# Presentation: 237412

## Designing Wetland Conservation Strategies under Climate Change

**Jiayi Li, Elizabeth Marshall, James Shortle, Richard Ready**
**The Pennsylvania State University**
**Carl Hershner**
**Virginia Institute of Marine Science **
**November 2005**

## Presentation Outline

- Introduction
- Objectives
- Uncertainties and Decision-Making Process
- Model
- Information Structure
- Experimental Design
- Results and Conclusions

**Notes:**
First is the introduction part, which talks about the background of this study and the essential problem that will be addressed in this study.

Then I will briefly talk about wetland types and the services they provide.

Then I will introduce the three major wetland conservation strategies considered in this study and how they fit into the wider framework of the policy responses to climate change.

 Then I will talk about the objectives of this study.

Then I will talk about the frameworks that are used to study the problem of decision-making under uncertainties and use a decision tree to illustrate the specific problem studied here. 

## Introduction

- A new threat to wetlands in recent years: sea-level rise associated with global warming and climate change.
- The essential problem: identify land parcels to be preserved given uncertainty about sea-level rise and future land development that would minimize the total cost of wetland conservation.

**Notes:**
Wetlands worldwide are being degraded and destroyed by natural forces and human activities. The resulting losses of wetland services have profound impacts on the environment and ecosystem. In recent years, a new threat to wetlands and wetlands restoration activities ahs been recognized – this being sea-level rise associated with global warming and climate change. With this new threat, effective coastal wetland conservation strategies must anticipate and adjust for rising sea levels. Yet, most of current conservation efforts fail to account for rising sea-levels. 

Risks of wetlands loss to sea level rise can be addressed through three strategies. One is protection of parcels that are identified as suitable migration sites based on their land use type and hydrologic conditions. A second is to acquire lands upon which to create new wetlands or restore former wetlands. A third is to acquire information that reduces uncertainty and helps to refine the first two strategies. This information would include improved forecasts of sea level rise and future land development. 

The essential problem is to identify which land parcels to preserve given uncertainty about sea-level rise and futureu

## Wetland Conservation Strategies

- Wetland conservation: both mitigation and adaptation response
- Three major wetlands conservation methods:
-     - migration
-     - creation
-     - restoration

**Notes:**
Wetland conservation can be viewed as both an adaptation and mitigation response. Conservation strategies that increase wetland acreage or restore and enhance wetland functions are adaptation responses. On the other hand, the protection and restoration of wetlands can reduce the land-based greenhouse gas emission. In this sense, wetlands conservation can be considered as mitigation response to climate change. 

Migration. Show graph in the next slide. Direct losses of coastal wetland due to sea-level rise can be offset by inland wetland migration. However, structures designed to protect developed areas, such as bulkheads and dikes, prevent new wetland from forming. Accordingly, facilitating migration requires protection of suitable undeveloped land near existing wetlands to preserve the opportunity for wetland migrations. As indicated by EPA, the amount of dryland available for migration is much less than the amount of wetlands that would be lost. Therefore, creation and restoration strategies will be essential to meet the national goal of “no net loss” of wetlands. 

Creation is the “construction of a wetland in an area that was not a wetland in the recent past (within the last 100-200 years) and that is isolated existing wetlands (i.e., not directly adjacent)”. Wetlands creation involves the flooding of soils and the subsequent establishment of aquatic plant communities and formation of organic sediment.

Restoration is defined as “the re-establishment of wetland conditions, including hydrologic condition or native hydrophytic vegetation, to an area where a wetland had previously existed” (1985 Food Security Act). The goal of restoration is typically to reestablish wetland ecosystems to levels that existed prior to human influence. Identifying potential sites with appropriate physical conditions and historical land use is essential to the success of wetland restoration. 

