Search Results (184)

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

Rapid urbanization and industrialization have intensified air pollution, posing severe challenges to sustainable development and public health. As a core economic zone in China, the Beijing–Tianjin–Hebei (BTH) region faces persistent air quality deterioration, highlighting the urgent need for accurate and intelligent prediction models. However, existing studies often suffer from limited adaptability of single models and subjective feature selection thresholds, constraining predictive performance and generalization capability. To address these challenges, this study proposes a feature-optimized hybrid deep learning framework for AQI prediction across Beijing, Tianjin, and Shijiazhuang. An adaptive feature selection strategy is first developed by integrating the Relief_F algorithm with the Bat Optimization Algorithm (BOA), which adaptively determines feature importance, thereby enhancing objectivity and effectiveness in identifying key pollutant and meteorological indicators. Subsequently, an attention-enhanced CNN–BiLSTM–GRU hybrid network is constructed, where the attention mechanism emphasizes critical temporal information that most influences prediction results. Experiments show that the proposed model achieves MAPE values of 1.00%, 1.15%, and 1.09% for Beijing, Tianjin, and Shijiazhuang, outperforming benchmark models by 18.43–45.05%. These results confirm the framework’s reliability for practical application with strong robustness and statistical validity. Full article

This study explores the dynamic relationship between industrial robots, education, and environmental sustainability in the United States, emphasizing their role in reducing CO₂ emissions. The research aims to quantify how automation, human capital, and the energy transition contribute to carbon mitigation within a data-driven, AI-oriented policy framework. Quarterly data spanning 2011Q1–2024Q4 were analyzed using the advanced Quantile-on-Quantile Autoregressive Distributed Lag (QQARDL) model, which captures heterogeneous long- and short-run effects across emission distributions. Results reveal that industrial robot adoption, education, and renewable energy transition significantly reduce emissions, with the strongest effects occurring at both high- and low-emission quantiles. Economic growth and financial development also support decarbonization when complemented by green finance and innovation, while urbanization increases emissions unless aligned with compact urban design and clean energy systems. The findings imply that AI-driven industrial robotics and education jointly foster sustainability through efficiency, innovation, and awareness. Policymakers are encouraged to integrate automation strategies, renewable energy incentives, and sustainability education into climate policy. This study provides empirical evidence supporting the Resource-Based View, highlighting human capital and intelligent automation as strategic assets for achieving long-term carbon neutrality. Full article

Urban underground spaces, including tunnels, subways, and underground commercial buildings, have grown quickly as urbanization has progressed. Fires frequently break out following industrial accidents and multi-hazard natural disasters, and they can severely damage human health. Fire smoke is a major contributor and a major hazard to public safety. The flow patterns of fire smoke in underground spaces, the risks to human casualties, and engineering and personal protective technologies are all thoroughly reviewed in this work. First, it analyzes the diffusion characteristics of fire smoke in underground spaces and summarizes the coupling effects between human behavior and smoke spread. Then, it examines the risks of casualties caused by toxic gases, particulate matter, and thermal effects in fire smoke from both macroscopic case studies and microscopic toxicological viewpoints. It summarizes engineering protection strategies, such as optimizing ventilation systems, intelligent monitoring and early warning systems, and advances in the application of new materials in personal respiratory protective equipment. Future studies should concentrate on interdisciplinary collaboration, creating more precise models of the interactions between people and fire smoke and putting life-cycle management of underground fires into practice. This review aims to provide theoretical and technical support for improving human safety in urban underground space fires, thereby promoting sustainable urban development. Full article

