Public goods exist in space and recent research has shown that accounting ...... endangered species in the western U.S. (Ando et al., 1998), global priority ...
PUBLIC GOODS AND SPATIAL LOCATION: STATED PREFERENCE PRIORITIZATION WITH SPATIAL INTERDEPENDENCIES
by Steven J. Dundas
A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Agricultural and Resource Economics
Spring 2011
Copyright © 2011 Steven J. Dundas All Rights Reserved
PUBLIC GOODS AND SPATIAL LOCATION: STATED PREFERENCE PRIORITIZATION WITH SPATIAL INTERDEPENDENCIES
by Steven J. Dundas
Approved:
___________________________________________________________ Joshua M. Duke, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee
Approved:
___________________________________________________________ Thomas W. Ilvento, Ph.D. Chair of the Department of Food and Resource Economics
Approved:
___________________________________________________________ Robin W. Morgan, Ph.D. Dean of the College of Agriculture and Natural Resources
Approved:
___________________________________________________________ Charles G. Riordan, Ph.D. Vice Provost for Graduate and Professional Education
ACKNOWLEDGMENTS First and foremost, I would like to thank Ann Marie Alliano, Susan Dundas, Mark Dundas, and Jean Moser for their love and tremendous support for my decision to return to graduate school. Without their encouragement, I would not have been able to complete this project. I would like to dedicate this thesis to my late grandfather, Robert Moser, who was a great inspiration to me. He was absolutely thrilled that I was returning to school to further my education and purse a new career. I am very grateful for the support of my advisor, Dr. Joshua M. Duke, who guided me through this process and provided valuable insights that will help me as I move forward in my academic career. I would like to extend my gratitude to Dr. Tom Ilvento and the Department of Food and Resource Economics for providing me with the opportunity to participate in the graduate program and for the assistantship offer that made this work possible. I would like to thank Dr. Kent D. Messer, Dr. Titus O. Awokuse, and Dr. Robert J. Johnston of Clark University for serving on my thesis committee and providing their knowledge and assistance on this project. I also would like to thank Will Allen of The Conservation Fund, Dr. Kent Messer, and Mike McGrath of the Delaware Department of Agriculture for graciously providing a crucial data set for this research and Dr. Jong-Soo Lee for his assistance with a statistical matter. I am grateful to Jacob Fooks for his help in developing and troubleshooting the coding for an algorithm used in the spatial interdependency analysis. Lastly, I want to thank all my fellow AREC students for their friendship and encouragement and for making these two years enjoyable and memorable.
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TABLE OF CONTENTS LIST OF TABLES ....................................................................................................... vii LIST OF FIGURES .................................................................................................... viii ABSTRACT.................................................................................................................. ix Chapter 1
INTRODUCTION ..............................................................................................1 1.1 Motivation for Research................................................................................1 1.2 Problem Setting.............................................................................................4 1.3 The Problem..................................................................................................5 1.4 Contribution to the Literature .......................................................................8 1.5 Organization of the Study ...........................................................................11
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BACKGROUND ..............................................................................................13 2.1 Spatial Location ..........................................................................................13 2.2 Brief Summary of Farmland Preservation ..................................................14
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LITERATURE REVIEW .................................................................................19
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3.1 A Spatial Perspective for Public Goods......................................................19 3.2 Justification for Intervention in Land Markets ...........................................21 3.3 Quantifying Preservation Benefits ..............................................................22 3.4 Costs of Preservation ..................................................................................25 3.5 Strategies for Prioritizing Selection ............................................................27 METHODOLOGY............................................................................................29 4.1 Why Economists Think Space is Important................................................29 4.2 Spatial Synergy Benefits.............................................................................30 4.3 Principles of Spatial Gravity .......................................................................31 4.4 Unknown Constants α and β .......................................................................33 4.5 Suboptimal and Near-Optimal Decision Rules...........................................36 4.6 Binary Linear Programming .......................................................................38 4.7 Spatially Dynamic Optimization.................................................................38
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DATA: DEFINING THE SAMPLE AREA .....................................................41 5.1 Formation of the Sample Area ....................................................................41 5.2 Minimum Parcel Size..................................................................................43 5.3 Sussex County Zip Codes ...........................................................................44 5.4 Protected Land ............................................................................................46 5.5 Land Uses....................................................................................................47
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DATA: BENEFITS...........................................................................................52 6.1 Deriving Benefits with Choice Model Data................................................52 6.2 Linking WTP Estimates to the Sample Area ..............................................53 6.2.1 Land Use and Development Risk ...................................................56 6.3 Predicting WTP on Sample Parcels ............................................................60
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DATA: COSTS .................................................................................................67 7.1 Easement Costs in the Sample Area ...........................................................67 7.2 Easement Cost Data ....................................................................................68 7.3 Assessing the Predictive Capacity of Multiple Hedonic Pricing Models.........................................................................................................71 7.4 Hedonic Pricing Model Results ..................................................................73 7.5 Predicting Easement Costs in the Sample Area ..........................................76
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RESULTS .........................................................................................................81 8.1 Parcel Prioritization Results........................................................................82 8.2 Impact of Space on Parcel Prioritization.....................................................89
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CONCLUSIONS AND POLICY IMPLICATIONS ........................................98
REFERENCES............................................................................................................101 Appendix A
COLLECTION OF NEW DATA ...................................................................116 A.1 New Variables Linking WTP Estimates to Sample Area ........................116 A.2 New Variables Linking HPM Coefficient Estimates to Sample Area .....117 A.3 New Variables for Spatial Synergy Benefits Analysis ............................118
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B
PRELIMINARY HEDONIC MODELS .........................................................119 B.1 Preliminary Hedonic Pricing Model.........................................................119 B.2 Preliminary Hedonic Pricing Model: Variation 1 ....................................121 B.3 Preliminary Hedonic Pricing Model: Variation 2 ....................................121
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LIST OF TABLES Table 1.1: State of Spatial Valuation Research.............................................................11 Table 4.1: Comparison of Decision Rules—Six Parcels, $15 Budget..........................37 Table 5.1: Selected Demographics by Zip Code in Sussex County..............................45 Table 6.1: Choice Model Mixed Logit Coefficient Estimates ......................................54 Table 6.2: Per-Acre Household WTP: PACE by State Agency and No Access .........61 Table 7.1: Descriptive Statistics for Sussex County Parcel Data .................................69 Table 7.2: K-fold Cross Validation (k=196) Comparison of Hedonic Models ............73 Table 7.3: Final Hedonic Model Results ......................................................................75 Table 7.4: Analysis of Variance and Summary of Fit for Final Hedonic Model..........75 Table 8.1: Comparison of Selection Results with Budget of $30 Million....................85 Table 8.2: Distribution of OPT Parcel Selection by Community .................................88 Table 8.3: Comparison of Selection with Spatial Synergy Benefits at Varying Levels of α and a Budget of $30 Million ....................................................91 Table B.1: Preliminary Hedonic Model Results .........................................................122 Table B.2: Analysis of Variance/Summary of Fit: Preliminary Hedonic Model........122 Table B.3: Variation 1 Model Results ........................................................................123 Table B.4: Analysis of Variance/Summary of Fit: Variation 1 ..................................123 Table B.5: Variation 2 Model Results ........................................................................124 Table B.6: Analysis of Variance/Summary of Fit: Variation 2 ..................................124
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LIST OF FIGURES Figure 4.1: Benefit Bonus vs. Distance with Varying α for Two Average Parcels........................................................................................................34 Figure 4.2: Benefit Bonus vs. Distance with Different β for Two Average Parcels........................................................................................................36 Figure 5.1: Decision Tree of Parcel Eligibility Process................................................42 Figure 5.2: Communities Used for Analysis.................................................................46 Figure 5.3: Targeted Land Uses for Parcel Eligibility ..................................................48 Figure 6.1: Development Risk for Eligible Parcels in Sussex County .........................57 Figure 6.2: Histogram of Parcel Level WTP ................................................................64 Figure 6.3: Capitalized Aggregate WTP for Eligible Parcels in Sussex County..........65 Figure 7.1: Histogram of Predicted Per-Acre Easement Costs in Sample Area ...........78 Figure 7.2: Predicted Parcel-level Easement Costs in the Sample Area.......................80 Figure 8.1: Benefit Cost Ratios of Eligible Parcels in the Sample Area ......................83 Figure 8.2: Spatial Comparison of Selection Strategies ...............................................87 Figure 8.3: Comparison of Parcel Selection Results—OPT vs. QKP (α=10) ..............92 Figure 8.4: Comparison of Parcel Selection Results—OPT vs. QKP (α=100) ............93 Figure 8.5: Comparison of Parcel Selection Results—OPT vs. QKP (α=500) ............96 Figure 8.6: Comparison of Selection Results at all values of α ....................................97 Figure B.1: Impact of Year and Year2 on Average Easement Values.........................120
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ABSTRACT Public goods exist in space and recent research has shown that accounting for the location of their provision can have many implications for policy. Non-market valuation techniques are often utilized to value benefits from the provision of public goods in monetary terms. However, if willingness to pay (WTP) for public goods varies with the spatial location of provision, then valuation studies without spatial specificity may lead to suboptimal provision. WTP for land preservation provides a public-goods context where space matters. This study examines the benefits of preserving farm and forest parcels, which are derived from existing survey data from community-level and state-level choice experiments (CE) conducted in 2005 in Delaware and Connecticut (Johnston and Duke 2007; Duke and Johnston 2010). Data on applicant parcels, both accepted and rejected, to the Delaware Agricultural Lands Preservation Foundation (DALPF) from 1995 – 2003 (Allen, et al., 2006) are utilized to develop a hedonic model to estimate conservation easement costs across all eligible parcels in Sussex County, Delaware. Using a geographic information system, nonuse benefits and easement costs are predicted for every eligible parcel in the County to cost-effectively prioritize parcel selection. Parcels are selected under a fixed budget with four different prioritization strategies: benefit-targeting (BT), cost-targeting (CT), benefit-cost targeting (BCRT), and an binary linear programming optimization method (OPT). This initial prioritization effort does not account for variability in benefits associated with the spatial location of selected parcels.
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To investigate potential spatial interdependencies in parcel selection, a distinct ‘gravity’ is calculated for each parcel i to every other parcel j in a feasible set based on the size of the parcels and the distance amongst them. An exponent (β) on distance captures the friction of these distances (i.e., high β, higher friction). The gravity values are then scaled by α to determine the spatial synergy (SS) benefits for all parcels i and j. Parcels are selected utilizing an algorithm for a quadratic knapsack problem (QKP), which finds a subset of parcels that maximizes net social benefits subject to the constraint of the knapsack—in this case, the budget. Because the true value of α would reflect WTP for spatial proximity and this is unknown, a sensitivity analysis is conducted by varying α to allow for policy makers to evaluate the level at which spatial preference might affect optimal choice. Results from the County-level model demonstrate that optimal (OPT) and near-optimal (BCRT) parcel prioritizations both generate $435 million more net benefits to society compared to BT and nearly $236 million more net benefits than CT. Furthermore, accounting for spatial interdependence dramatically alters the optimal set of parcels selected and thus the preserved landscape. Any study ignoring space, when public preferences vary systematically with space, will thus produce a suboptimal provision of preservation. The sensitivity analysis shows the degree to which this spatial preference varies with changes in the hypothesized values of α and β. This outcome indicates that welfare analysis based on a standard valuation study might, in fact, provide information that leads to misguided policy. Since the primary land preservation decision is whether to preserve a specific parcel, consideration of systematic elements, such as spatial interdependence, is needed to avoid potentially arriving at suboptimal policy guidance.
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Chapter 1 INTRODUCTION 1.1 Motivation for Research Location, location, location—a catchphrase first attributed to a 1926 classified ad in the Chicago Tribune (Safire, 2009)—is widely used to describe the three most important attributes to consider when purchasing real estate. Location is also of paramount importance when determining policies that alter the provisions of public goods, such as open space, clean air, and clean water. Research on policy changes impacting public goods has shown that strikingly different welfare estimates can result from spending a budget for procuring a public good in different locations (Walsh, 2007) and consumers adjust their locational preferences and willingness to pay (WTP) in response to large changes in the amenity (Sieg, et al., 2004). While these results highlight locational effects of large-scale market interventions, spatial considerations are also of fundamental concern when considering marginal changes in public good provision. Non-market valuation techniques, such as stated preferences (SP), estimate the marginal changes. Evidence from nonmarket valuation studies has shown that spatial considerations may factor into the preferences of survey respondents (Johnston, Swallow, and Bauer, 2002). Furthermore, spatial locations of individual welfare estimates generated from SP studies have the potential to improve
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methods for deriving aggregate benefit measures from public goods across various spatial dimensions (Hynes, Hanley, and O’Donoghue, 2010) and the transfer of those benefits across multiple jurisdictions (Campbell, Hutchinson, and Scarpa, 2009). A key tool of public policy to induce changes in the provision of public goods is land preservation efforts that promote positive externalities by precluding conversion of land to other uses. Generally, these policies take the form of regulations (i.e. zoning), voluntary programs (i.e. easements), incentive-based systems (i.e. impact fees), or policies that are a combination of different strategies (Duke and Lynch, 2006). SP techniques are useful to determine the public demand for these amenities; however, there is currently a gap between the welfare information provided by SP and the spatial information needs of the policy context in which it is applied. For example, in farmland preservation programs, parcel prioritization necessitates locating specific parcels in space and declaring that parcel i is more valuable preserved in its current state than parcel j. Studies that value average parcels at an average spatial location are likely inadequate because parcel i and parcel j have other systematic attributes that influence their benefit to society. As soon as a specific parcel of land is targeted, parcel i has a distance to parcel j—and that interaction warrants consideration. Policies have the potential to be improved by including spatial factors and their potential influence on preservation efforts (Bateman, 2009). There is evidence from the literature that preservation of contiguous parcels of land can increase the provision of public goods, including preservation of species (Diamond, 1975), ecosystem services (Margules and Pressey, 2000), and landscape amenities (Ahern,
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1991). Jointly preserving contiguous parcels may offer an increase in net benefits that SP studies are overlooking, and the spatial interdependence of parcels may therefore be directly relevant to which parcel ought to be chosen. Welfare analysis based on SP studies might, in fact, produce the wrong answer – not because of a lack of information, but due to the fact that the information used might be leading to misguided policy. Simple alteration of SP surveys may be inadequate to appropriately capture the complexities of spatial preferences; however, the guidance provided here would allow the policy process to incorporate spatial factors and potentially work more effectively. Although much of the research applying SP methods to land use changes values average parcels at unknown locations, there are a couple of previous studies that value parcels of land with a specific location using SP (Breffle, Morey, and Lodder, 1998; Duke, et al., 2010). Breffle, Morey, and Lodder (1998) recognize the importance of location by modeling WTP from a contingent valuation (CV) analysis as a function of the distance to a single specific parcel, while Duke, et al. (2010) examine a specific, well-known parcel within the context of a choice experiment (CE). Both studies incorporate specific spatial location within a SP framework; however, the policy relevance is limited to the single parcel being studied. In contrast, this study predicts average values for multiple parcels within defined jurisdictions (i.e., state or community), but without a specific location, from CE data and then takes a new approach by introducing a methodology to incorporate the specific location of all parcels in the sample area (5,315 farmland and forest
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parcels). The inclusion of multiple specific sites of intervention captures the systematic impact of spatial preferences at a policy relevant scale. The purpose of this paper is not to estimate what the spatial synergy benefits might be, but to demonstrate that the parcels chosen in a cost-effective preservation effort may be the wrong ones if spatial interdependence is ignored.
