Search Results (2,202)

The authorization and operation of the public data supply chain is an important pathway for unlocking the value of public data and cultivating the data element market. However, in practice, it faces challenges such as insufficient data supply and insufficient stakeholder incentives. This paper focuses on the differentiated subsidy policies of the fiscal department, constructing a differential game model involving multiple participants, including data providers, data managers, and data operators. The paper systematically compares the optimal effort decisions of each stakeholder, the evolution trajectory of public data product value, and the trajectory of overall system profits under two subsidy models: cost subsidies and transaction subsidies. It further analyzes the regulatory role of revenue distribution ratios and cost-sharing contracts in shaping the effectiveness of these subsidy mechanisms. The study finds that: (1) cost subsidies provide more balanced and stable incentives for all stakeholders and contribute more to the final value trajectory of public data products; transaction subsidies are more effective in improving overall system profits but offer weaker incentives for the supply and management sides, requiring flexible use in conjunction with cost-sharing contracts; (2) cost-sharing contracts play a regulatory role under different subsidy models and effectively reduce the data provider’s dependence on fiscal subsidies under the transaction subsidy mechanism; (3) the revenue distribution ratio only positively affects the effort decisions of the supply and management sides under the transaction subsidy model, and the optimal subsidy ratio of the fiscal department is closely related to the revenue distribution ratio. Therefore, differentiated subsidy strategies should be implemented based on specific decision-making contexts and internal revenue distribution ratios. This paper reveals the synergistic incentive mechanism between differentiated subsidy models and cost-sharing contracts, providing a theoretical basis for the design of subsidy policies for public data authorization and operation. Full article

Geographical indication (GI) products are often understood through terroir, certification, and branding, yet less attention has been paid to the feedback dynamics through which their market value is amplified and destabilized. This paper applies systems thinking to the post-harvest value–trust subsystem of Xinhui Chenpi, a traditional Chinese aged citrus pericarp product with GI protection, to examine how value amplification and systemic vulnerability emerge through interacting feedback mechanisms. Drawing on 28 semi-structured interviews conducted between September 2023 and December 2024, supplemented by participant observation, policy and standard documents, field-based market observations, and contextual media materials, the study develops a preliminary causal loop diagram (CLD) with 15 endogenous feedback variables, 3 boundary value-input variables, 4 exogenous contextual inputs, and 21 causal links (18 endogenous and 3 value-input). The model identifies two reinforcing loops and two balancing loops through which price expectations, holding incentives, credible circulation supply, perceived scarcity, misrepresentation, and trust erosion interact. The trust-erosion loop shows how premium-driven misrepresentation increases claim uncertainty, weakens open-market consumer trust, reduces credible open-market liquidity, and further contracts credible circulation supply. Buyer exit and delayed supply response operate as limited balancing mechanisms because their effects are segment-dependent and constrained by aging and verification delays. The proposed CLD suggests that high-value mechanisms in aging-dependent GI products may also generate structural vulnerabilities, with implications for managing consumer trust, claim verification, and credible circulation in premium markets. Full article

Electricity spot markets with time-of-use pricing create hour-by-hour variations in the economic value of thermal adjustments, requiring coal-fired units to adapt cold-end operation to real-time price signals. However, the nonlinear coupling between circulating water flow and condenser backpressure remains insufficiently characterized across the full operating envelope, and existing optimization strategies target steady-state heat consumption without accounting for the time-varying economic value of identical thermal adjustments under spot pricing. This study develops a quasi-steady-state thermodynamic–economic model that links real-time electricity prices with the nonlinear heat-transfer response of the circulating water system. The model enables the adaptive selection of pump combinations and blade-opening angles by balancing marginal pump power savings against marginal turbine output losses under time-of-use price signals. Using actual electricity spot market data from Zhejiang Province, simulations under different seasonal conditions show clear economic gains. The maximum hourly saving reaches 2190.79 CNY during summer negative-price periods, which is about 5.3 times higher than that in winter, while backpressure deviations remain within 12.5% of the design value. The seasonal disparity is governed by the initial heat exchange driving force, a fundamental thermodynamic property amplified by the negative-price superposition effect. The framework establishes a physical basis for market-responsive cold-end regulation across seasonal and load conditions, supporting the economic dispatch of coal-fired units in spot market environments. Full article

