To support the fulfillment of Sweden’s targets in term of climate change and economic growth, we need to do a distinct study to show the Environmental Kuznets Curve (EKC) pattern in different sector of the economy, as the GDP allocation, energy intensities, GHG emission, and technological development are different between sectors. This kind of study helps to figure out how the different sectors contribute to climate change and could appoint more particular and effective environment-energy policies. For this aim, we analyzed the existence of the EKC by implementing the ARDL Bound test approach in the whole and individual sectors of Sweden’s economy throughout 1990-2019. Our results indicated the contribution of a particular sector on total GHG emissions per capita. Results of the whole economy confirmed the EEKC hypothesis with a turning point in 1996, in which the AFF sector, unlike industry and service, had increased GHG emissions. Disaggregated sectoral analysis showed various results. The industry sector had efficient energy improvement. Policymakers should pay attention to AFF’s GHG emissions, as different sources of energy consumption had not impressive impact in both the short and long term. Also, effective fossil-related policies are necessary for the service sector due to the main contribution to transportation.

The first future challenge facing human beings is to supply the world's energy needs. However, energy consumption and resource depletion in industrial processes are significantly increasing. Therefore, life cycle assessment can be an excellent tool to quantify resources and energy consumption in different parts of industrial processes. The combination of process simulation and assessment of process life cycle can be resources & energy consumption in different parts is quantified and can be significantly reduced by optimizing the process, energy wastage. The process stimulation is done by HYSIS software, then by collecting output data, energy and materials flow, life cycle assessment is conducted using SIMAPRO software. According to output of the release list, 1709 items are released into the environment, of which 396, 407, 340 items are released into the air, water, soil, respectively and 556 items are extracted from sources. The most appropriate procedure to assess the life cycle of crude oil processing is Cumulative Energy Demand and Cumulative Exergy Demand energy approach. Based on the first-order analysis, the highest consumption of resources and energy is in the crude oil transmission sector; (Road construction with 44.95 petajoules and transmission pipelines with 19.85 petajoules). Also, regarding the second-order analysis, the highest consumption of resources and energy is related to crude oil production processes with 1.65 petajoules per operation and desalination unit, medium voltage electricity consumption with 0.002194 petajoules and exergy of power lines with 0.00087 petajoules.

Recently, making use of emerging fuels such as municipal waste has been proposed as an alternative for conventional fuels and also as a way for municipal waste disposal. This research, while modeling the thermal and electrical profiles of Ilam Industrial Town, examines the possibility of supplying the required fuel from municipal waste by the year 2041. For this purpose, different combined heat and power (CHP) scenarios were implemented in the LEAP software. According to the results, electricity generation will start gradually from the year of operation of the power plants in 2025 and reach more than 4.3 GWh in 2026. The production process will be incremental and is expected to reach 115.9, 119.1, 111.8, 118.4, 123.1, 118.9, 118.4, 118.4 GWh, respectively under the scenarios of gasifier CHP, CHP turbine incinerator, CHP steam incinerator, landfill CHP, syngas CHP, anaerobic digester CHP, combined gasifier and incinerator CHP, and ultimately improve to 118.9 GWh under the scenario of optimized gasifier and incinerator CHP. The required power plant capacity under the above-mentioned scenarios is expected to be approximately 21 MW by the year 2041and modify to 20.5 MW under the optimization scenario. The incinerator, combined-incinerator-and-gasifier, and optimization scenarios meet the supply and demand conditions of the generated waste, and in other scenarios, either the CHP supply share should be lower than 50% or the additional waste should be supplied from the nearby villages and towns.

Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines requires considerable amount of energy, it delivers a suitable surface slope with minimal soil deterioration as well as damage to plants and other organisms in the soil. Notwithstanding, in recent years researchers have tried to reduce fossil fuel consumption and its deleterious side effects, using new techniques such as Artificial Neural Networks (ANNs) and Adaptive Neuron-Fuzzy Inference System (Fuzzy shell-clustering algorithm) models that will lead to a noticeable improvement in the environment. The present research investigates the effects of various soil properties such as Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in energy consumption. The study consists of 90 samples, collected from three different regions. The grid size has been set on 20 m * 20 m from a farmland in Karaj Province, Iran. The aim is to determine the best linear model, using ANNs and ANFIS model to predict environmental indicatorsand find the best model for land leveling in terms of its output (i.e. Labor Energy, Fuel energy, Total Machinery Cost, and Total Machinery Energy). Results show that ANFIS can successfully predict labor energy, fuel energy, total machinery cost, and total machinery energy. All ANFIS-based models have R2 values above 0.995 and MSE values below 0.002 with higher accuracy in prediction, given their higher R2 value and lower RMSE value.