THE IMPACT OF ENVIRONMENTAL QUALITY ON LIFE SATISFACTION

Abstract

The issue of living quality is multifaceted and difficult to solve. It affects those who work in a variety of fields, including planners, designers, architects, economists, attorneys, and designers. Although it is difficult to determine the most relevant indicators for measuring quality of life, acceptable definitions may be developed based on the researcher's objectives. For several decades, the link between quality of life and the environment has been a major topic of research. Academic circles create a large number of articles. Economists, notably environmental economists, are becoming increasingly interested in subjective well-being, or Life Satisfaction. There are various reasons to believe that environmental quality (EQ), defined as a high level of environmental good and a low level of environmental 'bad,' will be linked to Satisfaction of Life. However, there is a scarcity of quantitative evidence in this area. Some studies employ cross-sectional data aggregated at the country level, although whether these aggregated measures accurately reflect individuals' actual EQ exposures is debatable. Other articles employ individual-level data, but they usually include spatial data at a very coarse resolution and only analyze a few number of EQ factors, all of which are restricted to the area around people's residences. The aim of the research is to examine the levels of life satisfaction in industrialized and developing nations, as well as to see whether improved environmental quality correlates with higher levels of life enjoyment. The major goal of sustainable development is to enhance people's quality of life, which is measured using a variety of criteria and indicators. One of the most significant indicators on quality of life is the quality of environment. Other important indications include: Unemployment, GDP growth, and the Globalization Index are all factors to consider. According to recent study, the influence of environmental quality on life satisfaction is both positive and considerable. The following model was used to produce econometric regression analysis: Ordinary least squares pooled (Pooled OLS). Endogeneity, multicollinearity, heteroscedasticity, autocorrelation, and cross-sectional dependency of variables have all been addressed using a variety of diagnostic tests. According to the model, there are five variables, namely, one dependent t variable and four independent t variables. Two independent factors were found to be significant in a 95 percent confidence interval, whereas the other two independent variables were not. Two variables, epi and unemployment, have a positive relationship with the dependent variable, whereas the other two, kofgi and GDP per capita, have a negative relationship.