Search Results (9,605)

The fundamental dilemma of energy transition lies in whether an economy can guide its energy system to break free from deep dependence on fossil fuels in a sustained and orderly manner. This requires not only institutional incentives for innovation but also, more critically, a social-level shift in focus from “physical expansion” to “human development.” This paper incorporates these two conditions into a unified causal framework. Taking the pilot program for the construction of IP-strong provinces in China launched in 2016 as a quasi-natural experiment, and using panel data from 30 provincial-level administrative regions in China over the period 2010–2022, this study employs the Spatial Durbin Difference-in-Differences (SDM-DID) model and the Double Machine Learning (DML) method to examine the joint impacts and transmission mechanisms of the regional IP strong chain and the deep synergy between investment in physical capital and investment in human capital on energy green controllability. The findings are as follows. First, both the IP strong chain and deep synergy significantly improve energy green controllability. The local effect of deep synergy is far greater than the direct effect of the IP system itself, making it the core structural force driving the green transition. Second, the institutional dividend of the IP strong chain generates positive spatial spillovers to neighboring regions through the patent information disclosure channel. In contrast, the spatial spillovers of deep synergy are obstructed by administrative barriers and fiscal boundaries. Third, deep synergy plays a significant partial mediating role in the process through which the IP strong chain affects energy green controllability, with more than one-third of the total policy effect being released through this channel. Fourth, a path-wise test reveals a notable structural difference: the human capital investment path significantly outperforms the physical capital investment path in terms of transmission efficiency and robustness. This indicates that, at the current stage, the institutional effectiveness of the IP system in driving the green transition is largely achieved by improving the quality, capacity, and security level of human capital, rather than by restructuring the physical capital stock. The above conclusions remain robust after replacing the machine learning algorithm, adjusting the sample split ratio, and excluding the interference of concurrent competitive policies. This paper reveals the complete causal chain through which institutional public goods are transmitted to system governance capacity via the factor allocation structure, providing new empirical evidence for understanding the deep-seated relationship between intellectual property governance and the energy transition. Full article

The rehabilitation of aging educational buildings has become increasingly important in the context of sustainable campus development and adaptive reuse of existing infrastructure. This study proposes an integrated BIM-based framework for the rehabilitation of underutilized academic spaces through the combined application of Building Information Modeling (BIM), biophilic interior design principles, and Artificial Intelligence (AI) predictive modeling. The methodology was implemented in a case study involving non-functional areas within the Faculty of Aerospace Engineering at the National University of Science and Technology POLITEHNICA Bucharest. Autodesk Revit was employed to develop a parametric digital model of the existing structure, support spatial reconfiguration, and assess environmental and functional performance indicators throughout the rehabilitation process. To evaluate the effectiveness of the proposed framework, multiple performance criteria were considered, including spatial efficiency, daylight performance, material sustainability, acoustic quality, and user-perceived visual comfort. Furthermore, a synthetic dataset generated through parametric simulation was utilized to train and compare four machine learning algorithms (Multiple Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Networks) to predict user comfort based on spatial and environmental variables. The rehabilitation strategy resulted in an 18% increase in usable floor area, a 26% improvement in average daylight factor, a 25% increase in renewable material utilization, and a 38% reduction in estimated acoustic reverberation time. Simultaneously, the predictive modeling revealed that the Artificial Neural Network (ANN) provided the highest accuracy (R² = 0.91) in capturing the non-linear relationship between biophilic design elements and perceived interior quality. By integrating Gilbreth’s principles of cognitive ergonomics, the AI framework actively prevents the rigid, purely quantitative optimization associated with “Digital Taylorism, The findings demonstrate that the proposed BIM-integrated rehabilitation framework can support both technical optimization and user-centered environmental enhancement in educational facilities. The study contributes a transferable digital methodology for sustainable academic building transformation, combining geometric precision, predictive environmental performance assessment, and human-centered design principles within a unified rehabilitation workflow. Full article

