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The Effect of Energy Consumption, Income, and Population Growth on CO 2 Emissions: Evidence from NARDL and Machine Learning Models

Author

Listed:
  • Mansoor Ahmed

    (Department of Finance and Economics, Software Engineering Institute of Guangzhou, Guangzhou 510900, China)

  • Wen Huan

    (Department of Finance and Economics, Software Engineering Institute of Guangzhou, Guangzhou 510900, China)

  • Nafees Ali

    (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Ahsan Shafi

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Muhsan Ehsan

    (Department of Earth and Environmental Sciences, Bahria School of Engineering and Applied Sciences, Bahria University, Islamabad 44000, Pakistan)

  • Kamal Abdelrahman

    (Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Anser Ali Khan

    (International Development, Community and Environment (IDCE), Clark University, Worcester, MA 01610, USA)

  • Saiq Shakeel Abbasi

    (Tyche Consultants (SMC) Pvt. Ltd., Islamabad 44000, Pakistan)

  • Mohammed S. Fnais

    (Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

Abstract

With population and income growth, the need for energy has increased in developing and emerging economies, which has inevitably led to an increase in carbon dioxide emissions (CO 2 e). This paper investigates the impact of energy consumption on CO 2 e influenced by population growth, energy consumption per capita, and income. In particular, this paper investigates whether or not an increase in energy consumption, energy intensity, energy consumption per capita, population growth, and income impacts CO 2 e in China, India, and the USA. The study applied the non-linear Autoregressive distributed lag (NARDL) and machine learning techniques. We found a significant impact of energy consumption per capita on the CO 2 emissions in China, India, and USA. Furthermore, the results revealed that, when income increased, CO 2 emissions increased in India, but decreased in the USA. The results confirmed that population growth increases CO 2 emissions only in India. The results revealed that a decrease in energy intensity significantly improves the environmental quality in China and India. Finally, we forecasted the CO 2 e trend from 2017 to 2025. The results revealed an upcoming increase in CO 2 e levels in China and India. Conversely, the forecasted results demonstrated a downward trend of CO 2 e emissions in the USA.

Suggested Citation

  • Mansoor Ahmed & Wen Huan & Nafees Ali & Ahsan Shafi & Muhsan Ehsan & Kamal Abdelrahman & Anser Ali Khan & Saiq Shakeel Abbasi & Mohammed S. Fnais, 2023. "The Effect of Energy Consumption, Income, and Population Growth on CO 2 Emissions: Evidence from NARDL and Machine Learning Models," Sustainability, MDPI, vol. 15(15), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11956-:d:1210017
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    References listed on IDEAS

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    2. Karambir Singh Dhayal & David Forgenie & Arun Kumar Giri & Nikmatul Khoiriyah & Wendy-Ann P. Isaac, 2026. "Modelling the nexus between green energy, agricultural production, forest cover, and population growth towards climate change for the transition towards a green economy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 28(4), pages 9935-9959, April.
    3. Qian Liu & Ruilin Xiang & Qiming Yang & Shamsheer ul Haq, 2025. "Growth Versus Green: How Financial Development and Energy Transition Shape Environmental Sustainability in Emerging Economies," SAGE Open, , vol. 15(4), pages 21582440251, December.
    4. Seth Acquah Boateng & Jiancheng Xi & Michael Provide Fumey & Jeff Kwaku Kumi, 2026. "Exploring the Nonlinear Relationship of Environmental Sustainability Factors and Economic Growth in West Africa: Novel Machine Learning Evidence," Sustainable Development, John Wiley & Sons, Ltd., vol. 34(S1), pages 1197-1220, January.

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