Search Results (137)

Renewable energy stands as a pivotal solution to environmental concerns, prompting substantial research and development endeavors to promote its adoption and enhance energy efficiency. Despite the recognized environmental superiority of renewable energy systems, there is a lack of globally standardized indicators specifically focused on renewable energy efficiency. This study aims to develop and apply a non-parametric data envelopment analysis (DEA) indicator, termed the Renewable Energy Indicator (REI), to measure environmental performance at the national level and to identify differences in renewable energy efficiency across countries grouped by development status and income level. The REI incorporates new factors such as agricultural methane emissions (thousand metric tons of ${\mathrm{CO}}_2$ equivalent), PM2.5 air pollution exposure (µg/m³), and aspects related to electricity, including consumption (as % of total final energy consumption), production from renewable sources, excluding hydroelectric (kWh), and accessibility in rural and urban areas (% of population with access), aligning with the emerging paradigm outlined by the United Nations. By segmenting the REI into global, developmental, and income group classifications, this study conducts the Mann–Whitney U test and the Kruskal–Wallis H tests to identify variations in renewable energy efficiency among different country groups. Our findings reveal top-performing countries globally, highlighting both developed (e.g., Sweden) and developing nations (e.g., Costa Rica, Sri Lanka). Central and North European countries demonstrate high efficiency, while those facing political and economic instability perform poorly. Agricultural-dependent nations like Australia and Argentina exhibit lower REI due to significant methane emissions. Disparities between developed and developing markets underscore the importance of understanding distinct socio-economic dynamics for effective policy formulation. Comparative analysis across income groups informs specific strategies tailored to each category. Full article

Ensuring the security of agricultural systems is essential for achieving national food security and sustainable development. Given that agricultural systems are inherently complex and composed of coupled subsystems—such as water, land, and energy—a comprehensive and multidimensional assessment of system security is necessary. This study focuses on Northeast China, a major food-producing region, and introduces the concept of agricultural system coupling security, defined as the integrated performance of an agricultural system in terms of resource adequacy, internal coordination, and adaptive resilience under external stress. To operationalize this concept, a coupling security evaluation framework is constructed based on three key dimensions: reliability (Rel), coordination (Cor), and resilience (Res). An Agricultural System Coupling Security Index (AS-CSI) is developed using the entropy weight method, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, while obstacle factor diagnosis is employed to identify key constraints. Furthermore, bivariate and trivariate Copula models are used to estimate joint risk probabilities. The results show that from 2001 to 2022, the AS-CSI in Northeast China increased from 0.38 to 0.62, indicating a transition from insecurity to relative security. Among the provinces, Jilin exhibited the highest CSI due to balanced performance across all Rel-Cor-Res dimensions, while Liaoning experienced lower Rel, hindering its overall security level. Five indicators, including area under soil erosion control, reservoir storage capacity per capita, pesticide application amount, rural electricity consumption per capita, and proportion of agricultural water use, were identified as critical threats to regional agricultural system security. Copula-based risk analysis revealed that the probability of Rel–Cor reaching the relatively secure threshold (0.8) was the highest at 0.7643, and the probabilities for Rel–Res and Cor–Res to reach the same threshold were lower, at 0.7164 and 0.7318, respectively. The probability of Rel–Cor-Res reaching the relatively secure threshold (0.8) exceeds 0.54, with Jilin exhibiting the highest probability at 0.5538. This study provides valuable insights for transitioning from static assessments to dynamic risk identification and offers a scientific basis for enhancing regional sustainability and economic resilience in agricultural systems. Full article

Given the problems related to drinking water supplies in rural and economically disadvantaged regions, point-of-use disinfection technologies are a viable alternative to improve access to drinking. Electrochlorinators are devices that produce chlorine-based disinfectants onsite via the electrolysis of a sodium chloride solution. In this research, we have constructed an innovative laboratory-scale three-compartment cell that includes two ion exchange membranes, fixed between two electrodes; in the anodic compartment, an acidic mixture of chlorine-based species (Cl₂, HClO, HCl and ClO⁻) is obtained, and, in the cathodic compartment, an alkaline solution is present (NaOH and hydrogen gas), while the central compartment is fed with a sodium chloride solution. The Taguchi methodology was used to examine the impact of the process operating conditions on the results obtained. The effects of the electrical potential levels (4.5, 6 and 7 V), electrolysis times (30, 60 and 90 min) and initial sodium chloride concentrations (5, 15 and 30 g/L) on the physical and chemical characteristics (concentrations of available chlorine and sodium hydroxide and pH of the solutions) and energy consumption were investigated. Variations in the electrical potential significantly influenced the concentration levels of active chlorine and sodium hydroxide produced, as well as the pH values of the respective solutions. The most favorable conditions for the production of electrolyzed water were an electrical potential of 7 volts, an electrolysis time of 90 min and a concentration of 30 g/L of sodium chloride, which was verified by ANOVA. The maximum concentration of active chlorine reached 290 mg/L and that of sodium hydroxide reached 1450 mg/L without the presence of hypochlorite ions under the best synthesis conditions. The energy consumption was 18.6 kWh/kg Cl₂ and 4.4 kWh/kg NaOH, while the average electric current efficiency for sodium hydroxide formation reached 88.9%. Similarly, the maximum conversion of chloride ions reached 24.37% under the best operating conditions. Full article

