Search Results (193)

Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R² = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments. Full article

Rural areas are currently facing a deepening “social-ecological divide,” where the fragmentation of natural, economic, and cultural data—often trapped in “data silos”—hinders effective systemic governance. To bridge this gap, in this study, the Rural Landscape Information Model (RLIM), an integrative framework designed to reconfigure rural connections through data fusion, process coordination, and performance feedback, is proposed. We validate the framework’s effectiveness through a comparative analysis of two distinct rural archetypes in China: the innovation-driven Yu Village and the heritage-conservation-oriented Hani Terraces. Our results reveal that digital technologies drive distinct empowerment pathways moderated by regional contexts: (1) In the data domain, heterogeneous resources were successfully integrated into the framework in both cases (achieving a Monitoring Coverage > 80%), yet served divergent strategic ends—comprehensive territorial management in Yu Village versus precision heritage monitoring in the Hani Terraces. (2) In the process domain, digital platforms restructured social interactions differently. Yu Village achieved high individual participation (Participation Rate ≈ 0.85) via mobile governance apps, whereas the Hani Terraces relied on cooperative-mediated engagement to bridge the digital divide for elderly farmers. (3) In the performance domain, the interventions yielded contrasting but positive economic-ecological outcomes. Yu Village realized a 25% growth in tourism revenue through “industrial transformation” (Ecology+), while the Hani Terraces achieved a 12% value enhancement by stabilizing traditional agricultural ecosystems (Culture+). This study contributes a verifiable theoretical model and a set of operational tools, demonstrating that digital technologies are not merely instrumental add-ons but catalysts for fostering resilient, collaborative, and context-specific rural socio-ecological systems, ultimately offering scalable governance strategies for sustainable rural revitalization in the digital era. Full article

Growing EV adoption is reshaping how Distributed Energy Resources (DERs) interact with the grid, playing a pivotal role in global decarbonization efforts and the transition towards a sustainable energy future. This study built a Virtual Power Plant (VPP) operation framework centered on EV behavioral dynamics, connecting individual driving and charging behaviors with the physical and economic layers of energy management. The EV behavioral dynamic model quantifies the stochastic travel, parking, and charging behaviors of individual EVs through an Agent-Based Trip and Charging Chain (AB-TCC) simulation, producing a Behavioral Flexibility Trace (BFT) that represents time-resolved EV availability and flexibility. The Forecasting Model employs a Bi-directional Long Short-Term Memory (Bi-LSTM) network trained on historical meteorological data to predict short-term renewable generation and represent physical variability. The two-stage optimization model integrates behavioral and physical information with market price signals to coordinate day-ahead scheduling and real-time operation, minimizing procurement costs and mitigating imbalance penalties. Simulation results indicate that the proposed framework yielded an approximately 15% increase in revenue over 7 days through EV-based flexibility utilization. These findings demonstrate that the proposed approach effectively leverages EV flexibility to manage renewable generation variability, thereby enhancing both the profitability and operational reliability of VPPs in local distribution systems. This facilitates greater penetration of intermittent renewable energy sources, accelerating the transition to a low-carbon energy system. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

A large number of state-owned companies were privatized in the Czech Republic after the end of the communist regime, mostly through their transformation into joint-stock companies. The water management sector was no exception from this process. The ownership of infrastructure networks was transferred to individual municipalities, which are legally obliged to provide their inhabitants with water supply and sewerage disposal. Subsequently, the municipalities joined together in joint-stock companies to enhance their capacity to provide sufficient financial resources for the rehabilitation and development of water infrastructure and also to enable the implementation of sustainable water management strategies, which are key to environmental protection. Assets contributed to joint-stock companies in the form of non-monetary contributions serve as a basis for a proportionate allocation of shares, representing the shareholder’s share of participation in the company’s management. An analysis of the asset performance within these companies indicates the necessity of developing an optimized methodology for determining the number of shares allocated for such non-monetary contributions. This need arises from significant disparities in both profitability and cost-efficiency among municipalities, depending on factors such as population size (revenues) and the length and technical characteristics of the infrastructure networks (costs) contributed to the joint-stock companies. The authors of the article present the research project results, aimed at developing a methodological procedure for determining the price (value) of municipal infrastructure assets contributed as non-monetary capital to a joint-stock company that owns and operates water management networks, from which the secondary objective of determining the fair value of a municipality’s water management infrastructure assets based on the developed methodology is derived. The proposed methodological procedure is primarily based on establishing the ratio between the fixed and variable costs of the municipality. Full article

