Mitigable Public Bads

The economic theory of public goods is sometimes assumed to be adaptable in a straightforward manner to public bads.  Here I consider some implications of the fact that such bads are usually mitigable.

Consider a region around an airport, subjected to noise from flights.  Noise arrives at every home in the region, but any household can choose, at a cost, to moderate the noise level inside its home by installing double glazing.  Such a choice by a household has no effect on the noise level inside any other home.

Suppose the sole water supply to a poor village is polluted.  Everyone in the village will be risking their health if they drink that water as supplied.  But (for some types of pollutant) any household can treat its water to make it safer to drink, perhaps by filtering or chemical treatment.  Such a choice by a household has no effect on the safety of the water consumed by any other household.

Similarly, suppose a region’s agriculture is affected by a drought.  All farms in the region receive less rainfall than normal but some, because of the crops they have chosen to grow or their cultivation methods, are better able than others to withstand the effects of the drought. Such choices by a farm have no effect on the resilience of other farms.

These are all examples of what I call mitigable public bads.  The idea of a public bad is derived from that of a public good, commonly defined as a good which is both rival and non-excludable. By non-rival is meant that one person’s consumption of or benefit from the good does not reduce the amount available to others. Non-excludable means that neither the provider of the good nor any other agent is able to pick and choose which individuals within the scope of provision of the good can consume it (hence the free-rider problem consisting in the fact that those who decline to pay for the good cannot be excluded from benefiting from it).  Street lighting in a city, for example, is non-excludable because no one can determine that certain individuals passing through the city’s streets will have their way illuminated and others will not. The fact that the provider could turn off the lights, excluding everyone, is irrelevant, as is the fact that only those who live in or visit the city can benefit from its street lighting.

Public bads have sometimes been defined as “goods” which are non-rival and non-excludable but which tend to lower rather than raise welfare (1).  For a bad to be non-rival is usually clear enough: my disturbance by noise from flights does not render my neighbours’ disturbance any less.  But non-excludability in relation to a bad is not so clear.  No one can be expected to pay for a bad, so a “provider” such as a factory causing air pollution has no interest in excluding non-payers. And whereas it can be assumed that few would wish to exclude themselves from a public good, sufferers from a public bad will certainly so wish if they can do so at reasonable cost. 

Usage in this context seems not to be settled. But I find it convenient to use the term public bad for any welfare-lowering “good” which is both non-rival and, in the sense that no one can pick and choose who within its broad scope suffers from it and who is excluded, non-excludable. If affected individuals can take action to moderate the effects of the bad on themselves, then I shall say that the public bad is mitigable. Environmental economists sometimes refer in this context to defensive expenditure or avertive expenditure, but as an adjective qualifying public bad, my sense is that mitigable is more appropriate than defensible or avertable.  Mitigable public bads are thus a subset of public bads, but an important one, as the above examples suggest.  Indeed, it seems plausible that most public bads are mitigable, at least for some individuals and to some degree. 

Mitigation need not be limited to individuals acting alone. In the case of installing double glazing, a decision by a household, or by a group of households making a bulk purchase to obtain a discount on the installation cost, can still be regarded as mitigation.  But for most public bads a clear distinction can be made between mitigation, action by potential sufferers to protect themselves from the bad, and what I shall term reduction, consisting in action at or close to the source of the bad to limit its scale or scope.  Examples of reduction would be a ban by an airport authority on flying to and from the airport in a certain direction, limiting noise for all households in that direction, or installation of equipment by a smoke-emitting factory to capture pollutants in the smoke, improving air quality for everyone in its vicinity.   

Two more definitions will be useful. I shall refer to the state of a public bad as its condition, across the whole of the affected region, after any reduction but before consideration of possible mitigation. By an individual’s exposure I shall mean the condition of the bad as experienced by that individual after any mitigation they have undertaken.  Thus exposure can differ between individuals both because some parts of the affected region may be affected more badly than others, and because the mitigation they have undertaken may differ.

A well-known result concerning public goods can be stated informally as below:

Proposition 1 The level of provision of a public good is optimal if the marginal cost of providing the good equals the sum over individuals of their marginal benefit from the good.

