Search Results (1,635)

In this work we present an integrated market, experimental, technical and economic approach to citrus waste valorization based on the extraction of essential oils from orange, lemon, lime and tangerine peels using supercritical CO₂, comprising nine varieties of citrus fruits with high production in Brazil and varying harvesting seasons. This approach has been applied to such a variety of citrus waste as a novelty, allowing the observation of specific results for each kind of citrus fruit and a robust evaluation of the proposed process solution, which includes pre-treatment and extraction and may feature energy recovery. The varieties of fruits and volumes of production were selected based on a market assessment for a proposed capacity of approximately 500 kg/d of citrus waste in the extraction facility. Samples of citrus fruit waste were pre-processed by drying at 60 °C for 24 h and submitted to extraction using CO₂ at 150 bar and 35 °C for 30 min. The provisional results of the extractions (0.33% to 4.00% wt. oil yield) were used to perform the simulation of the extraction facility and estimate the cost of manufacture of essential oil, which ranged from US$26.70 to US$323.86/kg of extract, depending on the variety of citrus fruit. As an illustrative result of the application of the present screening approach, the cost of the production of essential oils from terra, bahia, seleta and lima oranges, as well as lemons, limes and tangerines, was compatible with the market price, while the costs of manufacturing pera and valencia orange essential oils were higher than the average price for cosmetic-grade and food-grade orange oil. The use of energy recovery in the CO₂ cycle would further increase the environmental sustainability of this strategy, with 55% lower energy consumption. Full article

Grain price stability is fundamental to global food security, yet the extent to which major grain futures markets contribute to international price discovery and transmit price signals across borders remains insufficiently understood. This study constructs a measurement–mechanism–pathway framework to analyze the international price transmission dynamics of four grain futures: wheat, rice, corn, and soybean. We apply the Diebold–Yilmaz spillover index, OLS regressions with mediation analysis, and fsQCA. All four crops display predominantly negative net spillover indices, with rice exhibiting the strongest transmission and soybean the weakest. At the domestic level, yield is associated with stronger price transmission through an inventory channel, while the effects of price regulation are asymmetric. Notably, soybean producer subsidies are unexpectedly associated with stronger rather than weaker international price transmission. At the international level, crude oil prices are associated with weaker grain price transmission primarily through the freight cost channel, and speculative capital is associated with reduced transmission capacity through tail-price episodes. Configuration analysis reveals two shared improvement routes, namely capacity building with policy support and risk containment via volatility suppression, together with four commodity-specific pathways. These findings provide systematic evidence and policy insights for mitigating grain price contagion and strengthening food system resilience. Full article

Serving large language models (LLMs) on cloud spot and preemptible instances reduces costs by 60 to 90 percent compared to on-demand pricing, but unpredictable instance preemptions cause request failures, KV-cache state loss, and degraded user experience. We present MorphCloud-LLM, an elastic LLM serving system designed to achieve the reliability properties of on-demand serving at spot-instance pricing. MorphCloud-LLM integrates three synergistic components: (1) an asynchronous incremental KV-cache checkpointing engine that streams only delta state to disaggregated persistent storage with less than 3% throughput overhead, enabling sub-second KV-cache delta streaming and reconstruction for KV-cache sizes up to 32 GB on replacement instances (total end-to-end migration latency: 1390 ms); (2) a gradient-boosted preemption prediction model trained on spot market telemetry that achieves 89% recall at a 30-s prediction horizon, providing sufficient lead time for proactive migration before forced eviction; and (3) a speculative decoding continuity engine that offloads draft model token generation to on-demand fallback nodes during migration windows, bounding the user-visible interruption to a sub-second buffering pause. MorphCloud-LLM is deployed and evaluated on AWS and GCP using LLaMA-70B and Mixtral-8x7B across 521 trace-injected preemption events, achieving up to 76% cost reduction under active-serving accounting (69.8% for LLaMA-70B; 67% including warm standby fallback capacity) with only 2.1% p99 latency overhead and zero dropped requests. Extensive ablation studies confirm the contribution of each component to overall system resilience. Note that preemption events are reproduced via a trace-driven simulation framework built on empirical AWS and GCP spot interruption traces rather than fully uncontrolled live production preemptions. Production generalizability under uncontrolled preemption—including simultaneous multi-node failures, network congestion, storage contention, and replacement-instance scarcity remains subject to future validation in sustained live deployments. Full article

