Search Results (348)

Existing research on green transformation predominantly emphasizes “hard constraints” such as carbon taxes and environmental regulations, while neglecting “soft constraints” shaped by environmental sentiment expressions from key actors such as the public, financial institutions, media, and government. In particular, the collective influence of these multi-actor environmental sentiments remains insufficiently explored. This study fills that gap by constructing a collaborative governance framework using multi-source heterogeneous data from China spanning 2013–2023, including 330 provincial government work reports, 1862 bank annual reports, 2472 newspaper articles, and 68,519 Weibo posts, matched to 4708 firm-year observations of Chinese A-share energy companies. We quantify environmental sentiment tendencies through natural language processing, calculating the index as (negative word frequency − positive word frequency)/total word frequency at the province-year level, thus higher index value indicates more negative sentiment tendency, while green transformation is proxied by ln(green patent applications + 1). The results reveal the following: (1) More negative environmental sentiment tendencies from financial institutions, media, public, and government significantly promote green transformation in energy enterprises, with stronger effects observed from financial institutions and government. (2) Economic and climate policy uncertainty selectively weaken the impact of financial institutions’ sentiment, while the moderating effects for other actors are statistically insignificant. (3) The effect of multi-actor environmental sentiment is more pronounced for firms located in eastern China, operating under high competition or stricter environmental regulations. This study provides a novel, quantified approach to assessing multi-actor environmental sentiment tendencies, affirms the effectiveness of informal governance, and highlights the importance of stable policy in guiding corporate green transformation in emerging economies. Full article

Mountain regions across Europe face demographic decline and institutional fragmentation that hinder sustainable tourism development. This study analyzes the territorial governance system of the Val Canale and Canal del Ferro valleys (Italian Alps) with the aim of designing a stakeholder-based Organizational Model (OM) to strengthen sustainable tourism coordination in a peripheral mountain context. A qualitative single-case study approach integrates Stakeholder Analysis, Actor-Linkage Matrix, Appreciative Inquiry, and spatial contextualization to examine relational, institutional, and territorial dynamics. The findings reveal a territory rich in environmental and cultural assets—characterized by protected areas and extensive trail networks—yet constrained by fragmented inter-municipal cooperation and limited supra-municipal coordination. Governance fragmentation, rather than resource scarcity, emerges as the primary barrier to coherent territorial development. In response, the proposed multi-level Organizational Model introduces a valley-level coordination unit designed to institutionalize collaborative governance, enhance administrative capacity, and align local initiatives with regional strategies. By operationalizing stakeholder theory within a structured territorial framework, the study contributes to place-based governance literature and offers transferable insights for peripheral mountain regions facing similar coordination challenges. Full article

Entrepreneurial ecosystems are multi-actor, uncertain, and dynamic environments in which policymakers and investors must balance innovation, resilience, and cost. Despite the growing literature on entrepreneurial ecosystems, much of the existing research has focused on identifying the components and relationships among actors and has provided less prescriptive frameworks for evaluating resource allocation policies before implementation. To address this gap, this study presents a digital twin-based and fuzzy multiobjective optimization framework for resource orchestration in entrepreneurial ecosystems. The proposed framework combines dynamic ecosystem representation with multiobjective decision-making under uncertainty and allows for the testing of different resource allocation and policy scenarios before actual intervention. To solve the model, exact optimization in GAMS was used for small- and medium-sized samples, and NSGA-II and ACO algorithms were used for large-scale problems. The advantage of the proposed method is that, unlike purely descriptive approaches or deterministic models, it simultaneously considers uncertainty, time dynamics, and trade-offs between innovation, resilience, and cost in an integrated decision-making framework. Experimental evaluation was conducted based on simulated data calibrated with reliable public sources, and the performance of the algorithms was compared with reference methods in terms of computational time, solution quality, and stability. The results showed that metaheuristics, especially NSGA-II, significantly reduced the solution time in large-scale problems and at the same time produced solutions closer to the Pareto frontier and with greater stability. Sensitivity analysis also showed that in the designed scenarios, policy budgets have a more prominent effect on innovation, while resource capacity and structural diversification play a more important role in enhancing resilience. Also, improving resource efficiency has had the greatest effect on reducing the total system cost. From a theoretical perspective, the present study operationally models the logic of resource orchestration in entrepreneurial ecosystems through the integration of digital twins and fuzzy multi-objective optimization. From a managerial perspective, this framework acts as a decision-making engine that allows for ex ante testing of policies, clarification of trade-offs, and extraction of resource allocation rules under uncertainty. Full article