An individual’s marginal benefit from a public good can be interpreted as their marginal rate of substitution of the good for private goods.  This is equivalent to the slope of an indifference curve connecting combinations of goods which yield the individual the same utility. Since an individual’s utility function defines a whole set of indifference curves, the question then is which curve’s slope should be included in the sum when we are considering a possible re-allocation of production between private goods and the public good. The answer I will rely on here is to take the status quo distribution of private goods between individuals and assume that, in any re-allocation of production, individuals’ quantities of private goods would all change pro rata to the total quantity of private goods.  Any quantity of the public good together with an individual’s implied quantity of private goods would then define a point identifying a single indifference curve of that individual.  An alternative approach is to assume that all the indifference curves of any one individual have the same shape and therefore the same slope at any one quantity of the public good, regardless of the individual’s quantity of private goods.  This is equivalent to assuming that individual utility functions are quasilinear in the private goods (2), and is plausible if the quantity of private goods that individuals would have to forgo in return for the public good is small in relation to the total private goods.  Under this assumption it does not matter which curve of each individual we take: the sum of the slopes of one curve per individual will be the same whichever curves we take.

A point quite properly highlighted in most discussions of public goods is the sharp contrast between Proposition 1 and the optimality condition for a private good, which requires that the marginal cost of provision equal each individual’s marginal benefit. From this it follows that a public good will be under-provided in a free market (3).  However, other features of the proposition are sometimes overlooked. Firstly, the idea of optimal provision makes no sense for natural public goods like sunshine, except to the extent that they can be manipulated by human intervention. Secondly, for those cases where optimal provision does make sense, it is often a gross oversimplification to assume that the level of provision can be adequately characterised as a number of units on a single scale.  Just consider national defence, commonly given as an example of a public good. Thirdly, the relevant marginal cost of providing the public good is marginal social cost.  In the case of street lighting, for example, marginal cost should include the marginal social cost due to any greenhouse gases emitted in generating the electricity to supply the lights.  

Having noted these points, which apply equally to public bads, we may ask what is the equivalent of Proposition 1 for a mitigable public bad. In this case there are two kinds of decision to be made: the extent of mitigation by each individual; and the extent of action at source to limit or reduce the bad. 

Given the state of the bad, a rational individual will undertake mitigation up to but not beyond the point at which their marginal cost of mitigation (MCM) equals their benefit from a marginal lessening of exposure (BMLE). This is assuming normally shaped curves, that is, marginal cost increases and marginal benefit falls with additional units of mitigation.  For the purpose of optimal mitigation by individuals, it is not important how units of mitigation and exposure are defined: the only requirement is that, for any one individual, marginal cost and marginal benefit are measured with respect to the same units.  Note that, even if mitigation is possible for an individual, they will not undertake any mitigation if their marginal cost at zero mitigation exceeds their marginal benefit at that point.  In symbols, the condition under which an individual will undertake mitigation is:

MCM0  <  BMLE0                                  (A)

where the zero subscript indicates that the marginal quantities are measured at the state of the bad.

By analogy with the case of a public good, we expect that for a state S of a bad to be optimal, the marginal cost of reducing S must equal the sum over individuals of some sort of marginal quantity.  But what exactly?  The benefit to an individual from a marginal reduction in the state (BMRS) will depend upon whether, at S, they will undertake mitigation. If inequality A is not satisfied so that they do not undertake mitigation, then their benefit from a marginal reduction in S is simply their benefit from a marginal lessening of exposure:

BMRS(MCM0 > BMLE0)  =  BMLE0                    (B)

If however inequality A is satisfied so that they undertake mitigation, then their net benefit from mitigation at the margin will be BMLE minus MCM. Hence their benefit from a marginal reduction in S is the benefit from a marginal lessening of exposure less the benefit they could instead have obtained themselves from mitigation, the difference being the marginal cost of mitigation:

BMRS(MCM0 < BMLE0)  =  BMLE0 – (BMLE0 – MCM0)  =  MCM0           (C)

Putting the above together we have:

Proposition 2 Provided individual mitigation behaviour is rational, the state of a mitigable public bad is optimal if the marginal cost of reducing the bad equals the sum over individuals of the lower of a) their benefit from a marginal lessening of exposure and b) their marginal cost of mitigation.

