Economic Analysis of Wildfire Management

Organizations

David Rossi, Ph.D. student

Department of Applied Economics

Oregon State University

Olli-Pekka Kuusela, Assistant Professor

Department of Forest Engineering, Resources, and Management

Oregon State University

Presentation Outline

1. Motivating Questions

2. Literature Review

• Review C+NVC model

• Extensions of the C+NVC model

3. Discrete Choice Model

4. Discussion

2

Source: Hoover and Lindsay (2017); USFS Annual Budget Justifications

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Exp

end

itu

re S

har

e

Fiscal Year

Suppression Expenditure Shares (2005-2018)

Suppression's Share ofAnnual WFMAppropriations

Suppression's Share of TotalWildfire Mgmt.Expenditures

Suppression's Share of TotalUSFS Budget

Wildfire ManagementExpenditures as % of TotalUSFS Budget

• Complements or substitutes?

o Donovan and Rideout (2003); Snider et al., (2005); Thompson et al. (2013); Rideout et al. (2008)

• Fragmented budgeting?

o Rideout et al., (2008); Minas et al., (2015)

▪ Strategic behavior?▪ Rossi and Kuusela (2019)

▪ Externalities from availability of suppression reserve funds – misaligned incentives?

o Donovan and Brown (2005); Lueck (2012)

• Satisficing behavior, “selective rationality”, or inverse probability weighting amongst IMTs?

o Holmes and Calkin (2013); Wibbenmeyer et al., (2013)

• Non-neutral risk attitudes?

o Blattenberger et al., (1984); Canton-Thompson et al. (2008); Hand et al., (2017)

4

Literature Review

Deterministic Sequential C+NVC Model with Cost Externality

Pre-suppression

Appropriations

Suppression + Reserve Funding

(all costs not internalized)

Proposition #1

6

… or given 𝜃′ 𝑡 > 0…

𝐴𝑠 𝜃 ↑, 𝑆∗ ↓𝐴𝑠 𝜃 ↑, 𝑃∗ ↑

𝐴𝑠 𝑡 ↑, 𝑆∗ ↓𝐴𝑠 𝑡 ↑, 𝑃∗ ↑

• Shift arises from improvement in decision technology.

• The introduction of a better decision heuristic can enable the IMT to incorporate all costs with less cognitive/computational search time.

Incorporating Stochastic outcomes and Risk Preferences in the C+NVC model

(CARA Utility)

9

Pre-suppression

Appropriations

Suppression +

Downside Risk Aversion

10

Incorporating Downside Risk Aversion

Proposition #2 (when tail risk is present)

• 𝐴𝑠𝑠𝑢𝑚𝑒: 𝐴𝑠 𝑆 ↑, 𝜎2 ↓• 𝐴𝑠𝑠𝑢𝑚𝑒: 𝐴𝑠 𝑆 ↑, 𝜑 ↑. 𝑇ℎ𝑒𝑛…

• 𝐴𝑠𝑠𝑢𝑚𝑒 𝑖𝑛𝑠𝑡𝑒𝑎𝑑: 𝐴𝑠 𝑆 ↑, 𝜑 ↓. 𝑇ℎ𝑒𝑛 𝑤𝑒 𝑐𝑎𝑛 ℎ𝑎𝑣𝑒 𝑡ℎ𝑒 𝑐𝑎𝑠𝑒 𝑤ℎ𝑒𝑟𝑒 …

𝐴𝑠 𝑟 ↑, 𝑆∗ ↑𝐴𝑠 𝑟 ↑, 𝑃∗ ↓

𝐴𝑠 𝑟 ↑, 𝑆∗ ↓𝐴𝑠 𝑟 ↑, 𝑃∗ ↑

13

Recovering the Implicit Price of Suppression Resources

𝑠𝑖𝑡 = ቊ1 𝑖𝑓 𝑓𝑢𝑙𝑙 𝑠𝑢𝑝𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛0 𝑖𝑓 𝑚𝑜𝑛𝑖𝑡𝑜𝑟, 𝑝𝑜𝑖𝑛𝑡 𝑝𝑟𝑜𝑡𝑒𝑐𝑡𝑖𝑜𝑛, 𝑜𝑟 𝑐𝑜𝑛𝑓𝑖𝑛𝑒

