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...
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| मुख्य लेखकों: | , , , , |
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| स्वरूप: | लेख |
| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
Elsevier
2025-09-01
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| श्रृंखला: | Environmental and Sustainability Indicators |
| विषय: | |
| ऑनलाइन पहुंच: | http://www.sciencedirect.com/science/article/pii/S2665972725002053 |
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| 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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| आईएसएसएन: | 2665-9727 |