Search Results (874)

In the pursuit of global industrial sustainable development and carbon neutrality goals, the aviation manufacturing sector serves as a strategic pillar for advancing global economic growth, driving technological innovation and enhancing national competitiveness. Its green innovation has thus become a critical pathway to achieving carbon neutrality targets and spearheading the sustainable transformation of the industrial sector. This study investigates the enabling effect of artificial intelligence (AI) on green innovation within aviation manufacturing enterprises. The findings indicate that AI exerts a promotional impact on green innovation via three primary channels: technological empowerment, labor structure optimization and resource access improvement. Specifically, AI drives the digital transformation of operational processes in aviation manufacturing, rationalizes the human resource framework of the sector, and eases the financing pressures confronted by aviation manufacturing enterprises. A heterogeneity analysis reveals that regional resource endowments, enterprise production attribute characteristics and external market attention can form synergistic interactions with AI technology. What is more prominent is that the positive influence of AI on green innovation is especially distinct in three scenarios: in economically developed urban areas, among enterprises with traditional production attributes, and for enterprises that garner high levels of analyst attention. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

This study presents a comprehensive and systematic integrative review of Neighborhood-Level Energy Hubs (NLEHs) as pivotal enablers of sustainable and resilient urban energy systems. In response to accelerating climate pressures, rapid urbanization, and the decentralization of energy production, NLEHs are conceptualized as multi-carrier platforms that enable coordinated energy generation, storage, conversion, and exchange at the neighborhood scale. Utilizing a PRISMA-informed methodology to synthesize 125 core studies, the review systematically evaluates recent advances across five interconnected dimensions: conceptual foundations, system typologies, energy flow architectures, urban integration, and optimization paradigms. Unlike conventional reviews, this study explicitly bridges the critical gap between techno-economic optimization and socio-environmental priorities. A key novelty is the proposed mathematical integration of energy justice and Social Life Cycle Assessment (S-LCA) directly into optimization algorithms (e.g., MILP and MPC) as dynamic constraints and penalty terms. Particular emphasis is placed on participatory governance models, lifecycle sustainability metrics, and digitalization tools such as AI-driven energy management systems and urban digital twins. The analysis further reveals critical research gaps, highlighting a stark geographic dichotomy between high-tech, market-driven NLEHs in the Global North and resilience-oriented hybrid microgrids in the Global South, alongside the lack of adaptive regulatory frameworks. By proposing a unified Cyber–Physical–Social perspective, this study provides actionable insights for planners, policymakers, and researchers to support the development of scalable, inclusive, and context-sensitive NLEH implementations. Ultimately, the paper contributes to redefining neighborhood-scale energy systems as not only efficient and low-carbon infrastructures, but also as socially equitable, globally scalable, and institutionally adaptive components of future smart cities. Full article

Leveraging artificial intelligence (AI)―a cutting-edge technological tool―to drive the new quality transformation of Chinese manufacturing is a crucial foundation for China’s steady advancement of the new real economy, as well as an inevitable requirement for China to align with contemporary economic and technological trends. This study constructs a multi-sectoral equilibrium model to theoretically analyze the focal points of the new quality transformation in Chinese manufacturing and the impact AI has on it, followed by corresponding empirical tests. The results indicate that (1) AI has a positive impact on the qualitative transformation of China’s manufacturing sector; a one-unit increase in a firm’s AI level leads to a 0.171-unit increase in the sector’s qualitative transformation level. (2) This impact exhibits heterogeneity at the firm, industry, and regional levels. At the firm level, the impact varies depending on firm size, digitalization level, operational performance, internal control strength, and governance quality. At the industry level, the impact varies depending on technology intensity, industrial structure, strategic importance, and green development level. At the regional level, heterogeneity is reflected in geographical location, natural resource endowments, and the degree of urban agglomeration. (3) Artificial intelligence promotes the new quality transformation of Chinese manufacturing through the following mechanisms: reducing time lag costs and transaction costs in market penetration mechanisms; enhancing the quality of cutting-edge factor combinations and key core technologies in advanced innovation mechanisms; and improving resource utilization and operational management efficiency in lean production mechanisms. Full article

