عنوان مقاله [English]
One of the important foundations of industrial, economic and scientific life in the world is energy, which without life energy, especially in the industrial field, will face many problems. Experiences of recent decades show that with the increasing use of energy resources in the world and limited reserves of fossil fuels, the most important threats, waste and consumption of this variable is one of the main requirements for the proper use and consumption of energy resources. The growth of information and communication technology, rapid information exchange platforms, reducing transaction costs, increasing productivity and efficiency, have led to the emergence of views to support the idea that information and communication technology has the potential to reduce energy consumption without slowing economic growth. That information in the cycle of economic activities can play a role as an alternative input to energy. In other words, information causes the amount of energy consumption per unit of production to decrease or greater economic value to be created by consuming the same amount of energy). This approach raises the possibility of replacing information and energy, and states that these technologies can be used to store energy. Kelly (1999) believes that the added value created by ICT is usually due to human ideas and a small part of it is due to the use of materials and energy, so ICT changes economic structures and moves it from the use of materials and energy to non-physical inputs. And information. On the other hand, by changing the existing methods in the design, production, distribution and performance of various products, it increases efficiency and productivity in the use of resources and reduces damage to the environment (Fallahi et al., 2012). It should be noted that the impact of ICT and competitiveness on energy consumption is complex and has various aspects because ICT has direct and indirect effects and a reciprocal effect. Improving efficiency leads to lower energy costs, and this increase in cost-effectiveness due to cost savings may lead to more utilization of equipment and increase energy consumption. The impact of economic growth created by ICT and competitiveness may also increase energy consumption. Therefore, the purpose of this study is to investigate the effect of information technology and competitiveness index through testing the first hypothesis (information technology has a negative and significant effect on energy intensity) and the second hypothesis (the effect of competitiveness index has a negative and significant effect) on energy intensity. The organization of this research is as follows: in the next section, the theoretical foundations and empirical background of the research are discussed. After that, the method of researching and analyzing the data and performing the required tests will be done, and finally, conclusions and suggestions will be made.
The subject of the present study, due to its nature and the use of the results of documentary studies, is of the applied type and is classified as "semi-experimental" by "regression" method in terms of "data collection". This research is "descriptive" in terms of data collection method and "correlation" in terms of its type. Study period from 2011 to 2019 for 20 countries including: Brazil, Argentina, Colombia, Peru, Mexico, Chile, Malaysia, Bangladesh, Hong Kong, Indonesia, South Korea, India, Philippines, China, Taiwan, Thailand, Egypt, Turkey, Saudi Arabia Saudi Arabia is considered the Islamic Republic of Iran. Eveiws software has also been used to test hypotheses and statistical analysis.
• Research variables
Dependent variable: Energy intensity: A measure to evaluate and measure energy efficiency in the economy, which is obtained by dividing the units of energy consumed by a unit of gross domestic product (GDP). Higher energy intensity means higher price or cost of converting energy into national production. On the other hand, lower energy intensity indicates a lower price or cost to convert energy into production in the economy (Manzoor and Ancestors, 2014).
Independent variable, competitiveness: This index is a composite and weighted index that is presented in the form of three axes to assess the competitiveness of countries. These axes are: the level of meeting the basic requirements (which is the key to competitiveness of resource-based economies), the level of efficiency (which is the key to competing economies based on efficiency) and the level of innovation (which is the key to competing economies based on creativity). The indicators presented in each of the competitiveness axes do not affect competitiveness equally. Hence, the weights used for the indicators in one country are different from another and depend on the differences in the development stages. So that the first axis in relation to the basic requirements for countries that are in the early stages of their development have a high weight, while in developed countries, it has a low weight. Therefore, in order to find the weight of the axes, it is necessary to classify countries based on the stages of development. Are considered as resources) and countries are divided into three categories according to the table below. Mania (static) of variables: To avoid false regression, it is necessary to test the mania of variables. Because if the variables are not meaningful, they cause false regression. If the studied variables are constant, the estimates will not have the problem of artificial regression. In this section, first the root test of the panel data unit is performed, then the co-integration tests are used. In this study, Levin, Lin-Chou test was used to test the significance or reliability of variables. Given that the significance level of Levin-Lin-Chou test was less than 0.05 for all research variables, it can be said that the research variables are constant, so given the variability of the variables in regression analysis, there will be no problem of false regression. Had.
This study aims to investigate the impact of information technology and competitiveness on the intensity of energy consumption in 20 selected countries between 2010 and 2019, the data of which were collected based on reports from the World Bank and .... It was found that the research variables are meaningful, so given the meaning of the variables in regression analysis, there will be no problem of creating false regression. According to Levin's test, Lin-Chu confirmed the stability of the data used in the research before estimating the research models to test the significance of the variables. Despite this, in order to be more reliable and to obtain more validity of the model estimation results, the co-integration of the mentioned variables was also examined. In both models, it can be said that at the level of 99% confidence, the null hypothesis of the test based on the non-co-accumulation of variables is rejected and the variables are co-accumulated in the long run and there is a long-term relationship between them. It is in the long run. Jark's statistic also indicated that the research data was normal to indicate that the research data was abnormal, so Johnson's conversion method was used to normalize the dependent variables. After conversion, the significance level indicates the normality of the distribution of dependent variables. Also, the results of F-Limer test for research models show that the panel regression method is suitable for estimating research models with these data. Hausman test is used to determine the fixed and random effect. Based on the results of Hausman test, the panel method with random effects was used for the first model and the panel method with fixed effects was used to estimate the second regression model.