The affect heuristic and public support for three types of wood smoke mitigation policies

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Air Quality, Atmosphere & HealthAn International Journal ISSN 1873-9318 Air Qual Atmos HealthDOI 10.1007/s11869-014-0243-1

The affect heuristic and public supportfor three types of wood smoke mitigationpolicies

Navjot Bhullar, Donald W. Hine,Anthony Marks, Carol Davies, JohnG. Scott & Wendy Phillips

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The affect heuristic and public support for three types of woodsmoke mitigation policies

Navjot Bhullar & Donald W. Hine & Anthony Marks &

Carol Davies & John G. Scott & Wendy Phillips

Received: 19 November 2013 /Accepted: 4 February 2014# Springer Science+Business Media Dordrecht 2014

Abstract This study applied the affect heuristic model toinvestigate key psychological factors (affective associations,perceived benefits, and costs of wood heating) contributing topublic support for three distinct types of wood smoke mitiga-tion policies: education, incentives, and regulation. The sam-ple comprised 265 residents of Armidale, an Australian re-gional community adversely affected by winter wood smokepollution. Our results indicate that residents with strongerpositive affective associations with wood heating expressedless support for wood smoke mitigation policies involvingregulation. This relationship was fully mediated by expectedbenefits and costs associated with wood heating. Affectiveassociations were unrelated to public support for policiesinvolving education and incentives, which were broadly en-dorsed by all segments of the community, and were morestrongly associated with rational considerations. Latent profileanalysis revealed no evidence to support the proposition thatsome community members experience internal “heart versushead” conflicts in which their positive affective associationswith wood heating would be at odds with their risk judgmentsabout the dangers of wood smoke pollution. Affective associ-ations and cost/benefit judgments were very consistent witheach other.

Keywords Air quality . Affect heuristic . Risk perception .

Policy support .Mediational analysis .Latent profile analysis .

Audience segmentation

Introduction

Community members often differ dramatically with respect totheir preferred policy solutions for public health threats suchas air pollution, climate change, and infectious diseases. Ac-cording to the affect heuristic model, emotional responses tohealth threats, particularly in the form of affective imagery,represent a key driver of risk assessment and response(Finucane et al. 2000; Slovic et al. 2002). In the current study,we applied the affect heuristic model to better understand thepsychological processes that underlie support for polices toimprove air quality in a regional Australian community.

In 2011, 10 % of Australian households used wood fires astheir main source of energy for home heating—approximatelythe same proportion as in 2005 and 2008 (Australian Bureauof Statistics 2012). Burning firewood is beneficial in severalways. It is comparatively inexpensive relative to alternativehome heating solutions such as electricity and gas, is readilyavailable in many parts of rural and regional Australia, andelicits strong positive emotional associations in many of itsusers (Hine et al. 2007). However, one important drawbackassociated with wood burning is that, under certain conditions,it can generate substantial levels of wood smoke which con-tains a complex blend of air pollutants, including carbonmonoxide, nitrous oxide, air toxins, and particulate matter(PM), which have been linked to a wide range of negativehealth effects (Naeher et al. 2007).

The present study focuses on a small regional city innorthern New SouthWales, with a population of approximate-ly 25,000 residents. The city is located in a shallow valley,which creates surface inversion conditions contributing tohigh levels of wood smoke pollution on many winter nights.Approximately 85 % of winter air particulates in the cityoriginate from wood heaters (NSW Environment ClimateChange and NSW Environment Climate Change and Water2010), the primary source of heating for about one third of the

N. Bhullar (*) :D. W. Hine :A. Marks : C. Davies : J. G. Scott :W. PhillipsSchool of Behavioural Cognitive and Social Science, University ofNew England, Armidale, NSW 2351, Australiae-mail: [email protected]

Air Qual Atmos HealthDOI 10.1007/s11869-014-0243-1

Author's personal copy

households within the city. In 2012, PM2.5 levels in Armidaleexceeded the Australian National Environmental ProtectionMeasure advisory level (25 μg/m3) on 23 days (range ofmeasurements >25 μg/m3=28–65 μg/m3)1 (ArmidaleDumaresq Council 2013).

