Search Results (88)

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

Water insecurity, intensified by climate change, presents a significant challenge globally, especially in arid and semi-arid regions of Africa. In northern Ghana, where agriculture heavily depends on seasonal rainfall, prolonged dry seasons exacerbate water and food insecurity. Despite efforts to improve water access, there is limited understanding of how climate change preparedness affects water insecurity risk in rural contexts. This study investigates the relationship between climate preparedness and water insecurity in semi-arid northwestern Ghana. Grounded in the Sustainable Livelihoods Framework, data was collected through a cross-sectional survey of 517 smallholder households. Nested ordered logistic regression was used to analyze how preparedness measures and related socio-environmental factors influence severe water insecurity. The findings reveal that higher levels of climate change preparedness significantly reduce water insecurity risk at individual [odds ratio (OR) = 0.35, p < 0.001], household (OR = 0.037, p < 0.001), and community (OR = 0.103, p < 0.01) levels. In contrast, longer round-trip water-fetching times (OR = 1.036, p < 0.001), water-fetching injuries (OR = 1.054, p < 0.01), reliance on water borrowing (OR = 1.310, p < 0.01), untreated water use (OR = 2.919, p < 0.001), and exposure to climatic stressors like droughts (OR = 1.086, p < 0.001) and floods (OR = 1.196, p < 0.01) significantly increase insecurity. Community interventions, such as early warning systems (OR = 0.218, p < 0.001) and access to climate knowledge (OR = 0.228, p < 0.001), and long-term residency further reduce water insecurity risk. These results underscore the importance of integrating climate preparedness into rural water management strategies to enhance resilience in climate-vulnerable regions. Full article

This study aimed to evaluate the performance and robustness of the GR2m “Génie Rural à 2 paramètres au pas du temps Mensuel” rainfall–runoff model for simulating streamflow under past and future hydrometeorological shifts in the Medjerda, a data-scarce Mediterranean catchment in northern Tunisia characterized by limited hydrometeorological records and high climate variability. The evaluation was conducted across three subcatchments characterized by contrasting climatic conditions and representing the hydrometeorological pattern of the Medjerda catchment. To assess the model’s robustness, a calibration–validation process was applied. This method alternated between dry and wet periods and evaluated model performance through various criteria. Subsequently, GR2m was adopted to simulate projected discharge, using projections from the “Model for Interdisciplinary Research on Climate 5” (MIROC5) under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Standardized climate indices (SCIs) were employed to assess climate change impacts. The results demonstrate that GR2m performs well in simulating streamflow across different climatic conditions within the Medjerda catchment and maintains satisfactory performance when calibrated over a non-stationary climate period. The findings indicate a continuous decline in projected runoff and suggest a significant increase in extreme drought events. Full article

Background and Objectives: Breast reconstruction following mastectomy improves quality of life and psychosocial outcomes, yet it is not consistently performed despite multiple federal mandates. Current data shows decreased reconstruction in minority races, those with a low socioeconomic status, and those holding public health insurance. Many barriers remain misunderstood or unstudied. This study examines barriers to post-mastectomy breast reconstruction to promote a supportive clinical climate by addressing multifactorial obstacles to equitable access to care. Materials and Methods: The California Cancer Registry Data Surveillance, Epidemiology, and End Results (SEER) database and California Health and Human Services Agency Cancer Surgeries Database (2013–2021 and 2000–2021, respectively) were used in this retrospective observational study on mastectomy with immediate breast reconstruction (IBR), delayed breast reconstruction (DBR), or mastectomy only (MO) rates. Data were collected on age, sex, race, insurance type, hospital type, socioeconomic status, and residence. Pearson’s chi-square analysis was performed. Results: We found that 168,494 mastectomy and reconstruction surgeries were performed (82.36% MO, 7% IBR, 10.6% DBR). The 40–49 age group received significantly less MO (38.1%) compared to the 70–74 age group (94.8%, (p = <0.001). Significantly more reconstruction was carried out in patients with private, HMO, or PPO insurance (IBR 75.86%, DBR 75.32%, p = <0.001). Almost all breast surgeries were in urban areas as opposed to rural/isolated rural areas (96.02% vs. 1.55%, p = <0.001). There was no significant difference between races. Of all surgeries, 7.46% were completed in a cancer center with significantly higher rates of IBR. LA County, San Luis Obispo/Ventura County, and Northern CA had significantly more MO than other regions (p = <0.001). Conclusions: Reconstruction rates after mastectomy are low, with only 17.64% of patients undergoing reconstruction. Nationally, 70.5% of patients received MO, with 29.6% undergoing reconstruction. Significant factors positively contributing to reconstruction were private insurance, high SES, cancer center care, and urban residency. Identified barriers include public health insurance enrollment, rural or non-urban residence, older age, low SES, and non-white race/ethnicity, indicating potential monetary influences on care. Full article