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming the modeling of complex spatiotemporal urban processes such as urban growth, sprawl, shrinkage, redevelopment, and Land Use/Land Cover Change (LULCC). However, despite rapid methodological innovation, applications remain fragmented, and there is limited synthesis of how AI-based models complement, extend, or supersede conventional approaches. This study addresses this gap through a systematic review of 6356 records, from which 120 articles were selected for detailed analysis. It investigates: (i) how ML/DL techniques are embedded within spatiotemporal modeling frameworks; (ii) their use in simulating urbanization dynamics and land-use (LU) transitions; (iii) methodological and performance gains relative to traditional statistical and rule-based models; and (iv) emerging research frontiers and limitations. The review shows that LULCC dominates current applications, with Artificial Neural Networks (ANNs) as the most prevalent ML method, increasingly complemented by DL architectures. Across cases, AI is primarily used to learn non-linear transition dynamics, represent spatial and temporal dependencies, identify influential drivers, and improve classification performance and computational efficiency. Building on these insights, the paper synthesizes the roles of AI in spatiotemporal urban modeling and outlines forward-looking research directions to support more robust, transparent, and policy-relevant applications for urban sustainability. Full article

Rapid urbanization and the growth of data-driven planning have increased the need for tools that support integrated, transparent, and accountable urban governance. While Building Information Modeling (BIM) is well established in project delivery, its potential role in city-scale governance remains underexplored. This study conducts a structured qualitative evidence synthesis informed by PRISMA reporting principles and comparative case analysis to investigate how BIM, in combination with GIS, IoT, and AI, intersects with emerging digital governance practices. Through a synthesis of peer-reviewed research and documented case studies, the review evaluates how BIM supports data integration, interoperability, decision-making, regulatory compliance, collaborative governance, and sustainability. The findings suggest that BIM functions as a governance-support infrastructure when embedded within coordinated institutional frameworks, standardized data environments, and interoperable digital ecosystems. Based on these insights, the paper proposes a conceptual framework that organizes BIM governance into technical, institutional, social, and ethical–regulatory dimensions. The review suggests that BIM’s governance potential depends on institutional alignment, regulatory clarity, and sustained organizational capacity, rather than technological capability alone. Full article

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing agri-food supply chain resilience. Full article

Urban soil contamination poses persistent risks to redevelopment, public health, and ecological restoration, yet actionable evidence is scattered across site investigation reports, monitoring databases, and regulatory documents. Existing decision-support tools often depend on manual searches and provide limited structured reasoning. This study develops a domain knowledge graph (KG) and a KG-powered question-answering (KBQA) system for urban soil management to organize multi-source evidence and deliver precise, auditable answers to parcel- and pollutant-specific queries. The approach (1) defines an urban soil ontology covering parcels, land uses, pollutants, measurements, pathways, and regulatory thresholds; (2) extracts and links entities and relations from textual and tabular sources; (3) constructs a graph database with provenance; and (4) implements a KBQA pipeline that maps natural-language questions to constrained graph queries and verbalizes results with citations. The resulting system supports source identification, land-use-specific exceedance checks, affected-parcel listing, and remediation reference retrieval. Experiments on a curated QA set and a South China case study show higher answer accuracy and lower latency than text-only baselines, while consistently returning traceable evidence and reducing cross-document lookup effort. Compared to text-only RAG baselines, the KG-powered system achieved a 0.14 improvement in Exact Match scores (e.g., 0.81 vs. 0.58 for Threshold tasks) and maintained a competitive median latency of 0.75 s. The pipeline utilizes a 13B-parameter instruction-tuned LLM. The ontology, schema, benchmark QA sets, and sample queries are publicly released to support transfer to other regions. Full article

Generative AI has advanced rapidly in architectural design; however, existing building footprint generation models tend to emphasize stylistic exploration while insufficiently integrating site context as a fundamental physical constraint that facilitates alignment with the surrounding urban fabric. To address this limitation, this study proposes a context-responsive methodology for generating building footprints using a multi-layered four-channel representation of site conditions—including roads, sidewalks, adjacent buildings, and site boundaries—within a Latent Diffusion Model framework. The proposed approach encodes these physical conditions into a structured tensor and concatenates them directly to the U-Net input, enabling site context to function as an explicit spatial control variable during generation. An ablation study evaluated the effectiveness of the proposed contextual configuration. Compared with a single-channel model, the four-channel model achieved an 18.08% reduction in average pixel-wise information entropy, indicating a measurable decrease in generative uncertainty. Qualitative analyses further demonstrated that the enriched contextual input promotes geometrically coherent footprint configurations, such as context-responsive setbacks and spatial alignment with surrounding built forms. These findings suggest that structured multi-channel site information enhances contextual grounding in generative design processes and may contribute to more environmentally integrated and spatially coherent architectural outcomes. Full article

