Sage Journals: Discover world-class research

Abstract

Studies of climate attitudes are increasingly concerned with support for mitigation policies such as adequate indoor cooling to address heat exposure. Air conditioning has historically been marketed as a luxury good, but polling shows Americans are evenly split in whether they consider it a necessity or a luxury. Do they support utility aid programs that would increase access to air conditioning? Results from an original survey vignette experiment (N = 1,200) show respondents are less likely to support government aid to help low-income people pay utility bills when policy frames invoke air conditioning rather than heat. A replication (N = 703) finds this effect is most pronounced among White respondents. These findings have important implications for research and policy advocacy because they demonstrate a potential obstacle for public support in addressing the negative impacts of extreme heat in climate change.

Keywords

public opinion environment inequality climate change consumption

Research and advocacy increasingly focus on resilience in the face of the climate crisis. Innovative work on flooding (Douglas and Kirshen 2022), heat exposure (Burke, Hsiang, and Miguel 2015; Klinenberg 2002; O’Brien et al. 2020), and climate refugees (Portner et al. 2022) demonstrates that we will need to attend to the unequal distribution of climate risks and design solutions to protect many different communities (Klinenberg, Araos, and Koslov 2020; Mezey 2020; Olofsson, Öhman, and Nygren 2016; Pellow and Brehm 2013). Accordingly, work on public opinion is moving from a focus on beliefs about climate change to a focus on attitudes about specific climate policies (Dacey and Stewart 2023; Leiserowitz 2005; McCright and Dunlap 2011; McCright, Dunlap, and Xiao 2013; Mildenberger, Howe, et al. 2022, Mildenberger, Lachapelle, et al. 2022; Sparkman, Geiger, and Weber 2022; Stokes 2016; Stokes and Warshaw 2017).

One example of emerging concern is the case of extreme heat. Prior research and policy rightfully focused on the health effects of extreme cold, given that estimates tend to find cold weather conditions contribute more to excess mortality than hot weather conditions (Berko et al. 2014; Masselot 2023; cf. Dixon et al. 2005). But as climate change raises global temperatures and creates more extreme heat waves, people face additional and unique adverse health and social outcomes, including cardiovascular problems, escalated social conflict, and learning loss in schools, among others (Alahmad et al. 2023; Barlett et al. 2020; Burke et al. 2015; Klinenberg 2002; O’Brien et al. 2020; Ostro et al. 2010; Park et al. 2020; Spangler, Liang, and Wellenius 2022).

Access to adequate cooling is an increasingly important facet of climate resilience policy, but air conditioning has been historically marketed as a comfort good in the United States—even a luxury good tied to social status (Ackermann 2002; Vesentini 2017)—rather than a basic need for health equity. Research shows that support for policies to address inequality is often stymied by concerns about moral status, especially how much recipients “deserve” social assistance (Gilens 2009; Lamont 2018; Mohr 1994; Reid 2013; Soss, Fording, and Schram 2011; Steensland 2006). When policy leaders debate deservingness, they often lean on the status of goods people consume with government assistance (e.g., Scampini 2021). Advancing policies for heat mitigation requires understanding whether the status of cooling might impede policy solutions in a similar way.

The following study investigates whether people exhibit consistent attitudes about utility assistance programs or whether support for those programs is contingent on how they are used—for air conditioning or for heat. After reviewing literature on the material stakes of cooling inequality and the symbolic stakes of goods consumed with social assistance, I present results from an original survey vignette experiment showing that survey respondents are less willing to support a hypothetical policy that would provide utility aid for air conditioning in the summer than they are for heating in the winter. These results provide evidence that an implicit framing of air conditioning as a luxury good may hinder future support for initiatives to provide safer living and working environments in the climate crisis. I close by discussing the implications of this finding for research and advocacy addressing climate inequality.

