Forecasting sustainable development indicators using XGBoost: evidence from Brazil, Canada, China and India (2005–2023)
Bibliographic record
Abstract
Purpose: 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. Design/Methodology/Approach: 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. Findings: 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. Originality/Value: The paper identifies the application of machine learning to predict sustainable development, particularly economic modelling. Research Limitations/Implications: There is an implication of overfitting and data limitations, which implies that higher-frequency data and hybrid models are required. Practical Implications: The implication is based on evidence based policymaking in green investment planning and climate policy evaluation. Keywords: XGBoost; Economic Forecasting; Gross Domestic Product; Employment; CO₂ Emissions; Machine Learning. Citation: 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".