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

Agentic artificial intelligence—systems that reason, plan, and act autonomously within governed workflows—is converging with autonomous electric mobility and urban robotics to reshape how cities govern, move, and manage physical space. We argue that the simultaneous arrival of these three vectors is triggering a transformation comparable in scope to the Industrial Revolution. Cities that deploy across all three domains are becoming the new hubs of innovation: they concentrate talent, accelerate knowledge circulation, enable cross-fertilisation, and generate hybrid proposals that no single vector could produce alone. Just as Manchester, Birmingham, and the Ruhr became the defining centres of industrialisation because steam, textiles, iron, and coal recombined through the proximity of the engineers and entrepreneurs who moved between them, a small number of cities today are pulling ahead because they host the shared talent pool around which agentic governance, autonomous mobility, and urban robotics co-evolve. Conceptually, we extend the mirroring hypothesis in two directions: dynamically, arguing that organisations and urban ecosystems converge toward the configurations new technologies make possible; and ontologically, arguing that agentic AI introduces non-human agents into organisational architectures, requiring hybrid human–AI coordination. We formalise this dynamic as five propositions (P1–P5) of cumulative recursive hybridisation (CRH), operating through four reinforcing feedback loops—data, regulation, infrastructure, and talent. Together, these loops explain why the emerging urban order is path-dependent: early movers accumulate compounding advantages, while latecomers face exponentially rising costs of entry. We demarcate CRH from adjacent frameworks—general-purpose technologies, organisational complementarities, and complex adaptive systems—and test it against counterfactual evidence from failed, stalled, and Global South trajectories (Sidewalk Toronto, the Cruise rollback, Songdo, Bengaluru). We also examine its political-economy, equity, and surveillance limits. Drawing on comparative evidence from public-sector chatbot deployments, autonomous mobility ecosystems in the United States and China, and emerging urban robotics cases, we conclude that what is at stake is not incremental modernisation but the construction of a new urban order. The cities that act as innovation hubs for the agentic AI era will shape global standards, attract global talent, and define the institutional templates that others eventually adopt—much as the industrial cities of the eighteenth and nineteenth centuries did. Full article

Automatic building extraction from high-resolution imagery remains constrained by limited training data and domain shifts across geographic regions and spatial resolutions. Although data augmentation is widely applied in semantic segmentation, its capacity to compensate for scarce labeled samples under varying domain conditions remains insufficiently quantified in remotely sensed data. Here, we present a controlled data-centric evaluation to quantify how explicit, label-preserving augmentation influences model generalization under varying domain shifts, rather than proposing a new augmentation algorithm. The experimental design integrates DeepLabV3+ (CNN) and SegFormer (transformer) architectures to assess whether augmentation effects persist across distinct feature-learning paradigms. Four scenarios are constructed, including two intra-domain settings, a resolution shift (0.3 m to 0.1 m), and a geographic shift across heterogeneous urban environments. Training subsets are progressively sampled from 20% to 100% to isolate the interaction between data volume and distributional variability. Geometric, radiometric, and occlusion-based transformations are evaluated individually and in combination. Under cross-domain and low-data regimes, augmentation substantially increases predictive performance. Combined transformations increase mIoU from 0.572 to 0.688 at 20% training data in the resolution shift scenario, while geometric augmentation improves mIoU from 0.444 to 0.533 under geographic transfer. Models trained on 20% augmented data exceed the performance of 100% non-augmented configurations under pronounced domain discrepancies, establishing an operational threshold of data efficiency. Computational analysis indicates negligible overhead (approximately 1 s per epoch) through asynchronous data pipelines. Augmentation functions as a regularization mechanism in intra-domain settings and transitions to a distribution bridging mechanism under cross-domain conditions. Geometric invariance and engineered data diversity partially substitute for manual annotation, enabling improved cross-domain building extraction performance. Full article