Armidale residents hold divergent views about the magni-tude of the city’s air quality problem, and what, if anything,local government should do to address it. Previous research(Hine et al. 2007, 2011) has found that a substantial number ofresidents did not view wood smoke as a significant healththreat; a view that is somewhat at odds with the scientificliterature (Naeher et al. 2007). Among those who recognizethe threat, not all agree on the most appropriate policy re-sponse. Some residents favor public education about properwood storage and wood heater operation, whereas others haveargued for incentives for wood heater upgrades and replace-ments, fines for excessive emissions, or restrictions on whenand where wood can be burned.

The affect heuristic

Zajonc (1980) initially suggested that affective responses to astimulus precede rational deliberation and guide subsequentinformation processing, judgment, and choice. Finucane et al.(2000) and Slovic et al. (2002) extended Zajonc’s work andintroduced the concept of an affect heuristic. The affect heu-ristic is based on the premise that two distinct informationprocessing systems guide human decisional processes: (1) ananalytic system that is intentional, effortful, logic-based, andlargely affect free and (2) an experiential system that is pas-sive, effortless, rapid, and intimately tied to intuition andaffect. The analytic system is under the conscious control ofthe individual, which emphasizes the conscious evaluation ofrisks and benefits (e.g., goal-directed tasks, planned pur-chases). On the other hand, the experiential system is assumedto operate automatically, often with little conscious input fromthe decision maker (e.g., rash decisions, impulse buying).

According to Finucane et al. (2003), thoughts and imagesstored in memory are tagged with affective markers that varyin terms of valence and strength. Stimuli in the decisioncontext activate relevant thoughts and images, which in turnspread activation to their associated affective markers. Theactivated affective markers then combine to form an affectpool—a general feeling of goodness or badness about anobject or activity. In turn, this generalized affective responseguides subsequent judgments and decisions. The model pre-dicts that positive affective responses lead people to perceiveless risk and more benefits associated with an activity, where-as negative affective responses lead people to perceive morerisk and fewer benefits. An important implication of the affect

heuristic model is that when affect is the primary driver ofrisk–benefit judgments, perceptions of risk and benefit will beinversely correlated with each other (Alhakami and Slovic1994; Finucane et al. 2003), that is, individuals who view anactivity as highly beneficial will also tend to see it as less risky,and individuals who view an activity as risky will view it asless beneficial.

In a preliminary investigation of the role of the affectheuristic in preferences for public policy to mitigate woodsmoke pollution, Hine et al. (2007) found an inverse correla-tion between positive affective associations with woodheating and a composite index assessing support for localgovernment policies to mitigate wood smoke pollution. Theyalso found that affective associations moderated the predictiveeffect of perceived health risks on support for wood smokecontrol policies. Respondents who perceived more healthrisks associated with wood smoke expressed stronger supportfor wood smoke control. But this effect was substantiallyattenuated for individuals who possessed positive affectiveassociations with wood heating.

The current study

The current study extends this previous research in threeimportant ways. First, although Hine et al. (2007) assessedthe predictive effects of affective associations and risk percep-tions on policy support, they did not formally assess themediational paths implied by the affect heuristic model. Ac-cording to the model, affective associations should influencerisk–benefit judgments about wood heating, which, in turn,should influence support for wood smoke mitigation policies.In line with theoretical propositions and empirical findingsreported by Finucane et al. (2000, 2003) and Slovic et al.(2002), we predicted that individuals with strong positiveaffective responses to wood burning would perceive fewerrisks and more benefits associated with this form of homeheating. In turn, we predicted this pattern of risk–benefitjudgments would be associated with lower support for policiesaimed at controlling wood smoke emissions. We also predict-ed that respondents’ perceptions of the benefits of woodheating would be inversely related to their risk perceptions.

Second, whereas Hine et al. (2007) employed a unidimen-sional composite measure of policy support for wood smokecontrol, the current study employs a multidimensional mea-sure assessing public support for three distinct types of woodsmoke mitigation policies: education, incentives, andregulation. Assessing support for different types of woodsmoke mitigation policies will enable us to assess the extentto which Armidale residents support such control strategiesand also assess whether the mediational effects investigated inthe study hold for only certain types of policies. More specif-ically, we predicted that influence of the affect heuristic wouldbe more pronounced for policies that involve regulating

1 The measurements were obtained from the Armidale DumaresqCouncil.

Air Qual Atmos Health


behavior through bans, fines, and/or restrictions relative toless punitive policies such as education or incentives, wherethere are fewer direct negative consequences for wood burn-ing households.