1.2 Problem Setting Public goods are, by definition, non-excludable and non-rivalrous in consumption (Kolstad, 2003, p. 99). Because the external benefits produced from these goods are available to everyone in society, individuals tend to be willing to pay less than their true marginal value for receiving these benefits—known as the free rider problem. Thus, a market for a public good undersupplies the benefits of the good, which results in a Pareto inefficient outcome. This market failure creates a need for government entities to develop policy interventions to provide more of the good. These market interventions are quite common with public goods that produce positive externalities and policy examples include endangered species preservation, the creation of parkland, and farmland preservation efforts. Ideally, policy interventions would be designed to correct for the market failure by exactly accounting for the positive externality in a Pareto optimal outcome. However, in the real world, the true value of external benefits is difficult to accurately measure. Non-market valuation techniques offer potential avenues to approximate these benefits. While revealed preference (RP), such as hedonics and the travel-cost
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method, and SP (i.e., CV, CE) methods can both estimate values for public goods, SP benefit estimates are more thorough because they measure both use and non-use benefits derived from the public good not captured in markets. However, SP results typically generate average values and this may not guarantee a cost-effective outcome if any systematic element of WTP (i.e., spatial location) is overlooked. An additional complication in reaching an efficient outcome is the limited funding available for market interventions for the provision of public goods. This fact highlights the importance of choosing how to allocate these scarce resources. While efficiency may be the ultimate goal, the success of the policy interventions tend to rely on cost-effectiveness analysis (CEA) or benefit-cost analysis (BCA), when both benefits and costs can be measured in monetary terms. However, in practice, many policy interventions tend to distribute limited funding resources based on decision rules that are not necessarily based in sound economic principles (i.e., prioritizing investment by least cost interventions, ignoring potential benefits, until the budget is exhausted). Economic research has the potential to improve the policy process by developing prioritization strategies designed to incorporate systematic elements of WTP and increase the cost-effectiveness of public good market interventions.
1.3 The Problem Land preservation efforts seeking to increase (or maintain) the provision of public goods can improve benefit estimation by utilizing SP valuation techniques. However, even with improvements in benefit measures, different targeting strategies
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can vary dramatically in cost-effectiveness. Additionally, certain attributes of WTP may be overlooked in SP surveys, creating a systematic bias that can impact costeffectiveness of the policy intervention. Using farm and forest land preservation efforts in Sussex County, Delaware (herein referred to as the sample area) as a case study, this research will address the following questions: • Do economically prioritized parcel selection methods produce different outcomes than strategies currently employed in farmland preservation programs, and, if so, what is the gain in net benefits? • To what extent do net social benefits and the spatial orientation of selected parcels change if consideration is given to spatial interdependence at varying levels of importance within an optimal prioritization strategy? This effort is driven by Duke and Johnston (2010), where the authors note that an effective conservation strategy will establish benefits at the parcel level and use demand-side values to help prioritize investment. This study first predicts the benefits (WTP) for specific eligible farm and forest parcels in the sample area from CE data at the community level (Georgetown and Smyrna/Clayton Land Preservation Surveys) and the state-level (Delaware Land Preservation Survey) (Johnston and Duke, 2007; Duke and Johnston, 2010). The WTP predicted from both models are aggregated and capitalized to generate a total benefit value for each parcel. Next, data from Allen, et al. (2006) are utilized to develop a hedonic pricing model (HPM) that predicts the cost
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of placing an easement on the parcel to preclude development. Having both benefits and costs measured in dollars will allow the use of BCA to help prioritize the costeffective selection of parcels. This research demonstrates the gains in costeffectiveness from optimal and near-optimal targeting strategies compared to others currently in use. A methodology is developed to account for the spatial interdependence of parcels and its potential impact on cost-effective selection. This is accomplished by incorporating spatial synergy (SS) benefits into welfare estimates provided by SP in order to maximize net social benefits. A spatially-weighted matrix of SS values contains benefits that would accrue to jointly selected parcels. Adjustment of the relative importance of space in a particular jurisdiction can be accounted for by altering the value of a scalar (α) on the SS benefits. A sensitivity analysis shows the differences in net social benefits and the spatial patterns of parcel selection at three levels of α. It is important to note that WTP derived from the CE data is based on proposed setting to the survey respondent, creating an expectation of an average amount of agglomeration. The presumption for this analysis is that increased spatial agglomeration will increase WTP above and beyond the values observed in the CE so the values of α chosen for the sensitivity analysis are positive. This is because of the critical mass concept for farmland preservation. However, there is the potential in other settings for individuals to exhibit different agglomeration preferences, such as a preference for less agglomeration where α could take a negative value. For instance, a
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person may value high fragmentation for smaller amenities (i.e. community parks) that are closer to their home than large, agglomerated amenities further from their residence. While other preferences are possible, this study follows the assumption that increased agglomeration of public good interventions will generate extra benefits to society as a whole.
1.4 Contribution to the Literature This research focuses on public goods dependent on government intervention. This study first moves the literature forward by applying monetized public preferences at the policy level—individual parcel preservation decisions—and strengthens the recent related works highlighting the gains in cost-effectiveness from optimizing selection strategies. This study extends the results of Duke and Johnston (2010) to predict parcel-level WTP (in dollars) for public goods demanded by the general public. In combination with easement cost estimates from a HPM, these values are used to drive optimal and near-optimal targeting strategies. Results show benefit cost ratio targeting (BCRT) and a binary linear programming optimization (OPT) produce similar results with net benefits exceeding $812 million for a $30 million budget constraint, outperforming suboptimal strategies of benefit targeting (BT) and cost targeting (CT), by $438 million and $236 million, respectively. These results supports the findings of Messer and Allen (2010), who demonstrated potential cost-effectiveness gains in Delaware Agricultural Lands Preservation Foundation (DALPF) efforts by incorporating both benefits and costs to
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develop optimal targeting methods. However, benefits are valued in a different manner—by preservation experts through the analytic hierarchy process (AHP)—and applied with a non-monetized, weighted index (Messer and Allen, 2010). The main difference in these approaches lies in the methods used to obtain preferences. Comparison of AHP and CE on similar valuation efforts is limited; however, research on preservation policy options in Scotland finds preferences differ between the methods, and because of the price component, CE may offer a “more realistic reflection of the trade-offs to be made” than AHP (Moran, et al. 2007, p.52). Furthermore, this study, by soliciting preferences with a CE, allows for the monetization of preferences and subsequently, an appropriate use of BCA that can be useful to policy decision-making. BCA does have its fair share of issues when applied to environmental policies (see Hanley, 1992); however, despite these concerns, BCA remains of great importance in informing policy decisions and may still be the best instrument available to that end (Pearce, 1998). The second contribution of this research is an innovative methodology utilizing principles of gravity to integrate spatial location and interaction of public goods with SP techniques. Location is typically unaccounted for in SP methods, which can lead to a systematic bias that impacts the cost-effectiveness of public good market intervention if ignored within an otherwise optimal targeting strategy. Additionally, various spatial maps are provided to help overcome the usual disconnect between academic research and policy on the ground and provide an accessible tool to
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visualize the problem and improve the targeting and effectiveness of preservation efforts. Table 1.1 shows recent spatial SP research based on the spatial unit of interest (single vs. multiple sites) and the level at which WTP is examined. The simple schematic shows how this research fills a gap in the literature by combining public preferences at the parcel level at multiple sites of intervention. Borchers (2010) examines household (HH) WTP for preservation of a single site in Delaware and Breffle, Morey, and Lodder (1998) and Duke, et al. (2010) are discussed previously in section 1.1. This research builds upon related works examining multiple sites of intervention recently published in Ireland—Campbell, Hutchinson, and Scarpa (2009) and Hynes, Hanley, and O’Donoghue (2010)—which integrate spatial information into SP results. Campbell, Hutchinson, and Scarpa (2009) generate household WTP with a CE for landscape improvements. These WTP estimates exhibit spatial autocorrelation, an indication of their spatial interdependence, and the differences in WTP are smoothed into benefit gradients irrespective of political or property boundaries, highlighting very general areas to target for preservation efforts. Hynes, Hanley, and O’Donoghue (2010) utilize a CV study of farmer’s WTP to improve habitat for an endangered bird species. The study does not capture the WTP of the general public. Their combinatorial optimization approach can aggregate individual WTP to different general spatial scales (i.e., electoral division,
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Table 1.1: State of Spatial Valuation Research Household WTP
Parcel Owner WTP
Parcel WTP from Public
Single point of interest
Borchers (2010)
N/A
Breffle, Morey, and Lodder, (1998); Duke, et al., (2010)
Multiple points of Interest
Campbell, Hutchinson, and Scarpa (2009)
Hynes, Hanley, and O’Donoghue (2010)
This study
Source: Author review of the relevant literature
county level, national) and accounts for spatial differences in the preferences of the population. This research differs from both studies such that the site of the intervention is the parcel-level. Actual policy is typically conducted at this unit of analysis, so this research has the potential to have a more direct relevance to policy interventions. Benefit gradients (Campbell, Hutchinson, and Scarpa, 2009) and aggregate WTP by political jurisdictions (Hynes, Hanley, and O’Donoghue, 2010) can help inform policy in a general sense, but lacks the ability to link the benefits to the actual site of policy intervention.
1.5 Organization of the Study This remainder of this thesis is organized as follows. Chapter 2 provides a background on research investigating the importance of a spatial perspective for optimal public goods provision and a general history of land preservation policies.
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The third chapter is a literature review focused on the application of spatial analysis techniques to preservation, justification for farmland preservation efforts, the valuation of nonmarket benefits from agriculture, and optimal selection strategies. Chapter 4 details the economic background and methodology for this research. Chapter 5 defines the sample area, while Chapters 6 and 7 detail the data utilized in developing benefits (WTP) and easement costs for parcels in the sample area. Chapter 8 presents the results of parcel prioritization both with and without consideration of spatial dimensions. The final chapter provides concluding remarks and insights into the policy implications of the results. Appendix A is provided to detail the collecting of new spatial data for these analyses. Appendix B details the development of the preliminary HPM and provides results of this model and two other variations that were not utilized as the final model.
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Chapter 2 BACKGROUND
2.1 Spatial Location Spatial preferences are beginning to receive increasing research attention, and this study offers a new method for assessing spatial interdependencies of benefits and how it will affect optimal decisions for public good market interventions. The spatial distribution of land use lacks uniformity and explaining this heterogeneity allows for more accurate prediction of the “impacts on the environment, changes in the demand for public goods, and changes in the face of the landscape that matter so much to human well-being” (Bockstael, 1996, p. 1179). In their recent review of valuation studies, Bergstrom and Ready (2009) note the investigation of spatial impacts on amenity values as an important avenue for future research efforts. The use of spatial analysis tools, including the use of geographic information systems (GIS), has the potential to enhance economic modeling capabilities and to assist in the understanding of the models by providing a visualization of the output (Bateman, Lovett, and Brainard, 2003). Incorporation of spatial data may improve the effectiveness of land use policies by revealing the scale of and spatial distribution of WTP, although these elements tend to be overlooked in SP research (Campbell, Hutchinson, and Scarpa, 2009).
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2.2 Brief Summary of Farmland Preservation Currently in the United States (U.S.), farmland conservation and preservation efforts exist at many levels of government, from large national programs administered by the U.S. Department of Agriculture (USDA), to efforts at the state, county and municipal levels. Initiatives began to develop at the community level in the 1960s and 1970s, parallel to the environmental movement, and were rooted in concerns about rapid urban development and the growing scarcity of adequate land for food production (Lacy, 2006). Conversion of farmland in the U.S. has been intensifying largely unchecked as urban fringe development has increased the size of metropolitan areas from nearly 26 million acres in 1960 to approximately 65 million acres in 2000, and suburban sprawl expands as Americans demand more low density, large acre lot, housing options (Heimlich and Anderson, 2001). As a point of reference, the population in the sample area for this study (Sussex County, Delaware) grew nearly 25.9% between 2000 and 2010 (U.S. Census, 2010). Environmental concerns about the impacts of agriculture, including soil erosion, water and air pollution, aquifer depletion, and increased fertilizer and pesticide use, also affect policy (Horrigan, Lawrence, and Walker, 2002). These concerns have manifested in federal programs that seek to preserve and improve working agricultural land (i.e., preservation), such as the USDA’s Farm and Ranch Land Protection Program (FRPP), and others, such as the USDA’s Conservation Reserve Program (CRP), that have the goal of retiring farmland from production to reduce environmental damage (i.e., conservation).