Pr 𝑈 𝑠𝑖𝑡 = 1|𝐱𝑖𝑡 > 𝑈 𝑠𝑖𝑡 = 0|𝐱𝑖𝑡

Pr 𝑠𝑖𝑡 = 1 𝐱𝑖𝑡 = 𝐱𝑖𝑡′ 𝛃 + ത𝐱𝑖

′𝜻 + 𝑢𝑖 + 𝜀𝑖𝑡

• CRE-IV LPM

• Recover estimates of MWTP for each suppression resource 𝑥𝑖𝑡𝑘

𝑀𝑊𝑇𝑃𝑘 = −

𝜕 Pr 𝑠𝑖𝑡 = 1 𝐱𝑖𝑡𝜕𝑥𝑖𝑡

𝑘

𝜕 Pr 𝑠𝑖𝑡 = 1 𝐱𝑖𝑡𝜕𝑚𝑖𝑡

Estimation Sample

• 91.5% of 22,052 ICS-209 reports filed by USFS between 2002-2013 were classified as Full Suppression days

• Each time a manager fills out a report, a “choice occasion” is observed

• 3,439 Incident Managers for USFS managed fires over this period

0.2

.4.6

.81

Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Region 8 Region 9

USFS Region

=1 if Incident Manager chooses to suppress, =0 otherwise Mean

Incident Manager Suppression Decisions by Region of the U.S. Forest Service

Estimation Sample

Full Suppression

Decision

{0,1}

Pre FLAME Act

Period (2002-2009)

Post-FLAME Act

Period (2010-2013)

0 13.8% 0%

1 86.2% 100%

Estimation Sample

𝜒(1)2 = 1300.00 > 𝜒𝑐𝑟𝑖𝑡

2 ; 𝑅𝑒𝑗𝑒𝑐𝑡 𝐻0 𝑜𝑓 𝑛𝑜 𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑖𝑜𝑛

Full Suppression

Decision

{0,1}

Type 3,4,5 Incident

Complexity

Type 1,2 Incident

Complexity

0 12.6% 1.7%

1 87.4% 98.3%

Estimation Sample

𝜒(1)2 = 787.8849 > 𝜒𝑐𝑟𝑖𝑡

2 ; 𝑅𝑒𝑗𝑒𝑐𝑡 𝐻0 𝑜𝑓 𝑛𝑜 𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑖𝑜𝑛

0

10

00

020

00

030

00

040

00

0

Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Region 8 Region 9

USFS Region

Distance to Wildland-Urban Intrface or Intermix areas (in meters) Mean

Distances Between Wildfire Ignition Points and the WUI by Region of the U.S. Forest Service

Estimation Sample

Estimation Sample

CRE-IV LPM 2nd Stage Results

Dependent Variable: Suppress {0,1} N=22,052

Coefficient St. Error

ln(ACRES) 0.1747*** (0.0220)

DESTRUCTION -0.00008 (0.00002)

GROUNDCREWS 0.0001 (0.0001)

AIRRESOURCES -0.0002*** (0.00004)

FIREENGINES -0.0007* (0.0004)

BULLDOZERS 0.0004*** (0.00007)

ln(SIZE) -0.1122*** (0.0127)

MILESINF 0.0007*** (0.0001)

percAcRetAv -0.2777*** (0.0982)

COMPLEX {0,1} 0.0422*** (0.0160)

WINDSPEED -0.00007* (0.00004)

TEMP -0.0001*** (0.00004)

RELHUM 0.0004*** (0.0001)

WUIDIST -0.000008*** (0.0000009)

POSTFLAME {0,1} 0.1457*** (0.0087)

TC_HARVEST 0.000003*** (0.0000008)

Significant Region Interactions with Monetary Attribute: R2, R3, R4, R5, R6, R8, R9

Time-averaged Variables are Jointly significant @ 1% level

0.4256

0.2438

0.7529

Wald Statistic: 661.40***

Instruments: HUMAN, LIGHTNING, SUMMER, avgHUMAN, avgLIGHTNING,

avgSUMMER

𝜎

𝜎

=𝜎

𝜎 +𝜎

Postestimation Tests

• Fixed or Random Effects? → Hausman Principle.

oPerform F-test on time-demeaned terms.

𝐻0: 𝜻 = 0𝐻𝐴: 𝜻 ≠ 0

𝜒(21)2 = 37.25 > 𝜒𝑐𝑟𝑖𝑡

2

𝑝 = 0.0158 < 0.05; 𝑅𝑒𝑗𝑒𝑐𝑡 𝐻0

oEvidence suggests that unobserved manager-specific factors are systematically related to the observed determinants of suppression demand and resource use.

oPure Random Effects or RE-IV is thus invalid.

