Search Results (1,141)

This study focuses on Shanghai, a pioneer city in China’s community renewal practices. In recent years, community renewal driven by civic participation has become a prominent research topic, leading to the emergence of numerous exemplary cases in Shanghai. However, field investigations revealed that many projects have experienced varying degrees of physical deterioration and a decline in spatial vitality due to insufficient maintenance, reflecting unsustainable outcomes. In response, this study examines a bottom-up community renewal project led by the research team, aiming to explore how broad civic participation can promote sustainable community renewal. A multidisciplinary approach incorporating perspectives from ecology, the humanities, economics, and sociology was used to guide citizen participation, while participatory observation methods recorded emotional shifts and maintenance behavior throughout the process. The results showed that civic participatory actions under the guidance of sustainability principles effectively enhanced citizens’ sense of community identity and responsibility, thereby facilitating the sustainable upkeep and operation of community spaces. However, the study also found that bottom-up efforts alone are insufficient. Sustainable community renewal also requires top-down policy support and institutional safeguards. At the end, the paper concludes by summarizing the practical outcomes and proposing strategies and mechanisms for broader application, aiming to provide a reference for related practices and research. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Acid mine drainage (AMD) remains a critical environmental challenge in South Africa due to its severe impact on water quality, ecosystems and public health. Numerous studies on AMD management, treatment and resource recovery have been conducted over the past 20 years. This study presents a comprehensive review of research activities on AMD in South Africa from 1998 to 2025, highlighting key trends, emerging challenges and future directions. The study reveals a significant focus on passive and active treatment methods, environmental remediation and the recovery of valuable resources, such as iron, rare earth elements (REEs) and gypsum. A bibliometric analysis was conducted to identify the most influential studies and thematic research areas over the years. Bibliometric tools (Biblioshiny and VOSviewer) were used to analyse the data that was extracted from the PubMed database. The findings indicate that research production has increased significantly over time, with substantial contributions from top academics and institutions. Advanced treatment technologies, the use of artificial intelligence and circular economy strategies for resource recovery are among the new research prospects identified in this study. Despite substantial progress, persistent challenges, such as scalability, economic viability and policy implementation, remain. Furthermore, few technologies have moved beyond pilot-scale implementation, underscoring the need for greater investment in field-scale research and technology transfer. This study recommends stronger industry–academic collaboration, the development of standardised treatment protocols and enhanced government policy support to facilitate sustainable AMD management. The study emphasises the necessity of data-driven approaches, sustainable technology and interdisciplinary cooperation to address AMD’s socioeconomic and environmental effects in the ensuing decades. Full article

Construction and demolition waste (C&D waste) contains various types of substances, which require different processing methods to maximize benefits and minimize harm to realize the goal of the circular economy. Therefore, it is urgent to promote the on-site sorting of C&D waste and explore the determinants of managers’ waste sorting intention. Based on a comprehensive literature review of C&D waste management, seven determinants are identified to explore how antecedent factors influence waste sorting intention by symmetric and asymmetric techniques. Firstly, the partial least squares structural equation modeling (PLS-SEM) was adopted to analyze the data collected from 489 managers to assess the net impact of each determinant on their intentions. Then, the fuzzy-set qualitative comparative analysis (fsQCA) provided another perspective by determining the configurations of the causal conditions that lead to higher or lower levels of intention. The PLS-SEM results reveal that all determinants show a significant positive relationship with the intention except for the perceived risks, which are negatively correlated with managers’ attitudes and intentions regarding C&D waste sorting. Moreover, top management support and subjective norms from other project participants and the public exhibit a huge impact, while the influence of perceived behavioral control (PBC) and policies is moderate. Meanwhile, fsQCA provides a complementary analysis of the complex causality that PLS-SEM fails to capture. That is, fsQCA identified six and five configurations resulting in high and low levels of intention to sort the C&D waste, respectively, and highlighted the crucial role of core conditions. The results provide theoretical and practical insights regarding proper C&D waste management and enhancing sustainable development. Full article

