GIS-based regression analysis of the relationship between ecological footprint and economic development of selected countries

Abstract

Ecological footprint is an innovative concept to present the idea of consumption of natural resources and generation of waste by the human in terms of the Earth’s biological carrying capacity. The aim of this research is to analyze the interactive relationship between economic development and ecological footprints of the selected nations. The GIS based spatial regression tool Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) are used for this purpose. In addition, the individual components which forms the aggregate ecological footprints are also analyzed with the per capita GDP of the nations in order to learn about their interrelationship. The analysis has found that, there is a significant relationship between ecological footprint and economic development and the OLS model can explain approximately 64% of the variation in the dependent variable with the explanatory variables. The OLSR model has also found that, there is a statistically significant heteroscedasticity or non-stationarity between the dependent and independent variables. Hence, the GWR analysis is applied for mapping the variation in spatial pattern of the regression model which shows the strong and weak predictors regions for the analysis. More to this, it is found that nation’s economic development contributes much in increasing the carbon footprint as the Multiple R value is about 82%; R square and Adjusted R square value is around 67%. However, this study has not found any valid relationship with the other components like grazing, forest land, fishing ground and built-up land footprint with per capita GDP. The resulted outcome has enough significance for studying the spatial dimension of environment and economy. This can contribute to analyze the individual nation’s economic growth and their impact on environmental degradation which can ultimately influence the sustainability of the Earth and its natural environment.