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Experimental comparison between markets on dynamic permit trading and investment in irreversible abatement with and without non-regulated companies

Abstract

This paper examines the investment strategies of compliance companies in irreversible abatement technologies and the environmental achievements of the system in an inter-temporal cap-and-trade market using laboratory experiments. The experimental analysis is performed under varying market structures: firstly, in a market that is exclusive to compliance companies and subsequently, in a market that is open to both compliance and non-compliance entities. In line with theoretical models on irreversible abatement investment, the paper shows that regulated companies trade permits at a premium. Also, steep per unit penalties for excess emissions prompt early investments in irreversible abatement technologies. Further, the paper shows that by contributing to the permit demand and supply, non-compliance companies (i) enhance the exchange of permits, helping the system to achieve a zero-excess permit position, (ii) increase the price levels, but has no apparent effect on price variability.

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Notes

  1. 1.

    We refer to Stern (2007, 2008), respectively, for an overview and a comprehensive discussion on the economics of climate change.

  2. 2.

    Capandtrade programs are currently quite popular. Examples include the Acid Rain Program in the U.S., the European Union Emissions Trading Scheme in Europe, the Regional Greenhouse Gas Initiative signed by ten north-eastern states in North America, and the Australian and New Zealand programmes. China and South Korea, among other countries, are discussing plans to develop similar schemes.

  3. 3.

    Exceptions include Ben-David et al. (1999) and Stranlund et al. (2011).

  4. 4.

    Focusing on pricing contingent claim contracts in an equilibrium framework, the paper of Kijima et al. (2010) is the only article accounting for the participation of non-compliance entities in the permit market.

  5. 5.

    The ease with which firms can trade the amount of permits they offer to buy or sell at the corresponding bid or ask price.

  6. 6.

    The European Union Emission Trading Scheme, the largest existing market for permits, currently implements a grandfathering allocation criterion. This grants RCs an initial number of permits equal to a certain percentage of their pollution emitted at a fixed baseline year. In the first phase, 1990 was the baseline year for the majority of the participating European countries. We refer to Aihman and Zetterberg (2005) for a comprehensive discussion about other allocation criteria of emission permits.

  7. 7.

    We disregard RCs’ decision on output production. The analysis of the inter-relationship between permit and output markets has been undertaken by Misolek and Elder (1989), Hahn (1984), and Malueg (1990) respectively. Wräke et al. (2008) use a laboratory experiment to assess how much of the permits’ value is passed on by participants through output electricity prices.

  8. 8.

    Due to more stringent air quality regulations in recent years, newer plants typically have more sophisticated emissions control technology already installed. So, for a given industrial sector and a given level of output, companies employing old technology (High-RCs in our case) often emit relatively higher amounts of pollution emissions and can typically control them more cheaply.

  9. 9.

    According to the net-present-value approach, \(C^i(t)\) is the critical level at time \(t\) at which the decision to adopt the irreversible abatement should be taken by company \(i\). If the permit market price is higher than \(C^i(t)\), the \(i\)th company would be better off selling permits and using the proceeds to finance the irreversible abatement. A larger permit supply, and a corresponding reduction in the permit demand, would then induce lower future permit prices. Conversely, if the permit market price is lower than \(C^i(t)\), the \(i\)th company would be better off buying cheaper permits and not investing in the irreversible abatement. In this case we would expect an increase in the future permit market price.

  10. 10.

    Based on students’ answers to the questionnaire, most of them had a good understanding of the dynamics involved in the game. Although most of the students had a clear interest in the compliance managerial tasks, students’ motivation was not a concern given the fundamental trading decisions under investigation in the experiments.

  11. 11.

    A one time lag imposed on the observation of others’ emissions accommodates the realistic existence of non-perfect information. We refer to the model of Chesney and Taschini (2012) for further discussion about the modelling of partial information and its theoretical implication on dynamic equilibrium pricing.

  12. 12.

    Analytically, the company \(i\)th expected future emissions corresponds to \({\mathbb E}[\sum ^{T}_{s=t}Q^i(s)],\) and the expected cumulative and aggregate emissions corresponds to \({\mathbb E}[\sum ^{\mathcal {I}}_{j=1,j\ne i}\sum ^{T}_{s=t-1}Q^j(s)].\)

  13. 13.