Urban playgrounds are vital public spaces that support children’s play, social interaction, and well-being. However, many playgrounds in socially disadvantaged or aging urban areas experience physical deterioration, limited play diversity, and declining use. Although corporate social responsibility (CSR) initiatives have increasingly supported playground regeneration, many projects continue to emphasize short-term physical improvements rather than participatory processes and social value creation. This study conceptualizes CSR-supported, child-participatory playground regeneration as a social value creation process and examines how CSR enables process continuity through a structured six-stage participatory approach spanning planning, design, construction, and post-opening use. Two cases were selected from the “Save the Playground” program in Seoul, Korea: Saerok Children’s Park in a stable residential neighborhood and Mukjeong Children’s Park in a high-mobility, multicultural commercial district. Using a qualitative multiple-case study design, the study triangulates workshop outputs, observational records, facilitator field notes, and official program documents through thematic and cross-case analyses. The findings indicate that CSR support primarily ensured process continuity and facilitated multi-actor coordination across project stages. By securing implementation continuity and stabilizing governance arrangements, CSR support allowed participatory outputs to be iteratively translated into design development and post-opening evaluation. Post-opening outcomes differed by urban context; nevertheless, both cases showed social value creation through strengthened place attachment, responsibility-oriented use, and inclusive mixed-group play. This study advances a cross-case analytical framework linking urban context, participatory mechanisms, and post-opening social value outcomes, contributing to a more context-sensitive understanding of CSR-supported participatory design processes and their implications for sustainable urban public space development. Full article

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

The energy transition represents a complex, multi-level system subject to profound uncertainty and recurrent shocks. Current policy design approaches predominantly rely on static optimization frameworks (centralized, calculative models that presume stable conditions and predictable technological trajectories). Yet evidence from the 2021–2023 energy crisis in Europe, coupled with structural challenges in market liberalization and renewable integration, demonstrates persistent challenges in policy implementation. Price interventions affect competitive dynamics; subsidies influence technology selection; capacity mechanisms create coordination tensions; and rigid tariff structures create misalignments with evolving grid needs. This paper argues that these recurrent policy tensions stem not from implementation gaps, but from an inadequate theoretical foundation: the treatment of energy systems as optimizable rather than as complex, adaptive systems operating under Knight–Mises uncertainty and Huerta de Soto dynamic efficiency. This work explores an alternative framework grounded in dynamic efficiency, complex–uncertain systems, decentralized incentives, and adaptive governance (international–domestic, public–private, etc.). This review uses the theoretical and methodological framework of the Heterodox Synthesis, an alternative to the Neoclassical Synthesis. There is a reinterpretation of some insights from Knight and Mises (uncertainty), Hayek (distributed knowledge), Huerta de Soto (dynamic efficiency) and contemporary complexity economics into operational criteria applicable to energy policy design: (1) robustness to deep uncertainty; (2) preservation of price signals and risk-bearing mechanisms; (3) alignment of incentives across distributed actors; (4) institutional adaptability; and (5) minimization of ex post policy corrections. Through illustrative application to four critical policy instruments (price caps, renewable subsidies, capacity mechanisms, and network tariff design), it is shown how this framework identifies systematic tensions and consequences that conventional analysis overlooks. The contribution is exploratory in a bootstrap way: theoretical, by integrating classical and contemporary economics into energy governance; methodological, by operationalizing dynamic efficiency into evaluable criteria distinct from existing adaptive governance frameworks; and sectorial, by providing policymakers and regulators with diagnostic tools for assessing design robustness in conditions of deep uncertainty and rapid transition. According to this review, improved energy policy design under uncertainty is not achieved through more sophisticated optimization (in a calculative way), but through institutional architectures that preserve creative and adaptive learning, maintain distributed decision-making capacity, and remain functional when assumptions prove incorrect or not well-known. Full article

