Section 3: Benefit-Cost Analysis

This section attempts to estimate in financial terms the costs and benefits associated with carrying out the critical habitat hazard analysis. We consider the primary users of the end product to be policy makers, land-use planners and environmental/conservation groups promoting effective habitat conservation and management. Thus, we are making some key assumptions regarding the access of these groups and individuals to existing GIS technology. Those organizations from whom we have obtained data and with whom we have discussed this project are actively using GIS, though not necessarily for the kind of analysis we've developed here.

Technology Environment:

Based on discussions with representatives of these groups and reviews of the literature on hazard evaluation of critical habitat areas, this analysis complements and enhances existing methods and systems for quantifying and qualifying the nature of threats influencing these regions. It goes beyond what is commonly done to evaluate hazard factors by:

Geographic Scale: This project addresses critical habitats over a larger geographic region, the Puget Sound basin. The majority of conservation groups operate on a local scale and larger efforts have regional focuses. With the data automation and processing capacity offered by GIS, this project is capable of considering all identified critical habitats in the region of analysis.

Data Sources: Data sets that encompass the entire Puget Sound and contain sufficient detail to support critical habitat analysis are rare. This analysis utilizes detailed elevation and habitat coverages commonly utilized for conservation management along with land cover classifications from satellite imagery.

Habitat influence processes: Typical habitat analyses have focused on factors inherent in the habitat: species diversity and habitat quality. This analysis considers habitats' relationships with external influences associated with flow, adjacency and change. It accounts for habitats’ area/edge and relationship to uphill flow zones as predisposing characteristics. Slope and distance to habitat are used as aggravating factors in consideration of uphill and adjacent influences respectively.

Data Analysis: Each habitat region is considered independently of other habitats. This unusual approach to habitat analysis permits examination of each habitat in relationship to influencing land cover. GIS operations commonly available in standard software do not offer this ‘isolated object’ perspective and may underestimate the multiple effects of negative land cover impacts.

Antenucci Benefit Typology:

Type One Benefits: Increased Efficiency. Focuses on how users may more efficiently carry out an assessment of the nature and magnitude of hazardous influence on habitat areas.

1. Better at-a-glance tabular evaluative materials for rapid assessment
2. Reductions in time associated with synthesizing disparate data
3. Synthesis highlights data quality problems/needs to better focus future data development
4. Look-up table structure conducive to systematic updating as better data becomes available (updated land cover; 10 meter DEM, new critical habitats, etc.)
5. Opportunity to scrutinize data history of key data sets in circulation and consider the metadata needs of multiple users.

1. Type Two Benefits: Expanded Capabilities. Focuses on what is afforded by methods and procedures in terms of analysis that hasn't been done before.
1. Method for integrating slope and landcover in a dynamic hazard ranking system
2. Alternative habitat specific rapid assessment techniques
3. Development of operations to delineate watersheds/drainages based on polygons (habitat areas) rather than single pour points as the program is designed to do.
4. Method for more dynamically considering buffer zones and their surrounding landuses as they influence habitat areas.
5. Collaboration between data managers of various agencies responsible for various components of the model (State DNR, Nature Conservancy, County land planners, etc.) - map products which reflect the confluence of their interests.
6. May allow future landuse considerations to be incorporated via zoning related data sets thereby moving from managing existing danger to heading off future dangers.
7. May provide developers with advanced warning of potential building impacts for which they could be held responsible.
2. Type Three Benefits: Unforeseen events. Imagining problems or issues which might befall target groups for which this system might provide a solution.

1. Quick data responding to announced endangered species listing requiring immediate identification of prime areas requiring attention.
2. Expansion of user groups to builders associations or developers in an effort to better guide development of housing, utilities, etc. particularly at a time where growth places a high priority on rapid development
3. Could enhance the credibility of conservation planners by identifying specific up-slope hazards and linking these to anthropogenic change in very concrete ways.
4. Emergency ecological disaster mitigation planning.

