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An allocatively efficient auction for pollution permits

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Abstract

This article proposes a new auction design for the efficient allocation of pollution permits. We show that if the auctioneer restricts the bidding rule of the uniform-price auction—coupled with a simple ex-post supply adjustment rule—then truthful bidding is obtained. Consequently, the uniform-price auction is more allocatively efficient than conventional formats that are currently observed in pollution markets.

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Notes

  1. We focus on the efficiency of the initial allocation of permits and abstract from the secondary market. As (Krishna 2009) states, resale cannot guarantee efficiency, due to, for example, transaction costs or thin markets, therefore a regulator should aim for the most efficient initial allocation mechanism.

  2. The independence of a winning firm’s bid and the price paid is the result of the single-bid rule. Thus if buyer i’s bid is above the clearing price, then the clearing price is set by the bid of another buyer, which is independent of buyer i’s bid.

  3. The California Cap-and-Trade Program has over US$3bn of revenue generated per annum (CARB, 2018) yet the process of consignment requires a proportion of this revenue to be rebated back to auction participants (Khezr and MacKenzie 2018a). For RGGI results see https://www.rggi.org/Auctions/Auction-Results/Prices-Volumes

  4. Later in the article we provide a comparison between the proposed auction design and a conventional uniform-price auction to show the possibility of social welfare improvements as well as discussing potential limitations of using this new approach.

  5. In Section 3 we extend the model to allow for diminishing marginal values. We initially focus on constant values as there exists a number of justifications within an environmental setting. In the short run—when technologies are fixed—bidders’ values of the auction units can be interpreted as their expected value of selling the right on a secondary market or their marginal cost of using their abatement technology, which would be constant for the short-run capacity limits within this auction. As in existing permit auctions, we focus on scenarios where there are a relatively large number of bidders from similar industries (thus firm heterogeneity is not excessive).

  6. This can also be interpreted as the verified emissions of each firm in the previous compliance period as long as the aggregate emissions are decreasing over time.

  7. Within the Regional Greenhouse Gas Initiative (RGGI) the COATS tracking platform not only gives the auctioneer immediate access to estimates of bidders’ capacities but also provides public reports of emissions and allowance activity https://www.rggi.org/allowance-tracking/rggi-coats. The same is also true within the European Union Emissions Trading Scheme (EU-ETS) with their Union Registry https://ec.europa.eu/clima/policies/ets/registry_en and also the (publicly available) European Union Transaction Log (EUTL) http://ec.europa.eu/environment/ets/transaction.do?languageCode=en. The California Cap-and-Trade Program also has their Compliance Instrument Tracking System Service (CITSS) that reports emissions data and transactions similar to the other schemes.

  8. While we abstract from the explicit modeling of a secondary market, note that inclusion of the market would not alter our results. As the secondary market occurs after the auction has finished, firms within the auction would use backward induction and their marginal values in the auction \(v_i\) would represent their expected marginal value of a permit on the secondary market.

  9. We thus assume the process of quantity adjustment is costless.

  10. Note that despite the single-bid property of the auction proposed, the outcomes can vary from the Vickrey auction. In fact, given that firms have multi-unit demands with heterogeneous capacities, these two mechanisms could result in very different outcomes. For instance, denote \(\lambda _l\) as the capacity of the firm who submitted the highest losing bid. If, at least, one winning bidder has a larger capacity than \(\lambda _l\), the Vickrey auction results in lower prices equal to the second highest losing bid for at least some units. However, the single-bid auction results in a uniform price equal to the highest losing bid for all units independent of the capacities.

  11. The result in Proposition 1 can also be extended to values that are interdependent, similar to the model in Ausubel et al. (2014).

  12. Note that when the number of bidders is low and each bidder’s capacity is a significant proportion of the total units, the supply adjustment could potentially result in not allocating a significant amount of units. For instance, suppose there are only two bidders, one with a large capacity and one with a small capacity. If the bidder with a smaller capacity has the higher value and the total supply intersects the aggregate demand at a flat, then the auction could end up allocating zero units to the larger firm. However, note that given the application we noted for the current mechanism, we are unlikely to have such a scenario: the number of firms in a permit auction is relatively large (above 50 in some cases) and the capacity of firms are relatively small compared to the total number of permits available.

