The incentive mechanism design for emission reduction technology R&D projects: a stochastic differential game approach

This paper investigates the optimal incentive mechanism to motivate agents to exert effort for emission reduction technology R&D projects, where the incentive mechanism, the value process, and the cost function all take general forms. Since firms’ incentive strategies directly alter agents’ utility functions rather than the value process of the emission-reduction technology, this problem differs from the classic stochastic differential game. To address this distinction, we employ the first and second derivatives of the agents’ value function with respect to the state variable to represent compensation strategies and transform the problem into a classic one. The results indicate that the optimal agent compensation includes not only a base effort remuneration and a risk-sharing component but also a risk-adjustment component, which equals negative one-half of the agents’ risk preference factor multiplied by the quadratic variation of the agents’ risk exposure. It is positive for risk-averse agents, negative for risk-seeking agents, and can completely offset the influence of the risk-sharing component, thereby incentivizing participation by agents with varying risk attitudes. We then apply these results to a Cobb-Douglas model and get some useful findings.