1. Type Four Benefits: Intangibles. Difficult to foresee or quantify benefits
1. Greater faith in conservation and land use planners to realistically evaluate hazards to key areas resulting in greater public participation.
2. Potentially greater acceptance of sacrifices and other difficult policies which must be implemented to protect critical or endangered areas.
3. Sense of unified mission among disparate agencies involved in the protection of endangered species and their habitats.
2. Type Five Benefits: Sales The data generated by this project may be useful in data exchanges with over conservation organizations. The specialized analysis operations and habitat statistics will be of interest to other researchers and conservation planners who may be able to help support the analysis costs.
1. Habitat specific statistics on hazards stemming from land cover processes relating to flow and adjacency: of interest to other researchers and conservation efforts
2. Delineation of uphill flow zones with potential to impact critical habitats: of interest to land planners and regional development endeavors.
 

Cost Analysis in Three Categories: Capital, Operating and Opportunity/Hidden:

Cost Assumptions and Explanations:

Below is a more detailed delineation of the capital requirements and labor tasks associated with the project. These have been aggregated for the cost comparison chart which estimates the difference between attempting to carry out this kind of analyses manually (a task we feel is virtually impossible at this scale) and in the automated environment. Several decisions warrant some explanation in interpreting the chart.
 

1. First there was considerable discussion surrounding the allotment of staff labor time. We estimate each of the three analysts allotted 18 hours per week to the project. On average, 8 of these hours are spent in project management in discussion and brainstorming sessions. This appears under the Operations Section. The remaining time is spent building the data-base and conducting analysis and therefore constitutes the bulk of the costs associated with Data-base development under capital costs.
2. We define "Maintenance Costs" as those associated with the efficient upkeep of the network and other technical tools associated with automated analysis. Were this project to extend over a longer time period and should the data be utilized by other applications, we would expect these costs to grow considerably to include the upkeep and management of current data resources.
3. Training costs are those labor expenses associated with presentation and training of others on the system we've developed including staff time to educate on script development.
4. Hardware and Software costs associated with automated operations are based on depreciation costs of the equipment and software of the life span of the project. It is assumed that this operation has access to machines and software and therefore did not need to purchase it.

Capital Expenditures

Operations Related Expenditures

1. Labor.
1. Three analysts working .75 time for 10 weeks at $20.00 per hour.
2. Data preparation activity.
1. Procuring satellite/remote sensing imagery.
2. Interpretation time and remote sensing equipment.
3. DEM conversion/pre processing.
4. Landcover interpretation and ground truth.
5. Field surveys for habitat confirmation.
3. Promotion of and training in system use.
1. Staff to promote new system and educate various end users on its applications and products.
2. Systems Maintenance and Development.
1. Network manager – meet part of payment on widely used network manager.
2. Data management and upkeep.
3. Systems management and upkeep.
4. Researching survey control of integrated data.
5. Depreciation of Hardware (20% per annum).
3. Opportunity and Hidden Costs:
1. Management staff time away from other activities .
2. Key resources which undeveloped as money/time are put into the longer term payback of GIS development.
3. Transition period entails training and new procedure development which may result in a temporary reduction of services to the public and to other users.

The following information is presented in aggregate in the included graphs and charts. The graph provides the decision maker with some image of the break-even point over project duration and the cost loading of the project. The chart provides a more nuanced understanding of what categories dominate expenses at which project stage and may be a better tool for cash flow management requirements.

The graph illustrates an unusual pattern which differs markedly from the examples offered by Antenucci. This can be explained by the assumptions made during this analysis. Huge initial start up cost are often associated with the implementation of a GIS in a policy or other institutional environment. These frequently outweigh the costs associated with manual operations which may not exceed the automated model until well into the implementation of the system when efficiency benefits are realized.

However in this project, we assumed the existence of key equipment and data. The availability of both lowered important costs associated with the automated route and made the project cost-effective from the start. As the project proceeds toward final map production, the manual option which has required greater personnel costs throughout, becomes exceedingly expensive. This reflects our conviction that the types of maps and information products to come from this project would not be financially feasible without the support of a GIS.

In addition the GIS option may be more preferable based on benefits which may not be well captured here. Surveys of other staff members in the organization who have benefited from the project's products may better quantify some of the intangible benefits which may arise. An outside evaluation of efficiency in decision making before and after the project may also identify some measurable benefits derived from the project's implementation.