The northern foothills of the Qinling Mountains, a critical ecological barrier and urban–rural transition zone in China, face intensifying rainstorm–flood disasters under climate extremes and rapid urbanization. This study pioneers a remote sensing-driven, dynamically coupled framework by integrating multi-source satellite data, system resilience theory, and spatial modeling to develop a novel “risk identification–resilience assessment–scenario simulation” chain. This framework quantitatively evaluates the nonlinear response mechanisms of town–village systems to flood disasters, emphasizing the synergistic effects of spatial scale, morphology, and functional organization. The proposed framework uniquely integrates three innovative modules: (1) a hybrid risk identification engine combining normalized difference vegetation index (NDVI) temporal anomaly detection and spatiotemporal hotspot analysis; (2) a morpho-functional resilience quantification model featuring a newly developed spatial morphological resilience index (SMRI) that synergizes landscape compactness, land-use diversity, and ecological connectivity through the entropy-weighted analytic hierarchy process (AHP); and (3) a dynamic scenario simulator embedding rainfall projections into a coupled hydrodynamic model. Key advancements over existing methods include the multi-temporal SMRI and the introduction of a nonlinear threshold response function to quantify “safe-fail” adaptation capacities. Scenario simulations reveal a reduction in flood losses under ecological priority strategies, outperforming conventional engineering-based solutions by resilience gain. The proposed zoning strategy prioritizing ecological restoration, infrastructure hardening, and community-based resilience units provides a scalable framework for disaster-adaptive spatial planning, underpinned by remote sensing-driven dynamic risk mapping. This work advances the application of satellite-aided geospatial analytics in balancing ecological security and socioeconomic resilience across complex terrains. Full article

In China, retrofitting rural dwellings is a crucial step toward enhancing living conditions and lowering energy waste. One of the most important ways to enhance building performance is to retrofit the building envelope. The Qianbei Region’s (Northern Guizhou Province, China) rural dwellings are the subject of this study. It identifies the persistent issue of inadequate thermal comfort in local rural dwellings through indoor thermal environment measurements and questionnaire surveys. Using a parametric modelling tool (Rhino-Grasshopper-Ladybug Tools), multi-objective optimization was performed using a non-dominated sorting genetic algorithm (NSGA-II), with the types of external windows, walls, and roof insulation as optimization variables, and building energy consumption (E), annual thermal discomfort hours (TDT), and life cycle cost increment (ΔLCC) as optimization objectives. After the retrofitting, the building’s energy consumption was reduced from the baseline value of 96.41 kWh/m² to 42.40 kWh/m² (a 56% reduction), and the annual duration of thermal discomfort decreased from 6173 h to 5078 h (a 17.7% decrease). This resulted in a positive economic return, with a cost saving of ΔLCC = −56,329.87 CNY. The research proposes a scientific method for the energy-saving retrofitting of rural dwellings in the Qianbei Region, which also serves as a guide for the optimization of building performance in comparable climate zones. Full article

