Search Results (188)

Integration of informatization and industrialization (IoII) is a pivotal sustainable strategy in China, aimed at industrial modernization and high-quality economic development, which accelerates enterprise digitalization. This paper explores the economic impacts of China’s integration of informatization and industrialization (IoII) policy on enterprises’ environmental, social, and governance (ESG) performance, a core indicator of corporate sustainability. Employing the quasi-natural experiment setting offered by the pilot policy of “Integration of Informatization and Industrialization (IoII)” in China, this paper explores the effects of the IoII on corporate ESG performance through effect analysis, mechanism identification, and heterogeneity test, utilizing the data from A-share listed companies in China. Using the difference-in-differences (DID) analysis, the results suggest that the IoII significantly contributes to improving corporate ESG performance, primarily driven by improvements in the environmental (E) and governance (G) dimensions, while the social (S) dimension is not significantly affected, thereby enhancing long-term sustainability competitiveness. The findings from the mechanism identification indicate that the IoII can promote corporate ESG performance through the three mechanisms: green innovation, corporate governance, and information transparency, all of which underpin sustainable operational practices. The heterogeneity tests reveal that the IoII promotes corporate ESG performance more effectively in high-tech enterprises, non-SOEs, and enterprises with low growth rates, implying differentiated paths for sustainability-driven digital upgrading. By linking industrial digital integration with corporate sustainability, this study enriches the understanding of ESG determinants and provides meaningful implications for sustainability-oriented digital transformation. Full article

Unpolluted water, both freshwater and saltwater, is essential for achieving several United Nations Sustainable Development Goals, particularly SDGs 6, 3, 2, 14, and 15. This study maps emerging water-quality monitoring technologies at intermediate technological readiness levels (TRLs 4–5) and their potential patent markets (TRL 9). A total of 40,469 patent families were retrieved from the Espacenet worldwide database using IPC G01N33/18 and used to analyze sensing parameters. A subset of 2146 water-pollution-related patents was analyzed in detail. The analysis covered sensing parameters, temporal trends, compound annual growth rates (CAGR), legal status, geographic distribution of patent origins and markets, and the technological landscape, including application domains and niche clusters. The results show pronounced exponential growth in patent filings since 2014 and a high share of active documents, indicating sustained global investment. Innovation leadership is concentrated in China, South Korea, India, the United States, and Japan, with export-oriented patents largely held by transnational corporations, while African participation remains limited. Technological trends prioritize multiparameter environmental and biological sensing, addressing pH, temperature, turbidity, dissolved oxygen, nutrients, heavy metals, polycyclic aromatic hydrocarbons (PAHs), and oxidation–reduction potential. Emerging solutions integrate autonomous platforms, remote sensing, Internet-of-Things architectures, and machine-learning-based analytics. Persistent bottlenecks include sensor robustness in harsh aquatic environments and the reliable discrimination between background variability and early pollution signals. Strengthening low-cost and scalable deployment remains essential to ensure water quality, support environmental sustainability, and minimize risks. Full article

Against the background of high-quality development and urban renewal in China, old residential communities have become key areas for improving spatial quality and quality of life. We used the entrance pavilion of Baoshengli North Community as a case study to explore how spatial design and layout can meet residents’ psychological and social needs. Adopting a mixed-methods approach, combining field observation, behavioral mapping, a questionnaire (Total = 105), in-depth interviews, and statistical analysis, a post-occupancy evaluation (POE) was conducted on spatial effectiveness and social functions. The results show that user-oriented spatial design, safety, esthetic quality, and inclusive functions significantly enhance residents’ spatial perception, willingness to use the space, and social interaction. Differentiated spatial preferences and potential conflicts among diverse resident groups were also identified. Targeted design interventions can effectively strengthen the connection between spatial use and subjective perception, and participatory and equitable strategies help promote social harmony and justice. This study enriches the post-occupancy evaluation system for the renewal of old communities from psychological and social dimensions, and provides practical references for user-centered, inclusive, and sustainable public space design in urban renewal practices. One limitation of this study is that data were collected over a single period, which restricts the analysis of seasonal impacts on spatial usage. Full article