The integration of sustainable materials and Industry 4.0 technologies as the overarching paradigm offers significant potential to advance manufacturing sustainability. However, existing research remains fragmented, often focusing on isolated topics such as recycled plastics in additive manufacturing or the use of machine learning and material informatics in material processing. As a result, limited attention has been given to the combined effects of these approaches across the economic, environmental, social, and technological dimensions of sustainability. To address this gap, this study presents a systematic literature review of 156 peer-reviewed articles published between 2015 and 2025 and develops the concept of Sustainable Circular Manufacturing (SCM) as the sweet spot between sustainable materials and Industry 4.0. The review shows that SCM-related studies primarily enhance data acquisition, process transparency, intelligent decision-making, process optimization, automation, and human–technology interaction. The findings also indicate that the literature is strongly weighted toward environmental indicators such as carbon footprint, energy efficiency, and circularity, whereas social sustainability and economic viability remain comparatively underexplored. Building on these insights, the study proposes SCM as an integrative conceptual framework that clarifies the causal pathways linking sustainable material strategies and Industry 4.0 enablers to multidimensional sustainability performance. Finally, the review identifies key implementation barriers and outlines a future research agenda to support digitally enabled circular material strategies. Full article

Higher education is increasingly asked to prepare learners for societies shaped by artificial intelligence, ecological destabilization, labour-market reconfiguration, and declining institutional trust. Yet many universities remain governed by a scarcity model: knowledge transmission, durable credentials, and economic productivity. This article argues that the model is structurally misaligned with emerging conditions because the central educational challenge is shifting from knowledge scarcity to power abundance. The deeper crisis is not a deficit of knowledge production but a deficit of formation: higher education has underdeveloped the human capacities required to use technologically amplified power wisely, meaningfully and responsibly. The article develops this argument through a conceptual design with an embedded systematized scoping review and thematic synthesis across higher education studies, AI governance, futures and foresight, sustainability transitions, human flourishing, wisdom science, and research metrics. It proposes flourishing stewardship as a new first principle for higher education: the cultivation of persons and institutions capable of pursuing meaningful lives while preserving and advancing the conditions for shared human and planetary flourishing. The article contributes the Flourishing Stewardship Transformation Model, linking external transition conditions, scarcity-model misalignment, the power–wisdom gap, six formation capacities, and five institutional transformation levers. The model is operationalized through design questions, researchable indicators, and propositions for future empirical testing. The paper contributes to technological forecasting and social change by positioning higher education as a socio-technical transition infrastructure whose purpose is not merely to adapt learners to technological change, but to form the human agency needed to govern it. Full article

In South Africa, escalating consumption of digital devices poses significant threats to ecological sustainability and has far-reaching implications for environmental degradation. The ratio of digital devices to human beings is rapidly increasing, underscoring the urgency of interrogating the sustainability implications of digital expansion. This study investigated the complex relationship between digitalisation and sustainable development, focusing on the social, environmental, and economic impacts in the South African context. For this systematic review, researchers synthesised and analysed the findings of 44 articles, published between 2015 and 2025. Using a knowledge–implementation–effect (K-I-E) cycle, the research sought to bridge knowledge gaps, critically assess the ecological costs of digital innovation, and explore how digital technologies align or conflict with sustainability goals. The study hypothesised that unchecked digitalisation exacerbates climate vulnerability, e-waste, and energy consumption, thereby threatening environmental integrity and social equity. The findings underscore the digitalisation–carbon footprint paradox, capturing a contradiction of the digital age, whereby technological progress helps reduce environmental impact and at the same time drives new forms of environmental pressure. This paradox reflects a tension between efficiency gains and expanded energy consumption. The study has major implications for policymakers because it promotes eco-friendly principles. Notably, there is a paucity of literature on digitalisation and sustainability in South Africa, with existing studies largely confined to narrow, sector-specific analyses rather than providing integrated, systemic, and cross-sectoral perspectives. Full article

Due to the impacts of climate change and human activities, the regional climate of the Three Gorges Reservoir area in China has undergone significant changes, bearing important implications for regional sustainable development. Based on annual average temperature data from 15 cities in the reservoir area from 1980 to 2025, this study employs linear regression and anomaly analysis to systematically analyze the regional characteristics of urban temperature changes and their associations with the construction phases of the Three Gorges Project. The results show that: (1) The annual average temperature in the reservoir area exhibits a significant warming trend, with an overall increase of 1.66 °C (0.36 °C/decade) and a multi-year average temperature of 15.91 °C. (2) All 15 cities in the reservoir area show warming trends, with the downstream section of the reservoir (near the dam) experiencing higher warming rates than the midstream and upstream sections. (3) Divided into three construction phases, the annual average temperature in the reservoir area shows a warming trend in each phase, with warming rates of 0.028 °C/yr, 0.031 °C/yr, and 0.065 °C/yr, respectively, indicating an accelerating trend. Both the warming magnitude and the annual average temperature of the cities in Phase III are generally higher than those in the previous two phases. The findings of this study can provide scientific support for urban climate management and sustainable development in the reservoir area. Full article

