Search Results (281)

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

Against the backdrop of the “dual carbon” goals, carbon emission trading prices serve as a core signal of market operational efficiency. Accurately predicting carbon prices facilitates scientific decision-making, and model optimization is key to improving prediction accuracy. This study takes five major carbon trading pilots in China—Shenzhen, Guangdong, Hubei, Beijing, and Shanghai—as the research objects. An indicator system is constructed from four dimensions: macroeconomy, energy prices, climate and environment, and international markets. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is employed to identify the key influencing factors of carbon prices across different markets. Among them, “WTI crude oil price” and “EUA futures closing price” are consistently significant factors common to all five pilots. On this basis, four models—Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Transformer—are constructed for multi-method prediction comparison. The results show that ARIMAX and GRU achieve the best prediction performance among the four models. To further enhance prediction accuracy, hybrid optimization models are respectively developed: Support Vector Regression (SVR) is used to optimize the nonlinear residuals of ARIMAX (SVR-ARIMAX), and Genetic Algorithm (GA) is used to optimize the key hyperparameters of GRU (GA-GRU). The hybrid models significantly reduce prediction errors in most markets. Specifically, SVR-ARIMAX shows particularly notable improvements in Beijing and Hubei, while GA-GRU outperforms standard GRU in Guangdong, Shenzhen, Shanghai, and Hubei. Based on the optimized models, 12-month-ahead forecasts indicate that the Shenzhen market exhibits high volatility and greatest uncertainty; Guangdong remains relatively stable; Hubei, Beijing, and Shanghai are characterized by narrow-range fluctuations. The findings provide empirical support for corporate emission reduction decision-making, carbon market risk management, and price mechanism improvement. Full article

Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity market volatility in a global framework. Sustainability-related uncertainty is proxied by the Global GDP-Weighted ESG-Based Sustainability Uncertainty Index (ESGUI), while ESG market volatility is measured through a monthly proxy constructed from estimated daily conditional variances obtained from GJR-GARCH(1,1) models with Student-t innovations. The paper explicitly distinguishes sustainability-related uncertainty, understood as ambiguity in the ESG information environment, from ESG market volatility, understood as market-pricing instability in ESG equity benchmarks. Empirically, the study combines bootstrap full-sample Granger-causality tests, parameter-stability diagnostics, and rolling-window bootstrap analysis. Robustness and extended analyses use an EGARCH-based volatility proxy, alternative rolling-window lengths, macro-financial controls, an emerging-market ESG benchmark, impulse-response analysis, forecast-error variance decomposition, and out-of-sample forecasting tests. The full-sample results indicate an asymmetric predictive pattern: ESG market volatility contains Granger-causal predictive information for changes in sustainability-related uncertainty, whereas the reverse direction is not supported on average. However, parameter-stability tests reject constancy, and rolling-window evidence shows that predictive effects arise episodically in both directions, with changes in sign, magnitude and significance. The uncertainty-to-volatility channel becomes statistically relevant and locally stronger during stress episodes, especially around 2019–2021, while macro-control results show that broader market stress absorbs part of the volatility-to-uncertainty linkage. The findings indicate a regime-dependent uncertainty–volatility nexus and support dynamic approaches to ESG risk monitoring, portfolio management and regulatory communication. All results are interpreted as predictive evidence, not structural causality. Full article

This Perspective is dedicated to the memory of Professor Joseph Mark (Joe) Gani (1924–2016) and Professor Christopher Charles (Chris) Heyde (1939–2008), two scholars whose intellectual leadership profoundly shaped applied probability, mathematical statistics, and their interface with finance, insurance, and risk management. Their contributions extend beyond specific technical results to the development of research cultures grounded in probabilistic rigor, empirical relevance, and methodological transparency. We emphasize three enduring themes central to modern quantitative risk analysis. First, the systematic incorporation of heavy-tailed and non-Gaussian features in stochastic modeling, reflecting persistent empirical deviations from classical Gaussian assumptions in financial data. Second, the development of stochastic and time-series methodologies capable of handling dependence structures, including conditional heteroskedasticity and long-range dependence. Third, the principled integration of probabilistic modeling with data-driven and machine learning approaches, ensuring predictive performance is accompanied by interpretability and robustness. We situate these contributions within contemporary challenges in financial risk management, including systemic risk, environmental, social and governance (ESG) considerations, and climate finance. In particular, climate-related financial risks arise from both physical impacts (such as extreme weather events and long-term environmental change) and transition dynamics associated with the shift toward a low-carbon economy (including policy, technological, and market adjustments). These sources of risk introduce additional forms of dependence, nonlinearity, and model uncertainty, particularly in high-dimensional, data-rich settings. This Perspective highlights a forward-looking research agenda that preserves the foundational principles of applied probability while adapting them to modern financial systems characterized by real-time information flows and evolving risk structures. This legacy continues to shape how financial risk is modeled, measured, and understood in increasingly complex and interconnected environments. Full article

