Early work on environmental valuation estimated the benefits of improved environmental quality using direct methods that exploit easily obtained information about the monetary damage costs of pollution. These methods are still sometimes used (most often by people who are not environmental economists) because of their simple intuitive appeal. An analyst can measure costs associated with pollution; the benefits of environmental cleanup are then the reductions in those costs. Following are some examples:
- Production damage measures: Pollution has a deleterious effect on many production processes. For example, air pollution lowers corn yields, thus increasing the cost of producing a bushel of corn. An analyst could try to measure the benefits of eliminating air pollution by calculating the increase in net social benefits that would flow from the corn market as a result of higher yields.
- Avoided cost measures: Environmental degradation often forces people to spend money on efforts to mitigate the harm caused by that degradation. One benefit of reversing the degradation is not having to spend that money on mitigation—the avoided cost. For example, hydrological disruption from impervious surfaces in urban areas forces cities to spend money on expensive storm sewer infrastructure to try to reduce floods. A benefit of installing rain gardens and green roofs to manage stormwater might be avoided storm sewer infrastructure costs.
- Health cost measures: Pollution has adverse effects on human health. For example, toxic chemicals can cause cancer, and ground level ozone causes asthma. Some measures of the damages caused by pollution simply count the financial costs of such illnesses, including the costs of cancer treatment and lost wages from adults missing work during asthma attacks.
These measures seem appealing, but are in fact deeply problematic. One of the most serious problems with direct measures is that they often yield woefully incomplete estimates of the benefits of environmental cleanup. Consider the example of cancer above. Suppose a woman gets cancer from drinking contaminated well water. By the time her illness is diagnosed, the cancer is so advanced that doctors can do little to treat her, and she dies a few months later. The medical expenditures associated with this illness are not very large; she and her family would surely have been willing to pay much more money to have eliminated the toxins so she did not ever get sick. The direct health cost measure of the benefits of cleaning up the contaminated water is a serious underestimate of the true benefit to society of that environmental improvement.
A second set of valuation tools called revealed preference methods work to estimate WTP for environmental amenities and quality by exploiting data on actual behaviors and market choices that are related to the environmental good in question. People reveal their WTP for environmental goods with their actions. Three examples of such methods are below.
- Hedonic price analysis: We often cannot observe individuals taking direct action to change the quality of the environment to which they are exposed in a given location because they simply cannot effect such change; no one person, for example, can reduce the concentration of fine particles in the air near his house. We do, however, observe market data about the choices people make about where to live. If two houses are otherwise identical but one house is situated in a place with much cleaner air than the other, the benefit of breathing cleaner air will get capitalized in the value of that house. All else equal, neighborhoods with better environments will have more expensive homes. Analysts can gather data on housing prices and house characteristics (both environmental and nonenvironmental) and use a statistical analysis to estimate marginal WTP for elements of environmental quality that vary among the houses in the data set. The hedonic price analysis approach has been used to value amenities such as air quality, hazardous waste site cleanup, and open space.
- Hedonic wage analysis: Some forms of pollution cause people to face higher risk of death in any given year. Thus, one important goal of valuation is to estimate a dollar value of reduced mortality resulting from pollution cleanup. Except in the movies, we rarely observe people choosing how much money they are willing to pay to save a specific person from certain death. However, all of us make choices every day that affect our risk of death. One important choice is which job to accept. Elementary school teachers face little job-related mortality risk. In contrast, coal miners, offshore oil rig workers, and deep sea fishermen accept high rates of accidental death when they take their jobs. By analyzing data on wage rates and worker death rates in a variety of different industries, we can estimate WTP to reduce the risk of death. Using such hedonic wage analysis, economists have developed measures of the value of a statistical life (VSL), which can be applied to physical estimates of reductions in pollution-related deaths to find the benefits of reduced mortality.
- Travel cost analysis: Many natural amenities, such as forests, lakes, and parks are enjoyed by the public free of charge. While there is no formal market for “hours of quality outdoor recreation,” people do incur costs associated with such recreation—gas purchased to drive to the site, hotel expenses for overnight trips, and the opportunity cost of the time spent on the trip. If environmental quality is valued, people will be willing to pay higher travel costs to visit recreation sites with higher levels of environmental quality (e.g., cleaner water in the lake, more fish to catch, a better view from a mountain with low air pollution). In travel cost analysis, researchers gather data on the environmental features of a set of recreation sites and the choices people make about visiting those sites—which they choose to visit, and how often—and apply statistical analysis to those data to estimate WTP for improved quality of natural amenities.