The rising demand for cooling in hot semi-arid cities like Jaipur is putting increasing pressure on energy infrastructure and urban resilience. This study investigates the potential of Jaali, a traditional perforated screen used in Indian architecture, as a passive strategy to reduce energy demand in a contemporary office building through data-driven optimisation and computational analysis. Using detailed energy simulations in DesignBuilder, this research explores how variations in orientation, cavity depth, perforation ratio and screen thickness affect cooling performance during the summer months through a systematic parametric study generating 84 simulation configurations. The model is based on a 12-storey office building designed according to local energy codes. The results show that the optimal configuration differs by orientation. On the south façade, the optimal combination is a 100 mm Jaali with 20% perforation and a 1.5 m cavity, which delivers the best performance. The west façade performs best with a thicker 150 mm screen, the same 20% perforation ratio, and a 1.0 m cavity depth. On the east façade, the strongest performance is achieved with a 150 mm Jaali, 50% perforation, and a 1.5 m cavity, with cooling demand reduction of up to 8.71%. These findings demonstrate that traditional design elements, when optimised for modern use, can offer measurable energy savings through predictive modelling frameworks. More importantly, their widespread adoption could support urban cooling strategies, reduce peak electricity loads and contribute to sustainable development across rapidly growing cities in hot climates. The comprehensive dataset generated provides a foundation for future AI-enhanced building energy optimisation applications. Full article

Rapid urbanisation and intensified agriculture are major drivers of groundwater contamination in peri-urban agricultural aquifers worldwide. Contaminants including nitrates and phosphates accumulate through fertilizer use, wastewater infiltration, and groundwater overextraction, creating complex spatial and temporal patterns. Quantifying these impacts under multiple anthropogenic pressures remains a key challenge for effective water resource management. This study develops a Geo-AI ensemble modeling framework that integrates grid-based spatial analysis with advanced machine learning to assess groundwater contamination dynamics. A composite contamination index (CCI) was constructed to synthesize hydrochemical indicators into a unified measure of aquifer degradation. The AI framework uses Graph Neural Networks (GNNs), Light Gradient Boosting Machine (LightGBM), and Deep Long Short-Term Memory Networks (LSTM). Anthropogenic drivers include population growth, infrastructure density, agricultural intensity, groundwater abstraction, and hydroclimatic variability, providing a comprehensive understanding of contamination sources. The methodology was applied to the urbanised aquifer of Manouba, western suburban Tunis (Tunisia), using 295 samples collected from 85 monitoring wells between 2005 and 2025. Validation results show strong predictive performance, with LightGBM achieving R² = 0.986, RMSE = 13.14, and MAE = 1.72, outperforming GNNs (R² = 0.972) and LSTM (R² = 0.943). The spatial analysis reveals a major shift in contamination patterns, with severe contamination expanding to 55% of the study area in 2025, compared with 7% in 2005, while low and slight contamination declined from 45% to 20%. The results highlight how urban expansion reduces recharge, increases pollutant loading, and amplifies aquifer vulnerability, while agricultural intensification further accelerates contaminant accumulation and degradation processes. This framework provides a transferable, data-driven tool for mapping contamination hotspots and supporting targeted, sustainable groundwater management in peri-urban agricultural aquifers under increasing anthropogenic pressures worldwide. Full article

The rapid growth of digital communication platforms has generated vast volumes of user-generated textual data and digital footprints, creating growing demand for scalable artificial intelligence systems capable of supporting evidence-based decision-making. This study proposes and evaluates a human–AI collaborative analytical pipeline for multi-class sentiment and aggression analysis of large-scale social media data (N = 15,064 messages) related to an urban infrastructure project. The proposed framework integrates standard NLP preprocessing, machine learning-based classifiers, temporal aggregation, and controlled large language model (LLM)-assisted classification within a structured analytical workflow that incorporates expert validation and oversight. A stratified manual validation procedure (n = 301) demonstrated substantial inter-annotator agreement (κ = 0.70) and stable multi-class classification accuracy (80%). The results indicate that combining sentiment polarity and aggression detection as complementary linguistic indicators improves sensitivity to shifts in discourse dynamics and enables early identification of emerging social tension. The study demonstrates the potential of human–AI collaborative analytical frameworks for transparent, interpretable, and predictive large-scale social media analysis in decision-support contexts. Full article