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The value of incorporating bioindicators in economic approaches to water pollution control
ECOLOGICAL ECONOMICS ELSEVIER
Ecological Economics 19 (1996) 237-245
Analysis
The value of incorporating bioindicators in economic approaches to water pollution control 1 Andrew G. Keeler *, Donna McLemore Department of Agricultural and Applied Economics, The UniuersiO, of Georgia, Athens. GA 30602, USA
Received 20 December 1995; accepted 19 February 1996
Abstract Bioindicators provide better information about environmental quality than chemical and physical measures alone. This paper addresses how this information can improve the efficiency of water pollution control policy. We model bioindicators as a means of resolving uncertainty about the relationship of human activities and environmental effects. Two economic policy models are developed to formalize efficiency gains that result from biological information. We find that resolving uncertainty is valuable in both a benefit-cost framework and in implementing an exogenously-determined safety standard. The results imply that ecological research capable of providing this kind of information can have a direct economic payoff.
1. Introduction
The past decade has seen an intensified debate about how economics should be used to make decisions about environmental quality and the way it is affected by human activity. Neoclassical economics has focussed on the tradeoffs between environmental quality and other objectives. Ecological economists have criticized cost-benefit approaches on the grounds that they have a narrow species frame, a limited time frame, and an inadequate understanding of the complexity of ecological relationships. In this paper we argue that the economic analysis of water
Corresponding author. i This is a revised version of a paper presented at the International Society of Ecological Economics meeting in San JosC Costa Rica, October 1994.
pollution control policy can be advanced on a number of dimensions by directly incorporating ecological information - - using biological measures of environmental quality in addition to physical or chemical measures. Microeconomic models for water pollution policy analysis have used chemical levels as SmTogates for environmental quality (for example, see Braden and Segerson (1993) and Malik et al. (1993)). This has been a useful approximation for examining the costs of controlling pollution, but of limited value in understanding the damage it causes. Assessing the effects of human activities on ecosystems requires information that is not readily available from data on pollutant quantities or ambient pollution levels. The natural sciences have made significant progress in the use of biological information to indicate an ecosystem's health. A significant body of research
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exists on developing biological monitoring programs and selecting appropriate indicators for customized approaches to pollution problems. Biologists continue their efforts to identify new types of indicators and to test the robustness of others. Incorporating this information into the policy and enforcement process, however, remains a challenge for both natural and social science. Economic models incorporating biological measures have the potential to better reflect a holistic approach to resource management. This paper discusses the economic value of incorporating additional information given by biological indicators into microeconomic analysis and the policy decision-making process. It also provides some guidance on the factors that determine how much can usefully be invested in improving biological measures of environmental quality. We use a highly simplified model of ecological knowledge as improved information to illustrate why such knowledge has direct economic value, and to suggest what factors determine the extent of its value. We look at economic value in a conventional neoclassical approach that compares the costs and benefits of environmentally damaging activities. We also examine a minimum-standard approach where policy attempts to meet some exogenously determined level of environmental quality.
2. Biological indicators of water quality There are two types of biological indicators that are useful in measuring aquatic ecosystem quality - ambient biological monitoring and bioassay methods. Ambient biological monitoring is based on the fact that different species have different tolerances to pollutants and that evidence of impairments can be identified by noting the richness or number of taxa present. An index is constructed by sampling one or more organisms from a body of water and using the results of the sample to determine the extent to which the aquatic ecosystem has been degraded by human activity. This determination depends on comparison of the sample to a reference sample from a comparable 'healthy' ecosystem. This approach began in the USA with research at the Illinois River Biological Station in 1894, and was advanced by incorporation of indicator organisms in 1908 (Davis,
1995). Increasingly sophisticated use of multimetric indices have allowed ambient monitoring to incorporate multiple dimensions of ecological systems into interpretable reference measures. Considerable research has also been devoted to advancing understanding of the reference conditions against which biological assessment data are interpreted (Hughes, 1995). In addition to their primary advantage over chemical measures - - a more accurate assessment of how an aquatic ecosystem is affected by human activities - - ambient biological monitoring methods possess two other benefits. Many of the existing indices require relatively simple and inexpensive data collection techniques, and data is currently being collected by many volunteer citizen organizations. In addition, they have a widespread intuitive appeal to non-scientists which chemical measures lack. Bioassays have become increasingly important due to advances in ecotoxicology. They assess chemical, cellular, or genetic changes within an organism. Biomarkers are a primary tool in this assessment. A biomarker is defined as 'measurements of body fluids, cells, or tissues that indicate in biochemical or cellular terms the presence of contaminants or the magnitude of the host response' (McCarthy and Shugart, 1990). Biomarkers can include anatomical and cytological abnormalities, adaptive biochemical and immunological responses, including detoxication systems, reproductive competence and genotoxicity, response of stress proteins and effects of pollutants on DNA. Due to a short response time, biomarkers can provide an accurate indication of the type and source of pollutant causing observed ecological degradation (McCarthy and Shugart, 1990). However, they require greater laboratory sophistication and do not have the same intuitive appeal to non-scientists as ambient biological indices. The most commonly used biological indicator organisms are benthic macroinvertebrates, fish and algae. All three have important advantages to offer and are especially useful for different types of contaminants. Benthic macroinvertebrates, small organisms that live in sediments and debris, are most commonly used for measuring and monitoring contamination from metals, pesticides and polychlorinated biphenyls. Ohio developed an Invertebrate Community Index in 1986, and other states as well as the Environmental Protection Agency (EPA) have
A.G. Keeler, D. McLemore / Ecological Economics 19 (1996) 237-245
developed multimetric indices based on benthic macroinvertebrates (Davis, 1995). Fish are popular biological indicator organisms due to the popular public opinion of water as a fishery resource. Because fish are mobile and have long lives, they are good indicators of integrated long-term effects. The Index of Biotic Integrity (Karr, 1981) incorporates fish species composition, trophic composition, abundance and condition into a single measure of biological integrity. This approach has been adapted for use in a number of geographical areas. Algae provide information about nutrient enrichment. Biomass indicators like dry weight and chlorophyll a, a common algae pigment, are commonly used in evaluation of algae response (Raschke, 1994). Algae are highly responsive to nutrient loadings due to rapid reproduction and short life cycles, showing an advantage for pinpointing episodic loadings. Since they are at the bottom of the food chain, algae responses are mostly attributed to physical and chemical changes (U.S. EPA, 1990a). These measures have had an increasing influence on federal and state water protection efforts. For the most part, water quality regulation has been characterized by limiting ambient pollutant concentrations and maximum end-of-pipe loadings. The chemical standards in regulations act as a surrogate for true underlying environmental quality but only represent potential harm. Starting in 1977, concerns were expressed that the Environmental Protection Agency's monitoring efforts were insufficient (U.S. EPA, 1990b). The EPA in 1987 reported that new monitoring efforts were needed to assess the effect of current policies, to support the nonpoint source (NPS) pollution control efforts, and to enhance research (U.S. EPA, 1990b). As a result, the Environmental Monitoring and Assessment Program (EMAP) was developed as a national long-term project for assessing the health of the nation's aquatic ecosystems and for publishing support documents to help states implement assessment and monitoring programs under Section 303(c)(2)(B) of the Water Quality Act of 1987 (U.S. EPA, 1990b). While the great majority of states now collect at least some biological monitoring data for water resource management, only Delaware, Florida, Maine and Ohio have incorporated biological data as criteria in pollution control
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regulations. Other states, particularly North Carolina, Vermont and Connecticut, have incorporated biocriteria extensively into their water quality assessment and planning procedures (Southerland and James, 1995).
