Search Results (1,007)

Municipal solid waste (MSW) generation is a global problem affecting the environment and public health. The current landfill’s useful life is reaching its end, making new site selection a priority to guarantee proper MSW management. This research evaluated the suitability of the metropolitan area of the municipalities of Chihuahua, Aldama, and Aquiles Serdan, using Spatial Decision Support Systems (SDSS) integrated with Multi-criteria Decision-making (MCDM) and hierarchical analysis, and Geographic Information Systems (GIS) to determine potential sites for new Metropolitan landfill development in a semi-arid region. Results showed that 44.7% of the areas studied present a high suitability level, while 29.52% corresponds to a very high suitability level. These areas are located mainly in the north and center zones of the Chihuahua and Aldama municipalities, with some isolated areas in Aquiles Serdan. The key selection criteria were airport distance, land slope, and proximity to the intermunicipal boundary, which enabled the identification of sites with lower environmental impact and greater technical and economic feasibility. This study demonstrates that SDSS and GIS are efficient tools for identifying potential landfill sites. The results highlight the importance of integrating technical, environmental, and social criteria into MSW management planning to achieve sustainable, efficient management in the region. Full article

Tourism has become a structural driver of land-system transformation, influencing urban restructuring, rural land consumption, coastal development, and housing dynamics. Although tourism sustainability has received growing scholarly attention, less systematic evidence exists on how land governance and spatial planning frameworks mediate tourism-related land-use change. This study presents a systematic review of peer-reviewed journal articles published between 2000 and 2025 examining the relationship between spatial planning, land-use regulation, and tourism development. Following PRISMA guidelines, a structured search strategy and multi-stage screening process were applied using predefined inclusion and quality criteria, resulting in a final dataset of 58 studies. The findings indicate that tourism-driven land transformation is shaped by interconnected governance layers, including statutory planning instruments, institutional coordination mechanisms, and land administration infrastructures. However, these dimensions are rarely analyzed within an integrated framework. By synthesizing tourism planning and land administration scholarship through a land governance perspective, this review clarifies how regulatory tools and administrative systems interact in shaping spatial outcomes across scales. The study offers a structured basis for future comparative research and for more coherent policy responses to tourism-related land governance challenge. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Managers of protected areas (PAs) face growing challenges to conserve biodiversity while responding to multiple land uses such as recreation, tourism, and resource extraction. These pressures are intensified by the impacts of climate change on ecosystems. This highlights the need for planning approaches that support decision-making in the short, medium, and long term. This article profiles Wells Gray Provincial Park as a case study to demonstrate how an ecosystem-based planning approach can be incorporated into PAs planning. Wells Gray is situated in a unique ecosystem in the interior of British Columbia (Canada). We present an innovative model that integrates land cover types, ecosystem mapping, and Biogeoclimatic (BGC) zones derived from the Biogeoclimatic Ecosystem Classification (BEC) system using GIS tools to identify ecosystems and their associated services as Critical Decision Factors (CDFs). By explicitly linking ecosystems, land cover, and spatial patterns, this approach supports the systemic inclusion of ecosystems in management decisions. To account for future uncertainty, BGC zones were projected under climate change scenarios to inform interpretations of potential ecosystem impacts. The results indicate that this integrated analysis can initiate strategic thinking and facilitate dialogue to collaboratively plan with stakeholders. This approach can improve ecosystem-based planning processes in PAs across Canada. Full article

Sewage sludge management remains a critical challenge in Greece, where increasing regulatory pressure, environmental constraints, and limited stakeholder participation complicate regional decision-making. In particular, the revision of regional Waste Management Plans requires decision-support approaches that are both technically robust and socially legitimate. This study develops and applies a participatory, data-driven multi-criteria decision analysis framework to evaluate sustainable sewage sludge management strategies in the Region of Eastern Macedonia and Thrace. The framework combines structured stakeholder participation with quantitative performance assessment, enabling transparent, reproducible, and systematic comparison of alternative sewage sludge management options. Four realistic sludge management alternatives—composting fr agriculture, forestry use, land restoration, and thermal drying with energy recovery were assessed against fifteen economic, environmental, and social sub-criteria. Data were collected through structured questionnaires administered to forty-four representatives from five stakeholder groups: utilities (water and sewerage service providers), local authorities, scientists/experts, end-users, and citizens. Group preferences were aggregated using equal group weighting to ensure balanced representation. The results show that environmental and economic criteria outweigh social aspects. The highest mean weights were assigned to compliance with environmental requirements for products derived from the disposal method (0.105) and compliance with stricter national environmental legislation (0.104), followed by energy intensity (0.097), installation cost (0.065), and operation and maintenance (O&M) cost (0.061). Overall rankings identified composting and thermal drying as the most preferred options, followed by land restoration and forestry use; sensitivity analysis (±10% variation in sub-criterion weights) confirmed ranking stability. The proposed framework enhances decision transparency by embedding measurable criteria and stakeholder inputs within a structured analytical process. From a policy perspective, it addresses participation gaps in Greek waste planning and offers a transferable decision-support tool for future regional planning. Further extensions may include integration with life cycle assessment and cost–benefit analysis to support adaptive updates under circular economy objectives. Full article

