Introduction

The emission of greenhouse gases and the consequent pollution of the environment are important issues that are the concern of many policy makers in many countries. To reduce air pollution, the emission of greenhouse gases must be prevented; therefore, first, it is necessary to know the factors affecting the emission of greenhouse gases. By accurately knowing the factors affecting the emission of greenhouse gases and determining the effectiveness of each, in the next step, effective policies can be adopted to reduce environmental pollution; Therefore, in the continuation of this research, firstly, the reason for the importance of paying attention to greenhouse gas emissions and the factors affecting it will be discussed in detail, then the innovation of the research and the difference of the current research compared to the previous studies will be stated, and at the end of the introduction, the method and the way to respond to the questions are stated.

According to the National Aeronautics and Space Administration (NASA) research study, greenhouse gas emissions in the world are increasing, which has led to numerous environmental problems, including global warming. Environmental problems are one of the main problems around the world, which have been studied from different angles, including the Kuznets Curve Hypothesis. According to the environmental Kuznets curve hypothesis, there is an inverted U-shaped relationship between economic growth and environmental quality National Aeronautics and Space Administration (NASA).¹ Many studies such as Pita et al² and Rahman et al³ have confirmed the impact of economic growth and development indicators on the environmental quality of countries. Sustainable economic growth and development have always been considered by governments and policymakers in most advanced developing countries, but despite its many benefits, it generally has its problems, of which pollution is one of the most important. In other words, as economic growth is usually accompanied by increased use of resources and factors of production such as energy, it can also lead to environmental degradation by increasing the emission of pollutants and eventually posing a serious challenge to long-term economic growth and sustainable economic development. According to the World Health Organization (WHO), about 7 million people worldwide die each year from air pollution-related diseases. It is noteworthy that about 88% of these people live in low and middle-income countries.⁴

Iran is one of the developing countries facing severe air pollution, and in most populous cities, air pollution has caused irreparable damage to public health and the economy. Therefore, studying the factors affecting pollution seems very necessary given the issues raised.⁵ It is noteworthy that according to studies conducted by the Environmental Research Institute of Tehran University of Medical Sciences, and also according to the findings of other researchers, the air quality in various parts of Iran deviates significantly from the global standard values. Every year, about 40 000 people die in the country due to contact with particulate matter, which is about 10% of all deaths in the country.⁶ Pollution in Iran has increased over time, so one of the biggest challenges facing the country in the last 2 decades has been environmental problems. The problems related to pollution in Iran, especially in urban areas, are much greater and this problem has led to air pollution becoming a major socio-economic challenge for planners and people. It is also noteworthy that the group most affected by air pollution is the elderly and their proportion in the country will increase in the coming years, so the problem of pollution may have a much greater impact in the future. It is noteworthy that among the types of greenhouse gases, carbon dioxide gas is one of the most important because according to the latest national report of Iran’s greenhouse gases, CO₂, and methane account for 77.5% and 18.9% of the total mass emission, respectively. allocated; An increase in greenhouse gas emissions can cause environmental destruction and subsequent air pollution; Therefore, it is very important to investigate the variables that affect the emission of greenhouse gases and also the extent of their influence, and this is the main goal of this research. It is worth noting that the emission of carbon dioxide (CO₂) in Iran has continued to increase from 1991 to 2022, which can lead to sustainable economic growth in the face of major problems. Statistical data show that the quality of the environment and the amount of pollution in the air in Iran are not in a good condition compared to other countries in the world. Figure 1, shows the increasing trend of carbon dioxide emissions and it can be seen that the growth of carbon dioxide emissions in Iran is continuously increasing during the studied years.

Figure 1.

The rate of growth in carbon dioxide emissions‎ 1990 to 2019.

Source: World Bank Database.⁷

The role of housing sector activities in economic growth and development by adding value as a contributor to gross domestic product (GDP) and creating jobs and employment opportunities is critical for many developing countries. The impact of housing sector activities on other macroeconomic variables such as employment, public expenditure, GDP, and many other variables have been examined in most empirical studies and have found that despite the significant benefits of housing sector activities, the development of this sector also poses problems.⁸ It should be noted that activities related to the construction of buildings and their implementation generally have a high share of GDP in developing countries; this has also led to the housing sector being economically more important in this group of countries. For example, in Iran, between 5% and 10% of the national income is related to the added value of the housing sector. Figure 2 shows the share of the added value of the housing sector in the national income. As shown in the graph, despite its high fluctuations, is always a significant percentage of the national income related to the share of the housing sector.

