Revisiting the Nexus Between Energy Consumption, Economic Growth, and CO 2 Emissions in India and China: Insights from the Long Short-Term Memory (LSTM) Model

Bartosz Jóźwik (), Siba Prasada Panda, Aruna Kumar Dash, Pritish Kumar Sahu and Robert Szwed
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Bartosz Jóźwik: Department of International Economics, Institute of Economics and Finance, The John Paul II Catholic University of Lublin, 20-950 Lublin, Poland
Siba Prasada Panda: Department of Data Science, Mukesh Patel School of Technology Management & Engineering, NMIMS University, Mumbai 400056, India
Aruna Kumar Dash: Department of Economics, ICFAI School of Social Sciences, ICFAI Foundation for Higher Education (IFHE), Hyderabad 501203, India
Pritish Kumar Sahu: Economics & Public Policy, IMI Bhubaneswar, Bhubaneswar 751003, India
Robert Szwed: Department of Media Culture, Institute of Journalism and Management, The John Paul II Catholic University of Lublin, 20-950 Lublin, Poland

Energies, 2025, vol. 18, issue 15, 1-23

Abstract: Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO 2 emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide.

Keywords: energy consumption; economic growth; carbon emissions; machine learning forecasting; Long Short-Term Memory (LSTM); India; China (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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