Conventional applications of the CLUE-S model rely on a static driver assumption, using driver data and their associated coefficients from a base year to simulate land-use patterns for a future target year—an approach that implicitly assumes temporally invariant human–land relationships. To address this limitation, this study introduces and compares two simulation models: the Baseline-Driven Pattern (BDP), which follows the conventional protocol by employing base-year drivers to project future land use, and the Target-Driven Pattern (TDP), which instead utilizes driver data and coefficients that correspond synchronously to the target year, thereby capturing the dynamic evolution of driving mechanisms over time. In terms of implementation, the TDP involves updated driver datasets and regression coefficients, enabling a more accurate spatial allocation of land-use demand. Comparative experimental results from Xiamen in China demonstrate that the TDP achieves higher simulation accuracy than the BDP simulation, with notably greater sensitivity to dynamic factors such as transportation infrastructure and policy boundaries. For study periods 1989–2000 and 2000–2010, the accuracy of TDP simulation for all land-use types surpasses that of BDP simulation. As time progresses, the advantage of TDP simulation over BDP simulation becomes more pronounced, resulting in a significant improvement in the simulation accuracy. These findings confirm that the temporal alignment between driver data and the simulation period is a critical determinant of CLUE-S simulation accuracy. This methodological refinement holds significant implications for model-based land-use planning: it allows simulation procedures to explicitly incorporate future driver conditions articulated in planning documents. Moreover, it equips decision makers with a more realistic simulation tool for evaluating the land-use consequences of alternative planning interventions in scenario-based analyses. Full article

The urgency to mitigate greenhouse gas emissions and address the accelerating impacts of climate change has placed renewable energy as a core part of global climate strategies. However, the expansion of renewable infrastructures with a focus on solar systems often generates competition with other land uses, raising concerns about land availability, environmental integrity, and social acceptance. Renewable energy solutions deployment must be aligned with sustainable land-use planning, particularly in diverse and multifunctional landscapes. This study presents a GIS-based Multi-Criteria Decision-Making (MCDM) methodology to identify the most suitable areas for implementing a set of six land-use-based adaptation and mitigation solutions (LAMSs) focused on solar energy. Using Python-based processing algorithms and high-resolution spatial datasets, the methodology integrates technical, environmental, and socioeconomic criteria to generate suitability maps for three different case studies across Europe: Almería (Spain), Valle d’Aosta (Italy), and the Azores (Portugal). Results reveal significant geographical disparities in suitability due to the different land constraints. Almería and the Azores demonstrate high potential for photovoltaic and agrovoltaic farms, while Valle d’Aosta’s mountainous terrain is more limited for these measures. Floating solar and solar land management measures show limited applicability across all sites. The analysis highlights the value of place-based approaches in energy planning and the utility of GIS-MCDM tools to support evidence-based decision-making, enabling context-sensitive deployment of renewable energy infrastructure. Full article

A central goal of carbon (C) management and a critical outcome of sustainable land stewardship is reducing greenhouse gas (GHG) emissions from agriculture, forestry, and other land uses. Integrating GHG considerations into management can take many forms, but C credit markets are increasingly providing sources of private capital to offset the often high costs of stewardship. In Hawaiʻi, participation in voluntary C credit markets and the establishment of jurisdictional compliance C markets are constrained by a lack of institutional capacity, successful demonstrations, and high-quality data, making private capital for C market-based approaches in Hawaiʻi difficult to access. The State of Carbon in Hawaiʻi Hui (hui translates to partnership in ʻŌlelo Hawaiʻi, the Hawaiian language) convened landowners, researchers, federal and state government professionals, and for-profit and not-for-profit organization staff to better understand limitations to implementing C management in Hawaiʻi. This paper describes why the State of Carbon in Hawaiʻi Hui was formed, how we planned for, hosted, and assessed the success of a C-focused summit, and what outcomes resulted from this process. A Pathway Forward document, a decision support tool, and this article are outcomes. These products will serve as resources for those considering Hawaiʻi-based forest C projects, as well as contributing towards the legislated goal of reducing greenhouse gas emissions in Hawaiʻi. Our knowledge exchange process is readily replicable and can support a variety of efforts in environmental conservation and beyond. Full article

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings. Full article

This paper is focused on the evaluation of the implementation of landscape ecological documents in spatial planning processes in Slovakia. The implementation of landscape ecological regulations in spatial planning is a necessary condition for ensuring sustainable land use. The basic tools in Slovakia can be considered the landscape ecological plan and the territorial system of ecological stability, which are also enshrined in legislation. The landscape ecological plan is more complex. It represents a complex process of mutual coordination of the spatial requirements of human activities with the landscape ecological conditions of the territory. This paper presents the position of landscape ecological planning in legislation, presents basic theoretical and methodological approaches to the processing of landscape ecological documentation, barriers to the successful implementation in real practice, and also defines basic measures to improve the situation. Slovakia has a good theoretical and methodological basis for processing landscape ecological studies and also legislative support, but its implementation in real practice in spatial development is weaker. The specifics and strength of landscape ecological planning in Slovakia are the bridging of the sectoral approach to landscape management and its replacement with an integrated approach. Full article

