• Energy Research
• 2021-2025close
• 13. Climate action close
• 7. Clean energy close
• 8. Economic growth close
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Abstract Background and Aim: Increasing energy consumption has created an energy crisis in the world. Fossil fuels are limited and depleting. Biogas is considered a fuel that has attracted the attention of researchers. To increase biogas production, different pretreatments have been utilized. The purpose of this study was to investigate the optimal mixing ratio of Municipal Solid Waste (MSW) and Sewage Sludge (SS), as well as the effects of various conditions of alkaline pretreatment on biodegradability of wastes and the amount of biomethane production. Materials and Methods: This study was done in a laboratory digester with 1 L volume at 37 °C with different concentrations of NaOH in a completely randomized design. Biogas volume, methane volume, and changes in pH were measured daily. Measurement parameters in the anaerobic digestion including total solids, volatile solids, and carbon and nitrogen content in the feedstock were determined according to the APHA standard methods. Results: The optimal mixing ratio of MSW to SS was 60:40 with the highest methane yield of 254.87 mL/g VS. Next, the effects of 2, 6, and 10% NaOH concentrations were evaluated on the amount of gas produced, indicating that 6% NaOH concentration significantly improved waste decomposition. Methane production, VS, and TS removal were compared to the control treatment, and there were increases of 30, 27.94, and 27.25%, respectively. Conclusion:The results showed that the mixing ratio of MSW to SS at 60:40 with 6% NaOH improves the decomposition of organic wastes and increases biomethane production. Keywords: Alkaline Pretreatment; Anaerobic Digestion; Biogas; Municipal Solid Waste; Sewage Sludge

IntroductionThe problem of energy efficiency is one of the key pillars of economics, especially agricultural sector. In term of energy efficiency, a similar estimation for human actions and their consequents can be applied to the landscape system, which first introduced by Hall et al. (1986), and now referred to Energy Return on Investment (EROI). Many energy analyzes have been done, take into account a social system boundary and an input and output approach. This approach will inevitably hide the system's internal performance inside a black box. Recently, Tello et al. (2016) have proposed a novel approach for analyzing energy at the agricultural landscape scale with the aim of evaluating energy sustainability under multiple EROIs that views the landscape as a set of energy cycles between nature and society.The proposes of this study have been to consideration the theory and methodology of multiple EROIs, to investigate the efficiency of energy flow in Qazvin agricultural landscape and, to examine the relationship between energy efficiency and landscape heterogeneity in order to describe the interaction of landscape structure and energy efficiency.Materials and MethodsThe database of this case study was prepared from 46 counties of Agricultural Organization of Iran and land use map. Agricultural database was created based on agriculture, livestock, and pasture subsystems. Agricultural yield for each crop, number of agricultural, and horticultural labors, number and type of agricultural machinery, amount of fertilizers, herbicides and fungicides, used fossil fuels, electricity, and agricultural waste belonged to the agricultural sub-sector. Census of livestock, livestock and poultry production, livestock and poultry feed, livestock and poultry production, workers and machinery, fossil fuel and electricity needed and livestock waste were collected for the livestock sector. Pasture production used for livestock grazing, amount of livestock manure going back to rangelands were belong to pasture sector.All energy flows were converted to gross caloric value following research by Guzmán et al. (2014). In this method, the calculation of multiple EROIs has replaced the conventional methods of energy efficiency calculation. Landscape heterogeneity calculated using landscape metrics. Correlation coefficient was performed using SPSS between EROIs and heterogeneity.Results and DiscussionThe highest value of FEROI was found in Bashariyate Sharghi with 0.25 and the lowest was in Kharghane Gharbi with 0.018. EFEROI, which is the most similar to the conventional method of energy efficiency, had the highest rate with 0.666 in Bashariate Gharbi and the lowest rate with 0.020 in Kharqan Gharbi. IFEROI was 0.95 in Narjah and the lowest was in Shahidabad with 0.168. Lower IFEROI indicates a higher return biomass in the production system, which seeks to maintain reproduction in the system by closing the biophysical cycles. The highest NPPEROI were reported by Bashariate Gharbi at 1.122 and lowest by Kharqan Gharbi at 0.173.In this study the relationship between the EROIs index and the heterogeneity of the landscape structure was shown. The results have showed the inverse correlation between heterogeneity and energy efficiency, indicating the heterogeneous impact of landscape structure on these indicators. It can be deduced that the heterogeneity created by human in Qazvin province has reduced energy efficiency. To explain this inverse correlation between energy efficiency and the heterogeneity of the landscape, it should be noted that one of the factors affecting efficiency is that may final production come from land uses that needed more input energy and produce less output. By examining the relationship between these indices with land use and land cover of each county, it was found that these indices had their lowest level in dry farming. It means that in Qazvin province, energy efficiency in dry farming is low, and relay on external inputs, which was mainly fossil fuel.ConclusionThis study has explained how the calculation of several energy efficiency coefficients provides more complete information than conventional methods for decision making. The results of this study can be applied in land use planning to integrate energy considerations in planning and comprehensive agricultural development plan.