This study developed an eco-epidemiological model incorporating constant prey refuge, predator disease transmission, and anthropogenic harvesting to explore the coupled dynamical behaviors of ecological populations under natural infection and human interference. All feasible equilibrium points of the proposed system were solved, and the local stability of the boundary equilibria and threshold conditions for transcritical bifurcations were systematically analyzed. The global asymptotic stability of the positive coexistence equilibrium was verified by constructing the appropriate Lyapunov functions. Furthermore, a parameter sensitivity analysis was performed to identify the dominant parameters governing population persistence and system stability. To balance ecological sustainability and economic benefits, an optimal harvesting control problem was investigated, and time-dependent optimal harvesting efforts were obtained by applying optimal control theory. Numerical simulations demonstrated that the optimal harvesting strategy can dynamically adjust the harvesting intensity to maximize long-term discounted economic profit while maintaining stable population coexistence. The theoretical and numerical results provide reliable theoretical guidance for the sustainable exploitation and ecological management of infected predator–prey ecosystems. Full article

Additive manufacturing (AM) is applied in sustainable architecture and Construction 4.0 because it can support design flexibility, mass customization, material efficiency, and reduced reliance on conventional formwork. Prior reviews often addressed either construction applications or broader digital transformation, leaving the intersection of AM, construction sustainability, digital technologies, and lifecycle performance underexplored. This study conducts a systematic and bibliometric review of literature retrieved from Scopus and Web of Science covering the period from January 2016 to January 2026. The review protocol followed SPAR-4-SLR principles and PRISMA 2020 guidelines, with 58 records retained for analysis. Bibliometric and thematic analyses identified a marked rise in publication activity after 2021 with four major research themes: material development and innovation, digital fabrication and process control, lifecycle assessment and circularity, and digital integration and project implementation. Construction 4.0 technologies, including BIM, digital twins, automation, and robotics, were the most frequently represented digital enablers. The review further identifies future research opportunities and outlines a proposed conceptual pathway toward Construction 5.0. This pathway connects materials, robotics, lifecycle performance, and human-centered priorities as a future research agenda. Overall, this study contributes to a more integrated understanding of how AM can advance sustainable, digitally enabled, and human-centered construction practice. Full article

Wild boar populations have expanded globally due to factors such as habitat restoration, climate change, and adjustments in hunting policies. This expansion has led to agricultural losses, threats to public safety, disease transmission, and ecological damage, thereby exacerbating human–animal conflicts. Current strategies for mitigating conflicts between wild boars and humans face challenges, including insufficient assessment of long-term control effects and limitations in population dynamics monitoring technologies. This paper reviews mitigation strategies for global wild boar conflicts from ecological, economic, and social perspectives and provides a comprehensive evaluation for their effectiveness. Existing mitigation strategies include population control (hunting management and reproductive regulation), habitat management (landscape barriers and food resource regulation), physical protection (electric fences and multimodal deterrence devices), and community engagement mechanisms (ecological compensation and participatory management). The effectiveness of these strategies varies significantly across regions. Global case studies on human–wild boar conflicts demonstrate that multidimensional collaborative governance has achieved notable success in mitigating these conflicts. For example, dynamic management systems based on intelligent monitoring and community participation can effectively reduce the incidence of wild boar conflicts. Given the limitations of the current single-indicator evaluations, this paper proposes a comprehensive evaluation framework encompassing “strategy effectiveness, cost, and sustainability”. The application of synergistic multi-strategy approaches produces significant synergistic effects, exhibiting nonlinear superposition characteristics. Therefore, future efforts should integrate intelligent early warning systems and policy reforms to develop an adaptive social–ecological coupling framework. Full article

