# Presentation: 025994

## SETAC – Sydney, Australia
August 5, 2008

- SETAC – Sydney, Australia
- August 5, 2008

- C. Stahl, A. Cimorelli, M. Nicholson, C. Mazzarella, W. Jenkins, R. Pomponio, and J. Krakowiak;  U.S Environmental Protection Agency Region III, 1650 Arch Street, Philadelphia, Pennsylvania, USA

## What is Trans-disciplinary Learning?

- Creation of new knowledge from existing disciplines.
- Why does this matter?
- Systems approach
- Why is this hard?
- Specialty training
- Fields of specialties
- Organizational structures
- Complexity of problem
- Complexity of solutions

## Case Study

- U.S. Mid-Atlantic Region
- Nearly 200 indicators
- Health, Welfare, Future Vulnerability
- Air, water, waste, landscape (forest, waters, wetlands, oceans)
- Condition and stressors
- Geographic analytical unit = 12 digit Hydrologic Unit Code (HUC12)*; nearly 3700 in this region.
- *U.S. Geological Survey classification scheme based on water drainage areas.

## Analysis using MIRA

- Multi-criteria Integrated Resource Assessment
- MIRA approach:
- Transparent
- Data-driven
- Stakeholder inclusive
- Iterative
- Ideas/methods from a variety of disciplines:  risk management, adaptive management, decision analysis, consensus building, sustainability.
- A *Trans-disciplinary *approach to environmental analysis.

## MIRA Steps

**Decide** on the decision question.  **CONTEXT is key.**
**Decide** on decision criteria (indicator construct and data).
**Index** data (expert judgment of significance) – relative to this decision context.
**Preference **decision criteria (value sets using pairwise comparisons) – relative to this decision context.
**Iterate** – LEARN.
- MIRA Products:  ranked list (by area, by option,...); maps of ranked areas.

## Example of MIRA Indexing and Preferencing

- Index all indicator data
- Preference decision criteria** **

| Criteria | Good (1) |  | Bad | Worst (8) |
| --- | --- | --- | --- | --- |
| Waste | 12 | 30 | 50 | 80 |
| Forest | 10000 | 8000 | 2000 | 100 |

- Health

- Catastrophic

- Food Supply

- Disease

- 0.44

- 0.40

- 0.16

## Two Areas of Trans-disciplinary Learning

- Indexing
- Experts from different disciplines/fields of expertise representing decision criteria data determine the relative significance of that data within the context of the analysis.
- Preferencing
- Decision makers/stakeholders with different perspectives/different expertise experiment with different value sets – what’s more important and by how much?
- Both areas are transparent.

## Trans-Disciplinary Learning through Indexing:  Fine Particulate Matter (PM2.5, ug/m3)

| Indicator | Index  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | 1.0 – 2.0 | 2.0 - 3.0 | 3.0 – 4.0 | 4.0 – 5.0 | 5.0 – 6.0 | 6.0 – 7.0 | 7.0 – 8.0 |
| PM2.5D | 3.0 | 8.0 | 12.50 | 15.75 | 16.5 | 17.25 | 18.0 |
| PM2.5E | 10.0 | 14.0 | 16.0 | 17.0 | 18.0 | 19.0 | 20.0 |
| PM2.5V | 12.0 | 15.0 | 20.0 | 23.0 | 25.0 | 30.0 | 35.0 |

**Trans-Disciplinary Learning through Indexing:  Fine Particulate Matter (PM2.5, ug/m****3****)**

- Same PM2.5 data used three different ways:  Disease (D), Exercise (E), Vistas (V)

- PM2.5 Max = 30.7; PM2.5 Min = 5.8

## Trans-Disciplinary Learning through Indexing:  Fine Particulate Matter (PM2.5, ug/m3) with Bee Crops (hectares, ha) and Nitrogen Deposition (kg/ha) 

| Indicator | Index
1.0- 2.0 | 2.0- 3.0 | 3.0- 4.0 | 4.0- 5.0 | 5.0- 6.0 | 6.0- 7.0 | 7.0- 8.0 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| PM25DV-D | 3.00 | 8.00 | 12.50 | 15.75 | 16.50 | 17.25 | 18.00 |
| PM25DV-E | 10.00 | 14.00 | 16.00 | 17.00 | 18.00 | 19.00 | 20.00 |
| PM25DV-V | 12.00 | 15.00 | 20.00 | 23.00 | 25.00 | 30.00 | 35.00 |
| BEECROP | 1,000 | 10,000 | 50,000 | 80,000 | 120,000 | 180,000 | 350,000 |
| NITRODEP | 5.00 | 9.17 | 13.33 | 17.50 | 21.67 | 25.83 | 30.00 |

**Trans-Disciplinary Learning through Indexing:  Fine Particulate Matter (PM2.5, ug/m****3****) with Bee Crops (hectares, ha) and Nitrogen Deposition (kg/ha) **

- Using multiple criteria with disparate units in the same analysis.

## Trans-Disciplinary Learning through Preferencing

- Test the impact of different value sets on ranked watersheds.
- Learn what indicators/science drives which watersheds to rank high/low.
- Learn through the collective use of all indicators.

## Relative Ranks of Watersheds Change for Different Value Sets

| Name of HUC | Cons  | Equal  | Health | Welfare |
| --- | --- | --- | --- | --- |
| Goose Creek | 1001 | 472 | 1129 | 887 |
| Bald Eagle Creek | 1002 | 616 | 1454 | 70 |
| Grave Creek | 1003 | 3072 | 1361 | 1934 |
| Tunkhannock Creek | 1004 | 583 | 1483 | 750 |

**Relative Ranks of Watersheds Change for Different Value Sets**

**Value Set 1:  **Consolidated senior manager agreement (Cons)
**Value Set 2:  **Equal preferences (all criteria equally important) (Equal)
**Value Set 3: **Health focused (Health)
**Value Set 4: **Welfare focused (Welfare)

## Consolidated Run

- Equal Pref Run

- Health Focused Run

- Welfare Focused Run

## Science Drivers Differ Depending on Value Set

- Extreme Health Value set*

- Extreme Welfare Value set*

**Science Drivers Differ Depending on Value Set**

***Data is Identical.**

## MIRA informs:

- What science is important?
- Where is that science important?
- BUT: How do we use this information to budget our resources?

## Hypothetical Current and Potential Resource Allocation Schemes Based on 4 Different Value Sets (compared with the current allocation scheme)

**Programs**

**% of Available Resources**

## Linking Science to Budgets

- Use data and values to inform resource allocation.
- Compare/Evaluate current allocation with other allocation possibilities.

## Lessons Learned

- Trans-disciplinary learning isn’t just a concept.
- Using science to inform budget allocations is possible.
- The MIRA process facilitates communication and learning within the organization.
- We can use the MIRA process to identify gaps:  data, knowledge, communication,...
- We can do better – and we will!

## Contact Information

- Cynthia Stahl, U.S. EPA Region III, 215-814-2180, _[stahl.cynthia@epa.gov](mailto:stahl.cynthia@epa.gov)_