Search Results (11,149)

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation. Full article

The housing sector in major cities is facing escalating challenges due to rapid population growth and land scarcity. Consequently, vertical growth has been adopted as a strategic solution to optimize land use while balancing economic, social, and environmental needs. This study examines the phenomenon of vertical growth of the Dammam Metropolitan Area (DMA) in Saudi Arabia, from an urban sustainability perspective, focusing on evaluating the current state of multi-story buildings, their determinants, and their impact on quality of life and infrastructure efficiency. This study utilizes a systematic review methodology and a conceptual approach to develop an integrated framework for sustainable vertical growth. Furthermore, an empirical validation was conducted by projecting this framework onto vertical housing projects in Dammam, focusing on challenges related to design, construction quality, shared service management, and the suitability of apartments for family needs. The results indicate that the shift toward vertical growth achieves land-use efficiency, limits random horizontal expansion, and provides economic opportunities. However, it faces social and cultural constraints, most notably the resistance of some families to changing traditional ownership patterns, limited privacy and green spaces, and challenges in building maintenance and operations. The study highlights the importance of integrating urban planning, governance, architectural design, and infrastructure to ensure the sustainability of vertical growth and provide suitable housing alternatives. The study recommends further field research to assess social acceptance, improve quality-of-life indicators, and develop policies encouraging sustainable vertical expansion in alignment with Saudi Vision 2030 and the 2030 Sustainable Development Goals (SDGs), ensuring cities are more resilient, efficient, sustainable, and liveable. Full article

Developing countries in which infrastructure development is heavily dependent on overseas development aid face significant sustainability challenges, including financing gaps and inadequate maintenance. Increasing private-sector investment is crucial for addressing these challenges. This paper proposes an innovative framework linking environmental, social, and governance (ESG) principles with a revised joint credit mechanism (JCM) to attract private investment in infrastructure development, particularly in Pacific Island countries facing the climate crisis. Under the revised JCM, by allocating generated carbon credits to participating Japanese companies, rather than the Japanese government, corporations can monetise credits through market transactions, creating compelling economic incentives for private-sector engagement. In ESG-advanced markets, credits serve as strategic instruments for corporate value enhancement beyond revenue generation, while corporations require continuous credit acquisition to sustain investor confidence. Our revised framework provides a sustainable solution to both financing gaps and infrastructure maintenance challenges. Our analysis demonstrates that integrating market dynamics and corporate incentives into bilateral climate mechanisms holds substantial potential for mobilising private capital for sustainable climate infrastructure finance. This approach represents a promising departure from traditional donor-dependent models, effectively aligning corporate interests with sustainable development objectives while advancing national emission reduction commitments. Full article

Smart tourism destinations, embedded by the internet and information and communication technologies, have been improving tourists’ experiences and connectivity. However, Destination Management Organisations (DMOs) still lack knowledge of how digital technologies can enhance their role and bring greater competitive advantage to destinations. In this sense, this study aims to develop an integrated smart tourism destination management ecosystem model that clarifies the relationships between digital technologies, managerial functions, benefits and implementation barriers within the broader smart city context. The study adopts a mixed-review design, combining bibliometric analysis and a systematic literature review. Bibliometric mapping was conducted using VOSviewer to analyse co-occurrence networks, thematic clusters and research trends. At the same time, the systematic review, with a systems thinking approach, enabled an in-depth qualitative examination of technological applications, managerial roles and governance implications. Data was gathered from 29 Scopus-indexed articles. The analysis identifies key benefits, including enhanced visitor experiences, improved decision-making and increased destination competitiveness, alongside persistent barriers related to governance, digital literacy, interoperability and cybersecurity. Based on these findings, the study proposes a conceptual ecosystem model that illustrates how DMOs can orchestrate digital technologies to support smart, sustainable and adaptive destination management. This research contributes to the smart tourism and smart cities literature by integrating bibliometric insights with a systems thinking perspective to develop a holistic destination management ecosystem model. Unlike prior reviews that address technologies or outcomes in isolation, this study offers a structured and actionable framework that advances theoretical understanding of smart tourism destinations while providing practical guidance for DMOs engaged in digital transformation. Full article

In national park sections adjacent to large cities, protected wildlife habitats often intersect with roads, tourism, agriculture, forestry, and other community-use spaces. This overlap complicates the joint prioritization of multi-species conservation and potential human-wildlife conflict governance. Using species trace-point data from the Fourth National Giant Panda Survey, we developed 30 m MaxEnt distribution models for 12 mammal species in the Chengdu section of the Giant Panda National Park and integrated protected-species’ conservation priority with potential wild-boar-related conflict pressure. Test AUC values ranged from 0.702 to 0.897, and elevation was the dominant predictor for 11 species. The Top 15% weighted conservation priority area, based on protection status and rarity, covered 350.1 km². Potential wild boar conflict pressure was defined as wild boar suitability multiplied by human exposure, and the Top 15% risk area covered 348.3 km². Overlaying the two layers identified 61.6 km² of high-conservation-high-conflict areas. Functional-zone statistics showed that the core conservation zone concentrated higher multi-species conservation value, whereas the general control zone carried stronger potential wild boar conflict pressure. This framework provides a spatial basis for coordinating protected mammal monitoring, crop-damage warning, and community co-management. Full article