Sensory characteristics play a significant role in driving the market success of plant-based milk alternatives (PBMAs) in coffee applications. This research identified key sensory attributes affecting Thai consumers’ acceptance, purchase intent, and emotional responses toward chilled, sweetened coffee–PBMA formulations prepared with specific commercial products available in Thailand. Ten formulations—combining two coffee bases (100% Arabica or 70:30 w/w Arabica–Robusta blend) with five PBMAs (almond, pistachio, macadamia, white sesame, and riceberry)—were evaluated by nine trained descriptive panelists for 26 attribute intensities and by 100 Thai milk coffee consumers for overall liking, purchase intent, and emotional responses. Results showed that PBMAs primarily determined sensory profiles, consumer acceptance, and elicited emotions, whereas coffee bases exerted marginal influence. Macadamia and pistachio milks exhibited significantly (p ≤ 0.05) higher overall acceptance and top-two-box purchase intent than the remaining alternatives, while eliciting strong positive emotional responses. Four attributes, including nutty, beany, balanced/blended, and fullness, primarily fostered consumer liking and positive emotions (e.g., comforted, happy, healthy). Conversely, two attributes (dark brown flavor and powdery texture) served as major sensory barriers, linked to a decline in acceptance and negative emotions. These insights may provide valuable guidance for product developers to optimize specific coffee–PBMA formulations. Full article

Increasingly stringent regulations regarding climate policy and the sustainable development paradigm determine the transformation of contemporary multi-family housing typology, manifested by a growing share of single-aspect micro-apartments (units with exterior exposure on only one facade). This article identifies the phenomenon of the energy-efficiency paradox, wherein highly insulated buildings successfully trap winter heat but inadvertently escalate summer cooling demands. Consequently, the primary operational challenge becomes limiting excessive solar heat gains in summer, which directly translates into high cooling energy demand, rather than solely mitigating heat losses in winter. Sustainable construction requires moving beyond the narrowly defined reduction of envelope thermal transmittance towards holistic adaptation to climate change and ensuring adequate indoor environmental quality. The methodology is based on a coupled energy-economic analysis, evaluating thermal balances and their direct financial implications for end-users. The variant analysis of solar heat gains conducted for a reference 30 m² dwelling in Warsaw proves that architectural optimization should not be determined solely by short-term investment profit maximization. Effective engineering optimization in construction requires the implementation of a full building life cycle perspective. Unfavorable glazing orientation and the lack of cross-ventilation necessitate the use of energy-intensive air-conditioning systems, which directly increases the building’s carbon footprint and generates hidden operating costs (differences reaching over 145 PLN annually for heating and approximately 70 PLN for cooling). The findings highlight the necessity for a critical reevaluation of design priorities for compact apartments, integrating social justice (by reducing information asymmetry in the real estate market, where buyers are often unaware of these future cooling burdens) with long-term economic rationality and the resilience of the built environment to extreme weather events. Full article

Identifying the spatio-temporal evolutionary patterns and driving mechanisms of rural tourism network attention is essential for predicting the development trends of the rural tourism industry and delivering refined industrial governance. Taking 356 prefecture-level cities in China from 2015 to 2024 as basic research units, this paper constructs a comprehensive evaluation system for rural tourism network attention based on multi-source data. Furthermore, its spatio-temporal evolution characteristics and internal influencing factors are systematically investigated by means of spatial autocorrelation analysis and geographically weighted regression. The results indicate that the overall level of rural tourism network attention in China shows an obvious fluctuating growth trend, which can be divided into three successive stages, namely steady growth (from 0.8530 in 2015 to 1.2028 in 2019), explosive growth (from 1.9563 in 2020 to 3.7471 in 2021) and high-level fluctuation (maintained in the high range of 2.4–3.4). In addition, with the continuous iteration of internet communication media, the guiding influence of traditional search platforms has gradually weakened, while emerging social media and short-video platforms have become the core carriers of online tourism traffic. Correspondingly, media innovation persistently reshapes the spatial distribution pattern of rural tourism network attention. In terms of spatial characteristics, rural tourism network attention has undergone a significant transformation from geographical gradient polarization to overall regional equilibrium. Specifically, from 2015 to 2024, the overall Moran’s I index remained positive, with values ranging from 0.0116 to 0.1358, indicating an overall trend of gradual decline. High-attention areas are predominantly concentrated in economically developed urban agglomerations, whereas remote and economically underdeveloped regions exhibit contiguous low-value aggregation characteristics, which reveals a remarkable trend of balanced development nationwide. In view of driving mechanisms, highway network density, tourism income, rural tourism resource and enrollment of university students are identified as the core driving factors dominating the spatio-temporal evolution of rural tourism network attention. Moreover, the intensity of the influence of each factor presents distinct spatial heterogeneity. This study further reveals that the spatial heterogeneity of rural tourism network attention calculated using multi-source fused data shows a remarkable convergent characteristic, which can reflect the actual distribution of the rural tourism market more objectively and accurately. Meanwhile, rural tourism network attention is typically characterized by scale-dependent with the spatial distribution at the macro-scale being more balanced than that at the meso- and micro-scales. Full article