Mobile payment services are increasingly embedded in everyday digital consumption, yet their sustainability relevance should not be assumed solely from technological adoption. This study conceptualizes sustainable mobile payment consumption as a relational and digital sustainability issue, reflected in the continuity, trust, diffusion, and resilience of mobile payment ecosystems rather than as a direct measure of environmental sustainability. Drawing on perceived value theory, relationship marketing, social exchange theory, and trust-based consumption logic, this study examines how mobile payment perceived value (MPPV) is associated with customer advocacy through customer experience and customer loyalty, while considering customer trust as a boundary condition. Survey data collected from 382 mobile payment users in Lebanon were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings suggest that MPPV is positively associated with customer experience, customer loyalty, and customer advocacy. Customer experience is positively associated with loyalty while loyalty is positively associated with advocacy. The sequential mediation results are consistent with the proposed relational pathway in which holistic perceived value is linked to advocacy through experience and loyalty rather than through transactional evaluations alone. Customer trust strengthens the associations between MPPV and both loyalty and advocacy, suggesting that trust amplifies value-based relational outcomes in high-uncertainty financial environments. The central finding is that holistic perceived value becomes sustainability-relevant when channeled through accumulated experience and loyalty into advocacy, and that this relational pathway is contingent on trust, a mechanism particularly consequential in Lebanon’s high-uncertainty financial environment. By positioning advocacy as a sustainability-relevant relational outcome, this study clarifies how perceived value, experience, loyalty, and trust jointly contribute to sustainable digital consumption in an emerging economy. Full article

Increasing external uncertainty, supply disruptions, and market volatility have made resilience enhancement increasingly important for sustainable agricultural supply chains. While existing studies mainly examine agricultural supply chain resilience from macro or operational perspectives, limited attention has been paid to how firms’ strategic AI investment reshapes organizational resilience under external shocks. Using panel data on Chinese agricultural-related listed firms from 2010 to 2024, this study examines whether and how strategic AI investment enhances supply chain resilience. Empirical results show that strategic AI investment significantly improves both dimensions of supply chain resilience, namely resistance capacity and recovery capacity. Mechanism analyses indicate that this effect mainly operates through supply diversification, technological innovation, and information transparency. Further analyses reveal heterogeneous effects across supply chain positions, ownership structures, and regional digital development environments. In addition, compatibility analyses show that strategic AI investment not only strengthens supply chain resilience but also improves operational efficiency, R&D investment intensity, and financial stability. Overall, this study highlights strategic AI investment as an important organizational capability for strengthening agricultural supply chain resilience under increasing external uncertainty. Full article

Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitations of traditional centralized energy management and rule-based automation in highly distributed, data-intensive, and dynamically coupled energy infrastructures. In response, recent advances in artificial intelligence offer new opportunities for improving prediction, coordination, and adaptive control. This paper develops a reference architecture for Autonomous Energy Systems based on the integration of generative AI, agentic AI, digital twins, and distributed cyber–physical energy infrastructures. Rather than treating forecasting, control, simulation, and market coordination as separate research tracks, the paper organizes them within a common architectural perspective. Generative AI is positioned as a source of scenario intelligence, synthetic data generation, and uncertainty-aware forecasting, while agentic AI is framed as a bounded decision layer for perception, reasoning, planning, and coordinated action under operational constraints. The paper further clarifies the distinction between agentic AI, conventional multi-agent systems, and multi-agent reinforcement learning in energy applications. Representative application domains are discussed, including self-healing power grids, autonomous energy markets, and digital twin training environments. Major open challenges are identified in relation to scalability, physical consistency, safety verification, sim-to-real transfer, cybersecurity, interoperability with legacy infrastructures, and governance. The paper concludes by outlining a research agenda for the staged and safe development of increasingly autonomous energy systems. Full article

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs under multi-market synergy and develops a benefit allocation model based on the Nash–Harsanyi bargaining game. A Monte Carlo simulation was adopted to capture the uncertainties of market electricity prices and PV power output, and the stochastic dual-dynamic-programming (SDDP) algorithm was employed to solve the three-stage optimization framework consisting of day-ahead bidding, real-time optimization, and real-time frequency regulation. Bargaining power was quantified from four dimensions—the marginal contribution rate, PV prediction accuracy, energy storage capacity, and utilization rate—to establish a fair and reasonable internal benefit allocation mechanism. Case studies verified that the proposed method improved the single-day market revenue by up to 20.79% compared with traditional operation modes, achieved a near-zero curtailment rate for distributed PV, and maintained frequency regulation performance scores above 0.4 at all times. The benefits of all investment entities in the alliance increased by 3.36–99.43%, significantly enhancing the multi-market profitability of PV-storage VPPs and the stability of alliance cooperation. Full article

