جستجوی مقالات مرتبط با کلیدواژه "شاخص پیچیدگی اقتصادی" در نشریات گروه "اقتصاد"

Economic stability via Information and Communication Technology (ICT) has sparked interesting discussions among scholars. ICT plays a crucial role in realizing sustainable development objectives. Globally, the prospective advantages of ICT are widely acknowledged. Some research has solely emphasized ICT's role in mitigating air pollution, but the ecological implications of ICT have largely been overlooked. This article is pioneering in domestic studies of ICT's influence on ecological footprint. In addition, the present research uniquely computes the ICT index through the principal component method, distinguishing it from other ICT studies conducted within Iran. In recent times, the ecological footprint has been embraced as a broader gauge for assessing environmental damage. One reason for this choice is that other environmental harm indicators, such as air and water pollution, deforestation, and others, only represent a part of the total environmental degradation. However, the ecological footprint index incorporates diverse elements like agricultural lands, pastures, fishing areas, forests, carbon footprint, and constructed lands, hence offering a more holistic measure. Concerning the topic in question, it is evident from national studies that there has been little research on identifying the factors contributing to the ecological footprint.

In this research, we investigate the impact of the information and communication technology (ICT) index on selected oil-exporting countries' ecological footprint from 2006 to 2020. To do this, we use the generalized moments method. We extracted the model of this research from the studies of Higon et al. (2017) and Caglar et al. (2021) for carbon dioxide emissions. The variables of our study include the ecological footprint (as the dependent variable), the information and communication technology index (an explanatory variable calculated using the principal component analysis (PCA) method), and control variables such as GDP per capita, exports of goods and services, financial development, and economic Complexity Index which is chosen on the review of other studies. The data used for this study are taken from databases such as the World Bank and the Global Resource Footprint Network and the Atlas of Economic Complexity.
Discussion and

Given the challenges posed by global warming to current and future generations, this study aims to explore the impact of Information and Communication Technology (ICT) on the ecological footprint in chosen oil-exporting nations. This study studied the inverse U relationship of the information and communication technology index with the emission of ecological footprints from 2006 to 2020. The ecological footprint is an index of the amount of environmental pollution and a more comprehensive index than CO2. A data description was undertaken before estimating the model. The research model, built on theoretical underpinnings and past studies, was structured, and estimated by the Generalized Moments Method.The findings showed a non-linear connection between ICT and the ecological footprint in oil-exporting countries. ICT augments the ecological footprint per capita before a certain threshold, but it begins to diminish after that.The positive and significant coefficient of GDP per capita indicates the increase in ecological footprint per capita for the increase of GDP per capita. This result indicates that economic activities such as industrialization and development cause the exploitation of natural resources, which causes more pollution.Financial development has had a positive and significant effect on the ecological footprint. To prevent the destructive effect of financial development on the environment, governments in selected oil-exporting countries should develop financial markets in such a way that financial resources are available for investing in projects that help introduce clean energy technologies. The economic complexity index has had a negative and significant effect on the per capita ecological footprint. In fact, the expansion of economic complexity in the studied countries will lead to the reduction of the ecological footprint. According to the obtained result, the economic complexity index can be considered as one of the ecological footprint control factors; Therefore, the production of more complex goods that contain higher technology can lead to a reduction in energy consumption and ecological footprint; Therefore, governments can provide tax exemptions and subsidies for those companies that use new technology and clean energy, and also support knowledge-based products.The influence of goods and services exports on the ecological footprint has been negative and substantial. The significance of the quality and diversity of exported goods regarding environmental destruction has not yet been thoroughly considered. Therefore, the focus should be on enhancing the quality of export goods via cleaner production methods. Overall energy consumption should also be reduced in all countries, with policymakers prioritizing the use of renewable energy resources and promoting the reduction of fossil-fuel energy export products.The influence of urban population growth on the ecological footprint has been positive and substantial. Essentially, uncontrolled population growth, especially in developing countries, creates grave issues including scarcity of food, poor air and water quality, environmental contamination, degradation of the ecological structure, waste disposal problems, and high energy usage.

An economy that is based on simple production is under threat every moment. Therefore, one of the central strategies in the realization of economic growth and development is to rely on the production and export of complex and knowledge-based products. In complex societies, people with different knowledge must be able to communicate and combine their knowledge to produce a product. Economic complexity in relation to the composition of a country's products expresses a set of abilities to combine knowledge and skills. Therefore, societies lacking this set of abilities fail to produce complex products. Accordingly, the main goal of this article is to investigate the effect of economic complexity index on Iran's GDP.

In this research, seasonal data over the period 1995-2019 have been used in Iran, and Bayesian vector auto-regression (BVAR) model with Minnesota prior distribution has been used to investigate the effect of economic complexity on the level of GDP. The Bayesian vector auto-regression model provides more reliable predictions on the relationship between economic complexity and the level of GDP, due to the reduction of model parameters and the consideration of prior functions.

