Assessment of growth, and ion uptake of plant species, Conocarpus erectus and Dodonaea viscosa, on industrial solid waste.

Present study assessed the growth of two plant species and ion uptake by them grown on different proportion of industrial solid waste and garden soil. The industrial waste having high concentration of chemicals were used with garden soil at different proportion i.e. 0% (T0), 5% (T1), 10% (T2), 15% (T3) and 20% (T4). Two species namely Conocarpus erectus (alien plant) and Dodonaea viscosa (indigenous) were used as test plants in pot study. Different parameters including growth, physiology, and anatomy of plants and concentration of cations (Na+, K+, Ca2+, and Mg2+) in the plant shoot and root were measured at different time duration (initial, 1st, 2nd, 3rd and 4th month). The key objective of the study was to use these plants to establish their plantations on the barren lands where industrial solid wastes were being disposed of. C. erectus showed better growth than D. viscosa, as well as more uptake of ions. A significant increase in plant growth was observed in fourth month in T1, where plant height reached 24.5% and 46% for C. erectus and D. viscosa, respectively. At harvest, in C. erectus, no significant difference in the fresh (65-78 g) and dry weight (24-30 g) of the shoot was observed across treatments compared to the control. In D. viscosa, at the time of harvest, the fresh and dry weights of the root and shoot showed a strong, significantly decreasing pattern across T1, T2, and T3, leading to the death of the plant at T3 and T4. Further, optimum ratio of waste soil to garden soil was found as 10:90 and 20:80 to establish the plantations of D. viscosa and C. erectus, respectively in areas where such solid waste from industries are disposed. Findings can be used for the restoration of such solid waste for the sustainable management of industrial areas and their associated ecosystems.

The vulnerability of World Heritage seagrass habitats to climate change.

Seagrass is an important natural attribute of 28 World Heritage (WH) properties. These WH seagrass habitats provide a wide range of services to adjacent ecosystems and human communities, and are one of the largest natural carbon sinks on the planet. Climate change is considered the greatest and fastest-growing threat to natural WH properties and evidence of climate-related impacts on seagrass habitats has been growing. The main objective of this study was to assess the vulnerability of WH seagrass habitats to location-specific key climate stressors. Quantitative surveys of seagrass experts and site managers were used to assess exposure, sensitivity and adaptive capacity of WH seagrass habitats to climate stressors, following the Climate Vulnerability Index approach. Over half of WH seagrass habitats have high vulnerability to climate change, mainly from the long-term increase in sea-surface temperature and short-term marine heatwaves. Potential impacts from climate change and certainty scores associated with them were higher than reported by a similar survey-based study from 10 years prior, indicating a shift in stakeholder perspectives during the past decade. Additionally, seagrass experts' opinions on the cumulative impacts of climate and direct-anthropogenic stressors revealed that high temperature in combination with high suspended sediments, eutrophication and hypoxia is likely to provoke a synergistic cumulative (negative) impact (p < .05). A key component contributing to the high vulnerability assessments was the low adaptive capacity; however, discrepancies between adaptive capacity scores and qualitative responses suggest that managers of WH seagrass habitats might not be adequately equipped to respond to climate change impacts. This thematic assessment provides valuable information to help prioritize conservation actions, monitoring activities and research in WH seagrass habitats. It also demonstrates the utility of a systematic framework to evaluate the vulnerability of thematic groups of protected areas that share a specific attribute.

Capacity and capability of remote sensing to inform invasive plant species management in the Pacific Islands region.