The large-scale integration of renewable energy and the use of high-energy-consuming equipment in agricultural parks have a great influence on the security of rural distribution networks. To ensure reliable power delivery for residential and agricultural activities and sustainable management of distributed energy resources, this paper develops a distributed generation-supported interactive optimization framework coordinating distribution networks and agricultural parks. Specifically, a wind–photovoltaic scenario generation method based on Copula functions is first proposed to characterize the uncertainties of renewable generation. Based on the generated scenario, a bi-level interactive optimization framework consisting of a distribution network and agricultural park is constructed. At the upper level, the distribution network operators ensure the security of the distribution network by reconfiguration, coordinated distributed resource dispatch, and dynamic price compensation mechanisms to guide the agricultural park’s electricity consumption strategy. At the lower level, the agricultural park users maximize their economic benefits by adjusting controllable loads in response to price compensation incentives. Additionally, an improved particle swarm optimization combined with a Gurobi solver is proposed to obtain equilibrium by iterative solving. The simulation analysis demonstrates that the proposed method can reduce the operation costs of the distribution network and improve the satisfaction of users in agricultural parks. Full article

Energy communities led by local citizens are vital for achieving the European energy transition goals. This study examines the design of a regional energy community in a rural area of Spain, aiming to address the pressing issue of rural depopulation. Seven villages were selected based on criteria such as size, energy demand, population, and proximity to infrastructure. Three energy valorization scenarios, generating eight subscenarios, were analyzed: (1) self-consumption, including direct sale (1A), net billing (1B), and selling to other consumers (1C); (2) battery storage, including storing for self-consumption (2A), battery-to-grid (2B), and electric vehicle recharging points (2C); and (3) advanced options such as hydrogen refueling stations (3A) and hydrogen-based fertilizer production (3B). The findings underscore that designing rural energy communities with a focus on social impact—especially in relation to depopulation—requires an innovative approach to both their design and operation. Although none of the scenarios alone can fully reverse depopulation trends or drive systemic change, they can significantly mitigate the issue if social impact is embedded as a core principle. For rural energy communities to effectively tackle depopulation, strategies such as acting as an energy retailer or aggregating individual villages into a single, unified energy community structure are crucial. These approaches align with the primary objective of revitalizing rural communities through the energy transition. Full article

The growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to identify efficient scheduling plans and improve performance. In this paper, a new approach based on evolutionary algorithms (EAs) is designed, implemented, and tested on a real microgrid architecture to evaluate its effectiveness. The proposed approach effectively combines heuristic information with the optimization capabilities of EAs, achieving excellent results with reasonable computational effort. The proposed system is highly flexible, making it applicable to different network architectures and various objective functions. In this work, the optimization algorithm directly manages the microgrid Energy Management System, allowing for a large number of degrees of freedom that can be exploited to achieve highly competitive solutions. This method was compared with a standard scheduling approach, and an average improvement of 11.87% in fuel consumption was achieved. After analyzing the differences between the solutions obtained, the importance of the features introduced with this new approach was demonstrated. Full article

The rural revitalization strategy serves as a powerful engine, driving the coordinated development of urban and rural areas while propelling the modernization and increasing the quality of China’s construction. Based on the Asia-Pacific Integrated Model (AIM/Enduse model), this study constructed a Rural Areas in Guangdong model (RG-Enduse model) applicable to rural areas in Guangdong Province, China. The model includes 18 types of terminal technological equipment which are subdivided into 5 types of service needs for rural residents on the demand side. Through a supply-side analysis, this study explores the coordinated development paths of energy supply and demand in rural areas under the rural revitalization strategy across three distinct scenarios. The results show that energy consumption in the baseline (BL) scenario and the low-carbon (CM1) scenario will both peak in 2025 at 17.46 million tce and 14.35 million tce, respectively, and also show a continuous downward trend in the green low-carbon (CM2) scenario, falling to 9.77 million tce by 2060. Electricity will be the dominant energy resource in the carbon neutral path to 2060, with CM2 and CM2, accounting for 78.78% and 80.61% of green electricity consumption, respectively. In addition, the utilization of carbon capture, utilization, and storage (CCUS) for thermal power will be indispensable to ensure the stability of the green power supply. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

