Search Results (458)

Informal settlements are widely present and important urban spaces, providing valuable living spaces for many migrant populations, low-income groups, and indigenous peoples. However, urbanization faces a common challenge: the transformation of informal settlements often destroys social structures and belongingness. Despite its importance, few studies have examined how redevelopment is correlated with original residents’ belongingness. To address this gap, this study proposes a research framework of belongingness structured along the logical chain of “network–node–subject”. Social network analysis (SNA) is employed to identify the existing outdoor activity space network and its key nodes. Exploratory factor analysis (EFA) is then conducted to determine the key environmental features of outdoor activity spaces that influence original residents’ belongingness. Furthermore, K-means clustering is applied to explore the correlation mechanism between space and belongingness across different age groups. This study identifies differentiated and universal elements by analyzing the clustered conflict factors, in order to provide precise policy insights. The findings provide actionable insights for enhancing residents’ belongingness during the redevelopment of informal settlements in cities. Full article

This study investigates the “insurance-like effect” of corporate Environmental, Social, and Governance (ESG) performance amid financial restatement events among Chinese listed firms and examines the moderating role of ESG rating divergence. Employing an event study methodology on a sample of 1552 financial restatement events in China’s A-share market from 2013 to 2023, we measure market reactions using the cumulative abnormal return (CAR) over a [−1, +1] day window. Our findings reveal that strong ESG performance significantly mitigates the negative market reactions triggered by financial restatements. However, this protective effect of ESG is significantly weakened by the inconsistency in ESG assessments among rating agencies, known as ESG rating divergence, particularly when such divergence is persistent. We argue that the underlying mechanism is that rating divergence creates signal conflicts, exacerbates information asymmetry, and erodes the credibility of ESG signals. This, in turn, undermines the stakeholder trust and moral capital that underpin the insurance-like effect. This research sheds light on the complex impact of ESG rating divergence on the value-protective mechanism of ESG and contributes new empirical evidence to the literature on ESG and its insurance-like effect, especially within the context of an emerging market. Full article

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform. Full article

Rapid urbanization drives economic growth but also brings complex environmental and social issues, highlighting the urgent need for efficient urbanization monitoring techniques. However, datasets for urbanization monitoring are often lacking in rapidly developing urban areas. At the methodological level, Convolutional Neural Networks (CNNs) and Transformer-based models for urbanization monitoring exhibit limitations in balancing computational efficiency and global modeling. The recently emerging parallel large kernel convolutional networks partially alleviate the conflict between global modeling and computational efficiency, but they employ simple element-wise addition to fuse multi-scale features. This crude mechanism struggles to fully leverage multi-scale information. To address this, this paper takes Accra, the capital of Ghana, as a case study and proposes an urbanization monitoring framework covering both dataset construction and model design. Methodologically, we propose the Multi-Scale Spatial-Channel Attention Network (MSCANet). Its core component, the Multi-Scale Spatial-Channel Attention Module (MSCAM), jointly models spatial and channel dimensions to mitigate the common confusion problem in parallel large kernel convolutional architectures. Furthermore, we adaptively modified the MSCAM to propose the Multi-Scale Spatial-Channel Attention Feature Fusion Module (MSCA-FFM) module for effectively integrating multi-modal information during the fusion stage. Experimental results show that MSCANet achieves optimal performance on the self-built Accra dataset, with a mean intersection over union (mIoU) of 95.02%, an overall accuracy (OA) of 98.70%, and a mean F1 Score (mF1) of 97.43%. To further validate the model’s generalization capability, supplementary experiments were conducted on the public ISPRS Potsdam dataset. The results demonstrate that the MSCANet series of models remain competitive, achieving an overall mIoU of 80.92%, with particularly strong performance in the “Building” (mIoU 92.26%) and “Impervious surface” (mIoU 84.63%) categories. Full article

Global demand for food is expected to grow significantly by 2050, underlying the urgency of a sustainable transition in agriculture. In this context, the Circular Economy (CE) paradigm emerges as a promising strategy. This transition is still ongoing, underscoring the importance of sustainability assessment as the first crucial step in supporting this process effectively. Therefore, comprehensive and robust evaluation tools and methodologies are necessary to support effective decision-making processes in this context. This study addresses this topic by conducting a literature review focused on the main evaluation methodologies adopted to assess the sustainability of circular technologies in agriculture, as well as to identify emerging research trends and to identify current knowledge gaps. Therefore, the main objective of this research is to establish a well-defined framework that starting from existing researches, it will support the development of future research directions. The performed review identifies Life Cycle Assessment (LCA) as the most applied methodology for environmental impact assessment, due to its ability to analyze environmental impacts and resources consumption throughout the entire life-cycle of a product, followed by Multi-Criteria Analysis (MCA) and performances-based models for their capacity of integrating and managing many dimensions (environmental, economic, and social) within the evaluation process. Emerging trends highlight the increasing adoption of computational approaches, such as System Dynamics (SD), facilitating a more comprehensive assessment of complex agricultural systems. Despite this increasing attention, the review addresses the significant gap, or rather, the limited management of stakeholders’ conflicts and synergies. This gap will inform potential research directions within the Agritech project, especially regarding the development of Social Multi-Criteria Evaluation (SMCE) to integrate stakeholders’ perspectives in the sustainability assessment of circular technologies. Full article