Green bond markets have expanded rapidly across the European Union, but development remains uneven across Member States. Using a balanced EU-27 panel for 2021–2025, this study distinguishes sovereign market entry from the depth of the overall green debt market. Pooled Probit and Tobit estimates show that larger economies are substantially more likely to issue sovereign green bonds, whereas government debt and the budget balance are not significantly associated with entry. Market depth is positively associated with economic scale and a stronger budget balance, although the latter relationship partly overlaps with institutional quality. Environmental taxation is not significantly associated with market depth, providing no evidence of substitution for green debt financing. The findings support measures to lower entry costs and strengthen institutional capacity in smaller Member States. Full article

The high-value utilization market for crop straw renders the development of stubble management technology crucial. This study aims to reduce the energy consumption of L-shaped rotary blades during stubble-breaking. Based on a theory analysis of the rotary-cutting operation process, this study involved the burial of the in situ maize root–soil composite in an indoor soil bin, and investigated the effects of rotary speed (275, 330, 385, 440 rpm) and working depth (50, 85, 120 mm) on torque, power, and energy. Field verification yields an overall average relative error of 2.76% across six replicates, verifying that the indoor test method can reliably reproduce field cutting conditions. As the high-speed video images show, a reduction in rotary speed coupled with an augmentation in working depth has the potential to result in residue entanglement and secondary cutting, thereby leading to an escalation in consumption. As the working depth increased, peak torque appeared at a deeper penetration position. The analysis of the computer-aided geometric model section of the root–soil composite indicated that the diameter of the branching root was the primary factor influencing peak torque. At a working depth of 85 mm, the average power savings ranged from 2.26% to 24.8% compared to 50 mm and 120 mm. Despite the increase in average power, peak power, and specific energy requirements at all operational depths with increasing rotary speed, torque reached its minimum at 385 rpm. At 385 rpm, average torque hits its minimum to mitigate component wear, though power and specific energy rise monotonically with rotational speed. The multi-index evaluation balancing mechanical load, energy loss, and residue delivery identifies 385 rpm paired with 85 mm depth as the optimal parameter set. The optimized parameter combination delivers a quantifiable sustainable residue management scheme that balances ecological residue treatment and economic machinery operation costs, supporting low-carbon, sustainable production. Full article

To address the coupling among bidding decisions, resource allocation, and coordinated utilization of environmental rights for distributed resource aggregators in multiple markets, including the energy market, peak regulation ancillary service market, carbon trading market, and tradable green certificate market, this paper proposes a bi-level optimization model for multi-market joint trading considering a limited carbon–green certificate linkage mechanism. First, a quantitative mapping relationship between the emission reduction attribute of surplus green certificates and carbon emission reduction is established, and an upper limit constraint on the offset ratio is introduced to describe the limited conversion of green certificate environmental attributes into carbon emission reduction value. Second, an upper-level bidding model for the distributed resource aggregator is constructed, aiming at profit maximization while considering revenues from the energy, peak regulation ancillary service, carbon trading, and green certificate markets, as well as the operational constraints of gas turbines, energy storage, and flexible loads. This model characterizes the aggregator’s joint bidding strategy and internal resource coordination. Then, a lower-level unified market clearing model is developed to minimize system operating cost and simulate the segmented bidding and unified clearing process of the aggregator, wind power, and thermal power units in the energy and peak regulation ancillary service markets. Finally, the bi-level model is transformed into a solvable single-level model using the Karush-Kuhn-Tucker (KKT) conditions and the Big-M method, and case studies are conducted to verify its effectiveness. The numerical results show that under the complete multi-market mechanism, the distributed resource aggregator (DRA) obtains a net profit of 2245.03 yuan, which is higher than 1450.33 yuan in the scenario without the carbon–green certificate mechanism and 773.48 yuan in the energy-only scenario. The wind curtailment rate decreases from 8.53% in the energy-only scenario to 2.71%, and the carbon emissions accounted for within the DRA boundary are reduced to 2865.40 kg. Although the price-taker scenario obtains a slightly higher net profit of 2300.20 yuan, its average regulation price reaches 455.20 yuan/MWh, compared with 412.50 yuan/MWh under the proposed model, indicating that the proposed strategy achieves a better balance between aggregator revenue and system-side regulation cost. Full article