This study explores the importance of considering regional aspects and different calculation approaches when assessing the environmental impact of passenger cars in Germany. The transportation sector, in general, needs to improve its transition to comply with national and international goals, and more efficient measures are necessary. To achieve this, the spatial heterogeneity of underlying data, such as vehicle stocks, cubic capacity classes as a proxy for consumption values, and annual mileage, is investigated with respect to regional differences. Using data samples for the year 2017, the average emission values per car and year are calculated as well as Germany’s total emission values from the utilization of passenger cars. Conducting a spatially informed allocation algorithm, battery electric vehicles (BEVs) are added to certain regional fleets, replacing cars with internal combustion engines (ICEs). The results show significant regional differences in the underlying data, with a divergence between rural and urban areas as well as northern and southern regions, while the spread in mileage values is higher than that in consumption values. Comparing the tank-to-wheel (TtW) and well-to-wheel (WtW) approaches reveals different values with an increased spread as more BEVs are introduced to the fleet. Using the presented concept to allocate BEVs, emissions can be reduced by 1.66% to 1.35%, depending on the calculation perspective, compared to the extrapolation of historical values. Furthermore, rural areas benefit more from optimized allocation compared to urban ones. The findings suggest that regional distribution strategies could lead to more efficient reductions in GHG emissions within the transportation sector while incorporating both TtW and WtW approaches, leading to more comparable and precise analyses. Full article

With the development of society, energy application and building thermal comfort in rural residences are receiving more and more attention. The rural residences in this survey mainly cover the rural areas of 21 prefectures in Guangdong province, of which 24.7% are in the Pearl River Delta, 18.9% in western Guangdong, 13.1% in eastern Guangdong, and 43.2% in northern Guangdong. Rural household energy consumption is mainly used for lighting equipment, household appliances, and cooking equipment, where lighting equipment and household appliances mainly consume electrical energy, and cooking equipment consumes different types of energy due to the diversity of types. First, there is a wide variety and variation in rural energy consumption, with electricity and liquefied petroleum gas as the main sources of cooking energy. Hot water is mainly obtained by heating with electricity and natural gas. Secondly, for rural residents, renewable energy is too expensive to build, is also affected by the environment and weather, and is often not convenient to use. Third, rural residents generally experience a warm, humid indoor environment with adequate airflow, but poor kitchen ventilation reduces air quality satisfaction. To enhance renewable energy adoption, technological advancements and cost reductions are necessary, along with increased government efforts in awareness campaigns, policy incentives, and demonstration projects. This study analyses the rural energy structure in Guangdong, proposes the direction of rural energy optimization, and analyses rural energy use and the feasibility of renewable energy promotion, considering the population and income of rural households. Full article

The transportation sector is a significant contributor to global carbon emissions, thus necessitating a transition toward renewable energy sources (RESs) and electric vehicles (EVs). Among EV technologies, fuel-cell EVs (FCEVs) offer distinct advantages in terms of refueling time and operational efficiency, thus rendering them a promising solution for sustainable transportation. Nevertheless, the integration of FCEVs in rural areas poses challenges due to the limited availability of refueling infrastructure and constraints in energy access. In order to address these challenges, this study proposes a multi-objective energy management model for a hydrogen refueling station (HRS) integrated with RESs, a battery storage system, an electrolyzer (EL), a fuel cell (FC), and a hydrogen tank, serving diverse FCEVs in rural areas. The model, formulated using mixed-integer linear programming (MILP), optimizes station operations to maximize both cost and load factor performance. Additionally, bi-directional trading with the power grid and hydrogen network enhances energy flexibility and grid stability, enabling a more resilient and self-sufficient energy system. To the best of the authors’ knowledge, this study is the first in the literature to present a multi-objective optimal management approach for grid-interactive, renewable-supported HRSs serving hydrogen-powered vehicles in rural areas. The simulation results demonstrate that RES integration improves economic feasibility by reducing costs and increasing financial gains, while maximizing the load factor enhances efficiency, cost-driven strategies that may impact stability. The impact of the EL on cost is more significant, while RES capacity has a relatively smaller effect on cost. However, its influence on the load factor is substantial. The optimization of RES-supported hydrogen production has been demonstrated to reduce external dependency, thereby enabling surplus trading and increasing financial gains to the tune of USD 587.83. Furthermore, the system enhances sustainability by eliminating gasoline consumption and significantly reducing carbon emissions, thus supporting the transition to a cleaner and more efficient transportation ecosystem. Full article

