Citation

Ahmed, Elsadig Musa and Elfaki, Khalid Eltayeb and Abusham, Eimad Eldin (2025) Machine learning models predictive performance of Asian economies’ green technological progress. Sustainable Futures, 10. p. 101323. ISSN 2666-1888

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Abstract

This study introduces a novel Hybrid Deep Ensemble (HDE) model, which can maximize the accuracy of the prediction by combining the benefits of multiple learning architectures to examine the predictive performance of Machine Learning (ML) Models. The proposed model consists of three models: Multiple Linear Regression (MLR), Random Forest Regression (RF), and Gradient Boosting Regression (GBR) as the base learners. Meta-learners combine these models’ outputs to make final predictions using a regression model. The HDE model was applied to forecast Gross Domestic Product (GDP) and Carbon Dioxide (CO2) emissions by examining the in fluence of labor, capital, energy efficiency, and renewable energy in selected Asian Economies. The proposed HDE model performance is evaluated against two ensemble benchmark models: RF, which is based on bagging, and GBR, which is based on boosting; and MLR, which is a non-ensemble baseline algorithm. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) measures were employed to evaluate the accuracy of the models using a dataset of economic and environmental indicators. With the lowest MAE and RMSE values for both GDP and CO2 emissions estimates, the results show that HDE is always lower than the MAE and RMSE values of both GDP and CO2, revealing the better suitability to predict complex and nonlinear patterns. This study highlights the importance of selecting the appropriate modelling approaches based on the properties of the data and the feasibility of ensemble learning. Overall, three models demonstrate that CO2 emissions are the primary factor influencing economic development, revealing a strong correlation between industrial or energyrelated activities and economic performance. Renewable energy may also facilitate sustainable growth, while labour and capital have limited or adverse effects, exhibiting complex dynamics that vary by environment. The findings show that HDE and GBR are the best models for predicting economic growth and pollutant emissions and accurately capturing intricate non-linear interactions. Additionally, HDE, RF, and GBR offer greater insights into nonlinear interactions than MLR, revealing how these factors affect GDP. Green Total Factor Productivity (GTFP) indicates the influx of capital and labour in Asian countries, facilitating rapid development and indus trialisation progress through technological innovation and the development of human capital skills. CO2 emis sions and renewable energy influence economic growth, ensuring green technological progress through a productivity-driven approach to maximise the significant effects of energy efficiency and renewable energy, and to support GDP growth. An efficient strategy for utilising these factors is essential, leveraging the combined contributions of their qualities. These results underscore the significance of renewable energy in promoting sustainable development and the complex interplay between economic and environmental factors via imple menting Sustainable Development Goals 7 and 13, affordable and clean energy (SDG7), and Climate Action (SDG13) to achieve SDG 8 Decent Work and economic growth and other SDGs of the United Nations (UN) agenda 2030.

Item Type:	Article
Uncontrolled Keywords:	Machine learning models
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Business (FOB)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 06:49
Last Modified:	05 Oct 2025 10:30
URII:	http://shdl.mmu.edu.my/id/eprint/14583

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