A Monitoring Method for Corporate Environmental Performance Based on Data Fusion in China under the Double Carbon Target

Youying Mu, Chengzhuo Duan, Xin Li and Yongbo Wu ()
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Youying Mu: School of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China
Chengzhuo Duan: School of Business, Central South University, Changsha 410017, China
Xin Li: Artificial Intelligence Innovation Center, Central South University, Changsha 410017, China
Yongbo Wu: School of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China

Sustainability, 2023, vol. 15, issue 12, 1-16

Abstract: The production and operation of corporates have a significant impact on the environment, and it is crucial for corporates to operate in an environmentally friendly manner, especially in the context of the China double carbon target. Corporate environmental performance refers to the degree of impact on the environment and the degree of contribution to environmental protection by corporates in their business activities. Our study conducted an assessment and early warning system for corporate environmental performance by monitoring seven typical corporate environmental performance variables, including the green asset ratio (Gra), the proportion of environmentally friendly products (Pefp), and cash flow for environmental protection to total assets ratio (ECF), of 2718 non-financial listed corporates in China’s A-share market. The dataset comprised empirical data from the CSMAR database and multi-scale measurements collected by us. Among data-driven monitoring methods, deep learning is widely applied due to its powerful automatic feature extraction abilities. However, multi-time scale data is often encountered in industrial ecology-related data, as the different underlying physical quantities of various data result in inconsistent sampling rates. Multi-time scale data are incomplete and asymmetrical, making it difficult for traditional models to use directly for corporate ecological monitoring. In this article, an improved CNN-LSTM monitoring model based on data fusion is proposed to address this issue. This method employs unified vectorization processing to transform incomplete multi-time scale data into uniform complete data. An end-to-end diagnostic model is constructed to simultaneously optimize feature extraction and monitoring. In a multi-time scale corporate monitoring model, CNN can mine hidden features of data, while LSTM can further capture the time dependence of underlying time series. Compared to manual feature extraction that relies on prior knowledge, the proposed model can learn more effective data features. The effectiveness of the method has been demonstrated through empirical data experiments, which is beneficial for corporates in the context of double carbon emissions, providing a method for regulating corporate ecological indicators.

Keywords: corporate environmental performance; multi-time scale data; deep learning; data monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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