Abstract
This article assesses the environmental and economic efficiency of three different approaches to treat monitoring uncertainty in climate policy, namely prescribing uncertainty, setting minimum certainty thresholds and pricing uncertainty through a discount. Our model of the behavior of profit-maximizing agents demonstrates that under the simplest set of assumptions the regulator has no interest in reducing monitoring uncertainty. However, in the presence of information asymmetry, monitoring uncertainty may hamper the economic and environmental performance of climate policy due to adverse selection. In a mandatory policy, prescribing a reasonable level of uncertainty is preferable if the regulator has enough information to determine this level. For voluntary mechanisms, such as carbon offsets, allowing agents to set their own monitoring uncertainty below a maximum threshold or discounting carbon revenues in proportion to monitoring uncertainty are the best approaches for the regulator to mitigate the negative effects of information asymmetry. These conclusions are much more pronounced when agents do not accrue revenues from their mitigation action, other than carbon. Our analysis of monitoring uncertainty under information asymmetry, which results in heterogeneity in the agents’ benefits from abatement, generalizes the classical trade-off between production efficiency and information rents.
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Notes
Note that in this last scenario, we revoke the “no bias” assumption laid out in Sect. 3.1.
That is 784 kt \(\hbox {CO}_{2}\hbox {e}\) over 7 years for energy efficiency projects and 682 kt \(\hbox {CO}_{2}\hbox {e}\) over 7 years for LFG projects. For LFG projects, the cumulative MACC is truncated: projects with abatement costs excluding variable monitoring costs higher than 4.05 €/\(\hbox {tCO}_{2}\hbox {e}\) are excluded. Over this threshold, abatement cost rises steeply, thus strongly violating our assumption of linear increase. The truncated MACC covers 52 out of 77 projects in the database.
References
Antle J, Capalbo S, Mooney S, Elliott E, Paustian K (2003) Spatial heterogeneity, contract design, and the efficiency of carbon sequestration policies for agriculture. J Environ Econ Manag 46(2):231–250
Becker GS (1968) Crime and punishment: an economic approach. J Polit Econ 76(2):169–217. doi:10.1086/259394
Bellassen V, Stephan N (2015) Accounting for carbon: monitoring, reporting and verifying emissions in the climate economy. Cambridge University Press, Cambridge
Bellassen V, Stephan N, Afriat M, Alberola E, Barker A, Chang J-P, Chiquet C et al (2015) Monitoring, reporting and verifying emissions in the climate economy. Nat Clim Change 5(4):319–328
Bento AM, Kanbur R, Leard B (2015) Designing efficient markets for carbon offsets with distributional constraints. J Environ Econ Manag 70:51–71. doi:10.1016/j.jeem.2014.10.003
Brander M, Sood A, Wylie C, Haughton A, Lovell J (2011) Electricity-specific emission factors for grid electricity. Technical Paper, Ecometrica
Bultheel C, Morel R, Hainaut H, Deheza M, Shishlov I, Depoues V, Leguet B (2015) COP21—a successful end of the beginning. Climate Brief #38. I4CE—Institute for Climate Economics. http://www.i4ce.org/download/climatebrief__cop21/
Canton J, De Cara S, Jayet P-A (2009) Agri-environmental schemes: adverse selection, information structure and delegation. Ecol Econ 68(7):2114–2121. doi:10.1016/j.ecolecon.2009.02.007
Castro P (2012) Does the CDM discourage emission reduction targets in advanced developing countries? Clim Policy 12(2):198–218
Chakraborty I, McAfee P (2014) Let the punishment fit the crime: enforcement with error. J Public Econ Theory 16(2):274–292
Dasgupta P, Hammond P, Maskin E (1980) On imperfect information and optimal pollution control. Rev Econ Stud 47(5):857–860. doi:10.2307/2296917
Dimopoulos C (2015) Chapter 10. Direct measurement in the EU ETS. In: Bellassen V, Stephan N (eds) Accounting for carbon. Cambridge University Press, Cambridge, pp 313–338
EDF (2013) California. The world’s carbon markets: a case study guide to emissions trading. Environmental Defence Fund. http://www.ieta.org/assets/Reports/EmissionsTradingAroundTheWorld/edf_ieta_california_case_study_may_2013.pdf
IEA (2012) Electricity information. IEA Statistics, International Energy Agency
Laffont J-J, Tirole J (1993) A theory of incentives in procurement and regulation. MIT Press, Cambridge
Montero JP (2000) Optimal design of a phase-in emissions trading program. J Public Econ 75(2):273–291
Montero JP (2005) Pollution markets with imperfectly observed emissions. RAND J Econ 36(3):645–660
