Identifying Strategic Traders in China's Pilot Carbon Emissions Trading Scheme.

AuthorZhu, Lei

    In order to cope with global warming and achieve its greenhouse gas (GHG) emissions target, China has established seven pilot emissions trading schemes (ETS) (1). A nationwide ETS was also launched in late 2017, but only covers power generation sector in its first stage of operation. China's ETS platform, to a large extent, follows the European Union emissions trading system (EU ETS) to create a market for emissions rights or permits (Goulder et al., 2017). The seven pilot ETS programs involve 3,051 firms, and at the end of 2018, the total trading volume reached 105.1 million tons. Significant variations are found in allowance prices; for example, the Beijing ETS has the highest average price, at around RMB 50 per ton (approximately 7.5 US dollars), whereas lower prices of around RMB 20 per ton can be found in the Hubei, Tianjin, and Chongqing ETSs.

    The theoretical foundation of ETS is firstly given by Coase (1960), who suggests that the market equilibrium price should be independent of the initial allocation of allowances in the ETS in competitive market, and trading in a well-conceived market can reduce emissions to a target level cost effectively. Montgomery (1972) also shows that, in theory, such a system can effectively lower emissions, and an optimal equilibrium price can be achieved through a market competition mechanism. The effectiveness of ETS has also been proved in a series of relevant studies (e.g., Muller and Mestelman, 1994; Linn, 2008; Frey 2013; Holland et al., 2015). However, in practice, firms with market power can trade strategically and manipulate permit prices (Montero, 2009) to their own benefit, which potentially compromises the effectiveness of this platform, and, as a consequence, the optimal solution cannot be reached.

    As most firms that actively participate in emissions trading are from monopolistic industries (e.g., electricity suppliers), they can easily gain market power. For example, Montero (2009) points out that, between 1995 and 1999, 43% of the permits were allocated to the four largest firms in the US sulfur market. Ellerman et al. (2010) report that one-third of the cap was assigned to only 10 firms in the EU ETS. The capacity of these large players to trade and influence permit prices gains them market power (Burniaux, 1999). When these firms exert their market power in the permit trading market, manipulation is possible, which consequently affects the system. Goulder et al. (2017) suggest that firms with government ownership tend to gain market power naturally, moreover, local government has the incentive to protect local competitive power. Regulation and legislation support to avoid such issues are therefore important (Zhang et al., 2017; Karplus and Zhang, 2017) to ensure healthy development of the ETS. Zhang et al. (2015) suggest that the firms' decision in the ETS is affected by the rules of permit allocation, which gives stronger roles to the regulators. In general, it is therefore important for the designers of an ETS or regulators to pay attention to the decisions and strategic trading behaviors of firms in the ETS.

    The first and probably the most important issue here is identifying strategic traders in the market and then understanding how these traders act individually or collectively to move prices and affect the system. Hahn (1984) designs a model to study the role of market power in the permit trading market, which is then followed by extensive studies. The existing literature, mainly theoretical, provides a good description of how strategic traders may behave in the permit market. However, it offers no explicit guidance on how to identify strategic traders empirically (Godal, 2011).

    Theoretically, we follow the principle developed by Flam and Jourani (2003), Flam and Godal (2004) and Godal (2005) to establish the basic analytical framework. The paper is then set up an empirical strategy to identify strategic traders in the permit market with application to the pilot programs in China. China's plan to establish its own nationwide trading scheme seems too ambitious. Many questions arise in such a vast market with many political implications. Strategic trading is more likely, and it can have a significant impact on the effectiveness of China's ETS. Therefore, it is important for Chinese policy makers and market designers to understand the system better.

    Another reason for conducting an empirical investigation of the pilot programs in China is the availability of data. In 2011, the National Development and Reform Commission (NDRC) set up an interesting program to achieve the strategic goals in energy savings and emissions reduction outlined in the twelfth five-year plan. The NDRC proposed the "Top-10,000 Energy-Consuming Enterprises Program" which requires all those responsible for the largest carbon emissions to participate. A total of 1,867 firms in the program are from the pilot regions, and rich information on these firms is available for our empirical study (also used in Wang et al., 2018).