Community forestry is widely promoted as a mechanism for improving rural livelihoods while strengthening sustainable forest management. However, empirical evidence on the income effects of community forestry remains limited in fragile and conflict-affected settings. Therefore, this study examines the relationship between Community Forestry Associations (CFAs) and household income from the chilgoza pine (Pinus gerardiana) value chain in Ahmad Aba District, Paktia Province, Afghanistan. Using survey data from 365 forest-dependent households, the study applied descriptive statistics, reliability assessment, exploratory principal component analysis, Chi-square tests, Spearman correlations, and OLS regression with HC3 heteroscedasticity-robust standard errors. CFA-enabled market access and CFA-supported training were positively and statistically significantly associated with annual pine-nut income, whereas meeting participation and perceived fair pricing were not statistically significant. Households reporting CFA-enabled market access had approximately 88% higher expected income, while households receiving training had approximately 50% higher expected income, controlling for demographic characteristics. The findings demonstrate that community forestry institutions in fragile and market-constrained settings are more likely to generate measurable livelihood benefits when participatory governance is combined with direct market and capacity-building services. Full article

We study a class of evolutionary variational inequalities in a Hilbert space that models the long-run balanced-growth equilibrium of a competitive economy with transaction costs, time-dependent production and infrastructure constraints, and exogenous price dynamics. The paper makes four contributions. First, we introduce a parametrized monotonicity functional ${\mu }_{\alpha }(t;F;u,v;p)$ and prove an exact equivalence theorem: the inequality ${\mu }_{\alpha }\ge \beta {∥u-v∥}^2$ holds if and only if the operator F is strongly monotone with the explicitly computed constant $m=\beta -\alpha (1+{∥p∥}_{\infty })$. This turns the growth parameter $\alpha$ and the price level into explicit terms of a single admissibility threshold and, for $\beta <\alpha (1+{∥p∥}_{\infty })$, produces a scale of conditions that covers operators which are not monotone, i.e., economies with a bounded degree of increasing returns. Second, we prove well-posedness: for every admissible initial state there is exactly one Lipschitz equilibrium trajectory $u^{*}(·)$, obtained through Moreau’s catching-up algorithm for the associated perturbed sweeping process, together with the explicit velocity bound $∥{\dot{u}}^{*}∥\le L_K+2C_F$. Third, we derive one comparison estimate from which global exponential stability, the convergence rate $∥u\left(t\right)-u^{*}\left(t\right)∥\le r e^{-mt}+L_p{m}^{-1}\sup ∥\Delta p∥+\epsilon m^{-1}$, and robustness with respect to perturbations of prices and of the operator all follow; we also show that, when the constraint sets stabilize, the trajectory converges to the stationary equilibrium of the limit problem. Fourth, we prove that strong monotonicity implies the c-covering property with $c=m$, so that the shock-absorbing capacity of the economy is governed by the same constant as the speed of convergence. Two examples—a two-resource system and an n-market network with nonlinear transaction costs—are worked out with a complete verification of every hypothesis and with explicit numerical constants. Full article

Urban passenger hubs, such as airports and railway stations, generate concentrated last-mile demand from arriving passengers to spatially dispersed urban destinations. Fluctuating passenger arrivals and changing vehicle availability can create a mismatch between accepted demand and available service capacity. This paper aims to coordinate zone-level pricing and vehicle dispatching, so that fare-responsive accepted demand can be better aligned with available vehicle resources, while balancing operator financial performance and service reliability. Under the zonal pricing scheme, the transit operator determines a quoted zone-level fare for each service zone at every decision epoch. Newly arriving service requests accept the service when the quoted fare does not exceed their maximum acceptable per-passenger fare, after which the fare is committed. A rolling-horizon optimization model jointly determines zone-level fares and dispatching plans as request states and vehicle states evolve over time. The fare discretization property reduces the continuous pricing decision to a finite candidate zone-level fare selection problem, and a customized Rolling-Horizon Adaptive Large Neighborhood Search (RH-ALNS) algorithm is developed to solve the resulting problem efficiently. Case studies based on Nanjingnan Railway Station in Nanjing, China, demonstrate the operational value of coordinating pricing and dispatching decisions. In the baseline case, the proposed method achieves a passenger service rate of 76.75%, an accepted-passenger fulfillment rate of 96.07%, and an operating surplus of 1.145 CNY per passenger-kilometer. Holding the RH-ALNS dispatching method fixed, dynamic zonal pricing increases the objective value by 4.39%, the operating surplus per passenger-kilometer by 5.46%, and accepted-passenger fulfillment by 3.63 percentage points relative to fixed zonal fares. The findings indicate that coordinating dynamic zonal pricing with vehicle dispatching can better align accepted demand with available vehicle resources and provide practical guidance for designing reliable, resource-efficient, and financially balanced hub-based demand-responsive last-mile transit services. Full article

