protecting watershed ecosystems through targeted local land use policies

PROTECTING WATERSHED ECOSYSTEMS THROUGH

TARGETED LOCAL LAND USE POLICIES

CHRISTIAN LANGPAP, IVAN HASCIC, AND JUNJIE WU

Land use change is the most pervasive force driving the degradation of watershed ecosystems. Thisarticle combines an econometric model of land use choice with models of watershed health indicatorsto examine the effects of land use policies on watershed ecosystems through their effect on land use.Our results suggest that incentive-based land use policies and property acquisition programs can haverelatively large positive impacts on watershed health, while policies that change the returns to landuse are less effective. The results suggest that there is potential for targeting these policies becausetheir impacts vary across watersheds with different land use mixes.

Key words: land use, targeted policy, water pollution, water quality, watershed ecosystems.

Land use change is arguably the most perva-sive socioeconomic force driving the changeand degradation of watershed ecosystems. Thetransition from relatively undisturbed naturalhabitat to agriculture or urban developmentcan have widespread implications for water-shed ecosystems (see Hascic and Wu 2006 fora review of the literature). Agriculture is oneof the leading causes of impairment of surfacewaters (Baker 1992). The runoff generatedby agricultural practices increases nutrientloadings, leading to increased algae growth(Corbett et al. 1997) and dissolved oxygen fluc-tuations (Ryszkowski 2002). Urban develop-ment and the resulting conversion from per-vious to impervious surfaces cause increasedpollutant loads and surface runoff of toxic con-taminants, nutrients, and sediment, as well aslarger variances in stream flow and tempera-tures (Tang et al. 2005).

Land use decisions affecting watersheds de-pend in part on local land use regulations,which can affect land use in complex ways.

Christian Langpap is assistant professor and JunJie Wu is Emery N.Castle Professor of Resource and Rural Economics, Department ofAgricultural and Resource Economics, Oregon State University.Ivan Hascic is an economist at the OECD Environment Direc-torate in Paris, France. When part of this research was conducted,Hascic was a doctoral candidate in the Department of Agriculturaland Resource Economics at Oregon State University.

The authors would like to thank Ruben Lubowski, AndrewPlantinga, and Robert Stavins for providing some of the data onland use returns used in this article. They would also like to thankthree anonymous referees, Stephen Swallow, and participants atthe 2006 AAEA Annual Meetings, the 2007 AERE Summer Work-shop, and the 2007 ERS-Farm Foundation Workshop on “New De-velopments in U.S. Land Use Data Collection and Analysis: Im-plications for Agriculture and Rural Land” for helpful commentson previous versions of this article. All remaining errors are theirown.

Agricultural preservation programs, designa-tion of critical areas, and density restric-tions have been shown to delay development,whereas a clustering policy has been shown toindirectly hasten development by creating anopen space externality (Irwin and Bockstael2002, 2004). Other policies, such as develop-ment impact fees and conservation payments,may affect land use through their impacts onthe net returns to land use. To assess the im-pacts of these policies it is necessary to under-stand both the economic processes driving landuse choices and the ecological processes thatdetermine their effect on ecosystems. It is alsoimportant to use micro-level data to achieveconsistency with the economic theory under-lying land use choice models and to capturethe spatial variability in economic and envi-ronmental variables (Wu and Segerson 1995;Antle and Capalbo 2001; Wu et al. 2004).

In this article we take a significant step in thisdirection by combining an economic model ofland use choice with models linking land usewith water quality and aquatic life abundance.This allows us to examine the potential impactof land use policies on watershed ecosystemsthrough their effect on land use decisions. Westart by estimating an econometric model toanalyze the determinants of land use choice,including the returns to different land usesand local land use regulations. We then esti-mate econometric models for three measuresof watershed health: conventional water pol-lution, toxic water pollution, and the numberof aquatic species at risk. Finally, we combinethe results of these econometric models to as-sess the potential effects of different land use

Amer. J. Agr. Econ. 90(3) (August 2008): 684–700Copyright 2008 American Agricultural Economics Association

DOI: 10.1111/j.1467-8276.2008.01145.x

Langpap, Hascic, and Wu Protecting Watershed Ecosystems 685

policies on watershed ecosystems through pre-dicted land use choices. We take into account atemporal component by considering land usechoices in 1982, 1987, 1992, and 1997, and spa-tial heterogeneity by conducting the analysison land plots and watersheds in four states(California, Oregon, Washington, and Idaho).

This analysis builds on the existing litera-ture on land use and water quality. Specifically,our general approach follows that in Wu et al.(2004), but the environmental outcomes weexamine are based on models estimated withobserved data rather than on simulations. Weuse the local land use regulation indices devel-oped in Cho, Wu, and Boggess (2003), but wemodel the choice between five different landuses rather than focusing only on urban de-velopment. Furthermore, although there is alarge body of literature that examines land usepolicy at both the local scale (e.g., Irwin andBockstael 2002, 2004) and the national or re-gional levels (Stavins and Jaffe 1990; Schatzki2003; Wu and Cho 2007), these studies do notexamine how land use choices induced by poli-cies affect watershed health. There are alsostudies that examine the land use-water qual-ity linkage. Malmqvist and Rundle (2002) findthat stream biodiversity has been significantlyinfluenced by past land use. Hascic and Wu(2006) find that water pollution and the num-ber of endangered aquatic species are affectedby the amounts of land allocated to agricul-ture, development, transportation, and mining.These studies, however, do not estimate landuse choice or examine the effect of land useregulations. Finally, a number of studies haveexamined the effect of land use policies on landuse and the subsequent impact on water qual-ity (see Wu et al. 2004 for a review). Mostof these studies, however, are undertaken atthe farm or watershed level; only a few haveanalyzed micro-level decisions and landscapechanges and the resulting impacts on waterquality (Antle and Capalbo 2001; Wu et al.2004), and none has examined how the effectsof land use policies depend on the predomi-nant land use in a watershed or used multi-ple measures of watershed health. The maincontributions of this article are to use linkedeconometric models of land use and watershedhealth to analyze the possible impacts of localland use policies, focusing on the different ef-fects these policies can have in watersheds withdifferent land use mixes and examining the po-tential for targeting such policies.

Our results suggest that local incentive-based land use policies, such as preferential

property taxation for farm and forest land,agricultural districts, and density bonuses forsensitive design can have a relatively large pos-itive impact on water quality in watershedswith the highest percentages of urban land.Property acquisition policies, such as purchaseand transfer of development rights, are asso-ciated with less toxic water pollution in wa-tersheds with the lowest percentages of urban,agricultural, and forest land. Hence, it may bebeneficial to target implementation of theseland use policies according to the predominantland use in a watershed. However, becauseland use choices are insensitive to changes intheir relative returns, the water quality indi-cators are less responsive to policies that at-tempt to change the returns to different landuses, such as development impact fees, refor-estation payments, or agricultural subsidies inany type of watershed.

These results should be of interest to landuse planners, who will gain insight into howpolicies currently in use or under considera-tion may affect land use choices. Additionally,they may gain a better understanding of landuse patterns by identifying other determinantsof land use choices, such as location or the re-turns from different land uses. Furthermore,in recent decades ecological objectives havebecome important goals for land use planningin addition to traditional goals like preservingfarm or forest land. For instance, state-wideland use policies in Oregon and Washingtoncontain specific objectives for protecting waterresources and wildlife habitat, and improvingwater quality and conserving wildlife habitathave been some of the most important landuse policy concerns in many counties in theseand other western states. Hence, informationabout how regulatory tools could affect eco-logical objectives will be valuable to local landuse planners and policy makers.

