抄録
Recently, ecological footprint (EF) receives much global attention as a measure to estimate the human impact on the earth. However, the evaluation of EF values is not so easy because of the complexity of its process and the need of numerous data. For that purpose it is required to develop a convenient model for estimating EF values for a variety of countries based on easily available data such as gross domestic product (GDP) and others. A large-scale regression experiment to analyze the comprehensive determinants of EFs across many countries has been carried out. A nonlinear support vector machine (SVM) method was applied to the regression analysis between EFs (dependent variable) of 162 countries and 32 factors (explanatory variables) in various fields. Optimum factors for the modeling were determined by using the sensitivity analysis method as a variable selection technique in SVM. It is demonstrated that 19 factors satisfactorily reproduce the EFs of 162 countries with a coefficient of determination (R2) of 0.930, which is remarkably superior to those of the ordinary least squares (OLS) method. It also is revealed that various factors such as meat consumption and air pollution as well as geographical factors such as population and land area, and economic factors such as GDP and GDP per capita have to be taken into account to construct a model for estimating EFs with high accuracy.