The building sector faces an urgent challenge in balancing carbon neutrality goals with natural resource conservation. This study constructs a three-dimensional dynamic coupling model integrating Life Cycle Assessment, carbon footprint, and emergy analysis to evaluate the sustainability of a hydrogen–solar–geothermal hybrid energy system for an ecological office building in China’s hot summer and cold winter climate zone over a twenty-year horizon. The model incorporates dynamic factors including grid decarbonization, equipment efficiency degradation, and replacement cycles to overcome the systematic bias inherent in static LCA. Results reveal a significant trade-off: the hybrid system achieves a 29.8% reduction in global warming potential with a seven-year carbon payback period, yet non-renewable resource consumption doubles and resource scarcity damage increases by 173%. The carbon payback trajectory exhibits non-monotonic fluctuation, with electrolyzer replacement in year ten generating 360 tonnes of additional emissions that nearly reset the cumulative net value to zero. Multi-objective optimization identifies photovoltaic capacity as the system baseline (170–210 kW) and electrolyzer capacity as the primary regulating variable (35–62 kW), with the TOPSIS-recommended compromise solution of 200 kW photovoltaic, 50 kW electrolyzer, 30 kW fuel cell, and 32 m³ hydrogen storage achieving annual carbon emissions of 280 tonnes and a 33.3% reduction. Carbon pricing exhibits a nonlinear leverage effect with an incentive threshold of 200 RMB per tonne, substantially above China’s current 60–80 RMB per tonne level. This study concludes that while hydrogen–solar–geothermal hybrid systems offer substantial climate benefits, their comprehensive sustainability depends on proactive management of material scarcity costs, precise planning of equipment replacement cycles, and coordinated multi-level policy instruments. The findings provide methodological foundations for transitioning building carbon neutrality assessment from static LCA to dynamic coupling frameworks and from single carbon metrics to integrated carbon-resource-cost evaluations. All quantitative results presented herein are derived from this specific case study under the stated assumptions and parameter values; generalization to other building types or climate zones requires recalibration. Full article

This study investigates the effects of financial market instability, national security, and foreign direct investment (FDI) on economic growth in Sub-Saharan Africa from 1991 to 2023, while examining the moderating role of institutional quality. The study employs a panel autoregressive distributed lag (ARDL) model as the baseline estimator, complemented by fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) for robustness analysis. The results reveal that financial market instability constrains economic growth primarily through sovereign bond market instability and vulnerability to earnings manipulation, whereas stock price volatility exerts no significant long run effect. Improved peace conditions and domestic military expenditure promote growth, while terrorism undermines economic performance. Government effectiveness and control of corruption enhance growth, whereas foreign direct investment exerts a negative long-run effect in the absence of supportive domestic conditions. The interaction results show that institutional quality significantly conditions the effects of financial market conditions, national security, and foreign investment on growth. Stronger institutions reduce the adverse consequences of financial and security-related disturbances and improve the growth-enhancing capacity of foreign capital. The FMOLS and DOLS estimates largely confirm the baseline results. The study concludes that institutional quality constitutes a critical transmission mechanism through which financial stability, national security, and foreign investment influence long run economic growth in Sub-Saharan Africa. Full article

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is a settlement-aware learning objective that couples learned uncertainty, resource-time credible-capacity certification, network-constrained multi-stage dispatch, and counterfactual marginal-contribution allocation while retaining an exact revenue-adequacy identity. The 33-node Zhangjiakou-type regional case uses 15 min intervals and comprises five resource classes: independent storage, data-center flexibility, industrial adjustable load, commercial demand response, and electric-vehicle aggregation. Relative to a fixed-ratio reserve rule, the proposed method lowers the regional balancing cost from 950 to 618 thousand USD (34.9%), achieves 97.8% renewable accommodation, limits the shortage probability to 0.7%, and attains a settlement-fairness index of 0.92. The framework solves a 500-asset instance in 118 s. External validation uses 4027 half-hour observations from the 2025 Elexon/BMRS market, including measured wind and solar output, day-ahead forecasts, load, imbalance prices, and procured-reserve prices. On the 1487-interval December test set, the proposed model reduces the replay cost from 2953.3 to 2598.2 thousand GBP (12.0%), decreases the shortage-interval frequency from 4.64% to 1.28%, and reaches 99.74% renewable accommodation. Comparisons with forecast-then-optimize, Wasserstein distributionally robust optimization, off-policy reinforcement learning, and graph-based behavioral cloning establish that the improvement comes from jointly learning which uncertainty matters for dispatch and which flexible capacity is deliverable. Full article

