ASSESSING GLOBAL AND NATIONAL ECONOMIC LOSSES FROM CLIMATE CHANGE: A STUDY BASED ON CGEM-IAM IN CHINA

Tianpeng Wang, Fei Teng and Xiliang Zhang
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Tianpeng Wang: Institute of Energy Environment and Economy, Tsinghua University, Beijing, P. R. China
Fei Teng: Institute of Energy Environment and Economy, Tsinghua University, Beijing, P. R. China
Xiliang Zhang: Institute of Energy Environment and Economy, Tsinghua University, Beijing, P. R. China

Climate Change Economics (CCE), 2020, vol. 11, issue 03, 1-23

Abstract: Integrated assessment models (IAMs) have been widely used to inform policymaking on climate change. However, few Chinese IAMs have merely been involved in international model comparison. In this study, we develop an IAM, namely, CGEM-IAM, by coupling a global recursive dynamic CGE model with a simple climate model and establishing a climate impact assessment module based on a system of indirect damage functions. By using the CGEM-IAM, we analyze economic losses due to climate impacts under various scenarios. Results reveal that under the baseline scenario, the global annual loss due to climate change will reach $1628 billion (constant 2011 USD) in 2050, which represents approximately 0.79% of the global GDP in that year. The achievement of current commitments by countries promotes the slight reduction of the annual economic losses to $1398 billion in 2050. Under the 2-degree scenario, approximately 50% of the economic losses could be avoided. Developing countries and regions are vulnerable to the negative impact of climate change and could suffer from 85% of global economic losses, whereas China accounts for approximately 1% of global total climate impacts. Although the climate damage estimated by CGEM-IAM is comparable with the IAMs DICE and FUND models at the aggregate level, they are inconsistent at the sector distribution. Therefore, additional efforts should compare multiple models at the regional and sectoral levels in detail.

Keywords: Climate change; integrated assessment models; climate impacts; damage function (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S2010007820410031

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