However, extreme care is needed to ensure consistency in the units of measurement of the various marginal quantities.  Suppose we have a defined scale on which to measure the state of a bad.  For each individual, units of exposure must be such that the harm suffered from u units of state together with sufficient mitigation to limit exposure to u – 1 units must be the same as the harm suffered from u – 1 units of state with no mitigation.  A unit of mitigation is then simply that quantity of mitigation which will reduce exposure by one unit.

This has the important implication that both the physical requirements for and the cost of a unit of mitigation may vary greatly between individuals according to their circumstances.  Suppose the state of noise in the region around an airport is measured by average loudness at a defined location near the airport.  A given number of units will then be associated with more noise at some locations than others.  Suppose, as may be the case, that noise falls off with distance from the airport.  A reduction of one unit in the state of the noise may be quite significant for someone living close to an airport, but barely noticeable for someone on the edge of the affected region. Consequently the latter would need to do less than the latter, in physical terms, to achieve one unit of mitigation, and their marginal cost of mitigation will be lower.

Notwithstanding the above, a person living further from the airport will be less likely than someone nearer to it to undertake mitigation if, as is likely, benefit from a marginal lessening of exposure falls even faster with distance than marginal cost of mitigation, eventually reaching a point at which further lessening is imperceptible and offers no benefit.

The public policy implications of Proposition 2 are that those mitigable public bads which are due to human activity tend to be over-provided in a free market, but also that the extent of government action to correct that market failure should have regard to the availability and cost of mitigation by individuals.  If for example a tax is the preferred policy instrument, then it would be sub-optimal if the chosen tax rate per unit of state exceeds the sum over individuals as defined in Proposition 2.

An important question now is how the optimal state of a mitigable public bad compares with what it would be if no mitigation were possible.  This is illustrated in Chart 1 below.

The horizontal axis shows the state of the bad, reducing (ie improving) from left to right.  The vertical axis shows the quantity of a composite good representing all private goods.  It is assumed that there are no other public bads and no public goods.  The production possibility frontier PPF is the outer boundary of the possible combinations of the public bad and private goods, its concave shape reflecting the standard assumption of a diminishing marginal rate of transformation between goods.  The status quo is assumed to lie somewhere on the PPF.

S is the optimal state on the assumption that mitigation is impossible, and line I is the sum of individual indifference curves on the same assumption, chosen as explained above.  The slope of I at S will be the sum over individuals of their BMLE’s at S, and given the status quo assumption, I will be tangent to the PPF.

The red line IM is a sum of indifference curves passing through the intersection of PPF and I, on the assumption that mitigation is available. If inequality A above were not satisfied for any individual so that no mitigation would be undertaken, the slope of IM at S would also be the sum over individuals of their BMLE’s at S, and IM would be coincident with I in the vicinity of S, which would still be optimal despite the availability of mitigation.  If however A is satisfied for at least some individuals so that mitigation is undertaken, then the slope of IM will be a sum which includes MCM for at least some individuals, and therefore less than the slope of I.  Hence there is a region to the left of S within which PPF meets curves higher than IM, and the optimal state, given mitigation, will lie within this region, perhaps at SM.  So we have:

Proposition 3 Provided individual mitigation behaviour is rational, the optimal state of a public bad for which mitigation is available is greater (ie worse) than or equal to what the optimal state would have been if mitigation were not available, and is strictly greater if at least some individuals would have undertaken mitigation at the latter state.

In developing here a theory of mitigable public bads, I have passed over several points suggesting that Propositions 2 and 3 are in need of qualification.  Without following through the implications of each, I will just mention that an individual’s private cost of mitigation may not equal the social cost (for example the manufacture of double glazing may involve external costs not reflected in its price), that mitigation expenditure may be taxed or subsidised, and that rational mitigation behaviour may involve an element of gamesmanship if individuals believe that mitigation may lead to less action by government to reduce a bad.  It also needs to be borne in mind that optimality does not imply an acceptable income distribution: sometimes considerations of equity can properly take precedence over optimality.

Notes and References

1. Wikipedia  Public bad

2. Wikipedia Quasilinear utility

3. Wikipedia  Public good – Free rider problem

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The Marginal Value of Parks

Environmental valuation studies should be clearer as to whether the value they estimate is marginal value or something else. 