    The remainder of this paper is organized as follows: section 2 reviews the theoretical background and relevant studies. Section 3 explains our analytical framework and elaborates our empirical approach. Section 4 describes the data and presents some preliminary analysis of the sample firms. Section 5 discusses our empirical results, and then the last section concludes.


    Market imperfection in the tradable pollution permit market is introduced formally by Hahn (1984). In a static framework, he builds a model around a large dominant firm and its competitors. These firms are assumed to be price takers in the market, and permits are allocated freely. The model shows that initial distribution of permits can lead to market inefficiencies. Restricting all trades with a single price is the main difference from Coase (1960), who shows that initial allocation does not matter. Hahn and Stavins (2011) further prove the importance of initial distribution of permits. The dominant firm can obtain direct benefits through minimizing abatement costs (Bueb and Schwartz, 2011); and it can also gain indirectly in the product market through "exclusionary manipulation" (Misiolek and Elder, 1989). Malueg and Yates (2009) and Westskog (1996) extend the Hahn (1984) model to allow for two or more strategic actors. Montero (2009) also considers more than one large firm, and these firms may interact in the market. One special characteristic of the permit market is that firms can retain the current permit for future use, therefore a dynamic framework is needed. Hagem and Westskog (1998) first introduce dynamics via a two-period framework to the Hahn (1984) model. Liski and Montero (2010) and Montero (2009) extend this to a multiperiod permit market. The general idea is that an emissions trading market is developing, and it is expected that emissions limits will be tightened in the future.

    Another strand of the literature investigates the role of strategic trading at the country/regional level (e.g., Bohringer and Loschel, 2003; Bohringer et al., 2007; Carlen, 2003; Helm, 2002; Sartzetakis, 1997). Bohringer and Rosendahl (2009), for example, show potential efficiency losses due to strategic allowance allocation in the EU ETS. Most of these studies focus on theoretical investigation or draw conclusions based on numerical simulation in the computable general equilibrium (CGE) model. Price has important signaling effects, though distortions can exist. For example, under the assumption of incomplete information, Sengupta (2012) considers how firms' investment in cleaner technology is affected by the stringency of regulation, price can signal the market when regulation is weak. Zhang et al. (2015) find that emissions reduction efforts can be affected by price, awareness and government subsidies. Electricity market is perhaps the most relevant sector when considering market power in emission trading. Amundsen and Bergman (2012) show that the Nordic electricity companies can manipulate electricity market using market power in green certificate market. Others such as Pahle et al. (2013) and Limpaitoon et al. (2014) discuss strategic trading in electricity market and its impact on emission trading schemes.

    In general, an extensive number of studies show that, in theory, manipulation or strategic trading can take place in the permit trading market, and the impacts are well established. The empirical literature on how strategic trading affects an ETS, however, is relatively limited. One significant gap is the estimation and calibration of firm-level MAC curves. Following a bottom-up approach, an enormous survey has to be conducted to collect sufficient information on energy savings, emissions abatement costs, and the adoption of abatement technology at each firm, and it is difficult to do this if the firm sample is large (Hyman et al., 2003). Betz et al. (2010) has estimated four representative installations' bottom-up MAC functions to present their empirical study on the coverage of EU-ETS among installations with a trading fee, in which all the firms were assumed to face perfect competition. Ma and Hailu (2016) use a non-parametric approach to estimate MAC of emissions in China.

    The classical Hahn-Westskog type models assume a large dominant strategic trader (or a group of large dominant traders) in advance, whereas other firms are assumed to be price takers. The price-taker fringe must be numerous enough for the model to work well (Montero, 2009); otherwise, no effective market-clearing device exists. But this strand of models provides no clear guidance on how to identify these strategic actors. Intuitively, it might be simple for a firm with small trading volumes to be considered a price taker, and its behavior has a negligible impact. In light of Flam and Jourani (2003) and Flam and Godal (2004), Godal (2005) explores whether the equilibrium of the...

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