A third extension of previous research involves the use of alatent profile analysis to explore the extent to which residentshold affective associations with wood heating that are at oddswith their risk–benefit assessments. As some dual processresearchers have noted, outputs from the experiential and ana-lytical systems need not always agree, resulting in so-calledheart versus head conflicts (Epstein 1994; Hine et al. 2009). Ifsuch conflicts occur in the present context, one might expectto see examples of individuals who have strong positiveaffective associations with wood heating but also view thebenefits of wood heating as low and risk as high. Conversely,there may be individuals who have strong negative associa-tions with wood heating but perceive few risks and/or sub-stantial benefits. We did not make any predictions about thepossible discordant findings. However, we hoped to clarifythis “heart versus head” conflict by empirically assessing theextent to which residents hold conflicting experiential andanalytic cognitions about wood heating and how these con-flicts resolve themselves in terms of policy support.

Methods

Participants

Respondents were 265 Armidale residents (Mage=47.94 years,SD=15.23; median=48 years; 54 % female), each representinga different household. Over half of the sample (60 %) complet-ed either a university degree or a postgraduate training. Censusdata indicate that our sample was somewhat older (medianagecensus=35 years), and comprised more university postgrad-uates than the Armidale population (Australian Bureau ofStatistics 2011). Sixty per cent of the sample used wood heatingas their primary source of heating, which is substantially higherthan the rate of wood heater users in Armidale (∼33 %).

Procedure

Households were invited to complete an online questionnaireas part of a federally funded project to better understand theattitudes, beliefs, and wood heating practices of Armidaleresidents. To be eligible to participate in the study, potentialrespondents had to be over 18 years of age and be one of theprimary operators of their home’s heating system(s). Potentialrespondents were told that the information from the studywould be used to develop strategies to reduce winter woodsmoke pollution that were consistent with the needs andvalues of the community. The study was publicized throughnewspaper, television and radio interviews, email networks,

flyers posted in the community, and the local governmentcouncil’s web site. As an incentive to participate, all respon-dents were given the opportunity to be entered into a draw towin an iPad, SmartBurn canisters designed to reduce emis-sions from wood heaters, and $20 gift cards from a localbookseller.

Measures

The following measures were used in the present study2. TheCronbach’s αs reported below reflect the internal consisten-cies for each measure obtained in the current sample. Inaddition, respondents’ age (in years) and education (1 = lessthan high school to 5 = postgraduate or additional profes-sional degree) were assessed using single items. Respondents’perceptions of wood smoke pollution in Armidale wereassessed by an eight-point scale, ranging from 0 (not a prob-lem at all) to 7 (a very serious problem).

Affective associations Affective associations with woodheating were assessed using an approach developed byPeters and Slovic (1996) and previously applied to woodheating by Hine et al. (2007). Respondents were asked to listthe first three thoughts or images that came to mind whenpresented with the cue phrase “Using wood to heat homes inArmidale” and then rate each thought/image on a scale rang-ing from 1 (very negative) to 5 (very positive). The three affectratings were then averaged to form a composite index of affectvalence (Cronbach’s α=.72).

Expected benefits and costs of wood heating Wood heatingbenefits were assessed by six items addressing expected pos-itive outcomes associated with wood heating, whereas expect-ed costs of wood heating were assessed by 12 negative out-comes. Three items for expected costs were reverse scored.All benefit and cost items were assessed on a five-point scalefrom 1 (very unlikely) to 5 (very likely), averaged to form twocomposite indices, with a higher score indicating greater ex-pected benefits or costs of wood heating, respectively.Cronbach’s α was .76 for expected benefits and .93 for ex-pected costs.

Policy support Respondents’ support for policies to reducewood smoke pollution was assessed by 12 items addressing awide range of mitigation strategies, including education, in-centives, and regulation. A five-point scale from 1 (stronglyoppose) to 5 (strongly support) was used to assess all supportitems. A factor analysis was conducted to identify a latentstructure of these items. The details about this analysis andfactor loadings of the items comprising resulting subscales are

2 First author may be contacted to obtain a copy of the full questionnaire.



shown in the Appendix. Three subscales, support forregulation (six items, Cronbach’s α=.89), support forincentives (three items, Cronbach’s α=.69), and support foreducation (two items, Cronbach’s α=.73), were computed bytaking the mean response on items on each subscale.