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The existence of agricultural land produces a number of public goods. These goods are in essence positive externalities, such as scenic pastoral views, preclusion of development, and habitat for wildlife, and provide associated use and non-use benefits to the local community and society as a whole (Duke, Johnston, and Campson, 2007). When a farm parcel is sold for development, the demand for these public goods from agriculture does not impact the land price and thus creates a market failure (Duke, 2009). Because the free market does not provide the desired level of environmental benefits, there is justification for government intervention via preservation efforts (Gardner, 1977). These programs attempt to preserve the positive externalities that accrue to the general public by slowing development and attempting to adjust the market forces that have misallocated resources. As the movement to preserve farmland has grown since the 1970s, so have the number of non-profit and public conservation agencies and the amount spent on preservation activities. According to the 2005 National Land Trust Census Report, there are 1,667 private land trusts active throughout the U.S. and the pace of their conservation efforts has tripled since 1995 (Aldrich and Wyerman, 2006). Public expenditures by the federal government, authorized by the Food, Conservation, and Energy Act of 2008 (also known as the 2008 Farm Bill) will approach $26 billion1 on various farmland preservation efforts through 2012. These schemes are not limited to the U.S., as evidenced by the European Union’s plan to spend nearly €36 billion2 on agri-environmental payments from 2007 through 2013. Given the high levels of
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historic expenditures, it becomes necessary to assess the methods used to allocate these resources. Common methods to preserve farmland include the purchase of agricultural conservation easements (PACE), purchase of development rights (PDR), and transfer of development rights (TDR), where landowners voluntarily offer to have restrictions on development and management practices placed on their land in exchange for payment (Lynch and Musser, 2001). Despite the seemingly large outlay of funding, individual programs are constrained by limited budgets and cannot preserve all of the available land willingly offered by landowners. The typical conservation decision involves the selection of parcels from a pool of eligible applicants until the budget is exhausted. The bulk of the economic research on conservation focuses on how these selections are made. The measurements of benefits used to make these decisions are often a set of weighted values of environmental components, such as water quality, biodiversity, and soil erosion, which comprise a benefit index3. In the case of farmland preservation, these benefits arise from the idea of multifunctional agriculture, where farmland produces valuable nonmarket public goods in addition to commodity production (Randall, 2002; Batie, 2003; Abler, 2004). Appropriate measurement of these benefits is critical, as they direct billions of dollars in conservation spending (Hajkowicz, Collins, and Cattaneo, 2009). Unfortunately, benefit indices often do not align well with the positive externalities associated with land use. Payments made to landowners in preservation
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easements reflect development values determined by land market prices and fail to incorporate the social value of nonmarket benefits (Duke and Lynch, 2006). Economists use valuation methods to overcome this informational gap in hopes of better linking the way parcels are selected and their social benefits. Furthermore, many recent research efforts have demonstrated the gains in cost-effectiveness by including costs in the conservation decision (see for examples Ando et al., 1998; Balmford et al., 2000; Polasky, Camm, and Garber-Yonts, 2001; Naidoo et al. 2006; Messer and Allen, 2010).
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NOTES
1
– Author calculation based on data retrieved from Claassen (2010).
2
– Author calculation based on data retrieved from the European Union DirectorateGeneral for Agriculture and Rural Development (2009).
3
– See Hajkowicz, Collins, and Cattaneo (2009) for an overview of agrienvironmental indices (AEIs).
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Chapter 3 LITERATURE REVIEW
3.1 A Spatial Perspective for Public Goods There is mounting evidence that public good policies can be implemented more effectively by utilizing spatial tools, along with BCA, to identify desirable target areas for conservation and preservation efforts (Walpole and Sinden, 1997; van der Horst, 2006; Naidoo and Ricketts, 2006; van der Horst, 2007). Lovett and Bateman (1999) note the use of GIS in association with SP valuation methods is not as common as in RP studies; however, recent research has recognized several advantages of including a spatial perspective in SP valuation. Location of public goods is a critical factor that is not typically accounted for in SP surveys, and developing methods to integrate this information has the potential to improve the cost-effectiveness of policy efforts. SP approaches, combined with GIS tools, have the potential to generalize average values across space and improve the means for transfer of benefits across spatial scales (Eade and Moran, 1996; Campbell, Hutchinson, and Scarpa, 2009; Hynes, Hanley, and O’Donoghue, 2010). Recognition of heterogeneity in preferences and socioeconomic characteristics in different locations can also provide more robust WTP values when aggregating benefits by avoiding potential over/under estimation of WTP (Hynes, Hanley, and O’Donoghue, 2010).
19
Campbell, Hutchinson, and Scarpa (2009) conduct a CE in Ireland to determine household WTP for four rural landscape improvements and then smooth the WTP into benefit gradients using spatial neighborhood structures. The results exhibited strong positive spatial autocorrelation—a pattern of neighboring areas demonstrating similar WTP. The results indicate the demand for preservation of agricultural land attributes is highest where density agricultural land is highest and provides benefit maps that can help direct preservation investment (Campbell, Hutchinson, and Scarpa, 2009). Hynes, Hanley, and O’Donoghue (2010) take a national random sample from a CV survey that surveyed farmers, not the general public, to determine their WTP for providing habitat for an endangered farmland bird (corncrake) in Ireland. A synthetic estimation technique that statistically matches survey data with Ireland’s Census Of Agriculture data—via farm size, farm system, and soil type—was employed to generate a simulated population dataset and estimates WTP by electoral district from the national data (Hynes, Hanley and O’Donoghue, 2010). WTP measures were reweighted based on demographic/socioeconomic characteristics of each specific targeted region and the different aggregation methods produced different results (Hynes, Hanley and O’Donoghue, 2010). The main contribution of this study is that it provides methodology to systematically aggregate valuation data that accounts for heterogeneity in the target population and allows for benefit transfer to a variety of spatial scales, saving time and costly data collection efforts (Hynes, Hanley and O’Donoghue, 2010).
20
3.2 Justification for Intervention in Land Markets A popular tool for correcting for market failures in environmental goods is slowing land conversion through government preservation initiatives. Land preservation programs target a variety of goals through investment in outright purchases or easements to preclude development. Validation for these expenditures includes, but is not limited to, supporting food production and local agricultural viability, controlling urban and suburban development, and preserving open space and other environmental benefits (Gardner, 1977; Kline and Wichelns, 1996). Gardner (1977) disputes the soundness of the argument for the promotion of “food and fiber” production, as free markets will allocate the socially optimal level of agricultural land use. However, Gardner (1977) notes that policy action may be required to correct the market failure that exists for providing the optimal amount of environmental benefits derived from agricultural preservation. Current efforts still tend to pursue broad programs with multiple objectives through the preservation of farmland despite the potential efficiency gains from cost-effectively targeting environmental benefits directly (Rosenberger, 1998). Many studies support the view that the general public derives value from environmental benefits generated by the preservation of agricultural lands (Bergstrom, Dillman, and Stoll, 1985; Kline and Wichelns, 1996; Rosenberger, 1998; Duke and Ilvento, 2004). The multifunctional dimension of these efforts provides an array of public goods to society but often complicates the parcel selection process (Deaton, Norris, and Hoehn, 2003). Attempting to satisfy the demand for many nonmarket
21
goods through a single conservation action is challenging, and multiple aims may best be pursued with multiple methods (Lynch and Musser, 2001). However, preservation programs generally use parcel preservation as the sole method to accumulate public good benefits and tend to target parcels through the use of an index of multiple benefits to achieve these goals.
3.3 Quantifying Preservation Benefits Benefits measures used to drive selection can take the form of benefit indices that typically measure supply-side benefits, or values that measure the demand for the targeted benefits, such as monetized benefits (WTP) derived from RP and SP valuation or weighted indices from the analytical hierarchy process (AHP). Benefit indices are typically comprised of a series of quantifiable benefits that establish a relative value of one parcel compared to others under consideration (Hajkowicz, Collins, and Cattaneo, 2009). An example of one of these indexes is the USDA Natural Resources Conservation Services’ (NRCS) Land Evaluation (LE) and Site Assessment (SA) system. The LESA classification uses two parts—the LE component measures soil-based factors, and the SA component measures factors, such as agricultural productivity, development risk, and the value of public goods—which are then weighted by an appropriate committee and a total LESA score is derived for each parcel (Pease and Coughlin, 1996). The criteria used for LESA tend to focus on characteristics associated with agricultural production with little attention given to public demand for nonmarket goods (Duke and Johnston, 2010). Additionally, these
22
index scores are not measured in monetary terms, making conventional BCA difficult to implement. Determining the specific benefits demanded by society would facilitate and improve targeting of parcels to maximize overall net benefits (Smith, 2006). Smith (2006) considers the use of AHP, which uses panels of relevant stakeholders to determine relative weights for environmental amenities, as a “stop-gap strategy” (p. 1171) until methods that measure true demand for benefits from agriculture preservation can be developed and refined. AHP, developed by Saaty (1980), has been utilized for farmland preservation in previous research, including multiple studies in Delaware (Duke and Aull-Hyde, 2002; Messer and Allen, 2010). This strategy helps capture the demand for benefits but still lacks the ability to derive a monetized value for environmental amenities from multifunctional agriculture. Properly executed nonmarket valuation studies characterize the demand for environmental benefits from preserved agriculture through monetized WTP estimates and can allow for BCA to optimize the parcel selection process. Nonmarket valuation techniques utilized in previous research include SP methods, such as CE and CV, and RP methods, including HPM and the travel cost method (TCM) (Bergstrom and Ready, 2009). SP techniques elicit direct responses from the public through the use of surveys to determine WTP for a particular environmental amenity. RP methods also determine WTP; however, they do so through the use of proxies, such as property values, travel costs, and wages. SP methods are preferred for nonmarket amenity valuation from farmland because survey results measure benefit
23
values directly while RP studies use data on market goods to indirectly infer benefit values (Duke, 2009). The choice experiment (CE) is a stated preference methodology that is gaining popularity within the field of environmental economics. Choice experiments typically ask respondents to choose between varying collections of environmental benefits, illustrated by their characteristics and the levels at which they occur (Hanley, Wright, and Adamowicz, 1998). This choice between different packages of attributes differs from other stated preference techniques, where respondents are asked to simply rank the attributes (Adamowicz et al., 1998). This method can be useful in the valuation of environmental benefits from preservation, as it allows respondents to make substitutions, or trade-offs, between environmental amenity bundles and avoids the potential information accuracy issues involved with contingent valuation (Boxall et al., 1996). Boxall et al. (1996) also notes another advantage with choice experiments is that the model structures employed are similar in nature to those used with other valuation techniques. Furthermore, they can provide information on both use and non-use values, generate substantial data variation, and deliver qualitative information about the views of the respondents (Banzhaf, 2010). Since the 1980s, economists have been developing these valuation techniques to improve the targeting of environmental benefits from agriculture in areas experiencing rapid conversion of farmland to other uses. CV studies typically use a survey to directly ask participants their WTP to preserve farmland by providing information on the amount of acreage to be preserved and severity of the conversion
24
risk, among others (Bergstrom and Ready, 2009). Many studies focused on CV to estimate WTP for farmland preservation and Bergstrom and Ready (2009) report the range of per acre WTP between $0.0327 and $0.0001 from various CV efforts. In the early 2000s, economists began turning toward different SP survey methods that asked respondents to choose an attribute bundle between a set of multiple choices (Bergstrom and Ready, 2009). In their review of these studies, Bergstrom and Ready (2009) conclude that the mean per-acre WTP for multi-attribute studies ($0.3463) was substantially higher than for CV studies ($0.0114). These multiattribute methods are more realistic and closely related to actual market environments because they include several features for each respondent to consider instead of just one (Roe, Irwin, and Morrow-Jones, 2004). For example, Johnston and Duke (2007) conducted a CE in Delaware and Connecticut and demonstrated notable differences in WTP for farmland preserved under different policy processes.
3.4 Costs of Preservation The other major aspect of conservation action that is often overlooked is the costs associated with preservation. Generally speaking, conservation planners tend to prioritize benefits without consideration of the overall goals of the program and the actions needed to conserve the actual parcels (Wilson, Carwardine, and Possingham, 2009). These costs can impact the cost-effectiveness of a conservation program and include, but are not limited to, acquisition costs, management costs, transaction costs, and opportunity costs (Naidoo et al., 2006). Naidoo et al. (2006) also notes that non-
25
monetary proxies, such as total area, distance, or development threat, are used in conservation planning but often assume spatial homogeneity of costs. This assumption can be problematic and difficult to justify, and monetized costs should be utilized whenever possible (Naidoo et al., 2006). Numerous ecological studies, ranging from selection of reserves for endangered species in the western U.S. (Ando et al., 1998), global priority setting for mammal preservation (Balmford et al., 2000), and preservation of terrestrial vertebrates in Oregon (Polasky, Camm, and Garber-Yonts, 2001), have shown that incorporating costs into a budget-constrained conservation decision results in more cost-effective results. For instance, Ando et al. (1998) demonstrated that the same number of species could be preserved for 30% of the total cost if the heterogeneity of land prices was taken into consideration. Large federal government programs, like the CRP, have also adopted strategies that include conservation costs. The CRP incorporates costs as a weighted benefit within its Environmental Benefits Index (EBI), with higher scores going to landowners who offer lower annual per acre payments (Ribaudo et al., 2001). However, including costs in a non-monetized fashion, like the EBI, leaves open the possibility of exploitation by stakeholders with high environmental benefits and low opportunity costs in CRP auctions (Kirwan, Lubowski, and Roberts, 2005). This evidence highlights the importance of including monetized costs into the conservation decision to generate more cost-effective allocations of limited funding resources.
