Impacts of alternative allowance allocation methods under a cap-and-trade program in power sector

Energy Policy 47 (2012) 405–415

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Impacts of alternative allowance allocation methods under a cap-and-trade program in power sector Beibei Liu, Pan He, Bing Zhang n, Jun Bin State Key Laboratory of Pollution Control & Resource Reuse, School of Environment, Nanjing University, Nanjing 210093, PR China

H I G H L I G H T S c c c

The impact of allowance allocation methods is examined for a cap-and-trade program. The market efficiency would be distinct when the transaction costs are positive. The auction method would have lowest total emission control costs.

a r t i c l e i n f o

abstract

Article history: Received 22 March 2012 Accepted 7 May 2012 Available online 24 May 2012

Emission trading is considered to be a cost-effective environmental economic instrument for pollution control. However, the policy design of an emission trading program has a decisive impact on its performance. Allowance allocation is one of the most important policy design issues in emission trading, not only for equity but also for policy performance. In this research, an artificial market for sulfur dioxide (SO2) emission trading was constructed by applying an agent-based model. The performance of the Jiangsu SO2 emission trading market was examined under different allowance allocation methods and transaction costs. The results showed that the market efficiency of emission trading would be affected by the allocation methods when the transaction costs are positive. The auction allowance allocation method was more efficient and had the lowest total emission control costs than the other three allocation methods examined. However, the use of this method will require that power plants pay for all of their allowance, and doing so will increase the production costs of power plants. On the other hand, output-based allowance allocation is the second best method. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Allowance allocation Transaction costs Emission trading

1. Introduction In a simple model without transaction costs, income effects, and third-party impacts, the initial allowance allocation should not significantly affect the cost-effectiveness of an emission trading market (Hahn and Stavins, 2010). Any initial allocation should result in the same post market allocation (after trading) when the marginal emission abatement costs are equal to the market price under ideal conditions (MacKenzie et al., 2009; Montgomery, 1972). This conclusion has been widely cited and applied in subsequent research (Tietenberg, 1985); it follows directly from the assumption that the initial allocation does not affect the marginal abatement cost functions of a firm in a well-designed market (Hahn and Stavins, 2010). However, many factors may affect the marginal abatement cost functions of firms in an imperfectly competitive emission trading market (Georgopoulou et al., 2006; Michaelowa, 2004;

n

Corresponding author. Tel./fax: þ 86 25 89680536. E-mail addresses: [email protected] (B. Zhang), [email protected] (J. Bi).

0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2012.05.013

Tietenberg, 2006). In practice, the initial allocation matters a great deal, not only in terms of its impact on the fairness between companies, but also on the cost-effectiveness of the emission trading policy. Moreover, in many emission trading systems, the initial allocation process is the most controversial aspect of the imple˚ mentation process (Ahman et al., 2007; Tietenberg, 2006). The initial allocation of permits not only affects fairness or political expediency but also market efficiency. Hahn and Stavins (2010) identified six conditions under which the independence property may break down in a cap-and-trade system: transaction costs, market power, uncertainty, conditional allowance allocations, non-cost-minimizing behavior by firms, and differential regulatory treatment of firms. Transaction costs can be a major factor for cost-effective emission trading systems. Several authors have noted the potential importance of transaction costs in tradable permit markets, positing that transaction costs (e.g., finding a trading partner) may reduce cost-effectiveness (Montero, 1997; Woerdman, 2001). Although various economists define these transaction costs differently (Furubotn and Richter, 1997; Mullins and Baron, 1997; Stavins, 1995), all of them agree

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B. Liu et al. / Energy Policy 47 (2012) 405–415