The rapid development of new-generation information technologies, such as cloud computing, artificial intelligence, big data, and blockchain, is profoundly reshaping production and lifestyles, with regional development patterns. This study employs text analysis to extract the policy adoption timeline of cloud computing from official documents and constructs a quasi-natural experiment framework. First, spatial autocorrelation and hotspot analysis reveal significant spatial dependence in the urban green total factor productivity (GTFP). Accordingly, using panel data of 284 Chinese cities from 2000 to 2023, we apply a spatial difference-in-differences (SDID) model to empirically examine the impact of cloud computing on the urban GTFP. The results show that, first, the adoption of cloud computing significantly enhances the local GTFP, but simultaneously suppresses neighboring cities’ GTFP through the siphon effect, thereby generating negative spatial spillover effects. These findings remain robust across parallel trend tests, placebo tests, and multiple robustness tests. Second, mechanism analysis indicates that improved resource allocation efficiency and strengthened green innovation are the two core channels through which cloud computing promotes GTFP. Third, heterogeneity analysis reveals that cloud computing exhibits stronger siphon effects in smaller cities, generates significant positive spatial spillover effects in coastal regions, and effectively fosters GTFP growth within urban agglomerations, while exerting limited influence on non-agglomerated areas. Moreover, industrial agglomeration further amplifies the positive impact of cloud computing on GTFP. Additionally, from the perspective of regional policies, this study finds that promoting the integrated development of urban agglomerations, reducing administrative monopoly, facilitating free factor mobility, and advancing urban international economic activities are effective pathways to mitigate the siphon effect of cloud computing on the urban GTFP. Based on these findings, this study offers targeted policy recommendations to leverage cloud computing for advancing green and high-quality urban development. Full article

As an important driving force for intelligent transformation, the development and application of industrial robots have promoted the transformation of traditional production modes and the upgrading of energy utilization methods, playing a significant role in improving energy efficiency. Based on the panel data of 283 prefectural-level cities in China from 2008 to 2019, this study used a two-way fixed-effects model to examine the impact of industrial robots on urban energy efficiency. The study found that industrial robots significantly improve energy efficiency, with the mechanisms including scale effects, structural effects, and green technology effects. Heterogeneity analysis shows that this effect is more prominent in innovative cities, central and western regions, and areas with high human capital. The research provides a basis for understanding the pathways through which industrial robots promote the improvement of energy efficiency and offers policy insights for China to advance intelligent manufacturing and green development. Full article

As urbanization intensifies, the challenge of preserving industrial heritage while fostering authentic intergenerational connections has become increasingly salient. This study investigates how artificial intelligence (AI) and augmented reality (AR) technologies can be applied to enhance authenticity and promote both hedonic and eudaimonic well-being within the context of heritage tourism. Using a facility in Shanghai as a case study, we propose a cultural co-creation mechanism that transforms implicit intergenerational memories into shared cultural resources through digital interaction. The study first evaluates public awareness and participation needs in the context of industrial heritage revitalization. In response, we design an immersive platform that enables visitors of different generations to co-create meaning through historical scene reconstruction, multisensory engagement, and collaborative storytelling. A novel five-sense encoding strategy is introduced to reinterpret the enclosed spatial characteristics of industrial architecture as an experiential form of storytelling. This process fosters a deeper connection to place, contributing to authenticity and well-being. Prototype testing results suggest that this AI-AR-enabled co-creation system supports meaningful cultural attachment, improves authenticity, and facilitates the sustainable transmission of heritage. This research provides a replicable model for integrating digital technology, community participation, and authenticity in the well-being-oriented revitalization of industrial heritage sites. Full article

The integration of artificial intelligence (AI) into bioclimatic building design is reshaping the architecture, engineering, and construction (AEC) industry by addressing critical challenges in sustainability and efficiency. By aligning structures with local climates, bioclimatic design addresses global challenges such as energy consumption, urbanization, and climate change. Complementing these principles, AI technologies—including machine learning, digital twins, and generative algorithms—are revolutionizing the sector by optimizing processes across the entire building lifecycle, from design and construction to operation and maintenance. Amid the diverse array of AI-driven innovations, this research highlights digital twin (DT) technologies as a key to AI-driven transformation, enabling real-time monitoring, simulation, and optimization for sustainable design. Applications like façade optimization, energy flow analysis, and predictive maintenance showcase their role in adaptive architecture, while frameworks like Construction 4.0 and 5.0 promote human-centric, data-driven sustainability. By bridging AI with bioclimatic design, the findings contribute to a vision of a built environment that seamlessly aligns environmental sustainability with technological advancement and societal well-being, setting new standards for adaptive and resilient architecture. Despite the immense potential, AI and DTs face challenges like high computational demands, regulatory barriers, interoperability and skill gaps. Overcoming these challenges will be crucial for maximizing the impact on sustainable building, requiring ongoing research to ensure scalability, ethics, and accessibility. Full article