Introduction: The final energy consumption per capita in Iran in the agricultural sector is 3.4, as well for household sector is 2, besides the commercial and public sectors are 1.6, and transportation and industry are 1.4 times the global average. This is due to low efficiency in operation, high energy consumption, as well as the use of energy goods and services. The use of renewable energy in the agricultural sector, while increasing the security of energy supply, will reduce global warming, stimulate economic growth, create jobs, and increase per capita income and social justice and environmental protection in all areas. The purpose of this study is to investigate farmers' preferences for using solar energy in Sari.Materials and Methods: The Choice Experiment methods allow researchers to focus on valuing final changes as multidimensional features rather than discrete changes. Choosing between options encourages respondents to examine their preferences in detail related to different management programs. The Choice Experiment test approach consists of several steps, which include designing the Choice Experiment test, determining the sample size and method of data collection, estimation process, and modeling the Choice Experiment test. Designing a Choice Experiment test consists of five important steps which are defining attributes, determining the relevant levels, conducting an experimental design, constructing Choice sets, and measuring preferences. After determining the criteria affecting the prioritization of renewable energy, liketechnical, environmental, economic, social, and political criteria, in order to investigate the willingness to Pay of Sari farmers, a test questionnaire was designed. The criteria obtained from the review of prioritization of renewable energy were considered as the attributes of the Choice Experiment and the price attribute was added to the above criteria. A total of six technical, economic, social, political, environmental, and price attributes were considered to investigate farmers' willingness to pay. In the review of the studies and the current situation, the levels of each of the attributes were determined. To determine the levels of price attribute, these points were considered; the price of agricultural electricity per kilowatt-hour is 383 Rials, which was approximately 400 Rials for the current situation.Results and Discussion: To investigate the farmers' preferences for using solar energy, 98 questionnaires of farmers in Sari were completed in September 2019. Each questionnaire included 8 choice set cards and each card included three options, based on which, the number of observations in Sari is equal to 2352 observations. The purpose of this study is to investigate the preferences of farmers in Sari for the use of solar energy. For this purpose, the Multinomial logit, the Random parameter logit, the latent class, and the Random parameter logit latent class are used. Based on the results of the Multinomial logit method, environmental and price attributes at the level of one percent and economic attribute at the level of five percent are statistically significant, but political, social, and technical attributes are not statistically significant. The Alternative-specific Constants (ASC) in the first and second options are not statistically significant. Based on the results of the Random Parameter Logit estimation method, environmental, economic and price attributes are statistically significant at the level of one percent. Technical, political, and social attributes are not statistically significant, which shows that farmers do not make a significant difference between these two attributes. The Alternative-specific Constants (ASC) are significant in the first option at the level of five percent and the second option at the level of one percent. The results of latent class estimation show that in the first class, environmental, economic, political, social, and price attributes are statistically significant at the level of one percent and technical attribute at the level of ten percent. The Alternative-specific Constants (ASC) are statistically significant at the level of one percent in the first class. In the second class, technical attribute at the level of five percent and environmental attribute at the level of ten percent are significant, besides other attributes in the second class are not statistically significant. The most sensitive class is the first class and farmers of the second class are considered the base class. The results obtained from the Bayesian and Akaike criteria of different classes showed that the two classes have the lowest values of BIC and AIC criteria and the class is appropriate. After determining the appropriate class, the model was estimated. The results of model estimation were calculated by the Latent Class Random Parameter logit method. In the first class, environmental attributes and price are significant at the level of one percent and economical attributes at the level of five percent. Also, the Alternative-specific Constants (ASC) is significant at the level of one percent, but, in the second class, the attributes are not statistically significant. Technical, environmental, economic, political, social, and price attributes, as well as the option of status quo or the Alternative-specific Constants (ASC) in the second class, do not affect farmers' utility due to the lack of statistical significance.Conclusion: A comparison of the results obtained from the four methods shows that the highest value of the estimated coefficient for environmental attributes was in the latent class method and the lowest value was in the multinomial logit method; Comparison of fitted methods shows that the highest Log-likelihood is related to the latent class random parameter logit method and the lowest value is related to the multinomial logit method. Accordingly, the highest value of Akaike and Bayesian criteria is related to the multinomial logit method and the lowest value is related to the latent class random parameter logit method which is better than other methods according to the good fit criterion.

