题目：Impact of policy adjustments on low carbon transition strategies in construction using evolutionary game theory（政策调整对建筑低碳转型策略的演化博弈理论影响）
作者：Song Wang, Dongliang Zhu and Jiachen Wang
摘要：The construction industry is generally characterized by high emissions, making its transition to low-carbon practices essential for achieving a low-carbon economy. However, due to information asymmetry, there remains a gap in research regarding the strategic interactions and reward/punishment mechanisms between governments and firms throughout this transition. This paper addresses this gap by investigating probabilistic and static reward and punishment evolutionary games. The findings indicate that (1) Probabilistic rewards and penalties policies are more effective during the initial stages of the transition, whereas static mechanisms are more conducive to ensuring long-term stability. (2) The maximum values of rewards and penalties significantly influence the evolution of the low-carbon transition, with higher incentives enhancing motivation and more significant penalties imposing stricter constraints. (3) An increase in the cost of government involvement facilitates the low-carbon transition. (4) The benefits to both government and enterprises are critical in determining the application of static versus probabilistic rewards and penalties. The government may decide to cap probabilistic rewards and penalties by the magnitude of the benefits or adopt static rewards and penalties. This study offers theoretical support and a decision-making framework for developing effective low-carbon policies.
建筑业通常具有高排放特征，因此向低碳实践的转型对于实现低碳经济至关重要。然而，由于信息不对称，关于政府与企业在此转型过程中战略互动及奖惩机制的研究仍存在空白。本文通过研究概率性和静态奖惩演化博弈来填补这一空白。研究结果表明：(1) 概率性奖惩政策在转型初期更为有效，而静态机制更有利于确保长期稳定性。(2) 奖惩的最大值对低碳转型演化具有显著影响，更高的激励增强动机，更严厉的惩罚施加更严格的约束。(3) 政府参与成本的增加促进了低碳转型。(4) 政府与企业双方的利益对于确定静态与概率性奖惩的应用至关重要。 政府可以选择通过收益的幅度来限制概率性奖励和惩罚，或者采用静态奖励和惩罚。本研究为制定有效的低碳政策提供了理论支持和决策框架。
原文： Wang, S., Zhu, D. & Wang, J. Impact of policy adjustments on low carbon transition strategies in construction using evolutionary game theory. Sci Rep 15, 3469 (2025).
链接：https://doi.org/10.1038/s41598-025-87770-6
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