Allocating provincial CO2 quotas for the Chinese national carbon program1. (19th April 2018)

• Record Type: Journal Article
• Authors: Jiang, Minxing; Zhu, Bangzhu; Chevallier, Julien; Xie, Rui
• Is Part Of: Australian journal of agricultural and resource economics. Volume 62:Number 3(2018)
• Page Start: 457
• Page End: 479
• Publication Date: 2018-04-19
• Subjects: Chinese national carbon program -- CO2 quota allocation -- interprovincial differences -- marginal abatement cost; Agriculture -- Economic aspects -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; Natural resources -- Periodicals; Agriculture -- Economic aspects -- Australia -- Periodicals; Natural resources -- Australia -- Periodicals; 338.1
• DOI: 10.1111/1467-8489.12261
• Languages: English
• ISSNs: 1364-985X

Abstract

Abstract : In order to improve the efficiency of climate change initiatives China launched its national carbon market in December 2017. Initial CO2 quota allocations are a matter of significant concern. How should we allocate CO2 emissions reduction responsibilities among Chinese provinces, assuming that provinces will not or cannot trade these responsibilities among themselves? In this paper, we allocate CO2 quota from the perspective of cost minimisation. First, we estimate the national CO2 marginal abatement cost (MAC) function and deduce the interprovincial MAC functions. Second, we build an allocation model with nonlinear programming for cost minimisation. Finally, we obtain the allocation results under the emissions reduction target by 2030. The results are as follows. (i) The national MAC was 134.3 Yuan/t (at the constant price of 1978) in 2011, with an overall upward trend from 1990 to 2011. (ii) The interprovincial MACs differ significantly and decline gradually from east to west. Hebei has the largest emissions reduction quota, and Shandong has the largest emissions quota by 2030. (iii) Compared with other criteria of per capita, gross domestic product (GDP), grandfathering and carbon intensity, the proposed approach is the most cost‐effective in achieving the reduction target, with cost savings of 37.7, 34.5, 47.9 and 33.87 per cent, respectively.