School of Economics and Management, Xinjiang University, Urumqi, China.
PLoS One. 2020 Jun 30;15(6):e0235516. doi: 10.1371/journal.pone.0235516. eCollection 2020.
Methods of previous researches on green technology innovation will have difficulty in finite population. One solution is the use of stochastic evolutionary game dynamic-Moran process. In this paper we study stochastic dynamic games about green technology innovation with a two-stage free riding problem. Results illustrate the incentive and selection strength play positive roles in promoting participant to be more useful to society, but with threshold effect: too slighted strength makes no effect due to the randomness of the evolution process in finite population. Two-stage free riding problem can be solved with the use of inequality incentives, however, higher inequality can make policy achieves faster but more unstable, so there would be an optimal range. In this paper we provided the key variables of green technology innovation incentive and principles for the environmental regulation policy making. Also reminded that it's difficult to formulate policies reasonably and make them achieve the expected results.
以往的绿色技术创新研究方法在有限群体中会遇到困难。一种解决方案是使用随机进化博弈动力-Moran 过程。本文研究了具有两阶段搭便车问题的绿色技术创新随机动态博弈。结果表明，激励和选择强度在促进参与者对社会更有用方面发挥了积极作用，但存在阈值效应：由于有限群体中进化过程的随机性，过于微弱的强度由于随机性而没有效果。两阶段搭便车问题可以通过不平等激励来解决，然而，更高的不平等会使政策更快地实现，但更不稳定，因此会有一个最佳范围。本文为环境规制政策制定提供了绿色技术创新激励的关键变量和原则。同时也提醒人们，合理制定政策并使其达到预期效果是困难的。
