The present study in Maharashtra, India, assess 27 farmers adopting Saguna Rice Technology (SRT). SRT ensures economic and the environmental wellness. However, barriers like weed control, a lack of equipment, and behavior aspects may hinder the adoption. Qualitative approach, the Theory of Change framework, and Technology Adoption Model (TAM) are integrated to highlight enablers of SRT. The results indicate that awareness campaigns—with demonstrations, capacity-building initiatives, and supportive policies are essential for the expansion of SRT—a sustainable and resilient rice farming system. The study highlights the benefits, barriers and ways to scale up climate-smart farming practices like SRT. Study helps to ensure sustainable livelihoods, mitigation of climate change, and optimal use of resources.
In the Meknes region, groundwater is essential for drinking and irrigation. Facing growing agricultural and urban pressures, this study applies three complementary indices (Water Quality Index [WQI], Microbiological Quality Index [MQI], and Nitrate Pollution Index [NPI]) to provide an evidence-based environmental diagnosis. From December 2016 to November 2017, 14 wells were sampled. Physicochemical and bacteriological analyses followed standardized protocols. The assessment combined the WQI, MQI, and NPI with multivariate statistical analyses. Physicochemical quality complies with Moroccan standards, except at well W13. The NPI shows nitrate levels below 20 mg/L in most wells, except at W7 and W13 (moderate pollution). Systematic microbiological contamination (fecal coliforms, streptococci) was detected at all sites. The WQI ranged from 26.57 to 191.26 (high spatial variability), while the MQI (3.33–4.0) confirmed widespread fecal pollution. The contrast between good chemical quality and extensive bacterial contamination demonstrates that compliance with physicochemical standards does not guarantee water safety. Well W13, located near a landfill, illustrates the impact of point pollution sources. This study identifies priority sites (W7, W13), establishes a multi-index baseline, and proposes transferable tools for other semi-arid regions. It recommends preventive measures: well protection, improved waste management, and regular bacteriological monitoring.
Cities face increasing pressures from climate change, biodiversity loss, and accelerating urban expansion, making informed management of urban green infrastructure (UGI) essential for climate adaptation, public health, and long-term urban resilience. While UGI provides vital ecosystem services (ES), stakeholders, particularly university students as future planners, administrators, and civic actors, often have uneven understandings of their contributions to urban well-being and to evidence-based environmental governance. This case study examines how students from diverse academic disciplines perceive ES in Băneasa Forest, an urban forest in Bucharest exposed to development pressures, and engage in sustainability-related decision-making within a contested urban land-use context. Disciplinary background strongly shapes initial understandings and conservation priorities, reflecting knowledge asymmetries that parallel real-world governance dynamics between ecological experts, administrative sectors, and sociocultural stakeholders, yet a brief, targeted educational intervention improves conceptual clarity, ecological literacy, and willingness to participate in civic and service-learning initiatives. Notably, gains in the recognition of regulating and supporting services highlight the potential of interdisciplinary education to strengthen the cognitive foundations required for precautionary and ecosystem-based urban governance. By demonstrating how ES frameworks can foster interdisciplinary learning and informed action in a real metropolitan environmental context, this study offers a transferable model for integrating UNESCO-aligned Education for Sustainable Development into higher education curricula while simultaneously enhancing institutional capacity for cross-sectoral collaboration in urban forest management. In doing so, it positions universities as active intermediaries at the science–policy–society interface, contributing to more resilient and participatory urban environmental governance.
This case study examines the transformation of Muduligadia, a small forest-fringe village located near the Satkosia Tiger Reserve in Odisha, India, into the state’s first officially recognized eco-village. In 2011, Muduligadia consisted of 27 households facing severe livelihood insecurity, seasonal flooding, low literacy levels, and high dependence on forest resources. By 2019, the village had grown to 35 households and developed a diversified local economy centered on eco-tourism, organic agriculture, waste management, and community-led environmental stewardship. Central to this transformation was the formation of the Eco-Development Committee, which functioned as a local governance institution facilitating collective action, conflict resolution, and coordination with government programs. Through initiatives such as LPG adoption, sanitation improvements, community-managed eco-tourism at the Satkosia Sand Resorts, and organic farming practices, Muduligadia reported reduced dependence on forest extraction while generating new income streams. Village committee records and media reports suggested that household incomes increased following the expansion of eco-tourism and related livelihood activities, while villagers and local reports associated improved sanitation and cleaner cooking fuel adoption with better household health conditions. Local accounts and village records also suggested modest reverse migration as some households returned following the emergence of new livelihood opportunities. Drawing on concepts from commons governance, institutional theory, and the sustainable livelihoods framework, this case analyzes the institutional mechanisms that enabled Muduligadia’s transformation. It also critically examines the conditions under which elements of eco-village models may or may not be adapted across rural contexts. While Muduligadia demonstrates how community institutions can enable environmental stewardship and livelihood diversification, its success is shaped by context-specific factors including strong local leadership, small population size, and tourism potential. The case invites students and policymakers to examine how grassroots governance, state support, and local ecological conditions interact in shaping sustainable rural development.