Land Use Model

The theoretical framework for the land usechoice model is based on the land use deci-sion for an individual parcel. We assume thata landowner chooses a land use on the basis ofthe net returns to the various land uses, his ex-pectation regarding the future growth of thosereturns, and the uncertainty surrounding re-turns (Capozza and Helsley 1990; Capozza andLi 1994; Schatzki 2003), given local land useregulations. Let i index parcels, k = a (agri-culture), r (rangeland), f (forest), u (urban),

686 August 2008 Amer. J. Agr. Econ.

o (other) index land uses, and t = 1982, 1987,1992, 1997 index years. Additionally, let �ikt bethe annual net return to land use k on parcel i inyear t, let �G

ikt be the expected growth of returnsand �VG

ikt its variance, and let Zit be a vector oflocal land use regulations. Let Vikt (�ikt, �G

ikt,�VG

ikt | Zit) be the present value of expected netreturns to land use k on parcel i in year t, condi-tional on local land use regulations. Then landuse k is chosen on parcel i in year t if Vikt ≥Vilt for all l �= k, that is, if it yields the highestvalue.1

To move to a model that can be estimatedwith available data we make some transfor-mations. Data on net returns to land use donot exist at the parcel level. However, dataare available to estimate the average net re-turns to land use at the county level (�ikt). Wealso have data on land characteristics at in-dividual parcels and attributes of the countyin which they are located (Xikt). We assume�ikt is a function of �ikt and Xikt, and decom-pose Vikt into two parts: Vikt = �′

kvikt + εikt,where v′

ikt = (�ikt, �Gikt, �V G

ikt , Xikt, Zi t ), �k is avector of parameters to be estimated, and εiktis a random error.2 The choice rule can nowbe rewritten in probabilistic terms. The prob-ability that land use k is chosen on parceli in year t is Probikt ≥ Prob (�′

kvikt + εikt =�′

lvilt + εilt , ∀l �= k). If the errors have an IIDGumbel distribution, this probability can bespecified as a multinomial logit model (Mad-dala 1983):

Probikt = e�′kvikt

∑i=u,a,n, f,o e�′

i vilt, k = a, r, f, u, o.

(1)

The multinomial logit model has beenwidely used in economic analysis. In agricul-ture it has been used, for example, to modelfarmers’ land allocation decisions (Wu et al.2004) or the choice of crop management prac-tices (Wu and Babcock 1998).

1 We lack data that would allow us to control for land conversioncosts, but these should be captured partially through choice-specific(i.e., land use-specific) intercepts, which capture initial land use,and the land-quality measure.

2 Combining parcel-level and county-level data may lessen someof the advantages of an entirely micro-level study by limiting theaccuracy of the data to that of the coarser county-level information(Bell and Irwin 2002). In addition, we are unable to account forspatial externalities such as neighborhood effects (Irwin and Bock-stael 2002) because NRIs do not specify the locations of individualparcels. Nevertheless, our analysis provides a starting point for fur-ther modeling of the effect of local land use policies on watershedsat a finer scale.

Watershed Health Indicator Models

We use three indicators constructed by the U.S.Environmental Protection Agency (EPA) todescribe the health of watershed ecosystemsacross the four states in our study area.3 Theconventional water quality indicator is basedon water samples taken in a watershed overa period of nine years (1990–1998). It mea-sures the total number of samples over thenine year period that have concentrations ofone or more of four conventional water qual-ity measures (phosphorus, ammonia, dissolvedoxygen, pH) exceeding national reference lev-els. The concentrations of phosphorus and am-monia and the level of dissolved oxygen andpH are important indicators of water quality.High concentrations of phosphorous and am-monia are associated with excessive eutroph-ication. Acidification can disrupt the nitrogencycle and may lead to decreased diversity ofaquatic species (Vitousek et al. 1997). Exces-sive eutrophication and water pollution havebeen linked to agricultural land and chemicaluses, urban runoff, and hydrological character-istics.

The toxic water quality indicator is also basedon samples taken in a watershed over the pe-riod 1990–1998. It measures the total numberof samples over that period with concentra-tions of one or more of four toxic pollutants(copper, nickel, zinc, chromium) exceeding na-tional chronic levels. Contamination of waterbodies by heavy metals is a major concern dueto their sedimentation, persistence, and bioac-cumulation potential. Elevated concentrationsof toxic substances affect wildlife in a num-ber of ways, including changes in physiology,behavior, and reproduction (Handy and Eddy1990). The major sources of metallic pollutionof water bodies include urban land use, inten-sive agriculture, and mining (Malmqvist andRundle 2002).

The species-at-risk indicator measures thenumber of aquatic species at risk of extinctionper land area in a watershed in 1996. Amphib-ians are regarded as important indicators of en-vironmental health due to their susceptibilityto perturbations in the environment (Blausteinand Johnson 2003). Fish are considered usefulindicators of ecosystem health since they re-spond predictably to changes in factors suchas habitat and water quality (Davis and Simon

3 Watersheds are defined by the 8-digit hydrologic units fromthe set of watersheds in the Hydrologic Unit Classification Systemdeveloped by the U.S. Geological Service.


1995). Major factors affecting species compo-sition and population abundance in aquaticecosystems include habitat alterations (Faurieet al. 2001), peak flow, water temperature, andnutrient levels.

To specify econometric models for our in-dicators of watershed health we note thatthe number of samples violating water qual-ity standards and the number of endangeredspecies are event counts, which are usually es-timated using Poisson and negative binomialmodels (Maddala 1983; Cameron and Trivedi1998). An event count is a realization of anonnegative integer-valued random variable y,and the Poisson model is derived by assumingthat y is Poisson-distributed with the condi-tional density of y given by f (y | x) = e−� �y/y!,with � = E[y | x] and ln� = x′�, where x is avector of regressors and � is a parameter vec-tor. There are two potential problems with thePoisson regression model. First, it assumes thatthe sample size is constant, but sample sizes of-ten change in cross-sectional data (in our case,the number of samples taken to measure waterquality change across watersheds). To addressthis problem, Maddala (1983) suggests an al-ternative specification: let N be the total sam-ple corresponding to y so that the rate of occur-rence is y/N, and re-parameterize the Poissonmodel as ln � = ln N + x′�. Second, the Poissonspecification imposes a restriction by assumingthat E[y | x] = V[y | x] = �. The standard wayto relax this restriction is the NB2 model sug-gested by Cameron and Trivedi (1998), whoderive this negative binomial model from aPoisson-gamma mixture distribution.4 Hence,the models for the watershed health indicatorsare specified as

ln(CONVWQi) = ln N ci + �0 + b′

1lci

+ �′2 pc

i + εci

(2)

ln(TOXICWQi) = ln N ti + �0 + �′

1l ti

+ �′2 pt

i + εti

(3)

ln(SPERISKi) = �0 + �′1ls

i + �′2 ps

i + εsi(4)

where i indexes watersheds; Nci and Nt

i are thenumber of samples taken to measure conven-

4 In addition to y being conditionally Poisson-distributed, � isassumed to be the product of a deterministic term and a randomterm: � = ex′�+ε = ex′� eε = ��. By assuming a gamma distributionfor � (mean 1, variance ), the marginal distribution of y is thenegative binomial with the first two moments E[y | �, ] = � andV[y | �, ] = � + �2.

tional and toxic water quality, respectively; lci ,

lti , and ls

i are vectors of land use variables in-teracted with vectors of spatial dummies; pc

i ,pt

i , and psi are vectors of physical characteris-

tics of a watershed; and εci , εt

i, and εsi are er-

ror terms, with exp(εi) following the gammadistribution. Since the number of species ineach watershed is unknown, ln N is not presentin equation (4). However, the differences inspecies diversity are partially accounted for bythe spatial dummies representing the ecologi-cal conditions across the study region.

Data

The data on land use are from the 1982, 1987,1992, and 1997 Natural Resources Inventories(NRI)5 (U.S. Dept. of Agriculture 2001). TheNRI uses eleven broad land use categories,which we collapse into five: agriculture, range,forest, urban and built up, and other. The to-tal number of parcels in our sample is ap-proximately 128,680. The sample includes datafrom thirty-five counties in California, thirty inOregon, thirty-five in Washington, and twenty-seven in Idaho. By using the NRI data, we candetermine land use at each NRI site in eachyear. NRI also assigns a weight (called xfac-tor) to each site that reflects the acreage itrepresents. We use pooled data for 1982, 1987,1992, and 1997 to estimate the land use model,with “other” as the base land use. We controlfor federal land by including a variable thatmeasures the percentage of federal land in thecounty where a parcel is located.