Figure 2.

The ratio of added value of the housing sector to national income 2010 to 2020.

Source: Central Bank of Iran.

The housing sector is one of the leading economic sectors in most developing countries due to the creation of various jobs and poverty reduction.⁹ One of the most important problems in the development of the housing sector is the high consumption of natural resources and energy in this sector. For example, the housing sector in China is the second-largest producer of carbon. The high energy consumption in the housing sector has led to an increase in greenhouse gas emissions, which may also result in increased pollution.^{10 –12}

The housing sector in Iran is more important than in any other developing country because many people in Iran invest in the housing sector. In other words, due to high inflation in Iran, the housing sector is considered a durable goods investment sector in addition to meeting people’s consumption needs. Therefore, the housing sector in Iran has not only met people’s consumption needs but has always been considered an effective option for preserving the value of assets compared to other investments.¹³ Despite the great importance of the housing sector, this sector is also known as one of the sectors that pollute the environment. Regarding the impact of the housing sector on environmental pollution, important cases can be mentioned such as improper disposal of construction waste through demolition, non-compliance with laws and regulations related to energy conservation, and conversion of natural forests and agricultural land into residential areas. He pointed out that problems that are not addressed will lead to significant environmental pollution.

Another area that seems to be related to pollution is the structure of production. In recent studies, the relationship between the structure of CO₂ production and emissions has been investigated and confirmed by many researchers.^14,15 An economic complexity index can be used to show the structure of production. This index was developed by Hidalgo and Hausmann and is an indicator of the complexity of economies. In other words, this index is considered one of the determinants of production structure in terms of technology use. Economic complexity determines the productive capacity of a country with the aim of sustainable growth and development of human capital, institutional quality and innovation processes, and concentration on export goods based on technology and technical knowledge. Typically, countries with higher rankings in the economic complexity index export more complex products and also have higher GDP, per capita income, and growth rates.^16–19

Given the given definition, we can say that countries with higher economic complexity are likely to have more complex production structures. Accordingly, we expect that a country with higher economic complexity will practically use newer technologies and techniques in the production of its products. In other words, because countries with higher economic complexity are likely to use newer techniques and methods in manufacturing products, we expect emissions of environmental pollutants to decrease in this group of countries.²⁰

Experimental and field studies in Iran have shown that in Iran, due to the special conditions of environmental quality and severe pollution beyond global standards, especially in densely populated cities, it is necessary to pay more attention to effective factors. While until now, few coherent kinds of research have been done in terms of examining the effective factors at the macroeconomic level. Since many developing countries such as Iran, it is necessary to conduct such studies at the international level to reduce environmental problems. While previous studies have examined the effect of various variables on environmental pollution from different aspects, it seems that these studies are not comprehensive enough. For this purpose, in this research, while paying attention to the previous studies, we contribute to the development of the existing literature with a comprehensive model and investigate and analyze the variables affecting environmental pollution. This study uses the ARDL method to examine the relationship between the housing sector, economic complexity, household energy consumption, and environmental pollution indicators (CO₂ emissions). It is noteworthy that the purpose of this study is to investigate the impact of different variables on the amount of greenhouse gas emissions. In order to achieve this goal, we are trying to take a comprehensive look at variables affecting environmental pollution by developing previous studies and aggregating previous studies, so that we can take a step toward improving the literature of this important field by presenting a more comprehensive model than previous studies. According to the stated purpose, the results of this study can be useful for economic planners in the housing sector and related activities, so that the opinion of economic politicians could be directed to identify the effective factors in environmental pollution by using technical knowledge and the use of economic complexity in the production of goods and services and housing, to control the level of environmental pollution.

Following, we will examine the issue first from a theoretical point of view and then from an experimental point of view; Therefore, the continuation of the article is as follows: the second part is related to the review of theoretical literature, the third part is the review of the research method, the fourth part is the review and statistical analysis, and the fifth part is the conclusion and policy recommendations based on the results obtained in the research.