Background

Material Stakes: Inequality in Heat and Cooling

Heat exposure is an important emerging risk factor of climate change. These risks are not evenly distributed, both at the regional level given geographic variation across the United States (Spangler et al. 2022) and at the local level given neighborhood differences in population density, tree coverage, pavement, and other factors in the built environment (Harlan et al. 2006; Kuras, Hondula, and Brown-Saracino 2015; O’Brien et al. 2020). This has implications for many different sociodemographic groups. For example, the elderly are more vulnerable to heat-related mortality and more likely to be socially isolated and lacking support structures in heat waves (Klinenberg 2002; Kovats and Hajat 2008). Institutionalized populations are also at risk; research has documented increased risk of mortality and suicide watches in prisons during heat waves (Cloud et al. 2023; Skarha et al. 2023). Although younger people are more resilient to the health effects of heat exposure, heat exposure in schools exacerbates learning loss through reduced scores on standardized testing (Park et al. 2020). Air conditioning has been shown to mitigate the effects of heat on learning and health (Ostro et al. 2010; Park et al. 2020), but because access to cooling is also not equitable in the United States (Fraser et al. 2017), heat exposure is a mechanism that amplifies other inequalities across age, race, social class, and other demographic characteristics that condition selection into lower quality places to live, study, and work.

The development of high quality HVAC systems and air conditioning adaptation is closely tied to differences in local property values (Ahn and Uejio 2022; Noonan, Hsieh, and Matisoff 2015). According to a 2017 consumer trends report from the real estate company Zillow, for example, only about half of apartment rental listings in the United States (54.7 percent) offered central air conditioning, commanding a rent premium of up to 11.6 percent in New York State (Mikhitarian 2018). Although adequate heating is considered a basic necessity for habitable housing widely required in tenants’ rights, legal requirements for air conditioning vary much more state by state, depending on whether a state considers cooling a part of the implied warranty of habitability for housing (Franzese, Gorin, and Guzik 2016). Thus, many growing risks of heat exposure are in large, urban areas that now need more widespread access to air conditioning than in the past.¹

Even if people do have access to cooling infrastructure, they may not necessarily have the resources to afford using it. “Energy insecure” households struggle to pay for elevated utility costs for winter heating, and that insecurity increasingly stretches across the year as more households experience elevated need for air conditioning in warmer summer months that were previously more affordable relative to winter months (Adams, Carley, and Konisky 2024; Cook et al. 2008; Costello 2021; Hernández 2016; Zhao et al. 2020). Energy insecurity is linked to other social hardships as well—it is both a downstream effect of other life shocks (Finnigan and Meagher 2019) and a cause of additional hardship as families have to choose how to allocate scarce resources (Frank et al. 2006). Although governments can and do provide low-income home energy assistance programs, eligibility for those programs is often asset-tested—much like other social assistance programs, such as TANF and SNAP—limiting program participation (Graff and Pirog 2019).

This last point merits emphasis for motivating this study. Both public support for and bureaucratic organization of social assistance programs in the United States are often concerned with determining eligibility for aid. This means that research interested in advancing cooling equity needs to consider not only the material conditions of unequal exposure to heat but also the symbolic conditions that structure how people perceive aid and their willingness to extend aid to others (Reid 2013).

Symbolic Stakes: The Status of Air Conditioning

The design and execution of social aid programs is shaped by moral classifications—cultural assumptions and political frames about who deserves help and what kinds of help they deserve (Gilens 2009; Mohr 1994; Reid 2013; Skocpol 1996; Steensland 2010). This includes moral evaluations about whether recipients are sufficiently qualified to receive aid (Katz 2008; Steensland 2006) and whether they are sufficiently compliant with a bureaucratic system to continue receiving aid once granted (Schram et al. 2009; Soss et al. 2011). Political framing of moral boundaries in welfare policy is salient to public opinion (Foster 2008) because those frames activate core motives to help and to exclude from the social group (Edgell et al. 2020; Petersen et al. 2012). Public opinion, in turn, matters for the structure of future social welfare policies (Brooks and Manza 2006, 2007).

