Search Results (419)

The cement industry accounts for approximately 7–8% of global CO₂ emissions, primarily due to energy-intensive clinker production and limestone calcination. With cement demand continuing to rise, particularly in emerging economies, decarbonization has become an urgent global challenge. The objective of this study is to systematically map and synthesize existing evidence on technological pathways, policy measures, and economic barriers to four core decarbonization strategies: clinker substitution, energy efficiency, alternative fuels, as well as carbon capture, utilization, and storage (CCUS) in the cement sector, with the goal of identifying practical strategies that can align industry practice with long-term climate goals. A scoping review methodology was adopted, drawing on peer-reviewed journal articles, technical reports, and policy documents to ensure a comprehensive perspective. The results demonstrate that each mitigation pathway is technically feasible but faces substantial real-world constraints. Clinker substitution delivers immediate reduction but is limited by SCM availability/quality, durability qualification, and conservative codes; LC₃ is promising where clay logistics allow. Energy-efficiency measures like waste-heat recovery and advanced controls reduce fuel use but face high capital expenditure, downtime, and diminishing returns in modern plants. Alternative fuels can reduce combustion-related emissions but face challenges of supply chains, technical integration challenges, quality, weak waste-management systems, and regulatory acceptance. CCUS, the most considerable long-term potential, addresses process CO₂ and enables deep reductions, but remains commercially unviable due to current economics, high costs, limited policy support, lack of large-scale deployment, and access to transport and storage. Cross-cutting economic challenges, regulatory gaps, skill shortages, and social resistance including NIMBYism further slow adoption, particularly in low-income regions. This study concludes that a single pathway is insufficient. An integrated portfolio supported by modernized standards, targeted policy incentives, expanded access to SCMs and waste fuels, scaled CCUS investment, and international collaboration is essential to bridge the gap between climate ambition and industrial implementation. Key recommendations include modernizing cement standards to support higher clinker replacement, providing incentives for energy-efficient upgrades, scaling CCUS through joint investment and carbon pricing and expanding access to biomass and waste-derived fuels. Full article

Seawater reverse osmosis (SWRO) desalination generates a concentrated brine byproduct rich in dissolved salts and minerals. This study presents an extensive economic and technical analysis of recovering all major ions from SWRO brine, which includes Na, Cl, Mg, Ca, SO₄, K, Br, B, Li, Rb, and Sr in comparison to conventional mining and chemical production of these commodities. Data from recent literature and case studies are compiled to quantify the composition of a typical SWRO brine and the potential yield of valuable products. A life-cycle cost framework is applied, incorporating capital expenditure (CAPEX), operational expenditure (OPEX), and total water cost (TWC) impacts. A representative simulation for a large 100,000 m³/day SWRO plant shows that integrated “brine mining” systems could recover on the order of 3.8 million tons of salts per year. At optimistic recovery efficiencies, the gross annual revenue from products (NaCl, Mg(OH)₂/MgO, CaCO₃, KCl, Br₂, Li₂CO₃, etc.) can reach a few hundred million USD. This revenue is comparable to or exceeds the added costs of recovery processes under favorable conditions, potentially offsetting desalination costs by USD 0.5/m³ or more. We compare these projections with the economics of obtaining the same materials through conventional mining and chemical processes worldwide. Major findings indicate that recovery of abundant low-value salts (especially NaCl) can supply bulk revenue to cover processing costs, while extraction of scarce high-value elements (Li, Rb, Sr, etc.) can provide significant additional profit if efficient separation is achieved. The energy requirements and unit costs for brine recovery are analyzed against those of terrestrial or conventional mining; in many cases, brine-derived production is competitive due to avoided raw material extraction and potential use of waste or renewable energy. CAPEX for adding mineral recovery to a desalination plant is significant but can be justified by revenue and by strategic benefits such as reduced brine disposal. Our analysis, drawing on global data and case studies (e.g., projects in Europe and the Middle East), suggests that metals and salts recovery from SWRO brine is technically feasible and, at sufficient scale, economically viable in many regions. We provide detailed comparisons of cost, yield, and market value for each target element, along with empirical models and formulas for profitability. The results offer a roadmap for integrating brine mining into desalination operations and highlight key factors such as commodity prices, scale economies, energy integration, and policy incentives that influence the competitiveness of brine recovery against traditional mining. Full article