3. Information value of biological indicators Biological information in water quality assessment is important for many of the same reasons ecologically and economically. Chemical measures indicate the existence of a potential environmental stressor, but cannot indicate underlying damages to the ecosystem. To identify the affected state of the ecosystem, bioindicators integrate the effects of pollutants over spatial and temporal dimensions (McCarthy and Shugart, 1990). Studying biological indicators at different organism levels reveals effects of bioaccumulated and even quickly metabolized contaminants. Camargo, in a study comparing the results from physicochemical and biological surveys, demonstrated the importance of including biological monitoring in ecological risk assessment. Chemical and physical measurements in a water system receiving trout farm effluents revealed much less severe pollution effects than indicated by biological indicators (Camargo, 1994). Bioindicators are important for ecological risk assessment and water quality monitoring when several sources of degradation are present in a water body. Only a relatively small proportion of the interactions between different pollutants are understood. If multiple pollutants are present, biological indicators can be instrumental in identifying the pollutant causing environmental damage (McCarthy and Shugart, 1990). For example, a study of wastewater treatment controls using the index of biotic integrity showed that further abatement of ammonia emissions was not necessary for the improvement of environmental quality, but reduction of chlorine levels would cause significant improvement (Karr et al., 1985). By integrating the effects of anthropogenic and natural influences, the additional information provided by bioindicators gives a more refined measure of water quality than does chemical sampling alone.
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While chemical sampling only indicates potential effects of pollution on biological and ecological processes, biological indicators directly measure this effect. They are closely allied to our true underlying conception of environmental quality. A primary concern of the effects of NPS pollution is the damage done to biological integrity through sedimentation from erosion, eutrophication from nutrient loadings, and habitat degradation leading to increased temperatures. Relative to point sources, NPS polluters are difficult to regulate because polluters are hard to identify and effects of polluting activities vary greatly geographically. Runoff occurs during uncontrollable weather events. Therefore, due to the intertemporal nature and quick response systems of some indicators, the timing of episodic loadings can be determined. Sessile organisms help to identify polluters when a reference site such as an upstream position or a site in a characteristically similar stream is used for comparative purposes (U.S. EPA, 1990a). Since monitoring efforts are burdensome and expensive for measuring effluent from nonpoint sources, the dynamic nature of biomarkers offers a cost-effective advantage over chemical sampling alone (U.S. EPA, 1990a). Continuous or periodic biological monitoring will gauge the effectiveness of resource management policies allowing for revisions to be made on a timely basis if practices need to be updated. With better indicators of underlying environmental quality, negotiating groups will be able to focus more precisely on relevant issues for improved debate and reasoning. According to the U.S. EPA (1990a), 'implementing biological criteria in water quality standards provides a systematic, structured, and objective process for making decisions about compliance with water quality standards ... (A)nd increases the value of biological data in regulatory programs.'
4. Models for policy analysis The preceding section argued that adding biological indicators to chemical and physical data offers a
more accurate measure of underlying environmental quality. In the rest of the paper we model bioindicators in a simple information framework as providing a smaller variance around true environmental quality. In what follows we are abstracting from the problems of instrument choice and implementation that are extraordinarily difficult in water pollution control policy. Our intent is to show the importance of better biological information, and we believe this value will persist even with second-best instruments and imperfect implementation. We develop two models whose purpose it is to demonstrate how the use of bioindicators can improve the economics of pollution control regulation. These models are highly abstracted and stylized. They are useful to show the potential importance of these measures and how bioindicators offer different advantages in different pollution control paradigms.
4.1. Modeling bioindicators as information Let x be a physical measure of pollution (denominated in units such as parts per million). Assume that there is some underlying true level of environmental quality reflecting biological integrity. Quantifying such a measure is both conceptually and empirically difficult. Here we assume that it is possible to recognize and order different levels of environmental quality on a single scale. Let this underlying level be indexed by a continuous variable q. For notational convenience choose the values of the index variable q so that it corresponds with the measure of the pollution variable x. Let q be negatively indexed so that 0 corresponds to no ecosystem impairment, and higher levels of impairment correspond to higher levels of q. Let there be an imperfectly understood relationship between physical measures of pollution and environmental quality, and let q be indexed so that for any level of x, q = x + ~p, where ep ~ N(0, trp2). Note that the variable q is only an index; it has no particular units and its function is to mark some level of an ecosystem's ability to support biological diversity and ecological function. Since the level of an index is arbitrary, for the sake of analytical convenience it is defined as the level of expected environmental quality resulting
A.G. Keeler, D. McLemore / Ecological Economics 19 (1996) 237-245
from a given pollution level x in the absence of ecological information. 2 Now let there be a bioindicator b which provides additional information about the relationship between x and q. This information may result in improved understanding of environmental quality itself, or may provide more information about how the pollution control variable x affects quality. A reasonable way to think about the problem is that the bioindicator partially resolves the uncertainty parameterized by 00p2. The new information is not perfect there still exists an error term, but it is smaller than in the previous case. Let the mean of the new distribution shift by a random variable ~: for any given physical measure of pollution x the addition of bioindicators yields a new distribution q = x + 6 + e b, where b ~ N(0, o082) and ~ob N(0, O'b). The ex-ante (in the sense of before the information provided by the bioindicator) distribution of q around x should be the same in both cases: 6 + eb = ep, E[ 6 -r" ~'b] = E[,~p] = 0, and E[(6 + eb )2 ] = E[(oOp)2]. We assume that the information provided by the bioindicator is independent of the remaining uncertainty: E [ ( t S + e b ) 2 ] = E [ 6 2 + 2 6 e b+e~]=E[t5 2 + + + + e = 00g + 00~+= 00p-. The idea is that bioindicators provide information about the relationship of x and q such that the expected value of environmental quality for any x becomes x + ~. The value of ~ is known once bioindicators are collected and interpreted so the policy can be based on a new relationship between the pollution control variable and environmental -
-
~
z Note that we are specifying e as "measurement error'. There will also be true randomness in ecosystem outcomes having nothing to do with human activities, or at least with water pollution. We ignore this randomness. Note also that we are assuming a particular additive structure for the measurement error where many other possible structures are possible. This is overly simplistic for many reasons; in particular, we would certainly expect the size of the error term to depend on the pollution level. For example, we should be relatively sure of the effects of x at both very low and very high pollution levels, but much less certain between these extremes. Karr (1991) demonstrates that the ability of chlorine levels to predict a biological integrity index shows more variance at higher pollution levels. We feel that our simplifying assumption is justified given the demonstrative intent of our model, but for empirical work much closer attention to the form of this relationship will be necessary.