26
3.5 Strategies for Prioritizing Selection By including both benefits and costs, a BCA can be performed and optimal and near-optimal targeting strategies can be utilized to prioritize parcel selection. BCRT ranks parcels from highest to lowest based on the benefit cost ratio and selects projects until the conservation funds are exhausted, maximizing environmental benefits per dollar spent in the process (Ferraro, 2003). This selection strategy also maximizes social welfare, defined as the sum of consumer surplus, producer surplus, and overall environmental benefits (Wu, Zilberman and Babcock, 2001). Babcock et al. (1997) describes benefit-cost targeting as optimal, and demonstrate how relative variability and correlation of benefits and costs can impact total benefits accrued from suboptimal strategies (i.e. BT, CT). As an real world example, Baltimore County, Maryland moved away from benefit targeting and adopted a BCRT strategy in 2007, which resulted in the protection of an additional 680 acres with a cost savings of $5.4 million in the first three years of use (Kaiser and Messer, 2011, p. 271). In theory, BCRT is simply decision rule that will maximize net benefits from a constrained budget and produce a cost-effective outcome. However, a host of real external complexities can alter the effectiveness of this strategy. Taking farmland out of production or placing an easement on a parcel to preclude development can bring previously undeveloped land into production, a concept known also known as slippage (Wu, 2000; Wu, Zilberman, and Babcock, 2001). Ignoring land market feedbacks can actually accelerate conversion and render conservation spending
27
counterproductive (Armsworth, et al., 2006). Environmental benefits may not accrue if certain thresholds are not reached by a conservation effort (Wu, Adams, and Boggess, 2000). An additional force that can impact conservation decisions is the threat of development. Due to the correlation between probability of land conversion and cost of acquisition, trade-offs must be recognized to avoid suboptimal results (Newburn, et al., 2005). Directing resources toward areas at imminent risk for development can limit the immediate loss of the biological benefits and characteristics that provide public amenities (Costello and Polasky, 2004; Messer, 2006). Consideration must be given to these factors when selecting targeting criteria and true BCRT may not be the benefit maximizing strategy (Wu, Zilberman, and Babcock, 2001). Political and administrative restrictions can also complicate the selection process; however, these impediments can be expressed as mathematical constraints that can be handled with optimization programming methods, such as BLP. These methods have the ability to manage multiple program constraints to generate the maximum benefits for a given budget (Balmford et al., 2000; Messer, 2006).
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Chapter 4 METHODOLOGY
4.1 Why Economists Think Space is Important The importance of space and the reason policy makers should make every effort to include it in the decision-making process for the provision of public goods boils down to one economic principle: cost-effectiveness. The external benefits of public goods are sometimes locational, and this spatial dynamic impacts the configuration of the public goods within a landscape. This research argues that a systematic bias in the selection of public good investments is possible if spatial location of and spatial interactions between these investments are ignored. In other words, policy makers informed by methods that do not explicitly consider location are potentially making the wrong investment choices from a cost-effectiveness perspective. The external benefits of public goods are undersupplied without government actions. Cost-effectiveness occurs when resources dedicated to providing the public good are allocation in a manner that maximizes their net benefits to society. First, the external benefits of the public good need to be measured and economic research has developed non-market valuation techniques to derive these benefits. RP techniques (i.e, HPM, TCM) infer values from actual markets and can capture spatial
29
variation in the characteristics of a public good. However, these methods cannot account for non-use values and can inform policy incorrectly from a cost-effectiveness standpoint. Fortunately, SP techniques (i.e., CV, CE) can directly capture both use and non-use values associated with public goods. While SP methods offer many advantages in measuring social benefits of public goods, it cannot possibly value all factors that contribute to WTP. Systematic elements, such as spatial location, are often overlooked but are important because people tend to value improvements more the closer they are to them. This research uses principles of gravity1 and spatial interaction to posit an innovative methodology to explicitly account to for increased social benefits from procuring public goods in close proximity to one another, in conjunction with WTP predicted from a comprehensive CE survey. The following sections detail the methods and principles used to support the empirical analysis.
4.2 Spatial Synergy Benefits Spatial synergy (SS) benefits in this empirical analysis are defined as an additional joint benefit accruing to parcels of land preserved in close proximity to one another. For instance, two 20-acre forest parcels preserved in isolation may produce $10 and $20 of external benefits respectively as a habitat for an endangered tree frog species. However, if two contiguous forest parcels of similar size and external benefit were to be preserved, the endangered tree frog population may increase due to less fragmented habitat, generating $100 of social benefits. That additional $70 in
30
additional benefits may not be captured in a policy that does not account for the specific spatial location of preservation efforts. These extra benefits are not an “agglomeration bonus” as others (Parkhurst, et al., 2002; Parkhurst and Shogren, 2007; Drechsler, et al., 2010) have defined it. Previous experimental (Parkhurst et al., 2002; Parkhurst and Shogren, 2007), and empirical (Drechsler et al., 2010) agglomeration bonus research in land preservation has focused on the development of payment schemes to incentive landowners to overcome cooperation difficulties and preserve contiguous tracts of land for biodiversity conservation. The “bonus” in this study is not paid and this effort is not attempting to induce landowner cooperation. It takes a different direction by applying the specific spatial location of many parcels within a sample area and tracking the hypothetical value of agglomerated preservation in an empirical application under an optimal selection strategy that maximizes net benefits.
4.3 Principles of Spatial Gravity A hypothetical SS benefit structure is used to augment the predicted WTP by including a locational dynamic not explicitly accounted for in the CE survey. It is applied here by incorporating the basic principles of Newtonian gravity and developing a “gravity” for each parcel of land to every other parcel of land. This “gravity” is a function of the size of the parcels and the distance between them. A scalar is then used to determine the magnitude of the SS benefits, an indication of the relative importance of SS in a policy jurisdiction.
31
The spatial interaction of the gravity equation is based on the premise that the relationship or attraction between two objects is proportional to their size (mass) and inversely proportional to distance between the objects (Rodrigue et al., 2009). Gravity models have been utilized in many social science disciplines to explain phenomena such as the flow of goods across regional and national borders (Anderson, 1979; Anderson and van Winccop, 2003) and interurban transportation (Quandt, 1965). A simple form of the gravity model, presented by Haynes and Fotheringham (1984), takes the form:
(4.1)
where Tij is the interaction of areas I and j, Pi and Pj are the population (mass) of I and j, and Dij is the distance between the I and j. Thus, spatial interaction between locations i and j are proportional to their respective mass divided by their distance. In this project, the size of parcels in acres and the distance in meters between their respective parcel edges are used to calculate the spatial interactions of all parcels. This gravity is then scaled by a spatial interdependence constant (α) to calculate the additional benefits of preserving parcels i and j. The resulting equations take the form:
Gij =
Ai A j Dijβ
(4.2)
SSij = αGij
(4.3)
€
€
32
(4.4)
TBi = NBi + SSij
where Gij is the gravity of parcels i and j, Ai and Aj are the acreage of parcels of i and j
€ respectively, Dij is the distance between the i and j, and the β exponent on distance represents the variability of the impact of friction caused by distance. The larger the exponent, the more the expected benefit level will be reduced as the distance between the parcels increases (Haynes and Fotheringham, 1984). Additionally, SSij are the spatial synergy benefits in dollars to society if parcels I and j are both preserved, α is a spatial interdependence constant, TBi is the total benefits of preserving parcel i, and
NBi = WTPi − yˆ i , where NBi is the net benefits of preserving parcel i, WTPi is total WTP for parcel i, and yˆ i is the predicted easement cost for parcel i.
€
€
€
€ Constants α and β 4.4 Unknown The true values for α and β within the context of this application of the principles of gravity are unknown. Because the values are unknown, a rationale is provided here for the range of values selected. Sensitivity analysis helps assess the effects of changing these values. The choice of α scales the gravity between two parcels to produce the SS benefits (equation 4.3) and accordingly, larger values will increase the size and impact of the SS benefits. By varying the size of α, changes in the bonus structure can induce alterations in the optimal set of parcels selected. Figure 4.1 demonstrates how SIij changes with distance for two parcels of average size (55.2 acres) at different levels
33
$2,000
Benefit Bonus ($)
$1,800 $1,600 $1,400 $1,200
alpha=500
$1,000
alpha=100
$800
alpha=10
$600 $400 $200 $0 0
2000 4000 6000 8000 10000 12000 14000
Distance (meters) Figure 4.1: Benefit Bonus vs. Distance with Varying α for Two Average Parcels
of α. As a numerical example, assume three identical parcels, each with 55.2 acres, where parcels 1 and 2 are contiguous (D12 = 1), while parcel 3 is 10,000 meters from parcels 1 and 2 (D13 = D23 = 10,000). Assigning a low value to the scalar (α = 10) and a value of one to the exponent on distance (β = 1), the benefit bonus for simultaneously preserving parcels 1 and 2 (SI12 ) would be $30,415.23 compared to just $3.04 for the joint preservation of parcels 1 and 3 (SI13 ) or parcels 2 and 3 (SI23). Increasing α to 100 changes the bonuses (SI12 = $304,152.25, SI13 = SI23 = $30.42) and demonstrates that a larger α can potentially increase the probability of preserving parcel 1 and parcel 2 together from a larger set of eligible parcels in space.
34
A sensitivity analysis of α = {10, 100, 500} allows for a categorization of spatial interdependence in relation to parcel selection. This characterization defines selecting parcels in close€proximity to be of low importance (α = 10), of moderate importance (α = 100), or of high importance (α = 500). As α gets larger, it is expected that selected parcels will begin to cluster together, thus the choice of α can dramatically impact the preservation landscape. Although α is a continuous variable, it cannot be modeled as such because the iterative process needed to solve the branch-and-bound algorithm requires a discrete choice of α. Ultimately, this analysis allows for the policy makers to determine the relative importance of spatial interdependence in their jurisdiction when making preservation decisions. The value of β governs the relationship between the gravity of two parcels and the distance between them. Because this analysis is the first application of the principles of gravity to land preservation, to the author’s knowledge, the “correct” value for β is unknown and there is no guidance to be found in the literature. For this analysis, a value of 1 is chosen for β as it allows for parcels to be nearby, but not necessarily contiguous, to receive a bonus but that value of the bonus would drop soon
35
800
Benefit Bonus ($)
700 600 500 β=0.5
400
β=1
300
β=2
200 100 0 0
50
100
150
200
250
300
350
400
450
500
Distance (meters) Figure 4.2: Benefit Bonus vs. Distance with Different β for Two Average Parcels
afterwards. At a larger β, such as β = 2, the friction of distance would quickly minimize the potential for spatial benefit bonuses and these bonuses would likely only accrue to contiguous parcels. A smaller β, such as β = 0.5, could allow bonuses to accumulate for simultaneous preservation of parcels not considered spatially relevant. Figure 4.2 depicts the impact of different β’s on two parcels of average size (55.2 acres). 4.5 Suboptimal and Near-Optimal Decision Rules This section outlines the decision rules used to compare the costeffectiveness of parcel selection in the sample area with a preservation budget
36
Table 4.1: Comparison of Decision Rules—Six Parcels, $15 Budget Parcel ID A*** B C**, *** D** E* F**
WTP ($)
Easement Cost ($)
Benefit-Cost Ratio
15 11 9 6 19 4
9 8 6 5 15 4
1.67 1.38 1.50 1.20 1.27 1.00
Net Social Benefits ($) 6 3 3 1 4 0
* BT selects parcel E for $15, with net social benefit of $4. ** CT selects parcels F, D, and C for $15, with net social benefit of $4. *** BCRT selects parcels A and C for $15, with net social benefit of $9. Source: Hypothetical example created by the author.
constraint of $30 million dollars. These decision rules are benefit-targeting (BT), cost-targeting (CT), and benefit-cost ratio targeting (BCRT). Table 4.1 provides a simple example of the relative performance of these three decision rules with six hypothetical parcels and a preservation budget of $15. BT and CT are identified in the economics literature as suboptimal strategies for procuring external benefits from public goods because the decision rule prioritizes investment on either benefits or costs in isolation from the other critical element (Wu, Zilberman, and Babcock, 2001). BT selects the parcels with the highest total benefits until the budget is spent. CT selects the parcels with the lowest per-acre easement costs from the set of parcels predicted to have positive WTP. Parcels with negative WTP are excluded from the CT decision process because it is assumed that policy makers would not willingly select parcels that the public has a demonstrated disutility
37
for under a simple decision rule process (see section 6.3, p. 64 for further discussion of parcels with negative WTP). Lastly, BCRT produces a near-optimal prioritization strategy because it effectively incorporates both benefits and costs into the selection choice (Messer, 2006). It is a decision rule that ranks parcels based on their benefit to cost ratio,
WTPi
€
yˆ i , and selects parcels with the highest ratio until the budget is exhausted.
4.6 Binary Linear Programming In order to determine on an optimal set of parcels that maximizes net social benefits given the budget constraint, the following BLP model, herein referred to as OPT, is utilized: I
Max TB = ∑WTPiσ i σ
i=1
(4.5)
I
s.t.
∑c σ i
i
≤b
i=1
where TB is total benefits, WTPi is the aggregate capitalized WTP for parcel i, σ i is a
€ decision variables indicating whether or not parcel i is selected, c is the binary i
€ cost of parcel i, and b is the budget constraint. predicted easement
€
€ 4.7 Spatially Dynamic Optimization This section explains a methodology for assessing the potential impact of spatial synergy (SS) benefit “bonuses” given to preserved parcels in close proximity to
38
one another on optimal parcel prioritization. This involves solving for the optimal set of preservation from the 5,315 agricultural and forest parcels in the sample area with the joint benefits of spatial agglomeration factored in the decision at varying levels of importance. With α ∈{10,100,500} and β = 1 as defined in section 4.4, the selection of an optimal set of parcels in the presence of hypothetical spatial synergy (SS) benefits € can proceed. A branch and bound algorithm is utilized to solve a quadratic knapsack
problem (QKP) (Pisinger, 2007) and evaluate all possible combination of parcel bundles with the added value of spatial agglomeration (SSij) to determine the set that maximizes net benefits subject to a budget constraint. Max TB = σ T Ψα σ σ
(4.6)
s.t. σ T c ≤ b
σ ∈{0,1} where TB is total benefits, σ is a vector of binary decision variables , σT is the € of vector σ, Ψ is a matrix where the diagonal is the total capitalized transpose α
aggregate WTP for each parcel and the remaining elements are parcel gravities scaled by α (SSij), c€is the vector of easement costs for every parcel, and b is the budget constraint. This algorithm is run with the three specified values of α and outcomes will be compared by total net benefits and by the spatial patterns of preservation that result.
39
NOTES
1
– The idea of incorporating gravity into this analysis was first proposed by Dr.
Joshua M. Duke.