Wildfires threaten natural ecosystems and human livelihoods in the Tanzanian Miombo woodlands. This study presents the first locally calibrated, high-resolution wildfire susceptibility map for the Iringa region, developed using a robust machine learning framework. Multi-decadal remote sensing data (MODIS fire occurrences, 2001–2024) were integrated with climatic, topographic, vegetation, and anthropogenic variables to train four classifiers: Random Forest, XGBoost, support vector machine with RBF kernel, and Logistic Regression. A balanced dataset of 9096 fire points and an equal number of randomly sampled non-fire points was used. The data were split into 70% for training and 30% for testing. Model performance was evaluated using accuracy, area under the ROC curve (AUC), accuracy, precision, and F1-score. Random Forest achieved the highest overall performance (AUC = 0.845, accuracy = 0.759, precision = 0.789 and F1 = 0.771), followed by XGBoost (AUC = 0.828, accuracy = 0.736, precision = 0.700 and F1 = 0.757), SVM (AUC = 0.755, accuracy = 0.679, precision = 0.648 and F1 = 0.709), and Logistic Regression (AUC = 0.740, accuracy = 0.661, precision = 0.631 and F1 = 0.696). Feature importance analysis identified altitude as the most influential variable, followed by wind speed, distance to road, and NDVI. Kernel Density Estimation revealed spatially distinct fire clusters concentrated in central and southern hotspots. Temporal analysis showed that 94% of fires occur during the dry season (June–November), peaking sharply in October. These findings provide an evidence-based framework for fire prevention and sustainable management of Iringa’s Miombo woodlands. Full article

The future of the planet depends largely on human beings, who currently occupy a dominant position among living species. This condition highlights the importance of global efforts to ensure that the sustainability of life on Earth remains a central priority, as articulated in the Sustainable Development Goals (SDGs). This paper investigates how sustainability and education have been represented in research publications in Indonesia. This study reviews the development of sustainability and education research in Indonesia using a systematic literature review (SLR) supported by structured content analysis and descriptive mapping. A total of 362 documents were retrieved from the Scopus database using specific keywords. The systematic review reveals an upward trend in publications over the past nine years, with universities in Java playing a dominant role, and a significant acceleration in knowledge production during the last five years. This increase is accompanied by growing diversity in research topics, domains, keywords, and methodological approaches. One of the most notable findings is the prominence of service-based learning (SBL), which appears to be a distinctive feature of higher education pedagogy in Indonesia. Full article

Human health risk assessment of vapor intrusion caused by organic pollutants is commonly based on steady-state predictions of partition and vapor migration in the subsoil. This study develops a pseudo-dynamic, process-based Partition–Diffusion Risk Model (PDRM) using a one-dimensional numerical model for organic mixtures to assess the time evolution of cancer and non-cancer risks, indoor air concentrations, and non-aqueous phase liquid (NAPL) formation. The model has been applied to a low-carbon sandy soil without microbial degradation, which might be a worst-case scenario. Six simulation scenarios combined two source concentrations (1000 and 3000 mg/kg) and three source depths (1, 3, and 5 m) over 30 years. Results show that source depth governs exposure dynamics: shallow contamination poses unacceptable risks rapidly but declines quickly, whereas at greater depths, unacceptable levels appear later and persist throughout the exposure period. NAPL formation may act as a secondary source, sustaining vapor release and extending indoor exposure under high-loading conditions. Multicomponent partitioning induces nonlinear, compound-specific behavior, with the first 3–5 years representing a critical period for rapid risk changes. Conventional models show that neglecting NAPL formation and time variability may lead to an underestimation of cancer risk by up to an order of magnitude. These findings highlight the importance of incorporating depth and time-dependent characterization to reduce uncertainty in vapor intrusion risk assessments. Full article

