The Economic Effects of Initial Quota Allocations on Carbon Emissions Trading in China.

Author:Wu, Jie
 
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  1. INTRODUCTION

    Along with the rapid economic growth, China has been the largest emitter of greenhouse gas in the world. To combat climate change and accelerate the transition to a low-carbon economy, at the 2009 Copenhagen Summit, the Chinese government committed to the reduction of its carbon intensity by 40-45% from 2005 levels by 2020. In 2011, the State Council of China introduced a set of explicit carbon intensity reduction targets for each region by 2015, compared to 2010 levels, in the Twelfth Five-Year Plan (see Appendix Table A1). To achieve this commitment, seven trading pilots have been established and trials were started in 2013, which are expected to inform a national wide carbon market (Jotzo and Loschel, 2014).

    In theory, the emissions trading scheme is a market-based cost-effective reduction mechanism. It allows extra emissions cutbacks and the selling of superfluous quotas to obtain trading benefits in regions that have a lower marginal abatement cost (MAC), while regions with a higher MAC avoid too high abatement costs by buying quotas. This can reduce the total abatement cost of the society as a whole (Marshall, 1998; Montgomery, 1972; Tietenberg, 1985). Unlike a carbon tax, the emissions trading scheme uses a quantity cap on emissions rather than price intervention, and therefore has a more explicit reduction effect. It not only has the advantage of being cost-effective, but can also reduce the disparities among regional economies so as to promote equity and efficiency (Rose, 1992; Rose and Stevens, 1993; Vennemo et al., 2009). However, emissions trading may lead to a related problem known as "carbon leakage" whereby some industries with a high MAC in trading regions may simply transfer to other regions with more lax emission constraints or a lower MAC due to a decline in competitiveness (Bohringer and Rutherford, 2002; Kuik and Hofkes, 2010; van Asselt and Brewer, 2010).

    The initial quota allocation is one of the key points in the emissions trading design, and different quota allocations, which include the allocation criterion and allocation method, will affect the distribution of costs across regulated entities (Chen and Wu, 1998; Peace and Juliani, 2009). Currently, as Rose et al. (1998) summarized, the international initial quota allocation criteria are based mainly on three equity types--allocation-based, output-based, and process-based--which comprise nine criteria, namely Sovereignty, Egalitarian, Ability to Pay, Horizontal, Vertical, Compensation, Rawls' Maximin, Consensus and Market Justice. Some studies have also proposed various allocation principles based on equity among regions (Grubb, 1990; Grubler and Nakicenovic, 1994; Keverndokk, 1995).

    Beyond qualitative discussion, quantitative analysis comparing different quota allocation criteria has also been done by some researchers. Rose and Zhang (2004) and Bohm and Larsen (1994) simulated the emissions trading with a nonlinear programming model, while Edmonds et al. (1995) used a bottom-up model that consists of supply, demand, energy balance, and greenhouse gas emissions to evaluate different quota allocation criteria. Nevertheless, there is a lack of macroeconomic analyses of different quota allocation criteria.

    In terms of the initial quota allocation method, there are generally two possible options, namely free allocation and auction. Free allocation can be characterized as creating hidden subsidies in emission sectors; it also has the potential to overcompensate in high emission sectors and is not conducive to social investments flowing into low-carbon industry (Xuan and Zhang, 2013). Meanwhile, auction can improve the market efficiency and enhance the emission sectors' motivation to engage in autonomous reduction. In addition, auction revenue can be recycled in various ways to further promote energy conservation and emissions reduction (Cramton and Kerr, 2002; Goulder et al., 1999).

    Although the emissions trading scheme will be cost-effective irrespective of the initial quota allocations, production profits, investment distributions and the industrial structure are quite different under the different allocations (Coase, 1960; Rose and Tietenberg, 1993). Generally, the computable general equilibrium (CGE) model will be used to investigate the economic impact of initial quota allocation method (Edwards and Hutton, 2001; Hubler et al., 2014; Parry et al., 1999). Based on the previous research, we develop a multiregional CGE model that is coupled with an emissions trading model which describes the decision-making process of each trading sector in the emissions trading. This combination captures the connection between the micro decision making of emissions trading sectors and macroeconomic effects of carbon market policy.