Considering only the wetland conservation cost, migration is usually the best choice because it only entails the cost of preventing the development of parcels of land adjacent to existing wetlands. Creation and restoration entail additional costs related to flooding and establishment of aquatic communities and organic sediments. Comparing creation and restoration, creation is a more difficult undertaking because it essentially tries to produce a new ecosystem. In addition, the outcome of a creation project is often difficult to predict. Therefore, the wetland conservation strategies considered in this research will be focused on migration and restoration and the preservation of adequate supply of sites for the two activities. 

## Wetland Migration

## Elizabeth River Watershed

- Source: Moon Engineering Company, Inc.

**Notes:**
Wetlands serve as the link between land and water resources and they are important elements of a watershed. Therefore, we study the wetland conservation strategies within the context of watersheds and use Elizabeth River watershed in Virginia as our case study area. 

The Elizabeth River is located primarily within the cities of Norfolk, Portsmouth, Chesapeake and Virginia Beach. It empties into the Chesapeake Bay. It covers about 129,000 acres and is experiencing rapid growth. Elizabeth River watershed lost 52% of its wetland during 1944 and 1977. And wetland loss has become an increasingly important issue in recent years. 

## Objectives

- Develop optimal wetlands conservation strategies taking into account of uncertainties;
- Explore the sensitivity of the strategy.

**Notes:**
Wetland conservation strategies must be made with substantial uncertainty, both about sea-level rise and land development. Therefore, the problem is one of decision-making under uncertainty. Risk in this case can be addressed by taking actions to preserve undeveloped lands that are potential for wetland migration and restoration, and revising actions when new information becomes available. 

The objectives of this study are to (1) develop optimal wetlands conservation strategies taking into account of uncertainty about sea-level rise and land development; (2) explore the sensitivity of the strategy to land development, sea-level rise, land price, real land price appreciation, discount rate, wetland restoration cost and budget constraint; and (3) estimate the value of information of sea-level rise information. 

## Uncertainties and Decision-Making Process

- 2 wetland conservation methods: migration and restoration.
- 2 major types of uncertainties:
-     - acquisition of new sea-level rise information
-      - development of candidate undeveloped land parcels
- Two-stage decision process (2005-2030).

**Notes:**
1. We consider two conservation methods: migration and restoration. If there’s adjacent undeveloped land, when sea-level rises, wetlands can migrate inland and survive. Restoration is defined as the “return of an ecosystem to a close approximation of its condition prior to disturbance”. 

2. We realize that when look into the future, various kinds of uncertainties exist and will affect people’s decisions and their outcomes. In this study, we consider two major types of uncertainties. One is the acquisition of new information about sea-level rise, i.e., the level of inundation of existing and potential wetlands due to future sea level rise. We assume that new climate information will become available over time. The other type of uncertainty arises form the development probability of undeveloped land. It is necessary to consider this uncertainty, because when decision-makers consider whether to buy an undeveloped land parcel during any future time spot, they need information about the likelihood of the availability of the parcel. 

3. We model a two-stage decision process. The time period is from 2005 to 2030. The first decision is made in 2005, and the second decision will be made in 2030. 

## Illustration: One Parcel

- Decision

- Not Buy

- Buy

- High SLR (P0)

- Low SLR (1- P0)

- Low SLR/Undeveloped (P2)

- High SLR/Developed (P3)

- Low SLR/Developed (1-P1-P2-P3)

- High SLR/Undeveloped (P1)

- Not Buy

- Buy

- Not Buy

- Buy

- Stage I

- Stage II

**Notes:**
The decision-making problem can be characterized using a decision tree. 

## Wetlands Conservation Strategies Model

- Acquire undeveloped land: serves as migration buffers or potential restoration sites
- Objective function: minimizing the cost of land acquisition and wetland restoration:
-     - modeled as a dynamic stochastic decision problem
-     - solved using discrete stochastic sequential programming (DSSP)
- Constraint: “no net loss” of wetlands

**Notes:**
Both methods, migration and restoration, require available undeveloped land, so the goal really is to first identify the suitable land and then preserve them. 