Introduction: Today, the food-energy nexus is a vital issue. Energy in the food production chain is an essential feature of agricultural development and a critical factor in achieving food security. Energy use in the agricultural sector has increased to respond to the growing demand of the population, as well the limited supply of cultivated lands, and the desire for high standards of living. Therefore, the agricultural sector is heavily dependent on energy that affects agricultural prices. Agricultural price fluctuations are one of the most critical challenges for policymakers. The rapid rise in food prices has a significant negative impact on social welfare, especially the poor in developing countries, which is an issue that is more critical in developing countries than in developed countries. According to the Food and Agriculture Organization (FAO) report in 2018, the food world price index increased from 89.6 to 229.9 during the period from 2002 to 2011. Our literature review shows a distinct lack of research on modeling and analyzing the linkage between agricultural input price shock, especially energy and agricultural commodity prices in Iran. Materials and Methods: The Markov Switching model is a popular non-linear time-series model that involves multiple equations and can characterize the time-series behaviors in different regimes. This model is suitable for describing correlated data that exhibit distinct dynamic patterns during different periods. So, considering the sensitivity of food security and the impact of agricultural input, the main objective of this paper is to develop an econometric model to gain reliable insight into the impact of energy consumption on agricultural inflation, using the Markov Switching approach. To estimate this equation, we will run a MS-AR model, some preliminary tests, such as unit root test and stability test, are employed to ensure the reliability of MS-AR estimation results. Results and Discussion: Due to use of time series data, it is necessary to check the stationary status of variables. We performed a common non-linear unit root test (Kapetanios, Shin and Shell (KSS), Zivot and Andrews, Lee and Strazicich). These results reveal that we can significantly reject the null hypothesis of unit root for API, PPI, FPI, and EC, implying that all four variables considered in this study are stationary with structural breaks at levels. The Markov-Switching model has the various types that each of these is a particular component of the regime-dependent equation. Therefore, to choose the best type, the Akaike information criterion was used, and the model with the minimum value was selected as the optimal one. After model estimation and selection, the LR test indicated that the hypothesis of linearity could be rejected in favor of a Markov switching model. According to this model, the period of the Markov switching model estimation is classified into two regimes. Approximately, all the estimated coefficients of the MSIAH (2) - AR (5) model are found to be significant at the conventional level. Conclusion: The estimation results are consistent with theoretical foundations illustrating the importance of input prices and energy consumption on agricultural commodity prices. As with most experimental studies reviewed, this study has also shown energy consumption has a negative impact on agricultural commodity prices. In other words, it can be contended that during the study period, agricultural input prices have been influential factors on agricultural commodity prices. The findings revealed that the low inflation rate and high inflation rate regimes are stable and that only extreme events can switch regimes. The results of the MS model showed that the effect of input prices on agricultural inflation is different in regimes. In the case of energy, the impact of energy consumption on agricultural commodity prices in the high inflation rate regime is less than the low inflation rate regime because the elimination of energy subsidies policy has been applied in the second regime (high inflation rate). Thus, the results indicate the asymmetric impact of energy consumption shocks on agricultural commodity prices. The effect of agricultural input prices on agricultural commodity prices indicates that Iranian agriculture is significantly affected by changes in input prices. In this study, changes in input prices were caused by various shocks, such as the elimination of energy subsidies and drought. Therefore, it can be concluded that the elimination of energy subsidies and drought were, directly and indirectly, able to affect agricultural inflations through the price of inputs. In conclusion, planners and policymakers must pay attention to this asymmetry in agricultural commodity prices volatility to increase the price stability in agriculture as much as possible by appropriate policy tools.