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

The acceleration of industrialization and urbanization have led to the increasingly serious problem of waste gas pollution. Pollutants such as sulfur dioxide (SO₂), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH₃), formaldehyde (HCHO), and hydrogen sulfide (H₂S) emitted from industrial production, transportation, and agricultural activities have posed a major threat to the ecological environment and public health. Although traditional physical and chemical treatment methods can partially reduce the concentration of pollutants, they face three core bottlenecks of high cost, high energy consumption, and secondary pollution, and it is urgent to develop sustainable alternative technologies. In this context, probiotic waste gas treatment technology has become an emerging research hotspot due to its environmental friendliness, low energy consumption characteristics, and resource conversion potential. Based on the databases of PubMed, Web of Science Core Collection, Scopus, Embase, and Cochrane Library, this paper systematically searched the literature published from 2014 to 2024 according to the predetermined inclusion and exclusion criteria (such as research topic relevance, experimental data integrity, language in English, etc.). A total of 71 high-quality studies were selected from more than 600 studies for review. By integrating three perspectives (basic theory perspective, environmental application perspective, and waste gas treatment facility perspective), the metabolic mechanism, functional strain characteristics, engineering application status, and cost-effectiveness of probiotics in waste gas bioconversion were systematically analyzed. The main conclusions include the following: probiotics achieve efficient degradation and recycling of waste gas pollutants through specific enzyme catalysis, and compound flora and intelligent regulation can significantly improve the stability and adaptability of the system. This technology has shown good environmental and economic benefits in multi-industry waste gas treatment, but it still faces challenges such as complex waste gas adaptability and long-term operational stability. This review aims to provide useful theoretical support for the optimization and large-scale application of probiotic waste gas treatment technology, promote the transformation of waste gas treatment from ‘end treatment’ to ‘green transformation’, and ultimately serve the realization of sustainable development goals. Full article

As an integrated socio-technical system linking information technology with industrial infrastructure, the Industrial Internet is increasingly central to urban digital transformation. However, current research largely centers on national or sectoral scales, lacking systematic analysis at the city level—particularly regarding system structure, enabling mechanisms, and region-specific pathways. This study takes Dalian, a city with a strong industrial base and urgent digital transformation needs, leveraging the Industrial Internet Development Index (IIDI), employing a “system structure–mechanism–pathway” analytical framework, we conducted a comprehensive assessment of the spatiotemporal relationship between industrial structure and Industrial Internet performance in Dalian from 2020 to 2022. The study finds that, during the research period, Dalian’s Composite IIDI increased from 0.31 to 0.65, with substantial improvements in platform infrastructure, resource coordination, and data application capacity—providing key support for enterprise digitalization and intelligent consumption. A strong correlation (R² = 0.85) between industrial structure and Industrial Internet performance underscores the structural foundation’s critical role. However, comparative analysis reveals that Dalian still faces structural deficiencies in platform openness, international interface integration, and ecosystem synergy. The study introduces a systemic pathway for empowering Industrial Internet capabilities and offers actionable insights for policymakers seeking to foster regionally adapted digital transformation. Full article

Ensuring traffic safety within urban public transport systems is essential for achieving sustainable urban development, particularly in densely populated metropolitan areas. This study investigates the integration of artificial intelligence (AI) technologies to enhance safety performance in closed public transport environments, with a focus on the city of Belgrade as a representative case. The research aims to evaluate how AI-enabled systems can contribute to the early detection and reduction of traffic incidents, thereby supporting broader goals of sustainable mobility, infrastructure resilience, and urban livability. A hybrid methodological framework was developed, combining computer vision, supervised machine learning, and time series analytics to construct a real-time risk detection platform. The system leverages multi-source data—including video surveillance, onboard vehicle sensors, and historical accident logs—to identify and predict high-risk behaviors such as harsh braking, speeding, and route adherences across various public transport modes (buses, trams, trolleybuses). The AI models were empirically assessed in partnership with the Public Transport Company of Belgrade (JKP GSP Beograd), revealing that the most accurate models improved incident detection speed by over 20% and offered enhanced spatial identification of network-level safety vulnerabilities. Additionally, routes with optimized AI-driven driving behavior demonstrated fuel savings of up to 12% and a potential reduction in emissions by approximately 8%, suggesting promising environmental co-benefits. The study’s findings align with multiple United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 9 (Industry, Innovation, and Infrastructure). Moreover, the research addresses ethical, legal, and governance implications surrounding the use of AI in public infrastructure, emphasizing the importance of privacy, transparency, and inclusivity. The paper concludes with strategic policy recommendations for cities seeking to deploy intelligent safety solutions as part of their digital and green transitions in urban mobility planning. Full article