In the context of rapid urbanization and extreme climate change globally, balancing ecological resources and economic development for land spatial planning has become one of the pressing issues that need to be addressed. This study proposes a composite model to construct a spatial ecological security pattern. It identifies restoration areas with different risk levels based on the spatial distribution of land use, offering suggestions for optimizing spatial configuration. Focusing on the central Shaanxi region of the Yellow River Basin in China, ecological sources are identified by integrating ecological factors, and ecological corridors and restoration zones are extracted using the minimum cumulative resistance difference and circuit theory. The results indicate significant improvements in ecological quality and desertification in the study area from 2000 to 2020. Currently, the core area covers 51,649.71 km², accounting for 62.18% of all landscape types; the total ecological source area covers 31,304.88 km², representing 18.84% of the entire area. These ecological source areas are mainly distributed in the northern Loess Plateau and the southern mountainous regions. The area has 26 important ecological corridors, identifying 16 ecological pinch points and 12 ecological barriers, presenting an ecological security pattern characterized by a grid-like structure in the northern region and a dispersed pattern in the southern region. Additionally, 273.72 km² of ecological restoration priority areas and 197.98 square kilometers of ecological restoration encouragement areas are proposed as key planning regions for ecological environmental protection. This study provides references for optimizing spatial configuration to promote the sustainable development of urban and rural living environments in the Yellow River Basin. Full article

The development and implementation of regional protection plans for ecosystem carbon storage services have been recognized as crucial actions for mitigating global climate change. However, the supply areas of carbon sequestration in terms of ecosystem service flows in inland regions are still less evaluated. The goal of this study is to identify the priority-ranked supply areas for carbon sinks. Here, we conducted a case study in the Hexi Region of northwestern China and proposed a framework to quantify the priority supply areas for carbon sinks from the perspective of ecosystem service flows. Firstly, we quantified the carbon service supply and demand areas by combining carbon models (i.e., the Carnegie–Ames–Stanford Approach model and soil respiration models) with socioeconomic and natural factors. Then, we introduced a breaking point formula to estimate ecosystem service flow, specifically focusing on distance or range. Finally, we determined priority supply areas for carbon sinks based on the Zonation model. The results showed that significantly higher carbon sequestration values were detected in the Qilian Mountains, ranging from 2.0 to 3.0 t hm⁻², in comparison with desert oasis areas, where the supply values ranged from 0 to 0.01 t hm⁻². The urban areas and rural settlements within the study area are characterized by higher values of carbon emissions compared to those in the Qilian Mountains and deserts. The carbon flow analysis demonstrated that the middle and northern parts of the study area, being characterized by lower precipitation and sandy landscapes, were identified as locations with low carbon sequestration fluxes (<1.0 t hm⁻²). In addition, the mountainous regions were identified as the main highest priority area for ecosystem carbon sequestration, covering 8.33% of total area of the Hexi Region. Our findings highlighted the importance of the Qilian Mountains in terms of sustaining carbon sequestration service supply in the Hexi Region and targeted ecological protection practices to be implemented going forward. Full article

Adaptation to climate change has remained a major socio-ecological issue in the Northern Region of Cameroon since 1973. Presently, this region is subject to the severe chaos of drought, floods, and ecosystem degradation, causing harm and disrupting climatic patterns. Climate change results in the drying of surface water and crops, threatening food security and the well-being of households. It has a serious impact on the entire agricultural production system at global scale. Here, it is suggested that successive adjustments to deeper systemic and transformational adaptations through efforts from NGOs, the Government, and donors, as well as innovations, are necessary to offset the negative impact of climate change on the agricultural value chain. Therefore, this research aimed to identify adaptation strategies and practices for rural communities and households, who suffer from limited access to these agricultural innovations, for a transformative adaptation. Through surveys and focus group discussions carried out in several villages in the Northern Cameroon Region, this study provides empirical data on emerging agricultural innovations in contrasting socio-economic, agricultural, and ecological contexts. Our findings demonstrate that agricultural innovations fostered at the village level have several characteristics that contribute to adaptation and mitigation of the impact of climate change. To begin with, conservation agriculture is very interesting, because crop residues left on the soil protect it from rainfall and dry winds, and gradually add humus to the top soil. In addition, agroforestry plays an important role for the household regarding ecosystem services, including food supply, soil fertility, protection from erosion, regulation of water regime, and sociocultural value. Generally, heads of households (83%) were more involved in innovative initiatives than other social strata, resulting in unequal access and proximity to agricultural innovations. Furthermore, the results highlight a significant lack of coordination and poor visibility of permanent structures supporting agricultural innovations at local level, weakening the sustainable transformation of adaptation. From a scientific perspective, this study could help build a conceptual relationship between agricultural innovation and sustainability transformation, i.e., a climate-smart agriculture. In practice, it provides levers that can be used to multiply and expedite agricultural innovation processes, water conservation, and livestock sustainability, thus contributing to the sustainability of the whole agricultural system in Cameroon and within the Sahel region of Africa. Full article