The Pacific Islands region is home to several of the world's biodiversity hotspots, yet its unique flora and fauna are under threat because of biological invasions. These invasions are likely to proliferate as human activity increases and large-scale natural disturbances unfold, exacerbated by climate change. Remote sensing data and techniques provide a feasible method to map and monitor invasive plant species and inform invasive plant species management across the Pacific Islands region. We used case studies taken from literature retrieved from Google Scholar, 3 regional agencies' digital libraries, and 2 online catalogs on invasive plant species management to examine the uptake and challenges faced in the implementation of remote sensing technology in the Pacific region. We synthesized remote sensing techniques and outlined their potential to detect and map invasive plant species based on species phenology, structural characteristics, and image texture algorithms. The application of remote sensing methods to detect invasive plant species was heavily reliant on species ecology, extent of invasion, and available geospatial and remotely sensed image data. However, current mechanisms that support invasive plant species management, including policy frameworks and geospatial data infrastructure, operated in isolation, leading to duplication of efforts and creating unsustainable solutions for the region. For remote sensing to support invasive plant species management in the region, key stakeholders including conservation managers, researchers, and practitioners; funding agencies; and regional organizations must invest, where possible, in the broader geospatial and environmental sector, integrate, and streamline policies and improve capacity and technology access.

Capacidad y potencial de la telemetría para informar la gestión de especies de plantas invasoras en las islas del Pacífico Resumen Las islas del Pacífico albergan varios de los puntos calientes de biodiversidad del planeta; sin embargo, su flora y fauna únicas se encuentran amenazadas por las invasiones biológicas. Es probable que estas invasiones proliferen conforme incrementa la actividad humana y se desarrollan las perturbaciones naturales a gran escala, exacerbadas por el cambio climático. Los datos y las técnicas telemétricas proporcionan un método viable para mapear y monitorear las especies invasoras de plantas y orientar su manejo en la región de las islas del Pacífico. Usamos estudios de caso tomados de la bibliografía de Google Scholar, las bibliotecas digitales de tres agencias regionales y dos catálogos virtuales del manejo de especies invasoras de plantas para analizar la asimilación y retos que enfrenta la implementación de la telemetría en la región del Pacífico. Sintetizamos las técnicas telemétricas y describimos su potencial para detectar y mapear las especies de plantas invasoras con base en la fenología de las especies, características estructurales y algoritmos de textura de imagen. La aplicación de los métodos de telemetría para detectar las especies invasoras de plantas dependió en gran medida de la ecología de la especie, la extensión de la invasión y los datos disponibles de imágenes telemétricas y geoespaciales. Sin embargo, los mecanismos actuales de apoyo para el manejo de especies invasoras de plantas, incluyendo los marcos normativos y la infraestructura para datos geoespaciales, operan de manera aislada, lo que lleva a que se dupliquen los esfuerzos y se creen soluciones insostenibles para la región. Para que la telemetría apoye al manejo de especies invasoras de plantas en la región, los actores clave, incluidos los gestores, investigadores, practicantes, agencias financiadoras y organizaciones regionales, deben invertir, en lo posible, en un sector ambiental y geoespacial más amplio, integrar y simplificar las políticas y mejorar la capacidad y el acceso a la tecnología.

Multidirectional Fate Path Model to Connect Phosphorus Emissions with Freshwater Eutrophication Potential.

Excessive anthropogenic phosphorus (P) emissions put constant pressure on aquatic ecosystems. This pressure can be quantified as the freshwater eutrophication potential (FEP) by linking P emissions, P fate in environmental compartments, and the potentially disappeared fraction of species due to increase of P concentrations in freshwater. However, previous fate modeling on global and regional scales is mainly based on the eight-direction algorithm without distinguishing pollution sources. The algorithm fails to characterize the fate paths of point-source emissions via subsurface pipelines and wastewater treatment infrastructure, and exhibits suboptimal performance in accounting for multidirectional paths caused by river bifurcations, especially in flat terrains. Here we aim to improve the fate modeling by incorporating various fate paths and addressing multidirectional scenarios. We also update the P estimates by complementing potential untreated point-source emissions (PSu). The improved method is examined in a rapidly urbanizing area in Taihu Lake Basin, China in 2017 at a spatial resolution of 100 m × 100 m. Results show that the contribution of PSu on FEP (62.6%) is greater than that on P emissions (58.5%). The FEP is more spatially widely distributed with the improved fate modeling, facilitating targeted regulatory strategies tailored to local conditions.