The NRI data include the Land CapabilityClass (LCC), a land quality index of suitabilityfor agriculture, for each parcel. It ranges from1 (best quality) to 8 (worst quality). We incor-porate it into our model by defining a high-quality-land dummy, which is equal to 1 if a siteis in LCC categories 1 or 2 and to 0 otherwise.To control for climate variation that may affectthe productivity of different land uses, we in-clude the NRI’s climatic factor (WCFACT). Toproxy for urban development pressure we usea population-weighted distance index basedon the distances from the geographical centerof each county to the closest large metropoli-tan center (Portland, Seattle, Los Angeles, San

5 The NRI is conducted every five years to collect data at about800,000 randomly selected sites across the United States. The sam-ple is a two-stage stratified area sample of the entire country. Thefirst-stage, or primary, sampling unit (PSU) is a half-mile squarearea of land. The second-stage sampling units are points locatedwithin PSUs; three sample points are selected in most PSUs.


Francisco, and Boise) and to the closest majorcity (pop. ≥ 100,000) (Hardie et al. 2001).6 Wealso included the distances from the geograph-ical center of each county to the geographi-cal center of wilderness- and urban-type na-tional parks to measure access to nature-basedrecreational opportunities, with greater access(shorter distance) hypothesized to attract de-velopment. The data on the geographical cen-ter of each county and of national parks comefrom the U.S. Census Bureau.

The data on returns to agricultural and rangeland come from the Bureau of Economic Anal-ysis. We calculate county-level returns per acrefrom agriculture using income from crops tomeasure revenue and production expendituresto measure costs. Some expenditures can be al-located to crop production (purchase of seeds,fertilizer, and lime), but not others (petroleumproducts, labor, and other expenditures). Toallocate these expenditures we calculate theshare of total revenues that crop revenue rep-resents and we allocate the same share of theseexpenditures to agricultural production costs.We adjust the resulting measure of returns bythe CPI. Finally, to obtain returns per acrewe use the NRI’s xfactor to estimate the to-tal acreage allocated to agriculture in each pe-riod and divide total adjusted returns in eachcounty by this amount. Returns per acre fromrangeland are estimated in a similar way. Weuse income from livestock and products tomeasure revenue and production expenses tomeasure costs. Expenses that can be allocatedto range land are feed and livestock purchases;the remaining expenses are allocated in thesame way as was done for agricultural returns.

Returns to urban development and forestrywere obtained from Lubowski, Plantinga, andStavins (2007). They estimate net urban re-turns per acre as “the median value of a re-cently developed parcel, less the value of struc-tures, annualized at a 5% interest rate.” Theyestimate net returns from forestry by calculat-ing the net present value of a weighted aver-age of saw timber revenues from various foresttypes based on acreage, yields, prices, and costs,annualized at a 5% interest rate. We adjusttheir data by the CPI. To calculate the expectedgrowth of returns to each land use we assumethat the expected future growth is equal to the

6 The index was calculated using D = Popm/d2m + Popc/d2

c , wherePopm and Popc represent the population of the closest metropoli-tan center and major city, respectively, and dm and dc are the dis-tances from the county center to the closest metropolitan centerand major city.

average annual growth over the past five years.That is, let Gikt|t−1 = (�ikt − �ikt−1)/�ikt−1 bethe growth rate of returns to land use k be-tween t − 1 and t. Then the expected growthrate at time t is �G

ikt = 15

∑5j−1 Gikt− j t−( j+1),

and the corresponding variance is �Gikt =

15

∑5j−1 (G ikt− j t−( j+1) − �G

jkt)2.

Information on county-level land use regu-lations was obtained from a survey conductedin 1999. The survey was conducted using theDillman (1978) method and the overall re-sponse rate was 69% (see Cho, Wu, and Bogges2003). The survey asked county land use plan-ners whether different land use regulationswere in place by 1982, 1987, 1992, and after1993, and to judge the effectiveness of theregulation on a scale ranging from 1 (“NotEffective”) to 5 (“Effective”). We dividedthe regulations into four categories: Incentive-Based Policies such as preferential propertytaxation or agricultural districts, Property Ac-quisition Policies such as purchase or transferof development rights, Development Guide-lines such as an urban growth boundary orhousing caps, and Zoning Ordinances such asagricultural or conservation zoning. For eachcategory and each period we constructed aregulation index that measures the fractionof the maximum possible effectiveness scoreachieved in that period. For example, there arefive regulations included in the Property Ac-quisitions category, so the maximum attainablescore in any given period is 25. The scores forContra Costa county (California) add up to 12in 1982 and 14 in 1987, so the correspondingindexes are 0.48 and 0.56. We also includedtwo state-level regulations in our model asdummy variables: state land use planning pro-grams, which require adoption of a plan thatmeets state guidelines, and mandatory reviewof projects involving farmland conversion.

The variables included in the watershedhealth models were selected on the basis ofa comprehensive review of the scientific lit-erature (see Hascic and Wu 2006), which hasidentified the major land uses causing conven-tional and toxic water pollution as well as thedecrease in aquatic biodiversity. The water-shed health indicators were obtained from theEPA’s Index of Watershed Indicators (IWI).We use the following land use categories fromNRI: agriculture (crop, pasture, and rangeland), forest, urban, rural transportation, min-ing, and other land. These land use variablesare constructed as percentage of total landarea of the hydrologic unit and interacted with


regional dummy variables. Since the waterquality models are based on samples taken be-tween 1990 and 1998, for these models we av-erage the land use data over the years 1987,1992, and 1997.7 For the species-at-risk model,which is based on a measure from 1996, we useland use data from 1992. The spatial dummiesthat we interact with the land use variables(the U.S. Soil Conservation Service’s Land Re-source Regions (LRR) for water quality andthe U.S. Forest Service’s ecosystem divisionsfor species at risk) capture variability in factorslike soils, precipitation, and temperature.8 Wealso include the total acreage of irrigated land,permanent open water, and wetlands. Addi-tionally, we control for the average wind andwater erosion rates and the soil permeabilityindex of each watershed.9 The average windand water erosion rates were obtained fromthe NRI, and the soil permeability index fromthe IWI. Summary statistics for these variablescan be found in table 1.

Results

We start by discussing the results for the landuse model and then present the results for thewater quality and species-at-risk models.

Land Use Model

The multinomial logit model of land usechoices is estimated using data described inthe previous section. Because the estimatedcoefficients have no direct economic interpre-tation, only the estimated marginal effects ofindependent variables are reported in table 2.The model correctly predicts land use choicefor 60.8% of observations, which is compara-ble with previous studies (Wu et al 2004; Wu

7 Aggregating the land use composition data for watersheds overtime in this fashion is necessary because, although the water qualitydata are based on samples taken over nine years (1990–1998), onlythe total number of samples exceeding reference levels over thisperiod is available; the data are not available for individual yearsor shorter time periods. The results for these models are generallyrobust to using land use data from individual years rather than theaverage.

8 For details see www.soilinfo.psu.edu/soil lrr/ for LRRs andhttp://www.essc.psu.edu/soil info/soil eco/ for ecosystem divisions.LRRs and divisions were combined to ensure a sufficient numberof observations for each interaction term. Division 320 was in-cluded as a dummy rather than interacted with land use becauseonly a small part of it is included in our study area.

9 We also tested various specifications of the water quality mod-els that included a zoning variable, but found insignificant or coun-terintuitive effects. One possible reason is that zoning and waterquality may be jointly determined, since counties in watershedswith poor water quality may be more likely to adopt zoning regu-lations.

and Babcock 1998). To further validate themodel we estimated it with data from the firstthree periods (1982, 1987, 1992), used the re-sults to project total acreages in each land usein 1997, and compared actual and predictedoutcomes using Theil’s Inequality Coefficient(Ahn, Plantinga, and Alig 2000). The coeffi-cients range from 0.007 to 0.17, which indicatesgood predictive performance (lower values arebetter; the worst possible value is one).