  13. Ausubel et al. (2014), is one of the few papers that study diminishing marginal values, where buyers have identical utility functions. Unlike their model, we assume buyers have asymmetric demand functions.

  14. Note that the accuracy of the signal has a close relationship with the level of efficiency. In particular, one expects a less accurate ex-ante signal results in, on average, less accurate prediction of capacities and lower level of efficiency. In this context, the accuracy of signals could be translated to both the probability of estimating the correct capacity and the dispersion of the distribution. However, due to the probabilistic nature of signals, the ex-post efficiency of a particular auction may not monotonically decrease if the uncertainty regarding the signal increases.

References

  • Alvarez F, Mazón C, André FJ (2019) Assigning pollution permits: are uniform auctions efficient? Econ. Theory 67:211–248

    Article  Google Scholar 

  • Armstrong M (2000) Optimal multi-object auctions. Rev Econ Stud 67:455–481

    Article  Google Scholar 

  • Ausubel LM, Cramton P, Pycia M, Rostek M, Weretka M (2014) Demand reduction and inefficiency in multi-unit auctions. Rev Econ Stud 81:1366–1400

    Article  Google Scholar 

  • Ausubel LM, Milgrom P (2004) ‘The lovely but lonely Vickrey auction’, Discussion Papers 03-036, Stanford Institute for Economic Policy Research

  • Back K, Zender JF (1993) Auctions of divisible goods: on the rationale for the treasury experiment. Rev Financ Stud 6:733–764

    Article  Google Scholar 

  • Back K, Zender JF (2001) Auctions of divisible goods with endogenous supply. Econ Lett 73:29–34

    Article  Google Scholar 

  • CARB (2018) ‘California Cap-and-trade Program summary of auction settlement prices and results’, http://www.arb.ca.gov/cc/capandtrade/auction/results_summary.pdf, California Air Resources Board

  • Damianov DS, Becker JG (2010) Auctions with variable supply: uniform price versus discriminatory. Europ Econ Rev 54:571–593

    Article  Google Scholar 

  • Dasgupta P, Hammond P, Maskin E (1980) On imperfect information and optimal pollution control. Rev Econ Stud 47:857–860

    Article  Google Scholar 

  • Duggan J, Roberts J (2002) Implementing the efficient allocation of pollution. Am Econ Rev 92:1070–1078

    Article  Google Scholar 

  • European Commission (2017) Analysis of the use of auction revenues by the member states. Technical report. European Commission, Brussels

    Google Scholar 

  • Khezr P, MacKenzie IA (2018a) Consignment auctions. J Environ Econ Manag 87:42–51

    Article  Google Scholar 

  • Khezr P, MacKenzie IA (2018b) Permit market auctions with allowance reserves. Int J Ind Organiz 61:283–306

    Article  Google Scholar 

  • Krishna V (2009) Auction Theory. Academic Press, San Diego

    Google Scholar 

  • Kwerel E (1977) To tell the truth: imperfect information and optimal pollution control. Rev Econ Stud 44:595–601

    Article  Google Scholar 

  • LiCalzi M, Pavan A (2005) Tilting the supply schedule to enhance competition in uniform-price auctions. Europ Econ Rev 49:227–250

    Article  Google Scholar 

  • Lopomo G, Marx LM, McAdams D, Murray B (2011) Carbon allowance auction design: an assessment of options for the United States. Rev Environ Econ Policy 5:25–43

    Article  Google Scholar 

  • McAdams D (2007) Adjustable supply in uniform price auctions: non-commitment as a strategic tool. Econ Lett 95:48–53

    Article  Google Scholar 

  • Montero J-P (2008) A simple auction mechanism for the optimal allocation of the commons. Am Econ Rev 98:496–518

    Article  Google Scholar 

  • Shrestha RK (1998) Uncertainty and the choice of policy instruments: a note on Baumol and Oates propositions. Environ Res Econ 12:497–505

    Article  Google Scholar 

  • Shrestha RK (2017) Menus of price-quantity contracts for inducing the truth in environmental regulation. J Environ Econ Manage 83:1–7

    Article  Google Scholar 

  • Vickrey W (1961) Counterspeculation, auctions, and competitive sealed tenders. J Financ 16:8–37

    Article  Google Scholar 

  • Wilson R (1979) Auctions of shares. Quart J Econ 93:675–689

    Article  Google Scholar 

Download references

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Correspondence to Ian A. MacKenzie.