Water scarcity in Chile has been increasing in recent years, particularly in the central-northern region, associated with a sustained decrease in rainfall and the effects of climate change. This study characterizes the hydrosocial cycle in the Metropolitan Region of Santiago, Chile, with a focus on rural areas, examining the relationship between water availability and socioeconomic factors. For this, demographic data and data related to water demand and use, obtained from government databases, were used. In addition, geographic information systems (GIS) were used for spatial analysis and map creation. Finally, surveys were conducted in rural schools and households to obtain information on water use perceptions and practices. The results show inequalities in access to water with a moderate negative correlation between poverty and water connection/consumption. Rural areas exhibited stronger negative correlations, indicating a greater impact of poverty on water access. Water-saving practices, such as reusing washing water for irrigation, were prevalent in rural households. These results highlight the importance of the hydrosocial cycle to understand the dynamics and factors that shape water demand and consumption in a highly complex region. Full article

This study deals with the future of the traditional agroforestry systems (TAFSs) in the northern Mediterranean Basin. Important productive systems, such as ancient non-irrigated olive groves and extensive silvopastoral systems, from subalpine grasslands to coastal landscapes, are being irreversibly degraded, threatening the bio-cultural heritage of the Mediterranean region, an area recognized as a global biodiversity hotspot. In the midst of the global biodiversity and climate crisis, immediate actions are proposed for the protection and conservation management of TAFSs, within a new EU policy framework. Having the TAFSs of Greece in the spotlight of this analysis, a four-step approach was used in order to review the value of agroforestry in terms of (a) biodiversity (birds, mammals, invertebrates and soil biota), (b) agroforestry landscapes, such as traditional olive groves on terraces and valonia silvopastoral systems, (c) ecosystem services, especially concerning cultural values, and (d) the modern threats to traditional agroforestry. Through this research, problems are highlighted and European policy priorities are defined. Our conclusion is that there is an immediate need to revise the European Union rural, forestry, and environmental policies in the Mediterranean region, following the provisions of the new EU Nature Restoration Law, in order to revive agroforestry landscapes and make them productive and sustainable again for the benefit of rural economies, local communities, and biodiversity, especially in marginal Mediterranean mountainous and island areas, where depopulation and susceptibility to wildfires are major threats. Full article

As urbanization advances, rural construction and resource development in China encounter significant challenges, leading to the widespread adoption of standardized planning and design methods to manage increasing population pressure. These uniform approaches often prioritize economic benefits over climate adaptability and energy efficiency. This paper addresses this issue by focusing on traditional mountain villages in northern regions, particularly examining the wind and thermal environments of courtyards and street networks. This study integrates energy consumption and comfort performance analysis early in the planning and design process, utilizing Genetic and XGBoost algorithms to enhance efficiency. This study began by selecting a benchmark model based on simulations of courtyard PET (Physiological Equivalent Temperature) and MRT (mean radiant temperature). It then employed the Wallacei_X plugin, which uses the NSGA-II algorithm for multi-objective genetic optimization (MOGO) to optimize five energy consumption and comfort objectives. The resulting solutions were trained in the Scikit-learn machine learning platform. After comparing machine learning models like RandomForest and XGBoost, the highest-performing XGBoost model was selected for further training. Validation shows that the XGBoost model achieves an average accuracy of over 80% in predicting courtyard performance. In the project’s validation phase, the overall street network framework of the block was first adjusted based on street performance prediction models and related design strategies. The optimized model prototype was then integrated into the planning scheme according to functional requirements. After repeated validation and adjustments, the performance prediction of the village planning scheme was conducted. The calculations indicate that the optimized planning scheme improves overall performance by 36% compared with the original baseline. In conclusion, this study aimed to integrate performance assessment and machine learning algorithms into the decision-making process for optimizing traditional village environments, offering new approaches for sustainable rural development. Full article