Minimizing the cost of achieving a wetland protection goal requires forecasting the probability of development, the cost of acquisition, and the number of acres that we must be acquired to achieve wetlands protection goals. The cost-minimizing land acquisition problem can be modeled as a dynamic stochastic decision problem and solved using discrete stochastic sequential programming (DSSP). 

Constraint: “no net loss” of wetlands.

2. In this study, we use a technique called discrete stochastic sequential programming (DSSP). It was first introduced by Cocks in 1968. It is a mathematical programming structure that allows for explicit consideration of the priori known probabilities of uncertain events. The method involves the simultaneous generation of all (mutually exclusive) possible outcomes and the transference of all variability into the objective function. DSSP models are typically specified as linear programming structures, In this case, we construct an integer programming structure. 

3. There are several steps of this model. 

## Two-Stage DSSP Model

- Objective: minimizes the expected costs while satisfying the goal of “no net loss” of wetlands
- The model is specified as below:
-     *Minimize** *
-    *s.t.*
*           *

- X1i, X2ki, Y2ki are binary decision variables.
- i indicates parcels, and k indicates states
- of nature in stage II.
- C1i, C2ki, S2ki are costs associated with the
- decisions.
- Lh and Ll are the wetlands conservation
- goal under high and low sea-level rise.
- B is the budget constraint of stage I.

**Notes:**
Equation (1) is the expected cost minimizing objective function. k denotes occurrence of state of nature k in stage II, .   and  are two mutually exclusive main groups of states of nature in stage II.   denotes the vector of decision variables associated with stage I. We define them as binary variables, which can only take values of “0” or “1” representing “not buy” and “buy” an undeveloped land parcel, respectively.  and  denote the vectors of decision variables associated with stage II given state of nature k occurs, which are also defined as binary variables.  is similar to , which represents the “not buy” or “buy” decision.   represents “not sell” or “sell” decision of an undeveloped land parcel[i].  is the costs of buying parcel i in stage I;  and  are the costs of buying parcel i and the revenue of selling parcel i in stage II given state of nature k occurs, respectively.  is the probability of state of nature k occurring in stage II. 
Equation (2) and (3) are constraints that assure the satisfaction of the conservation goal of “no net loss” of wetlands under the occurrence of groups of state of nature  and . Equation (4) is the budget constraint in stage I[ii]. Equation (5) and (6) are logical constraints which assure that parcels can not be bought twice and un-bought parcels can not be sold, respectively. 
[i] At stage II, as new information becomes available, we may find that in order to meet a certain wetland conservation goal, we don’t need to preserve as much undeveloped land as we planned before. Therefore, we model the option that decision-makers sell a certain amount of undeveloped land.
[ii] The budget constraint of stage I is our interest. Because the budget constraint of stage II only affect whether the wetland conservation goals can be reached or not, but will not affect the optimization process.

## Information Structure

- Sea-level rise scenarios: 4-12 inches for 2030 (Warrick et al, 1996)
- 3 Land use scenarios: compact, dispersed and nodal development.
- Development vulnerability index: considers four major development drivers and uses cellular automata (CA)
-    - % undeveloped land in immediate vicinity
-    - distance to shoreline
-    - distance to primary roads
-    - distance to population center

**Notes:**
The state space, which is constructed using sea-level rise and land development scenarios, is the possible outcomes corresponding to each set of decisions. The information structure is the probabilities of the outcomes and the values of outcomes. 

We use the sea-level rise projection of Warrick, 1996 for the Mid-Atlantic region for year 2030. The range is from 4 inches to 12 inches. 

We use the concepts from 2026 Comprehensive Plan of City of Chesapeake. We have three patterns: compact, dispersed and nodal development. Compact development seeks a denser growth pattern inside a well-defined boundary and rural areas are preserved with low density development. Under this scenario, undeveloped land which is adjacent to or near existing developed land is assumed to have a higher probability to be developed. Dispersed development extends the current growth pattern, low density housing, throughout the area. In nodal development, new growth occurs in “nodes” along transportation routes. Rural areas are preserved with well-defined growth areas. 