This study explores the ecological and socioeconomic dynamics of the river Kosi, a vital tributary of the Ganga known as the “Sorrow of Bihar.” Flowing through Nepal and India, the Kosi sustains communities and supports rich biodiversity. The river’s heavy sediment load from rainfall causes significant soil erosion and aggradation in the plains. In March 2024, ICAR-Central Inland Fisheries Research Institute (ICAR-CIFRI) conducted a survey under the National Mission for Clean Ganga-III to assess the river’s ecological integrity and the socioeconomic conditions of local fishers. Five sampling sites in Bihar revealed 44 fish species from 18 families, alongside diverse plankton and benthic communities. Water quality analysis showed significant variations, highlighting the need for regular monitoring. The socioeconomic survey indicated declining fish stocks due to destructive fishing practices and habitat degradation, threatening fishers’ livelihoods. The findings emphasize the need for effective conservation strategies to ensure the sustainability of the Kosi river ecosystem and the well-being of its communities.
The existing scholarship on urban studies has given less emphasis on the interactions of the urban populace with their surrounding greeneries and their patterns, as well as the determinants that influence the interaction. To fill this existing gap, the present study examines the factors, that is, how age and occupation determine people’s interactions with the existing greeneries in the city of Kharagpur, West Bengal, India, utilising rigorous primary data collected through structured questionnaires employing a cluster random sampling method. The results show that the dweller’s average frequency of visiting an urban park (8.85 days per year) and participation in urban afforestation activities (2.96 days per year) is relatively low, indicative of limited people engagement with greeneries. Age and occupation emerged as significant factors contributing to this low park visitation. College and high school students emphasised the pressures of education, including home tuition and educational assignments. At the same time, working professionals indicated formidable time constraints exacerbated by weekday commitments and familial responsibilities even on the weekends to visit parks. It is because working professionals indicated that they don’t have time during the week or even on the weekends to visit urban parks because of their jobs, managing their families and offices. Moreover, regarding citizen willingness to participate in afforestation activities, environmental reasons predominate motivation (61.88%), followed by recreational (40.09%), social (29.79%), aesthetic (21.28%), commercial (2.08%), and other factors (8.32%), respectively. Lastly, the findings suggest that older people know more about plant diversities and their utilities than younger generations.
Lakes as ecosystems are sensitive to climatic variables, with Lake Surface Water Temperature affecting lake ecosystem health. This study analyzes the spatial and temporal dynamics of Lake Surface Water Temperature in Lake Sevan-Armenia's largest freshwater body-using Copernicus Global Land Operations (C-GLOPS) data from 2017-2023. It investigates the C-GLOPS lake surface water temperature relationship with climatic variables (e.g., air temperature, solar radiation). The study aims to characterize Lake Surface Water Temperature fluctuations and assess their correlations with climate drivers. Key methods include time series analysis using the Mann-Kendall test and Sen's slope estimator, linear correlation analyses, and Mean Shift Segmentation to detect spatial Lake Surface Water Temperature patterns. C-GLOPS data-based findings reveal Lake Surface Water Temperature exceeds air temperature in winter (December-March), with solar radiation dominating from February to July. Air and water temperatures align in spring, peaking in July, while solar radiation peaks in August. The seasonal trends are consistent. Time-lag effects are evident; early-year air temperatures influence Lake Surface Water Temperature later in the year. Spatially, two main clusters (Big and Small Sevan) are visible in winter, merging by spring. Summer shows diverse thermal zones, which stabilize by late autumn. Overall, the study reveals a complex but measurable link between Lake Surface Water Temperature and climatic variables and using C-GLOPS data for monitoring can help enhance understanding of how climatic variables affects lake systems.
Medicinal plant production in Eastern Iran plays a vital role in local livelihoods and traditional health care but faces increasing pressure from water scarcity and environmental stress. This study evaluates the resilience of social-ecological systems supporting medicinal plant cultivation across four eastern provinces using a structured perception-based Likert-scale questionnaire administered to 400 producers. Composite resilience scores were constructed from standardized ecological, agricultural, and social indicators to enable provincial comparison. Results indicate differentiated resilience patterns: North Khorasan emphasizes connectivity and plant protection, Razavi Khorasan demonstrates comparatively stronger ecological stability, South Khorasan highlights spatial heterogeneity, and Sistan and Baluchestan shows adaptive responses to environmental constraints. These findings reveal region-specific configurations of adaptive capacity rather than uniform resilience performance. The study provides a structured assessment framework to inform provincial-level strategic planning and decision-making in arid and semi-arid contexts while acknowledging the interpretive scope and limitations of perception-based resilience evaluation.