This research presents a straightforward and detailed method for calculating the cost of recycling solar panels and the associated economic benefits. The contribution of this research is to estimate the impact of the recycling process on the cost of energy and the payback period. The Full Recovery End-of-Life Photovoltaic (FRELP) method was utilized to assess the PV recycling process. Calculations were made for every 1000 kg of solar panels and converted to calculate the cost and revenue per square meter of panels. Calculations showed that the cost of recycling in Saudi Arabia reached 9.46 $/m² based on the geographical environment, fuel prices, and the various materials used in recycling processes, while the revenue was approximately 24.6 $/m² according to the current prices of materials resulting from the recycling process, especially the price of silver. The study results were applied to a 400 MW solar power plant to determine the feasibility of recycling the energy price and the payback period. The solar power plant was designed using variable-sized solar panels with capacities of 255, 330, and 580 watts. The recycling revenue for the plant with the smaller panels was the highest, being $2.6 M as an annual rate. Full article

Carbon pricing can change manufacturers’ material choices, while the costs of managing the residual waste remain geographically uneven. We formulate a manufacturer–regulator bilevel model for a household plastic-waste closed-loop supply chain with quantity-dependent recycled-bale prices, activity-specific carbon accounts, physical interregional waste routing, and a proportional regional cost-burden standard. The lower level is explicitly a single coordinating-regulator linear program rather than a game among independent regions. Its primal constraints, dual constraints, and strong-duality equality are embedded in the manufacturer problem; binary-continuous products are exactly linearized using the manufacturer’s SOS1 price-grid variables. Thus, every reported policy point is obtained from the same 12-region mixed-integer equilibrium formulation. Across 36 central policy combinations, HiGHS reports a zero mixed-integer programming gap, and the largest feasibility and optimality residual is $5.24\times {10}^{-8}$. Raising the carbon tax from 0 to 10 USD/tCO₂ increases the real recycling rate (RRR) from 15.33% to the bale-capacity limit of 29.85% and reduces physical emissions by 11.64%. Tightening the allowed regional burden deviation from 25% to 5% reduces the standard deviation of normalized residual-waste cost burden by 77.89% and interregional residual-waste transfers by 77.05%, but does not change the RRR. This zero-recycling effect overturns the earlier assumption-driven result: a pure routing-based cost-balancing rule cannot mechanically stimulate the manufacturer’s recycled-input demand. A global analysis of 300 parameter sets and five independent regional samples re-solves 1800 equilibrium models; all have a zero solver gap and pass the residual audit. Carbon-induced RRR increases have a median of 17.03 percentage points, while strict-versus-loose burden-threshold changes in RRR are zero in every set. The results distinguish carbon efficiency, regional cost incidence, and fiscal incidence and show that policy complementarity must be demonstrated through endogenous decision links rather than imposed response functions. Full article

The rapid expansion of the digital currency market and the growing role of stablecoins as potential intermediaries have brought its interconnectedness with traditional financial markets to the forefront of global financial research. Using daily data from 4 January 2021 to 30 September 2025, this study constructs a variable system with the price indices of USDT and USDC as core digital currency proxies, alongside traditional financial asset indices for stocks, bonds, and gold derived via the entropy weight method. We employ a comprehensive set of econometric techniques, including static correlation analysis, vector autoregression (VAR), impulse response functions, and extreme-event shock tests, to systematically investigate the interdependence structure, risk spillover dynamics, time-varying co-movements, and structural changes between the two markets during extreme risk episodes. The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets—particularly the equity market—exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies. Full article

