Search Results (3,193)

This study examines consumer preferences for goat cheese by jointly analysing sensory evaluations and willingness to pay more (WTPM) for artisanal and industrial products. Using a comparative experimental design conducted in Sicily (Italy) with 105 regular cheese consumers recruited through convenience sampling in a controlled laboratory setting, the study investigates how differences in farming systems and processing methods—from extensive pasture-based systems to intensive industrial production—affect sensory perception and WTPM. Two ordered logit models with increasing price levels were estimated to identify the determinants of WTPM, one for industrial goat cheese and one for artisanal goat cheese, including sensory evaluations and socio-demographic variables as explanatory factors. A paired t-test was also performed to assess whether sensory evaluations differed significantly between the two cheeses. The results indicate that sensory quality was the main driver of WTPM for artisanal goat cheese, with aroma emerging as the strongest sensory predictor in the exploratory ordered logit model (β = 3.535; p < 0.01). However, the sensory comparison revealed only modest differences between the two cheeses, with taste showing the clearest differentiation in favour of artisanal cheese. Among extrinsic attributes, only PDO certification positively affected WTPM, whereas the negative association observed for biodegradable packaging should be interpreted cautiously as an exploratory finding requiring further investigation. Origin-based signals such as PDO therefore appeared to enhance value alongside, rather than replace, sensory differentiation, acting as complementary rather than substitutive cues. Overall, the findings contribute to the literature on food systems and consumer behaviour by linking sensory perception with production systems and showing how artisanal dairy products can generate consumer value through the combination of sensory characteristics and extrinsic attributes related to origin and production identity. Full article

This paper analyzes the viability of Indian equity markets in response to global energy supply shocks. This study attempts to correct the missing-variable bias in earlier literature by using the USD-to-INR exchange rate as an exogenous explanatory variable. This will help determine the intricate synthetic relationship between Brent Crude Oil Returns and the carbon-efficient S&P BSE GREENEX. Vector Autoregressive with exogenous variables (VARX) models are employed to analyze the effects of structural shocks to Brent Crude Oil prices on the S&P BSE GREENEX. The empirical results found that global oil price shocks might immediately affect green equity values in India. Even without foreign currency changes, the Indian Green Exchange Index (GREENEX) maintains its long-term values, showing structural resilience. Institutional investors and Indian financial authorities, such as SEBI and the Reserve Bank of India, gain better risk-management insights amid international energy crises from this information. It also shows that carbon-efficient standards can hedge inflation induced by foreign import supply chain interruptions. Full article

Representative-day methods reduce the computational burden of energy storage dispatch and planning. Conventional clustering criteria typically preserve operating scenarios relevant to dispatch economics, whereas lifespan evaluation requires days that better represent battery degradation. Days selected only from load, photovoltaic generation, and electricity-price profiles may therefore miss cycling structures that determine degradation. We propose a representative-day selection method based on prior battery-behavior features for lifespan estimation in photovoltaic-storage systems. The method first clusters daily scenario profiles to maintain scenario coverage. Within each cluster, it constructs a capacity-aware prior state-of-charge sequence from net-load variations and applies rainflow counting to obtain depth-of-discharge bins. The method then standardizes and covariance-whitens the first- and second-order moments of this feature vector. A global moment-matching problem selects one real representative day from each cluster. Case studies using multiple annual user-side datasets show that the proposed method reduces lifespan estimation errors compared with the baseline. The method also retains a clear computational advantage over full-year daily dispatch, supporting repeated calculations for capacity searches and economic assessment. Full article

This study investigates the empirical links between climatic/socio-economic factors and Consumer Price Food Indices (CPFIs) across 47 advanced and emerging economies from 2001 to 2022. Utilizing a balanced panel dataset compiled from the World Bank’s World Development Indicators, FAO databases, and UNDP Human Development Reports, the analysis applies fixed-effects and random-effects panel data models to evaluate the drivers of food price dynamics. The empirical results reveal that socio-economic variables, specifically the Human Development Index (HDI), food price inflation, and agricultural productivity, are consistently associated with food indices, underscoring the critical role of structural development and nominal inflationary pressures. Climatic factors, particularly temperature anomalies, also exert a significant impact: a one-degree Celsius increase in temperature anomalies is associated with a 0.82-unit rise in the food price index level, whereas expansions in forest area and agricultural land are linked to price reductions. These findings indicate that food price dynamics are shaped by the intricate interplay of climate variability and socio-economic conditions. Consequently, policy recommendations emphasize targeted investments in socio-economic development, climate adaptation measures (including support for climate-resilient agriculture), and sustainable land management (forest conservation and optimized land use) to enhance food system resilience and support long-term food security. Full article

