Search Results (250)

This comprehensive review maps how China’s demand-side resources are aggregated, bid into markets, and monetized at the grid edge. We synthesize original studies and pilots to compare edge architectures for local estimation and privacy-preserving coordination, bidding frameworks that span deterministic, stochastic, chance-constrained, and robust designs, and retailer plan optimization that turns wholesale signals into simple user choices. Our headlined findings are fourfold. First, risk-aware bidding frameworks that use chance constraints or conditional value at risk (CVaR) reduce shortfalls without eroding expected revenue when penalties are strict and data are noisy. Second, joint design of retail prices with storage dispatch stabilizes delivery and consumer bills, raising participation and persistence. Third, intraday refresh of envelopes and redispatch improves balance and profit when provincial rules allow updates. Fourth, transparent baselines and settlement rules determine realized value and should be co-designed with aggregation and pricing. We organize reproducible metrics for revenue, reliability, latency, and consumer welfare, and provide simulation templates aligned with Chinese spot practice to enable head-to-head comparisons. The review closes with a research agenda on correlation modeling for heterogeneous portfolios, distribution-aware coordination, and long-run equipment impacts as areas where larger field trials and open data would unlock credible evaluation and faster deployment in China. Full article

With the continuous growth in the scale of engineering simulation and intelligent manufacturing workflows, more and more problem-solving tasks are migrating to cloud computing platforms to obtain elastic computing power. However, a core operational challenge for cloud platforms lies in the difficulty of stably obtaining high-quality scheduling solutions that are both efficient and free of symmetric redundancy, due to the coupling of multiple constraints, partial resource interchangeability, inconsistent multi-objective evaluation scales, and heterogeneous resource fluctuations. To address this, this paper proposes a Dual-Improved Whale Optimization Algorithm (DI-WOA) accompanied by a modeling framework featuring discrete–continuous divide-and-conquer modeling, a unified monetization mechanism of the objective function, and separation of soft/hard constraints; its iterative trajectory follows an augmented Lagrangian dual-rollback mechanism, while being rooted in a three-layer “discrete gene–real-valued encoding–decoder” structure. Scalability experiments show that as the number of tasks J increases, the DI-WOA ranks optimal or sub-optimal at most scale points, indicating its effectiveness in reducing unified billing costs even under intensified task coupling and resource contention. Ablation experiment results demonstrate that the complete DI-WOA achieves final objective values (OBJ) 8.33%, 5.45%, and 13.31% lower than the baseline, the variant without dual update (w/o dual), and the variant without perturbation (w/o perturb), respectively, significantly enhancing convergence performance and final solution quality on this scheduling model. In robustness experiments, the DI-WOA exhibits the lowest or second-lowest OBJ and soft constraint violation, indicating higher controllability under perturbations. In multi-workload generalization experiments, the DI-WOA achieves the optimal or sub-optimal mean OBJ across all scenarios with H = 3/4, leading the sub-optimal algorithm by up to 13.85%, demonstrating good adaptability to workload variations. A comprehensive analysis of the experimental results reveals that the DI-WOA holds practical significance for stably solving high-quality scheduling problems that are efficient and free of symmetric redundancy in complex and diverse environments. Full article