The own-return marginal effects are positiveand significant and the cross-return effects arenegative and significant, indicating that higherreturns to a land use increase the probabilitythat it is chosen and decrease the probabilitythat other land uses are chosen. The expectedgrowth of returns from agriculture and forestare not statistically significant. The marginaleffects for expected growth of returns to urbanland and range land are negative in their ownequations, suggesting that higher expected fu-ture returns decrease the present probabilityof choosing urban and range land and increasethe probabilities of choosing other land uses.Capozza and Li (1994) show that an increasein the growth expectation for the return to de-veloped land does not necessarily speed up de-velopment, because when the growth expecta-tion increases the option value of conversion(i.e., the value of delaying the project) also in-creases, so that it may be worthwhile to post-pone the project even though its net presentvalue has increased. The variances of growthof returns to forest and urban land have neg-ative own- and positive cross-marginal effects,suggesting that higher variability in the growthof returns lowers the probability of choosing aland use and increases the probability of choos-ing others.10

The marginal effects of the land use regu-lation indices yield mixed results. Incentive-based policies, such as preferential propertytaxation for agricultural and forest land, agri-cultural districts, and density bonuses have apositive and statistically significant effect onthe probability of choosing forest land and anegative and significant effect on the proba-bility of choosing range and urban land. Prop-erty acquisition programs, such as purchase ortransfer of development rights, have a nega-tive effect on the probability of choosing agri-cultural, forest, and urban land, and a positive

10 We leave out the variances of the growth of returns for agri-culture and range land because they are highly correlated with theexpected growth of returns. Including them would not change themain results of the land use model.


Table 1. Summary Statistics

Variable Mean Std. Dev.

Land use modelAg. returns/acre (thousands US $) 0.15 0.71Range returns/acre (thousands US $) −0.05 0.33Forest returns/acre (thousands US $) 0.02 0.02Urban returns/acre (thousands US $) 5.21 3.87Development guidelines 0.17 0.17Incentive-based policies 0.24 0.24Property acquisition 0.11 0.15Zoning policies 0.26 0.25Land use plan 0.76 0.43Mandatory review 0.18 0.38Land quality 1 0.06 0.23Weighted distance index 51.78 121.13Distance wilderness park (miles) 83.54 47.52Distance urban park (miles) 245.28 157.41Climatic factor 6.43 19.85

Watershed health modelsConventional ambient water pollution

Total number of samples 2343.28 9040.37Number of samples exceeding national reference levels 270.08 881.99

Toxic ambient water pollutionTotal number of samples 521.42 990.49Number of samples exceeding national reference levels 109.16 357.75

Number of species at risk (per million acres) 8.05 9.73Urban land (% of watershed)a 3.74 9.45Agricultural land (% of watershed)a 26.52 24.80Transportation land (% of watershed)a 0.78 0.62Mining land (% of watershed)a 0.15 0.50Other land 1 (% of watershed)a 47.97 30.09Other land 2 (% of watershed)a 47.05 30.63Irrigated land (1,000 acres)a 53.35 200.68Soil loss due to water erosion (tons/acre/year)a 1.17 2.29Soil loss due to wind erosion (tons/acre/year)a 1.43 9.56Index of soil permeability 3.83 0.98Area of water bodies (1,000 acres)a 15.68 45.90Area of wetlands (1,000 acres) 14.67 37.34Total land area (1,000 acres)a 804.17 660.84

aAverages are taken over four years (1982, 1987, 1992, 1997).

effect on the probability of choosing rangeland. On the other hand, zoning and develop-ment guidelines have positive effects on theprobability of choosing urban land, althoughonly the marginal effect of development guide-lines is statistically significant. The insignificanteffect of zoning may indicate that zoning af-fects the location, but not the total amount,of development, or it may reflect the fact thatsome zoning regulations set minimum par-cel sizes. The positive effect of developmentguidelines is consistent with the notion thatland use planning that preserves open spacemay spur additional development. Wu andPlantinga (2003) show that open space des-ignation may cause leapfrog development as

well as more development. Riddel (2001) re-ports evidence that open space programs inColorado increased growth in residential de-velopment. Irwin and Bockstael (2004) showthat, by creating a positive amenity, a policyintended to preserve open space may attractdevelopment.

One possible concern with the land usemodel is that the regulation indices may beendogenous. To avoid this potential problem,the indices for a given year are based on reg-ulations that were in place by that time, andhence land use choices in that year could nothave affected the adoption of the regulation.In addition, we model decisions of individuallandowners, who are likely to take land use


Table 2. Estimates of Marginal Effects for the Multinomial Logit Model of Land Use Choice

Marginal Effect on the Probability of Choosing a Land Use

Variable Agriculture Range Forest Urban Other

Constant −0.1076∗∗∗ −0.1216∗∗∗ −0.0506∗∗∗ −0.0315∗∗∗ 0.3112∗∗∗

Ag. returns 0.0031∗∗∗ −0.0005∗∗∗ −0.0003∗∗∗ −0.0001∗∗∗ −0.0022∗∗∗

Expected growth of ag. returnsa 0.1163 −0.0184 −0.0110 −0.0040 −0.0830Range returns −0.0127∗∗∗ 0.1178∗∗∗ −0.0118∗∗∗ −0.0042∗∗∗ −0.0890∗∗∗

Expected growth of range returnsa 0.0050∗ −0.0465∗ 0.0047∗ 0.0017∗ 0.0352∗

Forest returns −0.0968∗∗∗ −0.1499∗∗∗ 0.9569∗∗∗ −0.0323∗∗∗ −0.6779∗∗∗

Expected growth of forest returnsa −0.000 0.0014 −0.0091 0.0003 0.0064Variance growth of forest returnsa 0.0010∗∗∗ 0.0016∗∗∗ −0.0101∗∗∗ 0.0003∗∗∗ 0.0072∗∗∗

Urban returns −0.0003∗∗∗ −0.0005∗∗∗ −0.0003∗∗∗ 0.0032∗∗∗ −0.0022∗∗∗

Expected growth of urban returns 0.0031∗∗∗ 0.0048∗∗∗ 0.0029∗∗∗ −0.0321∗∗∗ 0.0214∗∗∗

Variance growth of urban returns 0.0218∗∗∗ 0.0337∗∗∗ 0.0203∗∗∗ −0.2281∗∗∗ 0.1524∗∗∗

Incentive-based policies 0.0035 −0.0158∗∗∗ 0.0402∗∗∗ −0.0084∗∗∗ −0.0195∗∗∗

Property acquisition −0.0593∗∗∗ 0.0753∗∗∗ −0.1039∗∗∗ −0.0033∗ 0.0912∗∗∗

Development guidelines 0.0910∗∗∗ 0.0472∗∗∗ −0.0730∗∗∗ 0.0148∗∗∗ −0.0800∗∗∗

Zoning policies −0.0843∗∗∗ −0.0227∗∗∗ 0.0505∗∗∗ 0.0001 0.0564∗∗∗

Land use plan 0.0082∗∗∗ 0.0057∗∗∗ −0.0305∗∗∗ −0.0188∗∗∗ 0.0354∗∗∗

Mandatory review 0.0308∗∗∗ −0.0195∗∗∗ 0.0881∗∗∗ 0.0082∗∗∗ −0.1076∗∗∗

High-quality land 0.1796∗∗∗ 0.0344∗∗∗ 0.0007 −0.0402∗∗∗ −0.1744∗∗∗

Weighted distance indexa 0.0073∗ 0.0181∗∗∗ 0.0149∗∗∗ 0.0323∗∗∗ −0.0725∗∗∗

Distance wilderness parka 0.0354∗∗∗ −0.0936∗∗∗ 0.2713∗∗∗ −0.0292∗∗∗ −0.1839∗∗∗

Distance urban parka 0.1205∗∗∗ 0.0265∗∗∗ −0.2696∗∗∗ −0.1203∗∗∗ 0.2429∗∗∗

Climatic factor 0.0007∗∗∗ 0.0012∗∗∗ −0.0004∗∗∗ 0.0002∗∗∗ −0.0017∗∗∗

Percentage of Federal land −1.6175∗∗∗ −0.1859∗∗∗ −0.7958∗∗∗ −0.2282∗∗∗ 2.8275∗∗∗

Note: Single asterisk (∗), double asterisks (∗∗), and triple asterisks (∗∗∗) denote statistical significance at = 10%, = 5%, and = 1% respectively.aMarginal effects are multiplied by 1000 to facilitate presentation. Observations: 512,201. Percent correct predictions: 60.8%.

regulations as given when making their landuse decisions. Nevertheless, we estimated landuse models with lagged and predicted regula-tion variables, and the qualitative results arethe same as those presented here.