Theoretical Background

Global warming and climate change and increasing awareness of the problems caused by it have increased the importance of knowing and investigating the factors affecting this problem. This issue has also led to the creation of extensive literature related to the effects of greenhouse gas emissions, especially carbon dioxide. Due to the high importance of the issue, almost all countries have agreed to implement policies to reduce carbon dioxide emissions. Global warming, environmental degradation and climate change are still considered the most dangerous issues in the world. To control these problems, many efforts have been made to identify the main factors of environmental degradation.^21–23 To reduce carbon dioxide emissions, we must first identify the factors affecting it; Therefore, in the following, we will examine the effect of different variables on the emission of carbon dioxide:

Material and Methods

Based on the objective of the study and the literature review of the previous empirical studies investigating the impact of the determinant macroeconomic variables on environmental changes in Iran considering the volume of carbon dioxide emissions as the dependent variable and the index of economic complexity, housing units produced, household energy consumption and housing sector expenditure as independent descriptive variables. For this purpose, the ARDL model has been used to estimate the model. One of the suitable methods for analyzing long-term and short-term relationships between variables is the ARDL approach. This method estimates the long-term and short-term patterns of the model at the same time and solves the problems related to the elimination of variables and autocorrelation. In the auto-regression distributed lag model developed by Pesaran et al,⁶⁹ the ARDL approach is suitable for smaller samples, while larger samples are required to trust the results of the VAR and VECM approaches. In this model, the dependent variable is affected by the lag of these variables and other independent variables. ARDL method estimates are sparse and efficient due to avoiding problems such as autocorrelation and endogeneity. Finally, we include the error correction component resulting from the long-term relationship estimation as an explanatory variable in the existing models. There is a close relationship between clustering and error correction models. In ECM, information related to data is usefully used to model short-term and long-term relationships. For this reason, despite the different interpretations of correction models, their use has been widely accepted in applied econometrics. The main reason for the popularity of these models is that they relate short-term fluctuations to long-term equilibrium values of variables. These models are actually a type of partial adjustment models in which by entering the stable residual from a long-term relationship, the effective forces in the short term and the speed of approaching the long-term equilibrium value are measured. Based on the stated cases and the following estimation model was developed following the study of Lavagna et al⁷⁰ as equation (1) for the years 1991 to 2019; using the autoregressive distributed lag model (ARDL).

(1)

Where LCO₂ is the logarithm of environmental effects in terms of CO₂ emissions, LECCI is the logarithm of economic complexity, LHENC is the logarithm of residential energy consumption, LHEXP is the logarithm of residential spending, and LRBCN is the logarithm of construction in terms of the number of building units constructed. It should be noted that the sample of the study is Iran and the study period is between 1991 and 2019 (Appendix 1).

Carbon Dioxide Emissions (CO₂) are the main cause of global climate change. Many researchers believe that greenhouse gas emissions must be reduced immediately to prevent the worst effects of climate change in the world. The relevant data collected shows the amount of carbon dioxide emissions.

Economic Complexity Ranking (ECCI) is a measure of a given country’s capabilities and knowledge based on the diversity, scale, and complexity of the products it exports.

Households Energy consumption (HENC) is used for a variety of purposes, including space and water heating and cooling, cooking, lighting, and electrical appliances, among other uses; therefore, the data in this section show household energy consumption.

Housing Sector Expenditures (HEXP) is used to emphasize the importance of the housing sector and its relationship with macroeconomic variables of other sectors’ economic activities. The housing sector is always considered one of the drivers of economic growth, and any investment in the housing sector is measured by the number of expenditures on the construction of buildings.

Residential Building Construction (RBCN) shows the number of new buildings constructed each year, and its value-added is counted annually in the gross domestic product.

The sources of data collection are reliable, as the data for CO₂ emissions are from the World Bank database, the data for economic complexity are from the Atlas of Economic Complexity, the data for energy consumption are from the World Energy Balance, and the data for housing and building construction expenditures are from the Central Bank of Iran database. Table 1 shows the descriptive statistics of the variables over the period 1991 to 2019.

Table 1.

Descriptive statistics of the variables, years 1991-2019.