Universities play a strategic role in advancing the Sustainable Development Goals (SDGs) through community engagement, particularly within medical education. This study examines medical students’ involvement in volunteering activities and evaluates how such engagement contributes to SDG 3 (Good Health and Well-being) and SDG 4 (Quality Education). A cross-sectional survey was conducted among 499 students from the Victor Babeș University of Medicine and Pharmacy in Timișoara, using a structured questionnaire assessing perceived community impact, soft skills development, motivation for volunteering, sustainability orientation, and institutional support. Results indicate a high level of availability and prior participation in volunteering, reflecting strong internalization of public health and social responsibility values. While no significant differences were observed between volunteers and non-volunteers regarding perceived community impact (SDG 3), volunteer experience was significantly associated with higher levels of soft skills development and motivation (SDG 4). Strong positive correlations were identified between perceived community impact and motivational, educational, and sustainability related dimensions. The intensity of involvement was modestly associated with sustainability orientation and institutional support. Despite high motivation, students reported limited structured institutional frameworks for sustained engagement. The findings suggest that medical volunteering functions as a dual mechanism, strengthening professional competencies while reinforcing community health orientation. Institutionalizing structured outreach programs, particularly in underserved areas, could enhance long-term impact and align medical education more effectively with the 2030 Sustainable Development Agenda. Full article

Managing supply chain risks is a core pillar of operational and supply chain resilience building in the global industrial chain system, which is essential for the high-quality and sustainable development of manufacturing firms. Against the backdrop of escalating global economic uncertainties and interconnected supply chain vulnerabilities, mitigating the adverse impact of supply chain risks on firms’ overseas market expansion has become a critical research and practical issue in the field of operational and supply chain risk management. Based on the textual analysis of annual reports of listed firms, this study constructs a systematic supply chain risk measurement indicator system through standardized text preprocessing, multi-dimensional feature keyword lexicon construction, context co-occurrence frequency calculation and so on. We further validate the effectiveness of the indicator system by comparing its trend with the global economic uncertainty index, confirming that it can capture firm-specific supply chain risk information effectively. Employing text analysis, this study constructs a systematic supply chain risk measurement indicator system for A-share manufacturing firms and empirically verifies that elevated supply chain risks significantly constrain their overseas market expansion. Three interrelated operational mechanisms, namely surging operating costs, tightened financing constraints, and slumping R&D investments, drive this inhibitory effect. Notably, firms can effectively offset this negative effect by broadening overseas operational scope and intensifying overseas digital and technological innovation. Heterogeneity analyses further reveal that the inhibitory effect is more pronounced for five types of firms: those with lower overseas revenue, located in less market-oriented regions, operating in upstream value chain sectors, with lower current liabilities, and with a lower degree of digital transformation. Full article

Since the 1970s, accelerated urban development in Doha has contributed to a disjointed and inefficient city structure. While the Doha Metro has begun to address spatial and mobility-related challenges, planners continue to call for a more integrated, strategic approach to ensure safe, accessible, and efficient transit connectivity. In response, the Qatar National Development Framework provides a long-term vision for sustainable urban transformation, with a central aim of embedding the Metro system within the existing urban context and aligning expansion with Transit-Oriented Development (TOD), which promotes dense, multifunctional, pedestrian-oriented neighborhoods along transit corridors. Within this context, this study investigates how TOD strategies can enhance quality of life in mixed-use environments, focusing on the area surrounding Al Mansoura metro station and the adjacent Najma and Al Mansoura districts. Using the Integrated Modification Methodology (IMM), the analysis assesses spatial structure across density, spatial diversity, and connectivity, and derives evidence-based recommendations to improve livability and support sustainable revitalization. To broaden regional applicability, the study also compares Al Mansoura with Olaya in Riyadh—two mid-to-late 20th-century, high-density mixed-use districts undergoing TOD-driven transition—highlighting how spatial form, infrastructure legacy, and urban governance shape TOD outcomes and inform adaptable TOD frameworks for Gulf cities. Full article