When developing these two scenarios, we construct a development vulnerability index using the idea of cellular automata model. So, I will talk about the CA model briefly first.

## Wetlands Restoration Sites Selection Protocol

- Developed by Center for Coastal Resources Management of Virginia Institute of marine Science.
- Based on basic criteria of restoration sites and has been applied to southeastern Virginia.
- A four-level hierarchical approach:
-     - foundation: land use
- Classification: potential, moderate, good, high and excellent.

**Notes:**
In order to identify the potential restoration sites, we use a GIS-based sites selection protocol developed by the Center for Coastal Resources Management of Virginia Institute of Marine Science. 

The protocol is developed based on the basic criteria of restoration sites and has been applied to a selected pilot region of southeastern Virginia, which includes our research area. 

This is a 4-level hierarchical approach and the foundation is land use. They identify agricultural and forested land are their targeting land use type. Level 1: Requires that hydric soils be present. All hydric soils greater than 0.25 acres within forested or agricultural land uses are considered plausible sites regardless of hydric soil type. 
Level 2: Assumes that in addition to hydric soils, hydrologic connectivity to the hydric soil polygon is present. 
Level 3: Integrates existing wetlands into the model. The premise behind this level is an assumption that success of a restored wetland should be enhanced if placed in a wetland landscape. 
Level 4: Integrates existing conservation areas into the model. Adjacency to existing conservation areas is considered as a positive factor. 

4. The protocol ranks potential sites based on this hierarchical approach and uses a simple appraisal system that classifies an area as potential, moderate, good, high and excellent. 

## Selected 

- Wetlands
- Restoration
- Sites

## Candidate 

- Conservation
- Sites

**Notes:**
This map shows the candidate sites we selected. The yellow area.

We first select the migration sites based on two criteria. One is that the undeveloped land cell has to be adjacent the wetland cells that will be inundated by 12 inches sea-level rise. The other one is that this cell has to have an elevation higher than 12 inches, so that in case of sea-level rise, this cell will not be inundated and can have wetland migrated on it. 

Then migration sites only can not compensate the wetland loss, so in order to meet the “no net loss” goal of wetland, we further selected restoration sites. 

Parcel 1 is in City of Hampton, Parcel 2, 3, 4, and 5 are in City of Norfolk, Parcel 6 is in City of Portsmouth, and Parcel 7, 8, 9, 10, 11, and 12 are in City of Chesapeake. 

## Development Probabilities of Compact Development Scenario

| Parcel | Conversion Rate |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 20.2% | 25.8% | 31.1% | 48.7% | 62.0% |
| No.1 | 0.9 | 1 | 1 | 1 | 1 |
| No.2 | 1 | 1 | 1 | 1 | 1 |
| No.3 | 1 | 1 | 1 | 1 | 1 |
| No.4 | 0.52 | 0.84 | 1 | 1 | 1 |
| No.5 | 0.68 | 0.94 | 1 | 1 | 1 |
| No.6 | 0 | 0 | 0 | 0 | 0.68 |
| No.7 | 0 | 0 | 0 | 0 | 0.8 |
| No.8 | 0 | 0.02 | 0.22 | 0.88 | 1 |
| No.9 | 0 | 0 | 0 | 0 | 0.06 |
| No.10 | 0.4 | 0.64 | 0.9 | 1 | 1 |
| No.11 | 0 | 0.02 | 0.3 | 0.96 | 1 |
| No.12 | 0 | 0 | 0 | 0.5 | 1 |

**Notes:**
1. Besides the four development drivers, we add a random term for each cell in order to capture other possible factors. We chose five conversion rate and for each rate, we generated 50 sets of random values and calculate the probability of each undeveloped cell being developed. Here are the results. 