241
quality, with a smaller variance o-b. We can now proceed with the specification of the regulatory problem under the alternative scenarios advanced earlier - - cost-benefit regulation and meeting an exogenous target level of environmental quality.
4.2. Cost-benefit regulation Assume that there is a known function which relates pollution control costs to ambient loading levels in the environment. Let c(x) be the control cost function: c ' ( x ) < 0 and c"(x) > 0. Since our arguments can be most transparently demonstrated with a particular functional form, let c(x)= K - ax + bx 2 with 0 < x < a / 2 b defining the economically relevant range of possible ambient loading levels. 3 The cost/benefit approach calls for monetizing environmental damages q. Greater dollar damages can be expected to be associated with lower levels of environmental quality (corresponding to higher values of our index variable q). We assume that there is a known damage function D(q) that relates ecosystem quality to the economic value of that quality. Although the function D(q) is known, the value of q which results from policy choices is not known with certainty. We make the normal assumption that increasing levels of environmental degradation lead to increasing marginal damages: D'(q)> 0 and D"(q) > 0. We ignore risk aversion so that the regulatory objective is to minimize the expected sum of control costs and damages. Again, we assume a quadratic form for expositional clarity, D(q) = y + ~q + flq2. When only physical measures are available the regu-
This model bears some similarity to modelling by Weitzman (1974) of the choice of price or quantity regulation under uncertainty. It is substantially different in that it is the value of reducing the uncertainty that is being explicitly modelled, and instrument choice is not at issue. The choice of quadratic cost and damage functions obviously limits the generality of our results. Our aim with this model is to demonstrate how better information translates into better choices, and we believe these forms do a good job of allowing this to be transparently demonstrated. Adoption of more general functional forms gives us results similar to those of Weitzman (1974) and Kolstad (1987): the arguments will generally hold with enough assumptions on the higher-order derivatives. We believe little is added with this approach.
A.G. Keeler, D. McLemore/ Ecological Economics 19 (1996)237-245
242
latory problem is to minimize the expected sum of damages and costs min~p = E[ K - ax + bx 2 + y + a q + / 3 q 2 ]
a.%/ & =
2~x+2/36+2~E(eb)
a+
=0
x
(6) a - a - 2/36
= E[ K -
ax + bx2 + 3'+ a ( x + ep)
+/3( x + ep)2].
Xb = (1)
Substituting x + ~ for q and differentiating with respect to x gives us the first-order condition for cost minimization O.~p/Ox = - a + 2 b x + ~ + 2 / 3 x + 2/3E(ep) = 0
(2) O--13/
xp
-a+2bx+
2b+2fl"
(3)
Since the expectation of ep is 0, the optimal level of pollution xp collapses to the certainty equivalence case: the optimal way to set x is the same as if the relationship between x and q were known with certainty. In other words, Xp is the pollution level where marginal control costs are equal to expected marginal damages. This is an artifact of the linearity of the marginal cost and damage functions; other specifications would yield a different rule. What we wish to emphasize here is not the choice of control level, but the value to the objective function (that is, the total damage to the environmental services plus total control costs to producers). Total minimum expected costs are given by substituting x~ into ~p,
= K-
axp
2/36
2b+2/3
Xp
2b+2/3'
The rule for determining the optimal pollution level under the expected cost-plus-damage minimization criterion changes in response to the information provided by the bioindicator. Values of 8 > 0 indicate that pollution is more damaging to biological integrity than was supposed, and the optimal regulation is correspondingly smaller than Xp. Values of 8 < 0 indicate that the ecosystem is more tolerant of pollution than was previously understood, and the optimal regulation is larger than Xp. Again, we are less interested in the regulation itself than in the expected savings from improved ecological information. Although 5 is known when the policy is set, the fact that different realizations of have different consequences for -~b* requires evaluating the function's expected value across the pdf of E[.9"b* ] = E
K-a
xe
2b+2/3 "2
[
+ b x~
2b + 2/3
+y+a
-[- b ( x; )2 -[- 'y-[- OlXp
xp
+/3 x;
2b+Z~+a+~b
min.~ab = E[ K - ax + bx 2 + "y q- a q +/3q2]
=c(x; ) + d(x; ) + l
]
+ (8)
The expected gains from incorporating bioindicators are given by
t3 2 =/3~p2 _ b +----~'~2_ / 3 ~
x
=K-
[ /32
(4)
The addition of biomonitoring information yields a virtually identical problem, except the observation of allows the policy to be set with a smaller error term, e b,
1
2b+2/3 +6+eb
+/3(x;)2+/3e[4]=K-ax; +b(x;) 2 + y + aXp + / 3 ( X p ) 2 +/3o'p2.
(7)
a x + bx 2 + E [ y + a ( x + 6 + eb)
+/3(x + a+ ~)2]
(5)
bfl
- b
bfl
= +b - - 7
[.p2 - . b : ] .