The aim of this research is to identify policy and practice interventions that support a just transition towards resilient practices for resource-dependent communities. We focus on Thai Nguyen and Phu Tho, two provinces in the Northern Midlands and Mountains of Vietnam. The region is reliant on agriculture but is assessed as highly vulnerable to climate change. We surveyed 105 farming households. A Likert-type questionnaire asked respondents to self-assess their experiences of weather extremes and of changes they had made to their farming practices. Our results show that for both Thai Nguyen and Phu Tho, farmers see the effects of climate change on their crops. Respondents in Thai Nguyen were more likely to report technically driven adaptation and engagement with extension services. Respondents in Pho Tho were more likely to continue traditional practices. For both, use of traditional knowledge and practices was related to taking measures to adapt to climate change. Our main conclusion is that at least three actions could support a just transition to resilient livelihoods. First is incorporating natural science and traditional knowledge into decision-making for just transitions. Second is considering long-term implications of interventions that appear to support livelihoods in the short term. Third is tailoring messaging and engagement strategies to the requirements of the most vulnerable people. The main message of this study is that a just transition for resource-dependent communities will inevitably be context-specific. Even in centralized and authoritarian contexts, flexibility to adapt top-down policies to locals’ own experiences of changing climates is needed. Full article

The global climate crisis has driven unprecedented agreements among nations on carbon mitigation. With China’s commitment to carbon peaking and carbon neutrality targets, the building sector has emerged as a critical focus for emission reduction, particularly because office buildings account for over 30% of building energy consumption. However, a systematic and regionally adaptive low-carbon technology evaluation framework is lacking. To address this gap, this study develops a multidimensional decision-making system to quantify and rank low-carbon technologies for office buildings in Beijing. The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. The results indicate distinct priority patterns across the building types: low-rise buildings prioritize roof-mounted photovoltaic (PV) systems, LED lighting, and thermal-break aluminum frames with low-E double-glazed laminated glass. Mid- and high-rise buildings emphasize integrated PV-LED-T8 lighting solutions and optimized building envelope structures. Ranking analysis further highlights LED lighting, T8 high-efficiency fluorescent lamps, and rooftop PV systems as the top-recommended technologies for Beijing. Additionally, four policy recommendations are proposed to facilitate the large-scale implementation of the program. This study presents a holistic technical integration strategy that simultaneously enhances the technological performance, economic viability, and carbon reduction outcomes of architectural design and renovation. It also establishes a replicable decision-support framework for decarbonizing office and public buildings in cities, thereby supporting China’s “dual carbon” goals and contributing to global carbon mitigation efforts in the building sector. Full article

Higher education and science (HES) is one of the key drivers of a country’s economic growth. In this study, we examine national projects and research capacity in HES in Kazakhstan from 2014 to 2024. We conducted a content review and scientometric analysis with network and temporal visualizations. Our data sources included policy documents, statistical reports, and the Scopus database. Our findings suggest that, while Kazakhstan aligns with global trends in the field (e.g., digitalization, scientometrics monitoring, and internationalization), these are achieved through a state-led, policy-driven approach shaped by its post-Soviet context. Additionally, we note a dual structure in Kazakhstan’s HES sector, characterized by a strong top-down direction and increasing institutional engagement. In terms of the thematic trends from the temporal analysis, the country experienced a three-staged evolution: foundational reforms and system modernization (2014–2017), capacity building and evaluation (2018–2021), and, most recently, strategic expansion, inclusivity, and globalization (2022–2024). Throughout the analyzed period, low R&D intensity, disciplinary imbalances, and structural barriers still undermine desired development efforts in HES. The analyzed case of Kazakhstan can serve as “lessons learned” for policymakers and researchers working in the science evaluation and scholarly communication area in similar emerging or transition countries. Full article

