CHAPTER 7
Pollution policy with
imperfect information
Concepts
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Risk and uncertainty: often used to characterise various situations in which less
than complete information is available.
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Risk: usually taken to mean situations in which some chance process is taking
place in which the set of possible outcomes is known and probabilities can be
attached to each possible outcome. However, it is not known which possible
outcome will occur.
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Uncertainty: usually taken to mean situations in which the set of possible
outcomes is known but probabilities cannot be attached to each possible
outcome.
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Radical uncertainty: circumstances in which it would not be possible even to
enumerate all the possible outcomes.
Difficulties in identifying pollution targets in the
context of limited information and uncertainty
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Much of the discussion of efficiency-based pollution targets in Chapter 5 implicitly
assumed that the policy maker was well informed, and so either knew – or by an
investment of resources could discover – the relevant cost and benefit functions.
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But this assumption is often not plausible
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The environmental agency will often not know with certainty the costs and/or benefits
of pollution (or equivalently the costs and benefits of pollution abatement).
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Where non-convexities are present, it is not sufficient to know the values of such things
near the current position of the economy; they have to be known across the whole range
of possibilities.
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For stock pollutants, stock effects and spatial considerations imply that the appropriate
functions vary from place to place and from time to time.
Limited information and uncertainty arises from:
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The costs involved in acquiring, collating, validating and processing
information, implying that census-data are likely to be prohibitively expensive.
Sampling error, associated with the use of sampling methods and statistical
inference from the sample data.
The data collected may not properly represent what the investigator is seeking
to obtain.
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Abatement costs: those who possess relevant information may have incentives not to truthfully
reveal it.
Difficulties in indentifying and evaluating the benefits of pollution abatement
(i.e. the benefits of avoided damages).
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Scientific knowledge about pollution impacts is far from complete, and arguably can never be
complete because of the stochastic and complex nature of ecosystem functioning.
Valuation of environmental services is beset by a host of theoretical and practical problems, and
there is little consensus about the validity of current valuation techniques.
Some methodological issues
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Relevant costs and prices (on both benefit and cost estimation sides) needed
for evaluation should be those that correspond to a socially efficient outcome;
these may bear little relation to observed costs and prices where the economy
is a long way from that optimum.
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Difficulties are compounded by ‘second-best’ considerations.
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Limited information and uncertainty do not simply mean that decisions should
be taken in the same way (but have less ‘accuracy’) as under conditions of full
information.
Sustainability-based approaches to target setting
and the precautionary principle
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Even taking the perspective of an economist, one would be very reluctant to rely
exclusively on efficiency-based targets given the difficulties identified in the previous
slides.
It seems sensible to at least give some weight to alternative approaches to pollution
policy that explicit address limited information and uncertainty.
Non-economists are generally suspicious of driving policy on what are perceived as
narrowly economic grounds and are critical of the importance that is often attached to
efficiency by economists in thinking about pollution targets.
Natural scientists, environmentalists and ecologists typically regard stability and
resilience as being more fundamental objectives.
These objectives – on the one hand, population stability and/or ecosystem resilience, and
on the other hand, maximisation of net economic benefits – are not necessarily mutually
contradictory.
Much of environmental economics (and, more so, ecological economics) consists of an
attempted synthesis of the two.
Precautionary principle
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In a world of certainty (and so complete predictability) taking precautions would be
unnecessary. But in a stochastic or complex environment where outcomes are not
certain, where processes are incompletely understood, or where non-linearities of
various kinds are thought to exist, some form of ‘playing safe’ is sensible.
The precautionary principle – in some of its guises at least – can be thought of as a
hybrid criterion.
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Of course, in trying to do several things at the same time, it runs the risk of not doing
any of them particularly well.
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It tries to bring together efficiency, sustainability, ethical and ecological principles, into a bundle that can
inform target setting.
But the approach is now being widely advocated.
