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Institutional linkages and information flow between agricultural organizations play a critical role in addressing sustainability issues and promoting agrarian innovation. The aim of this study was to evaluate institutional relations and information between the various actors within the agricultural knowledge and information system (AKIS). The study focused on eight actors within the AKIS in Dakahlia governorate of Egypt, namely policy, extension, research, agricultural cooperatives, higher education, secondary education, credit, and the private sector. Thus, the survey sample included 11 representatives of each actor with 88 respondents. Data were collected by a standardized questionnaire distributed online. The graph theoretical technique was used for the quantitative assessment of information flow and institutional linkages established among actors. The findings indicated that agricultural extension ranked first about their real cause and effect on the rest of the system, having a value of 7.95. Two critical information pathways within the AKIS sustained innovation outcomes: (1) higher education–extension–agricultural cooperatives, (2) research–extension–agricultural cooperatives. The results also revealed that agricultural cooperatives ranked second after the extension component on the extent of supplying information to other members in the AKIS, with a value of 4.8. In contrast, the highest component received information from other components (7.6). By analyzing institutional linkages and information flow, this article gives insights to policymakers on the mechanisms that still need to be strengthened and the information gaps between actors to address the challenges of sustainable rural development.

Abstract The growing concerns of global warming and depleting oil/gas reserves have made it inevitable to seek energy from renewable energy resources. Many nations are embarking on introduction of clean/renewable solar energy for displacement of oil-produced energy. Moreover, solar photovoltaic (PV)–diesel hybrid power generation system technology is an emerging energy option since it promises great deal of challenges and opportunities for developed and developing countries. The Kingdom of Saudi Arabia (K.S.A) being enriched with higher level of solar radiation, is a prospective candidate for deployment of solar PV systems. Literature indicates that commercial/residential buildings in K.S.A. consume about 10–45% of the total electric energy generated. The aim of this study is to analyze long-term solar radiation data of Dhahran (East-Coast, K.S.A.) to assess the techno-economic feasibility of utilizing hybrid PV–diesel–battery power systems to meet the load of a typical residential building (with annual electrical energy demand of 35,120 kWh). The monthly average daily solar global radiation ranges from 3.61 to 7.96 kwh/m 2 . National Renewable Energy Laboratory's (NREL) Hybrid Optimization Model for Electric Renewable (HOMER) software has been employed to carry out the present study. The simulation results indicate that for a hybrid system composed of 4 kWp PV system together with 10 kW diesel system and a battery storage of 3 h of autonomy (equivalent to 3 h of average load), the PV penetration is 22%. The cost of generating energy (COE, US$/kWh) from the above hybrid system has been found to be 0.179 $/kWh (assuming diesel fuel price of 0.1$/l). The study exhibits that for a given hybrid configuration, the operational hours of diesel generators decrease with increase in PV capacity. The investigation also examines the effect of PV/battery penetration on COE, operational hours of diesel gensets for a given hybrid system. Concurrently, attention is focussed on un-met load, excess electricity generation, fuel savings and reduction in carbon emissions (for different scenarios such as PV–diesel without storage, PV–diesel with storage, as compared to diesel-only situation), COE of different hybrid systems, cost of PV–diesel–battery systems, etc.

The current study aims to examine the reverse association between circular economy innovation (CEI) and digital sustainability (DS), as well as the dual mediation of government incentives (GI) among firms. Data was collected through a structured-questionnaire-based survey among financial institutions (banks, insurance, and financial companies) in Pakistan, Malaysia, and China. The study collected data via structured questionnaires in an online survey and analysed the data using partial least squares structural equation modelling (PLS-SEM), to find results. The results revealed that CEI has a two-way relationship with the DS among firms in the defined context. Furthermore, the results confirmed the dual mediating role of GI between CEI and DS among firms. The outcomes can guide the policy makers to focus on the practices of CEI in the settings of the relevant state support schemes, to enhance the practices of DS among firms in emerging markets. The implications of the study are presented at the end of this study.

Agricultural waste-based heterogeneous catalysts are emerging as efficient and green catalysts. The present study explored the agricultural waste-based heterogeneous catalyst utilized in the production of biodiesel. The plant waste is composed of organic compounds and various metals which, on combustion, produces ashes that mainly consist of various metal carbonates and oxides. The most commonly employed approach for the solid catalyst preparation from plant materials is the calcination process, and it is performed at temperatures ranging from 300 to 1200°C. It is known that the temperature employed for calcination plays a vital role in the composition and development of the morphology of the catalyst. The variation in alkalinity, porosity, and, accordingly, the catalytic activity of the catalyst is significantly influenced by the calcination temperature. It was found that the potassium present in the form of oxide and carbonate as the main constituent in such catalysts played a significant role in delivering catalytic efficacy. Therefore, a number of agricultural waste-based catalysts were reported as efficient catalysts. The selection of the catalyst may be one of the important issues for application in large-scale biodiesel production. Thus, the present study was undertaken for the preparation of a rank list among the reported catalysts by following the VIKOR (Višekriterijumsko Kompromisno Rangiranje) multicriterion decision-making approach. In this work, the ranking study was performed considering the reported optimum reaction conditions (ORCs) of biodiesel synthesis reactions. The study was conducted strictly on the basis of the parameters, viz., catalyst concentration ( C 1 ), MTOR ( C 2 ), reaction temperature ( C 3 ), reaction time ( C 4 ), and biodiesel yield ( C 5 ). The parameters are considered good if C 1 , C 2 , C 3 , and C 4 are low or minimum and if C 5 is high or maximum. The catalyst prepared from plantain peel showed the best performance and ranked as the first one followed by Musa paradisiaca peel and cocoa pod husk catalysts which are ranked second. Thus, the VIKOR method can be useful for comparison and ranking purposes if there are a large number of data, and this may be expanded for thorough study by considering more criteria which may give more fruitful results.

