Towards Carbon Transparency: A High-Resolution Carbon Emissions Database for China's Listed Companies
Xinlei Wang, Junhua Zhao, Haifeng Wu, Zhengwen Zhang, Guolong Liu,, Wenxuan Liu, Yuheng Cheng, Jing Qiu, Bohui Zhang, Jianwei Huang

TL;DR
This paper presents a high-resolution, AI-driven database of carbon emissions for Chinese listed companies, integrating multi-source data to improve accuracy and support climate risk management and policy goals.
Contribution
It introduces a novel methodology combining satellite data, self-reports, and AI algorithms to create a detailed, verifiable carbon emissions database for Chinese companies.
Findings
Enhanced accuracy of company emission data through AI and satellite integration
Robust validation of self-reported emissions
High-resolution data supports carbon management and market transactions
Abstract
The dual-carbon goals of China necessitate precise accounting of company carbon emissions, vital for green development across all industries. Not only the company itself but also financial investors require accurate and comprehensive company-level emissions data for climate risk management. This paper introduces the structure and methodology of the High-resolution Database for Carbon Emissions of China-listed companies, integrating three primary data sources: self-disclosed environmental data from listed companies, long-accumulated national power emission data, and regional high-precision emission data derived from multi-source satellites. The database's innovation lies in the employment of artificial intelligence (AI) algorithms to aggregate multi-source satellite data. This approach enables the precise identification of carbon emission sources and the prediction of company-level…
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Taxonomy
TopicsEnvironmental Impact and Sustainability · Energy, Environment, Economic Growth · Climate Change Policy and Economics
