Search Results (1,018)

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

This paper presents a novel framework for valuing ESG-based collaborative synergies in cross-border mergers and acquisitions (M&A) using a real options approach, with a specific application to L’Oréal’s acquisition of Aesop. The methodology integrates a Value-at-Risk (VaR) model to quantify and adjust for ESG-related risks, providing a more robust valuation framework. We demonstrate how linking sustainability practices with real option valuation in multinational corporations (MNCs) can enhance long-term value creation and reduce risk, thereby aligning synergy goals with ESG objectives. By applying our VaR-adjusted model to the L’Oréal–Aesop case, this study contributes to corporate finance by integrating advanced risk management and sustainability into synergy valuation, and to international business by providing an empirical example of this integrated valuation approach for cross-border acquisitions. Full article

This study investigates the essential competencies for engineers in the context of digital transformation, with the aim of proposing a refined framework to guide professional development across career levels. A mixed-methods, sequential approach was adopted: (1) a systematic literature review, conducted between 2014 and 2024, which identified 46 competencies organized into seven dimensions; (2) a quantitative survey with 392 engineers who self-assessed their level of mastery for each competency; (3) semi-structured interviews with 20 company representatives, who validated and contextualized the essential competencies according to hierarchical levels (junior, mid-level, and senior); (4) data triangulation, resulting in a final competency model by career level. The findings reveal a widespread deficit in digital competencies, regardless of hierarchical level. In total, 33 competencies assessed by career level showed statistically significant differences in employer perceptions and were identified as progressive throughout the career trajectory. Analysis of self-assessments and interviews indicates that for early-career engineers, there is a strong emphasis on personal and basic cognitive competencies. For mid-level engineers, the data show a significant valuation of social competencies. Senior engineers are perceived as having accumulated experience across all seven mapped dimensions. This study offers a practical model that can be used by educational institutions, companies, and professionals to align education, market demands, and career planning. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

This research examines road users’ willingness to pay for enhanced active mobility infrastructure at King Mongkut’s Institute of Technology Ladkrabang (KMITL), a suburban university campus in Bangkok, Thailand. The study addresses the need for sustainable transportation solutions in middle-income urban environments by analyzing factors that influence walking and cycling adoption among university community members. The research employed a comprehensive mixed-methods framework combining qualitative SWOT analysis, a stated preference survey of 400 participants, and regularized logistic regression modeling with cross-validation. The analysis revealed that specific infrastructure improvements significantly increase the likelihood of active mobility adoption. Rest areas demonstrated the strongest positive association (OR = 2.15, 95% CI: 1.08–4.27, p = 0.029), followed by CCTV security systems (OR = 1.89, 95% CI: 0.98–3.65, p = 0.047), and improved public transport connectivity (OR = 2.84, 95% CI: 1.42–5.68, p = 0.003). Demographic analysis uncovered notable resistance patterns, with male participants (OR = 0.48, 95% CI: 0.26–0.89, p = 0.020) and higher-income individuals showing reduced willingness to transition from motorized transportation. Using the Contingent Valuation Method with proper bias mitigation strategies, the study quantified potential behavioral changes, projecting a 12–18 min daily increase in active mobility engagement. This enhancement would generate measurable health benefits valued at 2840–4260 THB per person annually using WHO-HEAT methodology. The research contributes valuable insights to the limited body of active mobility literature from Southeast Asian suburban contexts, providing a replicable framework for similar investigations. Full article

In this study, the objective is to assess the impacts of NH₃ emissions from mink farming on human health and nature, which are sensitive to atmospheric nitrogen deposition. The impact-pathway approach is applied to follow the emissions from source to impact on human health in Europe (including Denmark) and from source to critical nitrogen load exceedances for NH₃-sensitive nature in Denmark. The Danish Eulerian Hemispheric Model (DEHM) is used for modelling the air pollution concentrations in Europe and nitrogen depositions on land and water surfaces in Denmark arising from NH₃ emissions from mink farming in Denmark. The Economic Valuation of Air (EVA) pollution model system is applied for deriving the health effects and corresponding socio-economic costs in Denmark and Europe arising from the emissions from mink farming. On a local scale in Denmark, the deposition resulting from the NH₃ emissions from mink farming is modelled using the results from the OML-DEP model at a high resolution to derive the critical nitrogen load exceedances for Danish nature areas sensitive to NH₃. From the analysis of the impacts through human exposure to the air pollutants PM_2.5, NO₂, and O₃, it is concluded that in total, ~60 premature deaths annually in Europe, including Denmark, can be attributed to the emissions of NH₃ to the atmosphere from the mink farming sector in Denmark. This corresponds to annual socio-economic costs on the order of EUR 142 million. From the analysis of critical load exceedances, it is concluded that an exceedance of the critical load of nitrogen deposition of ~14,600 hectares (ha) of NH₃-sensitive nature areas in Denmark can be attributed to NH₃ emissions from mink farming. The cost for restoring nature areas of this size, damaged by eutrophication from excess nitrogen deposition, is estimated to be ~EUR 110 million. In 2020, the mink sector in Denmark was shut down in connection with the COVID-19 pandemic. All mink were culled by order of the Danish Government, and now in 2025, the process of determining the level of financial compensation to the farmers is still ongoing. The socio-economic costs following the impacts on human health in Europe and nitrogen-sensitive nature in Denmark of NH₃ emissions from the now non-existing mink sector can therefore be viewed as socio-economic benefits. In this study, these benefits are compared with the expected level of compensation from the Danish Government to the mink farmers, and the conclusion is that the compensation to the mink farmers breaks even with the benefits from reduced NH₃ emissions over a timescale of ~20 years. Full article