BioHealing Optimization (BHO) is a bio-inspired metaheuristic that operationalizes the injury–recovery paradigm through an iterative loop of recombination, stochastic injury, and guided healing. The algorithm is further enhanced by adaptive mechanisms, including scar map, hot-dimension focusing, RAGE/hyper-RAGE bursts (Rapid Aggressive Global Exploration), and healing-rate modulation, enabling a dynamic balance between exploration and exploitation. Across 17 benchmark problems with 30 runs, each under a fixed budget of $1.5·{10}^5$ function evaluations, BHO achieves the lowest overall rank in both the “best-of-runs” (47) and the “mean-of-runs” (48), giving an overall rank sum of 95 and an average rank of 2.794. Representative first-place results include Frequency-Modulated Sound Waves, the Lennard–Jones potential, and Electricity Transmission Pricing. In contrast to prior healing-inspired optimizers such as Wound Healing Optimization (WHO) and Synergistic Fibroblast Optimization (SFO), BHO uniquely integrates (i) an explicit tri-phasic architecture (DE/best/1/bin recombination → Gaussian/Lévy injury → guided healing), (ii) per-dimension stateful adaptation (scar map, hot-dims), and (iii) stagnation-triggered bursts (RAGE/hyper-RAGE). These features provide a principled exploration–exploitation separation that is absent in WHO/SFO. Full article

This study hypothesizes that the impacts of landslides on housing prices are not uniform but instead vary depending on their spatial proximity to hazard zones, as well as on neighborhood, urban, and temporal characteristics of each city. To test this hypothesis, we analyze APT price responses to landslides across three South Korean cities with distinct urban characteristics: Seoul (capital city), Busan (metropolitan city), and Gunsan (medium-sized local city). Using 120 three-stage hierarchical linear regression (HLR) models, the analysis incorporates housing characteristics, neighborhood attributes, and urban–temporal factors to capture multilevel variations in price dynamics. The results reveal distinct spatial and temporal patterns. At the national level, immediate post-event changes are not uniformly negative: within 250 m of landslide zones, prices increase by 0.8%, while 500-m and 750-m groups rise by 0.5% and 1.9%, respectively, and only the 1000-m group declines by 0.9%. However, in the following year, the 250-m and 500-m groups experience notable declines before showing partial recovery in the second year. City-specific trajectories further underscore regional heterogeneity. In Seoul, medium- and long-term declines dominate, with post-event decreases of 1.9%, 4.2%, and 3.5% in the 500-m, 750-m, and 1000-m groups, respectively. Busan exhibits the sharpest and most persistent declines, with immediate decreases of 3.2% to 4.1% across distance bands, followed by sustained downturns in subsequent years. In contrast, Gunsan shows mixed but relatively faster recovery, as the 750-m group increases by 3.6% post-event and eventually surpasses pre-landslide levels. Full article

