A Novel Prediction Model: ELM-ABC for Annual GDP in the Case of SCO Countries

Xiaohan Xu (), Roy Anthony Rogers () and Mario Ruiz Estrada
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Xiaohan Xu: University of Malaya
Roy Anthony Rogers: University of Malaya

Computational Economics, 2023, vol. 62, issue 4, No 8, 1545-1566

Abstract: Abstract With the development of economic and technologies, the trend of annual Gross Domestic Product (GDP) and carbon dioxide (CO2) emission changes with time passes. The relationship between economic growth and carbon dioxide emissions is considered as one of the most important empirical relationships. In this study, we focus on the member of Shanghai Cooperation Organization, including China, Russia, India, and Pakistan and collect CO2 emission and annual GDP from 1969 to 2014. The statistical methods and tests are used to find the relationship between annual GDP and CO2 emission in these countries. Based on relationship between annual and CO2 emission, a novel multi-step prediction algorithm called Extreme Learning Machine with Artificial Bee Colony (ELM-ABC) is proposed for forecasting annual GDP based on CO2 emission and historical GDP features. According to the experimental results, it proved that the proposed model had a super forecasting ability in GDP prediction and it could predict ten-year future annual GDP for the corresponding countries. Moreover, the forecasting results showed that the annual GDP of China and Pakistan will continue to grow but growth will slow after 2025. The annual GDP in India will exhibit unstable growth. The trend of Russia will follow the pattern between 2010 and 2016.

Keywords: SCO; GDP; CO2 emissions; Extreme learning machine; Time series prediction; Optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10311-0

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