Chitala (Chitala chitala) fisheries in middle stretch of River Ganga, India, represent an important seasonal livelihood activity for small-scale fishing communities. This study examines a locally developed seed collection practice in the middle stretch of the river near Farakka, West Bengal, and explores its implications within a socio-ecological systems (SES) framework. The findings indicate that this practice provides a significant supplementary source of seasonal income, while also reflecting adaptive responses to ecological variability and market demand. However, concerns emerge regarding potential localized impacts on natural recruitment due to continuous extraction of eggs and hatchlings, alongside broader environmental pressures such as altered flow regimes and water quality degradation. The study highlights the need for context-specific management approaches that balance livelihood needs with ecological sustainability. The study contributes to understanding locally driven innovation in small-scale fisheries and identifies pathways for integrating conservation with livelihood resilience.
Land use and land cover (LULC) change represents a major driver of environmental degradation in forested landscapes undergoing increasing anthropogenic pressure. This study investigates multi-temporal land cover dynamics in the Shafarood watershed, northern Iran, over a 20-year period (2000-2020) using Landsat satellite imagery and Support Vector Machine (SVM) classification. Landsat 7 ETM+ (2000) and Landsat 8 OLI (2020) Level-1 terrain-corrected images were geometrically verified and analyzed following optimal band selection using the optimum index factor (OIF). Four land cover classes were identified: forest, agriculture, dense rangeland, and semi-dense rangeland. Classification accuracy was high for both years, with overall accuracy values of 96.75% (Kappa = 0.9472) for 2000 and 98.96% (Kappa = 0.9307) for 2020. Results reveal a net loss of 413 ha of forest and 577 ha of dense rangeland over two decades. In contrast, agricultural land expanded by 191 ha and semi-dense rangeland increased by approximately 800 ha, indicating vegetation degradation and land use conversion trends. The findings highlight ongoing ecological pressure in the Hyrcanian forest region and emphasize the importance of continuous satellite-based monitoring for sustainable land management. This case study demonstrates that medium-resolution satellite imagery combined with machine learning classification provides a reliable, cost-effective, and reliable framework for long-term environmental monitoring and sustainable land management.
India's rapid expansion of solar power capacity is central to achieving energy security, climate mitigation, and sustainable development goals. While national-level growth has been extensively documented, regional variations in deployment trajectories and policy effectiveness remain underexplored. This study presents a regionally disaggregated analysis of solar power development across Indian states between 2014 and 2025, examining spatial-temporal growth patterns, structural constraints, and policy implications. Using secondary data from national energy agencies, the study applies compound annual growth rate (CAGR) analysis, trend assessment, and policy mapping to evaluate state-wise and regional performance. During the study period, India's cumulative installed solar capacity increased from less than 5 GW in 2014 to over 75 GW by 2025, corresponding to an average annual growth rate exceeding 30%. However, the results reveal substantial regional heterogeneity: western and southern states account for more than 60% of total installed capacity, with several states exhibiting CAGRs above 35%, while eastern and northeastern regions contribute less than 10%, reflecting slower adoption and infrastructural constraints. The observed disparities are strongly associated with differences in policy execution, grid readiness, land availability, and institutional capacity. The findings highlight the necessity of region-specific policy interventions, targeted financial mechanisms, and governance reforms to enable a more balanced and resilient solar energy transition. By providing a quantitative regional perspective, this study contributes actionable insights for national energy planning and subnational sustainability strategies.
Voluntary demand for renewable energy could play a critical role in driving renewable energy deployment and grid decarbonization. An ongoing discourse explores how to optimize the impacts of voluntary demand on renewable energy deployment. Some stakeholders and scholars assert that broad classes of voluntary procurement strategies do not affect renewable energy deployment, despite the complexity of voluntary markets and the broad recognition of the importance of voluntary demand among many stakeholders. Here, a critical examination of that discourse is provided through insights from a case study of semi-structured interviews with renewable energy developers, investors, power producers, electricity buyers, and subject matter experts. The focus of this study is on demand for electricity generated from renewable energy resources, though the insights may be valuable for smaller voluntary markets for renewable fuels. The interviewee perspectives suggest that voluntary demand plays important roles in renewable energy deployment decisions and that the impacts of voluntary demand are more complex than what is often portrayed in simplified narratives in the literature. The case study provides insights into how practitioners view the role of voluntary renewable energy demand in driving the clean energy transition.