40
Chapter 5 DATA: DEFINING THE SAMPLE AREA
5.1 Formation of the Sample Area This study concentrates on the preservation of agricultural land and forests in Delaware, similar to the efforts of DALPF1, the state’s preservation program. The geographic focus is Sussex County (the sample area) because of existing WTP data from studies conducted in Delaware at both the state and community (Georgetown and Smyrna/Clayton) level (Johnston and Duke, 2007, 2009a; Duke and Johnston, 2010) that will be utilized to predict benefits for Delaware households at the parcel level. Previous research has extrapolated the results of these choice models to value hypothetical parcels in multiple municipalities in the County (Duke, Johnston, and Campson, 2007) and this study will use a similar technique to value actual parcels. After applying the conditions described in the following sections, the final study area contains 5,316 parcels in Sussex County that are deemed eligible for selection into a preservation easement program. These parcels have acreage above the lower bound of 10, are dominated by the specified land uses, and are grouped into 12 community units of analysis (based on zip codes) with similar demographics and rural character.
41
Sussex County Tax Parcels Map (132,160 parcels)
If < 10 acres, remove parcels (-123,244 parcels)
8,916 parcels
If zip code has influence from coastal areas or communities outside Sussex County, remove parcels (-627 parcels)
8,289 parcels
If federal, state, or county preserved land, remove parcels (-496 parcels)
7,793 parcels
If ≠ targeted LULC, remove parcels (-296 parcels)
7,497 parcels
If < 10 acres of targeted LULC, remove parcels (-1,681 parcels)
5,816 parcels
If ≥ 10 acres, but < 75% targeted LULC, remove parcels (-1,371 parcels)
4,445 parcels
If ≥ 10 acres, < 75% targeted LULC, but 95% of parcel is targeted LULC or wetland, add parcels (+871 parcels)
5,316 parcels
Figure 5.1: Decision Tree of Parcel Eligibility Process
42
Figure 5.1 provides a graphic flow chart of the decisions described in detail in the following sections. First, tax data show there are 132,160 individual parcels in the County, which marks the starting point for determining which parcels will be considered eligible for preservation. Spatial data on tax parcels (Sussex County Parcels, 2007) were obtained from the Delaware Office of State Planning and Coordination (DOSPC). To determine which of 132,160 parcels will be eligible for preservation, decisions are made on minimum parcel size, inclusion of zip codes, and land use classifications.
5.2 Minimum Parcel Size Current DALPF policy requires agricultural parcels (or a group of parcels) to be at least 200 acres in size2 or within 3 miles of an existing easement or preservation district3 and forest parcels to be at least 10 acres to be considered for an easement4. Considering the broad spatial locations of the agricultural preservation districts across the country’s second smallest state, nearly all the land in Delaware is eligible under these criterions (Duke, 2004), so DALPF requirements do not provide any insight into establishing a minimum parcel size for this research. Members of the University of Delaware Sussex County Cooperative Extension were consulted to determine the validity of a 5-acre minimum for eligible parcels. This discussion and further research established that county zoning regulations for buildings require setbacks at least 50’ from parcel boundaries and 200’ from dwellings on adjacent property5. These zoning
43
rules suggest that an agricultural operation would need to be at least 10 acres in order to be a viable operation. By establishing a lower bound parcel size at 10 acres, 123,244 parcels are removed from consideration for this study.
5.3 Sussex County Zip Codes The 25 zip codes in the County facilitate analysis. Spatial zip code data were obtained from the U.S. Census Bureau (Census, 2000). The sample area can be divided into two categories: inland and coastal. The majority of the inland of the County is relatively homogenous in terms of its rural character and socio-demographic characteristics. Table 5.1 is provided to demonstrate these similarities. It is assumed that residents across these zip codes will have similar preferences for land preservation. Eleven of the zip codes that have influences from external sources that may result in incomplete information on preferences are removed from the analysis. Seven zip codes are considered coastal and are removed due to the differences in the composition and character of the beach/resort communities compared to those inland. Other reasons for removal include shared zip codes areas with Kent County and incomplete information due to a strong influence from neighboring Salisbury, Maryland. Additionally, in two cases, adjoining zip codes with nearly identical demographics (Lincoln/Ellendale and Milton/Harbeson) are combined to further simplify the study area. The final analysis encompasses twelve (12) relatively homogenous, inland community units in the sample area and further eliminates 627 parcels from the study area. Figure 5.2 is provided to show these 12 areas.
44
Table 5.1: Selected Demographics by Zip Code in Sussex County Georgetown Bridgeville
Laurel
Milford Millsboro
Milton
Seaford
Population
15,524
6,694
13,451
15,232
17,768
6,552
21,416
Median Age
34.4
34.0
35.8
39.0
45.6
40.2
37.5
Percent (%) White
71.5
62.7
81.0
81.5
81.3
77.7
72.5
Average Household Size
2.79
2.74
2.63
2.48
2.31
2.47
2.57
Percent (%) H.S. Graduates
66.2
68.1
73.1
79.7
72.1
78.5
74.7
Median Household Income
$41,007
$31,131 $38,680 $37,863
$34,756 $39,833 $36,042
Source: Data collected from 2000 U.S. Census using Zip Code Tabulation Area (ZTCA) as the unit of geography -- http://factfinder.census.gov.
45
Figure 5.2: Communities Used for Analysis
5.4 Protected Land State and county protected land (Delaware State Parks, 1998; Delaware State Resource Areas, 1998), including state parks and forests, and federally protected land (USFWS Cadastral Geodatabase, 2010), specifically Prime Hook National Wildlife Refuge along Delaware Bay, represent parcels already preserved and removes 496 parcels from the analysis.
46
5.5 Land Uses According to the land use and land cover (LULC) spatial data (Delaware Land Use and Land Cover, 2007), 46 unique land use classifications exist across the sample area. To link actual parcels in the County to land use categories valued in the CE— food and dairy farms, nurseries, forests, or idle farmland—it is necessary to determine which spatial land use classifications can be grouped into those four designations. Food and dairy farms are defined to include cropland, truck crops, confined feeding operations, pasture, and three types of rangeland. The forest class includes deciduous, evergreen, and mixed woodlands along with forested wetlands. Both nurseries and idle farmland are each represented by a single land use from the GIS data. Figure 5.3 on the next page shows a map of the four targeted LULC categories in the sample area. Three other LULC decisions are worth noting here. “Farmsteads and Farm Related Buildings” exist on numerous parcels and are tagged to the dominant agricultural land use that exists on that parcel. The land use “Other Agriculture” does not provide enough classification information for the demands of this study and is therefore removed from the analysis. The final decision of note deals with wetlands. In Delaware, state law prevents wetlands from being developed. Therefore, wetland acres will not count toward the total acres of a parcel when calculating WTP, with a single exception: forested wetlands (both tidal and non-tidal) will be classified as forest acres when calculating WTP.
47
Figure 5.3: Targeted Land Uses for Parcel Eligibility
48
Only 296 parcels remaining in the analysis set did not have any of the targeted land uses and are therefore removed. To further refine the focus, parcels that do not have at least 10 acres of a targeted land use (1,681) are eliminated. To catch parcels that may have more than 10 acres of a targeted land use but the dominant use is not a targeted LULC, a decision is made to remove parcels with greater than 10 acres of targeted LULC where those land uses exist on less than 75% of the parcel. In other words, this decision means that a corporate office park with 20 acres of forested land will not be considered in the sample area because the targeted land use (forests) is not the dominant use for that particular parcel. This removes an additional 1,371 parcels from the study area. However, to account for wetlands, an amendment to the above condition applies. If the specification holds but 95% of the parcel is a targeted LULC or wetland, it is added back into sample area. This adjustment then increases the number of parcels by 871.
49
NOTES
1
– See Messer and Allen (2010) for an extensive background on the Delaware
Agricultural Lands Preservation Foundation (DALPF) and its selection procedures.
2 – Del.
Code, c. 9, t. 3, § 907a: “Any owner or owners of contiguous farmland and/or
forestland containing at least 200 usable acres of such lands located in the State may submit on a voluntary basis, on such forms as the Foundation prescribes, an application for establishment of an Agricultural Preservation District.”
3 – Del.
Code, c. 9, t. 3, § 907d: “An owner of farmland and/or forestland consisting
of less than 200 acres in the State may submit on a voluntary basis, on such forms as the Foundation prescribes, an application for expansion of an established Agricultural Preservation District if the application satisfies the criteria for eligibility established under § 908 of this title and any regulations adopted thereunder, as determined by the Foundation, and the farmland and/or forestland is either (1) contiguous to the established Agricultural Preservation District, or (2) located in whole or part within a 3-mile radius of an established Agricultural Preservation District.”
50
4–
Del. Code, c. 9, t. 3, § 932d: “Any owner or owners of contiguous forestland
containing at least 10 acres of such lands located in the State and outside of a designated growth zone may submit on a voluntary basis, on such forms as the Foundation prescribes, an application for establishment of a forestland preservation area.”
5–
Sussex County General Legislation, p. II, art. IV, § 115-20
6–
Two issues are important to note—differences between the attributes in the survey
area and in the surrounding area where WTP is to be transferred, and differences in the socio-economic demographics between the study site and the policy area (Colombo and Hanley, 2008). However, Colombo and Hanley (2008) also “present evidence…that simple, unadjusted benefit transfers are not consistently outperformed by more complex benefit-function transfer approaches” (pp. 145-146). Considering the incomplete evidence from the literature and the fact that inland Sussex County, Delaware is relatively homogenous in its rural landscape and demographic character, the most effective method may be simple extrapolation to other communities within the county, similar to the methodology used in Duke, Johnston, and Campson (2007).
51
Chapter 6 DATA: BENEFITS
6.1 Deriving Benefits with Choice Model Data This chapter will detail the derivation of the benefits accruing to residents of Delaware, measured as willingness to pay (WTP), for specific parcels in Sussex County. I have access to previously published data1 derived from a series of CE surveys conducted in Delaware and Connecticut in 2005 and 2006 (Johnston and Duke, 2007, 2008, 2009a, 2009b, 2010; Duke and Johnston, 2010). The focus here is on using the Georgetown, Smyrna/Clayton, and Delaware Land Preservation Surveys2. The community-level surveys (Georgetown and Smyrna/Clayton) asked 750 randomly selected respondents in each area to chose between different preservation alternatives for hypothetical parcels at non-specific locations within their community (Johnston and Duke, 2007). These data are then pooled from both communities (see Johnston and Duke (2009a) for justification) to generate community-level data. The state-level survey asked 1,000 random residential households to make a similar choice with hypothetical parcels at undefined locations in Delaware. Their survey contained three questions and respondents were given a choice to vote for one of four preservation options: two that would preserve a single parcel with varying levels of defining attributes, and two that would result in no conservation action. The attributes
52
for each hypothetical parcel are presented as number of acres (4 levels), land use (5 levels), policy technique and implementation (5 levels), public access (3 levels), development risk (3 levels) and yearly cost to the household (6 levels). Coefficient estimates from Duke and Johnston (2010) utilized to derive per-acre WTP benefit values for actual farm and forest parcels within the sample area are drawn from two mixed logit (ML) models. Because this research focuses on WTP, the reader is referred to Johnston and Duke (2007) and Duke and Johnston (2010) for ML model formulation and specifications. Table 4.2 displays the results of the ML models for both the state and community levels verbatim from Duke and Johnston (2010). The welfare estimates are then calculated as the mean of the parameter simulation from the median WTP determined by the coefficient simulation (Duke and Johnston, 2010). This mean of median simulation method is preferred over using the log sum difference because it is more appropriate for this research objective of estimating benefits of individual parcels and predicting off the support. The welfare results for each parcel will be practical for all estimates but most accurate within the supports of the experimental design, especially near the mean.
6.2 Linking WTP Estimates to the Sample Area Previous studies clearly indicate that size (acreage) is a significant factor influencing WTP and it should be included as a standard element in any valuation analysis (Bergstrom and Ready, 2009). Here, the impact of four other attributes—land
53
Table 6.1: Choice Model Mixed Logit Coefficient Estimates Community Neither (ASC) -0.93298 (0.20365)*** Fee (lognormal, sign reversal) -3.72041 (0.24206)*** Acres -0.00237 (0.00207) Acres*Nursery -0.00106 (0.00123) Acres*Forest 0.00008 (0.00124) Acres*Idle 0.00066 (0.00126) Acres*Trust Easement 0.00171 (0.00226) Acres*State Purchase 0.00421 (0.00189)** Acres*Trust Purchase 0.00096 (0.00197) Acres*State Easement 0.00573 (0.00209)*** Acres*Moderate Access 0.00803 (0.00156)*** Acres*High Access 0.00609 (0.00151)*** Acres*LowDevRisk -0.00061 (0.00097) Acres*ModDevRisk -0.00149 (0.00116) std NE 1.53389 (0.39456)*** std Cost 2.56899 (0.30388)*** Log-Likelihood Chi-Square 630.01*** 2 Pseudo-R 0.20 N
State -0.720424 (0.293863)*** -4.520530 (0.323230)*** -0.000009 (0.000043) -0.000027 (0.000026) -0.000006 (0.000029) -0.000011 (0.000027) 0.000098 (0.000047)** 0.000089 (0.000041)** 0.000091 (0.000042)** 0.000091 (0.000050)* 0.000086 (0.000030)*** 0.000072 (0.000029)*** -0.000106 (0.000023)*** -0.000019 (0.000022) 1.784680 (0.493429)*** 2.677350 (0.443947)*** 444.83*** 0.21
4,308
*, **, *** denotes p-values of 0.10, 0.05, and 0.01, respectively Source: Model results reproduced verbatim from Duke and Johnston (2010)
54
2,952
use, development risk, public access, and policy technique—will depend on the number of acres preserved within a set of defined characteristics. These attributes, along with their associated levels, produce 180 possible amenity combinations to describe a hypothetical acre (Duke and Johnston, 2010). In this analysis, an attempt is made to mimic the current farm and forest preservation environment in the sample area. A substantial amount of preservation in the County happens through the Department of Agriculture’s Delaware Agricultural Lands Preservation Foundation (DALPF). As of February 2011, the program has purchased 234 easements in the county, covering 33,556 acres at a cost of $65,754,629 (Delaware Agricultural Lands Preservation Foundation, 2011). Since purchase of agricultural conservation easements (PACE) is the dominant method utilized by DALPF, the preservation policy technique and implementation attribute will be restricted to one level, PACE by a state agency. Public access will not be considered as land preservation activities by the state via PACE do not commonly provide access for recreational activities. Therefore, the public access component will be restricted to “no access”. By restricting both public access and policy technique to one level, the variation in per-acre WTP will depend on four levels of land use and three levels of development threat, reducing the number of attribute combinations to describe a parcel to 12. The land use categories are food or dairy farms, forest, nurseries, and idle farmland, as described in section 5.5. Development threat in Sussex County will be divided into high (development likely within 10 years, as framed on the Johnston and
55
Duke survey), moderate (10 to 30 years), and low (not likely within 30 years) levels to correspond with the CE. The following sub-section describes the process of defining land uses and a development risk index for the 5,316 parcels in the sample area.