Agritourism is increasingly intersecting with digital technologies to foster rural resilience, economic growth, and sustainable development. This study conducts a comprehensive systematic bibliometric review to map the intellectual structure, thematic evolution, and collaborative networks characterizing the digitalization of agritourism from 2010 to 2025. Guided by the PRISMA framework, data from the Scopus database were analyzed using scientific mapping techniques, including keyword co-occurrence, thematic evolution tracking, and spatial collaboration analysis. The findings reveal a paradigm shift categorized into three evolutionary phases: an incubation period of basic web adoption (2011–2017), a disruptive phase catalyzed by the COVID-19 pandemic (2018–2022), and an exponential maturation phase driven by Industry 4.0 technologies such as Artificial Intelligence (AI), Big Data, and Virtual Reality (2023–2025). Four primary thematic clusters emerged: digital marketing and connectivity, smart tourism and advanced analytics, immersive technologies for heritage preservation, and macro-level sustainability policies. Geopolitically, research is driven by two distinct networks: an Asian-centric hub led by China focusing on state-sponsored smart villages, and a Western hub anchored by the USA and Italy emphasizing entrepreneurial diversification. The study concludes that digitalization has transitioned from a reactive survival mechanism to a proactive strategic necessity. It highlights the critical need to bridge the digital divide through human capital investment and provides a future research agenda focusing on the ethical application of AI, the circular economy, and the preservation of rural authenticity in emerging ’phygital’ environments. Full article

As one of the key vehicles to integrating culture and tourism in urban and rural development, cultural tourism-oriented characteristic towns are increasingly facing natural and social disaster risks caused by global climate variability, large-scale expansion of town areas, and intensified human engineering activities. In particular, characteristic towns that have rapidly developed through tourism based on historical and cultural heritage face challenges such as compact layouts of ancient architectural complexes, extensive outward expansion of newly developed areas, and inadequately planned emergency evacuation systems—making them ill-equipped to cope with increasingly uncertain disaster risks. In response to these issues, this study takes Zhangguying Town in Yueyang County, Hunan Province, as a case study. Through field investigations, interviews, and GIS-based hydrological simulations, the research systematically identifies the characteristics and influencing factors of disaster risks in the town. It also reveals the core dilemmas confronting current disaster prevention planning and proposes strategies such as enhancing chain disaster prevention measures, promoting micro-scale, site-specific disaster prevention retrofitting, and establishing a multi-scale disaster prevention system through “point-line” linkages. By reducing disaster risks, preserving cultural heritage, and optimizing emergency response capacities, this research effectively supports the sustainable development of cultural tourism-oriented characteristic towns from a disaster prevention perspective, enabling these towns to withstand natural hazards while sustaining their historical, cultural, and socio-economic functions. The findings provide a theoretical basis and methodological reference for comprehensive disaster prevention planning in similar cultural tourism-oriented characteristic towns. Full article

The reconstruction of vegetation in opencast mining areas constitutes an intricate process of ecological restoration within human-altered systems. A systematic characterization of the multi-dimensional synergistic successional pathways—encompassing morphology, structure, and function—and the corresponding delineation of key recovery phases holds significant potential to inform and refine land reclamation strategies. This study took the southern dump of the Antaibao Coal Mine within the Pingshuo mining area on the Loess Plateau as the study area. Using the Google Earth Engine (GEE) platform, time series Landsat remote sensing images from 1990 to 2023 were processed to derive three indicators representing vegetation coverage morphology, landscape pattern structure, and ecosystem service function: Vegetation Fractional Coverage (VFC), Mining Landscape Restoration Index (MLRI), and Remote Sensing Ecological Index (RSEI). A Reconstructed vegetation Restoration Comprehensive Index (RRCI) was established through the multi-dimensional collaborative analysis of morphology–structure–function. Based on the long-term evolutionary sequence of RRCI, the S-logistic growth curve model was employed for nonlinear fitting, and critical restoration stages of reconstructed vegetation were quantitatively delineated using preset threshold rules. The results demonstrate that time series RRCI data of the screened sample plots effectively characterize the spatiotemporal restoration dynamics of reconstructed vegetation, with a high model goodness of fit (R² > 0.7). In accordance with the criteria for delineating critical stages of reconstructed vegetation restoration, the average durations of the accelerated development period, consolidation development period, and overall recovery development period of reconstructed vegetation in the study area are 5.09 years, 4.64 years, and 9.73 years, respectively. Significant differences exist in the accelerated development period and overall recovery development period between arbor forest lands and arbor shrub forest lands (p < 0.05), and the time required for vegetation restoration at each stage is longer in arbor forest lands than in arbor shrub forest lands. This study constructs a multi-dimensionally collaborative RRCI and quantifies critical stages of reconstructed vegetation evolution, which is of great significance for promoting the sustainable evolution and dynamic management of reconstructed vegetation in opencast mining areas. Full article