(9)
A.G. Keeler, D. McLemore/ Ecological Economics 19 (1996)237-245
Eq. (9) shows that the value of additional information depends directly on how much of the uncertainty is resolved: the more information about true environmental quality and its relationship to pollution that bioindicators reveal, the higher the expected savings. In addition, savings depend on the shapes of the marginal damage and control cost functions. When small changes in biological integrity have a large effect on the marginal change in damages, bioindicators have a higher potential to increase regulatory efficiency. When marginal damages are relatively invariant to environmental quality, bioindicators will produce smaller savings. The same is true of the control cost function: when marginal control costs are steep, the information provided about damages by bioindicators becomes more important in regulatory considerations of efficiency. A flatter marginal control cost function decreases the importance of better ecological information. Note that Eq. (9) can also be interpreted as the maximum amount that it would be worth to conduct the research that would reduce the uncertainty about the effect of pollution from ~rp2 to cry. 4.3. Regulation with an exogenous environmental quality target
An exogenous environmental target is consistent with the safe minimum standard (SMS) approach developed by Bishop (1978, 1993), which results in a presumption that biodiversity must be protected unless it imposes intolerably high costs on the economic system. We use the SMS here in a slightly different way that we believe is consistent with the spirit of Bishop's definition. The idea is that there is some underlying level of environmental quality that maintains an ecosystem's biodiversity and resiliency. There may be threshold effects and non-linearities such that exceeding some level of pollution results in a discontinuously large change in ecosystem health. Since there will always be some uncertainty about the effects of economic activity on environmental outcomes, implementation of an SMS requires meeting an exogenous environmental goal with some high level of confidence. In practice regulations rarely result from a straightforward cost-benefit analysis. In general, water quality goals have been loosely based on maintaining the integrity of the nation's waters and on
243
meeting operational definitions of 'fishable and swimmable' (Freeman, 1990). EPA's proposed watershed protection approach calls for involving those most concerned with watershed quality in a process of problem identification and negotiation which results in corrective actions (U.S. EPA, 1993a; U.S. EPA, 1993b). This process can be expected to produce some set of environmental targets not necessarily identical to those which would result from the equalization of marginal costs and benefits. Our definition of policy with an SMS is also consistent with the idea that environmental quality is determined by a scientific, bureaucratic, and political process and the regulatory task is to implement the decision of that process with a reasonable degree of certainty. While this is not a precise description of current policy formation, neither is the cost-benefit approach more frequently adopted in economic analysis. Formally, we take the SMS to be some level of environmental quality that is exogenously determined and that must be met with some (exogenously determined) high degree of certainty. Let the exogenous standard of environmental quality be q * and the required probability of meeting that level be 7r. The regulatory problem is to minimize control costs subject to meeting the requirements of the SMS min c( x) s.t. pr( x + % < q* ) > Tr.
(10)
X
Let z~ be the critical value of the standardized normal distribution associated with the required confidence level w. The strategy to meet q * with a level of certainty "rr simply consists of choosing x such that q* - x - -
(11)
%
and the chosen level of pollution is given by xp=q* -z~%.
(12)
With a bioindicator meeting the assumed characteristics, the pollution level can be set after part of the uncertainty about the relationship of pollution and true environmental quality is resolved and ~ is observed. The problem becomes m i n c ( x ) s.t. p r ( x + 6 + e b < q * ) > ~ r
(13)
.r
and the solution is xb=q*-6-z~Cr
b.
(14)
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A.G. Keeler, D. McLemore/ Ecological Economics 19 (1996) 237-245
Actual pollution levels and control costs with bioindicators may be higher or lower than without, depending on the particular realization of 8. However, on average allowed pollution will be higher E[xb -xp]
= (o-p -
= (o-p -
however, that to the extent that this model reflects the way policy will be made with exogenous environmental targets, better biological information will have a higher payoff in the SMS case than in the cost-benefit case.
(15)
The expected savings from adding bioindicators is given by
=b(z~-l)
(O'p2 -- O-2 )
- z~( - a + 2bq* )(O'p - o'b).
(16)
The first half of the above expression reflects the interaction of the required certainty level with the variance of the environmental quality measure and the slope of the control cost function, This term will be positive whenever the required confidence level is higher than 85% so that z~ > 1. As higher degrees of certainty in meeting an environmental quality target are required, the economic gains of improving the precision of environmental quality measures becomes larger. Expected gains are also increasing in the part of the uncertainty that is resolved by adding bioindicators. The second term will always be positive - - it consists of the product of the negative of marginal control costs at the mean of the original distribution around q *, the reduction in the standard deviation provided by bioindicators, and the required level of certainty. Again, the more information provided by bioindicators and the higher the required confidence level of meeting q *, the larger the expected savings that result. As before, Eq. (16) can also be interpreted as the amount that we should be willing to pay for biological information that reduces uncertainty from trp2 to ~r~. In the cost-benefit model, expected savings depended on the levels of marginal cost of the control cost and damage functions. The savings resulted from reductions in expected deadweight efficiency loss. In this model, the savings closely approximate total savings between the 'without' and 'with' levels of allowed pollution. This is because the SMS model recognizes no gain from levels of environmental quality better than q * which will probably result when policy is set without bioindicators. It means,
5. Conclusion We have argued that the strength of bioindicators is that they are one step closer to the ecological processes which comprise 'environmental quality' than are conventional pollutant measures. One way to view the results of the models presented here is as the value of developing the scientific knowledge which relates bioindicators to biological integrity and to specific sources of environmental degradation. If reducing the variance of our measures of environmental quality produces more efficient outcomes, then the size of the efficiency improvements should provide one measure of the maximum worth of developing the ecological knowledge to relate bioindicators to pollution control policies. Our results here indicate that such knowledge can be expected to improve the efficiency of pollution control policy whether a cost-benefit or SMS approach to policy is followed. However, the value of the information produced by learning more about bioindicators and how to use them will ordinarily be higher in the SMS case. The obvious next step is to conduct empirical studies linking multimetric index values to levels of environmental controls and their corresponding economic costs. In addition, bioindicators can prove valuable in determining which of several potential pollutants is causing observed adverse environmental quality effects. Formal modeling of the value of biological information in this context would also be valuable.
Acknowledgements The authors are grateful to Jeff Dorfman, James Karr and an anonymous reviewer for helpful comments on an earlier version of this work.