6.2.1 Land Use and Development Risk The land use composition of each parcel is determined by first spatially overlaying a layer of targeted land uses on the study area and then computing the respective acreage for each land use on each parcel (see Appendix A for more detail). For instance, a 100-acre parcel may have both 75 acres of farmland and 25 acres of forest. By analyzing the parcel spatially, it allows for heterogeneity in land uses, which will impact the specific WTP for each parcel. To associate actual parcels in the sample area with the CE model measure of development risk, an index is created to define a high, moderate, or low risk of development for each parcel (Figure 6.1). Spatial data were collected for municipal boundaries (Delaware Municipal Boundaries, 2008), completed (Completed Delaware Annexations, 2008) and potential municipal annexation areas (Delaware Municipal Annexation Areas, 2008) from the DOSPC. Sussex County has relatively few incorporated municipalities, leaving a sizable majority of the land as unincorporated.
56
Figure 6.1: Development Risk for Eligible Parcels in Sussex County
57
The municipalities are required to have comprehensive future growth plans to annex areas if they wish to increase the size of the community (Delaware Code, 2001). Existing agricultural land or forests within the boundaries of the municipalities or in areas targeted for annexation are assumed in this study to be at a high risk of development. The existence of sewer and water hookups is also another potential indicator of potential development along the urban rural interface. Sussex County is divided into sewer districts, where hookups or planned expansions of the sewer system exist. Using spatial data from the Sussex County Department of Mapping and Addressing (Sussex County Sewer Districts, 2010), parcels in the study area that fall within these sewer districts are designated as at high risk for development. Two additional factors define a high development risk for a specific parcel. Delaware designates areas of the state into four investment levels (1, 2, 3, 4) as a way to guide public spending on roads, schools, and other public infrastructure in a way to minimize sprawl and promote sustainable development. Utilizing spatial shapefiles (Investment Levels, Strategies for State Polices and Spending, 2004), the parcels in the study area are classified by investment level. Levels 1 and 2 correspond with population centers and surrounding areas, therefore parcels in spending levels 1 and 2 will also be considered as high risk. Lastly, Delaware has a process to review landowners seeking a major land use change on their property before referring the matter to local governments for oversight. From 1996-2004, this process was guided by the Land Use Planning Act (LUPA), and since then has been replaced by the
58
Preliminary Land Use Service (PLUS). Because these parcels are already under review for potential land use changes, they are considered a high risk for development. Spatial data for LUPA (LUPA Review Polygons, 1999-2004, 2004) and PLUS (PLUS Project Areas, 2008) parcels were also obtained from DOSPC. Parcels that are located within a quarter of a mile from parcels at high risk for development are categorized as having moderate development risk. This classification will capture parcels that are not currently within municipal annexation plans, sewer districts or state investment levels 1 and 2, but their close proximity to these features causes them to be attractive candidates for expansion in the near future. With the total population of the County on the rise, parcels that lie near major roadways may be susceptible to development pressure. To account for this, annual average daily traffic (AADT) on all roadways in the County (Delaware Traffic Counts, 2008) were integrated into the spatial analysis. To be classified as a moderate risk of development, a parcel must be within 100 yards of a roadway that averages more than 5,000 AADT. Lastly, parcels that fall within the state investment level 3 are also considered moderate risk. Lands classified in level 3 are in close proximity to growing areas in spending levels 1 and 2 and are in the long term growth plans for municipalities, but where development is not likely to occur in the short term. Parcels that do not have any of the characteristics of high and moderate risk parcels are likely in state investment level 4 and are considered to be at a low risk of development. To summarize, parcels within municipal boundaries, potential annexation areas, and the state investment levels 1 and 2, within current sewer districts, or
59
PLUS/LUPA projects are designated as ‘high risk’ for development. Out of the 5,316 parcels eligible for this study, 20.7% (1,102 parcels) are at high risk. Parcels within a quarter of a mile of high risk parcels, 100 yards of road infrastructure with greater than 5,000 AADT, or the state investment level 3 are designated as ‘moderate risk’ for development. 1,457 parcels (27.4%) are captured in the moderate risk level. Parcels that do not fall into ‘high’ or ‘moderate’ risk are assigned as ‘low risk’ for development. Slightly more than half of the eligible parcels in Sussex County (2,759 parcels or 51.9%) are considered low risk for possible development.
6.3 Predicting WTP on Sample Parcels Each parcel in the sample area now has specific land uses (4 levels) and development risk (3 levels), and with policy technique (PACE by state agency) and access (no access) both restricted to one level respectively, there are 12 possible peracre household (HH) WTP combinations to consider for this analysis. Table 6.2 shows the 12 yearly, per-acre HH WTP values derived from the models of Duke and Johnston (2010) at both the community and state level. The following equations are utilized to derive a parcel-level HH WTP for a specific parcel located in a particular community in Delaware from the values in Table 6.2: (6.1) (6.2)
60
where WTPcpy is the yearly WTP for a parcel by households located in the same community, WTPspy is the yearly WTP for a parcel by households located in somewhere in the state outside of the community, i = {1, 2, 3, 4} representing different land uses (1 = Food or Diary Farm, 2 = Nursery, 3 = Forest, and 4 = Idle Farmland), j = {1, 2, 3} representing different development risks (1 = High , 2 = Low, 3 = Moderate), Aij is the acreage of a specific land use and development risk on each parcel, CWTPij is the corresponding per-acre HH WTP for parcels in their community, and SWTPij is the corresponding per-acre WTP for parcels by HH in the state not located in the same community as the parcel.
Table 6.2: Per-Acre Household WTP: PACE by State Agency and No Access Land Use Development Risk Acres WTP Community WTP State Food or Diary Farm High 1 0.14473 0.00782 Nursery High 1 0.10185 0.00537 Forest High 1 0.14653 0.00725 Idle Farmland High 1 0.16919 0.00693 Low 1 Food or Diary Farm 0.11933 -0.00236 Low 1 Nursery 0.07645 -0.00481 Low 1 Forest 0.12113 -0.00293 Low 1 Idle Farmland 0.14378 -0.00325 Moderate 1 Food or Diary Farm 0.08189 0.00601 Moderate 1 Nursery 0.03901 0.00356 Moderate 1 Forest 0.08369 0.00544 Moderate 1 Idle Farmland 0.10635 0.00512 Source: Calculations provided by Rob Johnston associated with work from Duke and Johnston (2010)1.
61
The calculations above derive a community HH WTP and a state HH WTP for each parcel per year. Community WTP will vary depending on the number of households in each zip code; however, state WTP will be invariant across all Sussex County communities (Duke, Johnston, and Campson, 2007). To calculate an aggregate yearly WTP for each parcel, the following equations are used: (6.3) (6.4) where TWTPyear is total yearly WTP for a parcel, HUc is housing units in the community, HUs is housing units in the state, TWTPcap is the total capitalized WTP for the parcel at a given r, defined as the discount rate, which for this study is set at 0.06, following Duke, Johnston, and Campson (2007). To illustrate this methodology for the derivation of benefits (WTP), an example from the study area is shown below using equations 6.1 – 6.4. Example 6.1 depicts a calculation with Parcel k, which has 26.41 acres of food and dairy farmland, 34.56 acres of forests at moderate risk for development. Parcel k is located in the Georgetown zip code containing 5,279 housing units while the state contains 343,072 housing units.
62
WTPcpy = 26.41(0.08189) + 34.56(0.08369) = $5.055 WTPspy = 26.41(0.00601) + 34.56(0.00544) = $0.3467 TWTPyear = $5.055(5,279) + $0.3467(343,072 − 5,279) = $143,798.18 TWTPcap =
$143,789.18 = $2,396,636.30 0.06
Example 6.1: Calculation of WTP on Sample Parcel
€ This methodology is applied to all 5,316 parcels across 12 jurisdictional units determined by zip codes within the study area in Sussex County. Each parcel now has a monetized benefit value - the aggregate, capitalized WTP of all households in the state of Delaware for preserving that specific parcel of land. Figure 6.2 shows the distribution of the WTP for parcels in the study area. Figure 6.3 depicts the spatial location of the parcels characterized by ranges of WTP values. As demonstrated in both Figures 6.2 and 6.3, a substantial number of parcels in the sample area (2,136 out of 5,315, or ~40%) are predicted to have a negative WTP. The existence of negative WTP is a controversial topic in environmental economics but conditions exist where the expression of negative WTP is legitimate (see Bohara, Kerkvliet, and Berrens, 2001). The potential for negative WTP exists in this study because respondents to the state survey responded negatively to the preservation of parcels classified with a low risk of development. These negative values are likely a reflection of respondents having a disutility for using public money (i.e., tax revenues) to preserve non-specific parcels somewhere in the state that are not likely to be
63
developed in the near future anyway. Therefore, in this study, parcels in the sample area with negative WTP will remain as is.
2500
Frequency
2000
1500
1000
500
0
Parcel WTP
Figure 6.2: Histogram of Parcel Level WTP
64
Figure 6.3: Capitalized Aggregate WTP for Eligible Parcels in Sussex County
65
NOTES
1
- I have access to this data because I am working directly with the authors, Joshua M.
Duke and Robert J. Johnston, on this research project.
2 - See
Johnston and Duke (2007) for a detailed description of the development,
experimental design, implementation, response rates, and other details for the surveys.
66
Chapter 7 DATA: COSTS
7.1 Easement Costs in the Sample Area This chapter outlines the development of a hedonic pricing model (HPM) that is used to predict easement costs for all parcels in the sample area. Participation in DALPF preservation efforts is voluntary, so easement values do not exist for all farm and forest parcels in Sussex County. However, historical easement data for parcels applying for the program can be used to predict the value of easements subject to specific parcel characteristics, such as size, land use, and proximity to urban areas, that may contribute to the easement cost. When developing a HPM for this purpose, it is necessary to not only fit a model with strong explanatory power, but also to fit a model that will minimize average prediction error (APE). Easement values in Delaware for the DALPF program are calculated by appraisers and are defined as the difference between the land market value of a parcel and the value in its current agricultural use. This system is designed to counteract the incentive for landowners to profit by selling their land to developers. Landowners offer easements to DALPF and then the program selects which easements to purchase based on the percentage discount off the appraised price. For example, a soybean farmer with 100 acres in soybean production and an appraised easement value of
67
$500,000 may offer to sell an easement to DALPF for $200,000. By offering his easement at a discount of 60%, the farmer is increasing his chances of getting his easement offer accepted compared to offering his easement for its full value.
7.2 Easement Cost Data Data on parcels that applied for DALPF easements in the sample area were graciously provided by Allen, et al. (2006). These data were originally compiled and organized by DALPF staff and additional data were augmented by The Conservation Fund (collectively, Allen, et al. (2006) data). The data set includes 196 parcels in Sussex County, cleansed of identifying and locational characteristics, which applied for an easement payment through DALPF from 1995 through 2003. Some parcels were eventually selected to be a part of the program, while others were not. For each parcel, the Allen, et al., (2006) data set provides easement value (dependent variable), and several key covariates, such as acres of agricultural land (AcTill), acres of forest (AcWood), acres of wetlands (AcWet), year accepted into program (Year), distances to various features, such as shoreline (DistShore), other protected areas (DistProt), major roads (DistMjrd), highways (DistHwy), and urban areas (DistUrban). Table 7.1 provides descriptive statistics for the selected variables from the data set.
68
Table 7.1: Descriptive Statistics for Sussex County Parcel Data Variable
Mean
Std Dev
Median
Max
Min
Easement ($)
$338,831
$332,648
$216,329
$1,942,827
$17,082
Tillable Acres (AcTill)
111.1
108.7
81
900
0
Wooded Acres (AcWood)
38.9
45.0
23
275
0
5.7
23.7
0
213.3
0
354.8
761.1
0
3371
0
2,248.6
2,158.7
1,582.5
11,026
0
Distance (m) to Major Roads (DistMjrd)
820.3
772.5
681.5
3985
0
Distance (m) to Shoreline (DistShore)
18,897.5
12,595.2
14,918
52,065
2,040
Distance (m) to Urban Areas (DistUrban)
2,174.4
1,702.9
1,999
8,193
0
Wetland Acres (AcWet) Distance (m) to Protected Areas (DistProt) Distance (m) to Highways (DistHwy)
Source: Data on DALPF applicant parcels in Sussex County provided to the author by Allen, et al., 2006.
69
The variables included as independent variables are chosen because of their explanatory power in determining the predicted value of the easement and the ability to link the estimated coefficients to similar data within the sample area. The three acreage variables (AcTill, AcWood, AcWet) help describe the size of the parcel and nature of the land uses. Parcel size is a critical factor in determining its value and intuitively, one would expect that as a parcel increases in size, its value also increases. The mean parcel size for this data set is 155.8 acres. Furthermore, it is expected that variation in the land use will drive at least some variation in the easement value. In this case, the assumptions above are likely to hold for tillable and forest acres, but because development of wetlands is restricted in Delaware, an increase in AcWet may negatively impact easement value. The variable Year is included to capture the impact of the fluctuations in the real estate market. For this model, the year a parcel was accepted into a DALPF easement is linearized (1995 = 1, 1996 = 2, … , 2003 = 9). This linear time trend is used instead of a dummy variable for each year to allow for prediction off the support, enabling the model coefficients to predict easement costs in 2005. The distance variables provide critical information about where each parcel is positioned relative to other geographic features that may impact the price. For instance, a 100-acre farmland parcel that is relatively close (5,000 meters) to the Atlantic Ocean, a major U.S. highway, and a growing municipality will likely have a higher market value than a similar parcel located a further distance away (30,000 meters) from those three features. Distance to shoreline (DistShore) is measured from
70
each parcel boundary to the closest point on Delaware’s coastline on Delaware Bay or the Atlantic Ocean. Distance to urban areas (DistUrban) is calculated from each parcel edge to the nearest borders of state investment levels 1, 2, and 3. Distance to highways (DistHwy) is determined as a parcel’s proximity to U.S. highways, while distance to major roads (DistMjrd) includes both U.S. and State highways. Lastly, distance to protected land (DistProt) is measured as the nearest distance from the edge of a parcel to the edge of a protected area, such as a state forest or county park. The following sections discuss the development of the HPM and its application to estimate easement costs for all parcels in the sample area.