    China is categorized by a diversity in industrial structure and a spatial heterogeneity of economic development because of its large territory. Therefore, the impact of emissions trading on regional macroeconomies is an important element of the emissions trading mechanism design (He and Li, 2010). Although many studies have discussed the initial quota allocation in China (Wu et al., 2010; Yuan et al., 2013; Zhou et al., 2013), most of which have had a qualitative focus or used an endogenous carbon tax to represent emissions trading, quantitative analyses on regional macroeconomies with in-depth consideration of emissions trading are still rare, and more of such research is needed. Therefore, the model developed in this paper contributes to the literature in developing an emission trading model that depicts the decision-making optimization of trading sectors in each region coupled with a multiregional CGE model.

    This paper analyzes the regional macroeconomic impacts of emissions trading in China under different quota allocation criteria and allocation methods with a multiregional CGE model. The remainder of the paper is structured as follows: Section 2 describes the methodology and data used for the model. Section 3 presents the different allocation criteria scenarios and empirical results, while Section 4 presents the different allocation method scenarios and empirical results. Section 5 states the conclusions that have been drawn from the results.

  2. MODEL AND DATA

    For this study, we developed a multiregional Chinese energy--environment--economy CGE model (CEEP Multiregional Energy-Environment-Economy Modeling System, [CE.sup.3]MS), in which the whole economy is divided into provincial regions based on administrative divisions and all regions form a national wide market through labor migration, capital flow, and commodity trading. There are 30 regions (1) and 17 production sectors in each region, including five energy sectors and 12 non-energy sectors (see Appendix Table A2). The model includes 30 regional governments and one central government, and it comprises six modules, as follows: production, commodity trading, institution, labor and capital flow, carbon emissions and trading, and macro-closure. The key features of [CE.sup.3]MS are outlined below.

    2.1 The Production Module

    The production sectors are perfectly competitive and produce generic commodities with capital, labor, energy and non-energy inputs. In production, energy is treated as a special resource rather than an intermediate input and is combined with value-added as a VA-E bundle. Thus, energy can be substituted for other energy or intermediate input. Electric power production is divided into eight kinds of technologies, as follows: thermal power, natural gas power, oil-fired power, nuclear power, hydro-power, wind power, solar power, and other technologies. In electric power production, coal, petroleum, and natural gas are the raw materials of thermal power, oil-fired power, and natural gas power, respectively, and cannot be substituted. Technical share factors are adopted in the composition of all technologies that can be calibrated in the benchmark. We assume fixed shares of technologies under simulation scenarios as this paper represents a static and short-term analysis.

    2.2 The Commodity Trading Module

    Commodity trading in the model includes import, export, and transfers among regions. The products of sectors in each region not only supply to the local market ([QRD.sub.j,r]), but also to other regions in China ([QRRE.sub.j,r]) and the rest of world ([QE.sub.j,r]), according to the following specification:

    [mathematical expression not reproducible] (1)

    [mathematical expression not reproducible] (2)

    where [[rho].sub.j,r] > 1 is the substitution elasticity parameter, [[alpha].sub.j,r] and [[delta].sub.j,r] are the efficiency parameter and share parameter of the CES function. [QA.sub.j,r] is the output of sector j in region r, and [QDS.sub.j,r] is the supply in domestic, which includes [QRRE.sub.j,r] and [QRD.sub.j,r].

    Composite commodities will be used for local intermediate input, governmental and household final consumption, fixed assets investment, and inventory. The supply function is represented by the constant elasticity of transformation (CET) function, while the demand function follows the Armington assumption.

    2.3 The Institution Module

    The model assumes that there is no direct linkage between households and the central government. Households' income ([YH.sub.h,r]) is composed of labor payment, part of capital compensation, and transfer payments from the local government. Households' consumption of different commodities ([QH.sub.h,j,r]) is determined by the Cobb--Douglas function under the principle of utility maximization with budget constraint:

    [mathematical expression not reproducible] (3)

    s.t. [summation over (j)][PQ.sub.j,r][QH.sub.h,j,r] = [mpc.sub.h,r](1-[tih.sub.h,r])[YH.sub.h,r] (4)

    where [PQ.sub.j,r] is the price of Armington commodity j in region r, [mpc.sub.h,r] and [tih.sub.h,r] are the propensity to consume and individual income tax rate of household h in region r.

    Regional enterprise income includes...

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