A novel text-based framework for forecasting coal power overcapacity in China from the industrial correlation perspective

Mao, J; Wang, D; Chen, F; Li, C; Shi, X; Zhang, Y

A novel text-based framework for forecasting coal power overcapacity in China from the industrial correlation perspective

Mao, J Wang, D Chen, F Li, C Shi, X

Zhang, Y

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Technological Forecasting and Social Change, 2024, 208
Issue Date:: 2024-11-01

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Field	Value	Language
dc.contributor.author	Mao, J
dc.contributor.author	Wang, D
dc.contributor.author	Chen, F
dc.contributor.author	Li, C
dc.contributor.author	Shi, X https://orcid.org/0000-0001-9653-7395
dc.contributor.author	Zhang, Y
dc.date.accessioned	2025-03-20T21:42:04Z
dc.date.available	2025-03-20T21:42:04Z
dc.date.issued	2024-11-01
dc.identifier.citation	Technological Forecasting and Social Change, 2024, 208
dc.identifier.issn	0040-1625
dc.identifier.issn	1873-5509
dc.identifier.uri	http://hdl.handle.net/10453/186061
dc.description.abstract	The accurate forecasting of coal power overcapacity in China has significant impact on the promotion of the dual carbon strategy. However, due to the complexity of the causes of coal power overcapacity, existing forecasting methods are unable to comprehensively identify influencing factors and incorporate them into forecasting models. Given this, we propose a text-based framework for forecasting coal power overcapacity. Specifically, a topic model and a sentiment analysis method are used to identifies and quantifies the influencing factors from policies and news texts of coal power and its related industries. In addition, under the dual carbon target, the development trend of coal power overcapacity under different environmental constraint scenarios was examined. The empirical test shows that the performance of the forecasting models based on the text data of the related industries is better than that based on the text data of the single coal power industry and the numerical data of the single objective variable, and the changes in capacity of the upstream and alternative industries and the industrial policy support of the downstream industries are the main causes of overcapacity. The scenario forecasting results indicate that the coal power overcapacity scale shows an upward trend from 2021 to 2060.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Technological Forecasting and Social Change
dc.relation.isbasedon	10.1016/j.techfore.2024.123690
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	10 Technology, 14 Economics, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Science Studies
dc.subject.classification	35 Commerce, management, tourism and services
dc.subject.classification	38 Economics
dc.title	A novel text-based framework for forecasting coal power overcapacity in China from the industrial correlation perspective
dc.type	Journal Article
utslib.citation.volume	208
utslib.for	10 Technology
utslib.for	14 Economics
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/DVC (International)
pubs.organisational-group	University of Technology Sydney/DVC (International)/Australia-China Relations Institute
utslib.copyright.status	in_progress	*
dc.date.updated	2025-03-20T21:41:59Z
pubs.publication-status	Published
pubs.volume	208

Abstract:

The accurate forecasting of coal power overcapacity in China has significant impact on the promotion of the dual carbon strategy. However, due to the complexity of the causes of coal power overcapacity, existing forecasting methods are unable to comprehensively identify influencing factors and incorporate them into forecasting models. Given this, we propose a text-based framework for forecasting coal power overcapacity. Specifically, a topic model and a sentiment analysis method are used to identifies and quantifies the influencing factors from policies and news texts of coal power and its related industries. In addition, under the dual carbon target, the development trend of coal power overcapacity under different environmental constraint scenarios was examined. The empirical test shows that the performance of the forecasting models based on the text data of the related industries is better than that based on the text data of the single coal power industry and the numerical data of the single objective variable, and the changes in capacity of the upstream and alternative industries and the industrial policy support of the downstream industries are the main causes of overcapacity. The scenario forecasting results indicate that the coal power overcapacity scale shows an upward trend from 2021 to 2060.

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http://hdl.handle.net/10453/186061