Search Results (49)

Accessibility indicators derived from web-map platforms are increasingly used in sustainable spatial planning, service allocation, and land-value modelling, particularly in data-constrained regions. Yet the reliability of such source-dependent measures for decision-making remains insufficiently examined. Using paired parcel-level data from Khyber Pakhtunkhwa, Pakistan, this study conceptualizes accessibility as a spatial measurement process with structured source uncertainty by directly comparing platform-derived (PD) and field-verified (FV) nearest-facility distances across five facility types. Cross-source analysis reveals substantial facility-specific discrepancies in both magnitude and rank ordering, with certain facility types exhibiting near-random or reversed parcel rankings between sources. Spatial diagnostics further demonstrate that discordance events are geographically clustered rather than randomly distributed. An exploratory local amenity-density check further shows that mismatch prevalence varies systematically with nearby POI context, although the relationship is heterogeneous rather than uniformly sparse-driven. Under spatial block cross-validation, land-value models using FV accessibility consistently outperform PD-based models, while PD-based models display fold-level instability. Moreover, coefficient sign orientation and relative importance vary systematically across sources, indicating interpretation sensitivity to measurement choice. Importantly, reducing magnitude error alone does not restore decision reliability when ordering instability persists. These findings show that accessibility source choice can reshape spatial prioritization and inferred price gradients, introducing decision risk into sustainability-oriented planning. We therefore propose a minimum reliability protocol—including discrepancy profiling, ordering diagnostics, spatial discordance mapping, and spatially structured validation—to support transparent and defensible accessibility analytics in data-constrained environments. Full article

In the context of rural sustainability and poverty alleviation within the developing world, a key dilemma facing the international community is to identify suitable strategies and mechanisms to bring multiple stakeholders together to work in efficient and sustainable ways. This paper focuses on the Public–Private–Producer Partnership (4P), a model that involves cooperation between government agencies, business firms, and small-scale producers to foster mutual trust and enhance collaboration through infrastructure development and capacity building in the agricultural value chain. Drawing on evidence from China, Indonesia, Rwanda, Ghana, and Nigeria, this study examines the impact of 4P on crop productivity, agricultural infrastructure, market access, stakeholder empowerment, employment, the land tenure system, and household income. This paper combines value chain analysis, Theory of Change mapping, and both qualitative and quantitative evaluation techniques to assess how the 4P model functions in different institutional and ecological contexts. While the model promotes inclusive growth, it also faces challenges such as price volatility, insufficient long-term sustainability, and limited integration of smallholder farmers into formal value chains. The paper discusses policy implications for improving the 4P model’s effectiveness in poverty alleviation and local economic development, highlighting the importance of better governance structures, financial mechanisms, and market stability. This paper sheds new light on inclusive, justified, and sustainable collaboration mechanisms for participatory agencies and individuals. Full article

This study applies an integrated analytical framework combining GeoDetector and multiscale geographically weighted regression (MGWR) to examine how the spatial distribution of cultural heritage values in the Chungcheong region of South Korea (Chungcheongnam-do and Chungcheongbuk-do) relates to regional socio-spatial contexts. Using the Korea Heritage Service’s heritage basic survey data (coordinates, attributes, and value assessments), we aggregated heritage value scores to a 1 km grid and modeled six value dimensions—historical, artistic, academic, social, rarity, and conservation—as separate dependent variables. We then integrated socio-spatial indicators derived from statistical grid maps published by the National Geographic Information Institute (official land price, building density, green space, road accessibility, total population, working-age population share, and aging rate). GeoDetector was first used to identify key determinants and interaction effects by value dimension, and MGWR was then used to estimate local effect heterogeneity and variable-specific operating scales. Results show that heritage values are better explained by multi-factor configurations—urbanization, land value, green space, accessibility, and demographic structure—whose importance varies by value dimension, and that the same factor can exert different directions and strengths across local contexts. By linking “what matters” (key determinants) with “where and at what scale it matters” (local effects and bandwidths), this study provides quantitative evidence to support place-based conservation and utilization strategies. The proposed GeoDetector–MGWR framework is transferable to other regions where spatial heritage inventories and comparable socio-spatial indicators are available. Full article