Human–wildlife conflicts arise from increasing human populations and the growing demand for land for agriculture and urban development. In Saudi Arabia, these dynamics have increased the impact of baboons on human communities, as expanding settlements encroach upon the natural habitats of baboons, while rising baboon populations increasingly invade urban areas in search of food, shelter, and water. We aimed to assess the effects of human–baboon coexistence on residents in the Al-Baha region, Saudi Arabia. From October 2021 to April 2022, we administered a 43-item semi-structured online questionnaire addressing emotional, social, environmental and financial impacts of nearby baboons. A total of 318 residents of the Al-Baha region completed the survey and shared their experiences regarding interactions with hamadryas baboons (Papio hamadryas) near human properties. In addition, three semi-structured life interviews with residents aged over 70 explored historical perceptions. Respondents attributed increased baboon presence to urban expansion, accessible waste, and intentional feeding and strongly supported government-led mitigation plans. Human–baboon interactions in Al Baha produce emotional stress, social disruption, and economic burdens for residents. Findings support integrated management combining public education, improved waste management, non-lethal deterrents, and carefully planned population control measures, developed with community consultation. Limitations of the study include convenience online sampling and reliance on self-reported impacts; future work should quantify baboon abundance and objectively measure economic losses. Full article

An effective housing policy must ensure affordability for individuals across all income levels by integrating advanced technological innovations with comprehensive socioeconomic strategies. Affordable housing fosters social inclusion, whereas sustainability supports long-term environmental protection and economic stability. The success and long-term sustainability of affordable housing initiatives are heavily influenced by current socioeconomic conditions, emphasizing the need for context-specific, inclusive, and sustainable housing solutions. Benchmarks are crucial in affordable housing to determine if it is climate-positive, aligning with the goals of the United Nations’ Sustainable Development Goal 11.1, which seeks to provide affordable and sustainable housing for everyone by 2030. This study uses the Scopus database to perform a scientometric analysis of 595 publications (2015–2024) on sustainability and affordability in housing. Using R-Studio 2025.05.1 + 513.pro3 and VOSviewer 1.6.20, it examines bibliographic trends, research gaps, and collaboration patterns across countries and journals. This study highlights performance thresholds related to economic, environmental, energy, territorial, and climatic factors. However, cost and ecological objectives can cause conflict with each other practically, and hence a balanced approach including green practices, efficient materials, and subsidies is crucial. There is a need for policymakers to address market gaps to prevent socially exclusive or environmentally harmful outcomes, maintain long-term urban resilience, and ensure sustained urban resilience and equitable access to affordable, sustainable housing by 2030. Integrating sustainable materials, circular and climate-resilient design, smart technologies, inclusive governance, and evidence-based policies is crucial for advancing affordable, equitable, and resilient housing. This approach guides future research and policy toward long-term social, economic, and environmental benefits. The findings and recommendations promote sustainable, affordable housing, emphasizing the need for further research on climate-resilient, energy-efficient, and cost-effective building solutions. Full article

While artisanal gold mining (AGM) has been credited as a sector that sustains many households in Zimbabwe, it has at the same time been criticized as the chief driver of ecological degradation and social vulnerability. This study qualitatively examines the environmental and socioeconomic impacts of AGM by conducting in-depth interviews with miners, residents, and policymakers across six central mining districts. The study findings indicate that the use of mercury has resulted in severe contamination of water bodies, while clearing land to pave the way for mining has led to severe deforestation, loss of biodiversity, and declining agricultural productivity due to the loss of fertile soils. It was also found that most AGMs were unregulated, and their unregulated operations have intensified health risks, social inequality, and land-use conflicts with the local community. This study provides an insight into how dependence on AGM has perpetuated a cycle of ecological degradation and poverty among many Zimbabweans. The study, therefore, attempts to combine community narratives with policy analysis, thereby proposing a framework for sustainable AGM in Zimbabwe. This involves advocating for the use of environmentally friendly technologies and promoting participatory environmental governance among all key stakeholders. The study contributes to achieving a balance between economic benefits and environmental management by advancing the discourse on sustainable development and community resilience in resource-dependent economies. Full article