Png IPL (1986) Optimal subsidies and damages in the presence of judicial error. Int Rev Law Econ 6(1):101–105. doi:10.1016/0144-8188(86)90042-6
Powell M (1999) Effect of inventory precision and variance on the estimated number of sample plots and inventory variable cost: The Noel Kempff Mercado Climate Action Project. Winrock International
Segerson K (1988) Uncertainty and incentives for nonpoint pollution control. J Environ Econ Manag 15:87–98
Shishlov I (2015) Chapter 11. Trend setter for projects: the clean development mechanism. In: Bellassen V, Stephan N (eds) Accounting for carbon. Cambridge University Press, Cambridge, pp 341–389
Shishlov I, Bellassen V (2014) Moving from the CDM to various approaches. CDC Climat. http://www.cdcclimat.com/Climate-Brief-no34-Moving-from-the.html
Shishlov I, Bellassen V (2015) Review of the experience with monitoring uncertainty requirements in the clean development mechanism. Clim Policy 1–29. doi:10.1080/14693062.2015.1046414
The Economist (2015) Dirty secrets. http://www.economist.com/news/leaders/21666226-volkswagens-falsification-pollution-tests-opens-door-very-different-car
Turner A, Jacob D, Benmergui J, Wofsy S, Maasakkers J, Butz A, Hasekamp O, Biraud S, Dlugokencky E (2016) A large increase in US methane emissions over the past decade inferred from satellite data and surface observations. Geophys Res Lett. http://onlinelibrary.wiley.com/doi/10.1002/2016GL067987/abstract
UNEP Risoe (2014) CDM/JI Pipeline Databases. United Nations Environment Programme. http://www.cdmpipeline.org/
van Benthem A, Kerr S (2013) Scale and transfers in international emissions offset programs. J Public Econ 107:31–46. doi:10.1016/j.jpubeco.2013.08.004
Warnecke C (2014) Can CDM monitoring requirements be reduced while maintaining environmental integrity? Clim Policy. doi:10.1080/14693062.2014.875285
Wartmann S, Groenenberg H, Brockett S (2009) Monitoring and reporting of GHG emissions from CCS operations under the EU ETS. Energy Proced 1(1):4459–4466
Weitzman M (1974) Prices vs. quantities. Rev Econ Stud 41:477–491
Acknowledgments
The authors would like to thank Stephane De Cara (INRA), David Driesen (Syracuse University College of Law), Franck Lecocq (CIRED), Juan-Pablo Montero (Economics Institute of the Pontificia Universidad Católica de Chile), Romain Morel (CDC Climat Research), Marie-Laure Breuillé (INRA) and Sophie Legras (INRA) for their valuable inputs. This work benefited from external funding from Agence Française de Développement; EIT Climate-KIC; Ministère français de l’Agriculture, de l’Agroalimentaire et de la Forêt; Ministère français de l’Ecologie, du Développement Durable et de l’Energie; and Union des Industries de la Fertilisation.
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Valentin Bellassen and Igor Shishlov have contributed equally to this work.
Appendices
Appendices
1.1 Appendix 1: Details on the Empirical Values Used in Simulations
For our simulations, we use the data publicly available for energy efficiency and landfill gas flaring (LFG) CDM projects, assuming that they represent a typical investment to reduce GHG emissions. The parameter sets are given in Table 1. The method to derive these parameters from existing databases is described below.
Carbon price p is assumed to be equal to EUR 30 per t \(\hbox {CO}_{2}\hbox {e}\), roughly the price of USD 40 at which the price-containment reserve of the Californian ETS is released (EDF 2013) and slightly above the average pre-crisis carbon prices in the EU ETS.
Non-carbon benefits b in energy efficiency projects are assumed to be equal to EUR 150 per t \(\hbox {CO}_{2}\hbox {e}\), the potential cost-savings from reduced electricity consumption at the average grid carbon intensity of 0.5 t \(\hbox {CO}_{2}\hbox {e}\) per MWh—the average level in the OECD countries in 2010 (Brander et al. 2011)—and the electricity price of EUR 75 per MWh —the average electricity price for industry in the OECD in 2009 (IEA 2012). For landfill gas flaring projects \(b = 0\) as there are no additional revenues other than carbon.
Variable monitoring costs parameter m is obtained by fitting the function “\(\hbox {costs} = \hbox {m/(relative error)}^\wedge {}2\)” on the estimates provided by Powell (1999) for a forestry project. We generalize it to energy efficiency and LFG projects based on two comforting points: the rationale of Powell (1999)—decreasing uncertainty through increased sample size—is consistent with our modelling approach and the resulting variable monitoring costs for a typical uncertainty of 10 % is EUR 30,000, that is half the average total MRV costs (including fixed costs) for these project types, as estimated for the CDM (Shishlov 2015).
