The Study of Enterprise Response to Carbon Trading Market Based on Agent-Based Modeling
Promoting the low-carbon transformation of industrial enterprises is an urgent requirement for China under the"dual carbon"targets.Carbon market policies serve as the main means to incentivize enterprises for autonomous emission reduction.However,the effectiveness of incentive policies is constrained by the technology adoption decisions and carbon emission adjustment behaviors of heterogeneous enterprises,posing challenges to policy design and implementation.This study takes 716 steel enterprises and 12 low-carbon technologies as cases,and based on Agent-Based Modeling method(ABM),designs five different scenarios of carbon quotas and five different scenarios of carbon market activity levels.It depicts the micro-level enterprise decisions on low-carbon technology adoption and the macro-level carbon emission reduction effects under the influence of carbon trading market policies.The study finds that:①Tightening carbon quotas and enhancing market activity can both promote enterprise adoption of low-carbon technologies and accelerate low-carbon transformation.Different enterprises exhibit characteristics of"technology adoption time windows"and time lag effects in their technology adoption behaviors.② Tightening carbon quotas and increasing carbon market activity can promote a decrease in the average carbon emission intensity of enterprises by 5%to 24%.Significant promotion of emission reduction occurs only when carbon quotas are tightened to below the average emission intensity.Market activity has a significant impact in both high and low ranges.③Increasing carbon market activity has a more significant effect on industrial low-carbon transformation compared to tightening carbon quotas.The former results in a 19%decrease in carbon emission intensity,while the latter leads to a 10%decrease.It is suggested that the government should promote market activity by strengthening the construction of trading platforms and encouraging intermediary participation.
万方数据
维普
