This paper discusses the potential of machine learning to predict sustainable development indicators, gross domestic product (GDP), employment, and CO₂ emissions in Brazil, Canada, China and India to build a sustainable policy.
An annual panel data (2005–2023) was employed to adopt a deductive and explanatory design. XGBoost (Extreme Gradient Boosting) is an algorithm that models nonlinear relationships and identifies important predictors based on macroeconomic, environmental, and policy variables.
XGBoost revealed high accuracy on GDP, and the balance of payments, climate policy, and green investment were some of the important predictors. CO₂ and employment forecasts were less certain because they were overfitted.
The paper identifies the application of machine learning to predict sustainable development, particularly economic modelling.
There is an implication of overfitting and data limitations, which implies that higher-frequency data and hybrid models are required.
The implication is based on evidence based policymaking in green investment planning and climate policy evaluation.
Knio, M. S. E. D. and Balikel, A. E. (2025): Forecasting Sustainable Development Indicators Using XGBoost: Evidence from Brazil, Canada, China and India (2005-2023). World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 21, No. 4, pp. 339-353.