The 15-min city (15 MC) is an urban planning concept that organizes cities through proximity-based systems, enabling residents to access essential services within a 15-min walk or cycle. Although the health and environmental benefits of this model are well documented, its effects on the real estate market have received limited attention. This paper examines the impact of 15-min proximity-based urban planning models on land use patterns, property values, and sociospatial interactions in urban settings. It adopts a scoping review approach (structured mapping and synthesis of the available literature) and, using a transparent source selection process (PRISMA-ScR), compiles evidence on how functional accessibility, mixed uses, and proximity to green/public spaces affect prices and rents in residential and/or commercial real estate. The synthesis shows that proximity is often capitalized as a proximity premium, but it can exacerbate inequalities and displacement risks without accompanying regulatory mechanisms. Based on the findings, an operational spatial–economic framework is proposed that links (a) planning interventions, (b) functional accessibility, (c) behavioral adaptation, (d) market valuation reactions, and (e) governance/redistribution tools (e.g., land value capture, inclusionary zoning), as a diagnostic tool for assessing surplus value and displacement risk and as a basis for future GIS/hedonic testing. Full article

District heating systems are central to Europe’s decarbonisation strategy and its 2050 climate-neutrality objective. However, district heating is deeply embedded in the socio-economic system and the built environment. This makes compliance with policy targets at the local level particularly challenging. The issues are attributable to two factors. Firstly, the process is characterised by a high degree of complexity and multidimensionality. Secondly, there is a scarcity of local resources (e.g., land, surface waters, waste heat, etc.). In Bucharest, Romania, the largest district heating system in the European Union, the process of decarbonisation represents a particularly complex challenge. The system is characterised by large physical dimensions, high technical wear, heavy dependence on natural gas, significant heat losses and complex governance structures. This paper presents a strategic planning exercise for aligning the Bucharest system with the Energy Efficiency Directive 2023/1791. Drawing on system data, investment modelling, and local resource mapping from the LIFE22-CET-SET_HEAT project, the study evaluates scenarios for 2028 and 2035 that shift heat generation from natural gas to renewable, waste heat, and high-efficiency sources. The central objective is the identification of opportunities and issues. Options include large-scale heat pumps, waste-to-energy, geothermal and solar heat. Heat demand profiles and electricity price dynamics are used to evaluate economic feasibility and operational flexibility. The findings show that the decarbonisation heat supply in Bucharest is technically possible, but financial viability hinges on phased investments, interinstitutional coordination, regulatory reforms and access to EU funding. The study concludes with recommendations for staged implementation, coordinated governance and socio-economic measures to safeguard heat affordability and system reliability. Full article

Understanding when and where to shift land from agriculture to forestry is essential for designing sustainable land use strategies that align with climate, biodiversity, and rural development goals. However, traditional profitability comparisons rely on long-term discounting, which is highly sensitive to assumptions and often misaligned with the shorter-term decision-making horizons that are relevant for policymakers. This study presents a deposit-based framework that interprets annual timber biomass growth as accumulating economic value, enabling direct, per-hectare comparisons with yearly agricultural profits. The framework integrates parcel-level spatial data, land quality indicators, national statistics, and expert inputs to produce high-resolution maps of annual profitability for both agriculture and forestry. Applied to the case of Latvia, the results show strong spatial variation in agricultural returns, particularly in low-quality areas where profits are marginal or negative. By contrast, forestry provides more stable, though modest, economic gains across a wide range of biophysical conditions. These insights help identify where afforestation becomes a financially viable land use alternative. The framework is designed to be transferable to other regions by substituting local data on land quality, prices and growth. It complements policy instruments such as performance-based CAP payments and afforestation support, offering a future-oriented tool for spatially explicit and economically grounded land use planning. Full article