At present, most plateau-constrained cities worldwide—plateau cities whose spatial form is strictly constrained by topography—have entered the late stage of urbanization. The relationship between urban form and the surrounding geographic spatial pattern has consequently exhibited distinctive new characteristics. However, planning and policy often continue to adopt green-space allocation schemes developed in the mid-stage of urbanization and based on the experience of plain cities, resulting in difficulties in plan implementation, intensified human–land conflicts, and imbalances in both the supply–demand relationship and equity of green public services with severe challenges to urban sustainable development, calling for urgent correction and reconstruction. Through a literature review and comparative case analysis, this study clarifies global trends in the paradigm shift in plateau-city planning and develops an evaluation system comprising “adaptability analysis of originally planned spaces within the built-up area + assessment of the potential for converting ecological value in green spaces outside the built-up area + integrated spatial optimization.” Building on Analytic Hierarchy Process (AHP) weighting and spatial analysis, the study establishes a comprehensive assessment framework and applies it empirically to Kunming as a typical case, with the aim of proposing a correction-and-reconstruction paradigm for green-space allocation tailored to plateau-constrained cities to achieve sustainable development goals. The results indicate a widespread paradigm shift in many cities from “pattern optimization during incremental expansion” and “passive adaptation to ecological patterns” toward “enhancing governance effectiveness during stock-based renewal” and “proactive innovation in governance instruments.” The Kunming case shows that, during the mid-stage of urbanization, numerous parks and green spaces were planned within the built-up area (flat land), yet many of these proposals proved infeasible due to excessive costs and trade-offs. Meanwhile, the adjacent mountainous ecological spaces with substantial scenic and recreational potential were long excluded from the urban public service system. In response, this study proposes a three-dimensional allocation model that combines “optimized adaptation” within the built-up area and “potential conversion” in adjacent peri-urban areas together with differentiated policy instruments and an implementation/transfer assurance mechanism. This approach not only offers practical planning guidance for Kunming but also provides a broadly applicable set of theoretical and practical tools for improving land-use efficiency and promoting green equity in similar cities worldwide. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development. Full article

Rapid urbanization has transformed the face of Texas by converting agricultural and natural lands into expanding built-up areas. This study analyzes and simulates land-use and land-cover (LULC) changes in Kaufman County, Texas, one of the fastest-growing counties in the United States, using a hybrid Cellular Automata–Artificial Neural Network (CA–ANN) model within the Quantum Geographic Information System (QGIS) Modules for Land-Use Change Evaluation (MOLUSCE) framework. Multitemporal NLCD datasets (2001, 2011, and 2021) and six spatial drivers: Elevation, Slope, Aspect, Distance from Roads and Rivers, and Built-up Density were used in the modeling framework. Transition relationships were calibrated using the 2001–2011 LULC data, and the model was validated by simulating the 2021 LULC map from the 2011 baseline. The calibrated model was then used to simulate future LULC scenarios for 2031, 2041, and 2051. Model validation yielded an overall Kappa value of 0.84 and a correctness of 90.9%, indicating high similarity between the observed and simulated maps. The results indicate simulated urban expansion, with built-up areas increasing by nearly 30% by 2051 at the expense of cropland and open areas, with forest and water bodies slightly increasing, and wetlands remaining stagnant. The CA–ANN model effectively captured the nonlinear, spatially dependent land-transition patterns using open-source tools. These findings provided useful information for sustainable land-use planning and environmental management, with the potential to incorporate spatial modeling into regional development strategies in rapidly urbanizing areas of Texas. Full article

Urban sprawl promotes significant changes in land use and occupation by interfering with the dynamics of functional ecosystems. Among other things, it encourages forest fragmentation, the degradation of woodland edges, and altered habitat integrity. This study aims to propose a simplified and low-cost methodological framework that integrates open data and open-source tools to monitor the potential of ecosystem services (ESs) at the municipal scale. Guided by the hypothesis that rapid suburbanization leads to measurable declines in ecological integrity, the InVEST Habitat Quality model was used as a proxy to analyze the landscape’s capacity to support ES. The procedure included data acquisition and organization, land use reclassification, and scores for the threats and sensitivities, implemented through the InVEST software 3.14.2. Results indicated that urban areas more than doubled between 1985 and 2005, while habitat quality scores declined across Campinas, reflecting a decrease in the potential for ES provision. Urban expansion, mainly concentrated in the central region, occurred at the expense of agricultural and pasture areas. Forest remnants, which currently occupy only 8.5% of the municipal territory, are small and fragmented, intensifying edge effects and reducing the potential capacity to provide regulatory ES. Fragmentation and adjacent land use changes limit these habitats’ capacity to provide ES. The proposed methodology demonstrates the potential for simple and reproducible monitoring of ecosystem services at the municipal scale, providing support to local governments with limited financial and technical capacity in geospatial data processing. This framework enables municipalities to incorporate environmental indicators into planning tools, offering a scalable approach for monitoring ecosystem dynamics in urbanized regions. Full article