The advent of 5G/6G technologies and the pervasive deployment of IoT devices are driving the emergence of demanding applications that necessitate ultra-low latency, high bandwidth, and significant computational power. Traditional cloud computing models fall short in meeting these stringent requirements. To address this, Software-Defined Edge Networks (SDENs) have emerged as a promising architecture, yet efficiently managing their heterogeneous and geographically distributed resources poses substantial challenges for optimal application provisioning. In response, this paper proposes a novel framework for intelligent task offloading, which reframes the intricate multi-component application task offloading problem as a Virtual Network Embedding (VNE) challenge within a SDEN environment. We introduce a comprehensive model where complex applications are represented as Virtual Network Requests (VNRs). In this model, each VNR consists of virtual nodes that demand specific computing and storage resources, as well as virtual links that demand specific bandwidth and must adhere to maximum tolerable delay constraints. To dynamically solve this NP-hard VNE problem in the face of stochastic VNR arrivals and dynamic network conditions, we leverage Deep Reinforcement Learning (DRL). Specifically, a Soft Actor-Critic (SAC) agent is employed at the SDN controller. This agent learns a sequential decision-making policy for mapping virtual nodes to physical edge servers and virtual links to network paths. To guide the agent towards efficient resource utilization, we define the reward for each successful embedding as the long-term revenue-to-cost ratio. By learning to maximize this reward, the agent is naturally driven to find economically viable allocation strategies. Comprehensive simulation experiments demonstrate that our SAC-based VNE approach significantly outperforms other baselines across key metrics, affirming its efficacy in dynamic SDEN environments. Full article

Green businesses represent a strategy for coordinating production with conservation in the Colombian Amazon; however, their consolidation continues to be limited by deficiencies in knowledge management and in the coordination of capacities for innovation. The objective of this study was to identify the relationships between capital, dynamic capabilities, and innovative performance in Amazonian green companies, using multivariate analysis. The results showed that knowledge-related capabilities (acquisition, transformation, and information management) are the factors that most strongly influence innovation. Pearson’s correlations confirmed positive associations between these variables and innovative performance. In the structural model, absorptive capacity emerged as the central axis of the system (β = 0.911; p < 0.001; R² = 0.83). We conclude that strengthening absorption capacity and organizational learning are the most important variables for improving innovation and sustainability in Amazonian green businesses. These findings provide robust evidence to inform the design of public policies in Science, Technology, and Innovation (STI) with a differentiated territorial approach, aimed at strengthening capacities, developing financing schemes for sustainable innovation, and consolidating multi-actor territorial governance structures, which are essential to foster resilient bioeconomy ecosystems in the Colombian Amazon. Full article

In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based Structure–Control Co-Design), a reinforcement learning framework for the co-design of manipulator link lengths and motion planning policies. The approach is implemented on a custom four-degree-of-freedom PRRR manipulator with manually adjustable link lengths, where a hybrid action space integrates configuration selection at the beginning of each episode with subsequent continuous joint-level control, guided by a multi-objective reward function that balances task accuracy, execution efficiency, and obstacle avoidance. Evaluated in both a simplified kinematic simulator and the high-fidelity MuJoCo physics engine, SAC-SC achieves 100% task success rate in obstacle-free scenarios and 85% in cluttered environments, with a planning time of only 0.145 s per task, over 15 times faster than the two-stage baseline. The learned policy also demonstrates zero-shot transfer between simulation environments. These results indicate that integrating structural parameter optimization and motion planning within a unified reinforcement learning framework enables more adaptive and efficient robotic operation in unstructured environments, offering a promising alternative to conventional decoupled design paradigms. Full article