Artificial intelligence (AI) is increasingly embedded in smart city strategies, yet its role in advancing participatory urban planning remains underexamined, particularly in rapidly urbanizing metropolitan contexts of the Global South. This exploratory, governance-centered study investigates how AI can support participatory urban planning for sustainable smart cities, emphasizing institutional mediation and trust dynamics. Using a convergent mixed-methods design, the research combines a purposive stakeholder survey (n = 260) with qualitative thematic analysis to assess AI awareness and use, participation quality, institutional and technical readiness, and public trust in the Dammam Metropolitan Area, Saudi Arabia. The findings reveal a participation paradox: relatively high AI awareness and digital readiness coexist with low perceived influence and limited confidence in participatory outcomes. Institutional coordination gaps, skill constraints, and regulatory ambiguity mediate the translation of AI adoption into meaningful engagement. Stakeholders favor AI applications, such as interactive mapping, predictive analytics, and digital twin visualization, that enhance transparency and deliberation over automated decision systems. Qualitative evidence further indicates that AI is perceived not as a standalone solution, but as a catalyst for institutional reform, capacity development, and sustainability-oriented governance. The study contributes to urban science by empirically validating a socio-technical framework that positions AI as a facilitative governance instrument embedded within institutional and trust-building processes. The findings offer policy-relevant insights for cities seeking to align AI-driven innovation with inclusive, accountable, and sustainable urban development. Full article

Active learning has increasingly been adopted as an evidence-aligned approach to improving learning quality in university physics—a domain characterized by high conceptual abstraction, persistent misconceptions, and substantial variability in student performance. Evidence from physics education research indicates that active-learning designs can outperform lecture-dominant instruction in conceptual learning and student engagement; however, reported effects vary substantially across instructional settings and implementation models. Here, empirical studies and review-level syntheses are integrated to delineate (i) the instructional components that most reliably underpin successful active learning, (ii) the mechanisms through which these components influence learning processes and outcomes, and (iii) the boundary conditions that moderate effectiveness across higher-education contexts. The synthesis is further situated within sustainability-oriented higher education by linking physics active-learning designs to competence development relevant to quality education, climate literacy, and collaborative problem solving. Evidence spanning flipped classroom implementations, peer instruction, collaborative problem solving, inquiry- and project-based approaches, and technology-enhanced formats is organized into a component–mechanism–outcome framework structured along cognitive, affective, and behavioral pathways. Two deliverables are advanced: an integrative mechanism model connecting instructional components to mediating processes, learning outcomes, and sustainability-aligned competencies, and an operational toolbox that translates the evidence into actionable design heuristics, measurement options, and scaling considerations. By redirecting attention from “which strategy works” to “which components work, how, and under what conditions,” the review aims to support instructors, departments, and institutions seeking scalable, evidence-aligned active learning in university physics. Full article

Public transport systems in rapidly urbanizing Gulf cities confront the simultaneous challenge of decreasing emissions while guaranteeing equal access for riders, many of whom rely on transit for economic reasons. Sustainable smart city development necessitates bus services that are both efficient and sensitive to rider needs in adverse weather conditions. This study develops and evaluates a wellbeing-focused planning framework for Dubai’s bus network, filling gaps in prior research that primarily focuses on temperate, choice-based transport environments. The study uses Generalized Structured Component Analysis (GSCA) to analyze how Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA) impact overall journey wellbeing, based on a cross-sectional survey of 491 riders collected from July–August 2024. Data were collected during peak summer conditions, and the analysis followed a structured workflow that operationalized the proposed constructs into measurable indicators and estimated both the measurement and structural components of the GSCA model to find planning relevant wellbeing drivers. The model shows a strong fit (FIT = 0.684; GFI = 0.991; SRMR = 0.056), with SEA (β = 0.504) having the greatest influence on wellbeing, followed by SOA (β = 0.344) and PEPC (β = 0.070). Affordability and information quality are key SEA metrics, highlighting the necessity of economic access and multilingual, real-time communication. Overall, the findings indicate that wellbeing is most strongly shaped by accessibility-oriented service experience attributes particularly affordability and information quality followed by operational assurance, while comfort-related conditions remain significant under high heat exposure during waiting and transfers. On the other hand, the research indicates that operational reliability helps mitigate environmental discomfort in hyper-arid areas. The report suggests focusing on equal prices, digital information accessibility, dependable operations, and climate-adaptive infrastructure to promote sustainable mobility and long-term public transport use. Full article