Results

Descriptive statistics and correlations

All missing data (1.1–6.8 %) were imputed using the expec-tation maximization technique in SPSS v20. Descriptive sta-tistics and inter-variable correlations are presented in Table 1.The means for affective associations and perceived benefits ofwood heating fell slightly above the mid-point on the five-point scale, whereas the means for perceived costs fell justbelow the mid-point. Support for wood smoke mitigationpolicies related to education and incentives was quite strongin the sample with mean scores above 4 on the five-pointsupport scale. Support for policies involving regulation wasmuch lower, just above 3, the mid-point of the scale. Aspredicted by the affect heuristic model, positive affectiveassociations with wood heating were associated with moreperceived benefits and less expected costs of wood heating.Also consistent with the affective heuristic model, respon-dents who perceived greater benefits viewed less costs asso-ciated with wood heating.

Breakdowns by wood heater user versus nonusers, alsopresented in Table 1, revealed that wood heater users relativeto nonusers reported significantly greater positive affectiveassociations with wood heating. They also perceived signifi-cantly more benefits and fewer costs associated with woodheating and also expressed significantly less support for woodsmoke mitigation policies involving regulation. Wood heaterusers and nonusers did not significantly differ in terms of theirsupport for mitigation policies related to education and incen-tives, with both groups strongly supporting these options.

Mediation analysis

A multiple mediation analysis was conducted to test thehypothesis that respondents’ affective associations with woodheating would influence support for wood smoke mitigationpolicy related to regulation indirectly through perceptions ofanticipated benefits and costs3. The analysis was conductedusing an SPSS macro developed by Preacher and Hayes

(2008). The macro estimates the path coefficients in a multiplemediator model and conducts the same set of analyses recom-mended by Baron and Kenny (1986) for assessing mediation.However, it generates bias-corrected bootstrap confidenceintervals (CIs) for total and specific indirect (mediational)effects. These bootstrapped CIs are less biased than Sobel’stest, the standard approach for assessing the significance ofindirect paths (Preacher and Hayes 2008). The results from theanalysis are summarized in Fig. 1.

Results suggested that respondents who had stronger positiveaffective associations perceived more benefits and fewer costsrelated to wood heating. In turn, high anticipated benefits andlower perceived costs were significantly associated with lesssupport for regulatory policies to reduce wood smoke pollu-tion, that is, participants who perceived more wood heatingbenefits and fewer costs were less likely to support regulatorypolicies. Examination of the indirect (mediational) effectsrevealed significant paths from affective associations throughexpected benefits (bias corrected 95 % CIb=− .16 to −.05) andexpected costs (bias corrected 95 % CIb=−.52 to −.35) tosupport for regulation. The full model explained 62 % of thevariability in support for regulation. The direct path betweenaffective associations and regulation support did not approachstatistical significance after controlling for the mediators, t(262)=.41, p=.68, indicating that perceived benefits and coststo wood heating fully mediated the effect.

Latent profile analysis (LPA)

The previous analysis suggests that much of the appar-ent influence of affective associations on support for woodsmoke regulatory policies flows indirectly through expectan-cies related to wood heating benefits and costs. This suggests ahigh degree of alignment between participants’ experientialand analytic cognitions about wood heating. However, dualprocess models of cognition also note instances in whichoutputs from experiential and analytic systems are discordant,producing a state of dissonance sometimes referred to as heartversus head conflicts.

To determine if such discordant patterns of experiential andanalytic cognitions existed in our sample, we conducted anLPA, a classification procedure that creates homogenousgroups of respondents based on their affective associationsand expectancies related to wood heating benefits and costs.The analysis was conducted using MPlus 4.1 (Muthén andMuthén 2006), which employs latent mixture model to iden-tify mutually exclusive groupings within the data. In ouranalysis, we used three fit indices: the Bayesian informationcriterion (BIC; Schwartz, 1978), the Akaike information cri-terion (AIC; Akaike, 1974), and the Lo-Mendel-Rubin (LMR)likelihood ratio test (Lo et al. 2001) to identify the optimalnumber of groups to retain.