To address the issues of parameter setting, reliance on human experience, and the limitations of traditional model-driven control methods in handling complex nonlinear dynamics in the tin smelting industrial process, this paper proposes a data-driven control approach based on improved deep reinforcement learning (RL). Aiming to reduce the tin entrainment rate in smelting slag and CO emissions in exhaust gas, we construct a data-driven environment model with an 8-dimensional state space (including furnace temperature, pressure, gas composition, etc.) and an 8-dimensional action space (including lance parameters such as material flow, oxygen content, backpressure, etc.). We innovatively design a Dual-Action Discriminative Deep Deterministic Policy Gradient (DADDPG) algorithm. This method employs an online Actor network to simultaneously generate deterministic and exploratory random actions, with the Critic network selecting high-value actions for execution, consistently enhancing policy exploration efficiency. Combined with a composite reward function (integrating real-time Sn/CO content, their variations, and continuous penalty mechanisms for safety constraints), the approach achieves multi-objective dynamic optimization. Experiments based on real tin smelting production line data validate the environment model, with results demonstrating that the tin content in slag is reduced to between 3.5% and 4%, and CO content in exhaust gas is decreased to between 2000 and 2700 ppm. Full article

The environmental and social crises affecting global agri-food systems highlight the need for a profound transformation of production models and their territorial relations. In this context, agroecology, understood as science, practice, and movement, has emerged as a paradigm capable of integrating ecological sustainability, social equity, and community participation. Within this framework, the work carried out by CREA in the AgrEcoMed project (new agroecological approach for soil fertility and biodiversity restoration to improve economic and social resilience of Mediterranean farming systems), funded by the PRIMA programme, investigates agroecology as a social and political process of territorial regeneration. This process is grounded in co-design with local stakeholders, collective learning, and the construction of multi-actor networks for agroecology in the Mediterranean. The Manifesto functions as a tool for participatory governance and value convergence, aiming to consolidate a shared vision for the Mediterranean agroecological transition. The article examines, through an analysis of the existing literature, the role of agroecological networks and empirically examines the function of the collective co-creation of the Manifesto as a tool for social innovation. The methodology is based on a participatory action-research approach that used local focus groups, World Café, and thematic analysis to identify the needs of the companies involved. The results highlight the formation of a multi-actor network currently comprising around 90 members and confirm the effectiveness of the Manifesto as a boundary object for horizontal governance. This demonstrates how sustainability can emerge from dialogue, cooperation, and the co-production of knowledge among local actors. Full article

The purpose of this article is to develop optimal control strategies for discrete-time multi-agent systems (DT-MASs) with unknown disturbances, with the goal of enhancing their consensus performance and disturbance rejection capabilities. Complex flight conditions, such as the scenario of multi-unmanned aerial vehicle (multi-UAV) maintaining consensus under strong wind gusts, pose significant challenges for MAS control. To address these challenges, this article develops an optimal controller for UAV-based MASs with unknown disturbances to reach consensus. First, a novel improved nonlinear extended state observer (INESO) is designed to estimate disturbances in real time, accompanied by a corresponding disturbance compensation scheme. Subsequently, the consensus error systems and cost functions are established based on the disturbance-free DT-MASs. Building on this, a policy iterative algorithm based on a momentum-accelerated Actor–Critic network is proposed for the disturbance-free DT-MASs to synthesize an optimal consensus controller, whose integration with the disturbance compensation scheme yields an optimal disturbance rejection controller for the disturbance-affected DT-MASs to achieve consensus control. Comparative quantitative analysis demonstrates significant performance improvements over a standard gradient Actor–Critic network: the proposed approach reduces convergence time by 12.8%, improves steady-state position accuracy by 22.7%, enhances orientation accuracy by 42.1%, and reduces overshoot by 22.7%. Finally, numerical simulations confirm the efficacy and superiority of the method. Full article

Marine protected areas (MPAs) are crucial for marine ecosystems, but they often pose significant challenges for the local fishing communities that rely on these ecosystems for their livelihoods. Identifying approaches that maintain ecological integrity while improving community livelihoods and well-being has become a central issue in marine sustainability. This study investigates the adaptive livelihood strategies of a community on Qi’ao Island, located in China’s Pearl River Estuary, which has gradually transitioned from traditional fisheries to tourism-induced livelihoods. Based on Actor–network theory (ANT), we developed a multi-level approach to examine interactions between human and non-human actors, institutions, and policies during livelihood adaptation. A mixed-methods approach was adopted, combining semi-structured interviews (n = 47), extended field observation, and policy analysis. Computational text analysis techniques included word frequency analysis, sentiment analysis, and co-occurrence network analysis using Python 3.8. These were integrated with thematic analysis and coding conducted in NVivo 15. This study demonstrates that the sustainability of tourism-based livelihood adaptation depends on equitable benefit sharing, flexible governance, and sustained community participation. Theoretically, this research extends livelihood studies by demonstrating how ANT captures the relational and processual dynamics of adaptation. Practically, it offers policy-relevant insights for designing adaptive and participatory governance strategies that reconcile conservation objectives with community well-being in MPAs. Full article