When people discuss who deserves social aid, they often invoke the status of goods that aid recipients consume with that assistance. For example, Scampini (2021:84) uses congressional hearings to demonstrate how a key rhetorical move in evaluating SNAP is the claim that people make “poor choices”—such as using SNAP to purchase food so that they can buy cigarettes or alcohol with their own money—implying that such consumption choices abuse the system. In another example, despite evidence that unconditional cash transfer programs are effective at mitigating homelessness and do not increase spending on “temptation goods,” survey respondents nevertheless predict that people who receive such aid will spend the money on these goods (Dwyer et al. 2023). The symbolic classification of goods is therefore an important aspect of determining whether and how people recognize others as legitimate recipients of aid (Lamont 2018, 2023; Lamont and Molnar 2002). In a paternalistic view of social aid (Soss et al. 2011), people are less willing to pay for specific goods that they perceive to provide comfort, temptation, or luxury beyond a minimum standard to vulnerable populations, and they are more likely to oppose programs on the grounds that they offer people too much leeway to choose those goods.

Utility assistance for heat and air conditioning is an interesting case of this theory because the status of these goods is asymmetric and subject to change with climate change. Home heating is generally considered a necessity in the United States given that a minimum heating standard is included in most warrants of habitability for housing nationwide. Air conditioning, in contrast, was originally marketed as a comfort good to consumers (Ackermann 2002), with early adaptation more prevalent in higher income and more educated areas (Biddle 2011). Vesentini (2017:93) argues that air conditioning advertising “articulated specific social dynamics that associated climatic comfort with social status,” placing it as a marker of the leisure class (Veblen 1899).

Public opinion polling initially reflected these marketing trends. In a 1973 Roper Reports poll, respondents were asked to distinguish which items from a list of consumer goods they would consider to be “a necessity” or “a luxury you could do without”— 72 percent of respondents classified air conditioning as a luxury good.² Since that time, however, attitudes about air conditioning have changed among the American public. Figure 1 presents results from three waves of the Pew Social Trends survey in 2006, 2009, and 2010, each of which presented a similar list of consumer goods. Most respondents in each survey classified home air conditioning as “a necessity,” and the gap has narrowed to a near even split in these later samples.

Figure 1.

Consumer classification of air conditioning, 2006–2010.

Moral boundaries around consumption are flexible and conditioned by social context (Brown 2011), and this shift in attitudes about air conditioning raises important questions about public support for heat-mitigation policies in climate change. If more people are exposed to extreme heat and more people view air conditioning as a necessity, it is possible that we may see a groundswell of support for improving access to cooling in the built environment as a basic requirement for social aid. If air conditioning remains understood as a luxury good, however, the public may be less likely to support its inclusion in social aid in the same way it is skeptical of other kinds of consumption choices made by vulnerable populations. This study aims to test that theory.

The Current Study

Given these trends in the literature, I conduct a study evaluating the role of air conditioning in attitudes about utility assistance programs to low-income Americans. I use an original survey experiment with a series of vignettes that evaluate support for general utility assistance, utility assistance for heat in the winter, and utility assistance for air conditioning in the summer.

There are two contrasting expectations for results. The first possibility is that rates of support for utility assistance will not vary by experimental condition. We might expect this to be the case if support for assistance is best predicted by other sociodemographic characteristics, such as partisanship, rather than the status of the goods consumed with utility assistance. We might also expect this if the status of air conditioning has changed such that a sufficient number of respondents see it as a necessity equal to home heating.

Hypothesis 1: On average, respondents will express equal rates of support for utility assistance in general, utility assistance for heat in the winter, and utility assistance for air conditioning in the summer.

If the status of heat and air conditioning remains different, however, the aforementioned theory implies that people will be less likely to support social assistance spending on luxury or comfort goods:

Hypothesis 2: Policy frames that discuss utility assistance for air conditioning in the summer will garner lower average support from survey respondents than (a) policy frames invoking heat or (b) frames invoking utility assistance in general.

Method

After institutional review board approval,³ I contracted the sampling firm Prolific to provide a sample of 1,200 U.S. adults, balanced to nationally representative demographic benchmarks on age, gender, and political affiliation. Due to intentional recruitment for academic research and improved participant pay, Prolific has demonstrated improved respondent quality relative to other online panel survey services, such as Amazon’s MTurk (Palan and Schitter 2018; Peer et al. 2017). Although Prolific does not use address-based sampling to recruit participants into the survey sampling pool, the sample collected was nonetheless broadly reflective of the U.S. population on demographic benchmarks (see Table 1).