The idea of a marginal quantity is one of the most-used in the economist’s toolkit.  In the theory of the firm, profit is maximised when marginal revenue equals marginal cost.  In macroeconomics, the effect of a change in disposable income depends on the marginal propensity to consume.  In the literature on valuation of non-market environmental goods, however, this tool seems not to be used as often as it should.  Travel cost studies estimating the value of recreational sites often fail to consider whether what they have estimated is the marginal or some other value of the site, and this can lead to inappropriate policy recommendations.

The sense of marginal value with which I am concerned here is that which considers a whole recreational site as one unit.  Admittedly that could raise a difficulty in respect of open countryside with no clear subdivisions, but for urban parks surrounded by developed land and for designated national or country parks with well-defined boundaries it is usually clear enough.  So I am not concerned with the difference in value between a park as it is and the same park with one less square metre of land.  The margin I mean is that between the status quo in respect of all the parks in a region and a situation with one less park.  One might argue that incremental would be a more correct term, but I consider that marginal has connotations that are relevant here, such as the idea of diminishing marginal utility.

A couple of examples will illustrate why it matters whether or not the value estimated by a study is marginal value. Suppose there is a proposal to convert a recreational site for housing development, which we want to evaluate by cost-benefit analysis.  This involves, for both costs and benefits, comparing the situations with and without the project (1).  The recreational value that should be used in the analysis is therefore a marginal value, the value that would be lost if the site were no longer available for recreational visits.  If there are other recreational sites in the vicinity, then people who would have visited that particular site might visit other sites instead.  If so, then the numbers of visits to the particular site and the costs incurred by its visitors will not give a good guide to the value that would be lost.  It should of course be borne in mind, here and throughout this post, that recreational value is only one component of the total economic value of undeveloped land – others include the values of water and air purification, biodiversity and carbon sequestration -, and decisions on land use conversions should have regard to all such components.

Sometimes, however, marginal value is not the value we need.  Suppose we want to include the value of a recreational site in the national accounts, as part of an estimate of the aggregate value of the country’s environmental assets or natural capital.  In that case the marginal value is not appropriate. Suppose two sites A and B are not far apart.  If we value site A on a marginal basis, excluding the value attributable to visitors who would have visited site B if site A had not been available, and if we apply the same approach, vice versa, when valuing site B, then we will not capture the full combined value of the two sites.  In simple terms, the value attributable to visitors with no strong preference between the sites will be missed.

How then should we estimate the marginal recreational value of a park?  And where marginal value is not what we want, what other value concepts are available and how should they be estimated?  A key consideration here is that parks are not a homogeneous good: they differ in location, size and other characteristics.  This has several implications.  We should not expect a smooth curvilinear relation between the number of parks in a region and their combined value.  Obtaining the marginal value of a park is certainly not a matter of estimating such a curve and then applying differential calculus.  Each park will have its own marginal value.  Furthermore, the average value of a park, obtained by dividing the total value of the parks in a region by the number of parks, is unlikely to be a useful statistic. 

To address the above questions, I shall consider the example below of a stylised small town with two parks.

The grey zone C2 is the commercial and shopping centre, with no residents.  The two green zones B2 and E1 are parks with free entry, also without residents.  The remaining seven zones are residential, each with the same number of residents: what the number is does not matter as all my calculations were per resident. 

In the interests of simplicity I make the following assumptions:

  1. All residents are alike in their behaviour in respect of park visits.  Their visit rates depend only on the travel costs of their visits to parks.
  2. Residents perceive the two parks as different but equally attractive, except in so far as visits require different travel costs.
  3. Distances are measured as straight lines between the centres of zones, the unit of distance being the side of one square zone.
  4. The travel cost (TC) of a resident’s visit to a park is measured in monetary units such that it equals the park’s distance from the resident’s zone.
  5. There are no complications arising from congestion or multi-purpose trips.

I pass over the important practical question, a key focus of attention in many published studies, of how the formulae modelling visit rates (trip-generating functions) might be estimated from observational data.  My focus here is on the determination of site values from the trip-generating functions, and I therefore start from plausible assumptions about those functions.  By plausible I mean that the functional forms are credible; the coefficient values are chosen so that all residents will make some visits to each park, but their visit rates will vary considerably depending on their zone of residence.

