Impact of Determinant Macro Economic Variables on Environmental Changes in Iran

Abstract

Sustainable economic development involves both improving the socio-economic quality of life in the present and improving environmental conditions for future generations. The main objective of the study was to investigate the impact of the determinant macroeconomic variables, including economic complexity, construction, energy consumption, and housing sector expenditures, on environmental changes in Iran over the period 1991 to 2019; using the autoregressive distributed lag (ARDL) model. The results show that at the macro level, an increase in the economic complexity index can reduce pollution in both the short and long term. The effects of further expansion of the housing sector and household energy consumption on pollution are positive and significant in both the short and long run. The error correction model (ECM = 0.65) shows that 65% of the imbalances in each period are corrected in the next period. Based on the obtained results, it is recommended to take serious measures to improve the structure of production and increase the country’s economic complexity index; in other words, to produce goods with higher knowledge and technology and apply policies to improve the level of household energy consumption leading to air pollution reduction. Jel: Q40, Q48, Q50, Q52

Stabilizing Error Correction Mechanism in the Presence of Explosiveness

“…Stock [31], and Engle and Granger [25] estimated equation (3) using OLS estimator since Yt and Xt are of order I(1) and Yt and Xt are of order I(0).…”

“…The ARDL model can be applied to study the relationship between different economic indicators such as gross domestic product, exchange rate, money supply, inflation, and interest rate over time, Pesaran et al [3], Adamu and Usman [4], Aronu et al [5], Ibrahim [6], Charles et al [7], Elem-Uche et al [8]; to examine the relationship between financial variables such as stock prices and economic indicators, Adeleye et al [9], Narayan and Smyth [10], Catau and Asmah [11], Mustafa [12], Celina, U.C. [13], Enisan and Olufisayo [14], Rostin et al [15], Liaqat et al [16]; to investigate the impact of exchange rate on trade balance; Bahmani and Narayan [17], Belloumi [18]; to examine the effect of environmental factors, policies, or regulations on economic variables, Ozturk and Acaravci [19], Hamid et al [20], Saida and Kais [21]; to study the long-run and short-term effect of healthcare policies, expenditures, and other health-related variables, Mamun and Sohag [22]; to examine the relationship between energy prices, consumption, and economic growth over time, Zhigang and Huang [23].The idea of differencing of integrated time series before modelization was advocated by Box and Jenkins [24] while Engle and Granger [25] formalized the idea of cointegration, which is used in a variety of economic models (Iyeli et al, [26], Nkoro and Uko [27]. In recent years, the cointegration method has been developed to address the issue of non-stationarity in time-series data.…”

Economic complexity and housing deficit: an econometric analysis in Brazil

“…The authorities must provide conditions to intensify innovation, competitiveness, and economic diversification to grow and modernize the economy. Furthermore, Sepehrdoust et al (2023) investigated the impact of economic complexity, construction, energy consumption, and housing sector spending on environmental changes in Iran from 1991 to 2019. According to the authors, economic complexity determines the productive capacity of a country aiming at sustainable growth and development of human capital, institutional quality, and innovation processes and the concentration on export goods based on technology and technical knowledge.…”