## Information Structure (cont.)

-  Land price: based on development vulnerability index.
-     - agricultural land: $4,500 - $8,000 / acre
-     - forested land: $2,000 - $6,000 / acre
-  Real land price appreciation: based on current land price.
-  Wetland restoration cost:
-     - range: $10,000 - $80,000 / acre
-     - average: $20,000 - $30,000 / acre

## Experimental Design

| Factor | Values Selected |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| Development Percentage | 20.2 | 25.8 | 31.1 | 48.7 | 62.0 |
| High SLR Probability | 0.1 | 0.3 | 0.5 | 0.7 | 0.9 |
| Land Price Adjustment (%) | -20 | -10 | 0 | 10 | 20 |
| Real Land Price Appreciation (%) | 0-1 | 1-2 | 2-3 | 3-4 | 4-5 |
| Discount Rate (%) | 1 | 2 | 3 | 4 | 5 |
| Budget Constraint of Stage I (%) | 40 | 55 | 70 | 85 | 100 |
| Restoration Cost ($1,000/acre) | 10 | 20 | 25 | 30 | 80 |

**Notes:**
In order to examine the sensitivity of the model result to the model parameters, we use the following experimental design. Six model parameters are considered: development percentage, high SLR probability, real land price appreciation, discount rate, budget constraint of stage I and wetland restoration costs. Five levels for each parameter are selected 

Italic: baseline 
The base case scenario is defined using the “best” predicted parameter values that we feel the most confident with. 

Land price adjustment: overestimate, underestimate

## Base Case Scenario Results

- Optimization results using base case values:

| Land Development Scenario | Expected Costs | Parcels Purchased in Stage I |
| --- | --- | --- |
| Compact | $13,904,312 | 1,2,3,4,5,8,11 |
| Dispersed | $13,994,928 | 1,2,3,4,5,11 |
| Nodal | No integer feasible solution exists |  |

- Purchasing decision in Stage I: migration sites and restoration sites with high development pressure

**Notes:**
Although the optimal results include the value of decision variables in both stage I and stage II, the decision variables in stage I and the value of the objective function are more interesting because in stage II results are state contingent which do not involve uncertainty. 

Larger budget can reduce the expected cost when real land price appreciation rate is greater than the discount rate. 

Purchasing decision in Stage I: migration sites and restoration sites with high development pressure.

Parcel 1 is in City of Hampton, Parcel 2, 3, 4, and 5 are in City of Norfolk, Parcel 6 is in City of Portsmouth, and Parcel 7, 8, 9, 10, 11, and 12 are in City of Chesapeake. 

## Conclusions

- Wetlands conservation efforts should be first focused on migration sites.
- Land development planning should take wetlands conservation into account.
- Wetlands conservation needs to be carried out NOW, although uncertainties of climate change exist.

## Questions?

## Development Percentage Sensitivity

|  | Development Percentage |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 20.2% | 25.8% | 31.1% | 48.7% | 62.0% |
| Compact |  |  |  |  |  |
| Exp. Cost | $13,749,482 | $13,885,786 | $13,904,312 | $14,018,176 | _
 |
| Parcel | 1-5 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11,12 |  |
| Dispersed |  |  |  |  |  |
| Exp. Cost | $13,932,684 | $13,932,782 | $13,994,928 | _
 | _
 |
| Parcel | 1-5 | 1-5 | 1-5,11 |  |  |
| Nodal |  |  |  |  |  |
| Exp. Cost | _ | _ | _ | _ | _ |
| Parcel |  |  |  |  |  |

**Notes:**
1. Next I will talk about the results of the sensitivity analyses. Sensitivity analyses are conducted for each of the model parameters while holding the other parameters at their base case values. 

