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Forecasting green hydrogen production in China: Hybrid deep learning assessment of economic, environmental, and renewable energy integration

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  • Xie, Yu
  • Ling, Xing

Abstract

Green hydrogen has emerged as a clean and scalable energy carrier as a result of the shift to low-carbon energy. Both opportunities and problems for large-scale production are presented by China's plentiful but unevenly distributed wind and solar resources. To anticipate renewable electricity generation under various Shared Socioeconomic Pathway (SSP) scenarios, this work creates a Bidirectional Long Short-Term Memory (AMBiLSTM) forecasting model based on Attention Mechanisms and combined with Multilevel Discrete Wavelet Decomposition (MDWD). Weighted electrolyzer efficiencies for Alkaline Water Electrolysis (AWE), Proton Exchange Membrane Water Electrolysis (PEMWE), and Anion Exchange Membrane Water Electrolysis (AEMWE), which represent current market shares, are then used to transform the anticipated electricity into green hydrogen production. The Levelized Cost of Hydrogen (LCOH) is used to evaluate economic viability, and saved CO2 emissions are used to assess environmental advantages. The northern area contributes 105.22 Mt of the total production of 429.02 Mt under SSP1. The Northwest has the lowest LCOH, and the model's Mean Absolute Percentage Error is 7.44 %. Through the integration of technologically specific efficiency modeling, advanced forecasting, and techno-economic-environmental analysis, the study provides practical insights to direct China's regional green hydrogen deployment strategies as it moves toward a low-carbon future.

Suggested Citation

  • Xie, Yu & Ling, Xing, 2026. "Forecasting green hydrogen production in China: Hybrid deep learning assessment of economic, environmental, and renewable energy integration," Renewable Energy, Elsevier, vol. 256(PG).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pg:s0960148125020786
    DOI: 10.1016/j.renene.2025.124414
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