Modernization in developing countries such as China has long been unsustainable. As a result, China has set the goal of achieving sustainable modernization characterized by harmony between humanity and nature. Against this backdrop, in this study, we apply spatial difference-in-differences (SDID) and double machine learning (DML) models using panel data from 30 provincial-level regions in China from 2009 to 2021. We examine the impacts of the National Big Data Comprehensive Pilot Zone policy and sci-technology financial ecology on the Chinese Path to Sustainable Modernization. The results show that big data pilot zones significantly enhance modernization and generate positive spatial spillover effects through demonstration and diffusion. Sci-technology financial ecology improves sustainable modernization and amplifies the role played by pilot zones. Heterogeneity tests reveal stronger effects in eastern provinces and in areas implementing urban–rural integration or green finance reforms. The results of the mechanism analysis show that big data innovation promotes modernization by strengthening sci-technology financial ecology, raising government attention, fostering inclusive intelligence development, enhancing green innovation efficiency, and upgrading industrial structures. Full article

Transforming the global legacy of abandoned industrial landscapes into vibrant, sustainable urban assets presents a critical yet complex opportunity, requiring solutions that simultaneously honor heritage and meet evolving urban demands. As multifunctional public spaces, their vitality significantly affects spatial quality and user engagement. We investigate the spatial vitality of post-industrial landscapes through a multi-source data framework, using Beijing’s Shougang Park as a case study. Integrating spatial syntax, point-of-interest (POI) analysis, and Baidu Heat Map data, the research constructs a comprehensive evaluation model encompassing spatial accessibility, functional diversity, heritage openness, and crowd dynamics. The findings reveal a marked spatial imbalance in accessibility, with global integration values ranging from 0.09 to 0.29 and a low intelligibility coefficient of 0.09, underscoring a mismatch between spatial structures and modern functional demands. The study identifies dynamic openness of heritage spaces and integrated community functions as key drivers for revitalization. Optimization simulations demonstrate that restructuring road networks significantly enhances spatial integration, increasing the global integration range to 0.10–0.87. This research contributes a replicable, data-driven framework for assessing and guiding the renewal of legacy industrial sites, offering valuable insights for post-industrial urban regeneration and heritage-based development. Full article

As urbanization intensifies, the quantification of methane (CH₄) emissions at city scales faces unprecedented challenges due to spatial heterogeneities from industrial and transportation activities and land use changes. This paper provides a review of the current state of top-down atmospheric CH₄ emission inversion at the city scale, with a focus on CH₄ emission inventories, CH₄ observations, atmospheric transport models, and data assimilation methods. The Bayesian method excels in capturing spatial variability and managing posterior uncertainty at the kilometer-scale resolution, while the hybrid method of variational and ensemble Kalman approaches has the potential to balance computational efficiency in complex urban environments. This review highlights the significant discrepancy between top-down inversion results and bottom-up inventory estimates at the city scale, with inversion uncertainties ranging from 11% to 28%. This indicates the need for further efforts in CH₄ inversion at the city level. A framework is proposed to fundamentally shape city-scale CH₄ emission inversion by four synergistic advancements: developing high-resolution prior emission inventories at the city scale, acquiring observational data through coordinated satellite–ground systems, enhancing computational efficiency using artificial intelligence techniques, and applying isotopic analysis to distinguish CH₄ sources. Full article