Concerns about the increasing ecological harm caused by human activities have led to greater recognition of the need to address environmental degradation. Policymakers are implementing actions and strategies to alleviate the detrimental effects of climate-change-driven environmental degradation. One of the policy tools for internalizing the external costs of environmental degradation is eco-taxation, which provides incentives for businesses and individuals to adopt cleaner technologies. Investment in renewable energy has surged in solar and wind due to technological advancements, policy backing, and cost reductions. This study examines the long-term environmental effects of eco-taxation and renewable electricity in France between 1998 and 2020, utilizing a novel Fourier autoregressive distributed lag (NARDL) econometric model. The results indicate that eco-taxation and renewable electricity have nonlinear and asymmetric effects on the environmental sustainability of France. In terms of policy implications, these findings provide policymakers in France with nonlinear and asymmetric insights. The government could optimize eco-taxation design and revenue recycling by integrating its existing green budget approaches with mainstream climate objectives into all government spending and taxation, thereby ensuring policy consistency and preventing environmentally harmful subsidies. Additionally, France could accelerate and diversify renewable deployment by committing to higher renewable generation targets, given the positive nonlinear impact without a rebound effect, or investing in grid flexibility and interconnection through grid modernization, smart grids, and cross-border interconnections. Full article

Cattle feed efficiency can influence operating costs and revenues, and it is a complex phenotype that can be measured by different metrices. The objective of this study was to determine which microbial taxa in the ruminal and fecal microbiomes of Angus bulls are associated with residual feed intake (RFI), residual average daily gain (RADG), feed conversion ratio (FCR), and adjusted feed conversion ratio (AFCR) by using a large sample size. The metrics RFI and RADG are calculated from the difference between an animal’s actual feed intake and actual gain, to their predicted intake and gain, respectively, and AFCR is the FCR adjusted for the animal’s size. Rumen (n = 1176) and fecal (n = 1179) samples were collected from Angus bulls from 10 contemporary groups located across the continental U.S. Animals were assigned to three classifications (High, Medium, or Low) for each feed efficiency metric within each contemporary group, according to their individual performance data. To compare the feed efficiency metrics, a cost analysis scenario was performed, and bulls classified by RADG had the greatest correlation with predicted profit (r² = 0.98). For alpha diversity, only classification by FCR resulted in differences within both rumen and fecal samples. In the rumen environment, only ‘X112’ was significant (p = 0.02) by RFI classification, and Succinivibrionaceae was significant (p = 0.01) by FCR classification. In the fecal environment, a total of 19 families significantly differed, with bulls classified by RFI having the greatest number of different families. Bull feed efficiency status had greater impacts in the fecal microbiome compared to the rumen, but results should be taken in context since the animals were classified within each contemporary group. Still, differences detected within the fecal microbiome may allow for more assessable microbiome applications; however, the metric used to classify cattle as feed efficient will determine which microbial families are relevant. Full article

Driven by carbon neutrality goals, electric–hydrogen coupled virtual power plants (EHCVPPs) integrate renewable hydrogen production with power system flexibility resources, emerging as a critical technology for large-scale renewable integration. As distributed energy resources (DERs) within EHCVPPs diversify, heterogeneous resources generate diversified market values. However, inadequate benefit allocation mechanisms risk reducing participation incentives, destabilizing cooperation, and impairing operational efficiency. To address this, benefit allocation must balance fairness and efficiency by incorporating DERs’ regulatory capabilities, risk tolerance, and revenue contributions. This study proposes a multi-stage benefit allocation framework incorporating risk–reward tradeoffs and an enhanced optimization model to ensure sustainable EHCVPP operations and scalability. The framework elucidates bidirectional risk–reward relationships between DERs and EHCVPPs. An individualized risk-adjusted allocation method and correction mechanism are introduced to address economic-centric inequities, while a hierarchical scheme reduces computational complexity from diverse DERs. The results demonstrate that the optimized scheme moderately reduces high-risk participants’ shares, increasing operator revenue by 0.69%, demand-side gains by 3.56%, and reducing generation-side losses by 1.32%. Environmental factors show measurable yet statistically insignificant impacts. The framework meets stakeholders’ satisfaction and minimizes deviation from reference allocations. Full article

The current evolution of the energy context and progress in sustainable energy technologies are enabling the development of new energy supply systems for the residential sector. However, the techno-economic assessment of such energy systems is not straightforward and depends, among others, on the building type, its thermal insulation rate, and user patterns, as well as on the climatic conditions or energy and technology prices. This study therefore aims to develop an investment model for a typical UK household energy system that is applied to a diversity of scenarios to highlight the sensibility of the output results over stochastic input data such as electricity and heat demands, ambient temperature, and global solar irradiation. This dwelling diversity dataset is generated using a thermal–electrical demand model that uses stochastic techniques to model uncertainty. This contribution concludes with a discussion on how end-users can effectively take part in the energy transition while minimizing their energy bill and potentially generate long-term revenues. The main results show stable economic performance, with capital expenditure (CAPEX) ranging from GBP 15,400 to GBP 17,000 and NPV from GBP 21,000 to GBP 26,000 over 2000 individual scenarios. This study also confirms the leveraging effect of policy instruments, such as subsidies, in shifting the optimal system design towards higher shares of renewable and storage technologies, further reducing the reliance on fossil fuels and the impact on distribution systems. Full article