In the context of rapid technological evolution and increasing market uncertainty, technoparks have emerged as critical ecosystems for bridging scientific research and high-tech industrial production; however, their effectiveness is often constrained by limited flexibility, fragmented control mechanisms, and delayed decision-making processes. Motivated by these challenges, this article investigates the automation of control processes in research-driven and flexible manufacturing environments within technopark infrastructures, positioning automation as a strategic lever for enhancing operational adaptability and innovation throughput. The study conceptualizes control process automation as a multi-stage framework encompassing data acquisition, processing, intelligent analysis, and real-time decision execution and examines the role of enabling technologies such as artificial intelligence, the Internet of Things (IoT), and cyber-physical systems in supporting this paradigm. The analysis demonstrates that the integration of these technologies significantly improves production flexibility, resource optimization, and responsiveness to dynamic conditions, while simultaneously accelerating the transformation of scientific and research outputs into measurable economic value. By combining theoretical foundations with illustrative practical applications, the article substantiates the effectiveness of automated control systems and highlights their strategic relevance for increasing the competitiveness of technoparks, fostering sustainable technological innovation, and shaping resilient long-term development strategies. Full article

Financial markets are characterized by non-stationarity, regime shifts, and complex cross-asset interactions, which challenge traditional portfolio optimization and motivate reinforcement learning (RL) for adaptive decision-making. However, many RL-based approaches remain predominantly return-centric, with risk, uncertainty, and human oversight only weakly integrated, limiting robustness and practical applicability. This review provides a critical synthesis of risk-aware and uncertainty-sensitive reinforcement learning for portfolio optimization from a human–AI collaboration perspective. We analyze major architectural paradigms—including single-agent, hierarchical, multi-agent, and modular systems—together with risk modeling strategies (e.g., reward shaping, constraint-based optimization, and downside risk measures such as CVaR) and probabilistic approaches to uncertainty estimation (e.g., Bayesian neural networks, Monte Carlo dropout, and ensembles). A structured analysis of 57 fully assessed studies reveals that only 5 (9%) explicitly couple uncertainty estimation with risk constraint mechanisms, while 38 (69%) treat risk and uncertainty as structurally independent components. We identify a central structural limitation: risk objectives are rarely conditioned on epistemic uncertainty, while uncertainty estimates seldom influence constraint mechanisms or capital allocation. This decoupling leads to fragmented frameworks that remain difficult to deploy in real financial environments. By integrating architectural design, risk modeling, uncertainty estimation, and evaluation practices, this review proposes a unified, deployment-oriented perspective for developing governance-aligned portfolio decision-support systems. Full article

The increasing number of sustainability claims may reduce the perceived reliability of formal eco-labels, creating an environment in which greenwashing can erode institutional trust. This study explores how consumers navigate significant information asymmetry when standardized environmental signals are absent. Using a qualitative research design, we conducted focus group discussions with Hungarian- and Romanian-speaking consumers in Transylvania, Romania, a multiethnic transitioning market. Computational text analysis, including topic modeling, was used to support this interpretive approach and effectively decode the complex typologies of green claim evaluation. The findings suggest that signal degradation among the participants was associated with culturally embedded heuristic substitution rather than a uniform rejection of green claims. Romanian-speaking participants described more analytical, information-seeking heuristics that are tightly integrated into routine purchasing decisions. Conversely, Hungarian-speaking participants articulated a looser connection between generalized skepticism and their purchasing routines. This study contributes to signaling theory and administrative science by suggesting that standardized governance tools may be less effective when they are not aligned with localized trust structures. Reconceiving greenwashing as a failure of signal fit rather than as deceptive marketing communication, the study contributes to a process-oriented understanding of how consumers evaluate sustainability claims under uncertainty. Future research should quantitatively test these heuristic pathways across diverse regulatory and cultural environments. Full article

