Ecological footprint model using the support vector machine technique

Area of Science:

• Environmental Science
• Ecological Sustainability
• Resource Management

Background:

• The per capita ecological footprint (EF) is a critical metric for assessing environmental sustainability.
• Understanding factors influencing EF is crucial for global resource management and policy development.
• Existing models may not fully capture the complex interplay of socio-economic drivers on EF.

Purpose of the Study:

• To identify and analyze key socio-economic factors affecting per capita ecological footprint (EF).
• To develop and validate a novel machine-learning model for calculating per capita EF.
• To assess the predictive accuracy of the developed model.

Main Methods:

• Literature review to identify influencing factors on per capita EF.
• Development of a support vector machine (SVM) based model for EF calculation.
• Validation of the SVM model using data from 123 nations, focusing on 24 specific countries.

Main Results:

• Five significant factors influencing per capita EF were identified: GDP, urbanization, income distribution, export dependence, and service intensity.
• The SVM-based model demonstrated high calculation accuracy, with average absolute error of 0.004883 and average relative error of 0.351078%.
• The model's performance indicates its suitability for estimating per capita EF.

Conclusions:

• The developed SVM model provides a robust and accurate method for calculating per capita ecological footprint.
• Socio-economic factors play a significant role in determining a nation's environmental sustainability.
• This approach offers a valuable tool for environmental policy and sustainable development planning.