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References Bishop, R., 1978. Endangered Species and Uncertainty: The Economics of a Safe Minimum Standard. Am. J, Agric. Econ., 60: 10-18. Bishop, R., 1993. Economic efficiency, sustainability and biodiversity. Ambio, 22: 69-73. Braden, J.B. and Segerson, K., 1993. Information problems in the design of nonpoint pollution policy. In: ed. C.S. Russell and J.F. Shogren, Theory, Modeling and Experience in the Management of Nonpoint-Source Pollution. Kluwer Academic Publishers, Boston, MA. Camargo, J.A., 1994. The importance of biological monitoring for the ecological risk assessment of freshwater pollution: A case study. Environ. Int., 20: 229-238. Davis, W.S., 1995. Biological assessment and criteria: building on the past. In: ed. W.S, Davis and T.P. Simon, Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making. Lewis Publishers, Boca Raton, FL. Freeman III, A.M., 1990. Water Pollution Policy. In: ed. P.R. Portney, Public Policies for Environmental Protection. Resources for the Future, Washington, DC. Hughes, R.M., 1995. Defining acceptable biological status by comparing with reference conditions. In: ed. W.S. Davis and T.P. Simon, Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making. Lewis Publishers, Boca Raton, FL. Karr, J.R., 1981. Assessment of biotic integrity using fish communities. Fisheries, 6: 21-27. Karr, J,R., 1991. Biological Integrity: A long-neglected aspect of water resource management. Environ. Appl., 1:66-84 Karr, J.R., Heidinger, R. and Helmer, E., 1985. Effects of Chlo-
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rine and Ammonia from Wastewater Treatment Facilities. J. Water PoUut. Control Fed., 57: 912-915. Kolstad, C.D., 1987. Uniformity versus differentiation in regulating externalities. J. Environ. Econ. Manage., 14: 386-399. Malik, A.S., Letson, D, and Cmtchfield, S.R., 1993. Point/nonpoint source trading of pollution abatement: choosing the right trading ratio. Am. J. Agric. Econ., 75: 959-967. McCarthy, J.F. and Shugart, L.R., 1990. Biological markers of environmental contamination. In: Biomarkers of Environmental Contamination. Lewis Publishers, Chelsea, MI. Raschke, R.L., 1994. Phytoplankton bloom frequencies in a population of small Southeastern impoundments. Lake Reserve Manage., 8: 205-210. Southerland, M.T. and James, B., 1995. Status of biological criteria development and implementation. In: ed. W.S. Davis and T.P. Simon, Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making. Lewis Publishers, Boca Raton, FL. U.S. Environmental Protection Agency, Office of Water, 1990a. Biological Criteria, National Program Guidance For Surface Waters. EPA-440/5-90-004, Washington, DC. U.S. Environmental Protection Agency, Office of Research and Development, 1990b. Environmental Monitoring and Assessment Program, Ecological Indicators. EPA/600/3-90/060, Washington, DC. U.S. Environmental Protection Agency, Office of Water, 1993a. The Watershed Protection Approach: Annual Report 1992. EPA-840-S-93-001, Washington, DC. U.S. Environmental Protection Agency, Office of Research and Development, 1993b. Regional Environmental Monitoring and Assessment Program. EPA/625/R-93/012, Washington, DC. Weitzman, M.L., 1974, Prices versus Quantities. Q, J. Econ., 41: 477-491.
Ecological Economics 19 (1996) 237-245
Analysis
The value of incorporating bioindicators in economic approaches to water pollution control 1 Andrew G. Keeler *, Donna McLemore Department of Agricultural and Applied Economics, The UniuersiO, of Georgia, Athens. GA 30602, USA
Received 20 December 1995; accepted 19 February 1996
Abstract Bioindicators provide better information about environmental quality than chemical and physical measures alone. This paper addresses how this information can improve the efficiency of water pollution control policy. We model bioindicators as a means of resolving uncertainty about the relationship of human activities and environmental effects. Two economic policy models are developed to formalize efficiency gains that result from biological information. We find that resolving uncertainty is valuable in both a benefit-cost framework and in implementing an exogenously-determined safety standard. The results imply that ecological research capable of providing this kind of information can have a direct economic payoff.
1. Introduction
The past decade has seen an intensified debate about how economics should be used to make decisions about environmental quality and the way it is affected by human activity. Neoclassical economics has focussed on the tradeoffs between environmental quality and other objectives. Ecological economists have criticized cost-benefit approaches on the grounds that they have a narrow species frame, a limited time frame, and an inadequate understanding of the complexity of ecological relationships. In this paper we argue that the economic analysis of water
Corresponding author. i This is a revised version of a paper presented at the International Society of Ecological Economics meeting in San JosC Costa Rica, October 1994.
pollution control policy can be advanced on a number of dimensions by directly incorporating ecological information - - using biological measures of environmental quality in addition to physical or chemical measures. Microeconomic models for water pollution policy analysis have used chemical levels as SmTogates for environmental quality (for example, see Braden and Segerson (1993) and Malik et al. (1993)). This has been a useful approximation for examining the costs of controlling pollution, but of limited value in understanding the damage it causes. Assessing the effects of human activities on ecosystems requires information that is not readily available from data on pollutant quantities or ambient pollution levels. The natural sciences have made significant progress in the use of biological information to indicate an ecosystem's health. A significant body of research
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exists on developing biological monitoring programs and selecting appropriate indicators for customized approaches to pollution problems. Biologists continue their efforts to identify new types of indicators and to test the robustness of others. Incorporating this information into the policy and enforcement process, however, remains a challenge for both natural and social science. Economic models incorporating biological measures have the potential to better reflect a holistic approach to resource management. This paper discusses the economic value of incorporating additional information given by biological indicators into microeconomic analysis and the policy decision-making process. It also provides some guidance on the factors that determine how much can usefully be invested in improving biological measures of environmental quality. We use a highly simplified model of ecological knowledge as improved information to illustrate why such knowledge has direct economic value, and to suggest what factors determine the extent of its value. We look at economic value in a conventional neoclassical approach that compares the costs and benefits of environmentally damaging activities. We also examine a minimum-standard approach where policy attempts to meet some exogenously determined level of environmental quality.
2. Biological indicators of water quality There are two types of biological indicators that are useful in measuring aquatic ecosystem quality - ambient biological monitoring and bioassay methods. Ambient biological monitoring is based on the fact that different species have different tolerances to pollutants and that evidence of impairments can be identified by noting the richness or number of taxa present. An index is constructed by sampling one or more organisms from a body of water and using the results of the sample to determine the extent to which the aquatic ecosystem has been degraded by human activity. This determination depends on comparison of the sample to a reference sample from a comparable 'healthy' ecosystem. This approach began in the USA with research at the Illinois River Biological Station in 1894, and was advanced by incorporation of indicator organisms in 1908 (Davis,