Land degradation in river basins is a critical issue influenced by hydrological changes, sediment deposition, and deforestation. This study presents an innovative Agroforestry-Based Multi-Criteria Decision Analysis Model integrating Fuzzy Logic to assess land degradation risks in the Ranganadi River Basin, India. By leveraging remote sensing and geographic information system, key risk factors such as rainfall intensity (2,500 mm/year), river sinuosity (1.73), sediment deposition (200 tons/km2/year), soil erosion (15 tons/ha/year), and land-use changes (17%) were analyzed. The model assigns weighted significance to these factors using Analytical Hierarchy Process and applies fuzzy logic to classify risk zones. Results reveal that the Ranganadi River Basin faces a moderate risk level (risk score = 0.53), warranting targeted agroforestry interventions. Alley cropping is recommended as a suitable agroforestry strategy to reduce runoff, stabilize soil, and enhance agricultural productivity. The study estimates a net agricultural loss of 2.38 million pounds (1,080,140 kg) of paddy production annually, leading to an economic loss of approximately US$262,000. The findings highlight the importance of integrating geospatial analysis with sustainable land management practices to mitigate environmental degradation. Future work will refine the model by incorporating climate resilience strategies and expanding its application to other river basins for broader environmental impact assessment.
Monitoring land use and land cover (LULC) dynamics is critical for regions experiencing rapid urbanization and agricultural change. The study discusses the dynamics of LULC in Kolar district, Karnataka, India, using the Sentinel-2 satellite image and Google Earth Engine platform of 2023. The hybrid classification approach has been adopted by combining spectral indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) with multispectral bands to enhance the classification accuracy. Four machine learning algorithms were evaluated: Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), and Classification and Regression Trees (CART). RF showed the greatest accuracy with an overall accuracy of 98% and kappa coefficient of 0.97. The RF performance had strong consumer accuracy for each land cover type, including built-up (98%), agriculture (96%), forest (98%), water bodies (100%), and wasteland (96%). In 2023, the landscape was led by agriculture (71.09%), followed by wasteland (11.41%), forest (7.49%), water bodies (6.61%), and built-up areas (3.40%) giving way to a slight but growing urban sprawl due to the district proximity to Bengaluru. The methods used in this case support Sustainable Development Goal (SDG) 11 by presenting spatial evidence towards control of urban sprawl and land degradation. From this case study, the readers can understand the comparative efficiency of different machine learning approaches towards LULC classification in a semi-arid area using Sentinel-2 data and assess the importance of integrating multiple spectral indices to improve mapping accuracy and sustainable land use planning.
Urban-industrial regions in the Global South face intertwined challenges of energy insecurity, freshwater scarcity, and environmental degradation that are strongly shaped by spatial context. This article presents a Geographic Information Systems (GIS) informed case study examining the integration of a solar-green hydrogen hybrid energy system with industrial wastewater reuse at a textile manufacturing facility in Karachi, Pakistan, a water-stressed and solar-rich megacity with concentrated industrial activity. Designed for sustainability science and GIS education, the case demonstrates how spatial analysis can inform real-world decision-making at the water-energy nexus. Using a transparent and replicable GIS workflow, the study integrates spatial datasets on solar resource availability, industrial land use, wastewater generation, and infrastructure proximity to evaluate the feasibility and sustainability implications of co-locating renewable energy generation, hydrogen storage, and wastewater treatment within an industrial zone. Results show that spatially integrated deployment improves energy reliability, substantially reduces freshwater withdrawal through wastewater reuse and water recovery, and delivers environmental benefits by displacing fossil-fuel-based electricity in dense industrial areas. The analysis further demonstrates that these benefits are highly location-dependent and diminish in settings where energy demand and wastewater availability do not spatially overlap. Through this case, readers will learn how GIS can be applied to evaluate integrated energy-water systems, assess spatial trade-offs, and develop place-based sustainability strategies applicable to other urban-industrial contexts.