3 Examination of the raw correlations in Table 1 indicated that affectiveassociations were strongly correlated with support for regulation policybut uncorrelated with support for policies related to education and rebates.Thus, we assessed mediation for regulation policy only.



The best fitting model for a data set is indicated by thesmallest BIC and AIC values generated among competingmodels. The LMR provides a p value that indicates whetherthe selected profile model is significantly better than thesolution with one less profile. For example, a nonsignificantLMR p value for a four-profile model indicates that a three-profile model fits the data better. For our analysis, we alsostandardized the three profiling variables to a mean of 0 and astandard deviation of 1 to facilitate interpretation of the pro-files. Table 2 presents fit indices for two to four profilesolutions. Results suggested that the three-profile solutionproduced the lowest BIC value, and the LMR indicated thatit fitted the data significantly better than the four-profile solu-tion. Thus, we retained three profiles to interpret (see Fig. 2).

All three profiles consisted of concordant patterns of expe-riential and analytic cognitions. Profile 1 (n=64, 24 %) com-prised wood heater advocates with strong positive affectiveassociations with wood heating, high expected benefits, andlow expected costs. Respondents in Profile 2 (n=124, 47 %)exhibited a more neutral outlook, with standardized scores justabove themean for affective associations and expected benefits,

and just below the mean for expected costs. We labeled respon-dents with this profile as fence-sitters. Profile 3 (n=77, 29 %),which we labeled alarmed comprised 77 respondents (29 %)with strong negative affective associations, low expected ben-efits, and high expected costs.

Overall, the results from the LPA suggest our respondents hadexperiential and analytic cognitions that were largely consistentwith each other, that is, positive affective associations tended toco-occur with assessments of high expected benefits and lowexpected costs. There were no profiles characterized by respon-dents with discordant patterns of experiential and analytic cog-nitions expected to be present during heart versus head conflicts.

Finally, a multivariate analysis of variance (MANOVA)was used to determine whether profile membership was sig-nificantly related to respondents’ age, education, judgmentsabout whether wood smoke pollution represents a seriousproblem in Armidale, and their support for wood smokemitigation policies involving education, incentives, and regu-lation. The results of the MANOVA and follow-up univariatetests for each of the profile groups, with post hoc groupcomparisons using Tukey’s HSD test, are presented in Table 3.

Table 1 Correlations, means, and standard deviations for the main variables used in the study

Variables 1 2 3 4 5 6

1. Affective associations – .63** −.73** .11 .02 −.59**2. Expected benefits – −.62** .16* .05 −.60**3. Expected costs – −.01 .16* .78**

4. Support—education – .33** .12*

5. Support—incentives – .25**

6. Support—regulation –

M (SD) total sample 3.29 (1.25) 3.51 (.82) 2.85 (.94) 4.43 (.61) 4.10 (.81) 3.05 (1.10)

M (SD) wood heater users 3.70 (1.09) 3.78 (.72) 2.46 (.77) 4.48 (.59) 4.08 (.83) 2.62 (.98)

M (SD) Nonusers 2.71 (1.25) 3.10 (.81) 3.41 (.91) 4.35 (.66) 4.12 (.80) 3.69 (.98)

Significant mean differences between wood heater users and nonusers are presented in bold; all significant t values (259)>6.00, p<.001. Fourparticipants did not provide information on their wood heater use

Fig. 1 Summary of mediationanalysis evaluating the affectheuristic model predicting publicsupport for wood smoke policiesinvolving regulation. Solid linesreflect significant paths. Valuesabove top right hand corner ofeach box reflect varianceexplained by predictors earlier inthe model. All path coefficientsare standardized. Results arebased on 1,000 bootstrap samples



In terms of demographics, the results in Table 3 indicated thatfence-sitters (Profile 2) and alarmed respondents (Profile 3) weresignificantly more educated than wood heater advocates (Profile1). The mean educational level for fence-sitters did not signifi-cantly differ from alarmed respondents. None of the groupsdiffered significantly on age. As expected, alarmed respondentsperceived the magnitude of wood smoke problem significantlyhigher than the other two profile groups, with fence-sitters per-ceiving the problem significantly greater than wood heater advo-cates. In terms of wood smoke mitigation policy preferences, allof the profile groups differed significantly from each other onpolicy support for regulation. Wood heater advocates expressedsignificantly less support for government regulation of woodheating than fence-sitters, who, in turn, expressed less supportfor regulation than alarmed respondents. Alarmed respondentsexpressed significantly more support for regulation than all othergroups. Profile membership did not significantly predict supportfor education or incentives. All three groups strongly supportedthese policy initiatives.