Urban resilience has emerged as a critical paradigm for addressing the intertwined challenges of climate change, rapid urbanization, and social inequality, positioning green public spaces as catalysts for social, ecological, and institutional transformation. This article presents a systematic review conducted under the PRISMA 2020 guidelines, examining how collaborative and community participation influenced transformative urban resilience in green public spaces between 2021 and 2025. A total of 6179 records were initially identified across ScienceDirect and MDPI (last search: July 2025), of which 26 empirical studies met the inclusion criteria (peer-reviewed, empirical, published 2021–2025). Methodological rigor was strengthened through the application of the Mixed Methods Appraisal Tool (MMAT, 2018) and confidence in qualitative evidence was assessed using the GRADE-CERQual approach, enhancing transparency and reliability. Data extraction and synthesis followed a theoretical-methodological coding framework, allowing for the comparison of participatory strategies, typologies of green spaces, resilience dimensions, and applied instruments. The results show that multi-actor co-management, co-design, and community self-organization are the most frequent participatory strategies, while urban green infrastructure, pocket parks, and urban gardens constitute the predominant spatial contexts. Socio-ecological and social-participatory resilience emerged as dominant theoretical perspectives, with qualitative and mixed-methods designs prevailing across studies. Evidence synthesis through GRADE-CERQual identified seven key pathways—multi-actor co-management, Nature-based Solutions, community-based actions, social equity, cultural identity, institutional innovation, and planned densification—each contributing differently to resilience dimensions. Overall, the findings highlight that transformative resilience depends on deep, inclusive participatory processes, multi-level governance, and the integration of social, ecological, and cultural dimensions. Despite the heterogeneity of designs and unequal data adequacy, this review confirms that transformative urban resilience is a co-produced process grounded in community action, ecological sustainability, and collaborative governance. Strengthening underexplored areas—technological innovation, cultural resilience, and standardized methodological instruments—is essential for advancing comparative research and practice. Full article

This paper develops a new conceptual and operational understanding of cultural heritage transformation, interpreting it as a systemic and dynamic process rather than a static state. It explores the realities and opportunities for action when cultural heritage is understood and managed as a complex, adaptive system. The study builds on a critical review of contemporary literature to identify the multi-scalar challenges currently facing urban heritage systems, such as climate change, disaster risks, social fragmentation, and unsustainable urban development. To respond to these challenges, the paper introduces a metamodel for heritage-based urban transformation, designed to apply systems thinking to heritage management that was developed based on cases from the Western European context. This metamodel integrates key variables—actors, resources, tools, and processes—and is used to test the hypothesis that a systems-oriented approach to cultural heritage can enhance the capacity of stakeholders to connect, adapt, use, and safeguard heritage in the face of complex urban transitions. The hypothesis is operationalized through scenario-based applications in the fields of disaster risk management (DRM), circular economy, and broader sustainability transitions, demonstrating how the metamodel supports the design of cross-over resilience strategies. These strategies not only preserve heritage but activate it as a resource for innovation, cohesion, identity, and adaptive reuse. Thus, cultural heritage is reframed as a strategic investment—generating spillover benefits such as improved quality of life, economic opportunities, environmental mitigation, and enhanced social capital. In light of the transition toward a greener and more resilient society, this paper argues for embracing heritage as a driver of transformation—capable of engaging with well-being, behavior change, innovation, and education through cultural crossovers. Heritage is thus positioned not merely as something to be protected, but as a catalyst for systemic change and future-oriented urban regeneration. Full article