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Appendix

Appendix

Proof of Proposition 1

Suppose bidder i with value \(v_i\) submits a bid \(b_i<v_i\) and the price at which all permits are sold is p. We claim that bidding \(b_i=v_i\) weakly dominates \(b_i<v_i\). If \(p>v_i\) in both situations the payoffs are zero. If \(p<v_i\), then bidding \(b_i<v_i\) either results in a zero payoff or results in a payoff equal to \(v_i-p\). However, bidding equal to \(v_i\) would always result in a positive payoff equal to \(v_i-p\). Therefore, bidding \(b_i=v_i\) weakly dominates bidding \(b_i<v_i\).

Now suppose bidder i submits a bid equal to \(b'_i>v_i\). We show this is also weakly dominated by a bid equal to her value. If \(v_i<b'_i<p\), then both cases would result in zero payoff. If \(v_i<p<b'_i\), then \(b'_i\) wins but results in a negative payoff, since \(v_i<p\). In this case bidding equal to \(v_i\) results in zero payoff. Finally, if \(p<v_i<b'_i\) both bids would result in similar payoffs. \(\square \)

Proof of Corollary 1

Efficiency is a straightforward result of truthful bidding. Since the mechanism allocates the objects to those with the highest values, then it is efficient. However, since there are instances that the seller may sell less than \(\bar{Q}\), then it is weakly efficient. \(\square \)

Proof of Proposition 2

To show that bidding truthfully is a weakly dominant strategy we start by a representative buyer and show that this buyer cannot benefit from demand reduction if all other buyers act truthfully. Suppose buyer i bids its true demand schedule, that is, \(p=v_i -\frac{v_i}{\lambda _i} q_i\). First, calculate the aggregate demand without the consideration of buyer i. Note that each buyer by submitting a demand schedule, creates a kink on the aggregate demand at their reported value. Define \(c_{-i}\) as the value of the buyer who sets the last kink before \(\bar{Q}\) when excluding buyer i in the aggregate demand. In other words \(c_{-i}\) is the lowest reported value that can win some permits, excluding i. There are two possible scenarios with respect to the relation of \(c_{-i}\) and buyer i’s value \(v_i\).

S1: If \(v_i > c_{-i}\), then buyer i wins some permits, say \(q_i\), and pays a price \(p<v_i\). Thus, the payoff for buyer i is,

$$\begin{aligned} \pi _t = \frac{(v_i-p)q_i}{2}. \end{aligned}$$
(5)

S2: If \(v_i < c_{-i}\), then two outcomes are possible. The kink at \(v_i\) on the aggregate demand could be either to the left of \(\bar{Q}\) or to the right and the last one before \(\bar{Q}\).

S2.1: When the kink is at the left of \(\bar{Q}\), but lower than \(c_{-i}\), they win \(q'_i\) permits and pay a price equal to \(p'\) with a positive payoff equal to,

$$\begin{aligned} \pi _{t'} = \frac{(v_i-p')q'_i}{2}. \end{aligned}$$
(6)

S2.2: When the kink is at the right of \(\bar{Q}\), buyer i receives zero payoff.

Now, suppose buyer i submits a value \(z_i <v_i\) in its demand schedule then in scenario S2.2 they obviously receive zero payoff again. But now in scenario S1 and S2.1 they would also receive a zero payoff due to the supply adjustment. To see this, suppose the kink at \(z_i\) is the kth highest kink. The slope of the aggregate demand after this kink is,

$$\begin{aligned} slope = \frac{\frac{\rho }{(k-1)}\rho '}{\frac{\rho }{(k-1)}+\rho '}, \end{aligned}$$
(7)

where \(\rho '<\rho \) is the slope of the buyer i’s demand function with \(z_i\). The above slope is less than \(\frac{\rho }{k}\) because,

$$\begin{aligned} slope = \frac{\frac{\rho }{(k-1)}\rho '}{\frac{\rho }{(k-1)}+\rho '}< \frac{\rho }{k} \Leftrightarrow \frac{\rho '}{(k-1)}< \frac{\rho + (k-1)\rho '}{k(k-1)} \Leftrightarrow k\rho ' <\rho + (k-1)\rho '. \end{aligned}$$
(8)

The last inequality is true because \(\rho '<\rho \). According to the supply adjustment, when the slope of aggregate demand is less than \(\frac{\rho }{k}\) after kink k, the supply would reduce to kink k and buyer i receives zero permits.