The results of the instantaneous reaction function of the level of gross domestic product (GDP) to the shock of the economic complexity index (ECI) show that this shock has a negative reaction on the level of GDP. Therefore, the impact of the economic complexity index shock on the level of GDP is negative in the long term. In addition, the results show that the financial freedom index (FIS) shock increases the level of GDP in the long term. The response of GDP to the shock of the Investment Freedom Index (INV) is also very weak and almost neutral during 10 periods. The reaction of the GDP level to the shock of the business freedom index (BUS) is negative. The effect of the corruption index shock (COR) on the GDP level is negative, and the reaction of the GDP level to the bank credits to the private sector (CRDT) shock indicates the negative effect of this shock on the GDP level. The reaction of the GDP level to the inflation shock (INF) is negative during the period under review. The effect of the shock of trade openness (OPN) on the level of GDP is positive and low during 10 periods. This means that the trade openness shock increases the level of GDP in the long run.  In addition, the reaction of the GDP level to the domestic direct investment (FDI) shock is positive. The results of the variance analysis also show that the shock variables of the economic Freedom Index (ECON), Foreign Direct Investment (FDI) and Inflation (INF), have the greatest impact on the GDP level, respectively. The variable shock of investment freedom index (INV) also has the least effect on the dependent variable.

The results show that the economic complexity index shock has a negative effect on the GDP level. According to Kremer's O-ring theory, greater complexity with increased specialized tasks and responsibilities leads to reduced production. In other words, when the production of goods is followed in a specialized manner, there is a possibility that in some stages of production, human capital and labor will not have the necessary ability to produce goods with high complexity due to lack of knowledge and skills. As a result, it causes a decrease in the production level. Therefore, the diversity of knowledge and skills in the production of products must be accompanied by the production of complex products. Iran's low rank in the economic complexity index shows the simplicity of the economy and the existence of structural weaknesses as well as vulnerability in the production and export structure. Therefore, in order to achieve economic complexity in the export portfolio and increase the level of production of products, the process of converting theoretical and scientific knowledge into complex and knowledge -based products should be followed with appropriate foundation.

Reducing the emission of pollution, especially polluting gases, is one of the important goals of the world's energy and environmental policies. The purpose of this research is to investigate the effect of energy consumption structure on the emission of polluting gases in some countries of the world, including industrialized developed countries and developing countries. To meet this end, while using data for the period 1995 to 2019, a panel smooth transition regression (PSTR) was applied. In this paper, energy consumption structure, economic complexity index, urban population and the degree of openness of the economy considered as effective factors on the emission of polluting gases. The results of the linearity test confirm the existence of a non-linear relationship between the research variables. A transfer function with a threshold parameter that represents a two-regime model was considered to specify the non-linear relationship between model variables. The slope parameter (transmission speed) is equal to 3.1964. The results of the tests indicate that in both regimes (first and second) in industrialized and developed countries, the structure of energy consumption has a positive effect on the emission of polluting gases.

Reducing greenhouse gas emissions is one of the most important goals of the world’s energy and environmental policies. Even though fossil fuels are one of the most important factors in creating pollution, their role in the structure of production and economic growth cannot be ignored. Nowadays, to measure economic growth, economists do not consider only the amount of production of goods and services, but also consider the structure of production of goods and services in terms of technical knowledge (technology level) used in them. Accordingly, in recent decades, the index of economic complexity has been proposed, and by calculating it the possibility of knowing the development of countries’ levels is provided. The purpose of this study is to investigate the effect of the economic complexity index on greenhouse gas emissions in some oil exporting countries in the period from 1995 to 2019 using the panel smooth transition regression (PSTR) model. The results of the linearity test confirm the existence of a nonlinear relationship between the considered variables. Also, considering a transfer function with a threshold parameter that expresses a two-regime model is sufficient to specify the nonlinear relationship between the model variables. The slope parameter (transition speed) equals 3/1964. The test results indicate that in both regimes (first and second), the economic complexity index has a negative effect on the amount of greenhouse gas emissions

Moving towards a knowledge-based economy, in which growth depends on the quantity, quality, and availability of existing information, is of great importance. The amount of technological knowledge available in a country reflects the level of development. One of the most reliable indicators for measuring the amount of technological knowledge of countries is the Economic Complexity Index. The research attempts to examine the causal relationship between the economic complexity and the components of the knowledge economy. To this end, a panel data model was used. The data consists of 113 countries over the period 2016-2006. For the knowledge economy, we used fourteen variables, which were introduced by the World Bank. By employing Principle Component Analysis, four components were calculated as indicators of the four knowledge economic pillars. Then, the Granger causality relation of these components with the economic complexity was investigated. The result shows that there is a two-way causal relationship between the components of Education, Communication and Information Technology, Economic Institution and economic complexity, and one-way causal relationship from Innovation to Economic complexity. Furthermore, the causal relationship varies among OECD and non-OECD countries.

Economic complexity approach is based on this idea that commodities produced in an economy represent the level of productive knowledge required for making it. Assuming that countries do no produce any output unless they have knowledge and skill to produce it, this approach computes the accumulation of productive knowledge available in the national economies.
This article uses economic complexity approach to identify the level of productive knowledge in Iran’s economy. To this end, we use data on exports to countries included in Iran Vision 2025 during 2005 to 2014. We apply both ubiquity and diversification components in economic complexity index (ECI) in order to rank the region’s products and economies. The findings indicate that Israel and Turkey are of the high levels of complexity in the region under the study while Iran ranks 17th among 19 countries. Finally, Yemen and Turkmenistan rank 18th and 19th in the region.