1995). Increasingly sophisticated use of multimetric indices have allowed ambient monitoring to incorporate multiple dimensions of ecological systems into interpretable reference measures. Considerable research has also been devoted to advancing understanding of the reference conditions against which biological assessment data are interpreted (Hughes, 1995). In addition to their primary advantage over chemical measures - - a more accurate assessment of how an aquatic ecosystem is affected by human activities - - ambient biological monitoring methods possess two other benefits. Many of the existing indices require relatively simple and inexpensive data collection techniques, and data is currently being collected by many volunteer citizen organizations. In addition, they have a widespread intuitive appeal to non-scientists which chemical measures lack. Bioassays have become increasingly important due to advances in ecotoxicology. They assess chemical, cellular, or genetic changes within an organism. Biomarkers are a primary tool in this assessment. A biomarker is defined as 'measurements of body fluids, cells, or tissues that indicate in biochemical or cellular terms the presence of contaminants or the magnitude of the host response' (McCarthy and Shugart, 1990). Biomarkers can include anatomical and cytological abnormalities, adaptive biochemical and immunological responses, including detoxication systems, reproductive competence and genotoxicity, response of stress proteins and effects of pollutants on DNA. Due to a short response time, biomarkers can provide an accurate indication of the type and source of pollutant causing observed ecological degradation (McCarthy and Shugart, 1990). However, they require greater laboratory sophistication and do not have the same intuitive appeal to non-scientists as ambient biological indices. The most commonly used biological indicator organisms are benthic macroinvertebrates, fish and algae. All three have important advantages to offer and are especially useful for different types of contaminants. Benthic macroinvertebrates, small organisms that live in sediments and debris, are most commonly used for measuring and monitoring contamination from metals, pesticides and polychlorinated biphenyls. Ohio developed an Invertebrate Community Index in 1986, and other states as well as the Environmental Protection Agency (EPA) have
A.G. Keeler, D. McLemore / Ecological Economics 19 (1996) 237-245
developed multimetric indices based on benthic macroinvertebrates (Davis, 1995). Fish are popular biological indicator organisms due to the popular public opinion of water as a fishery resource. Because fish are mobile and have long lives, they are good indicators of integrated long-term effects. The Index of Biotic Integrity (Karr, 1981) incorporates fish species composition, trophic composition, abundance and condition into a single measure of biological integrity. This approach has been adapted for use in a number of geographical areas. Algae provide information about nutrient enrichment. Biomass indicators like dry weight and chlorophyll a, a common algae pigment, are commonly used in evaluation of algae response (Raschke, 1994). Algae are highly responsive to nutrient loadings due to rapid reproduction and short life cycles, showing an advantage for pinpointing episodic loadings. Since they are at the bottom of the food chain, algae responses are mostly attributed to physical and chemical changes (U.S. EPA, 1990a). These measures have had an increasing influence on federal and state water protection efforts. For the most part, water quality regulation has been characterized by limiting ambient pollutant concentrations and maximum end-of-pipe loadings. The chemical standards in regulations act as a surrogate for true underlying environmental quality but only represent potential harm. Starting in 1977, concerns were expressed that the Environmental Protection Agency's monitoring efforts were insufficient (U.S. EPA, 1990b). The EPA in 1987 reported that new monitoring efforts were needed to assess the effect of current policies, to support the nonpoint source (NPS) pollution control efforts, and to enhance research (U.S. EPA, 1990b). As a result, the Environmental Monitoring and Assessment Program (EMAP) was developed as a national long-term project for assessing the health of the nation's aquatic ecosystems and for publishing support documents to help states implement assessment and monitoring programs under Section 303(c)(2)(B) of the Water Quality Act of 1987 (U.S. EPA, 1990b). While the great majority of states now collect at least some biological monitoring data for water resource management, only Delaware, Florida, Maine and Ohio have incorporated biological data as criteria in pollution control
239
regulations. Other states, particularly North Carolina, Vermont and Connecticut, have incorporated biocriteria extensively into their water quality assessment and planning procedures (Southerland and James, 1995).
3. Information value of biological indicators Biological information in water quality assessment is important for many of the same reasons ecologically and economically. Chemical measures indicate the existence of a potential environmental stressor, but cannot indicate underlying damages to the ecosystem. To identify the affected state of the ecosystem, bioindicators integrate the effects of pollutants over spatial and temporal dimensions (McCarthy and Shugart, 1990). Studying biological indicators at different organism levels reveals effects of bioaccumulated and even quickly metabolized contaminants. Camargo, in a study comparing the results from physicochemical and biological surveys, demonstrated the importance of including biological monitoring in ecological risk assessment. Chemical and physical measurements in a water system receiving trout farm effluents revealed much less severe pollution effects than indicated by biological indicators (Camargo, 1994). Bioindicators are important for ecological risk assessment and water quality monitoring when several sources of degradation are present in a water body. Only a relatively small proportion of the interactions between different pollutants are understood. If multiple pollutants are present, biological indicators can be instrumental in identifying the pollutant causing environmental damage (McCarthy and Shugart, 1990). For example, a study of wastewater treatment controls using the index of biotic integrity showed that further abatement of ammonia emissions was not necessary for the improvement of environmental quality, but reduction of chlorine levels would cause significant improvement (Karr et al., 1985). By integrating the effects of anthropogenic and natural influences, the additional information provided by bioindicators gives a more refined measure of water quality than does chemical sampling alone.
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While chemical sampling only indicates potential effects of pollution on biological and ecological processes, biological indicators directly measure this effect. They are closely allied to our true underlying conception of environmental quality. A primary concern of the effects of NPS pollution is the damage done to biological integrity through sedimentation from erosion, eutrophication from nutrient loadings, and habitat degradation leading to increased temperatures. Relative to point sources, NPS polluters are difficult to regulate because polluters are hard to identify and effects of polluting activities vary greatly geographically. Runoff occurs during uncontrollable weather events. Therefore, due to the intertemporal nature and quick response systems of some indicators, the timing of episodic loadings can be determined. Sessile organisms help to identify polluters when a reference site such as an upstream position or a site in a characteristically similar stream is used for comparative purposes (U.S. EPA, 1990a). Since monitoring efforts are burdensome and expensive for measuring effluent from nonpoint sources, the dynamic nature of biomarkers offers a cost-effective advantage over chemical sampling alone (U.S. EPA, 1990a). Continuous or periodic biological monitoring will gauge the effectiveness of resource management policies allowing for revisions to be made on a timely basis if practices need to be updated. With better indicators of underlying environmental quality, negotiating groups will be able to focus more precisely on relevant issues for improved debate and reasoning. According to the U.S. EPA (1990a), 'implementing biological criteria in water quality standards provides a systematic, structured, and objective process for making decisions about compliance with water quality standards ... (A)nd increases the value of biological data in regulatory programs.'
4. Models for policy analysis The preceding section argued that adding biological indicators to chemical and physical data offers a
more accurate measure of underlying environmental quality. In the rest of the paper we model bioindicators in a simple information framework as providing a smaller variance around true environmental quality. In what follows we are abstracting from the problems of instrument choice and implementation that are extraordinarily difficult in water pollution control policy. Our intent is to show the importance of better biological information, and we believe this value will persist even with second-best instruments and imperfect implementation. We develop two models whose purpose it is to demonstrate how the use of bioindicators can improve the economics of pollution control regulation. These models are highly abstracted and stylized. They are useful to show the potential importance of these measures and how bioindicators offer different advantages in different pollution control paradigms.