## High SLR Probability Sensitivity

|  | High Sea-Level Rise Probability |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 0.1 | 0.3 | 0.5 | 0.7 | 0.9 |
| Compact |  |  |  |  |  |
| Exp. Cost | $11,250,779 | $12,577,541 | $13,904,312 | $15,231,066 | $16,557,828 |
| Parcel | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 |
| Dispersed |  |  |  |  |  |
| Exp. Cost | $11,320,579 | $12,657,754 | $13,994,928 | $15,332,103 | $16,669,277 |
| Parcel | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,11 |
| Nodal |  |  |  |  |  |
| Exp. Cost | _ | _ | _ | _ | _ |
| Parcel |  |  |  |  |  |

## Land Price Sensitivity

|  | Land Price Adjustment |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | -20% | -10% | 0 | 10% | 20% |
| Compact |  |  |  |  |  |
| Exp. Cost | $12,557,382 | $13,230,847 | $13,904,312 | $14,577,778 | _
 |
| Parcel | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 |  |
| Dispersed |  |  |  |  |  |
| Exp. Cost | $12,629,918 | $13,312,423 | $13,994,928 | $14,677,433 | $15,359,938 |
| Parcel | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,11 |
| Nodal |  |  |  |  |  |
| Exp. Cost | $12,917,593 | _ | _ | _ | _ |
| Parcel | 1-6,10,11,12 |  |  |  |  |

## Real Land Price Appreciation Sensitivity

|  | Real Land Price Appreciation Rate |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 0-1% | 1-2% | 2-3% | 3-4% | 4-5% |
| Compact |  |  |  |  |  |
| Exp. Cost | $13,366,859 | $13,604,017 | $13,904,312 | $14,168,354 | $14,168,354 |
| Parcel | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11,12 | 1-5,8,11,12 |
| Dispersed |  |  |  |  |  |
| Exp. Cost | $13,219,455 | $13,561,619 | $13,994,928 | $14,303,446 | $14,565,353 |
| Parcel | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,7,11,12 | 1-5,7,11,12 |
| Nodal |  |  |  |  |  |
| Exp. Cost | _
 | _ | _ | _ | _ |
| Parcel |  |  |  |  |  |

## Discount Rate Sensitivity

|  | Discount Rate |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 1% | 2% | 3% | 4% | 5% |
| Compact |  |  |  |  |  |
| Exp. Cost | $18,980,036 | $16,150,290 | $13,904,312 | $12,068,920 | $10,639,608 |
| Parcel | 1-5,8,11,12 | 1-5,8,11,12 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 |
| Dispersed |  |  |  |  |  |
| Exp. Cost | $19,110,067 | $16,285,006 | $13,994,928 | $12,028,194 | $10,496,590 |
| Parcel | 1-5,7,11,12 | 1-5,7,11,12 | 1-5,11 | 1-5,7,11 | 1-5,7,11 |
| Nodal |  |  |  |  |  |
| Exp. Cost | _
 | _ | _ | _ | _ |
| Parcel |  |  |  |  |  |

## Wetland Restoration Cost Sensitivity

|  | Restoration Cost ($1,000 / acre) |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 10 | 20 | 25 | 30 | 80 |
| Compact |  |  |  |  |  |
| Exp. Cost | $9,602,509 | $12,470,365 | $13,904,312 | $15,338,240 | $29,677,538 |
| Parcel | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 |
| Dispersed |  |  |  |  |  |
| Exp. Cost | $9,692,997 | $12,560,945 | $13,994,928 | $15,428,902 | $29,768,647 |
| Parcel | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,11 |
| Nodal |  |  |  |  |  |
| Exp. Cost | _ | _ | _ | _ | _ |
| Parcel |  |  |  |  |  |

## Budget Constraint Sensitivity 

|  | Budget Constraint |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 40% | 55% | 70% | 85% | 100% |
| Compact |  |  |  |  |  |
| Exp. Cost | _ | _ | $13,904,312 | $13,904,312 | $13,904,312 |
| Parcel |  |  | 1-5,8,11 | 1-5,8,11 | 1-5,8,11 |
| Dispersed |  |  |  |  |  |
| Exp. Cost | _ | $13,994,928 | $13,994,928 | $13,994,928 | $13,994,928 |
| Parcel |  | 1-5,11 | 1-5,11 | 1-5,11 | 1-5,11 |
| Nodal |  |  |  |  |  |
| Exp. Cost | _ | _ | _ | $14,352,322 | $14,352,322 |
| Parcel |  |  |  | 1-6,10,11,12 | 1-6,10,11,12 |