The core of the United States’ tax incentive policies for the cultural industry is anchored in Section 501(c) of the Internal Revenue Code. By offering tax incentives, these policies incentivize individuals and enterprises to make donations to non-profit cultural institutions, thereby fostering the prosperity and development of the U.S. cultural industry. China’s tax incentive policies for cultural donations are categorized into three types: those applicable to enterprises, individuals, and special donations, with variations in the deduction benefits afforded by each category. In comparison, China’s tax incentives for cultural donations have shortcomings, including excessively lengthy approval processes, inadequate coverage, fragmented management, and insufficient supervision. Drawing on the experience of the U.S. tax system, measures such as simplifying the registration procedures for non-profit cultural organizations, enhancing tax declaration requirements, exploring industry self-governance mechanisms, and establishing robust supervision frameworks constitute crucial pathways to advancing the high-quality development of China’s cultural industry. Full article

This study aims to analyze the development of the Central Bank Digital Currency (CBDC) concept and create a design suitable for Turkey’s financial structure. Academic studies scanned in the Web of Science (WOS) database between 2018–2025 were analyzed by bibliometric and content analysis methods. Most of the studies focused on economics, and the most frequently emphasized topics in the 40 studies analyzed in the content analysis were the importance of CBDC design, its effects on the banking sector, and CBDC with interest rates. By analyzing Turkey’s tax revenues, informal economy, and interest rates, we propose an account-based, interest-bearing retail CBDC model that provides individuals with direct access to the Central Bank of the Republic of Türkiye. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

The island of St Lucia has undergone a dramatic transformation in a matter of decades, metamorphosing from a monocrop banana economy to a high end tourist destination which generates billions of dollars of revenue for those who operate it. This transformation has, however, seen a tourism industry develop that relies on Western models of environmental destruction and degradation. For Rastafari communities in St Lucia this represents a direct challenge to a deeply green ecological ethic which lies at the heart of the movement. Instead, some offer an alternative—eco-tourism. Several Rastafari are today engaged in offering an experience to visitors which brings them closer to this naturality rather than separating them from. To do so, however, is not without challenge. Many Rastafari seek to avoid as far as is possible all forms of engagement with a ‘Babylonian’ system that has persecuted the movement and debased all around it. For these individuals, it is only through the establishment of agrarian communes entirely separate from governmental systems and means of production that Rastafari might achieve a spirituality and a living modality free from corruption. These Rastafari eco-tourism ventures offer an alternative to this vision in presenting a different path to a shared ambition of self-sufficiency. Through ethnographic research on the island this paper seeks to situate and explore Rastafari eco-tourism as a vision for community autonomy in St Lucia. Full article

Demand-side flexible resources in building energy systems hold significant potential for enhancing grid reliability and operational efficiency. However, their effective coordination remains challenging due to the complexity of modeling and aggregating heterogeneous loads. To address this, this paper proposes a feasible region aggregation and coordination method for load aggregators based on a GNMTL-LSTM-Zonotope framework. A Gradient Normalized Multi-Task Learning Long Short-Term Memory (GNMTL-LSTM) model is developed to forecast the power trajectories of diverse flexible resources, including air-conditioning systems, energy storage units, and diesel generators. Using these predictions and associated uncertainty bounds, dynamic feasible regions for individual resources are constructed with Zonotope structures. To enable scalable aggregation, a Minkowski sum-based method is applied to merge the feasible regions of multiple resources efficiently. Additionally, a directionally weighted Zonotope refinement strategy is introduced, leveraging time-varying flexibility revenues from energy and reserve markets to enhance approximation accuracy during high-value periods. Case studies based on real-world office building data from Shandong Province validate the effectiveness, modeling precision, and economic responsiveness of the proposed method. The results demonstrate that the framework enables fine-grained coordination of flexible loads and enhances their adaptability to market signals. This study is the first to integrate GNMTL-LSTM forecasting with market-oriented Zonotope modeling for heterogeneous demand-side resources, enabling simultaneous improvements in dynamic accuracy, computational scalability, and economic responsiveness. Full article

This study investigates the drivers of individual and joint adoption of sustainable land use (SLU) practices—specifically crop choice and soil and water conservation—and their impact on farm performance (crop revenue) and production risk (crop yield skewness). Using a farm-level dataset of 504 households across three agro-ecological zones in Punjab, Pakistan, we address selectivity bias through the newly developed multinomial endogenous switching regression (MESR) model. Additionally, we assess land use sustainability across ecological, social, and economic dimensions using a comprehensive non-parametric approach. Our findings identify key determinants of SLU adoption, including farmer education, access to advisory services, FBO membership, hired labor, climate information, farm size, and perceptions of drought and heatwaves. We demonstrate that joint adoption of SLU practices maximizes crop revenue and reduces production risk, lowering the likelihood of crop failure. The study further suggests complementarity between these SLU practices in enhancing crop revenue. Moreover, joint adopters of SLU practices significantly outperform non-adopters in ecological, social, and economic sustainability dimensions. We recommend improving access to public sector farm advisory services and climate information to enable farmers to make well-informed decisions based on reliable data. Implementing these measures can support the transition toward sustainable land management, helping to mitigate risks like crop failure and declining revenues, which threaten farm income. Full article