Carbon market prices are jointly shaped by policy interventions, energy market fluctuations, and macroeconomic dynamics, and thus exhibit pronounced nonlinearity, non-stationarity, localized abrupt changes, and time-varying uncertainty. From an information-theoretic perspective, carbon price forecasting can be viewed as the extraction and fusion of effective information from a complex market system driven by heterogeneous endogenous and exogenous signals. To address the challenges of accurately characterizing local high-frequency fluctuations in carbon price series, effectively modeling the interactions between endogenous and exogenous variables, and mitigating the structural noise introduced by conventional serial forecasting frameworks, this study proposes ConvTimeXer, a hybrid model combining bidirectional temporal convolution and TimeXer for carbon market price forecasting. Specifically, the model first employs front-end bidirectional temporal convolutions to extract local multi-scale fluctuation features from the endogenous carbon price series. It then leverages the global token and cross-attention mechanism in TimeXer to achieve dynamic decoupling and deep interaction between endogenous and exogenous variables. Finally, residual fusion of shallow and deep features is introduced to enhance the preservation of local details. Experimental results based on data from China’s carbon market over the past three years demonstrate that the proposed framework delivers high predictive accuracy and strong robustness, effectively balancing responsiveness to local abrupt changes with global trend modeling. This study not only provides an effective approach for carbon price forecasting in complex and uncertain market environments, but also offers valuable insights into non-stationary time-series forecasting driven by multi-source heterogeneous information. Full article

Promoting the green transformation of the construction industry requires an in-depth understanding of the core elements of various national green building standards. However, the data heterogeneity of standard texts and the scarcity of high-quality annotated data limit the effectiveness of traditional supervised and single unsupervised keyword extraction methods. To address this, this study proposes an unsupervised keyword extraction method with multi-indicator integration, named EBert-MPI. This method integrates semantic indicators, term frequency indicators, and keyword network indicators, and introduces the entropy weight method to achieve objective weighting, enabling a multi-dimensional, comprehensive evaluation of keyword importance. Performance verification shows that EBert-MPI outperforms baseline methods, confirming its comprehensive effectiveness in keyword identification accuracy and importance ranking. Applying EBert-MPI to a cross-national comparative analysis of green building standards from China (ASGB), the United States (LEED), the United Kingdom (BREEAM), and Germany (DGNB) reveals the following results: at the macro level, LEED and ASGB exhibit a relatively balanced focus on the three sustainability dimensions of environment, society, and economy, whereas BREEAM and DGNB concentrate significantly on environmental issues; at the micro level, LEED emphasizes renewable energy and market-driven incentive mechanisms, ASGB uniquely highlights thermal comfort and performance optimization, BREEAM focuses on the standardized management of the construction process, and DGNB advocates for the principle of whole-life-cycle transparency. Meanwhile, the term “design” holds an extremely high and consistent importance ranking across all standards, indicating that integrating sustainability into front-end design has become an internationally recognized core technical paradigm. Full article