that the resources devoted to transactions have alternate uses and opportunity costs. A one-time initial allocation would likely lead to lower costs than an auction allocation approach, given the vast level of analysis that would be required by all of the generating firms (Dixit and Olson, 2000; Simshauser, 2008). There are three major criteria or methods of allowance allocation: auction, criteria exogenous to the firm receiving the permits, and output-based allocation (Boemare and Quirion, 2002; Persson et al., 2006). Grandfathering is the most common criterion in practice (e.g., Acid Rain Program, RECLAIM)(Cramton and Kerr, 2002). However, grandfathering has two negative effects: it reduces the incentive of regulated firms to develop environmental innovations to reduce the value of the permits, and it creates a bias against new firms entering the product market (Boemare and Quirion, 2002; Howe, 1994; Milliman and Prince, 1989). Of these three allowance allocation methods, auctions – in theory – are considered to be the most cost effective (Cramton and Kerr, 2002). Researchers suggest that the auction method is the most cost-efficient method, by which its revenue can be used to cut preexisting distortion taxes (Fullerton and Metcalf, 2001; Goulder et al., 1999). More specifically, compared to the grandfathering approach, auctions increase the flexibility of distributing costs and incentives for regulated firms to develop environmental innovations, (Cramton and Kerr, 2002; Milliman and Prince, 1989). Auctions also increase the equitableness (i.e., fairness) (van Dyke, 1991) as well as the incentives of firms to minimize costs and distribute them in the rents (Cramton and Kerr, 2002). Cramton and Kerr (2002) prefer auctions to grandfathering because auction allocation methods reduce tax distortions, provide more flexibility in distributing costs, increase incentives for innovation, and reduce the need for politically contentious arguments over the allocation of rents. In output-based allocation, the number of permits that a firm receives is based on the firm’s current production (Lennox and van Nieuwkoop, 2010). The output-based allocation method does not create rent scarcity because it provides no incentive to raise the price-cost margin (Boemare and Quirion, 2002). However, the difficulty is how to define ‘‘output’’ (Fischer, 2001). Burtraw et al. (2001) found that an output-based CO2 allowance allocation method would have similar costs to the grandfathering method, but approximately twice the costs of the auction method. Other allowance allocation methods, such as contest-based allocation (MacKenzie et al., 2009) and generation performance standard (GPS) allocation (Beamon et al., 2001; Zhu et al., 2003), have been developed for emission trading policy. This research examines the emission trading market performance of different allowance allocation methods in the presence of transaction costs. To examine the dynamic evolution of the emission trading market and individual property, we used a bottom-up approach – an agent-based model – to simulate the emission trading market. The agent-based model is widely applied in simulations of artificial markets (Guerci et al., 2005; Tesfatsion, 2006; Veit et al., 2009; Walter and Gomide, 2008) and emission trading markets (Cong and Wei, 2010; Genoesea et al., 2007; Mizuta and Yamagata, 2001; Sichao et al., 2010). Section 2 provides the analytic framework and model design, Section 3 presents the main results of market simulation, and Section 4 concludes with policy suggestions.

2. Methodology 2.1. Research site This research chose Jiangsu Province in China as a case study. In its 10th Five-Year Plan (2001–2005), the annual average SO2

concentration of Jiangsu Province and its frequency of acid rain increased. In 2005, the acid rain (pHo5.6) frequency reached 33.9%. In the 11th Five-Year Plan (2006–2010), the central government requires Jiangsu Province to reduce its SO2 emission by 18% of its 2005 level. In the new 12th Five-Year Plan (2011– 2015), Jiangsu Province plans to reduce its SO2 emission by 8% of its 2010 level. Emission trading is considered to be a feasibility mechanism to achieve total pollution control targets with lower economic costs. 2.2. Allowance allocation methods Based on a literature review, four allowance allocation methods were chosen for further research: grandfathering, output-based, auction and the GPS method. 2.2.1. Grandfathering allocation method According to the grandfathering allocation mechanism, each power plant will receive an average reduction ratio based on the 2006 emissions (see Eq. (1)). Ait ¼ ei,t1 

TEC t , TEDt1

ð1Þ

where Ait is the allocated allowance of firm i at time t; ei,t  1 is the emission discharged by firm i at time t 1; TECt is the total emission control target at time t; and TEDt  1 is the total emission discharge at time t  1. 2.2.2. Output-based allocation method An alternative method is to allocate the allowance on the basis of the electricity generation of the power plants (see Eq. (2)): Ait ¼ TEC t 

EGi,t1 TEGt1

ð2Þ

in this equation, EGi,t  1 is the electricity generation by firm i; and TEGt  1 is the total the electricity generation at time t  1. 2.2.3. Generation performance standard (GPS) allocation method The Jiangsu Provincial Government has allocated SO2 emission permits to firms according to the requirements of the State Environmental Protection Agency (SEPA) under the GPS method (JSEPB, 2002). The GPS method is based on the average kilogram per kilowatt-hour basis to set environmental regulations for the electricity industry (Lin et al., 2011). However, in Jiangsu Province, such performance standard also took regulatory requirement, location into consideration (Zhu et al., 2003). The calculation equation is present as follows: Ai ¼ F i  Di  K i  Li :

ð3Þ

In this equation, Fi is the emission discharge benchmark in Jiangsu (JSEPB, 2002). For older power plants Fi ¼4.51 g/kW h, while for newly installed power plants, Fi ¼0.9 g/kW h. Di is the electricity generated by firm i. Ki is the unit capacity parameter of firm i. If the unit capacity is larger than 600 MW Ki ¼0.75, if the unit capacity is between 300 MW–600 MW Ki ¼0.90, and if the unit capacity is between 50 MW–300 MW Ki ¼1.0. Li is the SO2 reduction parameter of firm i. If firm i is in the ‘‘two-control zone’’, Li ¼ 0.8, on the contrary Li ¼0.9. 2.2.4. Auction Many studies (Cramton and Kerr, 1998; Lai, 2008) consider an auction of pollutant permits to be the best way to effectively set and achieve pollution caps. A standard ascending clock auction is chosen for further analysis (Cramton and Kerr, 1998). For each price p, each bidder i simultaneously indicates the quantity, Ei(p), that he or she desires, where demands are required to be non

B. Liu et al. / Energy Policy 47 (2012) 405–415

increasing in price. The demand is based on the Cournot–Nash marketplace solution for a given price for permits. When a price p* is reached such that aggregate demand is less or equal to supply, the auctioneer concludes the auction. Each bidder i is then assigned the quantity Ei(p*), and each bidder i is charged a unit price of p*.

