Search Results (707)

This study critically examines residential tourism in Isla Cortés within the context of the real estate boom and the growing sustainability challenges facing coastal regions. Driven by global mobility, investment flows, and lifestyle migration, residential tourism is reshaping coastlines through intensive urban expansion. The analysis highlights the socio-environmental consequences of this model, including habitat fragmentation, mangrove loss, increasing pressure on water resources, and the gradual privatization of coastal areas. Using a qualitative research design that combines literature review, comparative case analysis, and territorial assessment, the study identifies structural similarities between Isla Cortés and other coastal tourism enclaves while emphasizing locally specific processes shaped by Mexico’s political economy and regulatory context. Findings suggest the structurally unsustainable character of this development pathway. Although residential tourism has stimulated short-term economic growth, it has also intensified socio-spatial segregation, commodified coastal commons, and generated long-term ecological and social vulnerabilities. The study challenges dominant narratives that portray residential tourism as inherently sustainable and instead draws on ecological reflexivity and socio-ecological systems perspectives to outline alternative planning pathways. It underscores the need for stronger regulatory frameworks, nature-based solutions, participatory governance, and regenerative planning strategies capable of aligning economic activity with ecological integrity and social inclusion in coastal territories. Full article

This research examines how the relationship between cities and rural areas has evolved in light of the profound transformation affecting rural areas of high landscape value, which has been driven by the expansion opportunities granted to the real estate sector by urban planning regulations. The role of the landscape dimension in interpreting the relationship between territorial wealth and landscape value is considered, based on the convergence of two complementary disciplinary perspectives on territory: land planning and valuation science. Against this backdrop, and with a view to containing the progressive contamination of rural and agricultural heritage by the real estate sector, this study proposes a structured observation, valuation, interpretation, and regulatory tool to support the development of territorial planning in areas significantly characterized in terms of rural landscape value. The proposed tool is based on evidence regarding the phenomenon of building expansion in the agricultural territory of a municipality in southeastern Sicily, where favorable conditions for the development of the building sector exist, such as the vastness of the municipal territory and extensive farming as the mainstay of agricultural activity. This wider sub-regional area has also received attention due to the over-tourism phenomenon that has occurred in its cities of art. The evaluation approach experienced is a value-based representation of the evolution of this process over three observation periods: 2000, 2007, and 2012, relating the quantitative observation of the building expansion to the connected qualitative impact on rural landscape. It is the result of coordinating a large set of data in a hierarchical model of indices that converge to construct a synthetic index of rural landscape resilience. This achievement is based on the linguistic progression of “lexicon”, “semantics”, “syntax”, and “pragmatics”, each of which robustly supports “observation”, “valuation”, “interpretation”, and “planning”, respectively. The final stage is based on the convergence of explanatory indices, which are developed by coordinating evidence and assessments (factual and value judgements). This stage enables the proposal of a constraints system that supports a modus vivendi between the interests of the real estate sector and the values of the rural landscape in such a rich and fragile area. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

Against the backdrop of urbanization and global warming, reducing carbon emissions and achieving carbon neutrality have emerged as focal points in current urban ecological research. Urban green infrastructure (UGI) serves as the primary natural carbon sink within cities; therefore, investigating and optimizing its carbon sequestration services is a crucial step toward realizing carbon neutrality and fostering sustainable urban development. As the core components of urban ecosystems, urban parks provide essential ecosystem services that play a pivotal role in expanding carbon sinks, facilitating energy conservation and emission reduction, and enhancing urban climate resilience. This paper takes 20 parks in Linyi City’s central urban area as examples, systematically quantifies the carbon sequestration effect of urban parks in the central urban area of Linyi City from 2019 to 2024 using methods such as the Carnegie–Ames–Stanford Approach (CASA) and the gravity model, and quantitatively evaluates the equity of urban residents’ access to these services. The study shows that the overall annual average carbon sequestration rate of urban parks in Linyi City’s central area over nearly six years ranges from 202.02 gC·m⁻²·a⁻¹ to 279.31 gC·m⁻²·a⁻¹, while individual park annual averages range from 171.29 to 332.76 gC·m⁻²·a⁻¹, falling within the normal range for cities at the same latitude; in terms of vegetation carbon sequestration capacity, woody plant communities dominate in this region, with annual average carbon sequestration rates approximately 10% higher than those dominated by herbaceous vegetation. In terms of intrinsic activity performance of carbon sequestration, overall, woody-dominated plant communities exhibit greater stability and resilience under extreme weather conditions, experiencing smaller impacts on ecological functions but longer recovery cycles to peak levels. Regarding equity in the supply and demand of ecosystem services, the Gini coefficient in the study area is 0.59, indicating an extremely imbalanced state; within the same park service range, up to 60% of residents do not benefit from carbon sequestration ecosystem services. The urban supply–demand mismatch reveals that approximately 20% of the population resides in high-demand–low-supply areas, experiencing extreme ecological deprivation; only about 13% of the population falls into the high-demand–high-supply category, this group being the high-benefit recipients who enjoy both spatial convenience and high-quality ecological welfare. The theoretical implications for urban green space planning: according to the results, merely expanding park green space area to increase per capita access is myopic and inadvisable in central urban park planning. Instead, greater emphasis should be placed on enhancing ecological service levels beyond basic area requirements, comprehensively improving vegetation quality and ecosystem service capacity of parks. In old urban areas constrained by land use, the hierarchical structure of vegetation should be strengthened, and micro green spaces should have enhanced ecological service capabilities to improve residents’ access rights through higher service quality. In newly developed urban areas, planning should balance quantity and quality to serve more people and alleviate urban ecological pressures. Overall, by quantitatively assessing the carbon sequestration capacity and the socio-spatial equity of ecosystem services provided by urban parks in Linyi City, this study offers robust empirical evidence and methodological tools for sustainable urban planning, ultimately fostering the sustainable development of urban ecosystems. Full article