Post-industrial waterfronts have become key arenas of urban transformation, where heritage, public space and social equity intersect. This study examined the Xuhui Waterfront in Shanghai under the ‘One River, One Creek’ initiative, which converted former industrial land into a continuous riverfront corridor of parks and cultural venues. The research aimed to evaluate whether this large-scale renewal enhanced social equity or produced new forms of exclusion. A tripartite analytical framework of distributive, procedural and recognitional justice was applied, combining spatial mapping, remote-sensing analysis of vegetation and heat exposure, housing price-to-income ratio assessment, and policy review from 2015 to 2024. The results showed that the continuity of the riverfront, increased greenery and adaptive reuse of industrial structures improved accessibility, environmental quality and cultural enjoyment. However, housing affordability became increasingly polarised, indicating emerging gentrification and generational inequality. This study concluded that this dual outcome reflected the fiscal dependency of state-led renewal on land-lease revenues and high-end development. It suggested that future waterfront projects could adopt financially sustainable yet inclusive models, such as incremental phasing, public–private partnerships and guided self-renewal, to better reconcile heritage conservation, public-space creation and social fairness. Full article

Amid growing demands for urban regeneration, metro station areas (MSAs) have emerged as critical spatial units for assessing renewal potential. However, their highly heterogeneous functional and spatial attributes pose challenges to precise classification and targeted strategy development. This study introduces the RFM (recency, frequency, and monetary) model—originally used in marketing—to the urban renewal domain. By mapping POI (point of interest) data, population density, and land price to the RFM dimensions, a three-dimensional evaluation framework is constructed. Using QGIS to process multi-source data for 118 MSAs in Beijing, we apply an improved five-quantile stratification method to classify station areas into eight renewal potential types. The results reveal a concentric spatial gradient: 24% of core-area MSAs are identified as Key-Value MSAs, while 23% of peripheral MSAs are categorized as General-Retention MSAs. Based on the classification, differentiated renewal strategies are proposed: high-potential MSAs should prioritize public space enhancement and walkability improvements, whereas low-potential MSAs should focus on upgrading basic transit infrastructure. The study provides a replicable method for classifying MSAs based on spatial and economic indicators, offering new theoretical insights and practical tools to guide evidence-based urban regeneration and station–city integration in high-density metropolitan areas such as Beijing. Full article

As technological progress and population growth continue to drive rising energy demand, renewable energy has emerged as a key focus of the global energy transition due to its environmental sustainability. However, in suitability assessments and site selection for green energy projects such as photovoltaic (PV) power generation, key criteria such as supply–demand balance and land price are often inadequately considered, despite their direct impact on decision outcomes. Moreover, excessive reliance on expert judgment for weighting, along with the neglect of inter-criterion relationships, introduces uncertainty. Combined with the presence of ill-posed problems, these issues limit the practical value of the evaluation results. This study integrates economic cost–benefit analysis into the evaluation criteria system alongside climatic and geographical criteria, constructing a set of 11 spatial indicators, including global horizontal irradiation (GHI), land prices, and regional power demand, to support PV site selection. Furthermore, a comprehensive evaluation framework is proposed that combines geographic information systems (GIS), multi-criteria decision analysis (MCDA), fuzzy comprehensive evaluation (FCE), and machine learning (ML). The framework enables the collaborative optimization of expert-constrained and data-driven criteria weighting. A national suitability zoning map for PV power plants was developed and validated against actual construction cases. The results demonstrate that the proposed methodology outperforms traditional approaches, achieving a 0.1178 improvement in weight determination compared to expert-based methods, producing a photovoltaic suitability map that more accurately reflects actual construction trends, thereby providing better and more effective support for PV site planning. Full article

