Affiliation:
1. Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong SAR China
2. College of Economics Shenzhen University Shenzhen China
3. Systems Engineering Cornell University Ithaca New York USA
4. Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong SAR China
5. Research Institute for Advanced Manufacturing The Hong Kong Polytechnic University, Hung Hom Hong Kong SAR China
Abstract
AbstractEnvironmental, social, and governance (ESG) disclosure has drawn much attention from listed companies, investors, and regulators. In response to the increasing demand of investors and regulators for non‐financial information, listed companies have paid attention to publishing ESG reports consisting of environmental, social, and governance information. Listed companies are increasingly required to provide high‐quality information that is clear and comparable. However, the lack of incentive to listed companies makes it hard to improve the quality of ESG disclosure, and the cost of ESG disclosure leads to the uncontrollable quality of ESG reports and may even manipulation by opportunistic behaviors. In this paper, we illustrate the moral hazard problem in ESG disclosure from the perspective of investors and listed companies, in which the effort level for listed companies to provide high‐quality ESG report cannot be observed by investors. Then we propose a blockchain‐based incentive mechanism for ESG disclosure from a principal‐agent perspective to improve the information quality of ESG disclosure, where investors act as principal and listed companies act as agents. Token in blockchain technology is utilized as the rewards to improve the listed companies' reputation, thus increasing their chance of being promoted to investors for preferential investment opportunities in the blockchain platform. We then design the first‐best (FB) and second‐best (SB) optimal contracts based on classic principal‐agent model to overcome the moral hazard problem. Extensive simulations are conducted to demonstrate the effectiveness and feasibility of the incentive mechanism.
Funder
Basic and Applied Basic Research Foundation of Guangdong Province
Innovation and Technology Fund