Aviation’s sustainability discourse often centres on flight emissions, but production and end-of-life phases also carry material, energy, and pollution impacts that are large enough to merit systematic intervention. With ~13,000 aircraft projected to retire over the next two decades—roughly 44% of the global fleet—the sector must scale responsible dismantling and material recovery to avoid lost opportunities for meeting future sustainability goals and to harness economic value from secondary parts and recycled feedstocks. Embedding major sustainability and circular economy principles into aircraft design, operations, and retirement can reduce waste, conserve critical materials, and lower lifecycle emissions while contributing directly to multiple SDGs. Furthermore, when considering particular aircraft types, thousands of narrow-body aircraft such as the Airbus A320 and Boeing 737 are due to reach their end of life over the next two decades. This research evaluates the economic and environmental feasibility of aluminium recycling from these aircraft, integrating material flow analysis, cost–benefit modelling, and a lifecycle emissions assessment. An economic assessment framework is developed and applied, with the results showing that approximately 24.7 tonnes of aluminium can be recovered per aircraft, leading to emissions savings of over 338,000 kg of CO₂e, a 95% reduction compared to primary aluminium production. However, scrap value alone cannot offset dismantling costs; the break-even scrap price is over USD 4200 per tonne. When additional revenue streams such as component resale and carbon credit incentives are incorporated, the model predicts a net profit of over USD 59,000 per aircraft. The scenario analysis confirms that aluminium recycling only becomes financially viable through multi-stream revenue models, supported by Extended Producer Responsibility (EPR) and carbon pricing. While barriers remain, aluminium recovery is a strategic opportunity to align aviation with circular economy and decarbonisation goals. Full article

The value of electronic waste as an urban mine has been extensively demonstrated, particularly regarding its rich content in precious metals. However, little is known about the economic feasibility in informal recovery contexts like in Burkina Faso. Previous studies were focused on formal and industrialized systems, overlooking informal dynamics in low-income countries. This study addressed that gap by applying a scenario-based Extended Cost–Benefit Analysis to assess metal recovery pathways in Burkina Faso. Six scenarios were modeled, combining technological selectivity, variations in local collection costs, and policy incentives such as Extended Producer Responsibility and eco-taxes as well as socio-environmental co-benefits. Results showed that e-waste recovery in the informal sector became economically viable when technological, financial, and policy instruments were combined. At a reduced e-waste cost of 5 USD/kg, manual dismantling and bioleaching technologies allowed for net benefits of 6.34 and 6.85 USD/kg, respectively, corresponding to improvements of 136% and 133% compared to baseline losses. Even at 10 USD/kg, both methods remained viable with positive returns and benefit–cost ratios above 1.06. It is impossible to generate net benefits with an e-waste purchase price of 10 USD/kg without EPR or eco-tax mechanisms, unless the price is reduced to 5 USD/kg; this could impose enormous constraints on collection activities. These findings confirmed that no single factor is sufficient to achieve profitability, highlighting the need to integrate supportive policies, technological appropriateness, and environmental co-benefits, a combination that aligns with circular economy principles and is essential to unlock the full potential of e-waste recovery in low-income countries. Full article

This paper develops a tractable method to solve a general equilibrium model with bank runs and exogenous leverage ratio restrictions, enabling welfare analysis of macroprudential policy across the business cycle. By computing bankers’ value functions via backward induction from steady state, the framework quantifies how leverage caps affect capital allocation, asset prices, and run probabilities during recovery from crises. Calibrated simulations show that welfare-enhancing policy is time-varying—lenient when households’ marginal utility of consumption is high, and restrictive in low-marginal-utility states. The results highlight a trade-off: tighter leverage restrictions improve stability but risk persistent efficiency losses if imposed too harshly after crises. Full article

This study examines how psycholinguistic features of online reviews relate to guest satisfaction in Bali’s spa hotel market. Using LIWC-22 category rates from Google Maps reviews, a corpus of 15,560 quality-filtered reviews from ten leading spa hotels was analyzed. Exploratory factor analysis yielded four interpretable dimensions—Social, Health and Wellness, Emotional Tone, and Lifestyle. In regressions predicting review star ratings (satisfaction), Social (β = 0.028) and Health and Wellness (β = 0.023) showed small but statistically detectable positive associations, whereas Emotional Tone (β = 0.006, t = 0.727) and Lifestyle (β = 0.004, t = 0.476) were not significant. The model’s explained variance is negligible (R² = 0.001; F = 5.283, p < 0.05), reflecting the many influences on ratings beyond review language; findings are interpreted as directional associations rather than predictive effects. Practically, the results point to prioritizing interpersonal service cues and wellness/treatment assurances, with tone monitoring being used for service-recovery signals. The design favors interpretability (validated, word-based categories; full-history snapshot) over black-box complexity, and transferability is Bali-specific and conditional on comparable market features. Future work should add contextual covariates (e.g., price and location), apply explicit temporal segmentation, extend to multilingual corpora, and triangulate text analytics with brief questionnaires and qualitative inquiry to strengthen validity and explanatory power. Full article