The economic impact of obtaining biomass could become significant to U.S. rural economies via the establishment of a bioeconomy. In 2023, the Bioenergy Technologies Office (BETO) and Oak Ridge National Laboratory provided a road map to obtain over a billion tons of biomass for conversion to bioenergy and other products. Using information from this roadmap, this study estimates the potential positive and negative economic impacts that occur because of land use change, along with increased technological advances. This is achieved by using the input–output model, IMPLAN, and impacting 179 Bureau of Economic Analysis regions in the conterminous United States. Biomass included in the analysis comprises dedicated energy crops, crop residues, and forest residues. The analysis found that managing pastures more intensively could result in releasing land to produce dedicated energy crops on 30.8 million hectares, resulting in the production of 361 million metric tons of biomass. This, coupled with crop residues from barley, corn, oats, sorghum, and wheat (162 million metric tons), plus forest residues (41 million metric tons), provide 564 million dry metric tons of biomass. Assuming the price for biomass in 2023 dollars was USD 77 per dry metric-ton, this additional production results in an economic benefit for the nation of USD 619 billion, an increase from the Business As Is scenario (Baseline) of almost USD 100 billion per year, assuming a mature biomass industry. An additional 700,000 jobs are required to grow, harvest/collect, and transport the biomass material from the land. Full article

High-density urban development is promoted by both global and local policies in response to socio-economic and environmental challenges since it increases mobility of different land uses, decreases the need for traveling, encourages the use of more energy-efficient buildings and modes of transportation, and permits the sharing of scarce urban amenities. It is therefore argued that increased density and mixed-use development are expected to deliver positive outcomes in terms of contributing to three pillars (social, economic, and environmental domains) of sustainability in the subject themes. Territorial quality of life (TQL)—initially proposed by the ESPON Programme—is a composite indicator of the socio-economic and environmental well-being and life satisfaction of individuals living in an area. Understanding the role of urban density in TQL can provide an important input for urban planning debates addressing whether compact development can be promoted by referring to potential efficiencies in high-density, mixed land use and sustainable transport provisions. Alternatively, low-density suburban development is preferable due to its benefits of high per capita land use consumption (larger houses) for individual households given lower land prices. There is little empirical evidence on how TQL is shaped by high-density versus low-density urban forms. This paper investigates this topic through providing an approach to spatially map and examine the relationship between TQL, residential expansion, and densification processes in the so-called NUTS2 (nomenclature of terrestrial units for statistics) regions of European Union (EU) member countries. The relative importance of each TQL indicator was determined through the entropy weight method, where these indicators were aggregated through using the subject weights to obtain the overall TQL indicator. The spatial dynamics of TQL were examined and its relationship with residential expansion and densification processes was analysed to uncover whether the former or the latter process is positively associated with the TQL indicator within our study area. From our regression models, the residential expansion index is negatively related to the TQL indicator, implying that high levels of residential expansion can result in a reduction in overall quality of life in the regions if they are not supported by associated infrastructure and facility investments. Full article

This study aims to understand the spatiotemporal changes in patterns of tropical crop cultivation in Eastern Thailand, encompassing the periods before, during, and after the COVID-19 pandemic. Our approach involved assessing the efficacy of high-resolution (10 m) Sentinel-2 dense image time series for mapping smallholder farmlands. We integrated harmonic regression and random forest to map a diverse array of tropical crop types between summer 2017 and summer 2023, including durian, rice, rubber, eucalyptus, oil palm, pineapple, sugarcane, cassava, mangosteen, coconut, and other crops. The results revealed an overall mapping accuracy of 85.6%, with several crop types exceeding 90%. High-resolution imagery demonstrated particular effectiveness in situations involving intercropping, a popular practice of simultaneously growing two or more plant species in the same patch of land. However, we observed overestimation in the majority of the studied cash crops, primarily those located in young plantations with open tree canopies and grass-covered ground surfaces. The adverse effects of the COVID-19 pandemic were observed in specific labor-intensive crops, including rubber and durian, but were limited to the short term. No discernible impact was noted across the entirety of the study timeframe. In comparison, financial gain and climate change appeared to be more pivotal in influencing farmers’ decisions regarding crop cultivation. Traditionally dominant crops such as rice and oil palm have witnessed a discernible decline in cultivation, reflecting a decade-long trend of price drops preceding the pandemic. Conversely, Thai durian has seen a significant upswing even over the pandemic, which ironically served as a catalyst prompting Thai farmers to adopt e-commerce to meet the surging demand, particularly from China. Full article