The global energy sector is undergoing rapid and, in many respects, irreversible transformation driven by the convergence of digital disruption, sustainability mandates, and shifting investor expectations. Technologies such as artificial intelligence (AI), blockchain, and digital twin systems are fundamentally reshaping energy operations and strategic decision-making, while ESG governance quality and renewable energy adoption have emerged as two of the most consequential determinants of corporate financial competitiveness and equity valuation. Despite growing practitioner and regulatory interest in these dynamics, limited empirical evidence exists on how ESG governance, renewable adoption, and digital disruption jointly influence financial performance and environmental outcomes across multiple sectors simultaneously. This study addresses that gap using panel data from 26 large-cap US-listed firms across five sectors over 2015–2022 (N = 208 firm-year observations for Revenue/Market Cap/ROA models; N = 91 for the CO₂ model). A multi-method econometric framework is employed, comprising Fixed Effects and Random Effects panel regression with Hausman specification testing, Difference in Differences quasi-experimental analysis, and sequential OLS path analysis with HC3 robust standard errors. Three of four hypotheses are supported. ESG governance quality generates a significant market capitalisation premium of approximately 10–14% per unit Bloomberg ESG Score improvement, after controlling for firm size and R&D intensity; no significant revenue channel effect is found once firm size is properly accounted for. Renewable energy adoption shows a marginal association with market capitalisation at the 10% significance level (FE β = 0.019, p = 0.086; RE β = 0.016, p = 0.077), suggesting capital markets may price clean energy adoption as a forward-looking signal. ESG governance quality drives within-firm CO₂ emission reduction substantially more powerfully than renewable energy quantity alone, with the Fixed Effects estimator identifying a governance-led eco-efficiency mechanism. Firm profitability functions as a cross-model financial capacity moderator, enabling simultaneous ESG investment and environmental improvement. The findings carry direct implications for corporate managers, institutional investors, and policymakers aligned with SDG 7, SDG 9, and SDG 13. Full article

To alleviate renewable energy curtailment and the high operating costs arising from the temporal and spatial mismatch of distributed generation, this paper develops a cross-park dispatch optimization approach for power systems under the joint participation of carbon trading and green certificate trading (GCT). The proposed approach aims to improve system flexibility and economic performance in coordinated multi-park operation. Specifically, adjustable resources in different parks are dispatched in a coordinated manner, and the total comprehensive operating cost is taken as the optimization objective. In addition, the Alternating Direction Method of Multipliers (ADMM) is adopted to determine inter-park electricity trading prices and exchanged power in a distributed framework. Furthermore, an asymmetric bargaining model is introduced to distribute the cooperative benefits, ensuring a balance between fairness and incentive compatibility. Simulation results demonstrate that inter-park electricity interaction reduces generation costs by 5.29%. The integration of carbon and green certificate trading further reduces costs by 7.4%. After asymmetric bargaining-based benefit allocation, the operating costs of parks with higher contributions decrease by up to 10.34%. The results conclude that the proposed strategy effectively leverages the complementary advantages of multi-park resources and optimizes the synergy between carbon markets, green certificate markets, and physical dispatch. Full article