Tourism destinations consume vast quantities of energy, water, food, and materials, yet these resource flows remain largely invisible in destination planning practice. The aim of this paper is to develop a conceptual framework that reconceptualises tourism destinations as industrial ecosystems and makes their material and energy flows visible, quantifiable, and amenable to destination-scale planning. Existing frameworks prioritise governance and demand management, leaving the material dimension of sustainability unaddressed. To this end, the paper proposes a multi-scale resource-flow framework grounded in industrial ecology. This is a conceptual framework paper: it develops analytical architecture for destination-scale resource accounting rather than reporting empirical measurements. The framework organises four analytical components—actors, flows, structural configurations, and feedback mechanisms—across macro, meso, and micro scales. Three planning capabilities are advanced: supply-chain-complete environmental accounting, resource hotspot detection, and policy design along the full causal chain from structural arrangement to environmental outcome. Material flow analysis, life cycle assessment, and industrial symbiosis mapping are presented as operational tools, illustrated through reference to high-intensity coastal tourism systems. Industrial symbiosis is positioned as a structural mechanism through which by-product valorisation reduces destination-level resource throughput. The study contributes a bridging framework between governance-oriented tourism planning and the material accounting rigour of industrial ecology, distinguishing it from circular economy models that supply a design principle but no material accounting, from urban metabolism approaches that assume temporally stable flows, and from regenerative development that is values-based rather than quantitative. The framework offers a foundation for more integrated and resource-efficient destination sustainability planning. Full article

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

While public sanitation is a fundamental component of urban infrastructure, it is often treated as a discretionary amenity rather than a core public service subject to legal standards of equality and dignity. This article challenges gender-blind approaches to urban planning by examining how inadequate public toilet provision constrains women’s everyday mobility and presence in public space, raising questions of indirect gender discrimination and regulatory responsibility. Drawing on an exploratory mixed-methods study (N = 97), the analysis combines quantitative assessment of access barriers, qualitative user narratives, and time-based measurement of total restroom use duration to examine patterns of use and waiting with particular attention to gender differences. The findings indicate that hygiene-related concerns are reported across both men and women, without clear evidence of a consistent gender-specific pattern, while women are disproportionately affected by throughput failures, long waiting times, and the absence of care-integrated facilities. At the same time, variation in support for gender-neutral toilet solutions suggests that user acceptance may not align with model-based proposals in the literature. These inequalities reflect an institutional accountability gap with legal implications in the governance of everyday public services. By shifting the focus from numerical potty parity to temporal inequality and responsibility, this article contributes to feminist legal scholarship by situating sanitation within questions of temporal inequality and institutional responsibility. While exploratory in nature, the findings offer empirically grounded insights into inequalities in everyday sanitation governance. Full article

A blockchain-based framework is proposed for secure vehicle registration and real-time authenticity verification in vehicular networks. To mitigate the risks of fake and stolen license plates, vehicle identification data is protected using a modular arithmetic-based cryptographic mechanism and indexed within an on-chain hash table structure. Role-based access control ensures system integrity by restricting all registration and modification operations to authorized government entities, while enabling public verifiers to validate vehicle legitimacy through privacy-preserving verification. Experimental evaluation demonstrates that the system achieves low verification latency, minimal storage overhead, and stable throughput. Furthermore, scalability and denial-of-service (DoS) resilience analyses confirm consistent performance under high verification demand. This framework offers an efficient and privacy-preserving solution for the secure and real-time verification of vehicle legitimacy in vehicular networks. Full article

This paper presents a multidimensional analysis of Historic Building Information Modeling (HBIM) segmentation, offering a roadmap towards standardization, a key dimension towards broader adoption within the Cultural Heritage (CH) sector. HBIM faces multiple challenges related to the lack of standardized protocols and varying definitions of Level of Detail (LOD) across applications. Amid the advancements of the fourth industrial revolution, integrating Building Information Modeling (BIM) improves sustainability and digital governance, aligning with the sustainable development agenda. Despite increasing academic interest, the implementation of HBIM remains limited, primarily due to the complexities and heterogeneities inherent in CH artifacts. This study begins with a purely qualitative strategy. Then, it introduces multidimensional and hierarchical clustering analysis to classify the unique characteristics of various HBIM applications such as segmentation, input, and data-capturing media. At the same time, it is a tool for fine-tuning keyword-based selection criteria, which is crucial in systematic or semi-systematic surveys in HBIM segmentation. The thematic analysis output is interrupted just before the conceptualization step, and theme extraction is diverted to correspondence analysis implemented in R, an open-source statistical package. Among the key findings of this paper is the classification of four distinct HBIM application clusters, revealing how specific workflows align with data acquisition methods, input formats, and Level of Detail (LOD) requirements. The analysis exposes critical standardization bottlenecks hindering wider-scale industry adoption, highlighting that challenges are domain-specific. Strong evidence shows that 3D modeling has not reached the required maturity level, with persisting challenges distributed non-uniformly within the applications spectrum. Finally, AI-driven automation relates with poor LOD outcome. Full article