Regional Integrated Energy Systems (RIESs) are pivotal in the low-carbon transition of energy-intensive hospital campuses. However, traditional multi-objective optimization for RIES planning often suffers from the subjective selection of weights, leading to configurations that lack robustness against varying decision-maker preferences. To address this, this paper proposes a robust optimization methodology integrating shadow cost theory and multi-scenario weight scanning. A high-fidelity dynamic simulation model of a hospital in Beijing was constructed using TRNSYS. By monetizing environmental externalities into shadow costs, a comprehensive objective function, including annual cost savings rate, primary energy savings rate, and environmental shadow cost savings rate, was established, and the Hooke–Jeeves algorithm was employed to scan ten distinct weight scenarios, ranging from profit-driven to eco-centric preferences. The results reveal that solar collectors lack economic competitiveness under current boundary conditions due to cooling–heating coupling constraints. Instead, a configuration featuring a large-capacity gas turbine (2790 kW) coupled with a moderate GSHP was identified as the optimal solution in over 80% of the scenarios, demonstrating high preference robustness. Crucially, this configuration achieves net-negative emissions by maximizing clean power exports to displace carbon-intensive grid electricity. Compared to the reference system, the optimized RIES reduces primary energy consumption by 82.7% and achieves substantial environmental benefits, subject to grid emission factors. These findings confirm that prioritizing clean power export is a resilient pathway for hospitals to balance economic feasibility with environmental goals under current policy frameworks, providing scientific guidance for policymakers and engineers. Full article

Promoting the value realization of ecological products is a central issue in practicing the concept that “lucid waters and lush mountains are invaluable assets.” This is particularly urgent for alpine regions, which are vital ecological security barriers but face stringent developmental constraints. This study takes Golog Tibetan Autonomous Prefecture in Qinghai Province as a case study. It establishes a Gross Ecosystem Product (GEP) accounting framework tailored to the characteristics of alpine ecosystems and conducts continuous empirical accounting for the period 2020–2023. The findings reveal that: (i) The total GEP of Golog is immense (reaching 655.586 billion yuan in 2023) but exhibits significant dynamic non-stationarity driven by climatic fluctuations, with a coefficient of variation as high as 11.48%. (ii) The value structure of the GEP is highly unbalanced, with regulatory services contributing over 97.6%. Water conservation and biodiversity protection are the two pillars, highlighting its role as a supplier of public ecological products and the predicament of market failure. (iii) The spatial distribution of GEP is highly heterogeneous. Maduo County, comprising 34% of the prefecture’s land area, contributes 48% of its total GEP, with its value per unit area being 1.68 times that of Gande County, revealing the spatial agglomeration of key ecosystem services. To address the dynamic, structural, and spatial constraints identified by these quantitative features, this paper proposes synergistic realization pathways centered on “monetizing regulatory services,” “precision policy regulation,” and “capacity and institution building”. The aim is to overcome the systemic bottlenecks—“difficulties in measurement, trading, coarse compensation, and weak incentives”—in alpine ecological functional zones. This provides a systematic theoretical and practical solution for fostering a virtuous cycle between ecological conservation and regional sustainable development. Full article

Cultural heritage institutions today are experiencing a digital transformation. Virtual Reality (VR), with the promise of immersive and interactive features, has drawn the attention of artists and curators. Some prior museology research has attempted to investigate digital innovations like virtual museums and VR-based exhibits to present the best of museum experiences; however, existing systematic research on the topic of interactive narrative experience with immersive VR technologies is rare. This paper reports on an original research project to understand the emergent issues concerning immersion, interactive and narrative in museum experience design. This research used multiple case studies, Claude Monet: The Water Lily Obsession; We live in the Ocean of Air; Mona Lisa: Beyond the Glass; Curious Alice. In total, 22 semi-structured interviews were conducted with VR experts and museum curators to understand the motivation of the designers and developers. This research hopes to contribute to the digital revolution of museums, providing a foundation for curators and artists who are interested in using VR technologies in exhibitions. Full article

