Search Results (1,077)

The transition toward a circular water economy addresses accelerating water scarcity and pollution. A PRISMA-2020 systematic review of 50 peer-reviewed articles (January 2018–April 2024) mapped current technologies and management strategies, seeking patterns, barriers, and critical bottlenecks. Bibliometric analysis revealed the following three dominant patterns: (i) rapid diffusion of membrane bioreactors, constructed wetlands, and advanced oxidation processes; (ii) research geographically concentrated in Asia and the European Union; (iii) industry’s marked preference for by-product valorization. Key barriers—high energy costs, fragmented regulatory frameworks, and low social acceptance—converge as critical constraints during scale-up. The following three practical action lines emerge: (1) adopt progressive tariffs and targeted tax credits that internalize environmental externalities; (2) harmonize water-reuse regulations with comparable circularity metrics; (3) create multi-actor platforms that co-design projects, boosting local legitimacy. These findings provide policymakers and water-sector practitioners with a clear roadmap for accelerating Sustainable Development Goals 6, 9, and 12 through circular, inclusive, low-carbon water systems. Full article

This article presents an empirical evaluation of the technical and economic performance of a building-integrated photovoltaic (PV) system implemented at the Faculty of Architecture and Urbanism of the University of Cuenca, Ecuador. This study explores both stages of deployment, beginning with a 7.7 kWp pilot system and later scaling to a full 75.6 kWp configuration. This hourly monitoring of power exchanges with utility was conducted over several months using high-resolution instrumentation and cloud-based analytics platforms. A detailed comparison between projected energy output, recorded production, and real energy consumption was carried out, revealing how seasonal variability, cloud cover, and academic schedules influence system behavior. The findings also include a comparison between billed and actual electricity prices, as well as an analysis of the system’s payback period under different cost scenarios, including state-subsidized and real-cost frameworks. The results confirm that energy exports are frequent during weekends and that daily generation often exceeds on-site demand on non-working days. Although the university benefits from low electricity tariffs, the system demonstrates financial feasibility when broader public cost structures are considered. This study highlights operational outcomes under real-use conditions and provides insights for scaling distributed generation in institutional settings, with particular relevance for Andean urban contexts with similar solar profiles and tariff structures. Full article

The study examines the impact of pricing strategies on the competition and cooperation of cross-border ports, focusing on unified pricing and differential pricing. The results show that the inside border port can adopt a differentiation strategy to enhance its benefits, as this strategy allows for better control. Additionally, while the differentiated pricing strategy is an equilibrium strategy for the inside border port, blockchain technology can enhance the economic benefits of the inside border port under certain conditions, which also demonstrates the commercial value of blockchain in data sharing. Moreover, the expansion of port capacity can reduce the congestion of the inside border port to some extent under specific conditions. Finally, the study analyzes the environmental impact, tariff impact, and influence of port cooperation, which provides some management implications for inside border port. In summary, the findings highlight the potential of blockchain to optimize pricing strategy and promote cooperation between regional ports, thus improving economic benefits. Full article

The widespread adoption of electric vehicles introduces significant challenges to power grid stability due to uncoordinated large-scale charging and discharging behaviors. By addressing these challenges, mobile energy storage systems emerge as a flexible resource. To maximize the synergistic potential of jointly scheduling electric vehicles and mobile energy storage systems, this study develops a collaborative scheduling model incorporating the prediction of geographically and chronologically varying distributions of electric vehicles. Non-dominated sorting genetic algorithm-III is then applied to solve this model. Validation through case studies, conducted on the IEEE-69 bus system and an actual urban road network in southern China, demonstrates the model’s efficacy. Case studies reveal that compared to the initial disordered state, the optimized strategy yields a 122.6% increase in profits of the electric vehicle charging station operator, a 44.7% reduction in costs to the electric vehicle user, and a 62.5% decrease in voltage deviation. Furthermore, non-dominated sorting genetic algorithm-III exhibits superior comprehensive performance in multi-objective optimization when benchmarked against two alternative algorithms. Full article

Accurate gas consumption forecasting is critical for modern energy systems due to complex consumer behavior and regulatory requirements. Deep neural networks (DNNs), such as Seq2Seq with attention, TiDE, and Temporal Fusion Transformers, are promising for modeling complex temporal relationships and non-linear dependencies. This study compares state-of-the-art architectures using real-world data from over 100,000 consumers to determine their practical viability for forecasting gas consumption under operational and regulatory conditions. Particular attention is paid to the impact of data quality, feature attribution, and model reliability on performance. The main use cases for natural gas consumption forecasting are tariff setting by regulators and system balancing for suppliers and operators. The study used monthly natural gas consumption data from 105,527 households in the Volyn region of Ukraine from January 2019 to April 2023 and meteorological data on average monthly air temperature. Missing values were replaced with zeros or imputed using seasonal imputation and the K-nearest neighbors. The results showed that previous consumption is the dominant feature for all models, confirming their autoregressive origin and the high importance of historical data. Temperature and category were identified as supporting features. Improvised data consistently improved the performance of all models. Seq2SeqPlus showed high accuracy, TiDE was the most stable, and TFT offered flexibility and interpretability. Implementing these models requires careful integration with data management, regulatory frameworks, and operational workflows. Full article