Finding and Results

This study aimed to investigate the impact of the determinant macroeconomic variables on environmental changes in Iran. For this purpose, the auto-regression distributed lag (ARDL) model and time-series data for the period 1991 to 2019 were used. In the auto-regression distributed lag model developed by Pesaran et al,⁶⁹ the ARDL approach is suitable for smaller samples, while larger samples are required to trust the results of the VAR and VECM approaches. In this model, the dependent variable is affected by the lag of this variable and other independent variables. This model has advantages compared to other statistical models. Considering that the ARDL method can be used only when the independent variables are stationary at level I(0) or I(1); therefore, the stationary of the variables should be checked first.

The stationary test of the variables

To avoid false regressions in model estimation, the significance of variables was examined using the extended Dickey-Fuller unit root test. The Schwartz-Bayes method was used to determine the optimal lag due to the small amount of data. Based on the results of the stationary test presented in Table 2, all variables are non-stationary at the level and all are stationary with the first difference. The results obtained confirm the use of the ARDL method. Since the variables are not stationary at the level, if general econometric models are used, there is a possibility of spurious regression. But studies show that there is no spurious regression problem in ARDL models if all variables are stationary at level I(1) level. Ghouse et al⁷¹ showed in a study that the ARDL model can be used as an alternative tool to avoid the spurious regression problem.

Table 2.

Results of augmented Dickey-Fuller test.

ARDL model estimation

After making sure that the conditions for using the ARDL method are met, Table 3 shows the model estimation along with the summarized results in terms of the extracted coefficients. It is noteworthy that the probability level of less than 5% indicates that the studied variables have a significant impact on the environment. Considering that the probability of F statistic is less than 5%, the whole model is significant and the coefficient of determination of 98% indicates the adequacy of the variables examined in this study. As can be seen in Table 3, the increase in economic complexity causes a decrease in pollution, so for a 1% increase in economic complexity, pollution decreases by 0.22%. It should be noted that the increase in energy consumption and investment in housing also causes an increase in pollution; the results of Table 3 also show that when energy consumption and investment in housing increase by 1%, pollution increases by 0.51% and 0.09%, respectively. The effect of the number of buildings produced on pollution is also positive but not statistically significant.

Table 3.

Model estimation results of ARDL (1,0,0,0).

Diagnostic tests

As with any other econometric model estimation method, the ARDL requires testing the best fit and goodness of the model using tools and indicators to ensure that it is appropriate. To this end, the fit of the model is tested and the results are summarized in Table 4. The test results, which can be seen in Table 4, confirm the goodness of the model. In other words, considering that the confidence level of all diagnostic tests is above 5%, the functional form of this model is appropriate, and this model is normal and does not have the problem of autocorrelation and heterogeneity of variance.

Table 4.

Diagnostic tests.

Long-run relationship test

The results of the long-term relationship of the variables are examined and summarized in Table 5. This shows that the variables of economic complexity, energy consumption, and housing expenditures affect the environment in the long run, and their effects are fully consistent with the theoretical basis. The results also show that CO₂ emissions in the previous year and the number of building units constructed in the long term do not have a significant impact on the environment, considering the technological improvement in construction in the long term. Therefore, this variable was omitted in the calculation of the long-term relationship.

Table 5.

Long-run ARDL model estimation.

The results of Table 5 show that the increase in economic complexity leads to a decrease in pollution in the long run; thus, for a 1% increase in economic complexity, pollution will decrease by 0.18% in the long run. The effects of increasing energy consumption and housing investment on pollution, in the long run, are similar to those in the short run, so for a 1% increase in the above variables, pollution increases by 0.42% and 0.07%, respectively.

Error correction model estimation (ECM)

After estimating the long-run pattern, we estimate the error correction model (ECM). The error correction model represents the fit of the short-term fluctuations to the long-term relationship. In other words, the error correction model relates the short-term fluctuations of the variables to their long-term values. The result of the error correction test can be seen in Table 6. The coefficient (ECM = −0.65) is negative, less than one, and significant, confirming the existence of convergence in the model and indicating that about 65% of the disequilibrium is corrected to approach a long-term relationship in each period.

Table 6.

Error correction model estimation (ECM).