This study aimed to forecast the gross domestic product (GDP) of the South Caucasian nations (Armenia, Azerbaijan, and Georgia) by scrutinizing the accuracy of various econometric methodologies. This topic is noteworthy considering the significant economic development exhibited by these countries in the context of recovery post COVID-19. The seasonal autoregressive integrated moving average (SARIMA), exponential smoothing state space (ETS) model, neural network autoregressive (NNAR) model, and trigonometric exponential smoothing state space model with Box–Cox transformation, ARMA errors, and trend and seasonal components (TBATS), together with their feasible hybrid combinations, were employed. The empirical investigation utilized quarterly GDP data at market prices from 1Q-2010 to 2Q-2024. According to the results, the hybrid models significantly outperformed the corresponding single models, handling the linear and nonlinear components of the GDP time series more effectively. Rolling-window cross-validation showed that hybrid ETS-NNAR-TBATS for Armenia, hybrid ETS-NNAR-SARIMA for Azerbaijan, and hybrid ETS-SARIMA for Georgia were the best-performing models. The forecasts also suggest that Georgia is likely to record the strongest GDP growth over the projection horizon, followed by Armenia and Azerbaijan. These findings confirm that hybrid models constitute a reliable technique for forecasting GDP in the South Caucasian countries. This region is not only economically dynamic but also strategically important, with direct implications for policy and regional planning. Full article

The recovery, rework, or remanufacturing of returned products has received significant attention, leading to considerable advancements in green supply chain management. However, the impact of recovery mechanisms under demand sensitivity remains understudied. This study develops a sustainability model that incorporates limited recovery rates and demand sensitivity to price, carbon emissions, and stock conditions. The analysis investigates the difference in profit when considering recovery and proposes a procedure for deriving optimal solutions using two key decision variables: unit sales price and cycle time, within a nonlinear profit model. The findings show that (i) the increase in total profit is significant and (ii) both sellers and consumers benefit from this mechanism. In addition, total profit is 15% higher, while the total cost is 22% lower than in the case without recovery. Consumers can purchase products at lower prices (−12%), and sellers can sell more products (+4%), thereby earning higher profit (+15%). Such a win–win policy aligns with environmental, social, and governance (ESG) regulations and supports a healthy, long-term supply chain relationship. Numerical examples and sensitivity analysis illustrate the characteristics of the proposed model. The results also provide managerial insights into enterprises’ limited recovery capacity. Full article

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks. Full article

Background: Rural fires, worsened by climate factors such as drought, biomass buildup, and ignition sources, threaten sustainability. Recovering residual biomass (RB) presents a promising way to lower fire risk by reducing fuel loads and generating renewable energy; however, logistical costs in the RB supply chain—due to poor stakeholder coordination—limit its feasibility. App-based models can help solve these issues by improving information sharing, but their economic viability remains largely unexplored. This study suggests that such models work well when large amounts of biomass are involved and moisture content is low. Still, they might need external incentives for widespread use and fire risk reduction. Methods: The study modeled recovery scenarios by comparing costs (harvesting, retrieval, transport, and pre-processing) with biomass market value, using expert inputs and sensitivity analysis on variables like fuel prices and wages. Results: The economic feasibility is possible for large volumes (e.g., 10-ton loads) with low moisture (<30%), allowing transportation distances up to 459 km; however, small-scale or high-moisture situations often are not viable without support. Conclusions: App-based models need external support, like subsidies, to overcome owner and RB challenges, ensuring effective fire mitigation and sustainability benefits. Full article