The accurate estimation of electricity supply costs has become increasingly relevant due to growing demand, variable generation sources, and regulatory changes in emerging power systems. This study models the average unit cost of electricity supply (USD/kWh) in Ecuador using multiple linear regression techniques and ARIMAX forecasting, based on monthly data from 2018 to 2024. The regression models incorporate variables such as energy demand, generation mix, transmission costs, and regulatory indices. To enhance model robustness, we apply three variable selection strategies: correlation analysis, PCA, and expert-driven selection. Results show that all models explain over 70% of price variability, with the highest-performing regression model achieving $R^2=0.9887$. ARIMAX models were subsequently implemented using regression-based forecasts as exogenous inputs. The ARIMAX model based on highly correlated variables achieved a MAPE below 5%, showing high predictive accuracy. These findings support the use of hybrid statistical models for informed policy-making, tariff planning, and operational cost forecasting in structurally constrained energy markets. Full article

The energy system of Ukraine needs to be decentralized, which aligns entirely with its intention to join the EU. The study focuses on regional peculiarities in establishing solar energy cooperatives and provides practical guidance on developing an energy cooperative in Ukraine. The article studies the different elements of electricity tariff composition for households, compares the existing support schemes (feed-in tariff and net metering), and defines which regions are the most suitable for establishing energy cooperatives (using solar installation). The primary methods employed are descriptive analysis, net present value analysis, and the integral assessment method, which collectively provide a comprehensive framework for evaluating both the economic viability and regional suitability of solar energy cooperatives. The findings indicate that the most suitable regions for solar energy cooperatives in Ukraine are located in the northeast and southwest of the country. The study highlights the importance of tailoring regional programs for energy cooperatives to enhance energy security and support the country’s low-carbon energy transition. The findings may be of interest and applicable in Ukraine and beyond. Full article

Microgrids and load shifting can improve resilience and lower costs for electricity customers. The costs to deploy each have decreased and helped accelerate their deployment in the U.S. and globally. However, previous research has focused minimally on the combined benefit or “stacked economic value” that these assets could provide jointly. This article evaluates the financial value when those assets are combined and optimized jointly. The methods are demonstrated for a U.S. government facility with an existing microgrid and building automation system, with optimizations that vary the percentage load shifted and the duration of time the load can be shifted. The economic benefits of load shifting are greater when combined with a microgrid and coordinated dispatch of loads and microgrid assets. The methods and case study results illustrate “stacked economic value” showing energy charge reductions are 56–252% greater and demand charge reductions are 96–226% greater when load shifting is combined with a microgrid as compared to load shifting without a microgrid. Increasing the amount and duration of load shifting improves the stacked economic value as more loads are scheduled coincident with on-site generation to offset or completely avoid utility purchases during peak pricing periods, an underlying behavior that enables stacked economic value and increased financial savings. The percentage reduction in demand charges is greater than energy charges—a generalizable finding—but the relative impact on utility expenditures is dependent on the utility tariff structure and composition of demand charges and energy charges in the utility bill. In this case study, demand charge reductions were four times greater than energy charge reductions, but the financial savings of demand charges are less due to their smaller proportion of utility charges. This suggests that the stacked economic value of microgrids and load control may be even more significant in locations with electricity tariffs that more heavily weight billing towards demand charges than energy charges. Full article

In response to the growing prevalence of tariffs as instruments of economic policy and strategic competition, this paper introduces a formal mathematical framework for optimizing counter-tariff strategies. We model the global trade ecosystem as a multi-layered, directed, weighted hypergraph, where vertices represent countries, industries, and subindustries, and hyperedges capture complex trade relationships and supply chain dependencies. The proposed framework employs bilevel optimization techniques to maximize strategic impact on target economies while minimizing self-inflicted economic costs. Through integration of graph theory, spectral analysis, and multilevel optimization methods, we develop a rigorous formalism that enables policymakers to identify optimal counter-tariff portfolios under various constraints. Our model explicitly accounts for industrial interdependencies, where export competitiveness depends on imported inputs, thus providing a more realistic representation of global value chains. Case studies applying our model to historical trade disputes demonstrate its capacity to generate superior strategic outcomes compared to conventional approaches. The framework’s axiomatic foundation allows for rapid recalibration in response to shifting economic conditions and policy objectives. Full article

The intersection of digitalization and innovation has emerged as the most vital strategy to spur economic development and shape consumer choice. The study examines sustainable development, digital economic governance, and sustainable consumption, emphasizing the role of consumer awareness, tariff policy, and managerial practice in aligning business strategy with sustainable goals. Amidst global environmental challenges, a suitable tariff policy is of utmost significance to propel the digital economy and encourage sustainable development. This paper investigates how emerging tariff policies can advance trade competitiveness and environmental impacts and draws on a literature review and data collection for the period between 2000 and 2025. The article examines the disruptive function of digital technologies, AI, and big data in driving sustainable business practices and digitalization. It also discusses how such technologies can simplify tariff implementation, compliance, and stakeholder trust through behavioral insights drawn from secondary data analysis on a cross-country basis and official reports. The study identifies best practices in the coordination of tariff policy with international governance institutions. Empirical observation shows that innovative tariff approaches in digital economic governance can support long-term growth, increase international coordination, and guide global governance efforts in environmental sustainability and SDGs. The implications of the findings are relevant to policymakers, business leaders, and legal experts working on fast-paced global changes. Full article