In response to recent policies on sustainable finance, nature restoration, soil protection, and biodiversity conservation, it is increasingly important for projects to assess their impacts on natural capital to safeguard Ecosystem Services (ES). Nature-Based Solutions (NBSs) are recognized as strategic tools for fostering cost-effective, nature- and people-centered development. Yet, standard economic and financial assessment methods often fall short, as many ES lack market prices. Indirect, ecosystem-based approaches—such as ES monetization and environmental cost accounting—are therefore critical. This study evaluates the feasibility of investing in NBSs by estimating their economic and financial value through indirect ES valuation. An empirical methodology is applied to quantify environmental costs relative to ES delivery, using Willingness to Pay (WTP) as a proxy for the economic relevance of NBSs. The proposed ES-Cost Accounting (ES-CA) framework was implemented across major NBS categories in Europe. Results reveal that the scale of NBS implementation significantly influences both unit environmental costs and ES provision: larger interventions tend to be more cost-efficient and generate broader benefits, whereas smaller solutions are more expensive per unit but provide more localized or specialized services. The findings offer practical guidance for robust cost–benefit analyses and support investment planning in sustainable climate adaptation and mitigation from an ES perspective. Full article

This study introduces a fully uncertainty-aware forecasting framework for NBA games that integrates team-level performance metrics, rolling-form indicators, and spatial shot-chart embeddings. The predictive backbone is a recurrent neural network equipped with Monte Carlo dropout, yielding calibrated sequential probabilities. The model is evaluated against strong baselines including logistic regression, XGBoost, convolutional models, a GRU sequence model, and both market-only and non-market-only benchmarks. All experiments rely on strict chronological partitioning (train ≤ 2022, validation 2023, test 2024), ablation tests designed to eliminate any circularity with bookmaker odds, and cross-season robustness checks spanning 2012–2024. Predictive performance is assessed through accuracy, Brier score, log-loss, AUC, and calibration metrics (ECE/MCE), complemented by SHAP-based interpretability to verify that only pre-game information influences predictions. To quantify economic value, calibrated probabilities are fed into a frictionless betting simulator using fractional-Kelly staking, an expected-value threshold, and bootstrap-based uncertainty estimation. Empirically, the uncertainty-aware model delivers systematically better calibration than non-Bayesian baselines and benefits materially from the combination of shot-chart embeddings and recent-form features. Economic value emerges primarily in less-efficient segments of the market: The fused predictor outperforms both market-only and non-market-only variants on moneylines, while spreads and totals show limited exploitable edge, consistent with higher pricing efficiency. Sensitivity studies across Kelly multipliers, EV thresholds, odds caps, and sequence lengths confirm that the findings are robust to modelling and decision-layer perturbations. The paper contributes a reproducible, decision-focused framework linking uncertainty-aware prediction to economic outcomes, clarifying when predictive lift can be monetized in NBA markets, and outlining methodological pathways for improving robustness, calibration, and execution realism in sports forecasting. Full article

Long-duration energy storage (LDES) can deliver system-wide flexibility and decarbonization benefits, yet investment is often hindered because these benefits are diffuse and not fully monetized under conventional market structures. A public-asset-oriented valuation and cost-allocation framework is proposed for LDES. First, LDES externality benefits are quantified through a system-level optimization-based simulation on a stylized aggregated regional network, with key indicators including thermal generation cost, carbon penalty, renewable curtailment cost, involuntary load shedding, and end-user electricity expenditures. Second, LDES investment costs are allocated among thermal generators, renewable operators, grid entities, and end users via a benefit-based Nash bargaining mechanism. In the case study, introducing LDES reduces thermal generation cost by 3.92%, carbon penalties by 5.59%, and renewable curtailment expenditures by 7.07%, while eliminating load shedding. The resulting cost shares are 46.9% (renewables), 28.7% (end users), 22.4% (thermal generation), and 0.5% (grid entity), consistent with stakeholder-specific benefit distributions. Sensitivity analyses across storage capacity and placement further show diminishing marginal returns beyond near-optimal sizing and systematic shifts in cost responsibility as benefit patterns change. Overall, this framework offers a scalable, economically efficient, and equitable strategy for cost redistribution, supporting accelerated LDES adoption in future low-carbon power systems. Full article