407

and will not be taken into account in this analysis. In addition, for the model to be tractable, I assume that the operating costs are continuous and linear in qi. C i ðqi Þ ¼ pc qi

ð7Þ

Here, pc is the sum of coal price and maintaining costs. Thus, the benefit of firm i is

2.3. Simulation model of the emission trading market

pi ¼ g i ðqi Þpe pc qi ci ðri Þpxi

The simple process of a cap-and-trade system can be explained as follows. A regulator determines the scope of the system (e.g., regions and sectors) and sets a limit (or cap) on the total emission allowed. Economic agents are provided with emission rights based on a certain standard, and each agent is required to hold the rights corresponding to its emission. If an economic agent does not hold enough emission rights to comply with the regulation, then the agent must abate its emission or buy emission rights from agents who emit a lower emission than the rights that they hold.

We also assume that yi is the emission removal rate, thus 1 yi is the pollution discharge rate. Thus

2.3.1. Agents and state variables Agents (i.e., power plants in this paper) aim to abate their SO2 emission to levels below their emission rights and to maximize profit. In contrast to traditional economic analysis, agents behave on the basis of local information. Each power plant considers the marginal profit (MP) function, SO2 emission, emission rights, strategies, and existing environmental regulations. Firms are free to trade permits at any time and may meet government standards by exercising pollution control and by possessing permits for their residual emission. We assume that all power plants are profit-maximizing agents and have perfect information of their own costs and profits. In addition, perfect monitoring and enforcement are assumed to be available to the environmental regulator in this research. We consider ei as the unconstrained emission in units per year; ri as the pollution reduction in units per year by firm i; Ai as the quantity of permits in units per year allocated to source i; and xi as the amount of emission traded. Here, we assume that firms employ three compliance strategies: abatement of emission, purchasing of allowances in addition to their initial allocation, and adjustment of output levels (Zhang, 2007). Therefore, we have xi ¼ ei r i Ai :

ð4Þ

If xi ¼ei  ri  Ai 40, firm i will buy emission permits; conversely, firm i will sell emission permits. The benefit of firm i is

pi ¼ Ri ci ðri Þpxi

ð5Þ

here, Ri is the received revenues of firm i. ci(ri) is the firm’s emission abatement costs, for which c0i ðr i Þ 40 and c00i ðr i Þ 40. The total cost of emission control is ci(ri)þ pxi. p is the price of emission permits. In each time period, the firm decides the electricity output (gi) and chooses the amount of coal (qi). Suppose electricity prices is pe, we have Ri ¼ g i ðqi Þpe C i ðqi Þ

ð6Þ

The production function with fuels is represented by gi(qi), which is assumed to be concave, increasing in argument, and twice differentiable everywhere (Zhang, 2007). We assume that electricity generation is described by g(q)¼Gqk, where G is a productivity parameter and 0 oko1; k is a constant determined by technology and power capacity. Ci(qi) is the operating costs function. When a firm undertakes production, it incurs costs that can be described in terms of three components: (1) fuel costs, (2) maintaining costs and (3) fixed costs. Since the power plants have been built, the initial capital cost is considered as sunk cost

r i ¼ ei yi ¼ ai yi qi

ð8Þ

ð9Þ

here, ai sulfur dioxide yield parameter of firm i. ci is the function of yi and qi and is assumed to be concave, twice differentiable everywhere (c0i ðyi Þ 40, c00i ðyi Þ 4 0). ci ðr i Þ ¼ ci ðyi ,qi Þ

ð10Þ

The emission abatement costs are further assumed to be a function of the emission reduction and emission reduction rate: ci ¼ fi ai qi yi k1=ð1yi Þ

ð11Þ

here, k and ji are the parameters of the emission abatement costs function, and yi is the emission abatement rate. All power plants are assumed to have the same value of k and different values of fi. aiqiqi is the emission reduction for power plant i. When a firm plans to participate in an emission trading market, transaction costs are inevitable. These costs include the need to set trading objectives, audit and calculate abatement costs, negotiating the trading price, and obtaining approval of the trade. Here, we assume two parts of transactions cost: (1) fixed cost and (2) costs related to gross emission trading (Collins and Frank, 1991). Thus, in this paper, we define the transaction costs as Tðxi Þ ¼ a þ b9xi 9

ð12Þ

here, a is the fixed part of transaction costs, and b9xi9 is the trading tax which is related to the emission trading quantity. Fixed costs can include administrative costs (e.g., additional monitoring and verifying costs for trading and approval costs). The government also provides an emission trading center to reduce transaction costs. However, there are costs in building and maintaining the emission trading center, and they are transferred to trading firms as a ‘‘trading tax’’. In addition, other kinds of transaction costs such as research costs and enforcement costs are not taken into account in this research. Finally, in most previous studies, the emission discharge fee (or emission discharge tax) is separated from the emission trading market. Firms do not need to pay any tax or fee for emission discharge that is under the cap. However, some countries, such as China, do have such an emission fee/tax system. According to China’s current environment levy system, we assume the emission fee/tax rate is pd. Thus, firm i should pay the emission discharge fee/tax (fe) is f e ¼ ai qi ð1yi Þ  pd