Table 1.

Sample Summary (N = 1,200).

Measure	Value	Mean	SD
Support for utility assistance	7-point scale (range, –3 to 3)	1.32	1.67
	Value	N	%
Age	18–25	149	12.4
	26–34	214	17.8
	35–54	408	34.0
	55–64	268	22.3
	65+	161	13.4
Sex	Female	600	50.0
Sex	Male	600	50.0
Party identification	Democrat	379	31.6
	Independent	511	42.6
	Republican	310	25.8

Note: Survey sample is comprised of 1,200 respondents, quota-sampled from Prolific’s survey panel to match nationally representative proportions on age, sex, and political party identification.

Supplemental analyses demonstrated this sample was of high quality. The experimental manipulation was placed first on the survey, yielding a 100 percent completion rate from respondents. Randomization was effective and not significantly associated with major demographic factors.⁴ Results reported below use the full survey sample, and they were also similar in a subsample removing respondents with excessively short or long survey completion times to evaluate respondent quality and attention.⁵

The measurement strategy for this study aimed to vary the specific kind of utility assistance to assess the impact of the status of the utility consumed: air conditioning versus heat and a general utility condition. Respondents were randomized to receive one of three vignettes at the start of the survey:

As people experience more extreme temperatures, utility bills to heat and cool homes get more expensive.

Some advocates are proposing additional government funding to help people with low incomes [pay their utility bills/pay their utility bills as they use heat for their homes in the winter/pay their utility bills as they use air conditioning for their homes in the summer].

How much would you support providing government help for low-income people to make sure they can [pay their utility bills/use heating in their homes/use air conditioning in their homes]?

Respondents indicated their support for the randomized proposal vignette on a 7-point Likert type scale that included strongly support, support, somewhat support, neither support nor oppose, somewhat oppose, oppose, and strongly oppose. For analysis, I recoded these items to range from −3 to 3, with 0 as the neutral midpoint and higher values indicating stronger support.

Results

Table 2 presents descriptive statistics and significance tests across each of the experimental conditions, with higher scores indicating stronger support for each policy vignette. An analysis of variance test for mean differences across the three conditions was statistically significant (F = 7.21, p < .001), challenging the expectation of equality across conditions in Hypothesis 1. The difference in support between the two control vignettes was statistically significant (utility vs. heat: Cohen’s d = −0.15, pairwise t-test p < .05). In a test of Hypotheses 2a and 2b, the difference in support between the general utility control and air conditioning vignette was not significant (d = 0.11, p = .13). In contrast, the difference in support between the heat and air conditioning vignettes was statistically significant (d = 0.26, p < .001). Average respondent support for the air conditioning vignette was about a quarter of a standard deviation lower than average support in the heat vignette.

Table 2.

Differences in Support for Utility Assistance.

	Air Conditioning in Summer	Heat in Winter	Utility Bills
	N = 387	N = 421	N = 392
	Mean (SD)	Mean (SD)	Mean (SD)
Support for assistance	1.11 (1.76)	1.55 (1.55)	1.30 (1.69)
One-way analysis of variance test***	F = 7.21
	Difference in Means	p Value
Pairwise t tests
Heat vs. air conditioning***	0.44	.0006
Utility vs. air conditioning	0.19	.1295
Utility vs. heat*	−0.25	.0423

p < .05. ***p < .001.

Figure 2 presents the distribution of respondents across all three conditions. Support for utility assistance was high across each case, but the distribution of support is substantively tempered for the air conditioning case, with more respondents selecting the neutral baseline and opposing options.

Figure 2.

Distribution of support for utility assistance by condition.

Finally, I consider potential treatment effect heterogeneity. Three demographic subgroups are relevant to this analysis. Treatment effects may vary with partisanship given that prior research indicates that Democrats and Republicans exhibit different associations between climate attitudes and outcomes (e.g., Dacey and Stewart 2023; Hazlett and Mildenberger 2020). Effects may also vary by race and ethnicity given that a range of literature demonstrates that attitudes about social assistance are tightly coupled with racial attitudes (Gilens 2009; Schram et al. 2009). Finally, effects may vary by respondents’ region of residence (Zhang et al. 2018) because local climate conditions impact both the necessity of heating and cooling and whether housing is more likely to include air conditioning by default.