Establishing a mechanism for ecological product value realizing (EPVR) is a critical component of China’s ecological civilization strategy, aimed at translating the concept that “lucid waters and lush mountains are invaluable assets” into actionable economic policies. Although central government investments in the form of project for EPVR have increased significantly, surpassing CNY 700 billion by 2024, studies rarely focus on these projects and how to evaluate them. Evaluating the performance of EPVR projects is essential for optimizing resource allocation, enhancing project accountability, and ensuring the sustainable realization of ecological, economic, and social values. This study innovatively defines the conceptual connotation of EPVR projects and constructs a comprehensive performance evaluation system based on a “benefit-cost” analysis, comprising a multi-dimensional indicator system, quantifiable calculation methods, and explicit evaluation criteria. As water source protection projects are typical EPVR projects, the comprehensive water environment management project of Hongfeng Lake is selected for an in-depth empirical study. The results reveal that (1) the total annual benefits amount to CNY 923.66 million, dominated by ecological benefits (84.04%); (2) with an investment of CNY 1194.66 million, the project yields a net loss and a moderate performance index (PCPI = 0.77); (3) the project performance is primarily affected by weak economic value conversion stemming from restrictive zoning policies and underdeveloped market mechanisms for ecological services; and (4) integrated development pathways—such as ecotourism, eco-aquaculture, and ecological branding—are proposed to enhance the long-term sustainability of the project. The Hongfeng Lake case establishes a replicable framework for global assessment of analogous projects and delivers actionable insights for enhancing benefit–cost ratios in public ecological initiatives, with costs confined to data collection, modeling, and validation. Therefore, this study contributes a quantifiable and reproducible tool for the full lifecycle management of EPVR projects, thereby facilitating more informed government decision-making. Key findings reveal the following: (1) A comprehensive “Benefit-Cost” performance evaluation framework, pioneered in this study and tailored specifically for individual EPVR projects, surpasses regional-scale accounting methodologies like Gross Ecosystem Product (GEP). (2) A novel consolidated metric (PCPI) is introduced to integrate ecological, economic, and social dimensions with cost input, thus enabling direct cross-project comparison and classification. (3) The framework operationalizes evaluation by providing a detailed, adaptable indicator system with explicit monetization methods for 26 distinct benefits, thereby bridging the gap between theoretical value accounting and practical project assessment. (4) The empirical application to a drinking water source protection project addresses a critical yet understudied category of EPVR projects, offering insights into “protection-oriented” models. Full article

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

To improve the low-carbon economic operation of integrated energy system (IES) with electric–thermal–hydrogen hybrid energy storage (ETH-HES), this paper proposes a low-carbon optimal dispatch strategy that jointly considers the bidirectional interaction between green certificate trading (GCT) and stepwise carbon emission trading (SCET), as well as lifetime degradation of electrolyzers and batteries. A coupled GCT–SCET interaction model is formulated by linking green certificate acquisition with carbon quota transactions, and a triple-incentive mechanism is introduced to monetize the low-carbon value of ETH-HES. In addition, degradation-aware models are established for the electrolyzer and battery to capture long-term operating costs. Case studies show that the proposed strategy reduces system operating cost and carbon emissions, increases wind and PV utilization, and improves the operating profit of ETH-HES. Full article

Sustainable development goals link policies addressing air quality and energy efficiency to synergistic benefits for climate mitigation. However, the coal-dominated energy system poses major challenges for Henan Province in mitigating air pollution and climate change. While the government has issued a series of clean air policies and low-carbon energy targets, the simultaneous achievements of low-carbon transition and air quality goals at the sub-national level remain unclear. This study evaluates the effectiveness of policy implementation in Henan’s energy system using an integrated assessment framework that combines emission scenarios, air quality simulations, and health impact assessments. The results indicated that, by 2030, without system-wide energy transformation driven by carbon mitigation policies, air quality improvements in Henan Province will be limited, even under stringent end-of-pipe emission control measures. In contrast, low-carbon policies would yield significant co-benefits for both air quality improvement and climate mitigation. Beyond stringent end-of-pipe controls, the implementation of carbon mitigation policies aligned with China’s enhanced climate targets could further reduce Henan’s average PM_2.5 concentration by up to 4.1 µg/m³. The monetized health co-benefits in Henan Province would reach 4.57 billion RMB under the stringent carbon mitigation scenario. These results highlight the critical role of effectively implementing existing air pollution and energy policies in simultaneously achieving air quality, public health, and carbon mitigation goals in Henan. Full article