ð13Þ

Thus, at each period, a firm observes the electricity price (pe), permit price (p), emission discharge fee (fe), price of carbon fuel (pc), and allocated permits (Ai). Each firm uses it own perspective in each time period to adopt a strategy that it considers to be optimal for that period. Thus, the firm’s strategy is a map from the Markov state 4 ¼{P,A} to choice variables {qi,yi,xi}, where P is a price vector, i.e., P¼ {pe,p,pc,pd}. Here, yi is the emission removal rate, and qi represents coal consumption; {qi,yi,xi} represents the three compliance strategies: abatement of emission, purchasing of allowances in addition to initial allocation, and adjustment of output levels. Let pi(P,Ai) denote the value of firm i.

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The maximization problem for the firm can be written as

pi ðP,Ai Þ  max ½g i ðqi Þpe pc qi ci ðri Þpxi pd ai ð1yi Þqi T i ðxi Þ

ð14Þ

subject to : Ai þxi ai ð1yi Þqi ¼ 0

ð15Þ

yi Z 0:

ð16Þ

yi ,qi ,xi

2.3.2. Agents’ behavior rule in the emission trading market In accordance with the recent practice in emission trading in China (Nie and Xu, 2009), this research used a continuous double auction emission trading market for further analysis. Double auction is a natural trading mechanism. In the double auction emission trading market, both sellers and buyers place a buy/sell limit order (including the maximal/minimal price and the desired/offered amount) at any time. When a new order matches the best waiting order of the opposite type, a trade is made (for the limit price of the counterpart); otherwise, the new order is placed on hold. The orders may also be canceled by their submitters (Cliff, 2000; Posada et al., 2005). All participants can learn from the previous bid-ask and adjust their next bid-ask amount and price. 2.3.2.1. Step one: Determining an order. When a firm is participating in the emission trading market for the first time or when it has been waiting for several trading rounds, each firm considers an order on the basis of its marginal profit, its emission allowance, the market price of emission permits, and the expected price of emission permits. The first stage of emission trading is the determination of the emission amount that each firm expects to abate and trade in a year, based on the current market price (p). We assume that MPA (see Supplementary information) is the marginal profit of a firm when the amount of emission discharge (E) is equal to the firm’s allowance (A). There are three types of choice according to the current emission price of each firm. If its MPA is higher than the current emission price, then the firm should be a buyer. If its MPA is lower than the current emission price, then the firm should be a seller. If its MPA is equal to the current emission price, then the firm should be neither a seller nor a buyer until the price changes. The buyers will select a strategy, determines a bid amount, and calculates the MP to determine a bid price. Because the actual trading amount is uncertain when an order is considered and all of the agents are risk-neutral, the optimal bid price is less than p when the bid amount is Ep  A. Here, Ep represents the emission discharge when the marginal profits are equal to p and p is chosen as the bid price. Thus, buyers will determine the bid price of p, and the bid amount will be Ep A. Similarly, the sellers will determine the bid price of p, and the bid amount will be A Ep. 2.3.2.2. Step two: Making an order. All of the agents determine an order, but only some agents actually make the order in accordance with their strategy. We assume that each agent has a constant activation probability of 25% per round. In other words, 25% of the agents are randomly chosen to bid in each round (Posada et al., 2005; Walsh et al., 2002). 2.3.2.3. Step three: Accepting an order. The buyer with the highest bid price is initially matched with the seller having the lowest asking price, as long as both sellers and buyers exist and the highest bid price to buy is not lower than the lowest bid price to sell (Nicolaisen et al., 2001). The purchase price is the average of the highest bid price of buyers and the lowest bid price of sellers. If the buyer’s and seller’s bid amounts do not match, then the buyer’s bid is matched with the seller’s bid so that the quantity of

tradable permits is the minimum of the two amounts (the buyer’s bidding amount and the seller’s asking amount). The carryover amount to buy or sell is calculated, and the next pair is matched in the same manner. 2.3.2.4. Step four: Learning from previous orders. The repeated auction constitutes an environment where the bidders can obtain and use information from previous auction results. Cliff and Bruten (1997) indicated that the price convergence of zero-intelligence traders is predictable from a priori analysis of the statistics of the system. Thus, more complex bargaining mechanisms or some ‘‘intelligence’’ are necessary for traders. Consequently, a type of agent with simple machine learning techniques was developed and is referred to as the zero-intelligence-plus (ZIP) agent. Further experiments showed that ZIP agents outperform their human counterparts (Das et al., 2001). Therefore, in this research, we endow agents with ZIP ask-bidding strategies. According to ZIP strategies, agents in the emission trading market will adjust their profit margin and bid price according to their previous bidding. If there was a transaction in the last round and the agent was not the winner, or if there was no transaction, then the agent decreases its profit margin in the current round. Contrarily, the winner has a greater profit margin in the current round (see Supplementary information). Thus, the decision-making process for each agent consists of the following four main steps (see Fig. 1). 2.4. Data and parameters We collected and analyzed data from 45 power plants in Jiangsu Province. The initial values of variables and initialization dates were collected or calculated on the basis of data for 2006 (see Table 1). We only collected two years’ production data from the power plants. We assume that all of the power plants have the same value of k; this assumption yields the regression equation lnðg i Þ ¼ klnðqi Þ þlnGi þ e,