Thus it is a weakly dominant strategy for buyers to bid their true demand. \(\square \)

Proof of Proposition 3

Given the result of Proposition 2, if all buyers except buyer i play their weakly dominant strategy, then there is no incentive for buyer i to deviate from this strategy. So submitting the true value is a dominant strategy equilibrium of this game. The efficiency part is straightforward because the mechanism allocates the objects to those with the highest values, since in equilibrium the aggregate demand represents the true demand of each buyer. Also, since the supply adjustment does not take place in equilibrium, all quantities available would be allocated to buyers. Thus the mechanism is fully efficient. \(\square \)

Proof of Proposition 4

Suppose all buyers except buyer i submit their true demands. We want to show it is not optimal for buyer i to follow the same strategy. First, rewrite Equation (4) as follows,

$$\begin{aligned} \pi _i = \frac{1}{2} (v_i-p) \left( \bar{Q} - \sum _{j\ne i} \left( \lambda _j - \frac{\lambda _j}{v_j}p\right) \right) . \end{aligned}$$
(9)

The first-order condition is

$$\begin{aligned} - \, \bar{Q} + \sum _{j\ne i} \left( \lambda _j - \frac{\lambda _j}{v_j}p\right) + \sum _{j\ne i} \frac{\lambda _j}{v_j} (v_i-p) =0. \end{aligned}$$
(10)

Given the market-clearing price condition, \(q^*_i =\bar{Q} - \sum _{j\ne i} (\lambda _j - \frac{\lambda _j}{v_j}p)\), one can rewrite the first-order condition as follows

$$\begin{aligned} q_i^* = \sum _{j\ne i} \frac{\lambda _j}{v_j} (v_i-p). \end{aligned}$$
(11)

Define \(\psi _j = \sum _{j\ne i} \frac{\lambda _j}{v_j} \), then the equilibrium demand schedule of bidder i becomes,

$$\begin{aligned} p^* = v_i - \frac{q_i}{\psi _j }. \end{aligned}$$
(12)

Since \(\frac{1}{\psi _j} < \rho \) for \(n>2\), the best response of player i is to submit a demand function with a strictly lower slope than their actual demand. Thus there exist no equilibrium with truthful demands.

What remains to show is that bidding above the actual demand is not an optimal decision for buyers. To see this, suppose bidder i submits a demand schedule that is above their actual demand function. The only situation that matters is the one where buyer i is pivotal and wins some permits. When the submitted demand is higher than the actual demand, buyer i wins more permits but pays a higher price. Let us focus on the margin and a case where buyer i is only marginally above their true demand. For any extra unit buyer i wins, there will be an extra loss to the surplus at the same price. Now given that price will also go up, the loss in the surplus will be even higher. Therefore, submitting the true demand dominates submitting any demand above the true demand. \(\square \)

Proof of Proposition 5

Given the result in Proposition 4, in any undominated equilibrium of the standard uniform-price at least some buyers reduce their demands. Therefore, any equilibrium aggregate demand of the standard uniform-price auction is strictly lower than the actual demand. Since we show that in the proposed mechanism the aggregate demand is the same as the sum of the true demands, then the auction clearing price for the proposed mechanism is strictly larger than the clearing price of a standard uniform-price auction. Thus, the revenue of the proposed auction, which is \(p\bar{Q}\), is also strictly higher than the revenue of a standard uniform-price auction. \(\square \)

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Khezr, P., MacKenzie, I.A. An allocatively efficient auction for pollution permits. Environ Resource Econ 78, 571–585 (2021). https://doi.org/10.1007/s10640-021-00543-3

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