Introduction:Today, environmental issues have affected human life in the world, including Iran. Environmental problems are mainly the result of human use of natural resources and improper treatment of the environment, including the inappropriate use of fossil fuels. To achieve sustainable development, specifically in the field of energy, the knowledge of the correct ways of using natural resources and non-profit behaviors towards the environment, as well as proper ways to protect it must be considered. Given that new development approaches emphasize the need for the participation of all individuals and different groups in society, the role of environmental non-governmental organizations in achieving these goals, protecting the environment, and reducing vulnerability is very important. The purpose of the present study was to analyze the strategies of environmental non-governmental organizations in climate change and energy adaptation programs with a focus on an urban area. Materials and Methods:The approach of this research was interpretive. This study was conducted using contextual theory. Participants in this study were selected using purposive sampling in the form of snowball sampling, based on which 16 members of environmental non-governmental organizations in Tehran in 2019 were selected. Sampling in this study continued until it reached theoretical saturation. Accordingly, at the end of 16 interviews, data saturation was obtained. The data were collected using semi-structured interviews and analyzed using open, axial, and selective coding steps. In open coding, the data were first grouped into separate lines and categories, and a code or concept was attached to them. In the axial coding stage, the related raw codes were subdivided in terms of features and concepts. At this stage, the categories were connected as a network, and finally, the main categories and the core category emerged in addition to a paradigm model were extracted. The extracted paradigm model was divided into four parts: causal conditions, intervening conditions, strategies, and consequences. The four parts were formed around the central phenomenon. Discussion of Results and Conclusions:Data analysis showed that the causal conditions affecting the ways of attracting the participation of non-governmental organizations and the causes of the phenomenon of persuasion and related strategies were a sense of responsibility for the environment, scientific and executive ability of members, self-efficacy, and belief in the effect of individual action (effectiveness). Also, the intervening conditions affecting the phenomenon of persuasion were the weakness of society in the field of awareness (about individual duties, environmental knowledge, and status quo), lack of appropriate contexts in the society, weakness in group interactions, lack of proper infrastructure in buildings, and restrictions on laws. Besides, the results showed that the strategies adopted by non-governmental organizations were training and promotion of environmental knowledge at both intra-organizational and extra-organizational levels, improving local knowledge, fear and hope (the promise of a better future and fear of potential risks), sustainable local change (empowerment, alternative jobs, the formation of a local organization), profit and creating consensus between policymakers and active energy actors for legitimacy, division at the government, industry and university levels.The consequences of adopting these strategies were raising the level of awareness, responsibility, empowerment and independence of local communities, earning money, and prosperity of ecotourism. In general, the results of the present study showed that the main method of members of environmental groups to attract the participation of target groups was persuasion. To achieve this, the members of the non-governmental organizations have focused their efforts on educating and benefiting various groups. The results of this study can help other environmental agencies and government organizations to use the various capacities of environmental agencies.

World climate change is an accepted important subject but its negative effects are severe in arid and semi-arid areas of Iran. So, in the present study, two climate scenarios including RCP 8.5 (critical scenario) and RCP 4.5 (moderate scenario) during 2020, 2030, and 2040 decades and their effects on temperature changes in the wheat growth period in five cities of Isfahan province including Isfahan, Najaf Abad, Chadegan, Burkhar, and Meimeh have been investigated. The survey of temperature changes during wheat growth in the next decades showed that Burkhar, Isfahan, Najaf Abad, Chadegan, and Meimeh, respectively will experience more days with a temperature higher than 30°C in 2020, 2030, and 2040 decades than the mean of two recent years (2017-2018). Furthermore, in comparison with present conditions, the most changes in the number of days with a temperature higher than 30°C in next decades climates (2020, 2030, and 2040 decades) will be in Burkhar, Meimeh, Chadegan, Najaf Abad, and Isfahan, respectively. The range of changes percent in the number of days higher than 30°C in next climate conditions rather than present condition will be varied between 5 percent (Isfahan) till 97 percent (Burkhar). The changes percent in all studied cities were more in RCP 8.5 than RCP 4.5. During wheat growth, the number of days less than zero°C will be less in Isfahan, Burkhar, and Meimeh while will be more in Najaf Abad and Chadegan. The evaporation- transpiration will be increased in the next decades during wheat growth. As a result, planning and using compatibility strategies for each city is important to guarantee wheat production.