Improving land green utilization efficiency (LGUE) is essential for achieving sustainable development in China. This study investigates the spatiotemporal evolution and localized driving mechanisms of land green utilization efficiency across 127 cities in six major Chinese urban agglomerations from 2011 to 2023. Previous research frequently overlooks the spatial non-stationarity and structural interactions within regional land governance. To address this theoretical gap, a comprehensive multiscale framework is employed. This framework integrates the Super-SBM model, Dagum Gini decomposition, Spatial Markov chains, and Multiscale Geographically Weighted Regression. The empirical results reveal an overall upward efficiency trajectory alongside persistent spatial inequalities. A pronounced scale-efficiency inversion is observed between developed eastern coastal and developing central-western inland regions. Furthermore, spatial interaction analysis identifies a significant backwash effect. This mechanism constrains the upward mobility of peripheral cities adjacent to high-efficiency core nodes. The multiscale regression demonstrates substantial spatial heterogeneity in the effects of key driving factors. Elements such as industrial structure and financial development exhibit highly localized associations dependent on regional institutional contexts. These findings bridge macroeconomic growth models with micro-environmental governance. The study provides critical empirical evidence for shifting from uniform administrative management to spatially targeted regional policy frameworks. Full article

Artificial intelligence has become constitutive of smart city governance, yet data and algorithmic challenges remain analytically separated in existing scholarship, obscuring their recursive coupling and consequences for the built environment. This review synthesises 82 peer-reviewed studies (2020–2025) drawn from a deduplicated corpus of 876 records, combining PRISMA-guided methodology with VOSviewer and CiteSpace bibliometric mapping. Annual output rose from 78 publications in 2020 to 224 in 2024, with ten leading countries contributing roughly 84% of the corpus. The keyword network organises into five thematic clusters spanning AI technical foundations, data governance, algorithmic governance, sustainability, and built-environment governance; emerging 2023–2025 couplings between digital twin and SDG 11, and between generative AI and SDG 11, mark a shifting research frontier, while the algorithmic governance → SDG 16 linkage constitutes the strongest single ribbon in the synthesis. The study advances a double-helix coupling mechanism specifying directional propagation, reverse modulation, and structural cross-linking between data and algorithmic strands, reframing building energy management, digital-twin operation, and smart infrastructure as governance arrangements whose sustainability legitimacy depends on the simultaneous integrity of both strands. Full article

In 2025, the Democratic Republic of the Congo (DRC) experienced its 16th Ebola Virus Disease (EVD) outbreak, centered in the Bulape Health Zone of Kasai Province, amid multiple concurrent epidemics and limited health infrastructure. Genomic sequencing revealed a novel zoonotic spillover genetically related to the 1976 Yambuku strain. A Root Cause Analysis (RCA) using the “5 Whys” framework, integrating epidemiological data, genomic analysis, and surveillance reports, identified key contributors to delayed detection and response, with comparative insights drawn from the 2018–2020 North Kivu outbreak. The Mweka outbreak resulted in 28 confirmed, probable, or suspected cases and 15 deaths, including four healthcare workers. Root causes included inadequate ecological surveillance, weak community alert systems, diagnostic delays due to reliance on centralized laboratories, health system overload from concurrent outbreaks, and structural underfunding of preparedness and coordination. Unlike North Kivu, where security issues drove response delays, systemic and ecological vulnerabilities predominated in Mweka. These findings highlight how ecological and structural weaknesses facilitate novel Ebola spillovers and their escalation, emphasizing the need for sustained investment in One Health surveillance, decentralized diagnostics, and resilient public health governance to strengthen outbreak response capacity. Full article

Aircraft digital twins are increasingly used for diagnostics, prognostics, and predictive maintenance, but their role as lifecycle-oriented, multi-stakeholder decision-support ecosystems remains insufficiently developed. This paper addresses this gap by proposing a conceptual systems-engineering framework for an aircraft digital twin ecosystem supporting sustainable aviation transport management. The framework integrates physics-based, data-driven, hybrid, probabilistic, and federated modelling approaches and includes a three-layer ecosystem model, formal mathematical representation of aircraft and digital twin lifecycle evolution, federated model updating, lifecycle decision-support scenarios, reference architecture, validation and trustworthiness principles, and a five-level maturity model. Representative aviation industrial cases are used to interpret the framework. The analysis shows that current industrial practice already contains elements of predictive maintenance, fleet analytics, engine health monitoring, and cloud-enabled MRO optimization, but full aircraft-level lifecycle governance, sustainability trade-off analysis, federated validation, and multi-stakeholder decision orchestration remain underdeveloped. The proposed framework positions aircraft digital twins as asset-level instruments for lifecycle planning, coordinated governance, and sustainability-oriented decision support. Full article