Fixed and variable abatement costs parameters \(c_0 \) and c are obtained from investments costs in CDM projects of type “Energy efficiency own generation” and “Landfill gas flaring”, as estimated by UNEP Risoe (2014). For each project in the database, an estimate of investment costs per \(\hbox {tCO}_{2}\hbox {e}\) abated excluding variable monitoring costs is obtained by subtracting EUR 30,000 to total investment costs (see above). The resulting cumulative marginal abatement cost curve (MACC), obtained from many different projects—245 for energy efficiency and 52 for LFG—is scaled down to a single project to be consistent with our modelling approach. To this end, we assume that the marginal abatement costs of our single project—the size of which is the average size of existing projects of the same typeFootnote 2—are assumed to be proportional to those of the cumulative MACC. \(c_{0}\) and c are then obtained by fitting the relevant equation (\(\hbox {cost} = \hbox {c}_{0} + \hbox {c} * \hbox {q}^{\wedge } 2\)) to this “scaled-down” MACC.
Plugging all parameters in the model results in the production of 9 Mt \(\hbox {CO}_{2}\hbox {e}\) for energy efficiency projects and 0.27 Mt \(\hbox {CO}_{2}\hbox {e}\) for landfill gas flaring projects under no information asymmetry, both of which are within the range of existing projects in the database (0.08-20 Mt \(\hbox {CO}_{2}\hbox {e}\) and 0.07–6 Mt \(\hbox {CO}_{2}\hbox {e}\) respectively).
1.2 Appendix 2: Comparison of Outcomes Under Different Scenarios (Analytical Results)
Scenario | Policy option | \({{ q}}_{i}^{*}\) | \({u^{*}}\) | \({W^{*}}\) | UT | \(\beta *\) |
---|---|---|---|---|---|---|
1. No asymmetry | (a) u prescribed | \(\frac{p+b}{2c}\) | \(+\infty \) | \(\frac{\left( {p+b} \right) ^{2}}{2c}-2c_0 \) | \(+\infty \) | NA |
2. Asymmetry | (a) u prescribed | \(\frac{p\times \left( {1+2u_i \times \varepsilon _i } \right) +b}{2c}\) | \(\root 4 \of {\frac{cm}{p^{2}}}\) | \(\frac{\left( {p+b} \right) ^{2}}{2c}-2c_0 -\frac{4pm}{\sqrt{cm}}\) | \(4\sqrt{p}\times q_i \times \root 4 \of {\hbox {cm}}\) | NA |
(b) u limited | \(\frac{p\times \left( {1+2u_i \times \varepsilon _i } \right) +b}{2c}\) | No obvious solution | No obvious solution | No obvious solution | NA | |
(c) u discounted | \(\frac{p\times \left( {1+2u_i \times \varepsilon _i } \right) \times \left( {1-\beta } \right) +b}{2c}\) | No obvious solution | No obvious solution | No obvious solution | No obvious solution | |
3. Information bias | (a) u prescribed | \(\frac{p\times \left( {1+2u_i \times \varepsilon _i } \right) +b}{2c}\) | \(\root 4 \of {\frac{4cm}{p^{2}}}\) | \(\frac{\left( {p+b} \right) ^{2}}{2c}-2c_0 -\frac{4pm}{\sqrt{cm}}\) | \(4\sqrt{p}\times q_i \times \root 4 \of {\hbox {cm}}\) | NA |
(b) u limited | \(\frac{p\times \left( {1+2u_i \times \varepsilon _i } \right) +b}{2c}\) | \(\root 4 \of {\frac{4cm}{p^{2}}}\) | \(\frac{\left( {p+b} \right) ^{2}}{2c}-2c_0 -\frac{4pm}{\sqrt{cm}}\) | \(4\sqrt{p}\times q_i \times \root 4 \of {\hbox {cm}}\) | NA | |
(c) u discounted | \(\frac{p+b}{2c}\) | \(+\infty \) | \(\frac{\left( {p+b} \right) ^{2}}{2c}-2c_0 \) | 0 | \(1/\left( {0.5+u} \right) \) |
1.3 Appendix 3: Comparison of Outcomes Under Different Scenarios (Numerical Results for EE Projects)
Q1*(Kt \(\hbox {CO}_{2}\hbox {e}\)) | Q2*(Kt \(\hbox {CO}_{2}\hbox {e}\)) | Q*(Kt \(\hbox {CO}_{2}\hbox {e}\)) | W* (M€) | W loss (%) | P2* (M€) | W2* (M€) | UT1 (M€) | UT2 (M€) | UTA (M€) | u1* (%) | u2* (%) | t* (%) | Beta* | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Case 1a | 9.06 | 9.06 | 18.13 | 1628 | 0.00 | 542.02 | 813.92 | 271.90 | -271.90 | 0.00 | 50.00 | 50.00 | 50.00 | NA |