Watershed Health Indicators

We estimate the watershed health indicatormodels to evaluate the effects of land use al-locations on water quality and aquatic species.Since the land uses included in the models com-pletely describe the landscape, we avoid per-fect multicollinearity by using forest land asthe reference category. The results in table 3show the coefficient estimates for the waterpollution and species-at-risk models. The coef-ficients for urban land are positive in all threemodels, but are only statistically significant forwatersheds in LRR C for conventional waterquality, in LRRs C and D-E for toxic waterquality, and in ecosystem divisions 240-m240and 260-m260 for the species at risk model.The coefficients for agricultural land are pos-itive and significant for watersheds in all re-gions in the conventional water quality model,but only in region C for toxic water quality. In

the species at risk model, the coefficients foragricultural land are negative and significantfor watersheds in divisions 240-m240 and 340,possibly reflecting the fact that certain typesof agriculture, such as biologically integratedfarming systems, integrated pest management,organic farming, or conservation tillage can bebeneficial for some species by either directlyproviding habitat or by reducing existing neg-ative environmental impacts (see, for exam-ple, Holland 2004; Hole et al. 2005). These re-sults suggest that in our study area watershedswith higher proportions of urban and agri-cultural land relative to forest are associatedwith more water pollution. Watersheds withmore urban land are also associated with morespecies at risk, but watersheds with more agri-cultural land could be associated with fewerspecies at risk, depending on the location ofthe watershed.

Simulating the Effects of Land Use Policies onWatershed Health Indicators

In this section we link the land use and wa-tershed health models to examine the effectof land use policies on water quality and the


Table 3. Coefficient Estimates for the Watershed Health Indicator Models

Conventional Toxic Water SpeciesVariable Water Quality Quality at Risk

Constant −2.648∗∗∗ −5.800∗∗∗ −11.763∗∗∗

Urban land × LRR ABa 0.002 0.066Urban land × LRR C 0.014∗ 0.041∗∗

Urban land ×LRR DE 0.020 0.243∗

Ag. land × LRR AB 0.015∗∗∗ −0.011Ag. land × LRR C 0.016∗∗∗ 0.060∗∗∗

Ag. land × LRR DE 0.023∗∗∗ −0.020Transportation × LRR AB 2.141∗∗

Transportation × LRR C −1.674∗∗

Transportation × LRR DE 1.365∗∗

Mining × LRR AB −2.118Mining × LRR C 0.586∗∗∗

Mining × LRR DE −11.066Urban land × Div 240 m240a 0.042∗

Urban land × Div 260 m260 0.014∗∗∗

Urban land × Div 330 m330 0.029Urban land × Div 340 0.045Ag. land × Div 240 m240 −0.027∗∗∗

Ag. land × Div 260 m260 0.001Ag. land × Div 330 m330 −0.008Ag. land × Div 340 −0.028∗∗∗

Transportation × Div 240 m240 0.418∗∗∗

Transportation × Div 260 m260 0.509∗∗∗

Transportation × Div 330 m330 −0.402Transportation × Div 340 0.173Mining × Div 240 m240 −1.283∗

Mining × Div 260 m260 0.102Mining × Div 330 m330 −0.128Mining × Div 340 0.252Div 320 −0.559∗∗∗

Other land 1b 0.010∗∗

Other land 2c 0.050∗∗∗ −7.58E-03∗∗∗

Irrigated land 2.02E-07 −6.53E-06 −6.95E-07∗∗∗

Soil loss-water erosion 0.033Soil loss-wind erosion −0.008Soil permeability index −0.118Area of water bodies −1.35E-06Area of wetlands −1.28E-06Total land area (offset) 1.000 1.000 1.000Observations 155 31 279Deviance/DFd 1.2364 2.0195 1.0427Pearson 2/DFd 0.9558 1.5337 1.3033

Note: Single asterisk (∗), double asterisks (∗∗), and triple asterisks (∗∗∗) denote statistical significance at = 10%, = 5%, and = 1%respectively.aLRR = Land Resource Region; Div = Ecosystem division.bOther land 1 includes transportation, minor land (incl. mining land), CRP land, and federal land.cOther land 2 includes minor land (excl. mining land), CRP land, and federal land.dValues of these measures close to 1 indicate a good fit of the regression model. Values greater (smaller) than 1 indicate over (under)dispersion, i.e., the true variance is greater (smaller) than the mean. Evidence of over (under) dispersion indicates inadequate fit.

number of species at risk. We do so in twosteps: First, we simulate the effects of landuse policies on land use choices using the esti-mated land use model. We obtain predictedland use probabilities at each NRI site andmultiply them by the NRI’s xfactor to trans-

late them into expected land use acreages. Wethen aggregate land use acreage to the water-shed level and calculate the predicted fractionof each watershed in each land use. Second, wefeed these predicted land use fractions into thewatershed models and obtain predicted values


Table 4. Mean Percentage of Watershed in Each Land Use

Land Use % of Watershed % of Watershed % of WatershedPolicy in Ag. Land in Urban Land in Forest Land

Baseline 23.33% 5.12% 10.62%Urban returns

↓Urban returns by $2 (0.04%) 23.33% 5.12% 10.62%↓Urban returns by $5 (0.10%) 23.33% 5.12% 10.62%↓Urban returns by $10 (0.19%) 23.33% 5.12% 10.62%↓Urban returns by $500 (9.6%) 23.38% 4.90% 10.65%

Agriculture returns↑Ag. returns by $2 (1.33%) 23.33% 5.12% 10.62%↑Ag. returns by $5 (3.33%) 23.33% 5.12% 10.62%↑Ag. returns by $10 (6.67%) 23.33% 5.12% 10.62%↑Ag. returns by $15 (10%) 23.33% 5.12% 10.62%

Reforestation payments on Ag. land↑Forest returns by $2 (10%) 23.31% 5.12% 10.67%↑Forest returns by $5 (25%) 23.28% 5.11% 10.78%↑Forest returns by $10 (50%) 23.23% 5.10% 10.91%

Incentive-based policies↑Inc.-based index 5% 23.30% 5.10% 10.69%↑Inc.-based index 25% 23.22% 5.01% 10.99%↑Inc.-based index 50% 23.13% 4.92% 11.33%↑Inc.-based index to 1.0 22.34% 4.28% 14.10%

Property acquisition↑Prop.-acq. index 5% 23.35% 5.12% 10.53%↑Prop.-acq. index 25% 23.45% 5.11% 10.19%↑Prop.-acq. index 50% 23.59% 5.08% 9.79%↑Prop.-acq. index to 0.6 24.43% 4.99% 6.19%

of the water quality and species at risk mea-sures. We initially carry out this procedure us-ing sample data to generate predicted baselinevalues. Then we repeat it for each policy sce-nario and compare the outcomes to the base-line by calculating percentage changes in thewater quality indicators and number of speciesat risk. Specifically, we evaluate the effects ofpolicies that discourage development or en-courage preservation or reforestation of agri-cultural land and of local land use regulations.

To simulate the effects of policies that af-fect the returns to land use we modified thesereturns by amounts chosen to represent rea-sonable percentage changes relative to meanreturns. Specifically, to analyze policies thatdiscourage development we decreased the re-turns to urban land by $2 (0.04% decrease rel-ative to mean returns), $5 (0.1%), $10 (0.2%),and $500 (9.6%) per acre.11 To simulate poli-cies that preserve agricultural land we in-creased the returns to agriculture by $2 (1.3%),$5 (3.3%), $10 (6.7%), and $15 (10%) per acre.To simulate the effects of reforestation policieswe increased the returns to forestry on land

11 We account for the irreversibility of development by not allow-ing the predicted probability of choosing urban land to decreaserelative to the prior year in parcels that are already developed.

used for agriculture by $2 (10%), $5 (25%),and $10 (50%) per acre.12 Finally, we evalu-ate the effects of local land use regulations byincreasing the value of the regulation indicesby 5%, 25%, and 50%, and by setting eachindex to the highest value found in the data.Since the zoning and development guidelinesindices do not have a significant effect on theprobability of choosing urban land, we onlyuse the incentive-based policies and propertyacquisition indices. Given that different poli-cies are designed to impact different land uses,we examine whether their effects vary with theland use mix across the watersheds. We sepa-rately evaluate the effects of each policy onwatersheds with the highest and lowest per-centages of developed, agricultural, and forestland.

The simulation results appear in tables 4–7.13

Table 4 shows mean percentages of watersheds

12 We also simulated the effects of forest preservation policies byincreasing the returns to forestry on all land uses. The qualitativeeffects are equivalent to those of reforestation payments, so we donot include them here because of space considerations.

