Forecasting Sustainable Development Indicators Using XGBoost: Evidence from Brazil, Canada, China and India (2005–2023)
World Journal of Entrepreneurship, Management and Sustainable Development, cilt.21, sa.4, ss.339-353, 2025 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 21 Sayı: 4
- Basım Tarihi: 2025
- Doi Numarası: 10.47556/j.wjemsd.21.4.2025.4
- Dergi Adı: World Journal of Entrepreneurship, Management and Sustainable Development
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
- Sayfa Sayıları: ss.339-353
- Anahtar Kelimeler: CO2 Emissions, Economic forecasting, Employment, Gross Domestic Product, Machine learning, XGBoost
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Kent Üniversitesi Adresli: Hayır
Özet
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.