Spatial Externalities, Policy Interventions, and Private Wildfire Risk Mitigation
An Experimental Study
Naoya Yukizaki1・Yohei Mitani2
1.

Background
Reducing wildfire risk to wildland-urban interface (WUI) homeowners has been a growing major issue in theUnited States.

Successful wildfire management in the WUI involves homeowners’ voluntary mitigation

actions. However, individual homeowners fail to take sufficient risk mitigation due to risk externalities (known as“the mitigation paradox”). Do policy interventions facilitate private mitigation efforts and increase theefficiency? We use a laboratory experiment wherein spatial-risk externalities exist to investigate whether financial
interventions promote private mitigation efforts and increase the efficiency
2.

Experimental DesignWe develop a public good game that models the WUI owners’ risk mitigation, where in we construct hierarchicalgroupings containing three local groups of three members nested in a nine members global group. This groupstructure captures the features of spatial risk externalities. For each player, there are two neighbors in the same localgroup and six players in the different local groups. Each players decides whether to conduct fuel reduction (𝑑𝑖 = 1)
or not (𝑑𝑖 = 0). Player i’s expected payoff function is defined as 𝜋𝑖 = 𝑌 − 𝑐 ∙ 𝑑𝑖 − 𝐿 ∙ 𝑃(∑ 𝑑𝑗 , ∑ 𝑑𝑘 |𝑊). Y, c, andL are income, private cost of fuel reduction, and loss caused by wildfire, respectively. P is the probability of wildfire
occurrence, which is the function of the sum of risk mitigation efforts in the player’s own local group (∑ 𝑑𝑗 ) andthose in the other local groups (∑ 𝑑𝑘 ). The vector W puts different weights between their own group and outsidegroups: 1 on ∑ 𝑑𝑗 and 1/3 on ∑ 𝑑𝑘 One control and two treatments were used in the experiment: the no intervention
group (CONTROL), the low intervention group (LOW), and the high intervention group (HIGH). The LOW (HIGH)intervention mandatory involves mild (high) risk mitigation efforts, financed by tax deducted from all players’
income. In the LOW (HIGH), 20% (40%) of total maximum risk mitigation is mandatory conducted. In eachtreatment, players made a decision ten times and received feedback about other players’ decisions after each round.
We conducted a laboratory experiment at Kyoto University in 2016. A hundred and eight undergraduate andgraduate students were participated.
3.

Results
The likelihood of individual fuel reduction (i.e. d=1) was highest in the LOW (see Figure 1). The likelihood ofrisk mitigation is higher for older, male, and more risk averse participants (see Table 1). In order to analyze how
financial interventions influence the total amount of mitigation, we calculate how much intervention substitutes
voluntary risk mitigation. We find that the LOW intervention incompletely crowds in voluntary mitigation and theDivision of Natural Resource Economics, Graduate School of Agriculture, Kyoto University
TEL: 080-3802-4677 E-mail: yukizaki.naoya.63w@st.kyoto-u.au.jp
2
Division of Natural Resource Economics, Graduate School of Agriculture, Kyoto University
1

HIGH intervention incompletely crowds out voluntarymitigation. We also analyze how intervention influences onplayers’ incentives. The intervention changes the expected
payoff given other players’ decisions in the previous round( ∆E ). We assume that the influence of ∆E through theintervention is the economic incentive of interventions andthe other influences of interventions are intrinsic incentives.
We analyze these effects using the logit model. We find thatFigure 1: Likelihood of fuel reduction

players in the treatment groups have lower economic
incentives than those in the control groups. Players in theLOW have higher intrinsic incentives, but players in theHIGH show no significant difference in intrinsic incentives
from players in the CONTROL (see Table 2). The total
amount of mitigation in the treatment groups is stable near
the social optimal amount of mitigation (see Figure 2) andthe efficiency of the treatment groups is higher than theFigure 2: Efficiency

control groups (see Figure 3)
4.

Conclusion
The socially optimal intervention cannot be expected

because most of the WUI is private. However, we find thateven low level of interventions can crowd in the likelihoodof risk mitigation and improve the efficiency. When we raise
the level of interventions, the incentive of voluntary fuelreduction is crowding out, but the efficiency stays at thesocially optimal level. We also find that older, male, and more

Figure 3: Total amount of risk mitigation

risk averse players are more likely to mitigate the risk.
Table 1: Logit Model 1 (Individual characteristics)

Table 2: Logit Model (History variables)

Variables
HIGH
LOW
AgeFemale
CRRA
Round Dummy
Stage Dummy
Constant

Variables
ΔE
HIGH
LOW
HIGH*ΔELOW*ΔE
Round Dummy
Stage Dummy
Constant

Coef.
0.184
0.590
0.071
−0.2930.210✓✓−1.775p-value
0.089
0.000
0.000
0.004
0.008

0.000

Coef.
3.761
−0.625−0.432−2.809−1.704✓✓−0.069p-value
0.000
0.699
0.008
0.0000.027

0.789