**Notes:**
Under base case values

## Budget Constraint Sensitivity (3-4%) 

|  | Budget Constraint |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 40% | 55% | 70% | 85% | 100% |
| Compact |  |  |  |  |  |
| Exp. Cost | _ | _ | $14,168,354 | $13,942,978 | $13,833,077 |
| Parcel |  |  | 1-5,8,11,12 | 1-8,11,12 | all |
| Dispersed |  |  |  |  |  |
| Exp. Cost | _ | $14,542,401 | $14,303,446 | $14,099,526 | $13,994,928 |
| Parcel |  | 1-5,11 | 1-5,7,11,12 | 1-8,11,12 | all |
| Nodal |  |  |  |  |  |
| Exp. Cost | _ | _ | _ | $14,387,910 | $14,193,907 |
| Parcel |  |  |  | 1-7,11,12 | all |

**Notes:**
When real land price appreciation rate = 3-4%

## Thank You!

- Dr. Steve Graham of Penn State and Ms. Tamia Rudnicky of Virginia Institute of Marine Science (VIMS)
- Ms. Marcia Berman, Mr. Walter Priest and Mr. Dan Schatt of VIMS
- Ms. Jaleh Pett of the Planning Department of the City of Chesapeake
- Global Change Research Program, Office of Research and Development, U.S. Environmental Protection Agency

## Development Probabilities of Dispersed Development Scenario

| Parcel | Conversion Rate |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 20.2% | 25.8% | 31.1% | 48.7% | 62.0% |
| No.1 | 1 | 1 | 1 | 1 | 1 |
| No.2 | 1 | 1 | 1 | 1 | 1 |
| No.3 | 0.88 | 1 | 1 | 1 | 1 |
| No.4 | 0.46 | 0.6 | 0.78 | 1 | 1 |
| No.5 | 0.74 | 0.98 | 1 | 1 | 1 |
| No.6 | 0 | 0 | 0 | 0.32 | 0.6 |
| No.7 | 0 | 0 | 0 | 0.44 | 0.78 |
| No.8 | 0 | 0 | 0 | 0.44 | 0.88 |
| No.9 | 0 | 0 | 0 | 0 | 0.12 |
| No.10 | 0.02 | 0.3 | 0.52 | 0.94 | 1 |
| No.11 | 0 | 0 | 0.1 | 0.72 | 1 |
| No.12 | 0 | 0 | 0 | 0.5 | 0.88 |

## Development Probabilities of Nodal Development Scenario

| Parcel | Conversion Rate |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | 20.2% | 25.8% | 31.1% | 48.7% | 62.0% |
| No.1 | 0.8 | 0.96 | 1 | 1 | 1 |
| No.2 | 0.66 | 0.82 | 0.96 | 1 | 1 |
| No.3 | 0.78 | 0.96 | 1 | 1 | 1 |
| No.4 | 0.54 | 0.72 | 0.78 | 1 | 1 |
| No.5 | 0.7 | 0.84 | 0.96 | 1 | 1 |
| No.6 | 0 | 0.08 | 0.16 | 0.46 | 0.72 |
| No.7 | 0.12 | 0.34 | 0.56 | 0.82 | 1 |
| No.8 | 0 | 0 | 0 | 0.36 | 0.66 |
| No.9 | 0 | 0 | 0 | 0 | 0.2 |
| No.10 | 0.12 | 0.28 | 0.46 | 0.88 | 1 |
| No.11 | 0.1 | 0.18 | 0.32 | 0.54 | 0.88 |
| No.12 | 0.26 | 0.38 | 0.48 | 0.92 | 1 |