The rapid accumulation of plastic waste has become a major environmental concern, while at the same time, it is necessary to create opportunities to rethink how these materials can be reintegrated into productive use, particularly within the construction sector. This study provides a sustainability-oriented review of the reuse of plastic waste, both fossil-based plastics and bioplastics, as building materials, with a specific emphasis on structured decision-support approaches. A systematic literature review was conducted to identify and analyze peer-reviewed studies examining the incorporation of plastic waste into construction applications, including composites, panels, insulation systems, and structural or non-structural components. Particular attention is given to research applying Multi-Criteria Decision Analysis (MCDA) and SWOT analysis as tools for evaluating sustainability performance across environmental, economic, technical, and social dimensions. The findings indicate that recycled plastic and bioplastic-based construction materials can deliver significant advantages, such as diverting waste from disposal pathways, reducing reliance on virgin resources, and, in certain cases, enhancing durability. However, these materials also face important challenges, including limitations in recyclability, concerns related to fire performance, regulatory acceptance, and uncertainties in end-of-life management. MCDA-based studies underscore the critical role of criteria selection and weighting, especially regarding environmental impact reduction and cost competitiveness, in shaping final rankings and decision outcomes. SWOT analyses, in turn, offer complementary strategic insights by highlighting issues related to market readiness, regulatory frameworks, and implementation barriers. By integrating these decision-oriented evaluation approaches, this review contributes to more transparent and evidence-based material selection processes and supports policy development aimed at strengthening circular economy strategies for plastic waste reuse in the built environment. Full article

In recent years, frequent extreme climate events have not only greatly intensified financial system volatility, but may have damaged urban financial resilience and hindered the sustainable development of cities. This paper examines the effects of extreme climate risk on urban financial resilience and further discusses its heterogeneous characteristics and transmission mechanisms, using the data from Chinese prefecture-level cities. The empirical findings show that, first, extreme climate risk significantly weakens urban financial resilience. Second, heterogeneity analyses reveal that extreme climate risk has a more significant effect on the financial resilience of northern cities, small-population cities and low-marketization cities. Finally, mechanism tests indicate that extreme climate risk negatively affects urban financial resilience by reducing asset liquidity, increasing energy consumption, and exacerbating climate policy uncertainty. The research findings provide solid empirical evidence for policymakers to formulate targeted financial policies, strengthen systemic risk management, and enhance urban financial resilience, so as to promote the coordinated and sustainable development of the urban economy and ecological environment. Full article

Against the background of climate uncertainty, market volatility, and evolving regulatory environments, firms embedded in agricultural value chains face increasing pressure to maintain sustainable growth. This study examines China’s A-share-listed Traditional Chinese Medicine (TCM) firms to explore how internal organizational factors and external institutional conditions jointly shape firm-level sustainable growth capability. This setting is characterized by strong ecological dependence, strict quality regulation, deep policy embeddedness, and supply-chain sensitivity. Drawing on the resource-based view, dynamic capability theory, contingency theory, and the institutional environment perspective, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to 2023 cross-sectional data from 59 A-share-listed TCM firms. The results show that no single condition constitutes a necessary condition for high sustainable growth capability. Instead, high sustainable growth capability is mainly achieved through three configurational pathways: innovation-driven growth, policy-supported development, and market-responsive strategy. Low sustainable growth capability follows asymmetric pathways, mainly reflected in the mismatch between innovation capability and the institutional environment, and the coexistence of high financing constraints and low agility response. The findings indicate that sustainable growth capability is not the result of isolated factors, but a context-specific configurational outcome shaped by innovation, agility response, internationalization, equity governance, ESG performance, government support, marketization level, and financing conditions. This study provides a configurational explanation for growth research on agriculture-related firms and offers differentiated pathway implications for managers and policymakers. Full article

Cryptocurrency adoption remains difficult to explain when treated as a single decision or static outcome. Addressing this limitation, the present study develops a qualitative, process-oriented account of cryptocurrency adoption among users in Central and Eastern Europe, with particular attention to how engagement emerges, changes, and stabilizes over time. Semi-structured individual in-depth interviews were conducted with 25 cryptocurrency users, and the material was analyzed using reflexive thematic analysis within an interpretivist framework. The findings show that adoption unfolds as a multi-phase process embedded in users’ biographies, financial practices, and socio-technical environments. Across accounts, cryptocurrencies were described not only as speculative assets but also as tools of financial autonomy, learning, and optionality under conditions of institutional uncertainty and constrained access to conventional financial pathways, making the CEE context particularly revealing for a process-oriented understanding of adoption. The analysis identified six interrelated themes: adoption as a project of financial autonomy; the “conscious investor” identity; the market as a school of cost and irreversibility; platforms and communities as adoption infrastructures; the relational politics of visibility; and practice stabilization. Together, these themes show that factors already highlighted in prior adoption research—such as trust, risk, autonomy, and knowledge—do not function as stable predictors, but change their meaning across different phases of engagement. The study contributes to FinTech adoption research by proposing a processual model that reconceptualizes cryptocurrency adoption as a phased, experience-dependent pattern of participation rather than a static outcome of parallel determinants. In doing so, it extends existing variable-centered frameworks toward a more dynamic and interpretive understanding of financial technology use. Full article