7.3 Assessing the Predictive Capacity of Multiple Hedonic Pricing Models Different formulations of the model are examined in an attempt to determine the best model for the purposes of prediction. The preliminary model contains 13 variables—AcTill, AcTill2, AcWood, AcWood2, AcWet, AcWet2, Year, Year2, DistShore, DistProt, DistHwy, DistMjrd, and DistUrban. Three additional variations of the preliminary model are tested to compare their predictive capabilities. These include: •
Variation 1—Preliminary model with all five distance variables removed.
•
Variation 2—Preliminary model with four of the five distance variables removed, leaving DistShore in the model.
•
Variation 3—Preliminary model with all insignificant variables removed (AcWet, AcWet2, Year, DistHwy, DistMjrd, DistProt).
71
For a full accounting of the results of the preliminary model and the other formulations not selected as the final model, the reader is referred to Appendix II. K-fold cross-validation is a statistical procedure used to assess the accuracy of the predictions of the model for easement costs for eligible parcels in the sample area. For each model formulation, the original data are divided into 196 (the number of observations) subsets, one subset (validation set) is separated from the other 195 subsets (training subset), and the estimates from the training set can predict a value for the validation set (Weisberg, 1985). This process is repeated 196 times, using each observation as the validation set once. When completed, the actual value and the predicted value for easement costs on each parcel are utilized to calculate the average prediction error (APE) as follows: (7.1)
(7.2)
where PRESS is the predicted residual sum of squares, yi is the actual easement value for parcel i, yˆ is the predicted value of parcel i from cross-validation, and APE is an estimate of the average prediction error. Table 7.2 compares the predictive abilities of
€
72
Table 7.2: K-fold Cross Validation (k=196) Comparison of Hedonic Models Model
R2
Adj. R2
Average Prediction Error (APE)
Preliminary Model
0.7163
0.6961
$213,473
Variation 1
0.6473
0.6322
$221,404
Variation 2
0.7090
0.6949
$216,743
Variation 3*
0.7078
0.6969
$206,811
* Selected as final HPM because it minimizes APE. Source: Original calculation made by the author using SAS and JMP.
each model formulation with R2, adjusted R2, and APE values. The APE was minimized (and adjusted R2 slightly improved) with model variation 3, which excluded all insignificant variables from the preliminary model. Therefore, this model formulation will be used to estimate the coefficients that will be employed to predict easement costs for parcels in the sample area.
7.4 Hedonic Pricing Model Results A natural log transformation of easement cost is used as the dependent variable as it generally produces more accurate estimates in a hedonic analysis by reducing potential for heteroskedasticity in the use of skewed price data (Ottensmann, Payton, and Man, 2008). The model is expressed as follows:
73
ln_ easement = β0 + β1 AcTill + β2 AcTill 2 + β3 AcWood + β4 AcWood 2 +β5Year 2 + β6 DistShore + β7 DistUrban
(7.3)
The final model run in SAS has 6 out of 7 variables that are statistically significant at the € 1% level, and the signs conform to expected predictions. The model captures nearly 71 percent of the observed variability in easement cost (R2=0.7078). The coefficient estimates with variable interpretations and the summary of fit/analysis of variance for the model are shown respectively in Tables 7.3 and 7.4. The numerical interpretations in Table 7.3 are the respective coefficient estimate multiplied by the mean easement value (µ = $338,831) from the sample data. For instance, for every one-acre increase in forested land, the average easement value increases by $4,235.39. The results of the final HPM match quite well with the intuitive hypotheses posited prior to running the model. The positive coefficients AcTill and AcWood indicate that as acres of both agricultural land and forests increase, the cost of the easement will also rise. The negative coefficients AcTill2 and AcWood2 show that as the number of acres for each land type gets increasingly large, the easement cost will increase at a decreasing rate. The estimate for Year2 reflects a rapid increase in easement cost as time goes on, consistent with the vigorous real estate market observed in the first half of the 2000s. The HPM also predicts that the value of an easement will decline the further the parcel is from the Delaware coastline. Overall, this model provides a solid foundation for prediction, with explanatory power for over 70 percent of the variation in easement costs, highly significant variables with expected signs, and a minimized APE relative to other model formulations.
74
Table 7.3: Final Hedonic Model Results Coefficient Variable Interpretation Estimate Intercept
11.275*** (0.106)
$78,826.14
Constant for Sussex County
AcTill
0.009*** (0.001)
$3,049.48
Increase per acre of tillable land
AcTill2
-7.43E-06*** (1.1E-06)
-$2.52
Value increases at decreasing rate as tillable acres increase
AcWood
0.0125*** (0.002)
$4,235.39
Increase per acre of forested land
AcWood2
-3.99E-05*** (9.61E-06)
-$13.52
Value increases at decreasing rate as forest acres increase
Year2
0.0088*** (0.002)
$2,981.71
Value increases quickly at increasing rate as year increases
DistShore
-1.96E-05*** (3.04E-06)
$-6.64
Decrease per meter away from shore
DistUrban
-3.40E-05 (2.16E-05)
-
Insignificant
Source: Original SAS regression results and author calculations utilizing Allen, et al. (2006) data set *** denotes p-values of F Squares Square Model 7 105.995 15.1421 65.0425 $20,000
0
Per Acre Easement Costs
Figure 7.1: Histogram of Predicted Per-Acre Easement Costs in Sample Area
Because of this exceptionally large value, the negative coefficient estimated on AcWood2 drives the value down and the model predicts an unrealistically low easement cost. The HPM is unable to predict an accurate value for this parcel and it is therefore removed from the analysis, leaving 5,315 parcels remaining in eligible set. In summary, by adapting the CE data of Duke and Johnston (2010) and the hedonic model using easement data from Allen, et al. (2006), and outlining techniques to use this data to predict WTP and cost values across the sample area, all parcels now have monetized estimates for both benefits and costs. These values are used to
78
analyze parcel selection under different targeting strategies and spatial considerations in Chapter 8.
79
Figure 7.2: Predicted Parcel-level Easement Costs in the Sample Area
80
Chapter 8 RESULTS The first result of this study compares the cost-effectiveness of four distinct parcel-targeting strategies and provides evidence that prioritizing parcels in an economically sound manner, by including both monetized benefits and costs, will generate substantial gains in overall net benefits to society. To facilitate analysis, benefit and cost values are mapped in GIS to create a spatial representation of benefits and the costs of procuring these public goods. These results set the baseline for investigating the impact of spatial location on cost-effectiveness in public good policy interventions. The second finding addresses the potential welfare impact of additional benefits accruing to spatially agglomerated parcels in the preservation landscape and demonstrates that using SP values alone may produce an ineffective policy. A sensitivity analysis is conducted to show these welfare changes, in terms of net social benefits, at varying levels within spatial synergy benefit structure. For all of the following analyses, parcel selection is limited by a budget constraint of $30 million dollars.
81
8.1 Parcel Prioritization Results This section will compare the cost-effectiveness of four different prioritization strategies – benefit targeting (BT), cost targeting (CT), benefit cost ratio targeting (BCRT), and binary linear programming (OPT). The optimal set for each strategy was calculated in the following manner: •
BT sorts the parcels in Excel by WTP is descending order and selects the highest ranked parcels available until the budget constraint is reached.
•
CT sorts the parcels in Excel by per-acre easement cost in ascending order, excluding parcels with negative WTP values, and selects the lowest cost parcels available until the budget constraint is reached
•
BCRT sorts the parcels in Excel by benefit-cost ratios in descending order and selects the highest ranked parcels available until the budget constraint is reached. Figure 8.1 is provided to show the spatial distribution of benefit-cost ratios across the sample area.
•
OPT uses an algorithm in Python programming language supported with Gurobi Optimizer 4.0 to select the set of parcels that maximizes net benefits subject to the budget.
Table 8.1 provides a detailed summary of the selection results. By accounting for both benefits and costs, OPT and BCRT both produce over $812 million in net social benefits, outperforming BT by more than $435 million and
82
Figure 8.1: Benefit Cost Ratios of Eligible Parcels in the Sample Area
83
CT by nearly $236 million. OPT sets the maximum net benefits available for the budget at $812,216,051 and BT, CT, and BCRT produce 53.612%, 29.032%, and 0.009% less net benefits respectively. The large difference in net benefits between the suboptimal strategies, BT and CT, compared to BCRT and OPT show that simple decision rules considering either benefits or costs in isolation do not produce a costeffective outcome. These findings further solidify previous research that reports improvements in social welfare by incorporating both benefits and costs into parcel selection strategies (Babcock, et al. 1997; Ando, et al., 1998; Messer, 2006; Kaiser and Messer, 2011; Messer and Allen, 2010). For instance, Messer (2006) found that selection with BLP represented a nearly 97% increase in conservation value at the highest budget examined ($5 million) compared to a 115% increase in WTP for a similar comparison in this study. These results differ slightly by incorporating the demand of residents for public good investments through a monetized measure of benefits (WTP). Interestingly, the 0.009% difference in net benefits produced by the nearoptimal BCRT strategy compared to OPT is essentially negligible. It demonstrates that an optimization algorithm like BLP can better manage the entire budget, as it shuffles around the last group of parcels selected to eke out a slight improvement in net benefits. In this case, 116 parcels selected were identical for both methods; however, OPT removed three selected by BCRT and picked up two different parcels that were able to push the cost-effectiveness of the budget slightly further. All
84
Table 8.1: Comparison of Selection Results with Budget of $30 Million Benefit Targeting (BT)
Cost Targeting (CT)
Benefit-Cost Ratio Targeting (BCRT)
Binary Linear Programming (OPT)
Parcels Preserved
24
131
119
118
Acres Preserved
8,729
19,012
16,746
16,710
Total Benefits
$406,680,194
$606,286,677
$842,121,826
$842,215,415
Total Cost
$29,952,871
$29,936,518
$29,990,920.89
$29,999,682
Total Net Benefits (NB)*
$376,774,453
$576,413,642
$812,139,985
$812,216,051
% of Possible NB
46.388%
70.968%
99.991%
100%
* Total Net Benefits = (Total Benefits – Total Cost) + Budget Remainder Source: Original data generated from the decision rules and optimization methods outlined in Chapter 4.
five parcels that were involved in the switch were selected near the end of each process and had very similar benefit-cost ratios, ranging from 21.14 to 21.83. Additional restrictions on the preservation program, such as an acreage minimum or a geographic constraint, furthers the need for a selection strategy that employs optimization techniques that can handle multiple constraints. However, in situations where the budget is the only constraint, this result shows that BCRT is far superior to strategies that do not jointly consider benefits and costs (BT and CT) and can nearly replicate the results of OPT.
85
BT selects the fewest number of parcels (24) with the largest average size (364 acres), CT selects the most parcels (131) with an average size of 145 acres, and BCRT and OPT select a similar number (118, 119) of similarly sized parcels, 141 and 142 acres respectively. CT preserves the largest number of acres (19,012) while BT preserves the fewest (8,729). These results demonstrate that a BT strategy focuses on preserving a few large high-benefit, high-cost parcels, and a CT strategy focuses on many smaller, low-benefit, low-cost parcels. Both BCRT and OPT preserves a very similar number of acres (16,746 and 16,710) with a focus on preserving smaller parcels than both BT and CT. Figure 8.2 shows the geographic distribution of the selections of each strategy. The spatial techniques employed here strengthen the results of the economic analysis and produce a visualization of policy options to assist the understanding of the models used. As shown on the map, BT selects a few large parcels and nearly all are located near a major roadway in the sample area. 71% of the BT parcels are at high risk of development. CT focuses selection on the southern and western portions of the sample area, where agricultural uses dominant the landscape and land prices are cheaper relative to other areas. 81% of the parcels selected by CT are at low (35%) to moderate risk (46%) of development, leaving a CT strategy susceptible to adverse selection (see Arnold, Duke, and Messer, 2010). Both OPT and BCRT chose many, high benefit, relatively low cost parcels, with the highest concentration of selection along the Route 13 corridor near Seaford. In terms of development threat, these strategies targeted high risk (67%) and moderate risk (33%) parcels. Eight parcels
86
Figure 8.2: Spatial Comparison of Selection Strategies
87
Table 8.2: Distribution of OPT Parcel Selection by Community Community Units (Zip-Code based)
Parcels Preserved Acres Preserved
Bridgeville
3
296
Dagsboro
0
0
Ellendale/Lincoln
3
1,237
Frankford
1
336
Georgetown
10
2,243
Greenwood
7
1,113
Laurel
27
3,341
Milford
0
0
Millsboro
12
1,773
Milton/Harbeson
1
446
Seaford
53
5,776
Selbyville
1
149
Total
118
16,710
Source: Original analysis of OPT parcel selection from Figure 8.2
were a rare mix of high benefits and low cost and were subsequently selected by all four prioritizations. Table 8.2 shows the parcels selected in each community with OPT. The scope of the original community CE survey for acres was a range from 20 to 200, though the state CE survey was 1,000 to 10,000 (Johnston and Duke, 2009a). Comparison to the average selected parcel size shows that in 9 out of the 12 communities, the scope of the hypothetical community intervention is 1.5 to 28.9
88
times above the upper limit in the CE survey. As a caveat to this analysis, this suggests that the actual WTP in those communities may be lower than predicted by the data, due to diminishing marginal utility. The state results are slightly biased as well, but not systematically in favor of one community over another. Additionally, there appears to be asymmetry in the results with a majority of communities having either more or less acreage preserved than the mean of 1,393. This systematic bias will tend to overstate benefits for communities with more acreage preserved than the mean (Georgetown, Laurel, Millsboro, and Seaford) and understate benefits for communities with less acreage than the mean (Bridgeville, Dagsboro, Ellendale/Lincoln, Frankford, Greenwood, Milford, Milton/Harbeson, and Selbyville).