4.1. Modeling bioindicators as information Let x be a physical measure of pollution (denominated in units such as parts per million). Assume that there is some underlying true level of environmental quality reflecting biological integrity. Quantifying such a measure is both conceptually and empirically difficult. Here we assume that it is possible to recognize and order different levels of environmental quality on a single scale. Let this underlying level be indexed by a continuous variable q. For notational convenience choose the values of the index variable q so that it corresponds with the measure of the pollution variable x. Let q be negatively indexed so that 0 corresponds to no ecosystem impairment, and higher levels of impairment correspond to higher levels of q. Let there be an imperfectly understood relationship between physical measures of pollution and environmental quality, and let q be indexed so that for any level of x, q = x + ~p, where ep ~ N(0, trp2). Note that the variable q is only an index; it has no particular units and its function is to mark some level of an ecosystem's ability to support biological diversity and ecological function. Since the level of an index is arbitrary, for the sake of analytical convenience it is defined as the level of expected environmental quality resulting
A.G. Keeler, D. McLemore / Ecological Economics 19 (1996) 237-245
from a given pollution level x in the absence of ecological information. 2 Now let there be a bioindicator b which provides additional information about the relationship between x and q. This information may result in improved understanding of environmental quality itself, or may provide more information about how the pollution control variable x affects quality. A reasonable way to think about the problem is that the bioindicator partially resolves the uncertainty parameterized by 00p2. The new information is not perfect there still exists an error term, but it is smaller than in the previous case. Let the mean of the new distribution shift by a random variable ~: for any given physical measure of pollution x the addition of bioindicators yields a new distribution q = x + 6 + e b, where b ~ N(0, o082) and ~ob N(0, O'b). The ex-ante (in the sense of before the information provided by the bioindicator) distribution of q around x should be the same in both cases: 6 + eb = ep, E[ 6 -r" ~'b] = E[,~p] = 0, and E[(6 + eb )2 ] = E[(oOp)2]. We assume that the information provided by the bioindicator is independent of the remaining uncertainty: E [ ( t S + e b ) 2 ] = E [ 6 2 + 2 6 e b+e~]=E[t5 2 + + + + e = 00g + 00~+= 00p-. The idea is that bioindicators provide information about the relationship of x and q such that the expected value of environmental quality for any x becomes x + ~. The value of ~ is known once bioindicators are collected and interpreted so the policy can be based on a new relationship between the pollution control variable and environmental -
-
~
z Note that we are specifying e as "measurement error'. There will also be true randomness in ecosystem outcomes having nothing to do with human activities, or at least with water pollution. We ignore this randomness. Note also that we are assuming a particular additive structure for the measurement error where many other possible structures are possible. This is overly simplistic for many reasons; in particular, we would certainly expect the size of the error term to depend on the pollution level. For example, we should be relatively sure of the effects of x at both very low and very high pollution levels, but much less certain between these extremes. Karr (1991) demonstrates that the ability of chlorine levels to predict a biological integrity index shows more variance at higher pollution levels. We feel that our simplifying assumption is justified given the demonstrative intent of our model, but for empirical work much closer attention to the form of this relationship will be necessary.
241
quality, with a smaller variance o-b. We can now proceed with the specification of the regulatory problem under the alternative scenarios advanced earlier - - cost-benefit regulation and meeting an exogenous target level of environmental quality.
4.2. Cost-benefit regulation Assume that there is a known function which relates pollution control costs to ambient loading levels in the environment. Let c(x) be the control cost function: c ' ( x ) < 0 and c"(x) > 0. Since our arguments can be most transparently demonstrated with a particular functional form, let c(x)= K - ax + bx 2 with 0 < x < a / 2 b defining the economically relevant range of possible ambient loading levels. 3 The cost/benefit approach calls for monetizing environmental damages q. Greater dollar damages can be expected to be associated with lower levels of environmental quality (corresponding to higher values of our index variable q). We assume that there is a known damage function D(q) that relates ecosystem quality to the economic value of that quality. Although the function D(q) is known, the value of q which results from policy choices is not known with certainty. We make the normal assumption that increasing levels of environmental degradation lead to increasing marginal damages: D'(q)> 0 and D"(q) > 0. We ignore risk aversion so that the regulatory objective is to minimize the expected sum of control costs and damages. Again, we assume a quadratic form for expositional clarity, D(q) = y + ~q + flq2. When only physical measures are available the regu-
This model bears some similarity to modelling by Weitzman (1974) of the choice of price or quantity regulation under uncertainty. It is substantially different in that it is the value of reducing the uncertainty that is being explicitly modelled, and instrument choice is not at issue. The choice of quadratic cost and damage functions obviously limits the generality of our results. Our aim with this model is to demonstrate how better information translates into better choices, and we believe these forms do a good job of allowing this to be transparently demonstrated. Adoption of more general functional forms gives us results similar to those of Weitzman (1974) and Kolstad (1987): the arguments will generally hold with enough assumptions on the higher-order derivatives. We believe little is added with this approach.
A.G. Keeler, D. McLemore/ Ecological Economics 19 (1996)237-245
242
latory problem is to minimize the expected sum of damages and costs min~p = E[ K - ax + bx 2 + y + a q + / 3 q 2 ]
a.%/ & =
2~x+2/36+2~E(eb)
a+
=0
x
(6) a - a - 2/36
= E[ K -
ax + bx2 + 3'+ a ( x + ep)
+/3( x + ep)2].
Xb = (1)
Substituting x + ~ for q and differentiating with respect to x gives us the first-order condition for cost minimization O.~p/Ox = - a + 2 b x + ~ + 2 / 3 x + 2/3E(ep) = 0
(2) O--13/
xp
-a+2bx+
2b+2fl"
(3)
Since the expectation of ep is 0, the optimal level of pollution xp collapses to the certainty equivalence case: the optimal way to set x is the same as if the relationship between x and q were known with certainty. In other words, Xp is the pollution level where marginal control costs are equal to expected marginal damages. This is an artifact of the linearity of the marginal cost and damage functions; other specifications would yield a different rule. What we wish to emphasize here is not the choice of control level, but the value to the objective function (that is, the total damage to the environmental services plus total control costs to producers). Total minimum expected costs are given by substituting x~ into ~p,
= K-
axp
2/36
2b+2/3
Xp
2b+2/3'
The rule for determining the optimal pollution level under the expected cost-plus-damage minimization criterion changes in response to the information provided by the bioindicator. Values of 8 > 0 indicate that pollution is more damaging to biological integrity than was supposed, and the optimal regulation is correspondingly smaller than Xp. Values of 8 < 0 indicate that the ecosystem is more tolerant of pollution than was previously understood, and the optimal regulation is larger than Xp. Again, we are less interested in the regulation itself than in the expected savings from improved ecological information. Although 5 is known when the policy is set, the fact that different realizations of have different consequences for -~b* requires evaluating the function's expected value across the pdf of E[.9"b* ] = E
K-a
xe
2b+2/3 "2
[
+ b x~
2b + 2/3
+y+a
-[- b ( x; )2 -[- 'y-[- OlXp
xp
+/3 x;
2b+Z~+a+~b
min.~ab = E[ K - ax + bx 2 + "y q- a q +/3q2]
=c(x; ) + d(x; ) + l
]
+ (8)
The expected gains from incorporating bioindicators are given by
t3 2 =/3~p2 _ b +----~'~2_ / 3 ~
x
=K-
[ /32
(4)
The addition of biomonitoring information yields a virtually identical problem, except the observation of allows the policy to be set with a smaller error term, e b,
1
2b+2/3 +6+eb
+/3(x;)2+/3e[4]=K-ax; +b(x;) 2 + y + aXp + / 3 ( X p ) 2 +/3o'p2.