Background: Pirtobrutinib has recently emerged as a promising first-line treatment option for chronic lymphocytic leukemia (CLL). Unlike currently established regimens, which are generally based on doublet combinations, pirtobrutinib can be administered as monotherapy. No head-to-head trials comparing pirtobrutinib with contemporary first-line combinations are currently available; hence, indirect comparative evidence may help define its potential role. Methods: A non-anchored indirect comparison based on reconstructed individual patient data (IPD) was conducted using published Kaplan–Meier curves from randomized controlled trials evaluating first-line treatments for CLL. Progression-free survival (PFS) was the endpoint of interest. Reconstructed IPD were generated using WebPlotDigitizer and the IPDfromKM algorithm. Then, Kaplan–Meier curves were plotted based on these patients, and the values of the restricted mean survival time (RMST) at 36 months were determined. Regarding PFS, pirtobrutinib monotherapy was compared indirectly with acalabrutinib plus obinutuzumab, venetoclax plus obinutuzumab, and venetoclax plus ibrutinib. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using univariate Cox models. The non-inferiority of pirtobrutinib monotherapy versus doublet regimens was assessed according to a non-inferiority margin set at HR = 1.15. Finally, the value-based prices of the new treatments were estimated based on a willingness-to-pay threshold of euro 30,000 per disease-free year gained and compared with the corresponding real prices in the Italian market. Results: The analysis included four randomized trials. Compared with pirtobrutinib monotherapy, HRs for PFS were 0.5544 (95%CI, 0.2696–1.1397) versus venetoclax plus obinutuzumab, 0.4583 (95%CI, 0.2066–1.0200) versus venetoclax plus ibrutinib, and 1.4453 (95%CI, 0.6684–3.1240) versus acalabrutinib plus obinutuzumab. Pirtobrutinib met the non-inferiority criterion compared with venetoclax plus obinutuzumab and venetoclax plus ibrutinib, but not with acalabrutinib plus obinutuzumab; however, these results were negatively affected by the small number of events in the pirtobrutinib arm, which generated wide CIs. In the preliminary pharmacoeconomic analysis, venetoclax plus obinutuzumab showed the most favorable profile in the comparison of value-based price vs. real price. Conclusions: This exploratory non-anchored analysis suggests that pirtobrutinib monotherapy may provide PFS outcomes broadly comparable to current first-line combination regimens for CLL. Given the methodological limitations inherent to indirect comparisons, prospective head-to-head studies are needed to clarify the optimal positioning of pirtobrutinib in treatment-naïve CLL. Full article

Digital markets increasingly coordinate prices, matches, orders, and allocations through automated systems whose decision–execution loops can close faster than humans can intervene. This article develops a microfounded framework in which latency is a temporal feasibility constraint that complements adjustment costs, information delay, costly information acquisition, queueing, and technological execution costs. The model distinguishes common latency, human intervention latency, and relative latency. Common latency is produced by platform and participant investment and affects welfare through the freshness of the state on which decisions are executed. Human intervention is represented by a smooth, task- and organization-specific probability q(L,z,s), derived from a distribution of completion times and modified by interface and organizational support. Human, hybrid, and algorithmic decision technologies differ in speed, accuracy, cost, and systematic misspecification risk. Relative speed is modeled as a strategic priority contest in which each intermediary’s best response depends on rivals’ investments, while platform rules determine the sensitivity and value of being first. The framework derives conditions for human-algorithm substitution, welfare-improving common-speed investment, socially excessive strategic speed investment, and welfare-enhancing batching or latency floors. It separates temporal from structural market distortions, integrates decision-technology quality and state freshness in a total-welfare function, and develops an incidence model that traces gains across heterogeneous users, intermediaries, and infrastructure owners. Robustness results cover diffusion, mean-reverting, jump, stochastic-volatility, and regime-switching state processes. An illustrative dynamic simulation, explicit scope conditions, and an operational empirical agenda show how the theory can be tested without claiming empirical calibration. The central contribution is a non-equivalence result: when latency enters the probability of successful intervention, shortening the decision window can change the technology and locus of marginal choice even when information, objectives, adjustment costs, and the substantive decision rule are held fixed. Full article

Telemedicine in emergency and remote care relays medical images from ambulances and rural clinics to a hospital edge-computing server over a congested wireless uplink. Existing work prices such transmissions per bit or per quality-of-experience; neither metric captures the clinical value of a medical transmission. Diagnostic utility vanishes below a modality-specific acceptability floor rather than degrading gracefully, the deadline is determined by triage acuity rather than by the network, and a missed finding is far costlier than a false alarm. A per-bit clearing price therefore disadvantages the node that has expended local compute to produce a compact, diagnostically sufficient stream. We propose SemAuc, a triage-aware truthful mechanism for medical-image admission over a rate-splitting uplink, in which the shared semantic knowledge base rides the common stream, and case-specific residuals ride private streams. SemAuc filters tiers below the diagnostic floor and beyond the clinical deadline, reserves a regulated-price lane for life-threatening cases, and allocates remaining capacity through a single-parameter contestable auction whose bid-independent pre-selection step satisfies the conditions of Myerson’s lemma. The contestable lane is dominant-strategy truthful, individually rational, near-linear in the number of nodes, and achieves a constant-factor density-greedy welfare guarantee; the clinical lanes follow from triage policy without disturbing these properties. Diagnostic value is grounded by an offline kernel fitted on BraTS and CheXpert. On a Rayleigh-faded uplink at two hundred contending nodes, SemAuc preserves the high-acuity diagnostic service-level objective where bit-centric benchmarks fail, and tracks the offline optimum. Full article

