The travel cost method looks at how far people are willing to travel to enjoy an environmental nonmarket good such as a beach, lake, river, or wilderness area. Travel is costly in terms of time, fuel, and other expenses. We expect people who live closer to a pristine lake to visit it more often, on average, than people who live far away, just as we expect people to buy more pizza when the price is low than when the price is high. So, if we can estimate a demand curve for pizza using its price, then we can also estimate a demand curve for a pristine lake using the travel costs as a proxy for its price (where distance from the site means differences in travel cost). That is exactly what economists have been doing since the 1960s.
To explain how the travel cost method works, a simple illustration probably works best. In the upper portion of figure 14.2, we designate three rings or zones for people who live at different distances from the lake (five, ten, and fifteen miles). We conduct
In addition to estimating benefits from environmental improvements, economic analysis can often help policymakers estimate the costs. In the case of reducing air pollution, for example, policymakers face great uncertainty about the costs of air quality improvements and how those costs may differ for a wide range of possible actions under consideration. After they combine technical and economic data, it is possible for economists to estimate the marginal cost and changes in emissions for individual actions such as imposing emissions standards, retrofitting high-use vehicles such as busses and taxis, inspections for enforcement, fuel improvements, or fuel taxes.
In one study of this kind for Mexico City (Eskeland and Devarajan 1996), such estimates made it possible to construct a marginal cost or supply curve for emissions reductions. This was done by estimating the emissions reductions and marginal cost for a range of actions and then ordering them from lowest to highest cost. The results of that analysis produced a marginal cost or supply curve that looks something like the one below.
Air Pollution in Mexico City: Assessing the Cost of Cleaner Air
■ FIGURE Marginal abatement cost for air pollution reduction in Mexico City
Notably, the authors found that liquefied petroleum gas (LPG) and natural gas retrofitting had a negative cost because it was both cheaper and cleaner. Both emissions standards and inspections had lower marginal costs when applied first to high-use vehicles. And when a gas tax was omitted from the package of interventions, the MC curve was shifted higher as indicated by the dotted line above. The study demonstrates the usefulness of estimating costs of alternative environmental policies and also highlights how indirect policies, both CAC and economic incentives, can be complementary parts of an overall policy strategy.
Source: Gunnar Eskeland and Shantayanan Devarajan, Taxing Bads by Taxing Goods: Pollution Control with Presumptive Charges (Washington, DC: World Bank, 1996).
surveys of visitors to a lake to see where they come from and how often they visit. From a sample of visitors, we can estimate how many people come to the lake from each zone and compare that to the population of people living in each zone (we also may want to collect other socioeconomic information about visitors that may affect visitation rates). With this data, we can estimate the visits per year per one thousand residents from each zone.
Next, we estimate the travel cost, taking account of the method, cost, and time taken to travel. Putting a value on people's travel time is a bit tricky, and there are different opinions about how to put a price on the "opportunity cost of time" for people when they travel for pleasure. Some people may actually enjoy the travel as part of the experience.
Having estimated these travel costs, we can compare the three zones by graphing the visitation rates against the travel cost as in the bottom portion of figure 14.2. What we find—not surprisingly—is that demand is lower when the travel cost is higher. These points give us a sense of what the demand curve looks like.
Once we have estimated the demand curve, we can evaluate the net benefit, or consumer surplus, for the lake. We apply this demand curve to the population that lives at each distance from the lake: some people who live there will never visit the lake, while some may go only once because they are indifferent to the
Panel A: Map of residential locations relative to a lake
Panel A: Map of residential locations relative to a lake
■ FIGURE 14.2 Illustration of the travel cost method for lake recreation benefits from the visit and the costs (of travel). And some will visit frequently because the net benefits far exceed the costs. For distance B, for example, net benefit is the area of the shaded triangle representing the consumer surplus for people paying that "price." At greater distances, the consumer surplus will be small; for populations living near the lake, consumer surplus will be greater.
A large number of travel cost studies have been conducted, looking at things like beach use, sport fisheries, and hunting opportunities. These studies have produced estimates of the average net benefit or consumer surplus as high as $4,000 per visitor per year. This method provides concrete evidence of the value of natural resources and recreation sites, measured in a way that makes comparisons possible with other market goods and services.
What these kinds of studies can also do is to show how the whole demand curve for a particular kind of nonmarket good is shifted up or down depending on quality. The demand curve for a "dirty" lake is shifted down and to the left compared to a pristine lake (after adjusting for the population densities and other differences between the two locations). By estimating and then comparing these two demand or willingness-to-pay (WTP) curves, we can actually estimate the value to society of cleaning up a polluted lake. The way this is done is illustrated in figure 14.3. The WTP curves for a clean and dirty lake are superimposed on the same graph (adjusting the information based on the location, demographics, and other factors). The present condition is dirty (lower WTP curve), and a clean lake is estimated to produce a WTP curve that is shifted up and to the right. The benefit to society of this change is equal to the area of the trapezoid between the two curves. Given the numbers in the graph, we can calculate the benefits per person as 10 x 10 + 1 (10 x 5) = $125/person. Multiplying that figure by the population produces an estimate of the value of cleaning up the lake, which can then be compared with the cost.
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