Adsorption of trace heavy metals through organic compounds enriched biochar using isotherm adsorption and kinetic models.

Trace heavy metals such as copper and nickel, when exceeds a certain level, cause detrimental effects on the ecosystem. The current study examined the potential of organic compounds enriched rice husk biochar (OCEB's) to remove the trace heavy metals from an aqueous solution in four steps. In 1st step, biochar' physical and chemical properties were analyzed through scanning electron microscope (SEM) and Fourier transforms infrared spectroscopy (FTIR). In the 2nd step, two biochar vis-a-vis glycine, alanine enriched biochar (GBC, ABC) was selected based on their adsorption capacity of four different metals Cr, Cu, Ni and Pb (chromium, copper, nickel, and lead). These two adsorbents (GBC, ABC) were further used to evaluate the best interaction of biochar for metal immobilization based on varying concentrations and times. Langmuir isotherm model suggested that the adsorption of Ni and Cu on the adsorbent surface supported the monolayer sorption. The qmax value of GBC for Cu removal increased by 90% compared to SBC (Simple rice husk biochar). The interaction of Cu and Ni with GBC and ABC was chemical, and 10 different time intervals were studied using pseud first and second-order kinetics models. The current study has supported the pseudo second-order kinetic model, which exhibited that the sorption of Ni and Cu occurred due to the chemical processes. The % removal efficiency with GBC was enhanced by 21% and 30% for Cu and Ni, respectively compared to the SBC. It was also noticed that GBC was 21% more efficient for % removal efficiency than the CBC. The study's findings supported that organic compound enriched rice husk biochar (GBC and ABC) is better than SBC for immobilizing the trace heavy metals from an aqueous solution.

Riparian trees in mercury contaminated riverbanks: An important resource for sustainable remediation management.

Mining operations generate sediment erosion rates above those of natural landscapes, causing persistent contamination of floodplains. Riparian vegetation in mine-impacted river catchments plays a key role in the storage/remobilization of metal contaminants. Mercury (Hg) pollution from mining is a global environmental challenge. This study provides an integrative assessment of Hg storage in riparian trees and soils along the Paglia River (Italy) which drains the abandoned Monte Amiata Hg mining district, the 3rd former Hg producer worldwide, to characterize their role as potential secondary Hg source to the atmosphere in case of wildfire or upon anthropic utilization as biomass. In riparian trees and nearby soils Hg ranged between 0.7 and 59.9 µg/kg and 2.2 and 52.8 mg/kg respectively. In trees Hg concentrations were below 100 µg/kg, a recommended Hg limit for the quality of solid biofuels. Commercially, Hg contents in trees have little impact on the value of the locally harvested biomass and pose no risk to human health, although higher values (195-738 µg/kg) were occasionally found. In case of wildfire, up to 1.4*10-3 kg Hg/ha could be released from trees and 27 kg Hg/ha from soil in the area, resulting in an environmentally significant Hg pollution source. Data constrained the contribution of riparian trees to the biogeochemical cycling of Hg highlighting their role in management and restoration plans of river catchments affected by not-remediable Hg contamination. In polluted river catchments worldwide riparian trees represent potential sustainable resources for the mitigation of dispersion of Hg in the ecosystem, considering i) their Hg storage capacity, ii) their potential to be used for local energy production (e.g. wood-chips) through the cultivation and harvesting of biomasses and, iii) their role in limiting soil erosion from riparian polluted riverbanks, probably representing the best pragmatic choice to minimize the transport of toxic elements to the sea.

Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks.

Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (DO) concentrations. Despite advancements, challenges persist regarding accuracy, scalability, and adaptability of data-driven models to diverse environmental conditions. Previous studies primarily employed singular models or basic combinations of machine learning techniques, lacking advanced integration of adaptive mechanisms to process complex and evolving datasets. The current study introduces innovative hybrid models that integrate temporal pattern attention (TPA) mechanisms with advanced neural networks, including feed-forward neural networks (FFNNs) and long short-term memory networks (LSTMs). This approach leverages the synergistic strengths of individual models, significantly enhancing the accuracy of DO predictions. The models were rigorously tested against water quality data obtained from two distinct riverine environments, the Illinois River (ILL) and Des Plaines River (DP). Daily measured water quality data, including DO, chlorophyll-a, nitrate plus nitrite, water temperature, specific conductance, and pH, from 2017 to 2024 provided a robust foundation for comprehensive analysis of DO dynamics in these rivers. We conducted 10 scenarios with different model inputs, wherein the hybrid TPACWRNN-LSTM-10 model particularly excelled, achieving coefficient of determination values of 0.993 and 0.965, and root mean squared errors of 0.241 mg/L and 0.450 mg/L for DO predictions at the ILL and DP stations, respectively. The model's reliability was further confirmed by Willmott's index values of 0.998 and 0.992 and Nash-Sutcliffe efficiency values of 0.990 and 0.961 at the ILL and DP stations, respectively. Additionally, Shapley additive explanations (SHAP) values were utilized to interpret each predictor's contribution, revealing key drivers of DO predictions. We believe the novel hybrid modeling approach presented in this study could benefit utilities and water resource management systems for predicting water quality in complex systems.

Monitoring anomalies on large-scale energy and water balance components by coupling remote sensing parameters and gridded weather data.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied with MODIS images and gridded weather data from 2007 to 2021, to monitor the energy balance components and their anomalies, in the Atlantic Forest (AF) and Caatinga (CT) biomes inside the coastal agricultural growing zone, Northeast Brazil. Considering the long-term data, the Rn values between the biomes are not significantly different, however presenting distinct Rn partitions into latent (λE), sensible (H), and ground (G) heat fluxes between biomes. The Rn values annual averages are 9.40 ± 0.21 and 9.50 ± 0.23 MJ m-2 d-1, for AF and CT, respectively. However, for respectively AF and CT, they are respectively 5.10 ± 1.14 MJ m-2 d-1 and 4.00 ± 0.99 MJ m-2 d-1 for λE; 3.80 ± 1.12 MJ m-2 d-1 and 5.00 ± 1.00 MJ m-2 d-1 for H; 0.50 ± 0.12 MJ m-2 d-1 and 0.40 ± 0.10 MJ m-2 d-1 for G, yielding respective mean evaporative fraction (Ef = λE/(Rn - G) values of 0.60 ± 0.12 and 0.50 ± 0.15. Anomalies on λE, H, and Ef were detected through standardized index for these energy balance components by comparing the results for the years 2018 to 2021 with the long-term values from 2007 to each of these years, showing that the energy fluxes between surfaces and the lower atmosphere, and then the root-zone moisture conditions for both biomes, may strongly vary along seasons and years, with alternate positive and negative anomalies. These assessments are important for water policies as they can picture suitable periods and places for rainfed agriculture as well as the irrigation needs in irrigated agriculture, allowing rational agricultural environmental management while minimizing water competitions among other water users, under climate and land-use changes conditions.

Economic globalization-energy diversification nexus and implications for environmental management in China.

Every nation on earth has the responsibility to implement effective environmental management measures for sustainable environmental quality. In doing so, this study scrutinizes the relationship between economic globalisation and energy diversification in the Chinese economy from 1995 to 2022 for designing and implanting policies for environmental management. It uses industrialization, foreign direct investment, foreign remittances, and information & communication technology as supplementary factors into augmented energy diversification demand function. This empirical analysis shows cointegration between the variables, with economic globalisation positively impacting energy diversification. Factors such as foreign direct investment, foreign remittances, and information & communication technology contribute to energy diversity. However, industrialization has an adverse relationship with energy diversification. The relationship forms an inverted-U shaped between economic globalization and energy diversification. Our causality analysis indicates that economic globalization positively causes energy diversification. This study also reveals a reciprocal and beneficial cause-and-effect association between foreign direct investment and energy diversification. Lastly, foreign remittances and information & communication technologies positively cause energy diversification.

