A Bayesian approach to analyzing the ecological footprint of 140 nations

The per capita ecological footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the Earth's biological capacity required to support human activity. This study presents a Bayesian approach to predict the EF of 140 nations. By formulating the linear regression in a probabilistic framework, a Bayesian linear regression model is derived, and a specific simulation method, i.e., Markov Chain Monte Carlo (MCMC), is utilized to estimate the model parameters. Bayesian MCMC methods allow a richer and more complete representation of complex EF data. It also provides a computationally attractive and straightforward method to develop a full and complete description of the inherent uncertainty in parameters, quantiles and performance metrics. Results show that the per capita EF is positively influenced by the nation's world system position (WSP) and its urbanization level. The distribution of income, as measured by the Gini coefficient, was found to be negatively related to per capita EF.