Case 2a | 9.19 | 8.93 | 18.13 | 1627 | -0.04 | 790.72 | 813.59 | 23.53 | -22.87 | 0.66 | 4.27 | 4.27 | 4.27 | NA |
Case 2b | 9.19 | 9.03 | 18.22 | 1625 | -0.19 | 805.50 | 811.11 | 23.52 | -5.61 | 17.92 | 4.27 | 1.03 | 4.27 | NA |
Case 2c | 9.18 | 9.02 | 18.20 | 1624 | -0.24 | 802.54 | 810.13 | 78.02 | -4.78 | 73.24 | 17.91 | 0.89 | NA | 1.17 |
Case 3a | 9.19 | 9.19 | 18.38 | 1627 | -0.04 | 837.12 | 813.59 | 23.53 | 23.53 | 47.05 | 4.27 | 4.27 | 4.27 | NA |
Case 3b | 9.19 | 9.19 | 18.38 | 1627 | -0.04 | 837.12 | 813.59 | 23.53 | 23.53 | 47.05 | 4.27 | 4.27 | 4.27 | NA |
Case 3c | 9.06 | 9.06 | 18.13 | 1628 | 0.00 | 813.92 | 813.92 | 0.00 | 0.00 | 0.00 | 50.00 | 50.00 | NA | 1/(\(\mathrm{u}+0.5\)) |
1.4 Appendix 4: Comparison of Outcomes Under Different Scenarios (Numerical Results for LFG Projects)
Q1*(Kt \(\hbox {CO}_{2}\hbox {e}\)) | Q2*(Kt \(\hbox {CO}_{2}\hbox {e}\)) | Q*(Kt \(\hbox {CO}_{2}\hbox {e}\)) | W* (M€) | W loss (%) | P2* (M€) | W2* (M€) | UT1 (M€) | UT2 (M€) | UTA (M€) | u1* (%) | u2* (%) | t* (%) | Beta* | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Case 1a | 0.27 | 0.27 | 0.55 | 4.30 | 0.00 | -6.09 | 2.15 | 8.24 | -8.24 | 0.00 | 50.00 | 50.00 | 50.00 | NA |
Case 2a | 0.31 | 0.24 | 0.55 | 4.02 | -6.49 | 1.07 | 2.01 | 1.22 | -0.94 | 0.28 | 6.53 | 6.53 | 6.53 | NA |
Case 2b | 0.31 | 0.26 | 0.57 | 3.88 | -9.72 | 1.35 | 1.87 | 1.22 | -0.52 | 0.70 | 6.53 | 3.39 | 6.53 | NA |
Case 2c | 0.29 | 0.25 | 0.54 | 3.90 | -9.34 | 1.08 | 1.78 | 2.35 | -0.43 | 1.92 | 16.77 | 2.97 | NA | 1.21 |
Case 3a | 0.31 | 0.31 | 0.62 | 4.02 | -0.04 | 3.23 | 2.01 | 1.22 | 1.22 | 2.43 | 6.53 | 6.53 | 6.53 | NA |
Case 3b | 0.31 | 0.31 | 0.62 | 4.02 | -0.04 | 3.23 | 2.01 | 1.22 | 1.22 | 2.43 | 6.53 | 6.53 | 6.53 | NA |
Case 3c | 0.27 | 0.27 | 0.55 | 4.30 | 0.00 | 2.15 | 2.15 | 0.00 | 0.00 | 0.00 | 50.00 | 50.00 | NA | 1/(\(\mathrm{u}+0.5\)) |
1.5 Appendix 5: Derivations for Case 1a
Note that our assumption that \(\varepsilon \) and q are independent is crucial here. Although we think it is generally warranted as \(\varepsilon \) is the relative standard error, it may not always be the case.
1.6 Appendix 6: Derivations for Case 2a
Profit of agent i:
Welfare:
Modelling this situation with a continuum of agents whose \(\varepsilon _i \) are equally distributed over [-1;1] leads to a similar result on welfare:
First order condition:
The solution is therefore very similar to the representation with two opposite agents:
Quite intuitively, a uniform distribution of agents over [-1;1] decreases the inefficiency generated by monitoring uncertainty compared to a distribution where \(\varepsilon _i \) clusters on {-1;1} since the effect of adverse selection is lower for values of \(\varepsilon _i \) closer to zero. As a result, the cost of monitoring weights relatively more on welfare and u* is 30 % higher.
1.7 Appendix 7: Derivations for Case 2b
For the case of underestimation of emissions reductions:
1.8 Appendix 8: Derivations for Case 2c
1.9 Appendix 9: Derivations for Cases 3a and 3b
For both agents:
Welfare:
1.10 Appendix 10: Derivations for Cases 3c
For both agents:
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Bellassen, V., Shishlov, I. Pricing Monitoring Uncertainty in Climate Policy. Environ Resource Econ 68, 949–974 (2017). https://doi.org/10.1007/s10640-016-0055-x
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DOI: https://doi.org/10.1007/s10640-016-0055-x