13 We note as caveats to these results that they are partially basedon some estimated coefficients that are statistically significant onlyat a 10% level and that they ignore possible spillover effects of thepolicies between counties, which could change the magnitude ofthe policy impacts.


Table 5. Mean Percentage Change in Conventional Water Pollution Relative to Baseline

Most Least Most LeastUrban Urban Most Ag. Least Ag. Forest Forest

Land Use Policy Land Land Land Land Land Land

Urban Returns↓Urban returns by $2 (0.04%) 0.00%∗∗∗ 0.00%∗∗∗ 0.00% 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↓Urban returns by $5 (0.10%) −0.01%∗∗∗ 0.00%∗∗∗ 0.00% 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↓Urban returns by $10 (0.19%) −0.02%∗∗∗ 0.00%∗∗∗ 0.00% 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↓Urban returns by $500 (9.6%) −0.75%∗∗∗ −0.04%∗∗∗ −0.04%∗∗∗ −0.07%∗∗∗ −0.05%∗∗∗ −0.08%∗∗∗

Agriculture returns↑Ag. returns by $2 (1.33%) 0.00%∗∗∗ 0.00%∗∗∗ 0.00% 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↑Ag. returns by $5 (3.33%) 0.00%∗∗∗ 0.00%∗∗∗ 0.00% 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↑Ag. returns by $10 (6.67%) 0.00%∗∗∗ 0.00%∗∗∗ 0.01% 0.00%∗∗∗ 0.01%∗∗∗ 0.00%∗∗∗↑Ag. returns by $15 (10%) 0.00%∗∗∗ 0.00%∗∗∗ 0.01% 0.00%∗∗∗ 0.01%∗∗∗ 0.00%∗∗∗

Reforestation payments on Ag. land↑Forest returns by $2 (10%) −0.06%∗∗∗ 0.00%∗∗∗ −0.08%∗∗∗ 0.00%∗∗∗ −0.07%∗∗∗ 0.00%∗∗∗↑Forest returns by $5 (25%) −0.16%∗∗∗ −0.01%∗∗∗ −0.20%∗∗∗ −0.01%∗∗∗ −0.18%∗∗∗ 0.00%∗∗∗↑Forest returns by $10 (50%) −0.32%∗∗∗ −0.02%∗∗∗ −0.42%∗∗∗ −0.01%∗∗∗ −0.37%∗∗∗ −0.01%∗∗∗

Incentive-based policies↑Inc.-based index 5% −0.09%∗∗∗ −0.02%∗∗∗ −0.03%∗∗∗ −0.02%∗∗∗ −0.10%∗∗∗ −0.01%∗∗∗↑Inc.-based index 25% −0.44%∗∗∗ −0.10%∗∗∗ −0.15%∗∗∗ −0.08%∗∗∗ −0.49%∗∗∗ −0.05%∗∗∗↑Inc.-based index 50% −0.88%∗∗∗ −0.20%∗∗∗ −0.30%∗∗∗ −0.15%∗∗∗ −0.95%∗∗∗ −0.11%∗∗∗↑Inc.-based index to 1.0 −6.01%∗∗∗ −1.44%∗∗∗ −2.74%∗∗∗ −1.59%∗∗∗ −3.66%∗∗∗ −1.81%∗∗∗

Property acquisition↑Prop.-acq. index 5% 0.11% 0.03%∗∗∗ 0.00% 0.03%∗∗∗ 0.05%∗∗∗ 0.03%∗∗∗↑Prop.-acq. index 25% 0.54%∗∗∗ 0.15%∗∗∗ 0.02%∗∗ 0.13%∗∗∗ 0.24%∗∗∗ 0.16%∗∗∗↑Prop.-acq. index 50% 1.07%∗∗∗ 0.30%∗∗∗ 0.05%∗∗∗ 0.27%∗∗∗ 0.48% 0.32%∗∗∗↑Prop.-acq. index to 0.6 1.37%∗∗∗ 3.85%∗∗∗ 0.10%∗∗ 3.94%∗∗∗ 3.71%∗∗∗ 4.07%∗∗∗

Note: Single asterisk (∗), double asterisks (∗∗), and triple asterisks (∗∗∗) denote statistical significance at = 10%, = 5%, and = 1% respectively.

in each land use for each policy scenario (to-tal land area in each watershed remains con-stant throughout all the scenarios) and tables5–7 show mean percentage changes relative tobaseline values for conventional and toxic wa-ter pollution and species at risk. The statisti-cal significance of the simulation results is de-termined based on standard errors calculatedusing a bootstrapping procedure.14 Negativechanges represent reductions in the numberof water samples with measures of conven-tional or toxic water pollution exceeding refer-ence levels or in the number of species at risk.Hence, we interpret them as improvements inwatershed health. Positive changes reflect in-creases in the number of samples with mea-sures of water pollution above reference levelsor in the number of species at risk. Hence, weinterpret them as deteriorations in watershedhealth. We report results for the 5% of water-sheds with the highest and lowest proportionsof urban, agricultural, and forest land. We refer

14 The bootstrapping procedure repeated each simulation sce-nario fifty times drawing random samples with replacement fromthe data and calculating the standard errors of the resulting per-centage changes from the baseline.

to these as the most/least urban, agricultural,and forested watersheds.15

The results in tables 5–7 suggest that poli-cies that change the returns to the differentland uses have comparatively small effects onwater quality and the number of species at risk.Policies that reduce the returns to urban landare associated with decreases in water pollu-tion and in the number of species at risk, butthe effects are mostly small, even when returnsdecrease by $500/acre (10%). In the most ur-ban watersheds the toxic water pollution indi-cator decreases by 3%, but the conventionalwater pollution and species at risk indicatorsdecrease by only 0.75% and 1.2%, respectively.

Policies to preserve agricultural land by in-creasing the returns to agriculture have practi-cally no effect on the watershed health indica-tors in any type of watershed. With an increasein agricultural returns of $15/acre (10%), the

15 The main difference between “most urban” and “least agricul-tural” or “least forested” watersheds are the fractions of water-sheds in urban land. For “most urban” the proportion of urbanland ranges from a minimum of 16.5% to a maximum of 53.9%,whereas for “least agricultural” watersheds the range is 0.4% -1.5% and for “least forested” it is 0.4% - 1.8%.


Table 6. Mean Percentage Change in Toxic Water Pollution Relative to Baseline



Urban Returns↓Urban returns by $2 (0.04%) −0.01%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ −0.01%∗∗∗ 0.00%∗∗∗↓Urban returns by $5 (0.10%) −0.03%∗∗∗ −0.01%∗∗∗ −0.01%∗∗∗ −0.01%∗∗∗ −0.03%∗∗∗ −0.01%∗∗∗↓Urban returns by $10 (0.19%) −0.06%∗∗∗ −0.01%∗∗∗ −0.02%∗∗∗ −0.02%∗∗∗ −0.05%∗∗∗ −0.02%∗∗∗↓Urban returns by $500 (9.6%) −3.01%∗∗∗ −0.56%∗∗∗ −0.89%∗∗∗ −0.97%∗∗∗ −2.52%∗∗∗ −1.18%∗∗∗

Agriculture returns↑Ag. returns by $2 (1.33%) 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↑Ag. returns by $5 (3.33%) 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↑Ag. returns by $10 (6.67%) 0.01%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ −0.01%∗∗∗ 0.00%∗∗∗↑Ag. returns by $15 (10%) 0.01%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ −0.01%∗∗∗ 0.00%∗∗∗

Reforestation payments on Ag. land↑Forest returns by $2 (10%) −0.23%∗∗∗ 0.00%∗∗∗ −0.12%∗∗∗ 0.00%∗∗∗ −0.03%∗∗∗ 0.00%∗∗∗↑Forest returns by $5 (25%) −0.58%∗∗∗ 0.00%∗∗∗ −0.30%∗∗∗ 0.00%∗∗∗ −0.07%∗∗∗ 0.00%∗∗∗↑Forest returns by $10 (50%) −1.17%∗∗∗ 0.01%∗∗∗ −0.60%∗∗∗ 0.00%∗∗∗ −0.14%∗∗∗ 0.00%∗∗∗