Spatial-temporal differentiation and control strategies of nitrogen environmental loss in China's coastal regions based on flow analysis.

Nitrogen pollution emissions from human production and living activities in coastal regions are important topics in the management of environmental pollution in coastal waters. However, to date, there has been relatively little research systematically assessing the environmental loss of nitrogen (NEL) from human activities that negatively affect marine ecosystems. This study categorised emission sources into five subsystems, namely livestock, farming, aquatic, industrial, and residential. Through flow analysis, the anthropogenic emissions of nitrogen in the gas, liquid, and solid phases from 11 coastal provinces in China in 2011, 2015, and 2020 were determined. A nitrogen cost index was constructed by combining the social indicators of each province. The effectiveness of nitrogen emission control since the land-sea coordination and the future challenges for the coastal region were discussed from various perspectives. The results of the study showed that the total NEL that poses a potential threat to marine ecosystems in coastal areas of China has decreased from 18.93 TgN to 14.66 TgN since the proposal of land-sea coordination, with livestock systems and aquatic systems emitting the most. The Bohai and Yellow Seas area were most threatened by nitrogen pollution. Among the three oceanic pathways, liquid-phase nitrogen discharge from each subsystem was effectively controlled, and the control of gas-phase nitrogen emissions is still the most numerous NEL state, although it has had a significant effect. The results of the correlation analysis suggest that NEL flow can characterize the regional management of nutrient-based organic pollutants. Past management tools and environmental investments in China have been more effective in controlling emissions from point and line sources involving artificial facilities, but less direct effect on mariculture. How to control surface source pollution from livestock and aquaculture will be an important challenge to reduce reactive nitrogen emissions in the future.

Green infrastructure design for the containment of biological invasions. Insights from a peri-urban case study in Rome, Italy.

Secondary shrublands and transitional woodland/shrub formations are recognised to be particularly susceptible to plant invasions, one of the main global threats to biodiversity, especially in dynamic peri-urban landscapes. Urban fringes are in fact often the place for the sprawl of artificial surfaces, fragmentation of habitats, and complex land transitions (including both agriculture intensification and abandonment), which in turn increase propagule pressure of exotic species over residual semi-natural ecosystems. Within this framework, the present study was aimed at analysing i) how landscape composition and configuration affect the richness of woody exotic species in shrubland and transitional woodland/shrub patches, and ii) how this threat can be addressed by means of green infrastructure design in a peri-urban case study (Metropolitan City of Rome, Italy). Accordingly, the occurrence of exotic plants was recorded with field surveys and then integrated with landscape analyses, both at patch level and over a 250 m buffer area around each patch. Thus, the effect of landscape features on exotic plant richness was investigated with Generalised Linear Models, and the best model identified (pseudo R-square = 0.62) for inferring invasibility of shrublands throughout the study area. Finally, a Green Infrastructure (GI) to contain biological invasion was planned, based on inferred priority sites for intervention and respective, site-tailored, actions. The latter included not only the removal of invasive woody alien plants, but also reforestation and planting of native trees for containment of dispersal and subsequent establishment. Even though specifically developed for the study site, and consistent with local government needs, the proposed approach represents a pilot planning process that might be applied to other peri-urban regions for the combined containment of biological invasions and sustainable development of peripheral complex landscapes.

Revisiting the EKC framework concerning COP-28 carbon neutrality management: Evidence from Top-5 carbon embittering countries.