8.2 Impact of Space on Parcel Prioritization Using the quadratic knapsack algorithm (4.6) at different spatial benefit levels produces significant differences in the net social benefits and the spatial configuration of the optimal set of preserved parcels, with the OPT results from section 8.1 as a baseline for comparison. The algorithm was written in Python programming language and supported with Gurobi Optimizer 4.0. The spatial synergy (SS) benefits were coded to scale the gravity elements of the matrix by the appropriate α for the model, leaving the total aggregate capitalized WTP for each parcel unchanged on the diagonal. Each model took 24 hours to load the elements of the algorithm (i.e., spatial benefit matrix, cost vector). After the set up time, algorithm
89
was able to produce a solution in 9.07 seconds when α=10, 70 hours when α=100, and 94 hours when α=500. Table 8.3 summarizes the results. By treating the spatial interactions of parcels as a low priority (α=10) the optimal set added two new parcels and removed four previously selected parcels. The four parcels dropped were isolated from other preserved parcels while the two new selections shared a border with a preserved parcel, as expected. A minimal increase in net benefits of 0.0084% was observed. Figure 8.3 shows that while this treatment does induce a change in the optimal selection, the overall impact was very small. At a moderate level of importance (α=100), SS benefits stimulate substantially more changes in selection from the OPT baseline than the lower level. Although 73% of the parcels selected are the same, thirty-seven parcels are removed from the original baseline selection and thirty new parcels are selected. By altering the optimal set in this manner, attributing a moderate priority on spatial interdependence produces a nearly 15% increase in net social benefits for the same budget. One large parcel even attracted two parcels with negative WTP as their values by being preserved jointly outweighed the negative individual values. As seen in Figure 8.5, setting α = 100 stimulates the clustering of preserved parcels in the optimal selection set. The parcels that were removed were preserved in isolation from other parcels and those added are pulled in by the gravity of large, high benefit parcels to create larger contiguous areas of preserved land.
90
Table 8.3: Comparison of Selection with Spatial Synergy Benefits at Varying Levels of α and a Budget of $30 Million α=0
α = 10
α = 100
α = 500
Parcels Preserved
118
116
111
123
Acres Preserved
16,710
16,685
16,165
16,630
Total Aggregate WTP
$842,215,415
$841,891,625
$782,432,221
$362,465,098
Number of Parcel Clusters1
82
76
45
22
Average Cluster Acreage
204
220
359
756
Number of Negative WTP Parcels Selected
0
0
2
43
Total Spatial Synergy (SS) Benefits
$0
$7,139,380
$180,685,000
$1,827,015,502
Total Benefits*
$842,215,415
$849,030,905
$963,117,221
$2,189,480,600
Total Cost
$29,999,682
$29,999,240
$29,999,149
$29,986,987
Total Net Benefits (NB)**
$812,216,051
$819,032,425
$933,118,923
$2,159,506,626
% Increase in Potential NB
0%
0.0084%
14.886%
165.878%
1
– A cluster is defined as group of contiguous parcels. A single parcel preserved in isolation is considered its own cluster. * Total Benefits = Total Aggregate WTP + Total SS Benefits ** Total Net Benefits = (Total Benefits – Total Cost) + Budget Remainder Source: Original data from QKP algorithm results calculated in Python and Excel.
91
Figure 8.3: Comparison of Parcel Selection Results—OPT vs. QKP (α=10)
92
Figure 8.4: Comparison of Parcel Selection Results—OPT vs. QKP (α=100)
93
Placing a high importance (α=500) on spatial interdependence produces dramatically different results than the baseline. Only twenty-six parcels (21%) are selected in both this scenario and original OPT model. The spatial bonuses produce a 166% increase in net benefits and a dramatically different spatial configuration of preserved parcels. As shown in Figure 8.5, this selection generates twenty-two distinct clusters of preserved parcels. Interestingly, setting spatial interdependency as a high priority generates the selection of relative large parcels (~ 80 – 200 acres) at low risk of development with negative WTP (35% of those selected). There are four distinct clusters of parcels where their value together far outweighs each parcel’s individual negative WTP, and two other clusters with parcels with both positive and negative WTP. Figure 8.6 shows all four results side by side for comparison. This result highlights a very important implication for land preservation policy. Even parcels that may not be valued by residents of a particular jurisdiction, they may have very high spatial values when preserved jointly with surrounding parcels. Policy makers cannot overlook these parcels if they believe spatial location has an important effect on net social benefits. These results imply that agricultural preservation districts, even in relatively rural areas with low risk of development, have the potential to be a part of an optimal solution if spatial interdependence is regarded of high importance. Overall, the differences in the optimal set of preserved parcels produced by the hypothetical spatial synergy benefits demonstrate the potential importance of including the spatial complexities of a landscape in the preservation decision. Raising
94
the level of α increases clustering of parcels in the optimal set and also has the potential to induce the selection of parcels with negative WTP values. Since the true value of α is unknown, these results cannot be interpreted as an exact solution, but rather a guidance tool for policy makers to incorporate spatial interdependence into prioritization strategies informed by SP techniques.
95
Figure 8.5: Comparison of Parcel Selection Results—OPT vs. QKP (α=500)
96
Figure 8.6: Comparison of Selection Results at all values of α
97
Chapter 9 CONCLUSIONS AND POLICY IMPLICATIONS
This research has applied results from choice experiments and hedonic models to examine the cost-effectiveness of land preservation under a fixed budget and built upon principles of the gravity model to investigate the impacts of spatial interdependence on parcel selection that maximizes net social benefits. The results reported support the conclusion of previous research by demonstrating dramatically higher net benefits through joint consideration of both benefits and costs of preservation in the selection method. Additionally, this analysis is more complete than previous research as it accounts for the preferences of society (WTP in dollars) in the benefit measure. The inclusion of hypothetical spatial synergy benefits accruing to spatially agglomerated parcels indicate that using valuation methods, such as SP, to prioritize selection without consideration of the systematic impact of spatial location has direct implications for land use policies and can lead to ineffective policy outcomes. While the true nature of the spatial interactions of land is unknown, the principles of gravity can provide a systematic foundation to address spatial interdependencies and the sensitivity analysis conducted here allows policy makers to adjust their selections by determining the relative importance of space in their
98
jurisdictions. Furthermore, this analysis uses spatial analysis tools to provide maps and visual representations of the results in the hope that including these elements will increase the chances of policy uptake. From a policy perspective, the results of this study present a stark warning to policy makers and preservation agencies. Even by incorporating WTP in dollars from SP methods into the selection process and utilizing an optimal selection strategy, you may still get an inefficient outcome and get less ‘bang’ for your budgetary buck if spatial considerations are overlooked. If SP results are utilized to choose which parcels to preserve, the average value gives us sufficient information to prioritize. However, the average value is not sufficient to guarantee the optimal answer because if the spatial interdependencies are present, the selections can be switching all across the preservation jurisdiction. Welfare analysis based on SP studies might, in fact, provide information that leads to misguided policy and produces the wrong answer. Although it may seem counter-intuitive, the selection of seemingly undesirable parcels (negative WTP) can occur in an optimal strategy if the spatial benefits from preserving contiguous parcels are high enough. This implies that preservation agencies could potentially use cost-saving strategies, such as preservation districts in areas of low development risk, within an optimal selection set if spatial interactions are given high priority. The findings in this research provide one possible avenue to quantify the spatial complexities involved in land preservation decisions. Future efforts could attempt to determine more refined values for the unknown parameters (α and β) that
99
could improve the robustness of the results. Additionally, research integrating the idea of spatial synergy benefits with the advances in the agglomeration bonus literature could generate an incentive structure to further improve preservation policies. This research is a step forward in attempting to assess spatial interactions in a preservation setting, but work remains to further enhance the economic applications that ultimately influence real world policy changes.
100
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APPENDIX A COLLECTION OF NEW DATA A.1 New Variables Linking WTP Estimates to Sample Area To link per-acre WTP estimates from CE models (Duke and Johnston, 2010) to actual parcels, I collected publicly available spatial data sets from various sources, including the U.S. Census, the U.S. Fish and Wildlife Service Cadastral Geodatabase, the Delaware Office of State Planning Coordination, the Delaware Department of Transportation, and the Sussex County Office of Mapping and Addressing. These shapefiles and data were incorporated into the analysis using ESRI ArcGIS 10 software. To create linking variables on acreages of the four targeted land uses— food and dairy farms, forest, nurseries, and idle farmland—on each parcel in the sample area, the calculate geometry tool was first used to determine the acreage of each parcel. To the calculate the acreage of each particular land use, land use and land cover (LULC) maps were overlaid on the parcel layer, and the clip tool was used to subdivide each parcel into their respective different land uses. The calculate geometry tool was then used again to calculate the acres of each specific land use on each parcel. Next, a linking variable was created to represent development risk. To determine if a parcel is at high risk for development, multiple shapefiles, including
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municipal boundaries, annexation areas, state investment levels 1 and 2, sewer districts, and PLUS/LUPA project areas, were added to the map on top of the parcels. The select by location tool was used to determine if the parcel was located within any of these areas. If a parcel fall in any of these boundaries, it is considered a high risk of development. For those parcels that did not fall within that designation, the select by location tool was again utilized to determine if a parcel is located within ¼ mile of a high risk parcel, within 100 yards of a road with greater than 5,000 annual average daily traffic (AADT), or within state investment level 3. Parcels falling within these constraints were considered moderate risk for development. Any parcel not selected in this methodology was considered a low risk for development. Lastly, each parcel was grouped into one of the 12 zip codes areas defined in section 5.3 by using the select by attributes tool.
A.2 New Variables Linking HPM Coefficient Estimates to Sample Area To predict easement values on parcels in sample area, ESRI ArcGIS 10 software is used to calculate the acres of different land uses on each sample parcel (agricultural land, forests, and wetlands) using the methodology described above in section A.1.1 above. The variables for acres of land use are labeled as follows: AcTill (acres of both active and idle agricultural land), AcWood (acres of forested land), and AcWet (acres of wetlands). Next, distance in meters is measured using the near tool from the parcel borders to the nearest features in other target layers. See Section 7.2
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for a description of the distance variables. These two operations provide the necessary variables to link the HPM coefficient estimates to the parcels in the sample area.
A.3 New Variables for Spatial Synergy Benefits Analysis In order to create a gravity for each parcel to every other parcel, variables for acreage and distances to every other parcel needed to be created. Acreage was calculated for each parcel as described in section A.1.1 with the calculate geometry tool. For the distance variables, the program calculates the distance from the edge of a parcel to the nearest edge of every other parcel in the set with the generate near table tool. This process took approximately 15 hours of computing time and produced 28,249,225 data points, although only 14,121,955 were needed for analysis. Because the diagonals of the matrix are simply distances of a parcel to itself and the lower triangle of the matrix is the mirror image of the upper triangle, the final number of distance values needed to calculate parcel gravity values for the analysis are [(5,315 * 5,315) " 5,315] 2 = 14,121,955. The gravity (equation 4.2) values are
computed and formatted into matrix form in Excel and aggregate capitalized WTP was !
added as the diagonal using various Excel macros to expedite the process. Both the spatial benefit matrix and the cost vector were converted to comma separated value (csv) files for better performance within the Python programming language and optimization algorithm.
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APPENDIX B PRELIMINARY HEDONIC MODELS B.1 Preliminary Hedonic Pricing Model The independent variables representing different land uses on the parcels (AcTill, AcWood, AcWet) are included in the initial model along with squared transformations of each to determine how the easement costs vary as the acreage of the different land types grow progressively larger. The five distance variables (DistShore, DistUrban, DistHwy, DistMjrd, DistProt) are also included in the initial estimation. To investigate, the impact of both Year alone and Year and Year2 together are isolated within the model to determine their individual impact on easement value (Figure B.1). The slope of the curve depicting the effect of both Year and Year2 more closely represents the trends of the real estate market during that time period. Therefore, both Year and Year2 will be included in the initial hedonic model. The model is formulated as follows: ln_ easement = "0 + "1 AcTill + "2 AcTill 2 + "3 AcWood +"4 AcWood 2 + "5 AcWet + "6 AcWet 2 + "7Year + "8Year 2 +"9 Dist Pr ot + "10 DistHwy + "11DistMjrd + "12 DistShore +"13 DistUrban
!
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(B.1)
Actual
Extrapolated
Figure B.1: Impact of Year and Year2 on Average Easement Values
The preliminary HPM has eight out of 13 variables statistically significant, and the signs of the significant variables conform to expected predictions. The model captures 72 percent of the observed variability in easement cost (R2=0.7163). Tables B.1 and B.2 show the coefficient estimates and the summary of fit/analysis of variance for the model, respectively.
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B.2 Preliminary Hedonic Pricing Model: Variation 1 Variation 1 starts with the preliminary model but removes the five distance variables. Model results from variation 1 are presented in Tables B.3 and B.4 respectively.
B.3 Preliminary Hedonic Pricing Model: Variation 2 Variation 2 starts with the preliminary model but removes the distance variables with the exception of DistShore. Model results from variation 2 are presented in Tables B.5 and B.6 respectively.
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Table B.1: Preliminary Hedonic Model Results Variable Estimate Std Error t Ratio Intercept AcTill AcTill2 AcWood AcWood2 AcWet AcWet2 Year Year2 DistProt DistHwy DistMjrd DistShore DistUrban
11.467 0.009 -7.32E-06 0.012 -0.00004 0.001 8.46E-06 -0.090 0.016 2.58E-05 1.71E-05 8.13E-06 -1.87E-05 -4.88E-05
0.235 0.001 1.11E-06 0.002 9.88E-06 0.004 2.30E-05 0.081 0.007 0.00005 0.00002 4.89E-05 3.27E-06 0.00002
48.79 12.62 -6.58 6.82 -3.95 0.28 0.37 -1.12 2.26 0.49 0.78 0.17 -5.73 -2.03
Prob > |t|