New-type urbanization (NTU) is a key driver of high-quality development and progress toward the Sustainable Development Goals (SDGs) in China. While existing studies acknowledge the multidimensional nature of this process, they often measure it as a single composite aggregate. This approach masks the system’s local sensitivity to internal structural changes and obscures the spatially stratified heterogeneity of dominant drivers. To address this gap, this study constructs construct a comprehensive evaluation index system using panel data for 280 prefecture-level and above cities in China from 2001 to 2023. This study integrates the entropy-weighted TOPSIS method, a modified coupling coordination degree model (MCCD), geographically and temporally weighted regression (GTWR), and the optimal parameters geographical detector (OPGD). Using this framework, this study investigates the spatio-temporal characteristics of the coordinated high-quality development (CHQD) in NTU, systematically dissecting the spatial heterogeneity of local sensitivities and dominant drivers. The results indicate that the following: (1) CHQD exhibits a continuous upward trajectory characterized by significant regional convergence, with the center of gravity gradually shifting southwest. Structurally, green and social dimensions demonstrate the most rapid growth, progressively superseding spatial expansion as primary growth poles. (2) The structural decomposition reveals clear spatially stratified heterogeneity in local sensitivity. The coastal East faces “diminishing marginal utility” of traditional factor inputs, whereas the Central and Western regions continue to reap “structural dividends” from factor accumulation. (3) The dominant drivers shaping spatial heterogeneity have undergone a sequential evolution from an early “resource-space orientation” to a later “innovation-service orientation.” For instance, in the eastern region, the proportion of construction land (L2) had a single-factor explanatory power (q-statistic) of 0.791. However, its interactions with science and technology expenditure (E3) and other factors yielded q-statistics exceeding 0.820, indicating a marked synergistic effect. These findings support region-specific policy recommendations to promote CHQD and inform sustainable urbanization pathways in China. Full article

Tea plantation ecosystems, as typical human–natural integrated systems, rely on biodiversity to sustain yield, quality, and ecological sustainability. With the global popularization of ecological agriculture concepts, eco-oriented tea production has emerged as a core development direction for the tea industry. However, a systematic elucidation of the mechanisms by which tea plantation biodiversity modulates tea quality, alongside standardized assessment methodologies for this biodiversity, remains inadequate. This paper comprehensively synthesizes how genetic, species, and ecosystem diversity regulate the accumulation of tea polyphenols, amino acids, and aromatic compounds—key determinants of tea quality. It evaluates mainstream assessment frameworks and identifies DPSIR (Driving Forces-Pressure-State-Impact-Response) as the most comprehensive and practical option. This paper further dissects the impacts of genetic, ecosystem, and species diversity (the three core dimensions of tea garden biodiversity) on tea quality formation. Genetic diversity shapes metabolic traits; ecosystem diversity modulates secondary metabolism via microclimate and soil; and species diversity (plants, animals, microbes) exerts synergistic effects on nutrient cycling and pest control. All these collectively improve tea sensory quality, safety, and stability. Future research should focus on plant–microbe interactions, quantitative biodiversity–quality models, and precision ecological management, laying a theoretical foundation for sustainable, high-quality tea production. Full article

Confronted with the severe imperatives to food security posed by a growing population and the urgent need for sustainable development amid climate change, traditional agricultural models face significant resource-intensive efficiency bottlenecks. Deep learning-based image recognition is driving a future-oriented intelligent agricultural revolution by enabling high-throughput phenotyping and autonomous decision-making across the production chain. This paper systematically reviews key advancements in image recognition within modern agriculture, mapping the fundamental paradigm shift from traditional hand-crafted feature engineering to adaptive deep feature learning. We critically analyze technological implementation and performance across five core application scenarios: high-precision pest and disease diagnosis, spatio-temporal growth monitoring and yield prediction through multi-source image fusion, agricultural robots for automated harvesting, non-destructive quality inspection of products, and intelligent precision management of farmland. The review further identifies critical challenges hindering large-scale technology adoption, primarily centered on the high costs of constructing high-quality agricultural datasets and model robustness in complex field environments. Consequently, this study provides a comprehensive and forward-looking reference for advancing the deep integration of vision technology, thereby offering a strategic path toward achieving more intelligent, efficient, and sustainable global agricultural production systems in the digital era. Full article