The digital economy is fundamentally transforming urban landscapes by disrupting traditional relationships between land use and land value. This scoping review aims to examine how digital transformations alter urban activities, human behaviours, and mobility patterns, and to assess the subsequent impacts on land use planning and land valuation frameworks. Following PRISMA guidelines, Scopus, Web of Science, Google Scholar, and ProQuest databases were systematically searched for peer-reviewed articles published between 2019 and 2024. Inclusion criteria comprised empirical studies, theoretical papers, and case studies examining digital economy impacts on urban land use or land value. Grey literature, non-English publications, and studies without clear urban spatial implications were excluded. The data were synthesised using bibliometric analysis and thematic analysis to identify patterns of disruption across three domains: urban activities, behaviours, and mobility. Of the 512 initially identified articles, 66 studies met the inclusion criteria. The evidence demonstrates significant geographic bias and methodological limitations, including the scarcity of longitudinal studies tracking actual land value changes and inconsistent metrics for measuring disruption intensity. Despite these limitations, findings indicate that the digital economy is decoupling land value from traditional determinants, such as physical proximity to services and employment centres. These transformations necessitate fundamental revisions to urban planning frameworks, land valuation models, and regulatory approaches to ensure equitable and sustainable urban development in the digital age. Full article

The increasing environmental concerns of plastic waste have encouraged more interest in environmentally friendly packaging, but consumer willingness to pay (WTP) for green alternatives in emerging markets such as Saudi Arabia is not fully explored. This research explores the relationship between awareness of sustainability and price sensitivity in determining WTP for green packaging in the Saudi retail market. The study utilizing a mixed method included both a Contingent Valuation Method (CVM) and a Discrete Choice Modeling (DCM). In it, data was gathered and analyzed using a sample of 424 urban consumers in Saudi Arabia’s major cities. The findings of OLS regression indicated awareness of sustainability had a significant, positive effect on WTP, whereas price sensitivity had a negative effect. There was a marginal interaction effect indicating that awareness could overcome price aversion. Logistic regression supported awareness as a dominant factor in binary product choice, although price sensitivity was not significant in the said model. The multinomial logit model also showed that the type of package, environmental labels (more so the “100% recyclable” type), and price had significant effects on consumer preferences. These results indicate that there is acceptance of sustainable packaging by consumers in Saudi Arabia if the product is communicated effectively and priced competitively. Full article

In the paper, using the three-statement financial modelling methodology as applied to a representative development project, we aim to analyse, ex ante, the industry-level impact of transition to mandatory escrow schemes in residential and mixed-use construction in Tashkent city (due to be implemented in Uzbekistan from 2026). Modelling single-milestone escrow plans against the current steep-discount advance-based system of off-plans as a baseline, the model accounts for salient institutional features of the Tashkent city development market, including land auctioning, full-cycle Value-added tax (VAT) accounting, and Tax loss carryforward provisions. It also incorporates a framework for demand-driven residual valuations for the development land element. Our findings indicate practically unchanged cashflow profitability of developers on the market in question. Around 30% p.a. in nominal Free-cashflow-to-equity based IRRs expressed in the national currency, provided that the transition to the greater use of leverage in funding unfolds as expected. The disappearance of steep off-plan discounts while the transition to escrows unfolds will be countervailed by the reliance on costly loans from escrow banks. Absent the greater use of leverage, the IRR (FCFE) profitability of the developers is expected to decline by some 5%. For the apartment buyers, this is effectively equivalent to increasing property transaction prices on the primary market in line with their headline asking amounts. Thus-generated economic surplus will be partially captured by the developers and partially passed through to escrow banks, increasing their gross profits by up to $50M, p.a. due to their new role in financing Tashkent city residential developments that are still largely equity-driven. Apart from this effect, we find only a moderate financial leverage influence on developers’ profitability due to the high-interest-rate environment prevailing in Uzbekistan. We also find a demand-driven pressure on land auction prices suggested by increasingly back-loaded alterations in project cashflow profiles. This study also purports to make a material contribution to the evolving body of literature on financial modelling of apartment and mixed-use property developments by offering a flexible three-statement modelling framework with innovative endogenised equity management features. Full article