This study systematically analyzes the temporal variation characteristics, driving mechanisms, and decoupling relationship between carbon emissions and economic output in China’s planting industry. Using a dynamic panel model, LMDI decomposition, and coupling coordination model, it explores the main influencing factors of carbon emissions and their dynamic evolution. The findings reveal that from 2003 to 2022, carbon emissions in the planting industry exhibited a phased trend of rising first and then declining, with a limited overall reduction. Carbon emissions demonstrated significant path dependency. Planting industry output and agricultural investment were identified as the primary driving factors for carbon emissions, while energy intensity and mechanization levels had significant inhibitory effects. Decoupling analysis showed that weak decoupling dominates, with strong decoupling achieved only in specific regions and periods, highlighting significant regional disparities. Coupling coordination analysis indicated that the coordination between positive driving factors and carbon emissions improved annually, whereas the coordination related to rural electricity consumption declined in recent years. This study suggests that promoting precision agriculture and clean energy technologies, optimizing agricultural investment structures, implementing region-specific policies, and enhancing land resource planning can help us achieve the coordinated goals of high-quality agricultural development and carbon reduction. The findings provide theoretical insights and policy recommendations for low-carbon agricultural development and serve as a reference for global agricultural green transformation. Full article

The poultry sector in Ecuador relies heavily on non-renewable energy, particularly electricity from the public grid. A typical poultry plant consumes an average of 57,313 MWh per year, with sheds accounting for 36% of the total energy consumption. This study aims to reduce operating costs and transition the energy matrix by modeling an optimal photovoltaic system tailored to Ecuador’s geographical conditions. To achieve this, historical data on solar radiation, geographical resources, and energy consumption patterns were collected. Based on this analysis, an isolated photovoltaic system was designed to meet the energy needs of the Type A shed (5.89 kWh) and Type B shed (6.59 kWh). The system was sized to account for the lower annual solar radiation values of 4.58 kWh/m², ensuring effectiveness even under reduced solar input. The approach aims to standardize the energy supply in rural poultry plants by using a modular solar energy system. It also enables the scalable addition of photovoltaic modules based on each plant’s consumption requirements. Full article

The aim of this study was to assess the impact of the on-farm solar photovoltaic (PV) generation to offset grid electricity consumption on a commercial poultry farm in the US. Hourly electricity production by PV systems was estimated using the System Advisor Model (SAM) of the National Renewable Energy Lab (NREL) and compared with the estimated electric load demand of a broiler house. We analyzed the economic benefits of installing solar systems of three capacities under net energy metering and net energy billing pricing scenarios. Results suggested that a smaller PV installation of 35 kW_p, while substantially short of meeting energy needs, resulted in an 85% self-consumption ratio, whereas the larger 70 kW_p and 105 kW_p PV systems, led to 59% and 42% self-consumption ratios, respectively. This is important when analyzing pricing schemes as lesser amounts of PV energy created are sold to the grid with the smallest system, and, thereby, the least pricing effects on profitability occur across pricing scenarios. Although all scenarios lead to positive net present values (NPVs), under either type of the pricing scenarios, farm business owners realize more favorable energy sales with NEM, which would spur PV system adoption. Results of this study thus provide information to both poultry farmers, considering the size of system installations and policy makers interested in affordably increasing renewable energy supplies and/or rural development. Full article

Rural areas are ideal for renewable energy facilities, supporting sustainable development and energy transition. Egypt aims to reduce greenhouse gas emissions in the electricity sector by 37% and energy consumption by 17% by 2030. Rural Egypt, hosting two-thirds of the population and building stock, consumes one-third of the total electricity. Thus, this paper provides an exploratory study to diagnose and benchmark the energy-use intensity of rural buildings and quantify the correlation between residential electricity consumption, built environment elements, and socio-economic factors, in addition to promoting techno-economic assessments of renewable energy from photovoltaic panels in rural Egypt, supporting national policies amid rapid rural development. The study utilized different analytical and field methods and statistical analyses. A typical agriculture-based rural village in the Delta region, northern Egypt, was selected; the built environment, building types, and socio-economic factors were examined. The results revealed a significant correlation between lifestyle, built-up area, household size, and floor numbers with residential buildings’ electricity consumption. The average annual electricity use intensity was benchmarked at 2.5–92.3 kWh/m² for six non-residential building typologies and at 22 kWh/m² and 6.67 kWh/dwelling for residential buildings. Under current regulations, rooftop solar panels can generate electricity significantly, but are not profitable. Eventually, insights for policymakers to inform energy transition policies and national initiatives for rural regeneration were provided. The research focused on a local context, but the methodology can be applied to rural settlements in similar contexts. Full article