ð17Þ

where e is the error. The regression results obtained with the real data of 45 power plants are presented in Table 2. The coefficient of lnq is the value k in Eq. (17). Therefore, we have k¼0.92011. In addition, we assume that all of the power plants have the same value of k and distinct values of fi. aiqiyi is the emission reduction for power plant i. Thus, we have the regression equation lnci ¼ lnfi þ lnRi þ xi lnk þ ei ,

ð18Þ

where xi ¼ 1=ð1yi Þ and e is the error. When we use the database of 45 power plants, the regression coefficient of xi is the value lnk. Therefore, we have lnk ¼ 0:0565 and k ¼ 1.0581363(see Table 3).

3. Results and discussion 3.1. Results of allowance allocation The total emission target set by the 11th Five-Year Plan of Jiangsu is 317,902 t (TECt ¼317,902), which is 55.7% of emission discharge level at 2006 (556,442 t). The allowance allocation results based on the four allowance allocation methods are shown in Fig. 2. The results based on the grandfathering and outputbased methods were calculated by using the above equations. The allowance allocation results based on the GPS method were collected from Jiangsu EPB (JSEPB, 2002). For the standard ascending-clock auction, when a price p* ¼ 4.20, the aggregate demand is equal to supply. The results for the GPS-based, output-based and

B. Liu et al. / Energy Policy 47 (2012) 405–415

409

Input data

Make an order last round?

i

To be a buyer at last round

yes

yes

Bidingstrategies Bidingprice = p Biding amount = Ep − Ai

Make an order ?

no

yes

no

yes

no MPA > p

Agent i

M PA < p

Accept an order

i

no

no yes

yes Askingstrategies Askingprice = p Asking amount = Ai − Ep

no

no

Winner?

Make an order ? yes

yes Accept an order

Winner?

no

yes i (t) = Hi (t) q (t) + Yi (t)

i (t) = Hi (t) q (t) + Yi (t) Δi (t) = vi (i (t) − pi (t))

Δi (t) = vi (i (t) − pi (t))

Hi ∼ [0.95, 1.0]

Hi ∼ [1.0, 1.05]

Yi ∼ [−0.1, 0.0]

Yi ∼ [0.0, 0.1]

pi (t+1) = pi (t)+Δi (t)

Next round

t = t+1

Fig. 1. Flow diagram of model processes.

3.2. Market performance with zero transaction costs

Table 1 Initial values of variables. Variable

Unit

Value

Reference

pe Ai

(CNY kW h  1) (kg) (kg ton  1) (CNY ton  1) (CNY kg  1) (CNY kg  1) – –

[0.32, 0.40] – – 420 1.26 – 1.0581363 0.92011

(JSFB, 2006), see Table 2 (JSEPB, 2002), see Table 2 Calculated with data from 2006 Data from 45 power plants (JSEPB et al., 2007) Calculated with data from 2006 Calculated with data from 2006 Calculated with data from 2006

ai pc pd

f

k k

auction allowance allocations were similar to each other. However, the results of the grandfathering method were distinct from those of the other three methods. The reason for this difference is that some of the old power plants that have not fixed the Flue Gas Desulfurization System (FGDS) receive a large allowance with the grandfathering method (e.g., power plant 11).

To examine the impact of transaction costs on market performance under different allowance allocation methods, we first simulated the market performance with zero transaction costs. We set a ¼0 and b ¼0 in the agent-based model. The artificial market model was run for 300 rounds to ensure sufficient trading. The market price of Jiangsu SO2 emission trading converged at 4.20 CNY/kg (see Fig. 3). The equilibrium price is equal to the auction price at its initial allowance allocation. The market price under the other three allowance allocation methods converged at 4.20 CNY/kg. The initial allocation methods have no impact on the equilibrium market price in a completely competitive market. However, as there were no transaction costs between the traders, all of the agents could trade their allowances sufficiently, and the market achieved the same total emission control costs. We examined the total emission control costs under the four allowance allocation methods and compared them with the command-and-control regulation scenario under which emission