It is convenient to define the functions in two stages.  The first stage consists of formulae stating what the visit rate to a park would be if, hypothetically, the other park were not available (to indicate the hypothetical nature of these visit rates I write VR’).  These formulae are:



The formulae for actual visit rates, given the availability of both parks, are then expressed in terms of these hypothetical visit rates:


The formula for VR(E1) is as above but with B2 and E1 interchanged throughout.  These formulae may appear complicated, but are chosen for various desirable properties (further details are in the download at the end of this post).  Note that a simple linear functional form as below will not work:


While it correctly indicates that a higher travel cost to park E1 will be associated with a higher visit rate to park B2, it has the very implausible implication that the relation between VR(B2) and TC(E1) is linear.  The higher TC(E1) is, the smaller we would expect the effect on VR(B2) of a unit increase  in TC(E1) to be.

To calculate values, starting from these trip-generating functions, I applied the standard method of deriving points on the demand curves by considering various price additions to the travel cost, then taking the area under the demand curve (the consumer surplus) to be the value (2).  Applying this method to the actual trip-generating functions, I obtained values per resident of 21.66 for park B2 and 16.06 for park E1, implying a total value of 37.72.  An appropriate description for these values would be “contribution of visits to the park to the total value of the two parks”.  This (generalised to all the parks in a country) is the value concept that would be relevant for inclusion in the national accounts as above.

The lower value for park E1 – the more distant park for more than half of the residents – is unsurprising, notwithstanding the equal attractiveness of the two parks.  There is much evidence that the recreational value of sites is lower when they are further from centres of population, other things being equal (3).

I also applied the method to the hypothetical trip-generating functions, obtaining values per resident (in each case in the absence of the other park) of 28.26 for park B2 and 22.26 for park E1.  This enabled the marginal values to be obtained as follows (calculations may not exactly agree due to rounding:

Marginal value of park B2

=  (Total value of two parks) less (Value of park E1 in absence of park B2) 

=  37.72 – 22.26  =  15.45

Marginal value of park E1

=  (Total value of two parks) less (Value of park B2 in absence of park E1) 

=  37.72 – 28.26  =  9.45

The table below summarises these results.

Total value is shown only for the “contribution” row: totals of the other rows would not be meaningful.

Once one has both the actual and the hypothetical trip-generating functions, the calculations of these distinct values do not present any special difficulty.  Why then do published travel cost valuation studies often fail to consider whether the values they obtain are marginal values or contributions to total value?

One possible reason is that researchers may not be entirely impartial.  They may be seeking results that would support a case for preservation of a site, and recognise that a marginal value, which could be low, would not be helpful.  Certainly, I have seen quite a few such studies that use their results in recommending preservation, perhaps with the help of government funding.  I cannot however recall a single study concluding that a site was not worth preserving.

Another reason is that many studies, perhaps because of resource limitations, focus on a single site.  They may, with the aim of avoiding omitted variable bias, estimate a trip-generating function that includes travel costs to alternative sites as independent variables.  But even then, it is impossible to obtain the marginal value of a site from a trip-generating function for that site only.  As we have seen above, that requires such functions for other sites too.

Suppose however that a researcher is both impartial and well-supplied with resources.  Suppose that they collect data on visit rates and travel costs for a number of sites in a region.  They could then estimate the actual trip-generating function for each site and hence calculate each site’s contribution to total value.  To obtain the marginal value of a site, however, they would still have to estimate the hypothetical trip-generating functions describing the unobservable visit rates to other sites that would prevail if that site were unavailable.  That seems, at best, statistically challenging. 

It is understandable, therefore, that many studies lead to value estimates for a site that are a reasonable approximation to “contribution to total value”, but do not estimate, even approximately, the marginal value.  What is less defensible is when such values are used to justify policy recommendations, for example whether a site should be preserved or converted to an alternative land use, that really require the marginal value.  One such example is a paper by Bharali & Mazumder (2012) which estimates the recreational value of Kaziranga National Park, Assam, India (4).  Starting from sample data on visit rates, travel costs and other relevant variables, the authors obtain an estimate of consumer surplus which, they state, “signifies the value of the benefits that the visitors derived from visiting the park” (5).  But they do not consider whether those benefits are gross, or net of the benefits that visitors would have obtained at alternative sites if the park had not been present.  Since the paper does not consider alternative sites at all, we can take it that these benefits are gross and that the value it estimates is not marginal value.  Nevertheless, the author’s conclude that the government should allocate large funds to preserve the site (6).  That conclusion would be much better supported if it had been shown that the marginal value of the site were significant.