Non-built-up green areas are essential for preserving the ecological functions of cities and fostering sustainable growth. Focusing on Shanghai, we developed a comprehensive framework of driving forces that integrates socioeconomic, natural, policy, and financial indicators. To assess the spatial-temporal changes in regional green space configurations and their underlying mechanisms between 2000 and 2020, we utilized stepwise regression alongside Geographically Weighted Regression (GWR) techniques. The results show that regional green space exhibited a clear stage-dependent evolution, with the total area decreasing from 580.56 km² in 2000 to 506.43 km² in 2005 and then increasing continuously to 905.70 km² in 2020. Forest land consistently expanded and became the dominant land type, while wetland showed a “decrease–increase” pattern and grassland experienced an early decline followed by partial recovery. The primary elements driving these changes underwent substantial transformations over the study period. During the initial phase, socioeconomic variables, particularly real estate investments (β = −0.296), demonstrated pronounced adverse impacts. Conversely, post-2005, financial allocations for landscaping and policy interventions emerged as the main favorable drivers (β = 0.598). Furthermore, environmental aspects like NDVI and waterway density provided a continuous positive influence on green space enlargement. Certain socioeconomic indicators, notably population density, transitioned from exerting adverse impacts to having beneficial effects during the latter periods. The primary drivers demonstrated considerable spatial variation; socioeconomic impacts were largely localized in regions undergoing urban growth, whereas environmental and policy variables exerted broader and more consistent influences. Overall, these outcomes highlight a shift from a socioeconomic-dominated evolutionary process to one governed by a synergy of multiple factors. This offers a theoretical foundation for refining urban ecological strategies and harmonizing city expansion with ecological conservation. Full article

The real estate market is a complex and dynamic sector that plays a key role in economic stability and wealth generation. In many regions, real estate assets represent around 80% of household wealth, while rising housing prices have turned access to housing into a major social and economic challenge. In this context, the availability of accurate and accessible information is essential for decision-making by buyers, investors, and public administrations. This study proposes the development of an advanced technological tool based on Artificial Intelligence and Machine Learning techniques to predict and analyze real estate market dynamics within a specific geographic area. Using the city of Madrid as a case study, the research presents a digital application capable of estimating the market value of a property by analyzing comparable recently sold properties and incorporating key housing characteristics. By entering an address and a set of property features, the system generates a precise and data-driven valuation. The results demonstrate that AI-based approaches can significantly improve the accuracy and accessibility of real estate valuation processes. The proposed methodology enables real-time price estimation, graphical comparisons, and dynamic market analysis. Furthermore, the framework is scalable and can be extended to other geographic areas where relevant data are available, providing valuable insights for both academic research and practical decision-making in the real estate sector. Full article