The rapid growth of distributed renewable generation and electric vehicles has increased the demand for flexible peak-shaving resources. Battery swapping stations (BSSs), which centrally manage standardized batteries under the battery-as-a-service model, can provide station-to-grid (S2G) services when aggregated by virtual power plants (VPPs). However, S2G adoption is influenced by contract design, market returns, subsidies, battery degradation, and heterogeneous consumer attitudes. This study develops a complex-network evolutionary diffusion model for VPP–BSS cooperation. The framework integrates a VPP profit-accounting module, a segmented Hotelling demand model, and an evolutionary game on a Newman–Watts small-world network. BSS strategies are updated through a partial asynchronous Fermi rule to reflect bounded rationality and investment inertia. Numerical simulations examine contract parameters, subsidy policies, consumer structures, exogenous variables, and network characteristics. The results show that S2G adoption follows an S-shaped trajectory but does not automatically reach full penetration. Successful diffusion requires a feasible combination of electricity prices, revenue sharing, settlement mechanisms, subsidies, consumer acceptance, and available battery capacity. The findings also reveal a trade-off between promoting BSS participation and maintaining VPP profitability, while robustness tests confirm the stability of the main conclusions. Full article

With the increasing penetration of wind and photovoltaic generation, microgrids are playing an increasingly important role in promoting renewable energy accommodation, enhancing operational flexibility, and enabling low-carbon energy management. However, the strong uncertainty of renewable generation and load demand, together with the coupling effects of electricity, hydrogen, and carbon markets, poses significant challenges to the optimal planning and operation of microgrid energy storage systems. To address these issues, this paper proposes a bi-level optimal sizing framework for an electric–hydrogen hybrid energy storage system (EHH-ESS) in a microgrid under multi-market coupling. First, typical wind–solar–load scenarios are generated using a Wasserstein generative adversarial network with gradient penalty (WGAN-GP), so as to capture the stochastic characteristics and temporal correlations of renewable generation and load demand. Then, a multi-market coupling index (MCI), integrating electricity price, hydrogen price, and carbon price signals, is constructed to characterize time-varying economic and low-carbon operating incentives and to guide coordinated dispatch decisions. On this basis, a bi-level multi-objective optimization model is established. The upper level determines the optimal capacities of battery storage, electrolyzers, fuel cells, and hydrogen tanks, while the lower level performs hourly coordinated operation of the microgrid under multi-market conditions. The model considers annual equivalent total cost, renewable energy curtailment rate, and carbon emissions as objective functions, and is solved using the NSGA-III algorithm. Compared with the no-storage benchmark, the proposed scheme improves the annual operating economics and renewable-energy accommodation under the studied market conditions. The proposed method significantly reduces annual operating cost and improves renewable energy accommodation. However, under the current carbon price and grid emission factor settings, the optimal economic solution increases carbon emissions relative to the baseline, indicating a trade-off between economic arbitrage and low-carbon operation. Full article

Data centers are large and rapidly growing electricity consumers, and cooling systems account for a substantial share of their energy demand. A key contribution of this study is a climate-sensitive, hourly techno-economic comparison of three data-center cooling configurations under consistent operating assumptions: a conventional water-cooled centrifugal chiller baseline, a magnetic bearing chiller (MBC) system, and an MBC system integrated with aquifer thermal energy storage (ATES). The comparison is performed for Phoenix, Arizona, and Fairbanks, Alaska, which represent substantially different cooling climates in the U.S. Hourly simulations use identical information technology (IT) load profiles, identical aggregate installed chiller capacity represented by two 4058 kW chiller units, common water-side economizer controls, and site-specific weather and electricity tariffs. Results show that the MBC system reduces annual cooling-system electricity consumption from 1169.4 to 957.4 MWh in Phoenix (18.1%) and from 361.6 to 319.4 MWh in Fairbanks (11.7%). Peak cooling-system electrical demand decreases by 119.4 kW in Phoenix and 71.6 kW in Fairbanks. Relative to the centrifugal baseline, the MBC case gives a 5.8-year simple payback in Phoenix but is not economically attractive in Fairbanks under the assumed tariff. The MBC-only case gives the lowest annual cooling electricity use in both climates. The MBC + ATES case is treated only as a screening-level, discharge-assisted cold-storage scenario rather than a full techno-economic assessment of seasonal ATES, and no site-specific hydrogeological feasibility assessment is performed. Under the assumed O&M cost structure, MBC + ATES gives a higher discounted value of savings than MBC-only, but this economic result is not caused by additional cooling-electricity savings relative to MBC-only. The MBC + ATES case also has a longer payback period because of its higher capital cost. These results show that the value of advanced cooling configurations depends on climate, free-cooling availability, electricity pricing, storage assumptions, and economic assumptions within the modeling framework considered in this study. Full article