    Negative values correspond to a zero payoff.

  14. 14.

    This scheme is presented in more detail in Schindler (2007). Cason and Plott (1996) and Cronshaw and Kruse (1999) employ a similar procedure.

    Fig. 2
    figure2

    Representation of a one-period bid and ask ordering. The intersection of the revealed demand (solid) and the revealed supply (dashed) identifies the quantity of permits that maximizes the offered quantity

  15. 15.

    The Kruskal–Wallis test assesses whether the medians of the arrays that contain the observations of the adoption periods differ significantly across the four rounds.

  16. 16.

    The lack of borrowing provisions is implemented with a marked separation between consequent rounds. The inability to borrow permits from future permit allocation—next round—can make the initial permit allocation insufficient to meet compliance obligations.

  17. 17.

    As highlighted by one of the referee, a constant maximum price for emissions in excess of their permit holdings may weaken the final environmental result. Because the payment of the penalty is in fact an alternative to compliance, the penalty is effectively a price ceiling and it acts like a safety valve. However, Fig. 4 shows that the presence of a certain safety valve does not hamper the intended environmental target. Figure 4 shows that the aggregated final emissions are lower than allowed emissions, ie. the cap in all sessions. Hence, the penalty used in the experiment is sufficiently stiff.

  18. 18.

    A detailed analysis of such an option value is beyond the scope of this paper and is left for future research.

  19. 19.

    The two-sided Wilcoxon rank-sum test assesses whether the medians of the arrays of the predicted prices and the ask prices differ significantly in each period.

  20. 20.

    Median prices and the observed market price are significantly higher than predicted prices also in the presence of non-RCs. A price premium is consistently present throughout all sessions. An expanded analysis of permit prices in the presence of non-RCs is presented in the next section.

  21. 21.

    A one-way analysis of the variance, ANOVA, assesses whether the mean of each proposed measure differs significantly among the three conditions. In our case the null hypothesis to be tested is whether the markets under the three conditions have the same means of a specific measure. The alternative hypothesis is that at least under one condition the mean of that measure is significantly different. Non significance of the test associated with the one-way analysis of variance implies that the presence of non-compliance companies has no effect on the liquidity level of the permit market. Conversely, significance implies that the liquidity level is statistically different in the presence of non-RC market participants. In this last case we know that under other conditions the market performs differently. We do not know, however, which conditions vary. By assuming equal variances among the tested groups, the post-hoc Tukey test offers an answer to this question. When the homogeneity assumption is violated, the post-hoc Games-Howell test should be used.

  22. 22.

    Which represents the quantity of permits successfully exchanged (per trade) among compliance firms.

  23. 23.

    The standard deviation of the price at which High-RCs offer to sell their permits is 9.47 for ‘RCs”, 9.41 for “RCs/1 NGO/1 FI”, and 6.02 for “RCs/3 NGO/3 FI”. The standard deviation of the prices at which Low-RCs offer to sell their permits is 10.98 for ‘RCs”, 9.01 for “RCs/1 NGO/1 FI”, and 6.38 for “RCs/3 NGO/3 FI”.

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Acknowledgments

The authors would like to thank Federica Buricco, Corina Comendant, Samuel Fankhauser, Achille Marcolin, Tatjana Puhan, John K. Stranlund, Alessandro Tavoni, and the participants of the EURO 2009 and WCERE 2010 for their helpful discussions and comments. The authors acknowledge Alessandro Vagliardo for his outstanding research support. This paper was partly written while Taschini was Research Visiting Fellow at the MIT Center for Energy and Environmental Policy Research, Cambridge, MA, USA. Taschini gratefully acknowledges financial support from the Centre for Climate Change Economics and Policy, which is funded by the UK Economic and Social Research Council (ESRC) and Munich Re. Part of Chesney’s and Wang’s research was supported by the University Research Priority Program “Finance and Financial Markets” and by the National Centre of Competence in Research “Financial Valuation and Risk Management” (NCCR FINRISK), research instruments of the University of Zurich and the Swiss National Science Foundation, respectively. The usual disclaimers apply.

