Carbon accounting method for industrial sectors at prefectural level: the case of Shanxi Province
Abstract
Accurately quantifying carbon dioxide (CO₂) generation from industrial sectors at the prefectural level is both critically important and highly amenable to methodological improvement. This study develops a systematic, machine learning–enhanced framework for precise sectoral CO₂ generation estimation and robust imputation of missing data. Utilizing this framework, an extensive carbon accounting analysis is performed for Shanxi Province, a major energy production hub in China, encompassing 30 industrial sectors across its 11 prefectural regions during the years 2007 to 2022. Furthermore, the resulting dataset is used to characterize the spatial distribution and sectoral heterogeneity of industrial CO₂ generation. This study advances the existing literature along four dimensions: (i) it achieves carbon accounting at a significantly finer spatial and sectoral resolution; (ii) it introduces a more sophisticated, algorithm-driven accounting framework grounded in state-of-the-art machine learning techniques; (iii) it incorporates a broader spectrum of energy types and explicitly accounts for sector-specific variations in carbon emission factors across energy sources; and (iv) it implements a rigorous reclassification of industrial sectors alongside nuanced, deflation-adjusted treatments of monetary indicators to ensure temporal consistency and comparability of industrial carbon dioxide generation estimates over time.
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BibTeX
@article{li2026carbon,
title = {{Carbon accounting method for industrial sectors at prefectural level: the case of Shanxi Province}},
author = {Zongyu Li and Guitian Huang},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-70211-3},
url = {https://www.nature.com/articles/s41598-026-70211-3},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Carbon accounting method for industrial sectors at prefectural level: the case of Shanxi Province
AU - Zongyu Li
AU - Guitian Huang
PY - 2026
DA - 2026/10/17
JO - Scientific Reports
DO - 10.1038/s41598-026-70211-3
UR - https://www.nature.com/articles/s41598-026-70211-3
AB - Accurately quantifying carbon dioxide (CO₂) generation from industrial sectors at the prefectural level is both critically important and highly amenable to methodological improvement. This study develops a systematic, machine learning–enhanced framework for precise sectoral CO₂ generation estimation and robust imputation of missing data. Utilizing this framework, an extensive carbon accounting analysis is performed for Shanxi Province, a major energy production hub in China, encompassing 30 industrial sectors across its 11 prefectural regions during the years 2007 to 2022. Furthermore, the resulting dataset is used to characterize the spatial distribution and sectoral heterogeneity of industrial CO₂ generation. This study advances the existing literature along four dimensions: (i) it achieves carbon accounting at a significantly finer spatial and sectoral resolution; (ii) it introduces a more sophisticated, algorithm-driven accounting framework grounded in state-of-the-art machine learning techniques; (iii) it incorporates a broader spectrum of energy types and explicitly accounts for sector-specific variations in carbon emission factors across energy sources; and (iv) it implements a rigorous reclassification of industrial sectors alongside nuanced, deflation-adjusted treatments of monetary indicators to ensure temporal consistency and comparability of industrial carbon dioxide generation estimates over time.
SN - 2045-2322
ER -