Against the backdrop of the deepening “dual carbon” strategy and the globalization of ESG investment, China’s construction industry, an important key carbon-emitting sector, faces a “triple institutional dilemma”. It includes high carbon lock-in, human capital alienation, and an ambiguous governance structure. Current research on the practical paths of ESG compliance and its localized adaptation in this industry remains limited. Drawing on the green transformation theory, this study systematically explores the theoretical logic, realistic picture, and breakthrough path of ESG compliance in the industry. Firstly, it clarifies the connotation of ESG compliance and maps out the industry’s policy framework and practical patterns. Secondly, it analyzes core dilemmas from three dimensions: environmental constraints related to technical pathways, social conflicts between labor and community arising from institutional imbalances, and governance inefficiencies caused by irregular information disclosure and imperfect structure. Finally, it proposes a “three-dimensional collaborative” mitigation mechanism. This study provides localized, practical pathways for ESG compliance in the construction industry and offers a theoretical reference for the sector’s green transformation, thereby contributing to advancing Chinese-style modernization and ecological civilization construction. Full article

This study examines the transformation of modern residential architecture in Cyprus between 1930 and 1974 through 14 representative residences selected across four historical periods. The study examines the political, social, and cultural contexts extending from the introduction of modern architecture to the island, through the post-Republican era, and the conflict between 1963 and 1974. It aims to comprehensively explore the spatial, climatic, and regional characteristics of modern Cypriot housing. The examples were selected based on criteria such as documentability, period representativeness, architectural originality, and spatial qualities. Data were tabulated based on plan structure, façade layout, use of semi-open spaces, material choices, and climatic adaptation strategies. These parameters were evaluated through comparative analysis to identify changes in modernist expressions, the emergence of regionalist trends, and how Mediterranean environmental approaches were reflected in architecture. The findings demonstrate that the spatial flexibility, interior-exterior continuity, and climatic sensitivity seen in modern housing from the 1930s onward evolved into a more pronounced Mediterranean regionalism with the contributions of local architects after 1950. The study offers a typological, spatial, and climatic analysis unique to the literature on the development of modern residential architecture in Cyprus. Full article

Although shale gas is an important energy source in the energy transition, its development faces multidimensional challenges across economic, environmental, social and technological domains. Traditional evaluation methods struggle to quantify interdependencies among indicators or capture their overall benefits. To address this, we propose a sustainable development evaluation framework for shale gas that integrates 25 indicators across four dimensions: economic, environmental, social and technical. Entropy weighting is used to determine indicator weights, and principal component analysis (PCA) is applied to reduce dimensionality, Gaussian copula functions are then used to model inter-indicator dependencies, and Monte Carlo simulation (10,000 iterations) is used to quantify the distribution of comprehensive benefits under uncertainty. The key findings are as follows: (1) the environmental and technological dimensions carry the highest weights at 29% and 28%, respectively; (2) the PCA–Monte Carlo (PMC) development model achieves a comprehensive benefit score of 0.567, and 22% higher than the traditional model’s score of 0.467 with a 90% confidence interval of [2%, 46%]; and (3) sensitivity analysis identifies the most influential drivers as the hazardous waste compliance rate (impact coefficient 0.92), the community conflict resolution rate (0.367), and community satisfaction (0.26). The marginal benefits of environmental compliance and social governance substantially exceed those of traditional economic indicators, offering scientific guidance for the green transformation of the shale gas industry. The integrated PCA–copula–Monte Carlo framework also provides a methodological reference for the sustainable assessment of other unconventional resources. Full article

In an increasingly interconnected world, the capacity of societies to withstand, adapt to, and recover from crises is a central challenge for security and sustainable development. Yet, despite extensive research on resilience, the mechanisms through which systemic vulnerabilities emerge and propagate across social domains remain poorly understood. This paper addresses this gap by proposing a network-based framework: the Complex Analysis for Socio-Environmental Adaptation (CASA), which models resilience as a graph-structured system. Each node in CASA represents a social or infrastructural component whose resistance is derived from indicators of installed capacities, while edges capture interdependencies among sectors. We formalize a damage propagation model in which the loss of capacity in one node dynamically affects connected components, revealing the topological patterns that drive systemic fragility. Comparative simulations demonstrate that CASA’s topology amplifies the impact of highly connected nodes, rendering them crucial for resilience planning. An application to a real-world case demonstrates how initial disruptions in access to drinking water cascade into governance, economic, and social instabilities. The results provide both theoretical and operational insights, highlighting that resilience depends not only on the strength of individual components but also on the network architecture that links them. CASA thus offers a replicable and data-informed approach for identifying drivers of social conflict and guiding anticipatory resilience strategies in complex territorial systems. Full article