410

B. Liu et al. / Energy Policy 47 (2012) 405–415

Table 2 Regression results of Eq. (17). Source

SS

df

MS

Number of observations

45

Model

23.2669

1

23.2669

Residual

0.4931

43

0.0115

Total

23.7600

44

0.5400

F(1, 43) Prob 4F R-squared Adj R-squared Root MSE

2029.1 0.0000 0.9792 0.9788 0.1071

Lng Lnq _Cons

Coef. 0.9201 8.1378

Std. err. .0216 .3126

t 45.05 26.03

P 4t 0.0000 0.0000

95% Conf. 0.9305 7.5073

Interval 1.0177 8.7682

Table 3 Regression results of Eq. (18). Source

SS

df

MS

Number of observations

45

45

Model

73.4143

2

36.7072

Residual

10.1025

42

0.2405

Total

83.5168

44

1.8981

F(2, 42) Prob4F R-squared Adj R-squared Root MSE

152.61 0 0.879 0.8733 0.49044

152.61 0 0.879 0.8733 0.49044

Lnc LnR x _Cons

Coef. 1.2232 0.0565  3.0840

Std. Err. .0936 .0213 1.4619

t 13.07 2.66  2.11

P 4t 0.0000 0.0110 0.0410

95% Conf. 1.0343 0.0136  6.0346

Interval 1.4121 0.0994  0.1337

Fig. 2. Allowance allocation results of 45 power plants.

trading is not allowed and power plants should not discharge more pollution than their allowance. The simulation results of this research also supported the conclusion that the allocation methods have no impact on the market efficiency (total emission control costs). The total emission control costs of the three allowance allocations all declined to 4.25  109 CNY1 (see Fig. 4). Compared with the command-and-control regulation scenario, using the emission trading mechanism would reduce the total emission control costs. The total emission control costs under the four allowance allocation methods were 6.25  109 CNY, 4.77  109 CNY, 4.37  109 CNY and 4.25  109 CNY, and the total

1

For auction method, we did not take the costs at auction into account.

emission control costs under the grandfathering allocation were higher than those under the other three methods. When emission trading is allowed, the emission trading market will reduce 32.09%, 10.99%, 2.88%, and 0% of total pollution control costs in comparison to the command-and-control regulation scenario. However, the amount and rate of cost savings depended on the gap between the initial allowance and equilibrium status. Thus, different allocation methods resulted in different cost savings. According to Fig. 4 the grandfathering allocation method had the largest variation from the optimal status (32.09%), and the auction allocation method had the least variation (0%). Emission trading is used to correct the initial allowance allocation distortion of the lowest pollution control costs. The larger the gap between the initial allowance allocation and equilibrium status, the

B. Liu et al. / Energy Policy 47 (2012) 405–415

larger the emission trading amount will be and the more significant the cost-saving will be. In this research, the grandfathering allocation method needs more trading than the other three methods. The

411

emission trading amount under the grandfathering allocation method is 1.75  108, while that under the auction allocation method is 0. However, if the transaction costs are positive in the emission trading market, the total emission control costs of the emission trading market will be affected by the allocation methods. 3.3. Market performance with positive transaction costs

Fig. 3. Prices in the Jiangsu SO2 emission trading market under three allowance allocation methods.

When the transaction costs in the emission trading market are positive, power plants will pay additional costs for trading. Taking the verifying costs as fixed transaction costs, we set a¼26,666 CNY. In addition, we changed b in the range of [0.0,0.5] as the trading tax, which is consistent with previous research (Zhang et al., 2010). We ran the agent-based model with different values of band different allocation methods. The equilibrium prices of 11  4 runs are illustrated in Fig. 5. The market prices in our model converged quickly to the equilibrium price. As the marginal profits of power plant are equal to each other, then no trading will occur under the auction allocation method. For the other three allowance allocation methods, the increase in transaction costs did not significantly impact the market price; the price will stay around the equilibrium price. The t-test of the equilibrium price under the three allowance

2.0E+08 1.8E+08

6.0E+09

1.6E+08 5.0E+09

1.4E+08 1.2E+08

4.0E+09

1.0E+08 3.0E+09

8.0E+07 6.0E+07

2.0E+09

4.0E+07 1.0E+09

emission trading amount (kg)

total emission control costs (CNY)

7.0E+09

2.0E+07 0.0E+00

0.0E+00 Grandfathering TEC under command&control

GPS

Output-based

TEC under emissions trading

Auction emissions trading amount

Fig. 4. Total emission control cost of three allowance allocation methods with zero transaction costs.

4.4 4.35

price (CNY/kg)

4.3 4.25 4.2 4.15 4.1 4.05 4 3.95 3.9 3.85 0

0.05

0.1

0.15

Grandfathering

0.2 0.25 0.3 trading tax Output-based

GPS

0.35

0.4

0.45

0.5

Equilibrium price

Fig. 5. Market price of Jiangsu SO2 emission trading market under three allowance allocation methods.