The calculations underlying the above results may be downloaded here: Marginal Park Value Calculation (MS Excel 2010 format).

Notes and References

  1. See for example quote from Watkins T at  (select Compare Aggregate Costs and Benefits)
  2. See for example Perman R, Ma Y, McGilvray J & Common M (3rd ed’n 2003) Natural Resource and Environmental Economics  Pearson Addison Wesley  pp 413-4
  3. See for example Bateman I et al (2013) Bringing Ecosystem Services into Economic Decision-Making: Land Use in the United Kingdom  Science Vol 341 Issue 6141 pp 45-50 (section headed National-Scale Implications)
  4. Bharali A & Mazumder R (2012)  Application of Travel Cost Method to Assess the Pricing Policy of Public Parks: the Case of Kaziranga National Park  Journal of Regional Development and Planning Vol 1(1) pp 41-50
  5. Bharali & Mazumder, as 4 above, p 47.  An unusual feature of this paper (an error?) is that, having estimated the consumer surplus, it then adds on the actual travel costs to arrive at what it describes as total recreational value.
  6. Bharali & Mazumder, as 4 above, p 48
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An Unnecessary Book on Environmental Economics?

In the internet age, a collection of fine articles may not make a worthwhile book.

Edward Elgar Publishing have recently published a seventh edition of Economics of the Environment: Selected Readings (1), a collection of 34 articles on a wide range of topics in environmental and natural resource economics, edited by Robert Stavins.  According to the publisher, it “serves as a valuable supplement to environmental economics textbooks and as a stand-alone reference book of key, up-to-date readings from the field”. 

A virtue of the book is that, while the articles are all republished from academic journals, most of them are towards the more readable and less mathematical end of the spectrum of academic writing on economics.  I will comment briefly on a few.

Article 2 – Coase’s The Problem of Social Cost (1960) – is a seminal work in which he challenges the view that the appropriate policy response to a negative externality is either a Pigovian tax or regulation. Instead, he lays emphasis on a comparison of the overall economic effects of alternative social arrangements defining the respective rights of the parties, and on the transaction costs which arise if a party attempts to negotiate agreement with another party to infringe their rights in return for payment.  His numerous detailed examples illustrate the range of circumstances to which his arguments apply – but many readers will probably prefer to skip some sections in order to focus on the main points.

Articles 5 to 7 by, respectively, Carson, Kling et al and Hausman (2012), set out alternative positions on the contentious issue of the role of the contingent valuation method for valuing non-market environmental goods.  They focus especially on the conditions under which contingent valuation studies may be subject to hypothetical bias, where individuals overstate their willingness to pay for an environmental good.  I discussed these articles in this post.

Article 12 by Schmalensee & Stavins (2017) is a useful and fairly concise analysis of the performance of and lessons from seven cap and trade (marketable permit) systems intended to reduce emissions at a lower cost than would a command-and-control approach.  It considers six US systems differing in type of emission addressed and regional scope – notably the national Sulphur Dioxide Allowance Trading Program that began in 1995 –, as well as the EU Emissions Trading System, a multi-country system focused on CO2 emissions which started in 2005.  Among the lessons identified are a) not to require prior approval by a central authority of trades in emissions allowances; b) to establish rules and obtain accurate data well before the start of the first compliance period; c) to limit price volatility by price collars and by permitting allowances to be carried forward to the following period (allowance banking).

Article 16 by Covert, Greenstone & Knittel (2016) asks whether natural supply and demand forces (that is, without policy intervention) can be expected to significantly reduce fossil fuel consumption and so help to mitigate climate change.  They consider two kinds of such forces: increases in the costs of extracting fossil fuels; and technological advances improving the energy efficiency of existing technologies and developing new carbon-free technologies.  Their conclusion that fossil fuels will remain the primary energy source without policy intervention may not be a surprise, but it is useful to have the evidence for this set out in detail.