Due to the high population density and limited land availability in Bangkok, the capital of Thailand, land values have been increasing every year, posing challenges to sustainable housing development. Accurate land valuation is critical not only for investment decisions but also for promoting economic efficiency, social equity, and sustainable urban land use. Inaccurate analysis can lead to losses for real estate developers, project residents, and surrounding communities. However, this process requires extensive knowledge and experience. This research presents an approach for analyzing land values in Bangkok using Deep Learning techniques, which can help real estate developers assess appropriate land values more accurately and precisely. The study collected data on vacant land in Bangkok from an online feasibility study database and analyzed them using Deep Learning techniques. The results showed 30 determinants categorized into five groups. The study conducted 80 parameter adjustments with a ratio of 128:64:32 using a Quadratic Loss Function. The model was validated using k-fold cross-validation to ensure robustness and a Model Simulator operator to test sensitivity analysis. The Deep Learning model resulted in an R-square value of 0.917 and an RMSE of 2620 USD. The results of this research can be used as an effective decision-making tool for real estate developers, landowners, and brokers in determining appropriate buying or selling prices for land to support real estate sustainable development. Full article

Despite the rapid financial expansion over the past two decades, South Africa’s economic growth has remained sluggish, raising concerns about the disconnect between financial sector development and overall economic performance. This study aims to investigate the relationship between financialization and economic growth in South Africa using three proxy variables, finance, insurance, real estate, and business services as a percentage of GDP; money supply (M3) as a percentage of GDP; and credit to the private sector as a percentage of GDP, alongside a composite financialization indicator. Using quarterly time-series data from 1994Q1 to 2025Q2, this study employs the autoregressive distributed lag (ARDL) approach to examine both short- and long-term dynamics and cointegration between financialization and economic growth. The empirical findings reveal that financialization exerts a positive and statistically significant influence on South Africa’s economic growth. Meanwhile, the estimation results reveal that financialization has a positive and highly significant impact on economic growth in South Africa, demonstrating the need for policies that promote and enhance its effects. Full article

This study examines the transformation of the Balaton region in Hungary from a traditional tourist destination into an international retirement migration destination for older adults from Western Europe. Migration theories and models are applied to illustrate the relationships between migration and development and to explore how tourism, lifestyle aspirations, and socio-economic factors influence the settlement decisions of older migrants. Empirical findings suggest that prior tourism experience can mitigate the uncertainty associated with migration and foster belonging. However, many retirees move to Hungary with limited knowledge of the country, relying on social networks and real estate agents for information. These retired migrants also utilize local services and infrastructure, including healthcare and community spaces, which shape their daily lives and help them integrate into the community. The migration of older adults stimulates the development of peripheral rural areas through real estate purchases, renovations, and small-scale entrepreneurial activities, particularly in the accommodation sector. This challenges the traditional perception of older-age migrants as inactive. Full article

The global landscape of sustainability challenges has become increasingly complex, characterized by varying regulatory frameworks and market maturity across different nations. The financial significance of environmental, social, and governance (ESG) factors is influenced by industry and firm-specific attributes. Therefore, this study employs an integrated decision support framework that combines grey relational analysis (GRA) models including Deng’s GRA, absolute GRA, and a second synthetic grey relational analysis (SSGRA) with firm-level panel regressions to compare ESG and financial performance linkages across 11 Middle East and Africa (MEA) countries and industrial sectors. Furthermore, the study utilized a sensitivity analysis to check the robustness of SSGRG. Results indicate considerable variability in the relationships between ESG and financial performance across the region. The economies of the Gulf Cooperation Council (GCC) showed the most robust positive relationship between ESG factors and financial performance based on SSGRG, with Kuwait (0.82), Qatar (0.81), and Saudi Arabia (0.80) predominantly influenced by the social and governance dimensions. Conversely, a weak correlation was demonstrated in Egypt (0.54), Nigeria (0.53), and Kenya (0.56). Moreover, financials, communication services, and materials sectors exhibit the greatest integration of ESG factors into financial performance, with composite SSGRG values ranging from 0.75 to 0.78. In contrast, the information technology and energy sectors demonstrate weak association, with composite SSGRG values falling below 0.60. Furthermore, a conservative maximin analysis reveals that corporate governance in Kenya and environmental performance in Oman are identified as the weakest relationship at the country level, while governance in the information technology and energy sectors, environmental management in real estate, and social performance in consumer discretionary sectors are highlighted as weak connections. This study addresses a gap in the literature by developing a novel decision-support framework, providing fresh empirical evidence from emerging markets, and offering theoretical insights into the into influence of stakeholder and institutional factors on ESG value creation. This study provides implications for investors, corporate managers, and policymakers on sustainable finance in emerging markets and presents a decision-making framework that emphasizes ESG initiatives to enhance financial performance. Full article