If efficiency and sustainability criteria yielded identical policy recommendations, their
relative importance would not matter. But analysis suggests they do not.
Safe Minimum Standard
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The precautionary principle can be thought of as proposing a lexicographic approach to
setting targets.
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We regard one criterion (in this case sustainability) as being of overriding significance, and require that any
target do as well as possible in terms of this measure.
If this leaves us with more than one option, then other desirable criteria can be employed (perhaps in a
hierarchical way too) to choose among this restricted choice set.
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Alternatively, a constraint approach could be adopted: pollution policy should in general
be determined using an efficiency criterion, but subject to an overriding sustainability
constraint.
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Chapter 13 explains the notion of a safe minimum standard (SMS) of conservation.
When applied to pollution policy, the adoption of a strict version of the SMS approach
entails that threats to survival of valuable resource systems from pollution flows are
eliminated.
A modified SMS would eliminate the pollution flow, provided that so doing does not
entail ‘excessive cost’.
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Taxes
Permits
t*
P*
MC
M*
MC
L*(= M*)
Emissions, M
M
PH
t*
P*
MCH
MCH
PL
MC
MC
MCL
MCL
ML
M*
MH
M
L*(= M*)
M
Figure 7.1 A comparison of emissions taxes and marketable emissions permits when abatement costs
are uncertain
Establishing a ‘baseline’ against
which the efficiency losses from
errors due to uncertainty can be
measured.
Figure 7.2 Target setting under perfect information
MD
The efficient target, M*, is that level
of emissions which equates the
marginal cost of emissions abatement
(MC) and the marginal damage of
emissions (MD).
The shaded area represents the total
net social benefit that would be
generated at that level of emissions.:
the maximum net benefit available.
Efficiency losses from uncertainty
have in mind are those in which
emissions are at any level other than
M*, and so attained net benefits fall
short of their maximum level.
t*
MC
M*
Emissions, M
Figure 7.3 Uncertainty about abatement costs – costs overestimated
MD
Loss when
licenses used
tH
t*
MC
(assumed)
Loss when
taxes used
MC (true)
Mt
M* LH
Emissions, M
Figure 7.4 Uncertainty about abatement costs – costs underestimated
MD
t*
tL
MC (true)
MC
(assumed)
LL M*
Mt
Emissions, M
Figure 7.5 Uncertainty about abatement costs – costs overestimated
MD
MD
tH
t*
MC
(assumed)
MC (true)
Mt
M*
LH
Emissions, M
Figure 7.6 Uncertainty about abatement costs – costs underestimated
MD
MD
t*
tL
MC (true)
MC
(assumed)
LL
M*
Mt
Emissions, M
General results for abatement
cost uncertainty
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What differentiates these two pairs of cases is the relative slopes of the MC
and MD functions. We obtain the following general results:
1. When the (absolute value of the) slope of the MC curve is less than the
slope of the MD curve, licences are preferred to taxes (as they lead to
smaller efficiency losses).
2. When the (absolute value of the) slope of the MC curve is greater than
the slope of the MD curve, taxes are preferred to licences (as they lead
to smaller efficiency losses).
Figure 7.7 Uncertainty about damage costs – damages underestimated
MD (true)
MD (estimated)
t
MC (true)
M*
L
Emissions, M
Figure 7.8 Consequences of a threshold in the damages function
Total
D
damages
M
Emissions, M
Figure 7.8 (Panel (b))
Marginal
The non-linearity in damages implies that a price-based
policy has attractive properties where errors are not too
large. However, when the estimation error goes just
beyond some critical size, the efficiency loss can switch
to a very large magnitude.
damages
&
marginal
(abatement)
costs
MD
t2
MC1
t1
MC2
M1
M
M2
M22
Emissions, M
Quantity controls
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We leave the reader to explore the use of quantity controls.