Article 24 by Tol (2018) considers the overall economic effects of future climate change, drawing on the conclusions of many previous studies of general and specific effects of climate change, and also considers recent estimates of the social cost of carbon (which should inform the welfare-maximising rate of a carbon tax).  As might be expected, he concludes that the overall effects of climate change in the long run are negative.  On the crucial issue of quantifying the negative effect, he suggests that the welfare effect of a century of climate change is unlikely to exceed that of losing a decade of economic growth.  But this conclusion is importantly qualified by recognition both that the effect on poor tropical countries is likely to be especially large and that the range of uncertainty is very wide.

Article 30 by Shogren & Taylor (2008) assesses the relevance of behavioural economics to environmental and natural resource economics, referring to much previous literature on this topic.  One of various arguments considered is that markets tend to encourage rational behaviour, in aggregate if not at the level of individual participants, and that the insights of behavioural economics are therefore especially relevant to behaviour in respect of non-market environmental goods. The conclusions reached are in my view balanced: neither dismissing the behavioural approach, nor over-stating the extent to which it requires modification of conventional analysis and policy recommendation based on an assumption of consistently rational behaviour. 

Although the book certainly contains some fine and mostly recent articles, I find it difficult to see who would want to buy it at a (current online) price of £130.50 (hardback) or £35.96 (paperback).  The world of publishing has changed since the first edition (1972).  Those affiliated to an academic institution will probably have free access to most if not all the articles via institutional arrangements with the original journals.  Moreover, nine articles are from the Journal of Economic Perspectives, an open access journal.  Another four (articles 3, 12, 24 & 27) I found to be freely accessible on the websites of the Review of Environmental Economics and Policy and the American Economic Review, subscription journals which however make the full text of selected articles freely available.  The Coase article is available within the open-access part of JSTOR.  So even without an institutional affiliation, it is possible to obtain free (and legal) access to at least 14 of the articles. 

Notes & References

  1. R N Stavins (ed) (2019)  Economics of the Environment: Selected Readings, 7th edition,  Edward Elgar Publishing
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Some Thoughts on this Blog

When I started this blog in 2012, I considered – having looked at many other blogs – that posts should be short and frequent, and my early posts were all well below 1,000 words.  But it gradually became apparent that for many topics I could not present the sort of sustained argument I wanted to make within that limit. I do aspire to persuade, and not just address readers who already agree with what I say.  Hence my posts became longer and less frequent.   Many are in the region of 2,000 to 3,000 words, and the longest – a review of Dieter Helm’s Natural Capital: Valuing the Planet – extends to some 8,000 words.

Through WordPress I can access statistics giving some idea of which of my 54 posts have received most views.  The statistics are not as helpful as they could be, with a high proportion of views classified under the catch-all category of “Home page / Archives”.  Based on the classification of the remainder, some posts have been viewed far more times than others.  The ten most-viewed posts are as below, in descending order of views (counted from the date of posting to the end of 2018).

  1. Some Economics of a Carbon Tax (20/2/2013)
  2. Of Fish, Fishers and Consumers (23/6/2013)
  3. Explaining Environmental Policy Failure (1/12/2017)
  4. Pollution Control and Output (30/12/2016)
  5. A Valuation Case Study: The Great Barrier Reef (15/7/2017)
  6. Green Space: An Important Use of Urban Land (28/7/2013)
  7. Net National Product and Sustainability (17/5/2017)
  8. In Defence of the Linear Demand Function (21/6/2016)
  9. Reducing Pollution with a Combined Tax and Subsidy (6/9/2012)
  10. Lessons from the Industrial Revolution (7/3/2013)

Of the less-viewed posts, the following are some with which I am especially pleased:

While some posts relate to matters that were in the news at a particular time, all are intended to address or illustrate more general issues in the field of environmental and natural resource economics and related disciplines.  I’m not trying to build up an encyclopaedia – which would be absurdly ambitious -, but I hope that some may find my posts, including some of the older ones, a useful resource.  With that hope comes a responsibility to try to improve older posts where possible, which may include explaining points more clearly, improving layout and – yes – on occasion correcting errors.  In that spirit I have recently made substantial amendments to Net National Product and Sustainability and minor changes to several other posts.

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