As a supplementary analysis, we compared the proportionof wood heater users in each of the three profile groups. Thevast majority of wood heater advocates were wood heaterusers (88 %), compared to 63 % of fence-sitters and 29 % ofalarmed profile members.

Discussion

This study applied the affect heuristic model in a communityadversely affected by wood smoke pollution, to better un-derstand the psychological factors underpinning public sup-port for air quality policies. Our results indicate that positiveaffective associations with wood heating were associatedwith opposition for wood smoke mitigation policies involv-ing regulation, and this relationship was fully mediated byrespondents’ expectancies related to benefits and costs ofwood burning. We found no evidence for the presence ofheart versus head conflicts involving discordant affectiveassociations and analytic risk/benefit assessments of woodheating.

The affect heuristic and support for wood smoke mitigationpolicies

In the present study, we examined the affect heuristic modelby assessing whether Armidale residents’ affective associa-tions with wood heating predicted their support for three typesof wood smoke mitigation policies and whether these predic-tive effects were mediated by expectations about the costs andbenefits of wood burning. Consistent with this model,

Table 2 Model fit indices for latent profile solutions

No. of profiles BIC AIC LMR

2 2,084.17 2,048.37 −1,152.56*3 2,069.80 2,019.69 −1,014.19*4 2,075.54 2,011.11 −995.84

Best fit model indicates a lower BIC and AIC values and a significantLMR value

BIC Bayesian information criterion, AIC Akaike information criterion,LMR Lo-Mendel-Rubin likelihood ratio test

*p<.01

Fig. 2 Latent profile analysis ofexperiential and analyticcognitions related to woodheating. Scores on all variableshave been standardized tofacilitate interpretation. Profile 1= wood heater advocates (24 %),2 = fence-sitters (47 %), 3 =alarmed (29 %)



respondents’ affective associations correlated strongly withtheir expectancies related to wood heating benefits and costs.Respondents with positive affective associations with woodheating, primarily but not exclusively wood heater users,expected more benefits and less costs from wood burning. Inaddition, expectancies about the benefits of wood heatingcorrelated negatively with perceptions of costs, as predictedby the affect heuristic model. This pattern of relationships wasalso reflected very clearly in the results of our LPA. Threedistinct subtypes of respondents were present in the samplewith each group exhibiting concordant patterns of affect andanalytic cognitions. Respondents who exhibited positive af-fective associations with wood heating tended to perceivemore benefits and fewer costs. Conversely, respondents withnegative associations with wood heating perceived fewerbenefits and more costs.

When interpreting these results, it is important to note thatthere is no logical necessity that judgments of the perceivedbenefits of an activity should be inversely related to perceivedcosts. Many activities, such as unprotected sex, investing inthe stock market, and playing contact sports, can be perceivedto be both beneficial (e.g., in terms of the pleasure they bring)and risky (e.g., in terms of potential negative health or finan-cial outcomes). The strong inverse correlation observed in thestudy is suggestive of a third factor (the affect heuristic) thatmay drive both types of judgments (Finucane et al. 2003;Leiserowitz 2006; Siergrist et al. 2008; Slovic 2001; Slovicand Peters 2006). Our overall pattern of results is also consis-tent with previous experimental findings indicating that affectis an important driver of risk–benefit judgments (Johnson andTversky 1983; Lerner et al. 2003).

Our results also indicated that respondents with posi-tive affective associations with wood heating were

significantly less likely to support regulatory policiesaimed at restricting wood burning or fining householdsfor emitting excessive wood smoke pollution. The rela-tionship between affective associations and support forregulatory policies to mitigate wood smoke pollution wasmediated by expected benefits and costs of woodheating. As noted above, community members with astrong positive emotional attachment to domestic woodheaters tended to perceive the benefits of wood burningto be high and the costs to be low. Given this combina-tion of emotional and cognitive responses, it is not sur-prising that individuals with this type of profile did notview wood smoke as a problem and were opposed to theintroduction of policies that would threaten an activitythat they valued and enjoyed and, according to theirbeliefs, posed little risk.