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You should find that if the EPA set a control at the quantity M1 or M2 (depending on
which MC function it deems to be relevant), the likelihood of extremely large efficiency
losses is reduced, but at the expense of losing some efficiency for relatively small errors
in estimation.
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Hartwick and Olewiler conjecture that a best policy in the case analysed in this section
is one that combines a tax (price) control and an emissions (quantity) control.
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They propose a tax equal to the lower value of the MD function, and an emissions limit
equal to the threshold level. The tax bites – and generates efficient emissions – if
marginal abatement cost lies in the neighbourhood of MC1. Where MC is sufficiently
large to intersect MD in its upper segment, the quantity constraint bites. This composite
policy does not eliminate efficiency losses, but it prevents them being excessively large.
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The authors argue that such a combined policy is also prudent where uncertainty
surrounds the position of the MD function.
General conclusions
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Where functions are linear, and uncertainty relates to the marginal abatement cost (MC)
function, then an EPA should prefer a quantity policy (licences) to an emissions tax if
MC is flatter than MD, and an emissions tax to a licence system if the reverse is true, if
it wishes to minimise the efficiency losses arising from incorrect information.
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However, where uncertainty pertains to the MD function, knowledge of relevant slopes
does not contain information that is useful in this way.
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Once the existence of non-linearity and/or threshold effects is admitted, general results
are harder to find. In some circumstances at least, combined tax–quantity-control
programmes may have attractive properties.
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The presence of uncertainty substantially weakens the general presumption in favour of
incentive-based instruments over quantitative regulations that we developed in the
previous chapter. They may be better in some circumstances but not in all.
Information requirements: asymmetric
information and incentive compatibility
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Imperfect information puts restrictions on the ability of the EPA to devise ‘good’ targets
and to attain them at least cost.
It also considerably complicates its choice of instrument because comparative
advantages depend on the prevailing circumstances.
Moreover, limited information and uncertainty may prevent the EPA from knowing
which circumstance actually pertains.
Faced with all this, there are strong incentives on the EPA to become better informed.
One would expect that it would invest in systems that deliver greater information. There
are three ways that the EPA might do this:
1. undertake its own research to gather data;
2. build long-term institutional relationships with regulated businesses;
3. create reward structures that give firms incentives to reveal information
truthfully to the regulator.
Incentive compatibility
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An instrument is incentive-compatible if the incentives faced by those to
whom the instrument applies generate behaviour compatible with the
objectives of the regulator.
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In general, none of the instruments we have discussed so far has this property.
Where polluters think that the numbers they report can influence the severity
of regulation, they have an incentive to lie about the costs of complying with
abatement targets.
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This is true whether the instrument being used is command and control,
emissions tax, abatement subsidy or a marketable permit scheme.
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We illustrate two examples of such incentive effects.
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If firms expect tax schemes to be used they have an incentive to understate abatement costs.
If they expect a marketable permit scheme, the incentive is to overstate these costs.
We also outline one possible instrument – a mixture of abatement subsidy and
marketable permits – that is incentive-compatible.
Figure 7.9(a) Incentive effects with permit systems
MD
P1
*
P2
MC
(reported)
If firms honestly report
their actual abatement
costs, L* permits are
issued, allowing an
efficient emissions
level M*. In a
competitive permits
market the equilibrium
permit price would be
μ*. If firms overstate
their abatement costs,
the EPA incorrectly
believes that the
efficient target is MP,
and so issues that
number of permits
(LP). Exaggerating
abatement costs is
better for firms than
being truthful as more
permits are issued, and
so they incur lower real
emission abatement
costs.
MC (true)
M*
MP
=L*
=LP
Emissions, M
With an emissions tax,
firms have an incentive
to understate their
abatement costs.
Figure 7.9(b) Incentive effects with an emissions tax
MD
Firms benefit because
they emit more than if
they told the truth (and
so incur lower real
abatement costs). Also,
the tax rate is lower
than it would be
otherwise.