Incentive-based policies↑Inc.-based index 5% −0.45%∗∗∗ −0.01%∗∗∗ −0.06%∗∗∗ −0.01%∗∗∗ −0.30%∗∗∗ −0.01%∗∗∗↑Inc.-based index 25% −2.21%∗∗∗ −0.05%∗∗∗ −0.30%∗∗∗ −0.04%∗∗∗ −1.47%∗∗∗ −0.03%∗∗∗↑Inc.-based index 50% −4.35%∗∗∗ −0.11%∗∗∗ −0.59%∗∗∗ −0.08%∗∗∗ −2.67%∗∗∗ −0.07%∗∗∗↑Inc.-based index to 1.0 −18.61%∗∗∗ −0.76%∗∗∗ −5.38%∗∗∗ −2.47%∗∗∗ −7.64%∗∗∗ −3.19%∗∗∗

Property acquisition↑Prop.-acq. index 5% 0.30%∗∗∗ −0.04%∗∗∗ 0.00% −0.04%∗∗∗ 0.02%∗∗∗ −0.05%∗∗∗↑Prop.-acq. index 25% 1.46%∗∗∗ −0.20%∗∗∗ −0.03% −0.18%∗∗∗ 0.08%∗∗∗ −0.25%∗∗∗↑Prop.-acq. index 50% 2.78%∗∗∗ −0.39%∗∗∗ −0.06% −0.36%∗∗∗ 0.10%∗∗∗ −0.51%∗∗∗↑Prop.-acq. index to 0.6 5.31%∗∗∗ −4.74%∗∗∗ −1.35%∗∗∗ −6.12%∗∗∗ 0.61%∗∗∗ −6.86%∗∗∗


toxic water pollution indicator decreases byonly 0.01% in the most forested watersheds,and the species at risk indicator decreasesby 0.01% in the most agricultural and mostforested watersheds.

Policies to encourage reforestation of agri-cultural land have positive effects on waterquality, but the changes are also small. Thewater pollution indicators decrease relative tothe baseline when returns to forest land in-crease, but the changes are mostly smaller than1%, even when the net returns to forest landgo up by as much as $10/acre (50%). With theexception of the most urban watersheds, thesepolicies are associated with small increases inthe species at risk indicator. They cause a smalldecrease in the percentage of agricultural landin a watershed and this effect dominates in wa-tersheds where higher fractions of agriculturalland are associated with lower species at riskmeasures.

Table 4 suggests that these policies are asso-ciated with small changes in the water qualityand species at risk measures because they yieldonly minor changes in the mix of land use ina watershed. Policies that lower the returns to

urban land lead to small increases in agricul-tural and forest land and a somewhat largerdecrease in urban land, but only when returnsdecrease by $500/acre, or 10% of mean returns.Policies that increase the returns to agriculturalland have essentially no effect on land use, andpolicies that increase the return to forest landlead to small decreases in agricultural and ur-ban land and a small increase in forest land.These outcomes are consistent with previousresults that land use allocations are relativelyunresponsive to changes in returns to alter-native land uses (Stavins and Jaffe 1990; Wuet al. 2004). However, a limitation of our anal-ysis is that it ignores the effects of conserva-tion and reforestation payments on how landis managed, which could augment their im-pact on our measures of watershed health. Forinstance, California’s range livestock industryis voluntarily implementing Best ManagementPractices (Larson et al. 2005), and farmers andranchers in Oregon, with the support of thestate’s Soil and Water Conservation Districts,implement numerous practices such as ripar-ian buffers or no-till planting (Oregon Depart-ment of Agriculture 2005). By focusing on land


Table 7. Change in the Number of Species at Risk Relative to Baseline



Urban returns↓Urban returns by $2 (0.04%) −0.01%∗∗∗ 0.00%∗∗∗ 0.00%∗∗ 0.00%∗∗∗ −0.01%∗∗∗ 0.00%∗∗∗↓Urban returns by $5 (0.10%) −0.01%∗∗∗ 0.00%∗∗∗ −0.01% 0.00%∗∗∗ −0.01%∗∗∗ 0.00%∗∗∗↓Urban returns by $10 (0.19%) −0.03%∗∗∗ 0.00%∗∗∗ −0.01% 0.00%∗∗∗ −0.02%∗∗∗ 0.00%∗∗∗↓Urban returns by $500 (9.6%) −1.24%∗∗∗ −0.13%∗∗∗ −0.66%∗∗∗ −0.07%∗∗∗ −1.14%∗∗∗ −0.06%∗∗∗

Agriculture returns↑Ag. returns by $2 (1.33%) 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↑Ag. returns by $5 (3.33%) 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗ 0.00%∗∗∗ 0.00%∗∗∗ 0.00%∗∗∗↑Ag. returns by $10 (6.67%) 0.00%∗∗∗ 0.00%∗∗∗ −0.01% 0.00%∗∗∗ −0.01%∗∗∗ 0.00%∗∗∗↑Ag. returns by $15 (10%) 0.00%∗∗∗ 0.00%∗∗∗ −0.01% 0.00%∗∗∗ −0.01%∗∗∗ 0.00%∗∗∗

Reforestation payments on ag. land↑Forest returns by $2 (10%) −0.03%∗∗∗ 0.01%∗∗∗ 0.05% ∗∗∗ 0.00%∗∗∗ 0.04%∗∗∗ 0.00%∗∗∗↑Forest returns by $5 (25%) −0.09%∗∗∗ 0.01%∗∗∗ 0.12%∗∗∗ 0.01%∗∗∗ 0.10%∗∗∗ 0.00%∗∗∗↑Forest returns by $10 (50%) −0.18%∗∗∗ 0.03%∗∗∗ 0.25% ∗∗∗ 0.02%∗∗∗ 0.21%∗∗∗ 0.00%∗∗∗

Incentive-based policies↑Inc.-based index 5% −0.11%∗∗∗ 0.02%∗∗∗ −0.01% 0.01%∗∗∗ −0.06%∗∗∗ 0.01%∗∗∗↑Inc.-based index 25% −0.55%∗∗∗ 0.08%∗∗∗ −0.03%∗∗∗ 0.07%∗∗∗ −0.27%∗∗∗ 0.04%∗∗∗↑Inc.-based index 50% −1.10%∗∗∗ 0.17%∗∗∗ −0.05%∗∗∗ 0.14%∗∗∗ −0.41%∗∗∗ 0.09%∗∗∗↑Inc.-based index to 1.0 −5.08%∗∗∗ 1.24%∗∗∗ −0.11%∗∗∗ 0.86%∗∗∗ −0.72%∗∗∗ 0.48%∗∗∗

Property acquisition↑Prop.-acq. index 5% −0.03%∗∗∗ −0.04%∗∗∗ −0.01%∗∗∗ −0.03%∗∗∗ −0.07%∗∗∗ −0.03%∗∗∗↑Prop.-acq. index 25% −0.16%∗∗∗ −0.20%∗∗∗ −0.06%∗∗∗ −0.17%∗∗∗ −0.36%∗∗∗ −0.12%∗∗∗↑Prop.-acq. index 50% −0.39%∗∗∗ −0.41%∗∗∗ −0.13%∗∗∗ −0.33%∗∗∗ −0.76%∗∗∗ −0.25%∗∗∗↑Prop.-acq. index to 0.6 −0.18%∗∗ −4.99%∗∗∗ −0.83%∗∗∗ −3.41%∗∗∗ −2.82%∗∗∗ −2.15%∗∗∗


use choices rather than management practiceslike these, our analysis may underestimate thepotential effect of conservation payments.

The results in tables 5–7 indicate that localincentive-based land use policies could haverelatively large positive impacts on water qual-ity and species at risk and that their effectsvary considerably across different types of wa-tersheds. Incentive-based policies yield slightlyless agricultural land relative to the baseline,less urban land, and more forest land (seetable 4). Hence, they are associated with thelargest reductions in water pollution, in partic-ular when implemented at the highest level ofeffectiveness observed in the sample (an indexvalue of 1.0). The conventional water pollu-tion indicator decreases by 6% relative to thebaseline in the most urban watersheds, 3.7%in the most forested watersheds, and 2.7% inthe most agricultural watersheds. The toxic wa-ter pollution indicator decreases by 18.6% inthe most urban watersheds, 7.6% in the mostforested watersheds, and 5.4% in the most agri-cultural watersheds. Incentive-based policiesare only associated with a lower species at riskmeasure in the most urban watersheds, where

the indicator decreases by 5.1%. In other wa-tersheds, such as the least urban, agricultural,and forested watersheds these policies are as-sociated with small increases in the numberof species at risk indicator. These are water-sheds with small percentages of urban and for-est land, and are located in regions where moreagricultural land is associated with a lowerspecies at risk measure. For these watershedsthe effect of a decrease in agricultural landdominates the effects of a decrease in urbanland and an increase in forest land.

