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中国低碳政策的随机冲击效应——基于多主体 DSGE 模型的分析。

Effects of China's low-carbon policy under stochastic shocks-a multi-agent DSGE model analysis.

机构信息

School of Business Administration, Northeastern University, Chuangxin Road, Hunnan District, Shenyang, 110167, China.

出版信息

Environ Sci Pollut Res Int. 2023 May;30(24):65177-65191. doi: 10.1007/s11356-023-26942-1. Epub 2023 Apr 20.

Abstract

China has announced a target of achieving carbon peaking by 2030 and carbon neutrality by 2060. Therefore, it is important to assess the economic impacts and emission reduction effects of China's low-carbon policies. In this paper, a multi-agent dynamic stochastic general equilibrium (DSGE) model is established. We analyze the effects of carbon tax and carbon cap-and-trade policies under both deterministic and stochastic conditions, as well as their ability to cope with stochastic shocks. We found that (1) from a deterministic perspective, these two policies have the same effect. Every 1% cut in CO emissions will bring a 0.12% output loss, a 0.5% drop in demand for fossil fuels, and a 0.05% rise in demand for renewable energy; (2) from a stochastic perspective, effects of these two policies are different. This is mainly because economic uncertainty does not change the cost of CO emissions under a carbon tax policy, but it does change the price of CO quotas and the emission reduction behaviors under a carbon cap-and-trade policy; (3) from an economic volatility perspective, both two policies can act as automatic stabilizers. Compared to a carbon tax, a cap-and-trade policy can better ease economic fluctuations. The results of this study provide implications for policy-making.

摘要

中国宣布了 2030 年碳达峰和 2060 年碳中和的目标。因此,评估中国低碳政策的经济影响和减排效果非常重要。本文建立了一个多主体动态随机一般均衡(DSGE)模型。我们分析了在确定性和随机性条件下碳税和碳配额交易政策的影响,以及它们应对随机冲击的能力。我们发现:(1)从确定性角度来看,这两种政策的效果相同。每降低 1%的 CO 排放,产出将损失 0.12%,化石燃料需求下降 0.5%,可再生能源需求上升 0.05%;(2)从随机性角度来看,这两种政策的效果不同。这主要是因为经济不确定性不会改变碳税政策下 CO 排放的成本,但会改变碳配额交易政策下 CO 配额的价格和减排行为;(3)从经济波动性角度来看,这两种政策都可以作为自动稳定器。与碳税相比,配额交易政策可以更好地缓解经济波动。本研究的结果为政策制定提供了启示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acbe/10116111/98558a4eeb9a/11356_2023_26942_Fig1_HTML.jpg

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