The Pacific Alliance (PA), established in 2011, aims to foster economic integration among its member states—Peru, Chile, Colombia, and Mexico—by promoting trade liberalization and economic cooperation. However, recent political shifts within these countries have influenced trade policies, affecting intra-bloc commerce and relations with external markets, particularly China and the United States. This study explores how the political environment within the PA has shaped sustainable trade, considering economic policies, trade agreements, and shifts in regional priorities. Using a qualitative and descriptive approach, this research is based on a documentary review of academic literature, official reports, and international trade data. Content analysis was applied to assess the impact of political decisions on PA trade sustainability, including the examination of tariff structures, trade flows, and capital movements. The findings indicate that intra-regional trade within the PA remains limited, with an intraregional trade index below 4%. Mexico continues to prioritize exports to the U.S., while Peru and Chile strengthen ties with China. Although PA member states have maintained liberal economic policies, disparities in trade liberalization levels hinder integration. Furthermore, despite the reduction of tariffs and the implementation of digital trade facilitation measures, political instability and differences in economic strategies among member states challenge the PA’s long-term sustainability. Strengthening institutional frameworks and increasing investments in research and development are crucial for enhancing economic integration and ensuring trade resilience within the bloc. Full article

The primary objective of this study is to investigate the relationships among perceived price fairness, consumer attitude, and loyalty in the context of egg consumption. In addition, the study examines the moderating effects of tariff-related concern and education level on these relationships. Data were collected through an online survey administered via the Clickworker platform, resulting in a sample of 311 U.S. consumers. The proposed hypotheses were tested using Hayes’s Process Macro (Model 7). The results reveal that both perceived price fairness and consumer attitude have significant positive effects on loyalty toward eggs. Moreover, a strong positive association was found between consumer attitude and loyalty. The analysis also identifies the moderating roles of tariff concern and education level, particularly in the relationship between price fairness and loyalty. This research contributes to the existing literature by offering insights into the dynamic interplay of five key variables in the context of consumer behavior within the egg market. Also, this research proposes practical implications focusing on various stakeholders. Full article

This paper proposes a non-invasive, data-driven methodology for monitoring and optimising machine utilisation in manufacturing environments. By analysing high-resolution power consumption data, the system automatically classifies machine states (off, idling, and working, and segments operational periods into discrete production events. Unsupervised learning techniques enable the identification of production patterns, product typologies, and anomalies, supporting improvements in operational efficiency and quality control. The approach also estimates energy consumption and cost using time-of-use tariffs, offering insights into both performance and sustainability. Experimental evaluations across multiple industrial settings demonstrate the method’s robustness, with high agreement with production records and significant potential for reducing idle time, improving scheduling, and enhancing resource allocation. This work presents a scalable and interpretable analytics framework to support data-driven decision-making in modern manufacturing operations. Full article

Green and cool roofs have significant potential to reduce energy consumption in buildings, but high initial costs and the need for local adaptation limit their adoption. This study aims to compare the life cycle energy assessment (LCEA) and life cycle cost analysis (LCCA) of green, cool, and standard (fibre cement) roofs in three Brazilian cities with different climatic and economic contexts. Computer simulations were carried out on a multifamily residential building model to assess the energy performance of the roofs. The simulation results and literature data were used to estimate the roofs’ energy consumption and cost over the life cycle. Over a 40-year life cycle, green and cool roofs reduced energy consumption by 13% to 22% compared to standard roofs. Cool roofs showed the lowest life cycle costs, while green roofs faced cost-effectiveness challenges due to high initial and maintenance costs. However, in areas with high energy demands and electricity tariffs, the life cycle cost of green roofs may be decreased. The study highlights the crucial role of material selection in embodied energy and emphasises the dominant impact of the operational phase on energy consumption and life cycle costs. These findings underscore the need for customised design strategies and localised assessments to support decision-making. Full article

This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition of residential grid feed-in, which limits the use of conventional peer-to-peer energy models. Additionally, fixed pricing is required to ensure simplicity and trust among users. SEAMS coordinates prosumer and consumer households, a shared battery energy storage system (BESS), and a centralized aggregator (AGG) to minimize total electricity costs while maintaining financial neutrality for the aggregator. A mixed-integer linear programming (MILP) model is developed to jointly optimize PV sizing, BESS capacity, and internal buying price, accounting for Time-of-Use (TOU) tariffs and local policy limitations. Simulation results show that a 6 kW PV system and a 70–75 kWh shared BESS offer optimal performance. A 60:40 prosumer-to-consumer ratio yields the lowest total cost, with up to 49 percent savings compared to grid-only systems. SEAMS demonstrates a scalable and policy-aligned approach to support Thailand’s transition toward decentralized solar energy adoption and improved energy affordability. Full article