The integration of solar photovoltaic (PV) systems with smart home technologies represents a key strategy for addressing environmental challenges in residential energy management, such as high consumption, grid dependency, and equipment degradation. This case study is structured in two parts: the first examines the basic synergy between SunPower solar PV systems and energy-controlling smart home devices (e.g., thermostats, lights, and plugs), demonstrating a 24% reduction in household energy use and associated environmental benefits like lower carbon emissions. The second part advances this by incorporating an artificial intelligence-driven integration layer between Tesla Solar PV systems and Google Nest Smart Thermostats, enabling real-time optimization via API communication, predictive analytics, and weather forecasting while maintaining user-defined comfort levels. Simulations show 35% savings on HVAC operations (equating to an additional 15% total energy reduction), 20-30% equipment lifespan extension, and 15% maintenance cost reductions, yielding overall 39% energy savings and further minimizing environmental impacts. This prospective case, grounded in real-world data, offers interdisciplinary insights into sustainable energy transitions, with pedagogical tools for exploring technology, policy, and equity.
This study quantifies the carbon dioxide (CO2) emissions generated by vehicles using a restaurant drive-thru, aiming to raise awareness among stakeholders and to provide site-specific insights that may inform strategies for emission reduction. A high-traffic fast-food outlet in St. John’s, Newfoundland and Labrador, Canada, was selected as an exploratory, single-site case study. Over a 20-hour observation period, 1,136 vehicles were recorded, with data collected on brand, model, and total time spent in the drive-thru. Emissions were estimated using manufacturer-reported emission rates and idling characteristics, including fuel consumption rates and idle times. Despite an average drive-thru time of just over 4 minutes—reflecting the restaurant’s efficiency policy—approximately 270 kg of CO2 was released during the observation period at this site. While this estimate is specific to the observed location and timeframe, it illustrates the potential for non-negligible emissions associated with drive-thru operations. These findings highlight the need for further research to examine similar contexts and better understand the variability and broader implications of emissions from drive-thru services and to explore mitigation measures, including policy changes, behavioral adjustments, and technological solutions, in support of Canada’s net-zero targets.
As decarbonization continues to be a key focus across the globe, the heating sector remains a critically challenging area to reduce greenhouse gas emissions. Geothermal district heating presents a low-carbon alternative to traditional fossil fuel-based heating systems, yet its deployment in the United States remains relatively limited. In this paper, we conduct case studies of existing geothermal district heating systems in the Western U.S. states and find that project economics can vary greatly from system to system. For instance, systems with more funding through grants tend to have better project economics, while only three systems have a cheaper levelized cost of heat than average state natural gas costs. Our findings underscore the need for stronger geothermal policy, along with an increased public awareness of geothermal district heating. By identifying and quantifying the key economic and regulatory barriers, this study contributes to the broader discourse on the clean energy transition and the global pursuit of net-zero emissions, all while better discerning the role of geothermal district heating in the decarbonization of heating systems. Finally, our study provides key insights on the current feasibility of geothermal district heating in the United States to inform future policymakers, developers, and related stakeholders of geothermal district heating.
Road salt-driven salinization threatens drinking water quality, public health, and water infrastructure in cold-climate regions, such as central New York. While rising sodium and chloride levels in surface water and groundwater are well-documented in the Mohawk River basin and Adirondacks region, the extent of salinization across New York are unknown. Here, we provide a case study examining salinization of Payne Brook, a losing stream connected to the shallow alluvial aquifer that is used by the village of Hamilton, New York, as a municipal drinking water supply, to evaluate whether deicing practices are salinizing Hamilton’s surface waters and how salt runoff may affect groundwater quality, infrastructure, and public health. By combining analysis of historical water quality data with seasonal surface-water sampling and complementary weather and stream-gage time-series data, we find steadily increasing sodium concentrations over time in Hamilton’s village water supply and indicators of road salt inputs characterized by elevated chloride-to-sulfate mass ratios in Payne Brook water that are consistent with a high potential for infrastructure corrosion. As a result of engaging with this case study, readers will learn how low-cost monitoring can detect potential freshwater salinization. The case study provides a framework for comparable municipalities looking to maintain winter road safety while preserving freshwater resources and reducing risks to public health.
The Talcher-Angul industrial belt in Odisha, India, is a major coal mining and power generation hub that plays a critical role in national energy security. However, rapid industrial expansion has led to escalating environmental degradation and growing public health concerns. This case examines the policy dilemma facing regional authorities: how to balance economic development and employment generation with increasing air and water pollution risks. Using field-based environmental data, the study evaluates particulate matter concentrations, water quality indicators, and heavy metal contamination through standard indices including Air Quality Index, Water Quality Index, contamination factor, geo-accumulation index, and health risk assessments. Findings indicate that particulate matter levels frequently exceed permissible limits, while water sources show signs of cumulative contamination and potential health hazards. These environmental stresses disproportionately affect nearby communities, raising concerns about long-term sustainability. The case invites readers to analyze stakeholder tensions, regulatory effectiveness, and feasible pollution mitigation strategies. It provides a decision-focused framework for examining environmental trade-offs in coal-dependent industrial regions.