(7)
a x + bx 2 + E [ y + a ( x + 6 + eb)
+/3(x + a+ ~)2]
(5)
bfl
- b
bfl
= +b - - 7
[.p2 - . b : ] .
(9)
A.G. Keeler, D. McLemore/ Ecological Economics 19 (1996)237-245
Eq. (9) shows that the value of additional information depends directly on how much of the uncertainty is resolved: the more information about true environmental quality and its relationship to pollution that bioindicators reveal, the higher the expected savings. In addition, savings depend on the shapes of the marginal damage and control cost functions. When small changes in biological integrity have a large effect on the marginal change in damages, bioindicators have a higher potential to increase regulatory efficiency. When marginal damages are relatively invariant to environmental quality, bioindicators will produce smaller savings. The same is true of the control cost function: when marginal control costs are steep, the information provided about damages by bioindicators becomes more important in regulatory considerations of efficiency. A flatter marginal control cost function decreases the importance of better ecological information. Note that Eq. (9) can also be interpreted as the maximum amount that it would be worth to conduct the research that would reduce the uncertainty about the effect of pollution from ~rp2 to cry. 4.3. Regulation with an exogenous environmental quality target
An exogenous environmental target is consistent with the safe minimum standard (SMS) approach developed by Bishop (1978, 1993), which results in a presumption that biodiversity must be protected unless it imposes intolerably high costs on the economic system. We use the SMS here in a slightly different way that we believe is consistent with the spirit of Bishop's definition. The idea is that there is some underlying level of environmental quality that maintains an ecosystem's biodiversity and resiliency. There may be threshold effects and non-linearities such that exceeding some level of pollution results in a discontinuously large change in ecosystem health. Since there will always be some uncertainty about the effects of economic activity on environmental outcomes, implementation of an SMS requires meeting an exogenous environmental goal with some high level of confidence. In practice regulations rarely result from a straightforward cost-benefit analysis. In general, water quality goals have been loosely based on maintaining the integrity of the nation's waters and on
243
meeting operational definitions of 'fishable and swimmable' (Freeman, 1990). EPA's proposed watershed protection approach calls for involving those most concerned with watershed quality in a process of problem identification and negotiation which results in corrective actions (U.S. EPA, 1993a; U.S. EPA, 1993b). This process can be expected to produce some set of environmental targets not necessarily identical to those which would result from the equalization of marginal costs and benefits. Our definition of policy with an SMS is also consistent with the idea that environmental quality is determined by a scientific, bureaucratic, and political process and the regulatory task is to implement the decision of that process with a reasonable degree of certainty. While this is not a precise description of current policy formation, neither is the cost-benefit approach more frequently adopted in economic analysis. Formally, we take the SMS to be some level of environmental quality that is exogenously determined and that must be met with some (exogenously determined) high degree of certainty. Let the exogenous standard of environmental quality be q * and the required probability of meeting that level be 7r. The regulatory problem is to minimize control costs subject to meeting the requirements of the SMS min c( x) s.t. pr( x + % < q* ) > Tr.
(10)
X
Let z~ be the critical value of the standardized normal distribution associated with the required confidence level w. The strategy to meet q * with a level of certainty "rr simply consists of choosing x such that q* - x - -
(11)
%
and the chosen level of pollution is given by xp=q* -z~%.
(12)
With a bioindicator meeting the assumed characteristics, the pollution level can be set after part of the uncertainty about the relationship of pollution and true environmental quality is resolved and ~ is observed. The problem becomes m i n c ( x ) s.t. p r ( x + 6 + e b < q * ) > ~ r
(13)
.r
and the solution is xb=q*-6-z~Cr
b.
(14)
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A.G. Keeler, D. McLemore/ Ecological Economics 19 (1996) 237-245
Actual pollution levels and control costs with bioindicators may be higher or lower than without, depending on the particular realization of 8. However, on average allowed pollution will be higher E[xb -xp]
= (o-p -
= (o-p -
however, that to the extent that this model reflects the way policy will be made with exogenous environmental targets, better biological information will have a higher payoff in the SMS case than in the cost-benefit case.
(15)
The expected savings from adding bioindicators is given by
=b(z~-l)
(O'p2 -- O-2 )
- z~( - a + 2bq* )(O'p - o'b).
(16)
The first half of the above expression reflects the interaction of the required certainty level with the variance of the environmental quality measure and the slope of the control cost function, This term will be positive whenever the required confidence level is higher than 85% so that z~ > 1. As higher degrees of certainty in meeting an environmental quality target are required, the economic gains of improving the precision of environmental quality measures becomes larger. Expected gains are also increasing in the part of the uncertainty that is resolved by adding bioindicators. The second term will always be positive - - it consists of the product of the negative of marginal control costs at the mean of the original distribution around q *, the reduction in the standard deviation provided by bioindicators, and the required level of certainty. Again, the more information provided by bioindicators and the higher the required confidence level of meeting q *, the larger the expected savings that result. As before, Eq. (16) can also be interpreted as the amount that we should be willing to pay for biological information that reduces uncertainty from trp2 to ~r~. In the cost-benefit model, expected savings depended on the levels of marginal cost of the control cost and damage functions. The savings resulted from reductions in expected deadweight efficiency loss. In this model, the savings closely approximate total savings between the 'without' and 'with' levels of allowed pollution. This is because the SMS model recognizes no gain from levels of environmental quality better than q * which will probably result when policy is set without bioindicators. It means,
5. Conclusion We have argued that the strength of bioindicators is that they are one step closer to the ecological processes which comprise 'environmental quality' than are conventional pollutant measures. One way to view the results of the models presented here is as the value of developing the scientific knowledge which relates bioindicators to biological integrity and to specific sources of environmental degradation. If reducing the variance of our measures of environmental quality produces more efficient outcomes, then the size of the efficiency improvements should provide one measure of the maximum worth of developing the ecological knowledge to relate bioindicators to pollution control policies. Our results here indicate that such knowledge can be expected to improve the efficiency of pollution control policy whether a cost-benefit or SMS approach to policy is followed. However, the value of the information produced by learning more about bioindicators and how to use them will ordinarily be higher in the SMS case. The obvious next step is to conduct empirical studies linking multimetric index values to levels of environmental controls and their corresponding economic costs. In addition, bioindicators can prove valuable in determining which of several potential pollutants is causing observed adverse environmental quality effects. Formal modeling of the value of biological information in this context would also be valuable.
Acknowledgements The authors are grateful to Jeff Dorfman, James Karr and an anonymous reviewer for helpful comments on an earlier version of this work.
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