This study examines whether recognized intangible assets carry information about bank performance in an emerging market, and whether their information value is conditioned by the maturity of macro digital infrastructure. Using a balanced panel of 28 Indonesian commercial banks over 2015–2024 (280 firm-year observations), we estimate two-way fixed-effects models with macro digital infrastructure, an economy-wide principal component index of internet penetration, mobile and broadband subscriptions, and electronic payment volume as a cross-layer moderator. Intangible investment intensity, proxied by the ratio of reported intangible assets to total assets, shows weak direct associations with performance; only the operating efficiency ratio displays a marginally significant short-run cost, consistent with transition-cost dynamics. The central result is conditional: the interaction between intangible intensity and macro digital maturity is strongly significant for operating efficiency (β = −2.587, p = 0.005), with the implied efficiency cost contracting by a model-implied 88 percent across the observed range of digital maturity (an estimate computed from the estimated coefficients over the observed sample variation, not a structural causal magnitude). Heterogeneity is pronounced across regulator-defined bank tiers (KBMI): the four largest banks realize positive profitability effects, whereas mid-tier banks bear transition costs. Results are robust to Driscoll–Kraay standard errors, system GMM, sub-sample splits, and outlier exclusion. The findings show that the information value of recognized intangibles in banking is state-contingent, extending the intangible-asset and digitalization literature to emerging-market banking. Full article

Electricity price uncertainty directly affects the economy and reliability of scheduling, especially when flexible loads are scheduled only according to point forecasts. To improve the coupling between price forecasting uncertainty and load scheduling, this paper proposes a two-stage affine adjustable robust optimization method for flexible loads based on the confidence level of electricity price prediction via an improved hybrid convolutional neural network temporal convolutional network (CNN-TCN) model. An attention-enhanced CNN-TCN model is used to obtain day-ahead electricity price forecasts, and conformalized quantile regression (CQR) is introduced to construct calibrated asymmetric prediction intervals under different confidence levels. The interval bounds are then converted into a budgeted price uncertainty set and embedded in a two-stage affine adjustable robust optimization model for industrial, commercial, and residential loads. The model considers power limits, ramping constraints, total energy requirements, baseline deviation limits, and smoothing penalties, enabling load transfer from high-price periods to low-price periods while preserving operational feasibility. Case studies based on Spanish electricity market data show that the proposed method reduces operating costs under forecast, worst-case, and abnormal disturbance scenarios compared with the original load plan. The results also show that the 90% confidence level provides a suitable balance among cost reduction, risk coverage, and scheduling conservatism in the studied case. Full article

Urban–rural integration, as a pivotal strategy for reshaping the relationship between cities and the countryside and promoting balanced regional development in the current era, carries considerable weight for the green transition of agricultural systems. This study utilizes panel data from 31 Chinese provincial units over the 2011–2023 period to construct a four-dimensional (economic, social, spatial, and ecological) assessment framework for urban–rural integration. It then applies the SBM-GML model to estimate agricultural green total factor productivity and systematically investigates the impact and underlying mechanisms of integration on that productivity. The principal findings are as follows: (1) Urban–rural integration significantly boosts agricultural green total factor productivity, and this outcome remains robust after a variety of sensitivity checks and corrections for potential endogeneity. (2) Mechanism tests further reveal that the primary conduits through which such integration raises AGTFP are the upgrading of human capital and the proliferation of agricultural socialized services. (3) Analysis of moderating effects indicates that both innovation in agricultural science and technology and government fiscal support for farming positively reinforce the facilitative impact of urban–rural integration on agricultural green total factor productivity. (4) Results from heterogeneity analysis suggest that the green productivity effect of such integration is strongest in major grain-producing areas, weaker in major grain-selling areas, and not statistically detectable in grain production–marketing balance regions. Full article

This paper studies the effects of price-cap regulation in an oral healthcare market by developing a duopoly model of price–quality competition between a public provider and a private provider. The results show that, in the absence of regulation, the public provider charges a high price and offers high quality, whereas the private provider adopts a low-price, low-quality strategy. This vertical differentiation softens price competition but leaves some highly price-sensitive patients untreated. Under price-cap regulation, market outcomes depend critically on the stringency of the cap. An intermediate price cap narrows the quality gap by inducing the private provider to improve quality while only weakly reducing the public provider’s quality, thereby expanding the market from partial to full coverage. A high price cap generates heterogeneous quality responses depending on the public provider’s unregulated quality level but does not improve market coverage. By contrast, a low price cap expands coverage but compresses profit margins, leading both providers to reduce quality and resulting in a low-price, low-quality equilibrium. These findings highlight the trade-offs between affordability, access, and quality in healthcare price regulation and suggest that intermediate price caps may achieve a better balance between these objectives. Full article