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allocation methods showed that the average values were equal to each other (Table 4). In addition, positive transaction costs associated with the different allocation methods did not increase or decrease the equilibrium price. The latter finding is distinct from or contrary to previous reports that the transaction costs would decrease the equilibrium price (Stavins, 1995). In comparison to the completely competitive market, the increase of the trading tax will depress the activity of emission trading market transactions and heighten the total emission control costs. The existence of the trading tax not only increased the cost of emission trading but also blocked the trading by a small amount. Fig. 6 reveals the total emission trading amount under different trading-tax scenarios. The total emission trading amount decreased when the trading tax is increased. For example, if the trading tax was 0.5 CNY/kg, the total emission trading amount was 3.98  107 kg, which is 68% of the total emission trading amount with a zero trading tax under the output-based allowance allocation scenario. In addition, although the market was run as long as possible, the emission trading market did not achieve the equilibrium in which all of the firms’ marginal abatement profits (costs) were equal to the market price. The power plants stopped trading when the marginal profits were higher than p  b for sellers and lower than p þb for buyers. Thus, the existence of transaction costs would reduce the emission trading amount as well as the effectiveness of the performance of the emission trading market. The three allowance allocation methods (except the auction, which has no transaction costs associated with finding a trading partner) resulted in different emission trading amounts and

Table 4 Hypothesis testing of price under different allowance allocation methods. Mean(diff) ¼mean(grandfathering – output-based) Ho: mean(diff) ¼0 Ha: mean(diff) o0 Ha: mean(diff)! ¼0 Pr(T ot) ¼0.7415 Pr(T4t)¼ 0.5170

t¼ 0.6717 Degrees of freedom ¼ 10 Ha: mean(diff) 40 Pr(T4t)¼ 0.2585

Mean(diff) ¼mean(output-based—GPS) Ho: mean(diff) ¼0 Ha: mean(diff) o0 Ha: mean(diff)! ¼0 Pr(T ot) ¼0.1885 Pr(T4t)¼ 0.3770

t¼ –0.9244 Degrees of freedom ¼ 10 Ha: mean(diff) 40 Pr(T4t)¼ 0.8115

Mean(diff) ¼mean(GPS—grandfathering) Ho: mean(diff) ¼0 Ha: mean(diff) o0 Ha: mean(diff)! ¼0 Pr(T ot) ¼0.4861 Pr(T4t)¼ 0.9721

t¼  0.0358 Degrees of freedom ¼ 10 Ha: mean(diff) 40 Pr(T4t)¼ 0.5139

transaction costs. The output-based allowance allocation method had the least emission trading amount (approximately 3.98  107 kg) when the trading tax was 0.5 CNY/kg. The trading amounts in the grandfathering and GPS allocation methods, respectively, were 3.61 times (1.43  108 kg) and 1.84 times (7.31  107) that of the output-based allowance allocation method. Consequently, the grandfathering allowance allocation method has higher transaction costs: 3.61 times those of the output-based allocation method and 1.97 times those of the GSP allocation method. Lower emission trading motivation as well as more and higher transaction costs would lead to lower market efficiency in Jiangsu Province’s SO2 emission trading market. While examining the total emission control costs under different transaction costs scenarios, we found that the transaction costs significantly reduced the efficiency of the emission trading market. Fig. 7 shows the total emission control costs under different levels of the trading tax. When the trading tax was increased from 0.0 to 0.5 CNY/kg, the total emission control costs also increased 3.44% (1.46  108 CNY), 1.01% (4.29  107 CNY), and 1.65% (7.0  107 CNY) (see Fig. 7), respectively. The slope of the grandfathering allowance allocation was steeper than that of the other two allowance allocation methods. This finding indicated that the market efficiency of grandfathering allocation is more sensitive to transaction costs than that of the other allocation methods. The output-based allocation method was more efficient than the other two allocation methods because it had the lowest total emission control costs. When examining the component of pollution control costs of power plants, the grandfathering allocation method has a higher proportion of transaction costs. When we set the trading tax to be 0.5 CNY/kg, the transaction costs are 3.30%, 1.72%, 0.96%, and 0.00% of total pollution control costs under grandfathering, GPS, output-based and auction allocation methods, respectively (see Fig. 8). Thus, the cost-savings of emission trading policy will reduce to 29.75%, 9.43%, 1.89% and 0.00%, respectively. However, the final total pollution control costs of the emission trading market under the four allowance allocation methods did not make much difference. More specifically, the final total pollution control costs of the grandfathering, GPS, and output-based allocation methods are 103.36%, 101.65%, and 100.94% of those of the auction method, respectively. 3.4. Discussion The approach to allocating emission allowances for pollution is important not only for the tremendous potential transfer of

Fig. 6. Emission trading amount and transaction costs of Jiangsu SO2 emission trading market under different allowance allocation methods.

B. Liu et al. / Energy Policy 47 (2012) 405–415

413

total emission control costs (CNY)

4.5E+09

4.4E+09

4.4E+09

4.3E+09

4.3E+09

4.2E+09

4.2E+09 0

0.05

0.1

0.15

0.2 0.25 0.3 0.35 trading tax (CNY/kg)

Grandfathering

Output-based

0.4

GPS

0.45

0.5

Auction

Fig. 7. Total emission control costs of Jiangsu SO2 emission trading market under three allowance allocation methods.