Globally, winery wastewaters (WWWs) are estimated to account for about 62.5 billion L annually (2021), with COD levels up to 300,000 mg O₂/L primarily attributed to residual ethanol, posing serious environmental concerns. Conventional treatments are effective in COD removal, but they often miss opportunities for energy recovery and resource valorization. This study investigates the aqueous phase reforming (APR) of ethanol-rich wastewater as an alternative treatment for both COD reduction and energy generation. Two scenarios were assessed: electricity and heat cogeneration (S1) and hydrogen production (S2). Process simulations in Aspen Plus^® V14, based on lab-scale APR data, provided upscaled material and energy flows for techno-economic analysis, life cycle assessment, and energy sustainability analysis of a 2.5 m³/h plant. At 75% ethanol conversion, the minimum selling price (MSP) was USD0.80/kWh with a carbon footprint of 0.08 kg CO₂-eq/kWh for S1 and USD7.00/kg with 2.57 kg CO₂-eq/kg H₂ for S2. Interestingly, S1 revealed a non-linear trade-off between APR performance and energy integration, with higher ethanol conversion leading to a higher electricity selling price because of the increased heat reactor duty. In both cases, the main contributors to global warming potential (GWP) were platinum extraction/recovery and residual COD treatment. Both scenarios achieved a positive energy balance, with an energy return on investment (EROI) of 1.57 for S1 and 2.71 for S2. This study demonstrates the potential of APR as a strategy for self-sufficient energy valorization and additional revenue generation in wine-producing regions. Full article

As global economies accelerate their energy transitions, the photovoltaic sector faces critical challenges linked to material supply, security, and sustainability. Solar-grade silicon, enabling over 90 percent of photovoltaic technologies, has become a strategic commodity underpinning the expansion of renewable energy infrastructures. This short communication examines the evolving role of solar-grade silicon within the global energy transition, moving beyond its traditional classification as a technical material to frame it as a commodity of geopolitical and economic significance. We analyze recent price trends, regional production asymmetries, and trade dependencies, identifying key vulnerabilities in current supply chains. Although alternative photovoltaic materials such as perovskites and organics attract research interest, their commercial immaturity reinforces the centrality of silicon. The novelty of this contribution lies in treating solar-grade silicon through a commodity lens, integrating techno-economic metrics with policy and investment considerations. We highlight opportunities for reinforcing supply resilience through domestic production, circular economy strategies such as silicon recovery and reuse, and diversification of technological pathways. Our findings advocate for the inclusion of solar-grade silicon in strategic resource planning and industrial policy frameworks. Recognizing its unique position at the intersection of energy, technology, and trade is essential to achieving secure, scalable, and sustainable photovoltaic deployment worldwide. Full article

This study examines the dynamic interplay between interest rates, inflation, and GDP growth in Southeast Asian economies from 2000 to 2023, employing the Panel ARDL framework with the Pooled Mean Group (PMG) model. The findings confirm a robust long-term relationship among the Deposit Interest Rate (DIR), Lending Interest Rate (LIR), Consumer Price Index (CPI), and GDP growth. Higher deposit rates consistently promote economic expansion by encouraging savings and investment, while lending rates support long-term growth but limit short-term activity due to higher borrowing costs. Inflation adversely affects long-term growth by reducing purchasing power but boosts short-term demand. Historical GDP trends highlight the region’s susceptibility to global shocks, such as the 2008–2010 financial crisis and the 2020 COVID-19 pandemic, with forecasts indicating a gradual recovery from 2021 to 2025. The study emphasizes the importance of balanced monetary policies to enhance growth and stability in Southeast Asia, providing practical insights for policymakers addressing global and regional economic challenges. Full article