air-pollution interactions, sleep disruption, and cumulative exposure. However, the framework is not presented as a validated predictive model; rather, it provides a structured basis for future empirical testing, local calibration, comparison with existing heat vulnerability approaches, and validation against observed health outcomes. By integrating exposure, vulnerability, biological response, and governance into a single analytical pathway, the framework supports the design of heat-health early warning systems, real-time dashboards, policy triggers, targeted cooling interventions, and equity-centered adaptation strategies. The paper contributes a structured foundation for future empirical testing, model calibration, cross-city comparison, and decision-support development aimed at reducing heat-related morbidity and mortality in rapidly urbanizing cities. Clinical trial number: Not applicable.
Quantifying hydrological responses of river basins to combined hydro-climatic variability and land use/land cover (LULC) change (1999–2019) is fundamental to sustainable water resource management in regions experiencing intensifying anthropogenic pressures. This study investigates how climatic variability and land-use dynamics drive hydrological change in the Tawa River Basin, a strategically important tributary of the Narmada River in central India, whose hydrological behaviour underpins regional agricultural productivity, ecosystem functioning, and water supply security. The primary objective is to detect, attribute, and quantify shifts in surface runoff generation, streamflow variability, and water balance partitioning driven by interacting climatic and land-use forcing. Long-term hydro-climatic data were analysed using ordinary least squares regression, coefficient of variation, standardized anomaly, Mann–Kendall trend detection, and Sen’s slope estimation. Basin rainfall exhibits moderate inter-annual variability (CV ≈ 19.7
This study examines the association between climate risk disclosure and corporate energy efficiency using Chinese A-share data from 2012 to 2024. A firm-level disclosure index is constructed via text mining based on the TCFD framework. Two-way fixed effects models show a positive association, with a one-standard-deviation increase in disclosure corresponding to approximately a 3.6