Regression analyses included in Section B of the Supplemental Appendix indicated that all three of these factors were significantly associated with the outcome measure. Independents and Republicans expressed significantly lower support than Democrats regardless of the vignette condition (p < .001), Black respondents expressed significantly higher support than White respondents (p < .001), and respondents in the southern United States expressed significantly lower support than respondents in the Northeast in fully specified models (p < .05). In models that controlled for these factors, however, the effects of the experiment persisted in both significance and magnitude, as we would expect with proper random assignment.

Additional regression analyses used an interaction term for treatment and each of these demographic indicators to test for potential treatment effect heterogeneity (Table 4 in Appendix B). No interaction terms were statistically significant at conventional levels, suggesting little evidence of treatment effect heterogeneity in this sample, but the interaction model for race found substantive differences in the treatment effect for Hispanic respondents (p < .10), which informed the design of a replication study.

One limitation of this experiment is that the moral framing of social welfare policies is often racialized (e.g., Schram et al. 2009), but nationally representative samples often undersample respondents from minoritized racial and ethnic groups. To address this problem, I conducted a replication in a second sample (N = 703) of respondents recruited through Prolific who self-identified as Black or Hispanic/Latino. This version of the survey included the original manipulation along with an optional Spanish language translation of the survey. Details of the replication analysis are provided in Section C of the Supplemental Appendix.

Table 3 summarizes results from the replication study. The original manipulation did not replicate in a sample of exclusively Black and Hispanic respondents. Results did replicate in an aggregated sample that included both waves of data collection (N = 1,903), and here, the test for treatment effect heterogeneity between White and Hispanic respondents was statically significant (p < .05; Table 6 in the Supplemental Appendix). In short, this replication provided evidence that the survey manipulation was most effective among White respondents. Figure 3 uses predicted support scores to illustrate this pattern—support is significantly and substantially lower for White respondents between the air conditioning and heat conditions, whereas Black and Hispanic respondents report relatively higher and consistent support across conditions.

Table 3.

Replication Analysis for Differences in Support for Utility Assistance.

	Replication Sample				Aggregated Sample
	Black- and Hispanic-Identified Respondents (N = 703)				Original Study + Replication Oversample (N = 1,903)
	Air Conditioning in Summer	Heat in Winter	Utility Bills		Air Conditioning in Summer	Heat in Winter	Utility Bills
	N = 236	N = 241	N = 226		N = 623	N = 662	N = 618
	Mean (SD)	Mean (SD)	Mean (SD)		Mean (SD)	Mean (SD)	Mean (SD)
Support for assistance	1.94 (1.33)	2.00 (1.31)	1.98 (1.35)	Support for assistance	1.42 (1.66)	1.71 (1.48)	1.55 (1.61)
One-way analysis of variance test	F = 0.16			One-way analysis of variance test**	F = 5.62
Pairwise t Tests	Difference in Means	p Value		Pairwise t Tests	Difference in Means	p Value
Heat vs. air conditioning	0.06	.830		Heat vs. air conditioning**	0.29	.003
Utility vs. air conditioning	0.04	.830		Utility vs. air conditioning	0.13	.184
Utility vs. heat	−0.02	.830		Utility vs. heat	−0.16	.079

p<.05, **p<.01, ***p<.001.

Figure 3.

Heterogeneous treatment effects by race and ethnicity.

Discussion and Conclusion

Addressing unequal heat exposure as a serious social problem under climate change will require expanding access to reliable cooling. In line with other social assistance programs, such as SNAP and TANF, however, public support for those programs may be conditional on how the public perceives the status of the goods that they provide to aid recipients. This study examined whether varying a reference to air conditioning versus heat would prompt respondents to express different rates of support for utility assistance programs.

Three key findings emerge from this survey experiment. First, respondents were significantly less likely to support utility assistance for people to use air conditioning in the summer than they were for people to use heat in the winter. Second, they were also significantly less likely to support utility assistance in general than they were to support assistance for heating specifically. Finally, a replication study demonstrated that these effects were most pronounced among White respondents rather than Black or Hispanic respondents.