7.0E+09 6.0E+09

CNY

5.0E+09 4.0E+09 3.0E+09 2.0E+09 1.0E+09 0.0E+00 Grandfathering

GPS

transaction costs

pollution discharge fee

Output-based

Auction

pollution abatement costs

cost saving

Fig. 8. The component of pollution control costs under four allowance allocation methods (b ¼ 0.5 CNY/kg).

Table 5 Comparison of four allowance allocation methods. Index

Unit

Grandfathering

GPS

Output-based

Auction

Total allowance Variance Total pollution control costs (command & control scenario) Emission trading scenario with zero transaction costs

kg kg CNY CNY % kg CNY CNY % kg

3.18  108 2.53  108 6.25  109 4.25  109 32.09% 1.75  108 4.39  109 1.45  108 29.75% 1.44  108

3.18  108 1.38  108 4.77  109 4.25  109 10.99% 9.16  107 4.32109 7.43  107 9.43% 7.31  107

3.18  108 8.33  107 4.37  109 4.25  109 2.88% 5.84  107 4.29  109 4.10  107 1.89% 3.98  107

3.18  108 0 4.25  109 4.25  109 0.00% 0 4.25  109 0 0 0

Emission trading scenario with positive transaction costs (b ¼ 0.5 CNY/kg)

Total pollution control costs Cost saving Trading amount Total pollution control costs Transaction costs Cost saving Trading amount

wealth within the economy but also for its effect on the economic cost of achieving emission reductions (Burtraw et al., 2001). This research examined four well known allowance allocation methods and their impact on an SO2 cap-and-trade program. The initial allowance allocation will cause different marginal pollution control costs, and emission trading will correct the inefficiency of allocation. However, the transaction costs will bring additional costs and block the market from reaching equilibrium status. Therefore, the larger gap between initial allocation results and equilibrium status in addition to the higher transaction costs will result in higher total pollution costs. In this research, the grandfathering allocation method is based on historical SO2 discharge; in other words, power plants that have

not installed FGD will receive more allowance. The GPS method also distinguished power plants with or without FGD for a different emission discharge benchmark. Once again, power plants that have not installed FGD will also receive more allowance. The outputbased allocation method and the auction allocation method do not take historical emission discharge into consideration. We measured the variance by the sum of the absolute D-value of the initial allowance allocation and equilibrium status. The results presented in Table 5 showed that the variance of the initial allowance allocation of grandfathering is higher than that of the other three methods, which results in higher trading and transaction costs. The auction allocation method is more efficient than other allowance allocation methods in this research, which is also found out in

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other kinds of research (Fullerton and Metcalf, 2001; Goulder et al., 1999). Two important reasons for this outcome are that all bidders receive their demands at the market price, and a secondary market allows the sale and purchase of permits as circumstances change. In this research, we only model one time period of the emission trading market that an auction allocation method eliminates the need for the firms involved to incur transaction. The EU has approved amendments to the EU ETS that require member states to distribute approximately two-thirds of their allowances through the auction allocation method; this requirement may provide a useful price signal (Hahn and Stavins, 2010). However, the auction allocation method will require power plants to pay for all of their allocation allowance, which will, in turn, increase their costs and bring political obstruction. Aside from the auction allocation method, the output-based allowance allocation is the next best choice or priority for emission trading market design in this research. However, for the emission trading programs including distinct sectors, output-based allowance allocation may reduce the incentive to save on pollution-intensive goods and services (Quirion, 2009). Moreover, this research only took transaction costs into the emission trading model. Power plants will do emission trading when it is profitable. When in a multi- period emission trading market, power plants with an economic profit or a residual allowance may bank for future use. In that case, the impact of allowance allocation will be more significant than it is for a one-period model.

4. Conclusion Emission trading is considered to be an environment friendly, cost-effective economic instrument for pollution control and is widely piloted in China (Chang and Wang, 2010). However, the policy design of an emission trading program has a decisive impact on its performance. Allowance allocation is one of the most important policy design issues in emission trading, not only for equity but also for the policy performance. In this research, an artificial market for SO2 emission trading was constructed by applying an agent-based model. The performance of the Jiangsu SO2 emission trading market under different allowance allocation methods and transaction costs was also examined. The results showed that the market efficiency of emission trading is affected by the allocation method when the transaction costs are positive. The results also showed that the grandfathering allocation method will yield more trading, transaction costs and total emission control costs. However, the application of the auction allowance allocation method in Jiangsu Province was more efficient than the application of the other allowance allocation methods. However, the auction allocation method increases the costs of power plants, and it needs to be re-designed to give the plants some of the revenue to cut preexisting distortion taxes. In addition, this research applied an agent-based modeling approach to illustrate the impact of allocation methods on the performance of emission trading policy. This research only considered transaction costs; other factors that were not included may impact the decision making of power plants. The next step is to include more conditions that may affect the behaviors of individual firms (Cason et al., 2003; Hahn and Stavins, 2010) in our agent-based model. In addition, long-term market efficiency should be investigated in place of short-term efficiency (del Rı´o Gonza´lez, 2008).

Acknowledgment This research is supported by the National Science Foundation of China (Grant No.70903030).

Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.enpol.2012.05.013.

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