بررسی اثر فقر بر کیفیت محیط زیست در کشور های در حال توسعه

Abstract
Introduction
Attention to the environment and pollution caused by human activities is receiving more and more attention, due to the impact it has on people's lives and well-being. This attention, the laws enacted in this area, and the level of compliance with the laws enacted have a significant relationship with the income of countries worldwide.
The issue of poverty and environmental protection are both complex issues that have different dimensions and both have a great impact on the quality of life of the people, and it is because of these complexities that policymaking in this area becomes difficult. For this reason, studying it can reveal the connection between these two sectors and provide a lot of information to policymakers in this sector. This issue becomes more important, because a large part of the people who are economically vulnerable usually live in areas that are under various environmental pressures.
Theoretical Framework
Ravallion (2015) in his book The Economics of Poverty states that poverty exists in a given society when individuals in the society have not achieved a minimum reasonable amount of economic well-being commensurate with the standards of the society. Criteria of measuring the poverty vary in different societies, and therefore it is difficult to examine it globally. To solve this problem, economists have sought to create criteria that can be used globally. To measure poverty, cannot rely on a single unit of measurement, but several measures must be used. However, because there is a need for a criterion for measuring poverty, the one dollar a day line has been used as the basis for measurement, based on which approximately one billion of the poorest people in the world can be identified.
To further refine the classification, the World Bank has developed new standards for poverty for those living in middle-and high-income countries. These poverty lines are set at $3.2 per day for lower-middle-income countries, $5.5 per day for high-middle-income countries, and $21.7 per day for high-income countries.
Environmental Kuznets Curve
Various theories and reasons have been put forward in explaining the EKC. According to one theory based on the Solow growth model, the EKC can represent three stages of the development process, in such a way that: 1) in the first stage, most of the production is related to the agricultural sector, which is relatively less polluting. 2) in the next stage of development, the production of heavy industries increases, which cause relatively high pollution, and then 3) the production of industries with advanced technology and services dominate over other sectors, which produce relatively lower levels of pollution.
Methodology
Many economic relationships involve dynamic processes. Dynamic models in the framework of panel data are very popular in labor economics, development economics, and macroeconomics in general. Including the lag of the dependent variable as independent variable provides dynamic adjustment in an econometric model. At the same time, the lag of the dependent variable is associated with cross-sectional special effects and can cause the endogeneity problem. The endogeneity problem may cause inconsistency of OLS estimators. The use of instrumental variable (IV) methods or the GMM method allows for consistent parameter estimation for limited time-period data with large cross-sections. Among these estimators, the GMM system estimator has become increasingly popular. This is because it provides efficient inference using minimal statistical assumptions.
The GMM method is used when the number of sections is greater than or equal to the number of time periods. Also, the number of instruments should be less than the number of groups. There are several tests to check the suitability of using this method. Serial correlation test of regression residuals, such that in GMM estimation, error terms can have first-order serial correlation (AR (1)) and not have second-order serial correlation (AR (2)). The Sargan and Hansen test tests the validity of the instruments used in the estimation, which if the null hypothesis is rejected, the instrumental variables used in the model are valid. The research model is based on the research of Marson and Subramaniam (2019). According to previous research, we predict that the estimated coefficient related to poverty will be positive. In this study, according to the research of Rizek and Slimin (2018), the per capita valueadded variable of the industrial sector and also considering the importance and impact of the unemployment variable, this variable is also added to the model and it is examined whether these two variables have an effect on environmental degradation.
Results
According to the results of the stationarity test, the null hypothesis of the existence of a unit root for all variables has been rejected at the 99% confidence level, indicating that the variables under study are stationary.
According to the results of the estimates, the effect of poverty on environmental degradation is significant. Based on the results in the model with the dependent variable of ecological footprint, the estimated coefficient of the poverty variable is 0.021 and in the model with the dependent variable of per capita carbon dioxide emissions, it is 0.05. This means that a one percent increase in the population below the poverty line in the first model leads to a 0.021 percent increase in environmental degradation and in the second model to a 0.05 percent increase in carbon dioxide emissions. The reason for this difference in the coefficient in the case where the dependent variable is the ecological footprint and the estimate that per capita carbon dioxide emissions are the dependent variable may be that, given that the ecological footprint includes a wider range of pollutants and environmental destructors, some of the activities that lead to the production of these pollutants may have a different impact on poverty and the outcome of these effects may be different from the case where we only examine the per capita effect of carbon dioxide production.