Global value chains (GVCs) generate rising volumes of electronic waste (e-waste), of which only 22.3% is formally collected and recycled, and operationalizing circular economy principles within GVCs requires reverse logistics networks that existing optimization models only partially capture. This paper develops a multi-echelon mixed-integer linear programming (MILP) model for integrated forward–reverse e-waste network design that jointly optimizes facility locations, material flows, hybrid distribution–collection co-location, and the collection price offered to consumers. Returns follow uniformly distributed consumer reservation prices, and the resulting price-dependent return mechanism is linearized exactly through a discrete price menu, yielding a fully linear formulation without big-M constants; recyclable fractions re-enter manufacturing as secondary inputs, closing the material loop. The model is evaluated on thirty randomly generated instances of three sizes, with parameter ranges anchored to the literature, solved with the open-source HiGHS solver; the largest instances solve to within 0.1% of optimality in under two minutes. Endogenizing the collection incentive raises total profit by 4.5 to 20.1% over an exogenous-return baseline and lifts material recovery from roughly 25% to 36 to 49%, while co-location adds modest, scale-dependent value and the two mechanisms show a directionally consistent but not statistically significant tendency toward substitutability (Wilcoxon signed-rank test, p > 0.05 across all size classes). These figures characterize the calibrated synthetic instances studied here and should not be read as generalizable empirical estimates. Sensitivity analyses identify consumer responsiveness to incentives, rather than waste stream quality, as the binding determinant of achievable recovery. Full article

This paper investigates platform promotional subsidy strategies under price regulation. We construct a dynamic game model of incomplete information. The model involves a regulator, a monopoly platform, and heterogeneous consumers. The regulator sets a subsidy cap. The platform chooses the price and subsidy after observing its cost type. Consumers decide whether to purchase based on their valuation and the perceived subsidy value. We solve the game by backward induction. We characterize the perfect Bayesian equilibria. We derive closed-form solutions for the critical subsidy threshold, separating/self-selection equilibrium conditions, and optimal regulatory policies. The analysis yields three main findings. First, the sign of the net social benefit of the promotional subsidy determines the optimal policy. A positive sign calls for a high subsidy cap. This induces full market coverage. A negative sign calls for a ban on subsidies. Second, when the subsidy cap lies between the two platform types’ critical thresholds, a natural separating/self-selection equilibrium emerges. It operates at no cost. The feasible interval widens linearly in the cost gap. Third, diseconomies of scale have a stronger marginal effect on the critical subsidy than marginal cost. There exists an endogenously determined critical proportion of high-value consumers, which depends on the model parameters, such as $v_H,v_L,c_I,{\eta }_I$. This proportion divides the policy space into two regimes. These results provide a basis for low-cost information screening and differentiated regulation. Full article

The service range of a district cooling system is strongly affected by the energy consumption of chilled-water distribution in the supply-side pipe network. With increasing cooling distance, pump operating energy consumption, pump-induced temperature-rise loss, and pipeline cooling loss all increase, which may weaken the supply-side distribution-energy advantage of centralized cooling. This study proposes a method for determining the maximum cooling distance of a district cooling system based on a supply-side energy-equivalent boundary. The boundary is defined as the distance at which the total supply-side energy consumption of the district cooling system equals that of the decentralized cooling system under the same load conditions. Unlike cost-based evaluation methods, the proposed criterion does not rely on economic parameters such as electricity price, material cost, construction cost, or maintenance cost, but determines the boundary based on supply-side energy consumption. A district cooling system in Guangzhou is used as the case study. Under the baseline condition, the maximum cooling distance is 1564 m. Increasing the supply–return water temperature difference from 7 °C to 12 °C increases the maximum cooling distance by 294.38%. Increasing the decentralized system main-pipe length from 250 m to 600 m increases it by approximately 175%. When the number of users increases from 8 to 12, the distance increases by 23.1%. A front-concentrated layout of high-load users increases the distance by 36.93% compared with a uniform layout. The proposed method provides an energy-based planning reference for cooling-source siting, user connection range determination, and district cooling pipe-network scheme evaluation. Full article