Global supply chains face unprecedented complexity as organizations must simultaneously achieve sustainability objectives and operational resilience amid evolving risk landscapes. Despite extensive research, the absence of systematic knowledge synthesis has limited understanding of how these dual imperatives intersect. This study conducts the first comprehensive scientometric analysis of global supply chain risk management research, examining 1228 peer-reviewed articles from major databases published from 2016 to June 2025. The study employed co-occurrence analysis, temporal burst detection, and network visualization to map the intellectual structure and evolutionary dynamics of this field. Our study reveals four distinct research clusters: risk factor identification (traditional and unconventional threats), environmental and social sustainability integration, technology-driven challenges, and innovative risk management methodologies. Temporal analysis demonstrates significant research acceleration post-2020, driven by pandemic disruptions, with emerging focus on cyberattacks, geopolitical conflicts, and ESG compliance challenges. The findings reveal critical gaps at the sustainability-resilience intersection, particularly paradoxical tensions where short-term resilience measures may compromise long-term sustainability goals. We propose four priority research directions: digital transformation frameworks balancing sustainability-resilience trade-offs, ESG-integrated early warning systems, adaptive governance mechanisms for unconventional risks, and policy frameworks addressing regulatory complexity. This systematic knowledge mapping provides theoretical foundations for future research and practical guidance for supply chain managers navigating dual sustainability-resilience objectives in an uncertain global environment. Full article

The global maritime industry faces a conflict between economic competition and sustainability standards. Economic pressure often incentivizes ship registries toward regulatory leniency, degrading environmental and social standards. Traditional static models often overlook how current flag choices impact future inspection risks and financing costs. To address this, we propose a Deep Reinforcement Learning framework that models flag selection as a sequential decision problem. Using a Markov Decision Process, we integrate economic, environmental, and social rewards. We analyze Port State Control records, AIS data, and 27 policy factors to quantify policy effectiveness within the simulation environment. The results show significant heterogeneity in policy performance. Reducing corporate income tax yielded the highest reward improvement (+131.37, p < 0.001). This suggests that, within the model, economic viability serves as a foundation for environmental investments. Enhanced safety standards also generate significant value (+58.35, p < 0.001) by reducing accident penalties and improving reputation metrics. Conversely, increasing tonnage taxes incentivizes the agent toward registries with lax oversight (−87.61, p < 0.001). These findings demonstrate that economic competitiveness and sustainability are mutually reinforcing. This framework provides maritime administrations with a “policy sandbox” for evidence-based decision-making, enabling a transition to sustainability without sacrificing competitiveness. Full article

In response to the coexistence of multi-objective conflicts and environmental complexity in the renewal of contemporary urban industrial heritage, this study develops a simulation and decision-making methodology for architectural and environmental renewal based on a digital twin framework. Using the Xi’an Old Steel Plant Industrial Heritage Park as a case study, a community-scale digital twin model integrating multiple dimensions—architecture, environment, population, and energy systems—was constructed to enable dynamic integration of multi-source data and cross-scale response analysis. The proposed methodology comprises four core components: (1) integration of multi-source baseline datasets—including typical meteorological year data, industry standards, and open geospatial information—through BIM, GIS, and parametric modeling, to establish a unified data environment for methodological validation; (2) development of a high-performance dynamic simulation system integrating ENVI-met for microclimate and thermal comfort modeling, EnergyPlus for building energy and carbon emission assessment, and AnyLogic for multi-agent spatial behavior simulation; (3) establishment of a comprehensive performance evaluation model based on Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP); (4) implementation of a visual interactive platform for design feedback and scheme optimization. The results demonstrate that under parameter-calibrated simulation conditions, the digital twin system accurately reflects environmental variations and crowd behavioral dynamics within the industrial heritage site. Under the optimized renewal scheme, the annual carbon emissions of the park decrease relative to the baseline scenario, while the Universal Thermal Climate Index (UTCI) and spatial vitality index both show significant improvement. The findings confirm that digital twin-driven design interventions can substantially enhance environmental performance, energy efficiency, and social vitality in industrial heritage renewal. This approach marks a shift from experience-driven to evidence-based design, providing a replicable technological pathway and decision-support framework for the intelligent, adaptive, and sustainable renewal of post-industrial urban spaces. The digital twin framework proposed in this study establishes a validated paradigm for model coupling and decision-making processes, laying a methodological foundation for future integration of comprehensive real-world data and dynamic precision mapping. Full article