Drawing on a panel dataset of 27,972 firm-year observations from Chinese A-share listed companies spanning 2009 to 2022, this study employs fixed-effects models to examine the nonlinear relationship between firm-level climate policy uncertainty (FCPU) and corporate green governance expenditure (GGE). The results reveal a robust inverted U-shaped pattern: moderate levels of FCPU encourage firms to increase GGE, while excessive uncertainty discourages it. Financing constraints mediate this relationship; specifically, FCPU exhibits a U-shaped impact on financing constraints, initially easing and then tightening them. Older top management teams accelerate the GGE downturn, while government environmental expenditure delays it, acting as a buffer. Heterogeneity analyses reveal the inverted U-shaped effect is more pronounced for non-polluting firms and state-owned enterprises (SOEs). This study highlights the complex dynamics of FCPU on corporate green behavior, underscoring the importance of climate policy stability and transparency for advancing corporate environmental engagement in China. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

The integration of artificial intelligence into sustainable agriculture holds significant potential to transform traditional agricultural practices. This transformation of agricultural practices through AI directly intersects with several critical sustainable development goals, such as Climate Action (SDG13), Life Below Water (SDG 14), and Life on Land (SDG 15). However, such implementations are fraught with multifaceted challenges. This study explores the technological, organizational, and environmental challenges confronting top management in the agricultural sector utilizing the technological–organizational–environmental framework. As interest in AI-enabled sustainable initiatives continues to rise globally, this exploration is timely and relevant. The study employs an interpretive case study approach, drawing insights from a carbon sequestration project within the agricultural sector where AI technologies have been integrated to support sustainability goals. The findings reveal six key challenges: sustainable policy inconsistency, AI experts lacking farming knowledge, farmers’ resistance to change, limited knowledge and expertise to deploy AI, missing links in the existing system, and transition costs, which often hinder the achievement of long-term sustainability outcomes. This study emphasizes the importance of field realities and cross-disciplinary collaboration to optimize the role of AI in sustainability efforts. Full article

This paper applies a multi-level social network analysis to examine Aragón’s innovation ecosystem, focusing on a decade of competitive public projects (2014–2023) aligned with the region’s Smart Specialisation Strategy (S3) 2021–2027. By mapping and weighting the participation of regional entities across regional, national, and European calls, the study uncovers how all types of local actors organise themselves around key specialisation areas. Moreover, a comparative benchmark is introduced by analysing more than 33,000 Horizon 2020 and Horizon Europe initiatives without Aragonese partners, revealing how to fill structural gaps and enrich the regional ecosystem through international collaboration. Results show strong funding concentration in four fields—Energy, Health, Agri-Food, and Advanced Technologies—while other historically strategic areas like Hydrogen and Water remain underrepresented. Although leading institutions (UNIZAR, CIRCE, ITA, AITIIP) play central roles in connecting academia and industry, direct collaboration among them is limited, pointing to missed synergies. Expanding previous SNA-based assessments, this study introduces a diagnostic tool to guide policy, proposing targeted actions such as challenge-driven calls, dedicated support programs, and cross-border consortia with top EU partners. Applied to two contrasting specialisation areas, the method offers sector-specific recommendations, helping policymakers align Aragón’s innovation capabilities with EU priorities and strengthen its position in both established and emerging domains. Full article

In an era where sustainable development is increasingly a core strategic issue for businesses, how top management, as the architects of corporate strategy, can achieve a synergy of economic, social, and environmental benefits through internal management mechanisms to promote corporate sustainability is a central focus for both academia and practice. This study aims to explore how Executive Cognitive Flexibility (CF) influences Firm Performance and to uncover the mediating effects of Non-market Strategy. We use panel data from Chinese A-share listed companies between 2016 and 2022 to examine and empirically analyze this mechanism. Our findings indicate that CF has a positive impact on Firm Performance. This relationship is realized through the pathway of Non-market Strategy, specifically manifesting as a reduction in Corporate Social Responsibility (CSR) and an increase in Corporate Political Activity (CPA). Further analysis reveals that the impact of executive cognitive flexibility on firm performance is differentially influenced by internal and external environmental contexts. The findings of this study provide important practical insights and policy recommendations for companies on cultivating executive cognitive flexibility, optimizing non-market strategies, and enhancing firm performance in various internal and external environments. Full article