Understanding the drivers of water yield (WY) changes in ecologically sensitive, data-scarce watersheds is crucial for sustainable management, particularly in the context of accelerating forest expansion and urbanization. This study focuses on the upper Yellow River Basin (UYRB), a critical headwater region that supplies 60% of the Yellow River’s flow and is undergoing rapid land use transitions from 1990 to 2100. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Future Land-Use Simulation (FLUS) model, we quantify historical (1990–2020) and projected (2025–2100) WY dynamics under three SSP scenarios (SSP126, SSP370, and SSP585). InVEST, a spatially explicit ecohydrological model based on the Budyko framework, estimates WY by balancing precipitation and evapotranspiration. The FLUS model combines cellular automata (CA) with an artificial neural network (ANN)-based suitability evaluation and Markov chain-derived transition probabilities to simulate land-use change under multiple scenarios. Results show that WY increased significantly during the historical period (1990–2020), primarily driven by increased precipitation, with climate change accounting for 94% and land-use change for 6% of the total variation in WY. Under future scenarios (SSP126, SSP370, and SSP585), WY is projected to increase to 217 mm, 206 mm, and 201 mm, respectively. Meanwhile, the influence of land-use change is expected to diminish, with its contribution decreasing to 9.1%, 5.7%, and 3.1% under SSP126, SSP370, and SSP585, respectively. This decrease reflects the increasing strength of climate signals (especially extreme precipitation and evaporative demand), which masks the hydrological impacts of land-use transitions. These findings highlight the dominant role of climate change, the scenario-dependent effects of land-use change, and the urgent need for integrated climate–land management strategies in forest-urbanizing watersheds. Full article

This study assesses the projected impacts of climate change on sediment retention and soil loss in Savannakhet Province, Lao PDR, through the application of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. Using climate projections under SSP2-4.5 and SSP5-8.5 scenarios for the mid- and late-21st century (2050 and 2080), compared against a 2015 baseline, the analysis quantifies changes in sediment dynamics and ecosystem service provision. Results reveal a substantial increase in sediment retention, particularly in forested and flooded vegetation areas, under moderate and high-emission pathways. However, an overall rise in soil loss is observed across croplands and urbanized zones, driven by intensified high-risk areas, which requires conservative management. This study advocates for ecosystem-based adaptation (EbA) strategies—including afforestation, intercropping, and riparian restoration—to enhance watershed resilience. These nature-based solutions align with national adaptation goals and offer co-benefits for biodiversity, climate regulation, and rural livelihoods. Full article

Our contemporary society is powered by fossil fuels, which results in environmental catastrophes. The combustion of these materials results in the release of CO₂, which accelerates the progression of climate change and its catastrophic consequences. The environmental repercussions of fossil fuel extraction have been highlighted through research into alternative energy sources. This inquiry uses the Tapio-Z decoupling approach to assess energy inputs and emissions. Furthermore, the fuzzy logic model is used to inspect the economic growth of the USA and China, as well as the impact of environmental factors, energy sources, and utilization, through decoupling effects from 1994 to 2023. The findings are substantiated by the individual perspectives of the environmental factors regarding decoupling, which ultimately lead to the acquisition of valuable results. We anticipate a substantial reduction in the total volume of CO₂ emissions in both the USA and China. Compared to China, the USA shows a significant increase in CO₂ emissions due to its reliance on fossil fuels. It is evident that a comprehensive transition to renewable resources and a broad range of technology is required to mitigate CO₂ emissions in high-energy zones. In their pursuit of sustainability, these two nations are making remarkable strides. The percentage change in CO₂ emissions indicates that effective changes in economic growth, energy input, and energy utilization, particularly sustainable energy, transmute energy output, as does the sustained implementation of robust environmental protection policies. The percentage change in CO₂ emissions indicates a remarkable transformation in energy input, energy consumption, and economic growth. This transition has been most visible in the areas of energy transformation, sustainability, and the maintenance of strong environmental protection measures. Full article

The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R² and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

Before the development of 3D cadastre, cadastral systems were based on 2D representations, which now require transformation or updating. In this context, the first issue is that existing 2D rights are not aligned with recent 3D data acquired using advanced technologies such as Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR). The second issue is that point clouds of objects captured by UAV-LiDAR, such as fences and exterior building walls—are often neglected. However, these point cloud objects can be utilized to adjust 2D rights to correspond with recent 3D data and to update 3D building models with a higher level of detail. This research leverages such point cloud objects to automatically generate 3D rights and building models. By combining several algorithms, such as Iterative Closest Point (ICP), Random Forest (RF), Gaussian Mixture Model (GMM), Region Growing, the Polyfit method, and the orthogonality concept—an automatic workflow for generating 3D cadastral models is developed. The proposed workflow improves the horizontal accuracy of the updated 2D parcels from 1.19 m to 0.612 m. The floor area of the 3D models improves by approximately ±3 m². Furthermore, the resulting 3D building models provide approximately 43% to 57% of the elements required for 3D property valuation. The case study of this research is in Indonesia. Full article