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Correspondence to Luca Taschini.

Appendix: Excerpt of the instruction paper

Appendix: Excerpt of the instruction paper

You are participating in an academic experiment. Please read carefully the following instructions that explain you how to play. In case of ambiguities please ask the instructors.

The session consists of four rounds with 20 periods each. The sessions restarts at each round. You earn money by collecting Gulden. Your earnings depend on your decisions and on those of the other players. All Gulden you collect during the experiment are converted into Swiss Francs: 100 Gulden = 60 Rappen. In addition, you will receive 15 Swiss Francs for participating.

In each period you receive information about your and yours colleagues’ present and past emissions, and about the permit market price. Your objective is to offset your emissions with permits and maximize your profits. Based on the information reported on your screen you can decide to (i) change technology and reduced emissions, (ii) buy or sell permits, and (iii) hold on to your permits. In each period you have 60 s for deciding. After 60 s the next period starts.

Fig. 8
figure8

An example of a screen-shot. Close-up of the evolution of the permit price (upper part) close-up of the evolution of the cumulated emissions (lower part)

The following figures summarize the information reported on the scree of your vide (see Fig. 8):

  1. 1.

    Here we report your current budget and the costs for technology change. It also reports the probabilities of the pollution emission states (sharp increase or mild increase).

  2. 2.

    Here we report the current amount of permits you possess, the amount of permits used and the amount of permits unused. If this number is below 0, you are short of permits and at period \(T=20\) you have to pay a fixed per unit penalty of 40 Gulden per excess emissions.

  3. 3.

    Here we report the aggregated probability of a sharp increase and a mild increase, respectively, in the emissions of all other players.

  4. 4.

    This cell reports whether you successfully bought or sold permits in the last period.

  5. 5.

    Minimum (Maximum) shows your minimum (maximum) expected amount of emissions, i.e. final emissions in case of permanent mild (sharp) pollution emissions.

  6. 6.

    This field reports (5) for all other players with one time lag—in other words with respect to the previous period.

  7. 7.

    This graph shows the evolution of emission prices up to the previous period.

  8. 8.

    This graph shows the evolution of your company’s cumulated pollution.

  9. 9.

    This graph shows the evolution of the cumulated pollution of the other companies, up to the previous period.

  10. 10.

    Here you can insert an order to sell permits. You should specify quantity and price. You cannot sell more permits than you currently possess.

  11. 11.

    Here you can insert an order to buy permits. You should specify quantity and price. The total offer (in monetary value) cannot exceed your “bank” account. Remark: You can buy or sell in a period. After having placed an offer you might need to wait for other players to place their orders.

  12. 12.

    Here we report the time series of the permit price.

  13. 13.

    If you don’t want to buy or sell permits you can press this bottom (please press as soon as you decide you are not interested in entering orders).

  14. 14.

    Pressing this button you change technology. Technology can be changed only once and it costs you 400 Gulden. The new technology reduces the sharp emissions state by half. In this example, by changing technology your higher pollution state reduced from 30 to15.

  15. 15.

    Here you see the rank of all players with respect to their net emissions. This ranking does not report players’ performance in the game.

  16. 16.

    This is your position in the ranking of box (15).

  17. 17.

    Room for extra info—when available.

This instruction paper was distributed to all participants playing the role of compliance companies. The instruction paper distributed to participants playing the role of non-compliance companies includes also information about NGOs’ and FIs’ final payoffs. Obviously, the NGOs’ and FIs’ screen did not report information about personal accumulated pollution or cost of the technology change. Yet, these players obtain information about accumulated pollution and price level.

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Taschini, L., Chesney, M. & Wang, M. Experimental comparison between markets on dynamic permit trading and investment in irreversible abatement with and without non-regulated companies. J Regul Econ 46, 23–50 (2014). https://doi.org/10.1007/s11149-013-9238-3

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Keywords

  • Irreversible abatement
  • Stochastic emissions
  • Dynamic trading
  • Participation restrictions
  • Non-compliance entities

JEL Classification

  • Q50
  • C02
  • C91
  • D40