*
T
MC (true)
MC
(reported)
MT2
M*
MT1
Emissions, M
An incentive-compatible instrument:
Kwerel (1977)
• Can an instrument be found which will encourage truthful
behaviour and allow the EPA to achieve its objective?
• We are looking for an instrument that creates an incentive
to report abatement costs truthfully and which allows the
EPA to achieve whatever target it wants in a cost-effective
way.
• Several schemes with such properties have been identified.
One is proposed by Kwerel (1977).
• The scheme involves a combination of marketable permits
and subsidies on ‘excess’ emissions reduction.
Intuition
• The scheme involves a combination of marketable permits
and subsidies on ‘excess’ emissions reduction.
• The costs that firms report have two effects:
1. they influence the number of permits issued
2. they also influence the subsidy received for excess
emissions reduction.
• The scheme balances these two influences so as to reward
truthful reporting.
Mechanism
Kwerel’s scheme works in the following way. Firms are told that:
1. permits will be allocated through auction;
2. they will receive a subsidy for any emissions reduction over and above the number of permits
they hold;
3. the subsidy rate will be set at the intersection of the marginal damage curve and the reported
marginal abatement cost curve.
• Given this information, firms are then asked to report their abatement
costs, the subsidy is set accordingly, and the permit auction takes
place.
• The total cost of the scheme to all firms in the industry is equal to
actual emission abatement cost plus the cost of acquiring permits less
the subsidy payments received on any emissions reduction over and
above the permitted amount of emissions.
Notation
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We use the following notation: M = volume of emissions; L = number of
permits made available to industry; P = price of permits; s = subsidy per unit
of emissions reduction.
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Then we can write an expression for pollution abatement costs (PCC) for the
whole industry.
PCC = Abatement costs (area under MC curve)
+
Permit costs P • L
Emissions reduction subsidy s • (L - M)
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To demonstrate that this instrument is incentive-compatible, we compare the
benefits to firms of being truthful with the benefits of (a) understating costs
and (b) exaggerating costs.
Figure 7.10(a) An incentive compatible instrument and under-reporting costs
MD

P
P*

s
MC (true)
MC
(reported)


M L
M*= L*
Emissions, M
Figure 7.10(b) An incentive compatible instrument and over-reporting costs
Diagram shows the losses that firms make as a result of exaggerating abatement costs. The shaded area is the additional
abatement costs, the hatched area is the additional price paid for permits. It can be seen that as MC (reported) goes
towards MC (true), these losses disappear. The best that the firm can do is to be truthful!
MD
s
MC
(reported)
P*
MC (true)
M
M*
L
Emissions, M
Transactions costs and environmental regulation
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In carrying out its responsibilities, an environmental protection agency necessarily
incurs transaction costs. This is a generic term for a variety of costs that include:
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acquiring relevant information;
creating, monitoring and enforcing contracts (of which one category is the EPA’s regulations);
establishing, implementing and revising the instruments it employs;
monitoring performance, and ensuring compliance.
Transactions costs do include the costs of the personnel and the structures an
organisation puts in place that allow it to carry out its activities, and any equivalent costs
imposed on other parties, including the regulated firms or individuals.
They do not include what are sometimes called the real resource costs of the controls –
that is, the costs of pollution control equipment, higher fuel bills for cleaner fuel, more
expensive exhaust systems and so on.
They also do not include any induced indirect costs that might occur such as loss of
national competitiveness or increased unemployment.
Summing up all those costs gives the total compliance costs of environmental
regulation.
Transactions costs are just one part – often a not insignificant part – of that overall total.
Figure 7.11 The net benefits of regulation
Marginal real resource cost of abatement + induced
indirect costs + transactions costs
Marginal real resource cost of
abatement + induced indirect costs
D
Marginal real resource cost of
abatement
C
Gross marginal benefits of
abatement
B
A
ZC
ZB
ZA
Emissions abatement