Urban transport systems are major contributors to air pollution in rapidly growing post-Soviet metropolitan areas. Almaty, Kazakhstan’s largest city, has experienced a sharp rise in private vehicle ownership, accompanied by persistent exceedance of guideline values, particularly for particulate matter (PM₁₀ and PM_2.5). In response, successive municipal administrations have introduced transport and mobility reforms aimed at mitigating traffic-related emissions and improving urban environmental sustainability. This study applies a descriptive longitudinal policy analysis to review and systematize major transport policy interventions implemented in Almaty between 2005 and 2019, including metro expansion, Bus Rapid Transit (BRT) corridors, pedestrianization programs, cycling infrastructure development, and public transport fleet modernization. A brief comparative reference to Bishkek (Kyrgyz Republic) is included as a contextual benchmark rather than as a full comparative case, situating Almaty’s experience within the Central Asian urban context. Using aggregated time-series air-quality data from RSE Kazhydromet, transport infrastructure statistics, and demographic indicators, the study examines how successive reform phases align with long-term particulate matter dynamics. The analysis suggests that people-oriented mobility restructuring and investments in high-capacity public transport tend to coincide with downward trends in particulate matter concentrations, whereas road-expansion-oriented measures demonstrate limited long-term environmental benefits. The study applies a descriptive longitudinal policy framework, focusing on system-level patterns and indicative alignment between transport policy phases and air-quality dynamics rather than causal attribution. The findings provide policy-relevant insights into other rapidly motorizing post-Soviet cities, subject to data and methodological limitations. Full article

Accurately assessing urban green perception is crucial for sustainable urban development and human well-being, yet conventional approaches often depend on simplistic objective metrics and non-immersive, screen-based subjective surveys, undermining ecological validity. This study develops and validates a multidimensional assessment framework that integrates Large Vision Models (LVMs) and Mixed Reality (MR) to couple objective environmental features with immersive human perception. The framework comprises 30 objective and 6 subjective indicators; state-of-the-art LVMs including DINOv2 and Depth Anything were applied to accurately extract objective features from Street View Imagery (SVI); and the MR device, Meta Quest 3, was utilized for the immersive collection of high-quality subjective data. In an empirical study with 74 volunteers in Shenzhen, China, machine learning models trained on MR-based data achieved 20–50% higher R² for subjective perception than models trained on traditional screen-based data. The validated framework was then applied to 61,131 SVIs citywide to map the spatial distribution of multidimensional green perception and to quantify relationships between objective and subjective indicators. Going beyond technical validation, this study demonstrates how the framework serves as a critical tool for urban planning and landscape upgrading. By diagnosing perceptual deficits where greening quantity does not translate into quality experiences, the framework supports a paradigm shift from quantity-oriented greening to perception-oriented spatial optimization. These findings offer actionable insights for policymakers to prioritize interventions that effectively enhance public health and environmental equity in high-density cities. Full article

Against the backdrop of rapid development in the IST industry, addressing issues such as regional homogeneity and uneven spatiotemporal development requires scientific identification and analysis of related resources to support sustainable regional IST development and promote high-quality regional economic growth. This study proposes a framework based on “policy orientation–theoretical support–regional adaptation,” utilizing machine learning to construct a multi-dimensional evaluation index system for IST development potential. By combining subjective and objective criteria to determine indicator weights, a scientific evaluation system is established, with visual analysis conducted through Geographic Information System (GIS). The research selects 22 indicator factors across four dimensions: natural environmental suitability, socio-economic support capacity, regional transportation accessibility, and tourism appeal. Through weighted superposition analysis, it achieves visual representation of spatial differentiation characteristics in the development potential levels of IST in Heilongjiang Province. Results demonstrate a distinct “V”-shaped distribution of high development potential, primarily concentrated in the Greater Khingan Range region, Harbin–Mudanjiang border zone, and Jiamusi, with gradual decline from the core “V”-shaped area to both sides. The proposed evaluation index system provides scientific quantitative decision-making support for regional IST planning, and this methodology also holds reference value for evaluating other tourism industry developments. Full article