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Effect Analysis of Carbon Information on Enterprise Value Based on Big Data

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  • Guangqi Ma
  • Miya Liang
  • Wenlin Sun
  • Lianhui Li

Abstract

An effect analysis approach of carbon information on enterprise value based on big data is proposed. This study first systematically expounds on the sources of research data and data collection methods and then comprehensively analyzes corporate carbon information disclosure status and characteristics. It also conducts an empirical study on the short-term impact of carbon information disclosure on corporate value creation and draws the following conclusions. Industry classification has an important impact on corporate carbon information disclosure in terms of the status and characteristics of corporate carbon information disclosure. Except for the financial and insurance industry, the average amount of carbon information disclosure in susceptible sectors such as the extractive industry and construction industry is relatively high. In terms of carbon information disclosure content, the carbon information disclosed by enterprises is mainly related to low-carbon technology and low-carbon product plans. Through empirical analysis of the impact of carbon information disclosure on short-term stock market performance and investor returns, it is found that the trading volume and value of stocks in the 5 trading days after the information event were higher than those in the previous 5 trading days. Still, the increase in the overall stock market value was not significant. This shows that the occurrence of carbon information disclosure events can improve the liquidity and trading activity of the stock market, trigger the stock market’s market response to carbon information disclosure, and enable investors to obtain more abnormal returns among short-term investors. It has a certain impact on short-term enterprise value creation.

Suggested Citation

  • Guangqi Ma & Miya Liang & Wenlin Sun & Lianhui Li, 2022. "Effect Analysis of Carbon Information on Enterprise Value Based on Big Data," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, July.
  • Handle: RePEc:hin:jnlmpe:4406064
    DOI: 10.1155/2022/4406064
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