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

In this study, a generalized and advanced form of the near soft set theory (NST) framework is proposed for information aggregation (IA) processes. The primary motivation of the study is to address the lack of similarity-based uncertainty modeling in the literature by integrating the parametric structure of soft sets with the similarity-oriented structure of nearness approximation spaces. Within this framework, the AND-product and OR-product operations are introduced as the main methodological tools, and their algebraic structures are analyzed in detail. It is mathematically demonstrated that these operations satisfy fundamental properties such as idempotency, absorption, distributivity, and De Morgan identities. The principal original contribution of the study is the development of a novel Uni–Int-based decision-making mechanism that enables the systematic distinction between strong and acceptable alternatives. In addition, the boundary frequency indicator ($b_r$), which quantitatively evaluates the reliability of objects under perceptual uncertainty and is introduced for the first time in the literature, is proposed. The applicability of the proposed model is demonstrated through a real-estate selection problem, and a sensitivity analysis is conducted to reveal the determining effect of the nearness parameter r on decision granularity. The obtained findings indicate that the proposed NST framework provides a more flexible, more discriminative, and structurally robust decision-support model than classical approaches, particularly for similarity-based IA problems. Full article

This contribution examines the luxury real estate sector in the Caribbean Island of Sint Maarten. Drawing upon an analysis of ethnographic observations, interviews, property market data and marketing materials, we pose two core questions to the data: (1) How are fragments of the Dutch-Caribbean past deployed in luxury real estate marketing? (2) How does cyclical hurricane damage influence the luxury real estate market and heritage preservation? Proportionally very few of the luxury real estate listings directly reference cultural history. Yet when “Dutch-style and “plantation-era” esthetics are referenced, they appear to add value to the properties while enhancing a sense of exclusivity but erase the history of colonial violence. In conjunction with these discursive effects are the material realities of the cyclical destruction of property by hurricanes through which distressed properties are sold at a discount to be redeveloped for luxury builds aimed largely at foreign purchasers. This disaster development model systematically destroys artifacts of tangible heritage while displacing residents from communal spaces. As climate change intensifies, we raise questions about the sustainability of this model on the island going forward. Full article

In recent years, the Turkish real estate market has emerged as a key driver of economic growth while simultaneously shaping the dynamics of social life. This study employs multivariate analysis methods to classify metropolitan cities and potential metropolitan candidate provinces that exhibit similarities in terms of housing market characteristics, based on 22 socio-economic and sectoral variables influencing the real estate sector. Additionally, the study identifies the metropolitan clusters to which the 10 candidate provinces structurally correspond within this classification framework. To achieve this, conventional classification techniques such as Decision Trees and K-Nearest Neighbors (k-NN) were integrated with artificial intelligence-based methods, including Random Forest (RF) and Support Vector Machines (SVM). The analysis resulted in the categorization of 40 metropolitan and candidate provinces into five distinct groups. Findings indicate that multivariate indicators capturing demographic, economic, and structural differences across metropolitan areas play a critical role in shaping the housing market and guiding strategic urban development decisions. Furthermore, the results highlight that determining metropolitan status solely based on population figures is insufficient and that a more scientific and comprehensive approach—grounded in a broader set of socio-economic and structural indicators yields more meaningful classifications. Full article

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and stakeholder participation. Although smart governance frameworks increasingly recognise participation as a normative principle, limited empirical attention has been paid to the participation motivators that drive engagement among different urban stakeholder groups. This study addresses this gap by analysing the key motivators influencing stakeholder participation in urban development within a smart city context. Building on established behavioural and participation theories, the article develops an Urban Participation Motivator Model comprising four core motivators: social pressure, emotional trigger, rational motivation, and reward for participation. The model is empirically tested using quantitative survey data from 620 respondents representing four stakeholder groups in Riga, Latvia: municipal residents, municipal employees, municipal politicians, and real estate developers. Data are analysed using descriptive statistics and non-parametric methods, including the Kruskal–Wallis test. The results reveal statistically significant differences in the perceived importance of participation motivators across stakeholder groups. Emotional triggers and social pressure emerge as the most influential motivators overall, while rational motivation is particularly salient for professional stakeholders. Reward for participation plays a weaker but differentiated role, being most relevant for municipal employees. These findings highlight the need for differentiated motivator-sensitive urban communication and participation strategies to enhance inclusiveness, democratic legitimacy, and long-term engagement in smart city development. Full article