Purpose The purpose of this paper is to predict the amount of energy consumption by using a suitable statistical method in some sectors and energy carriers, which has shown a significant correlation with greenhouse gas emissions. Design/methodology/approach After studying the correlation between energy consumption rates in different sectors of energy consumption and some energy carriers with greenhouse gas distribution (CO2, SO2, NOX and SPM), the most effective factors on pollution emission will be first identified and then predicted for the next 20 years (2015 to 2004). Furthermore, to determine the appropriate method for forecasting, two approaches titled “trend analysis” and “double exponential smoothing” will be applied on data, collected from 1967 to 2014, and their capabilities in anticipating will be compared to each other contributing MSD, MAD, MAPE indices and also the actual and projected time series comparison. After predicting the energy consumption in the sectors and energy carriers, the growth rate of consumption in the next 20 years is also calculated. Findings Correlation study shows that four energy sectors (industry sector, agriculture, transportation and household-general-commercial) and two energy carriers (electricity and natural gas) have shown remarkable correlation with greenhouse gas emissions. To predict the energy consumption in mentioned sectors and carriers, it is proven that double exponential smoothing method is more capable in predicting. The study shows that among the demand sectors, the industry will account for the highest consumption rate. Electricity will experience the highest rate among the energy careers. In fact, producing this amount of electricity causes emissions of greenhouse gases. Research limitations/implications Access to the data and categorized data was one of the main limitations. Practical implications By identifying the sectors and energy carriers that have the highest consumption growth rate in the next 20 years, it can be said that greenhouse gas emissions, which show remarkable correlation with these sectors and carriers, will also increase dramatically. So, their stricter control seems to be necessary. On the other hand, to control a particular greenhouse gas, it is possible to focus on the amount of energy consumed in the sectors and carriers that have a significant correlation with this pollutant. These results will lead to more targeted policies to reduce greenhouse gas emissions. Social implications The tendency of communities toward industrialization along with population growth will doubtlessly lead to more consumption of fossil fuels. An immediate aftermath of burning fuels is greenhouse gas emission resulting in destructive effects on the environment and ecosystems. Identifying the factors affecting the pollutants resulted from consumption of fossil fuels is significant in controlling the emissions. Originality/value Such analyses help policymakers make more informed and targeted decisions to reduce greenhouse gas emissions and make safer and more appropriate policies and investment.

Given that people in many jobs suffer from intense pressure being imposed on their muscles, work-related disabilities such as musculoskeletal disorders have turned into a major concern in industrial countries. Considering the significant financial and physical burden these disorders can put on people and society as a whole, preventing these issues seems more reasonable than remedying them. In this respect, there is a need for further studies concerning the prediction of muscle fatigue and activity under different working conditions. Accordingly, the present study considers an important aspect of this issue by focusing on postures in which the workers do not have access to the work station in the frontal direction. More specifically, the main purpose of this study is to present a statistical model to predict muscle fatigue, for which electromyographic signals are collected from the muscles of individuals while working at a simulated workstation, according to which the activities of the Longissimus thoracis and Iliocostalis Cervicis muscles are evaluated. Afterward, the wavelet transform is employed via Rbio 3.1 function at seven levels to process the collected signals, followed by using the normal mean absolute value index for feature extraction. Finally, some statistical models are created by the generalized estimating equation method. According to the results, posture factors, assembly cycle time, and rest intervals between cycles, which are variables, revealed significant impacts (p < .05) on muscle fatigue. It should be mentioned that the most suitable levels of the mentioned variables are also determined based on the Taguchi design of the conducted experiments. The presented statistical models can be used for designing and comparing workstations with respect to pressure on muscles for more effectively assigning workstations to employees, planning, and scheduling work cycles, and designing industrial machinery.

International electricity trade as a strategic commodity plays a prominent role in the foreign trade market of countries.Electricity export forecasting leads to better production planning, supply security, blackouts reduction, and obligations fulfillment. This paper aimed to provide a model for electricity export forecasting. In this regard, electricity consumption in different consumer sectors, gas consumption, population, GDP, and electricity prices have been entered into the multiple regression model as predictor variables. Although R 2 =.976, F=66.110, and SIG<.05 indicate the model appropriateness, the high correlation between the predictor variables created collinearity. In other words, Tolerance, VIF (variance inflation factor), Eigenvalue and the Condition Index are less than .2, more than 10, close to zero, and more than 15 respectively. To solve this problem, two hybrid methods of Multiple Regression-First Difference Function and Multiple Regression-PCA have been used. In the first hybrid method (R 2 =.553) the Tolerance and VIF index still show the presence of collinearity. In the second hybrid method (R 2 =.936, F=169.9, SIG<.05) due to all the mentioned indicators, the collinearity has been completely resolved. So, the MLR-PCA method is the most appropriate model for electricity export forecasting. The data collected from Iran have been used to illustrate the model.