Iran is located in the dry belt of the earth and is predicted to face water stress in the next half-century. Currently, the area of sugarcane cultivation in Khuzestan is over 85,000 hectares and due to the high water needs of sugarcane and drought conditions, optimization of water consumption and irrigation management is necessary to continue production. Therefore, in this study, the values of soil moisture, canopy cover, biomass yield in five treatments and irrigation levels (start of irrigation at 40%, 50%, 60%, 70%, and 80% soil moisture discharge) during 2 planting dates in the crop year 2015-2016 on sugarcane cultivar CP69-1062 in Amirkabir sugarcane cultivation and industry located in the south of Khuzestan was simulated by AquaCrop model. The measured data on the first culture date (D1) and the second culture date (D2) were used to calibrate and validate the model. The results of NRMSE statistics in canopy cover simulation in calibration and validation sets with values of 2.1 to 15.6% and 3.8 to 18.3%, respectively, and in biomass simulation with values of 6.2 to 15.2%, and 9.5 to 12.6%, respectively and coefficient of determination (R2), range 0.98 to 0.99 indicated that the high ability of the AquaCrop model in simulation canopy cover and biomass yield. whereas, the values of NRMSE of soil depth moisture in the calibration and validation sets ranged from 11.6 to 23.8, and 12.2 to 22.7, respectively, with a coefficient of determination (R2), 0.73 to 0.96 (calibration) 0.8 to 0.93 (validation) showed less accuracy of the model in the simulation. The best scenario is related to the third proposal that water consumption, water use efficiency, and yield are 1710 mm, 1.53, and 42.27 tons per hectare, respectively, which shows a reduction in water consumption of 360 mm.

Objective: Energy saving regarding its high share in energy consumption of industries has a significant impact on the growth and development of countries. This study aims to evaluate the performance of Iran's electricity generation, transmission, and distribution processes. Methods: By using a network data envelopment analysis (DEA) model, the overall efficiency scores, and efficiency scores of production, transmission, and distribution processes are calculated. The network structure considers the main and surplus inputs (fuel consumption costs, internal consumption, transmission substation capacity, power transmission lines length, transformers capacity, low and medium voltage network length), intermediate sizes (net power generation, gross power generation, and delivered energy), desirable (nominal power, actual power, and delivered energy) and undesirable outputs (environmental pollutants, and energy losses). Results: An algorithm based on a multi-objective programming model is presented to evaluate network performance and simultaneously to evaluate processes efficiency. The proposed algorithm is used to evaluate 16 electricity areas in Iran. Conclusion: The results showed that Tehran, Khorasan, Khuzestan, and Zanjan are the most efficient areas.

The aim of this study was to analyze the annual, monthly, and seasonal rainfall changes over a thirty-year period as well as before and after the emergence of declining oak. (Quercus brantii var. persica) trees (2000) in Ilam and Lorestan provinces. For this purpose, the authors used long-term data of daily rainfall in 1987–2017 period recorded by four synoptic meteorological stations; Ilam, Dehloran, Khoramabad, and Aligudarz. The Mann-Kendall non-parametric test was used to determine the rainfall trends. The decreasing trends of annual rainfall as well as mean differences in annual precipitation before and after emerging oak decline were not statistically significant at all meteorological stations. The average rainfall of four stations was 502 mm in the first decade (1987-1997), while it decreased to 422 and 371 mm in the second (1998-2007) and third decades (2008- 2017), respectively. The difference in the amount of annual rainfall before and after the emerging oak decline (442 against 401 mm) did not alter the mean rainfall event (̴7 mm). Although the thirty-year trends of seasonal rainfall were not significant, winter rainfall was decreased after emerging oak decline by 11% and spring, autumn, and summer rainfalls were increased by 5, 4, and 2%, respectively. Rainfall fluctuations in the Zagros vegetation region may act an accelerating factor for the emergence and extension of oak trees declining in the Zagros forests of Lorestan and Ilam provinces.

Considering that a significant portion of the share of energy consumption among consumer sectors is in the home and commercial sectors and this share is still increasing, there is a need for research in energy consumption management and evaluation of effective indicators in the building industry. Building the country is vital to optimizing energy consumption. One of the most appropriate ways to optimize fuel consumption in the building and housing sector is the implementation of zero energy buildings which is considered as the main solution in the world. In this research, after analyzing the population and statistical sample, data were collected to evaluate effective criteria and options related to energy consumption management. First, fuzzy Delphi method was used to evaluate the sub-criteria and select the main options of each. At first level the criterion is discussed and at the second level the data analysis and research model is analyzed through paired comparisons using fuzzy hierarchical analysis technique. Finally, the most important indicators and criteria are obtained using fuzzy hierarchical analysis. According to the research findings, among the energy consumption optimization management indices investigated by the FAHP method, the economic, construction and utilities indices with 0.333, 0. 201 and 0.176 have the highest management priority, respectively. Energy-efficient optimization has a zero-energy building approach.