Discussion and Conclusions

With the emergence and spread of environmental problems, both in developed and developing countries, the importance of environmental protection has been felt more than in the past. Since the early 1960s, the environment has prompted leaders and thinkers in Western countries to consider how to escape its dire consequences. It is noteworthy that more attention has been paid to the environment in the last 2 decades than in the past, which shows the great importance of the environment for sustainable development. The study of factors affecting environmental pollution is important and necessary in all societies, but it should be noted that the environment in Iran has gone through difficult times in the last decade due to the imposition of extensive financial and economic sanctions. Increased pollution in Iran, especially in densely populated cities, has caused many problems, including threats to people’s health and the closure of many polluting factories.

Economic complexity is an important variable whose relationship with pollution has recently received much attention. It is believed that the more complex the economy, the less it pollutes the environment because it uses newer technologies. The estimation results of the model show that economic complexity has a negative and significant impact on environmental change. In other words, the results of this research show that the increase in economic complexity reduces environmental pollution in both the short and long term.

The results of the statistical tests conducted in this study showed that with a 1% increase in economic complexity, environmental pollution decreases by 0.22% and 0.18% in the short term and long term, respectively. It is noteworthy that in the study of You et al,⁷² the existence of a causal relationship between economic complexity and environmental pollution was confirmed. In the study of Caglar et al,⁷³ as well as Khezri et al,⁷⁴ the existence of a relationship between economic complexity and environmental quality was confirmed. In the present study, in addition to confirming the existence of the relationship between the mentioned variables, the degree of relationship in the short and long term has also been determined. In other words, in this study, the same as the study of You et al, Caglar et al, and Khezri et al the effect of the economic complexity variable on environmental pollution was confirmed; In addition, in this study, the impact of economic complexity on the environment has been separated in 2 short-term and long-term period. Considering the significant influence that economic complexity has on pollution, special attention should be paid to this variable to reduce pollution. In other words, the use of advanced technologies in the production of goods can play an important role in reducing pollution in both the short and long term. In the study of Qin et al,²¹ the positive effect of increasing research and development (a key variable on the increase of economic complexity) on the reduction of carbon dioxide emissions was confirmed.

On the other hand, research results show that with an increase in energy consumption in the residential sector, pollution also increases. In other words, energy consumption in the residential sector increases pollution both in the short term and in the long term. It should be noted that the impact of energy consumption in the residential sector on the environment is significant both in the short term and in the long term. The results of the statistical tests concluded in this study showed that with a 1% increase in energy consumption, environmental pollution increases by 0.51% and 0.42% in the short term and long term, respectively. The obtained result is consistent with the studies of Khezri et al⁷⁴. Also, the study of Majeed et al²² also confirmed the negative impact of energy consumption on environmental pollution. Considering the large impact of energy consumption in the residential sector on pollution, this is one of the sectors that should be paid special attention to reduce pollution. In conclusion, efforts to increase the efficiency of energy consumption in the residential housing sector can reduce pollution in the short and long term.

The housing sector is very important in the Iranian economy for several reasons, including the high inflation in the country. This sector not only meets the consumption needs of people but also meets the needs of investors. In the Iranian economy, the housing sector is a leading sector whose prosperity can also affect other sectors of the country’s economy. Despite many advantages of housing sector activities in economic growth and development, it is considered one of the most important sectors in the field of environmental pollution. The results show that the impact of housing expenditures on pollution is positive and significant in both the short run and the long run.

The results of the statistical tests in this study showed that with an increase of 1% in spending on housing construction, environmental pollution increases by 0.09% and 0.07% in the short term and long term, respectively. This means that with the increase in spending on housing construction, pollution increases both in the short and long term. According to the results extracted, it can be said that despite the great importance of housing sector activities on other macroeconomic variables in the economy, their negative effects should not be neglected. In other words, through complementary policies to the investments and while maintaining the positive effects of the investments, their negative effects on the environment should be reduced as much as possible. Special attention to the materials used in the construction of buildings, the building architecture, the control of heating and cooling systems, and similar factors can have a significant impact on reducing energy consumption and, consequently, on reducing environmental impact. Among the cases mentioned, the role of building materials in reducing pollution caused by construction is very impressive.