The protection of biological diversity in nature and in agriculture, including the production of ornamental crops, has become increasingly important in Poland as well as worldwide. The Convention on Biological Diversity, signed in 1992 at the Earth Summit of the UN in Rio de Janeiro and ratified by the Polish government in 1995, imposed new regulations related to the protection of nature and the genetic resources of cultivated crops in Poland. The conservation of the genera, varieties and cultivars of ornamental geophytes—a group of plants of great interest from a botanical and physiological, but also a horticultural point of view—takes place in situ (both in nature and in the places of cultivation) and through the establishment of ex situ gene banks and collections. The natural genetic resources of ornamental geophytes include species from the genera Allium, Fritillaria, Gladiolus, Iris, Leucojum, Lilium and Muscari, among others, and more than a dozen species are protected by law due to varying degrees of threats. Botanical gardens play an essential role in the conservation of endangered species. Their activities focus on genus monitoring, managing ex situ gene banks (including National Collections), developing propagation methods and carrying out their reintroduction. In order to protect the national genetic resources of cultivated plants, the National Centre for Plant Genetic Resources at the Plant Breeding and Acclimatisation Institute—National Research Institute, under the auspices of the Ministry of Agriculture and Rural Development, was established. Concerning ornamental geophytes, the National Centre coordinates two field collections of cultivars of the genera Gladiolus, Lilium, Narcissus and Tulipa, which are of great economic importance and have a long tradition of breeding in Poland. The first one is located at the National Institute of Horticultural Research in Skierniewice (central Poland), and the second one is at the Experimental Substation of Variety Testing in Lisewo (northern Poland). The history of tulip collections in Poland dates back to the 1960s. At that time, the first breeding work for this species began. The collection of bulbous crops in Skierniewice is currently one of the largest in Poland, with a total of 934 accessions. Most of them are tulips (522) and lilies (222). Other plants in the collection in Skierniewice are gladiolus and narcissus. The most valuable accessions are grown under special protection (tunnels with dense nets) to guard against insects and maintain a mild climate inside. The genetic resources of the ornamental bulb plant collection in Lisewo currently consist of 611 accessions, mainly tulips (358), daffodils (121) and gladioli (132). All bulbous crops in both collections (Skierniewice and Lisewo) are grown in accordance with all principles of agrotechnics (negative field selection, fertilisation, soil maintenance). A particularly important task of botanical gardens, universities, research institutes and the National Centre is leading research on the methods of storage for survival organs, in vitro cultures and cryopreservation. We have discovered that the various activities for the species conservation of ornamental geophytes require a great deal of constantly deepening knowledge and extraordinary measures, including frequent monitoring of the effects of the applied measures. Full article

Climate change presents significant challenges to agriculture in developing nations, affecting farmers’ livelihoods and food security. In Ethiopia, agriculture is crucial to the economy and the well-being of millions. This study focuses on analyzing the determinants that affect smallholder farmers’ adoption of climate-smart agriculture (CSA) technologies in the North Wello administrative zone, northern Ethiopia. Through multivariate and ordered probit econometric models, data from 411 rural household heads were analyzed. Results reveal the synergy among climate-smart agricultural practices in rainfed farming systems, essential for crafting a comprehensive CSA package within an agroecological framework. The multivariate probit estimation results show that education, membership in local organizations, farm size, tropical livestock unit (TLU), irrigated land ownership, plot number, steep slope, farmland distance to home, and access to a local media source are positive determinants for the decision to use the type and several climate-smart agricultural practices. Lack of credit, large family size, distance from extension services, and proximity to the nearest market were all associated with lower adoption of all CSA technologies. The findings suggest that blanket recommendations for climate-smart agricultural technologies for smallholder farmers can be avoided. The complementarities that exist between CSA technologies may require further investigation into how such complementarities have benefits in terms of improving land productivity and food security and reducing climate-related risks for smallholder farmers in Ethiopia and other contexts. Moreover, by emphasizing an agroecological framework, the study promotes environmentally sustainable and socially equitable agricultural practices that are economically viable, contributing to broader environmental sustainability and development goals. Full article

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring. Full article