In the aftermath of the 28th Conference of the Parties (CoP) climate summit in the UAE, the majority of developing countries encounter challenges in attaining their objectives of carbon neutrality for a sustainable economy. The association of economic factors such as economic growth, governance structures, forest area, renewable energy consumption, technological innovation, and urbanization with environmental elements (carbon footprint) is vital for sustainable economic development and environmental management strategies. Therefore, this research reveals this association in five selected high-emitting countries spanning from 1990 to 2022. This research utilizes the Environmental Kuznets Curve (EKC) framework to investigate the interrelationship between these variables. To do so, this study employs the cross-sectional autoregressive distributed lags (CS-ARDL) statistical technique to determine the short- and long-term impacts of the variables under investigation on carbon footprint. In contrast, the mean group (MG) and common correlated effect mean group (CCEMG) have been applied for robustness. The findings revealed that GDP, urbanization, and forest area have positive associations with carbon footprints, whereas GDP square, renewable energy consumption, technological innovation, and governance effectiveness have inverse relationships with carbon footprints. These findings provide all stakeholders with valuable policy recommendations and management advice for accelerating the transition of renewable energy to low-carbon and green growth.

Integrating policy, data and technology in pursuing effective management of ecosystem services.

With the growing concerns about the protection of ecosystem functions and services, governments have developed public policies and organizations have produced an awesome volume of digital data freely available through their websites. On the other hand, advances in data acquisition through remote sensed sources and processing through geographic information systems (GIS) and statistical tools, allowed an unprecedent capacity to manage ecosystems efficiently. However, the real-world scenario in that regard remains paradoxically challenging. The reasons can be many and diverse, but a strong candidate relates with the limited engagement among the interest parties that hampers bringing all these assets into action. The aim of the study is to demonstrate that management of ecosystem services can be significantly improved by integrating existing environmental policies with environmental big data and low-cost GIS and data processing tools. Using the Upper Rio das Velhas hydrographic basin located in the state of Minas Gerais (Brazil) as example, the study demonstrated how Principal Components Analysis based on a diversity of environmental variables assembled sub-basins into urban, agriculture, mining and heterogeneous profiles, directing management of ecosystem services to the most appropriate officially established conservation plans. The use of GIS tools, on the other hand, allowed narrowing the implementation of each plan to specific sub-basins. This optimized allocation of preferential management plans to priority areas was discussed for a number of conservation plans. A paradigmatic example was the so-called Conservation Use Potential (CUP) devoted to the protection of aquifer recharge (provision service) and control of water erosion (regulation service), as well as to the allocation of uses as function of soil capability (support service). In all cases, the efficiency gains in readiness for plans' implementation and economy of resources were prognosed as noteworthy.

A novel integrated urban flood risk assessment approach based on one-two dimensional coupled hydrodynamic model and improved projection pursuit method.

Urban flood risk assessment is a complex task, as it requires extensive knowledge about hydrological features of the catchment, hydraulic characteristics of the drainage network and social characteristics of residential areas. How to accurately and efficiently quantify regional risk has always been a challenge in this field. To solve the problem, this study is developed to propose a novel integrated urban flood risk assessment approach based on one-two dimensional coupled hydrodynamic model and improved projection pursuit method. Two open source software like urban storm flood management model (SWMM) and TELEMAC-2D are introduced to build the one-two coupling hydrodynamic model through proprietary programming, which can accurately simulate urban inundation process. Based on the simulation results of hydrodynamic model and literature review, a set of urban flood risk assessment index system containing physical mechanism and statistical mechanism related index is established, including a total of 12 indicators covering three dimensions like hazard factor, exposure factor and vulnerability factor. Then an Improved Projection Pursuit (IPP) method coupling k-means clustering algorithm is proposed to determine the index weight. The novel integrated urban flood risk assessment approach is implemented in Suyu district, China. The results demonstrate that the accuracy and efficiency of evaluation urban flood risk assessment are greatly improved by the integrated approach. In conclusion, this research offers a novel methodology for urban flood risk assessment and contributes to decision-making in environmental management.

Multi-model assessment of potential natural vegetation to support ecological restoration.