Affective associations were not significantly related tosupport for “public friendly” wood smoke mitigation policiesinvolving public education or rebates/discounts for house-holds wishing to upgrade their wood heaters or implementtechnological solutions to reduce emissions. These latter twotypes of policies were broadly supported by all segments ofthe community, regardless of whether they held positive ornegative affective associations and risk–benefit cognitionsabout wood heating. Interestingly, these public friendly poli-cies were even supported by segments of the community whodid not believe that the Armidale community faced a woodsmoke problem. This may be due to the fact that policiesinvolving education and rebates/discounts attempt to reduceoverall levels of wood smoke emissions without placing anylegal restrictions on wood burning. They do not pose anythreat to residents’ perceived “right to burn wood” and theircurrent (potentially positive) wood heater experiences.

Table 3 Outcome variables: means, standard errors, and mean differences across profiles

Outcome variables Profile 1, Advocates Profile 2, Fence-Sitters Profile 3, Alarmed Univariate

(n=64) (n=124) (n=77)

M SE M SD M SE F (2, 262) Partial η2

Age 49.82a 1.86 47.40a 1.34 47.26a 1.70 .68 .01

Education 3.16a .14 3.91b .10 3.75b .13 9.49** .07

Perceived magnitudeof wood smoke problem

2.33a .18 3.62b .13 5.00c .16 63.09** .33

Support—education 4.42a .08 4.49a .06 4.34a .07 1.42 .01

Support—incentives 3.97a .10 4.16a .07 4.10a .09 1.14 .01

Support—regulation 1.84a .09 2.97b .06 4.19c .08 197.71** .60

N=265. Wilks’ Λ=.33, F(14, 512)=26.99, p<.001, partial η2 =.43. Means with different superscripts (in rows) differ significantly at p<.05 based onTukey’s HSD post hoc test. Education level and all the policy support variables were assessed on scales from 1 to 5. Perceived magnitude of the woodsmoke problem was assessed on a scale from 0 to 7, and income was assessed on a scale from 1 to 8.

*p<.01, **p<.001



Evidence for heart versus head conflicts

We found no evidence in support of the notion that a signif-icant segment of the population may be experiencing aninternal cognitive conflict about their positive feelings aboutwood heating on one hand and beliefs about wood heatingbenefits and costs on the other. Our LPA did not identify anyprofiles suggesting the presence of such conflicts. There aresome possible explanations for this finding. As suggested bythe affect heuristic model, initial affective reactions to anactivity such as wood burning play an important role indriving our conscious evaluation of risks and benefits. In otherwords, our risk/benefit assessments naturally fall into line withour initial affective responses. However, it may also be possi-ble that most respondents are predisposed to present them-selves as internally consistent and therefore respond to thesurvey in a manner that ensures consistency rather than onethat reflects their actual beliefs. Future studies might usemeasures such as the Balanced Inventory of DesirableResponding (Paulhus 1998) to evaluate and control for possi-ble self-presentation biases.

Policy implications

Our results indicate that there appears to be widespread sup-port within our sample, comprising wood heater users andnonusers, for policy options based on community educationand incentives for upgrading wood heaters and implementingtechnological solutions to reduce household wood smokeemissions. Thus, for the local government responsible formanaging local air quality, this appears to be the most straight-forward and arguably the preferred path forward. The rate ofpublic acceptance is high, and there is evidence from at leastone field study (Hine et al. 2011) demonstrating that commu-nity education about wood heater operation and the provisionof technology (in the form of SmartBurn canisters that helpfires burn more cleanly) produced significant reductions in thehousehold smoke emissions.

However, it is also important to acknowledge that edu-cation and incentives, by themselves, may not be sufficientto bring wood smoke levels down to acceptable standards.The education and technology effects reported by Hineet al. (2011) were statistically reliable but were modest ineffect size (dSmartBurn=.29; dEducation=.27). Although thesetypes of interventions can undoubtedly be fine-tuned andimproved, it is not clear that they will ever be potent enoughto satisfactorily address the problem. This is particularlyevident in the present context of rising electricity and gasprices which may encourage even more households toadopt wood burning as their primary source of homeheating. In this instance, local governments then may needto consider more interventionist and/or punitive policiesinvolving burning restrictions and fines. Implementing

such policies will be challenging in cities such as Armidalein which substantial segments of the population do notbelieve that wood smoke poses a serious threat and areopposed to regulatory restrictions on wood burning.

