Spatiotemporal Heterogeneity of Regional Decarbonization in China: Forecasting and Differentiated Strategies Based on LSTM and Spatial Econometric Modeling

DOI

10.2991/978-94-6463-742-7_39How to use a DOI?

Keywords

Regional Decarbonization; Renewable Energy Consumption; Spatial Econometrics

Abstract

This study examines the spatiotemporal heterogeneity of decarbonization pathways across China’s four major regions (2000–2020) by integrating LSTM forecasting with spatial econometric analysis. Utilizing panel data, three-dimensional visualization, and machine learning, we quantify regional disparities in renewable energy adoption, per capita CO₂ emissions, and industrial efficiency. The results reveal significant disparities: the eastern region exhibits the highest renewable energy consumption(2.05 billion kWh)with moderate emissions(0.061 hundred tons per person),while the northeast shows the lowest renewable energy adoption(0.29 billion kWh)and the highest emissions(0.074 hundred tons per person).LSTM projections(2021–2035)forecast divergent trajectories—renewable energy consumption grows nationally by 24.9%,yet emissions in the west rise sharply(+24.6%)due to industrial inefficiencies, contrasting with moderated growth in the east(+7.7%).Spatial econometrics identifies structural bottlenecks, including seasonal fossil fuel dependence in the northeast and technology diffusion lags in central provinces. The findings underscore the necessity for region-specific strategies: industrial restructuring in energy-intensive western and northeastern regions and cross-regional technology transfer to align decarbonization with China’s 2030/2060 climate goals. The methodological integration of machine learning and spatial analysis advances granular policy assessments for heterogeneous energy transitions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Changhao Wang
AU  - Rui Ye
AU  - Zhengrui Xie
AU  - And Peng Zhang
PY  - 2025
DA  - 2025/05/31
TI  - Spatiotemporal Heterogeneity of Regional Decarbonization in China: Forecasting and Differentiated Strategies Based on LSTM and Spatial Econometric Modeling
BT  - Proceedings of the 2025 4th International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2025)
PB  - Atlantis Press
SP  - 380
EP  - 387
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-742-7_39
DO  - 10.2991/978-94-6463-742-7_39
ID  - Wang2025
ER  -