The shade-grown coffee agroecosystem is rich in ecosystem services (ES). In recent years, pests and the decrease in coffee prices have caused producers to change their agricultural activities. These changes in land use have resulted in alterations in the vegetation cover that lead to the loss of ES. The objective of this research was to analyze the effects of land cover and land use changes on the ES associated with coffee production in Cumbres de Huicicila, a coffee-growing region in western Mexico. For this purpose, we analyzed land cover and land use maps for the period 2007–2019, calculated the annual rate of change and estimated the future rate of change to 2030. We used a literature review through the SALSA method to identify and estimate the impact of the ES of coffee plantations under the approach of nature’s contributions to people. As a result, we found alterations with a decreasing trend in agroecosystem cover and loss of ES related to biodiversity. We hope that this research will serve to consolidate efforts for the conservation and sustainable use of the ES of the shade-grown coffee plantations. Full article

In recent years, climate change has greatly affected agricultural activity, sustainability and production, making it difficult to conduct crop management and food security assessment. As a consequence, significant changes in agricultural land and land cover (LC) have occurred, mostly due to the introduction of new agricultural practices, techniques and crops. Earth Observation (EO) data, cloud-computing platforms and powerful machine learning methods can certainly support analysis within the agricultural context. Therefore, accurate and updated agricultural land and LC maps can be useful to derive valuable information for land change monitoring, trend planning, decision-making and sustainable land management. In this context, this study aims at monitoring temporal and spatial changes between 2001 and 2021 (with a four 5-year periods) within the Chi River Basin (NE–Thailand). Specifically, all available Landsat archives and the random forest (RF) classifier were jointly involved within the Google Earth Engine (GEE) platform in order to: (i) generate five different crop type maps (focusing on rice, cassava, para rubber and sugarcane classes), and (ii) monitoring the agricultural land transitions over time. For each crop map, a confusion matrix and the correspondent accuracy were computed and tested according to a validation dataset. In particular, an overall accuracy > 88% was found in all of the resulting five crop maps (for the years 2001, 2006, 2011, 2016 and 2021). Subsequently the agricultural land transitions were analyzed, and a total of 18,957 km² were found as changed (54.5% of the area) within the 20 years (2001–2021). In particular, an increase in cassava and para rubber areas were found at the disadvantage of rice fields, probably due to two different key drivers taken over time: the agricultural policy and staple price. Finally, it is worth highlighting that such results turn out to be decisive in a challenging agricultural environment such as the Thai one. In particular, the high accuracy of the five derived crop type maps can be useful to provide spatial consistency and reliable information to support local sustainable agriculture land management, decisions of policymakers and many stakeholders. Full article

Satellite remote sensing (RS) has been drawing considerable research interest in land-cover classification due to its low price, short revisit time, and large coverage. However, clouds pose a significant challenge, occluding the objects on satellite RS images. In addition, snow coverage mapping plays a vital role in studying hydrology and climatology and investigating crop disease overwintering for smart agriculture. Distinguishing snow from clouds is challenging since they share similar color and reflection characteristics. Conventional approaches with manual thresholding and machine learning algorithms (e.g., SVM and Random Forest) could not fully extract useful information, while current deep-learning methods, e.g., CNNs or Transformer models, still have limitations in fully exploiting abundant spatial/spectral information of RS images. Therefore, this work aims to develop an efficient snow and cloud classification algorithm using satellite multispectral RS images. In particular, we propose an innovative algorithm entitled UCTNet by adopting a dual-flow structure to integrate information extracted via Transformer and CNN branches. Particularly, CNN and Transformer integration Module (CTIM) is designed to maximally integrate the information extracted via two branches. Meanwhile, Final Information Fusion Module and Auxiliary Information Fusion Head are designed for better performance. The four-band satellite multispectral RS dataset for snow coverage mapping is adopted for performance evaluation. Compared with previous methods (e.g., U-Net, Swin, and CSDNet), the experimental results show that the proposed UCTNet achieves the best performance in terms of accuracy (95.72%) and mean IoU score (91.21%) while with the smallest model size (3.93 M). The confirmed efficiency of UCTNet shows great potential for dual-flow architecture on snow and cloud classification. Full article