The affect heuristic model provides useful insights aboutpossible strategies for increasing public acceptance of regula-tory policies to reduce wood smoke pollution. Simplyinforming the community about the scientific evidence linkinghealth problems to wood smoke exposure is unlikely to beeffective. If, as the model suggests, positive affective associ-ations with wood heating prevent members of the communityin accurately perceiving the risks associated with exposure towood smoke pollution, interventions could be introduced toreplace positive images with more negative ones. This ap-proach of using highly graphic and emotive negative imageryhas been used effectively in campaigns to reduce tobacco use(Dunlop et al. 2014; Taylor et al. 2003) and driving under theinfluence of alcohol (Elder et al. 2004; Tay 2005). By increas-ing awareness of the affect heuristic and the interplay betweenthe experiential analytic systems, educators and policy makershelp the general public recognize affect-driven biases in judg-ment and decision-making processes and how the analyticsystem can be engaged to override them. This represents animportant step on the path toward rational self-determination(Stanovich 2004).

Audience segmentation as utilized in our study canfurther inform the tailoring and targeting of messages tospecific segments. Tailored messages enhance personalrelevance, thus making them resonate with the segments(Maibach et al. 2011). For example, appeals aimed atincreasing awareness and understanding of the trade-offsof the costs and benefits of wood burning can be moreeffective for particular segments in the community (e.g.,“fence-sitters” group in our sample) not holding strongviews for and against wood heating. On the other hand,educational strategies, targeting wood heater advocates,may effectively present materials promoting wood heateruse in a more environmentally friendly manner.

Limitations

Readers should keep the following issues in mind wheninterpreting the results of this study. First, the studyemployed cross-sectional, correlational research design,thus causal inferences should be made with caution.Second, random sampling was not employed, whichmay limit our ability to generalize our findings. How-ever, the main aim of the present study was to assessthe affect heuristic model in the present context, thusminimizing the impact of this limitation. In addition, bygrouping respondents into three categories (wood heateradvocates, alarmed, and fence-sitters), our LPA resultsprovided a good spread of our sample’s response



patterns on affective associations and expectancies relat-ed to wood heating benefits and costs, giving credenceto the present findings. Third, the current study exam-ined the affect heuristic model in relation to a specificcase of risk assessment and policy support relating towood smoke pollution. It is not clear whether the samepattern of results would be found in relation to other airpollution issues, such as coal-burning power plants orauto emissions. Further research is needed to addressthis issue.

Conclusions

This study investigated how the affect heuristic modelcould be applied to better understand public support forwood smoke mitigation policies. Our results were con-sistent with the view that affective imagery for woodheating is a key driver of cost–benefit judgments relatedto this activity and support for wood smoke mitigationpolicies involving regulation. The affect heuristic ap-peared to be less relevant to predicting public supportfor policy interventions, such as education and incen-tives, which are broadly supported by all segments ofthe community and are more strongly associated withrational consideration.

Appendix

The 12 items were subjected to a principal axis factoranalysis to identify the latent structure. The analysisproduced three factors with eigenvalues greater than 1,explaining 68 % of the variance in the item set. Thescree plot also indicated that three factors should beretained. Following a Direct-Oblimin rotation, all itemshad loadings greater than .60, and there were no cross-loadings above .20. The first factor, labeled “support forregulation,” included six items primarily dealing withplacing restrictions on wood burning and implementingstricter standards for wood heaters. Three items relatingto providing rebates for purchasing tools and technologyto reduce emissions loaded on the second factor labeled“support for incentives.” The last factor, “support foreducation” consisted of two items related to providinginformation to the general public about proper firewoodmanagement and wood operation. Policy support scoreswere computed by taking the mean response for itemson each subscale. Pattern matrix item loadings for eachsubscale are presented in the table below.

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Table 4 Summary of exploratory factor analysis for wood smoke miti-gation policy support items

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Policy support—regulation

Restrict installation of wood heaters in new homes .90

Restrict areas in Armidale in which wood heaterscan be used

.85

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The affect heuristic and public support for three types of wood smoke mitigation policies

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