Ecological restoration is imperative for controlling desertification. Potential natural vegetation (PNV), the theoretical vegetation succession state, can guides near-natural restoration. Although a rising transition from traditional statistical methods to advanced machine learning and deep learning is observed in PNV simulation, a comprehensive comparison of their performance is still unexplored. Therefore, we overview the performance of PNV mapping in terms of 12 commonly used methods with varying spatial scales and sample sizes. Our findings indicate that the methodology should be carefully selected due to the variation in performance of different model types, with Area Under the Curve (AUC) values ranging from 0.65 to 0.95 for models with sample sizes up to 80% of the total sample size. Specifically, semi-supervised learning performs best with small sample sizes (i.e., 10 to 200), while Random Forest, XGBoost, and artificial neural networks perform better with large sample sizes (i.e., over 500). Further, the performance of all models tends to improve significantly as the sample size increases and the grain size of the crystals becomes smaller. Take the downstream Tarim River Basin, a hyper-arid region undergoing ecological restoration, as a case study. We showed that its potential restored areas were overestimated by 2-3 fold as the spatial scale became coarser, revealing the caution needed while planning restoration projects at coarse resolution. These findings enhance the application of PNV in the design of restoration programs to prevent desertification.

Toward sustainable environmental management: Insights from Chinese first-tier cities' consumers' response to green electricity payment policies.

China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay.

Environmental reporting in Italian thermal power plants: insights from a comprehensive analysis of EMAS environmental statements.

The global energy sector heavily relies on fossil fuels, significantly contributing to climate change. The ambitious European emissions' reduction targets require sustainable processes and alternatives. This study presents a comprehensive analysis of 73 Italian thermal power plants registered to the European Eco-Management and Audit Scheme (EMAS) aimed at assessing EMAS effectiveness in addressing and quantifying the environmental impacts of this relevant industrial sector. The analysis was based on EMAS environmental statements, publicly disclosing verified and certified data, with the secondary objective of evaluating if EMAS could be an efficient tool to improve the plants' environmental performances. An inventory of technical and environmental aspects, adopted indicators, and allocated budgets was based on 2023 data. A strong correlation was found between the significance of the environmental aspects and the number of adopted indicators. Gaps were observed in describing aspects like "biodiversity" and "local issues". Improvement objectives and budget allocation showed discrepancies and lacked correlation with the significance of the related environmental aspects. "Energy production" accounted for 68% of the total allocated budget; "environmental risks", "emissions to air", "electricity consumption", and "local issues" were also key focus areas. Insufficient information on emission control technologies and progress tracking of improvement objectives was detected. This study highlights the need for thermal power installations to improve the selection of appropriate indicators and to better relate allocated budget to improvement objectives when implementing EMAS. Such measures would facilitate the quantification of the effective environmental impacts of the energy production sector, supporting future research on this topic, allowing stakeholders a better comparison among plants, and driving industry-wide improvements.

Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia.

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 µg/m3 to 73 µg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

In search for climate neutrality in ice hockey: A case of carbon footprint reduction in a Finnish professional team.

Mitigation actions in all sectors of society, including sports, to limit global warming have become an increasingly hot topic in public discussions and sports management. However, so far, there has been a lack of understanding and practical examples of how these organizations, especially in team sports, can holistically assess and reduce their climate impacts to achieve carbon neutrality. This paper presents a carbon footprint assessment, implemented actions for GHG emission reduction, and offers the example of a professional Finnish ice hockey team that achieved carbon neutrality. The study is based on a life cycle assessment method. The Results show that the team's carbon footprint was reduced from 350 tCO2eq by more than 50% between seasons 2018-2019 and 2021-2022 in the assessed categories. The most GHG emission reductions were achieved in the team's and spectators' mobility and ice hall energy consumption. Furthermore, the team compensated for their remaining emissions to achieve carbon neutrality. Multiple possibilities for further GHG emission reductions were recognized. The majority of the GHG emissions were linked to the Scope 3 category, indicating that co-operation with partners and stakeholders was a key to success in attaining carbon neutrality. This paper also discusses the possible limitations and challenges that sport organizations face in assessing climate impacts and reducing GHG emissions, as well as the prospects of overcoming them. Since there are many opportunities for sports to contribute to climate change mitigation, relevant targets and actions to reduce GHG emissions should be integrated into all sport organizations' management.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak.

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.