Single-stage submersible pumps have widespread applications in industry. A special casing used to efficiently cool an electric motor with pumped liquid in such pumps leads to lower efficiency. This factor negatively affects the mass and dimension parameters of pump units. The efficiency can be essentially increased through impeller and casing optimization. When used in pumps, such parts with complex geometry can be manufactured in small bulk at a low price and in a short time using AM methods with different technologies and materials. Therefore, the objective of this research was to design a numerical optimization methodology to improve pump unit efficiency and to compare experimental characteristics during AM of impellers and vaned diffusers from non-metallic and metallic materials for the original and optimized flow passage. This article examined the impact of the vaned diffusers on the flow structure in the casing. The first optimization stage included studying the correlation between the input parameters and the objective function. The parameters that had the greatest impact on the hydraulic efficiency of the pump were revealed. The second optimization stage included studying the effect of the number of calculation points on the objective function using the LHS method. The global maximum of the pump's hydraulic efficiency was determined. The third optimization stage included studying direct methods for searching for the local maximum of the objective function. The best calculation point with the highest hydraulic efficiency for the pump was revealed. Recommendations regarding 3D printing for BJ and FDM technologies for AM of impellers and vaned diffusers were provided. The results of these studies revealed that velocity and flow swirl reduction in the casing increased the experimental efficiency of the pump unit by 5% at the nominal flow rate. The CFD characteristics are consistent with the experimental data. The experimental characteristics also revealed good correlation. Full article

The present research investigates the relationship between urban regeneration initiatives and residential real estate market dynamics by assessing market price appreciation associated with the factors most commonly considered in housing transactions. The study focuses on the Esquilino district in the city of Rome (Italy) with particular attention to the redevelopment of Piazza dei Cinquecento, the major public space located in front of Roma Termini railway station. The intervention aims to improve urban accessibility, reduce traffic congestion, and enhance public space quality through a new spatial configuration and the creation of a tree-lined area. The objective of the study is to verify whether, and to what extent, the ongoing regeneration project has influenced residential property values. To achieve this goal, an econometric analysis is implemented to quantify the contribution of different housing and locational attributes to residential asking prices and to identify the variables that significantly affect value formation within the local market. Given that the initiative is still in progress and approaching completion, the analysis adopts a diachronic perspective by comparing two distinct temporal stages: the ante project phase (second half of 2021) and the in itinere phase (first half of 2025). Building on the findings of a previous pre-intervention study, the research systematically examines changes in market behaviors over time, with the dual purpose of identifying variations in price determinants and analyzing the associations between the current urban transformations and the residential real estate market. The results indicate a substantial stability in the main determinants of residential property prices across the two periods, suggesting that the regeneration initiative has not yet been fully capitalized into market behaviors. However, variations in the contribution and functional relationships of some spatial variables highlight preliminary signs of market adjustment during the ongoing transformation process. The study highlights the importance of monitoring for assessing how urban regeneration processes are progressively incorporated into real estate market dynamics. The proposed framework provides a transferable tool for evaluating regeneration processes in different urban contexts, supporting evidence-based decision-making and the comparative assessment of urban transformation strategies. Full article

Addressing global climate change and China’s dual-carbon goals, advancing near-zero-carbon substations is imperative. However, existing studies often isolate carbon accounting, economic evaluation, and market mechanisms, particularly lacking analysis on the economic feasibility of multi-technology combinations. To fill this research gap, the primary objective of this paper is to establish a comprehensive systematic framework that integrates life-cycle emission accounting, life-cycle cost (LCC)–benefit evaluation, and carbon market dynamics. This framework is specifically designed to achieve two interrelated goals: (1) identification of the optimal multi-technology portfolio that balances high abatement rates with economic viability for a typical 110 kV substation; (2) determining the most economically feasible pathway toward life-cycle carbon neutrality under current carbon pricing. Based on life-cycle emission accounting, the operation stage is identified as the primary source. A technology library covering direct/indirect reductions and carbon sinks is built with LCC–benefit models. Four scenarios (S1–S4), following the “source control” to “smart management” logic, are designed to assess abatement rates, unit costs, and comprehensive performance. Introducing the carbon market mechanism, neutrality costs, and break-even points are evaluated. Results show S1 (clean air GIS + envelope optimization) achieves the best performance with a unit cost of 132.6 CNY/tCO₂e and a 36.36% reduction rate. As complexity increases, marginal abatement costs rise sharply while economic efficiency declines. Under current carbon prices, the “S1 reduction + allowance purchase” strategy is the most economical path toward life-cycle neutrality. This study provides quantitative support for technology selection and neutrality pathway planning. Full article