Balancing economic growth and carbon peaking in China: an integrated LSTM-NSGA-III framework for sustainable energy transitions

The urgent need to reconcile economic development with climate commitments presents a critical policy dilemma for emerging economies. This study proposes a novel decision-support framework integrating Long Short-Term Memory (LSTM) neural networks with the Non-dominated Sorting Genetic Algorithm III...

पूर्ण विवरण

में बचाया:
ग्रंथसूची विवरण
मुख्य लेखकों: Xin-Bo Zhang, Yi-Jun Lou, Jing-Ran Yang, Yang Zhang, Cheng-Liang Wu
स्वरूप: लेख
भाषा:अंग्रेज़ी
प्रकाशित: Elsevier 2025-09-01
श्रृंखला:Environmental and Sustainability Indicators
विषय:
ऑनलाइन पहुंच:http://www.sciencedirect.com/science/article/pii/S2665972725002053
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विवरण
सारांश:The urgent need to reconcile economic development with climate commitments presents a critical policy dilemma for emerging economies. This study proposes a novel decision-support framework integrating Long Short-Term Memory (LSTM) neural networks with the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize China's Economy-Energy-Environment (3E) system transition. Our dual-objective model simultaneously targets maintaining annual GDP growth at 5.0–5.5 % and achieving carbon peaking between 2028 and 2032 through strategic energy restructuring. The hybrid architecture combines a high-accuracy LSTM energy demand predictor (R2 = 1) with evolutionary multi-objective optimization, generating Pareto-optimal transition pathways with quantified energy mix configurations. Empirical results demonstrate that increasing non-fossil energy share to 28.5–32.7 % could enable China to peak CO2 emissions at 15000 megatons while sustaining economic targets, requiring annual new installations increasingly dominated by renewable energy about 50 %. The study provides a transferable toolkit for developing nations navigating the clean energy transition paradox, while the China-specific findings offer timely insights for refining national carbon neutrality implementation plans.
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