Abstract Industrial sustainability has gained huge attention in the scientific community owing to the concern of global warming. Establishing sustainability indicators by the application of exergy analysis can guide and motivate industrial sustainability. In this context, an attempt is made to establish sustainability indicators for the industrial sector. Different measures to improve the sustainability of the industrial sector of Bangladesh are also discussed. Based on the energy consumption data from the year 2000–2015, energy, exergy, and sustainability analyses are performed. It is found that the energy efficiency varies from 55.01% to 59.67% and exergy efficiency varies from 53.11% to 56.97%. Exergy efficiency is found to be at lower side due to the accounting of irreversibility. Various sustainability indicators such as sustainability index, depletion number, exergetic renewable share, cumulative exergy loss, and non-renewable exergetic share have been studied as well. It is observed that depletion numbers vary from 43% to 45% and the sustainability index varies from 2.21 to 2.32. Non-renewable exergetic share is as high as 98% and maximum cumulative exergy loss is found to be 217.3 PJ in 2015. Improvement potential shows a continuous increase from 22.75% in 2000 to 101.89% in 2015. Waste heat recovery, energy audit, waste minimization, and adopting renewable energy sources are recommended to increase the efficiency and sustainability of this sector. The outcome of this study reveals that exergy analysis is an effective technique to develop energy conservation policies for this sector.

In this article, the optimal design of a novel multi-generation system for the production of electricity, cooling, heat and freshwater is discussed. In this system, a Proton exchange membrane fuel cell (PEM FC) is used to generate electricity, and the heat produced by it is absorbed by the Ejector Refrigeration Cycle (ERC) and used to provide cooling and heating capacity. A reverse osmosis (RO) desalination system is also used to supply freshwater. The esign variables in this research are operating temperature and pressure and current density of FC, as well as the operating pressure of the HRVG, evaporator, and condenser of the ERC system. In order to optimize the considered system, the exergy efficiency and total cost rate (TCR) of the system are considered as optimization objective functions. To this end, the genetic algorithm (GA) is used and the Pareto front is extracted. Also, three refrigerants R134a, R600 and R123 areused as ERC system refrigerant and their performance are evaluated. Finally, the optimal design point is selected. At the mentioned point, the exergy efficiency is 70.2% and the TCR of the system is 1.78 S/h.

In Saudi Arabia, housing projects account for almost half of the total electricity consumed by the construction industry because of the large number of housing projects compared to other types of buildings. This paper proposes a quantitative approach using a multiple linear regression assessment model to predict the energy cost and environmental cost of housing building in Saudi Arabia. It was developed to assist house owners in Saudi Arabia in estimating the monthly energy cost and associated operational carbon cost according to several predictor parameters. Based on related literature, these parameters were reviewed and discussed by experts from the Ministry of Housing. They included building location, wall type, number of occupants, window type, envelope insulation, building age, building area, number of air-conditioning units and their systems, and lighting system. The model development process included five main stages: collecting the energy and carbon cost data from completed operating housing units, categorizing the collected data based on parameters, diagnosing the quality of gathered data and filtering outlier data if any, building and generating a model, and lastly, testing and validating the model. More than 77 datasets were collected across the country during different times of the year. The findings of this study reveal that the relationship between the number of users and the building area with the energy cost is significant and that the number of users is more correlated to the energy cost than the age of the building or the number of central air conditioners installed. Moreover, the results show that the developed model has the ability to predict energy and carbon costs with high accuracy. The developed model serves as a decision support tool for householders and decision makers in the Ministry of Housing to control the predicted parameters. This would be beneficial for the housing unit owners for allocating constrained budgets.

Multistep power consumption forecasting is smart grid electricity management’s most decisive problem. Moreover, it is vital to develop operational strategies for electricity management systems in smart cities for commercial and residential users. However, an efficient electricity load forecasting model is required for accurate electric power management in an intelligent grid, leading to customer financial benefits. In this article, we develop an innovative framework for short-term electricity load forecasting, which includes two significant phases: data cleaning and a Residual Convolutional Neural Network (R-CNN) with multilayered Long Short-Term Memory (ML-LSTM) architecture. Data preprocessing strategies are applied in the first phase over raw data. A deep R-CNN architecture is developed in the second phase to extract essential features from the refined electricity consumption data. The output of R-CNN layers is fed into the ML-LSTM network to learn the sequence information, and finally, fully connected layers are used for the forecasting. The proposed model is evaluated over residential IHEPC and commercial PJM datasets and extensively decreases the error rates compared to baseline models.