This study examines the role of public–private partnerships in promoting the sustainable development of travel destinations through a comparative analysis of two emblematic heritage-based hospitality projects: Dar Tantora in Al Ula, Saudi Arabia, and Sextantio Le Grotte della Civita in Matera, Italy. These case studies were analysed through both architectural–urban and economic–legal perspectives to highlight how public–private partnership models can support heritage conservation, community engagement, and responsible tourism development. A mixed-methods approach was employed, combining quantitative indicators—such as projected profitability, tourist volume, and employment—with qualitative insights from interviews with key stakeholders. The analysis reveals that while both models prioritise cultural authenticity and adaptive reuse, they differ significantly in funding structures, legal frameworks, and governance dynamics. Dar Tantora exemplifies a top-down, publicly funded model integrated into Saudi Arabia’s Vision 2030 strategy, whereas Sextantio reflects a bottom-up, private initiative rooted in social enterprise. The findings offer insights into how different public–private partnership configurations can foster sustainable tourism development, depending on local context, institutional frameworks, and strategic goals. The study contributes to the broader discourse on regenerative tourism, architectural conservation, and policy-driven heritage reuse. Full article

This study analyzes how Vietnam’s Ministry of Culture, Sports and Tourism (MoCST) communicates policy implementation in tourism governance through 35 official responses to citizen petitions, using Heidbreder’s Multilevel Policy Implementation Strategies Framework (centralization, agencification, convergence, networking). Content coding, frequency analysis, co-occurrence network, and sentiment analysis reveal a dominant centralization pattern, with MoCST maintaining strong top-down control in decision-making and resource allocation. Convergence reflects increased inter-ministerial coordination, while agencification is limited, and networking with private or civil sectors remains weak. This weak networking limits participatory decision-making and hinders the development of adaptive, community-based tourism initiatives, which are crucial for sustainable tourism governance. Positive sentiment is more associated with centralized and convergent actions, indicating institutional trust. The study extends Heidbreder’s framework to a non-Western, centralized context and calls for stronger local agency roles and inclusive networks to enhance resilience and community ownership in policy implementation. Full article

Renewable energy stands as a pivotal solution to environmental concerns, prompting substantial research and development endeavors to promote its adoption and enhance energy efficiency. Despite the recognized environmental superiority of renewable energy systems, there is a lack of globally standardized indicators specifically focused on renewable energy efficiency. This study aims to develop and apply a non-parametric data envelopment analysis (DEA) indicator, termed the Renewable Energy Indicator (REI), to measure environmental performance at the national level and to identify differences in renewable energy efficiency across countries grouped by development status and income level. The REI incorporates new factors such as agricultural methane emissions (thousand metric tons of ${\mathrm{CO}}_2$ equivalent), PM2.5 air pollution exposure (µg/m³), and aspects related to electricity, including consumption (as % of total final energy consumption), production from renewable sources, excluding hydroelectric (kWh), and accessibility in rural and urban areas (% of population with access), aligning with the emerging paradigm outlined by the United Nations. By segmenting the REI into global, developmental, and income group classifications, this study conducts the Mann–Whitney U test and the Kruskal–Wallis H tests to identify variations in renewable energy efficiency among different country groups. Our findings reveal top-performing countries globally, highlighting both developed (e.g., Sweden) and developing nations (e.g., Costa Rica, Sri Lanka). Central and North European countries demonstrate high efficiency, while those facing political and economic instability perform poorly. Agricultural-dependent nations like Australia and Argentina exhibit lower REI due to significant methane emissions. Disparities between developed and developing markets underscore the importance of understanding distinct socio-economic dynamics for effective policy formulation. Comparative analysis across income groups informs specific strategies tailored to each category. Full article