Property acquisition policies are associatedwith a small increase in agricultural land, asmall reduction in urban land, and a rela-tively larger reduction in urban forest land (seetable 4). When implemented at the highestlevel of effectiveness observed in the sample(an index value of 0.6), property acquisitionprograms can reduce the toxic water pollutionand species at risk indicators. The toxic wa-ter pollution indicator decreases by 4.7% inthe least urban watersheds, 6.1% in the leastagricultural watersheds, and 6.9% in the leastforested watersheds. The species at risk indi-cator decreases by 5.0%, 3.4%, and 2.2% in


the least urban, agricultural, and forested wa-tersheds, respectively, and by 2.8% in the mostforested watersheds. These are watersheds lo-cated in regions where more agricultural landis associated with lower species at risk mea-sures. For these watersheds the effect of an in-crease in agricultural land, combined with adecrease in urban land, dominates the effectof a decrease in forest land. In contrast, prop-erty acquisition policies are associated with in-creases in the conventional water pollution in-dicator in all types of watersheds because thecombined effects of a decrease in forest landand an increase in agricultural land dominatethe effect of a reduction in urban land. This isthe case as well for the toxic water pollutionindicator in the most urban watersheds.

In sum, these results suggest that local landuse regulations could have a greater positiveimpact on measures of watershed health thanpolicies which change the returns to differ-ent land uses. In particular, positive effectson water quality and the number of speciesat risk could be achieved if more countiesadopted incentive-based land use policies andproperty acquisition programs similar to themost effective ones in our sample. Incentive-based policies such as differential property tax-ation can have a large impact on the costsof development (for instance, property taxesin Benton County, OR, can be as much asthirty times higher for developed than foragricultural land). Other incentive-based poli-cies, such as agricultural districts, encouragelandowners to keep land in less developed usesby providing protection from eminent domain.The property acquisition policies include feesimple purchase and transfer or purchase ofdevelopment rights. These policies can be di-rectly used to set aside land for uses that bet-ter preserve watershed health. In contrast, landuse choices are relatively insensitive to changesin their relative returns. Hence, it may takelarge changes to have an impact on land al-location.

An alternative way to see this is to examinethe changes in returns necessary to achieve agiven policy goal. For instance, a 3% reductionin the conventional water pollution indicator inthe most urban watersheds could be achievedby decreasing returns to urban land by 40%($2,100/acre) relative to mean returns, increas-ing returns to forest land by 150% ($30/acre),or increasing the effectiveness scores of theincentive-based regulation index by 0.4. In themost forested watersheds it would take a dou-bling of forest returns ($20/acre) or an increase

of 0.5 in the incentive-based regulation index.Similarly, a 3% reduction in the toxic waterpollution indicator in the most urban water-sheds would take a 10% ($500/acre) reduc-tion in urban returns, a 60% ($12/acre) in-crease in forest returns, or an increase of 0.11in the incentive-based regulation index. In theleast urban watersheds this outcome would re-quire a drop of 67% ($3,500/acre) in urbanreturns, or an increase of 0.35 in the property-acquisition index. In the most forested water-sheds this outcome could be achieved with an11% ($550/acre) reduction in urban returns,a 140% ($28/acre) increase in forest returns,or an increase of 0.18 in the incentive-basedregulation index. Finally, a 3% reduction inthe species at risk indicator in the most ur-ban watersheds could be achieved if urban re-turns decreased by 25% ($1,300/acre), returnsto forest land increased by 240% ($48/acre), orif the incentive-based regulation index wentup by 0.4. In the most forested watersheds,this outcome would require a drop of 25%($1,300/acre) in returns to urban land, or anincrease of 0.5 in the property acquisition in-dex. These results suggest that even modestimprovements in the watershed health indica-tors would require significant, and often unre-alistic, changes in the returns to land use. Therequired changes in the effectiveness scores ofincentive-based or property acquisition poli-cies, while not trivial, are more reasonablegiven the highest observed indexes of 1.0 and0.6 in our sample.16

The results in tables 5–7 also highlight thepotential for targeting these policies accord-ing to the predominant land use in a water-shed. Given that the effects of the policiesvary considerably across different types of wa-tersheds, targeting these policies could helpland use planners avoid unintended effects thatmight counter the intended effects on water-shed health. For instance, property acquisitionpolicies are associated with a decrease in toxicwater pollution in the least urban, least agri-cultural, and least forested watersheds, but in-creases in the most urban watersheds. Thus, ifthis policy is applied uniformly, improvementsin some watersheds may be offset by declinesin others. Additionally, implementation of landuse policies is costly, so targeting could helpland use planners achieve watershed health

16 The relatively small changes generated by the policies consid-ered here may partly be explained by the fact that they are allindirect policies. That is, they address water quality through landuse rather than directly.


goals more cost effectively by focusing imple-mentation on watersheds where the policieshave the largest effects. For instance, incentive-based policies would have positive effects onwater quality in all types of watersheds, soapplying these policies uniformly would yieldpositive net effects. However, these policieshave smaller impacts in the least urban water-sheds. Hence, the same watershed health goalscould be achieved at lower cost by not imple-menting the policies in these watersheds, andtargeting only those watersheds where they areexpected to have significant effects.

Our results indicate that incentive-basedland use policies should be targeted to themost urban or most agricultural watershedswhen the main policy concern is water pollu-tion. Agricultural and urban land uses have anegative impact on water quality in these wa-tersheds, and incentive-based policies are as-sociated with reductions in these land uses.Property acquisition policies should be tar-geted to the least urban, least agricultural,and least forested watersheds when the pol-icy concerns are toxic water pollution or thenumber of aquatic species at risk. For thesewatersheds the effect of an increase in agricul-tural land, combined with a decrease in urbanland, dominates the effect of a decrease in for-est land. However, property acquisition pro-grams tend to have a negative impact on con-ventional water quality because they increasethe total acreage of agricultural land. Thus, itmakes sense to pick policies that affect landuses which have the largest impact on water-shed health, and target those policies to water-sheds where those land uses are predominant.

Summary and Conclusions

This article develops an empirical frameworkto examine the effects of land use policies onthree watershed health indicators in four west-ern states of the United States. The frameworkconsists of an econometric model of land usechoice and three models of watershed healthindicators. The framework is used to simulatethe potential effects of policies that decreasethe returns to developed land or increase thereturns to agricultural and forest land, as wellas of incentive-based local land use policiesand property acquisition programs.

Our results suggest that the impacts of thesepolicies vary considerably across watershedswith different land use mixes. Hence, there ispotential for enhancing their efficacy and cost-

effectiveness by targeting them according tothe land use mix of watersheds. By identifyingland uses, which have the largest impact on wa-tersheds, policy makers could choose land usepolicies that have the largest effects on thoseland uses and target them to watersheds wherethose land uses are predominant. This wouldallow policy makers to avoid unintended ef-fects in watersheds with different land usemixes, where the outcome of the policies mightbe different. Targeting policies to watershedswhere they are expected to be most effectivecould also allow policy makers to achieve wa-tershed health targets at lower implementationcosts.

For the watersheds considered here, we findthat local incentive-based land use policies,such as preferential property taxation for farmand forest land, agricultural districts, and den-sity bonuses for sensitive design, are most ef-fective in bringing about water quality im-provements in watersheds with the highest per-centages of urban land or agricultural land,and thus should be targeted to those water-sheds. Property acquisition policies are associ-ated with increases in agricultural land and re-ductions in urban and forest land, and shouldbe targeted to the least urban, least agricul-tural, and least forested watersheds when thepolicy concerns are toxic water pollution andthe number of aquatic species at risk. In con-trast, policies that attempt to change the rela-tive returns to the different land uses are asso-ciated with small changes in water quality andthe number of species at risk in any type of wa-tershed because these land uses are insensitiveto changes in their relative returns.

[Received March 2006;accepted January 2008.]

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protecting watershed ecosystems through targeted local land use policies

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