Urban roadside forests are vital components of green infrastructure that provide multiple ecosystem services, contributing to climate regulation, environmental quality, and urban resilience. This study assessed the multifunctional ecosystem services of roadside tree communities along four representative road types—Coastal Scenic, Commercial Arterial, Residential Secondary, and Industrial Park Roads—in Weihai, a coastal city in eastern China. Based on a complete tree inventory (6742 individuals from 38 species) integrated with the i-Tree Eco model, we quantified three key ecosystem services, carbon storage and annual sequestration, air-pollutant removal, and stormwater interception, and monetized their benefits. Results indicate that roadside forests stored approximately 1120 tons of carbon and sequestered 78 tons annually (≈USD 0.53 million; CNY 3.85 million), removed 1.28 tons of air pollutants per year (≈USD 9370; CNY 68,400), and intercepted 1560 m³ of stormwater (≈USD 5560; CNY 40,600). Commercial Arterial and Coastal Scenic Roads yielded the highest total ecosystem-service values, while Residential Secondary Roads achieved the greatest per-area efficiency. These findings highlight the significant contribution of urban roadside forests to sustainable and climate-resilient city development and underscore their potential role in urban forest planning and management. Full article

Short-video platforms such as TikTok, Douyin, and Instagram Reels have transformed digital consumption into an immersive, algorithmically mediated commerce ecosystem. This study examines how compulsive short video use (CSV), a maladaptive pattern linked to diminished self-regulation, shapes purchase intention (PI). Drawing on compulsive consumption theory, dual-process perspectives, and socio-technical systems theory (STST), we estimate a structural equation model using survey data from 542 active short-video users. The results show that CSV exerts a strong and consistent positive effect on PI, indicating that compulsive engagement functions as a commercially consequential psychological state. This relationship is contingent on socio-technical conditions: technical support and platform familiarity substantially amplify the CSV–PI pathway, social belonging provides weaker but positive reinforcement, and social interaction attenuates the effect by redirecting attention away from transactional cues. These findings position CSV as both a form of digital pathology and a commercially activating mechanism within socio-technical environments. The study also offers guidance for platform managers seeking to balance monetization with ethical responsibility in short-video commerce ecosystems. Full article

Digital platforms face a fundamental strategic decision between subscription-only, advertising-only, and freemium (hybrid) monetization models. We develop a game-theoretic framework that unifies these strategies, explicitly modeling consumer heterogeneity in both willingness-to-pay and advertising disutility, while incorporating network effects through the platform’s valuation of user-base size. Our analysis yields closed-form solutions identifying optimal strategy thresholds based on advertising market conditions. We show that subscription-only dominates when advertising prices are low, advertising-only prevails when prices are high, and freemium emerges as strictly optimal in the intermediate region. Under freemium, we demonstrate strategic complementarity: both subscription fees and advertising intensity exceed their levels in pure strategies because each instrument’s effectiveness is amplified by the other through user reallocation across tiers. Network effects universally reduce monetization intensity but alter instruments’ relative sensitivities differently across regimes—when advertising prices are moderate, freemium adjusts ad length more aggressively, while the opposite holds at high prices. Critically, freemium’s profitability requires sufficient consumer heterogeneity in ad tolerance. As consumer preferences converge, the screening mechanism fails and freemium collapses to the superior pure strategy. These results provide operational guidance for platform monetization decisions and clarify when hybrid models create value beyond traditional approaches. Full article