Postestimation Tests

• IVs are strong predictors of the Endogenous Variables

• Sargan-Hansen Test of Overidentifying Restrictions (are IVs valid)?

𝐻0: 𝑖𝑛𝑠𝑡𝑟𝑢𝑚𝑒𝑛𝑡𝑠 𝑢𝑛𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑖𝑡ℎ 𝐶𝑅𝐸𝐼𝑉 𝑒𝑟𝑟𝑜𝑟𝐻𝐴: 𝑖𝑛𝑠𝑡𝑟𝑢𝑚𝑒𝑛𝑡𝑠 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑤𝑖𝑡ℎ 𝐶𝑅𝐸𝐼𝑉 𝑒𝑟𝑟𝑜𝑟

𝜒(2)2 = 4.526 < 𝜒𝑐𝑟𝑖𝑡

2

𝑝 = 0.1041 > 0.1; 𝐹𝑇𝑅 𝐻0@ 10% 𝑙𝑒𝑣𝑒𝑙

oEvidence suggests that Instruments are valid/exogenous (unrelated to CRE-IV residual).

Estimates of Implicit Prices by Region

Questions/Comments?References

Arrow, K., Lind, R. 1970. Uncertainty and the evaluation of public investment. American Economic Review 60, 364-378.

Blattenberger, G.; W. Hyde; T. Mills. 1984. Risk in Fire Management Decisionmaking: techniques and criteria. Gen. Tech. Rep PSW-80. U.S. Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station. 12 p.

Donovan, G.; Brown, T. 2005. An alternative incentive structure for wildfire management on national forest land. Forest Science 51(5), 387-395.

Donovan, G.; Rideout, D. 2003. A Reformulation of the Cost Plus Net Value Change (C+NVC) Model of Wildfire Economics. Forest Science 49(2), 318-323.

Hand, M.; Katuwal, H.; Calkin, D.; Thompson, M. 2017. The influence of incident management teams on the deployment of wildfire suppression resources. International Journal of Wildland Fire 26, 625-629.

Hoover, K., Lindsay, B. 2017. Wildfire Suppression Spending: Background, Issues, and Legislation in the 115 th Congress. CRS Report 7-5700. R44966.

Lueck, D. 2012. Economics and the Organization of wildfire Suppression. In “Wildfire Policy: Law and Economics Perspectives” (Bradshaw and Lueck, Eds.). P. 71-88.

Maguire, L.; E. Albright. 2005. Can behavioral decision theory explain risk-averse fire management decisions? Forest Ecology and Management 211, 47-58.

Mercer, D.E.; Haight, R.; Prestemon, J. 2008. Analyzing Trade-offs between fuels management, suppression, and damages from wildfire. In “The Economics of Forest Disturbances: Wildfires, Storms, and Invasive Species,” Holmes et al. (Eds.). P. 247-272. Springer Science+Business Media.

Minas, J., Jearne, J., Martell, D. 2015. An integrated optimization model for fuel management and fire suppression preparedness planning. Annals of Operations Research 232, 201-215.

Raudla, R. 2014. “Budgetary Institutions.” Encyclopedia of Law and Economics. Springer. New York, NY.

Rideout, D., Wei, Y., Kirsch, A., Botti, S. 2008. Toward a Unified Economic Theory of Fire Program Analysis with Strategies for Empirical Modeling. P. 361-380 in “The Economics of Forest Disturbances: Wildfire, Storms, and Invasive Species.” Holmes et al. (Eds.). Springer.

Samuelson, P. 1964. Discussion. In: “Proceedings of the seventy-sixth annual meeting of the American Economic Association; volume 54(3); 1963 December 27-29; Boston, MA: American Economic Association, 93-96.

Sparhawk, W. 1925. The use of liability rating in planning forest fire protection. Journal of Agricultural Research 30, 693-792.

Teeter, L.; A. Allen Dyer. 1986. A Multiattribute Utility Model for Incorporating Risk in Fire Management Planning. Forest Science 32(4), 1032-1048.

Thompson et al., 2013. Quantifying the Potential Impacts of Fuel Treatments on Wildfire Suppression Costs. Journal of Forestry 111(1): 49-58

USFS. 2015. “The Rising Cost of Wildfire Operations”25