One of the greatest strengths of revealed preference valuation methods is that they use information about real behavior rather than hypothetical choices. These approaches also yield estimates of WTP that are often more complete than the results of direct market measure studies.
Revealed preference studies do, however, have weaknesses and limitations. First, they only give good estimates of WTP for environmental goods if people have full and accurate information about environmental quality and associated risks. For example, hedonic estimates of WTP to avoid living with polluted air will be biased downward if people in a city do not know how air pollution varies among neighborhoods. Second, some revealed preference approaches are only valid if the relevant markets (labor markets for a wage study, housing markets for a hedonic price study) are not plagued by market power and transaction costs that prevent efficient equilibria from being reached. For example, if workers find it too daunting and costly to move from one region to another, then coal miners may fail to earn the wage premium that would be associated with such a risky job in the absence of relocation hurdles. Third, revealed preference approaches cannot be used to estimate values for levels of environmental quality that are not observed in real-world data. If all the lakes in a region are terribly polluted, we cannot use a travel cost study of lake site choice to identify WTP for very clean lakes. Fourth, revealed preference methods can capture only use values, not non-use values.
The limitations of revealed preference valuation tools motivated environmental and natural resource economists to develop valuation methods that do not require analysts to be able to observe real-world behavior related to the amenity being valued. These stated preference methods are now highly refined, but the essential idea is simple. These studies design a survey that presents people with information about hypothetical scenarios involving an environmental good, gather data on their responses to questions about how much they would pay for something or whether they would choose one scenario over another, and then analyze the data to estimate WTP for the good or WTA compensation for elimination or degradation of the good.
- Contingent valuation: The methodology called contingent valuation (or CV) gained prominent attention when it was used by economists to estimate the damage done to society by the oil spilled by Exxon’s Valdez oil tanker in Prince William Sound in 1989 (Carson et al., 2003). A CV survey gives a clear description of a single environmental amenity to be valued, such as a wetland restoration, whale populations, or improved water quality in a local lake. The description includes details about how the amenity would be created, and how the survey respondent would pay any money they claim to be willing to pay in support of the amenity. Respondents are then asked a question to elicit their WTP. This value elicitation question can be open ended (“How much would you be willing to pay in taxes to increase whale populations?) or closed ended (“Would you be willing to pay $30 to increase whale populations?). The resulting data set is analyzed to find the average WTP of people in the sample population.
- Conjoint analysis: Conjoint analysis is also referred to as choice experiment survey analysis. It was developed first by analysts in business marketing and psychology, and only later adopted by economists for environmental valuation. The main difference between conjoint analysis and CV is that CV elicits WTP for an environmental amenity with a single fixed bundle of features, or attributes. Conjoint analysis estimates separate values for each of a set of attributes of a composite environmental amenity. For example, grasslands can vary in bird species diversity, wildflower coverage, and distance from human population centers. A conjoint analysis of grassland ecosystems would construct a set of hypothetical grasslands with varied combinations of attributes (including the cost to the respondent of a chosen grassland). The survey would present respondents with several choice questions; in each choice, the respondent would be asked to pick which of several hypothetical grasslands they would prefer. The resulting data would be analyzed to find how each attribute affects the likelihood that one grassland is preferred over another. This would yield estimates of marginal values for each attribute; those values could then be used to find WTP for composite grasslands with many different combinations of features.
Both CV and conjoint analysis methods can be designed to estimate WTP for improvement or WTA degradation depending on which context is most appropriate for the problem at hand. These stated preference methods have two main strengths. First, they can capture non-use values. Second, their hypothetical nature allows analysts to estimate WTP for improvements out of the range of current experience (or WTA for degradation we have fortunately not yet experienced).
However, stated preferences approaches do have weaknesses and limitations. For example, many economists are uncomfortable using value estimates derived from hypothetical choices, worrying whether consumers would make the same choices regarding payment for public environmental goods if the payments were real. Scholars also worry about whether people give responses to stated preference surveys that are deliberately skewed from their true WTP. Understatements of value could arise to protest a government policy (“Why should I have to pay to clean up the environment when someone else made it dirty in the first place?”) or out of a desire to free ride. Finally, the hypothetical nature of stated preference surveys can mean that some respondents are not familiar with the thing being valued, and thus may have trouble giving meaningful responses to the questions. Stated preference surveys must be designed to give respondents enough information without biasing their responses.
"An interesting piece to start conversations about sustainability. "