There are, of course, some limitations to these findings. To my knowledge as of writing, this study is one of the first experimental evaluations of public support for cooling assistance in the climate crisis, and future work can further test the replicability and generalizability of these findings to other programs using additional experiments with this same format. For example, this approach could be modified to evaluate support for bond initiatives to modernize HVAC systems in schools. Additional work could also evaluate whether this pattern is present among political representatives and directly structures the policymaking process through allocation of funding for infrastructure development and repair. At the time of writing, this link between public opinion and policymaking has yet to be tested in the same way it has for other social welfare programs (Brooks and Manza 2007).

Despite these current limitations, these findings have important implications for future research in environmental policy and inequality. For research on environmental policy, these results provide important evidence that efforts to mitigate harm from climate change may face unique barriers in public support beyond partisanship alone. Although polling shows the public is evenly split on whether it considers air conditioning a necessity or a luxury, these results indicate that access to cooling does not yet have similar levels of public support for assistance as access to heat. Although we might expect this asymmetry, given air conditioning’s historic status as a luxury good and elevated rates of excess mortality due to cold exposure, the emergence of new threats to health and social well-being due to heat will require that policy advocates address this asymmetry if they seek expanded support for cooling access in the future. As with other major policy concerns, such as accommodations for climate refugees (Berchin et al. 2017) or the provision of emergency transitional housing after storms or wildfires (Reid 2013), research will need to continue to investigate both the explicit moral frames that social movements and political leaders can use to advocate for equity and the implicit frames that structure opposition to policy solutions.

For research on inequality more broadly, these results provide another empirical example of how different consumption goods matter for attitudes about social assistance programs. Research on the politics of social assistance often finds that people justify their position by leaning on status distinctions between aid recipients who spend on necessities and recipients who “cheat the system” by spending on luxury goods or frivolous goods, regardless of whether those distinctions are factually correct (Dwyer et al. 2023; Scampini 2021). This is a key obstacle to social aid policies and one case where symbolic distinctions can reinforce material inequalities (Edgell et al. 2020; Lamont 2018, 2023; Lamont and Molnar 2002). These results demonstrate that support for utility assistance is most closely tied to one specific good consumed—heat—and is less likely to extend to general utility needs or air conditioning. They also provide additional support for the theory that attitudes about social assistance are deeply racialized (Gilens 2009) because they indicate the manipulation was most effective among White respondents. Under what conditions might the status of air conditioning change? Future research on inequality and the sociology of culture can build on these findings to understand additional status differences in the goods that people consume with social assistance (Warde 2015; Wilk 2001) and how these may present additional challenges with changing consumption patterns to adjust to the climate crisis (Schor 2007; Shove 2003).

Addressing climate change requires overcoming a wide range of policy obstacles including emissions regulation, infrastructure funding, and zoning for new, sustainable development. It is also important for researchers and advocates to recognize the cultural challenges posed by the climate crisis and understand how the crisis may force us to extend our understanding of who deserves help and what kind of help they deserve. This study demonstrates that getting aid to vulnerable populations—in this case, adequate cooling—will require overcoming both material and status boundaries.

Supplemental Material

sj-docx-1-sdr-10.1177_23780231241278540 – Supplemental material for Necessity or Luxury? Air Conditioning and Support for Utility Assistance in the Context of Climate Change

Supplemental material, sj-docx-1-srd-10.1177_23780231241278540 for Necessity or Luxury? Air Conditioning and Support for Utility Assistance in the Context of Climate Change by Evan Stewart in Socius

Footnotes

Acknowledgements

The author would like to acknowledge research support from Timothy Dacey and Katsyris Rivera-Kientz.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The author would like to acknowledge funding for data collection from the UMass Boston Endowed Early Career Faculty Award.

ORCID iD

Evan Stewart

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biography

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. His research on belonging in public life, religious change, and public opinion has appeared in American Sociological Review, Social Forces, Social Problems, and Sociological Theory, among other outlets. He earned a PhD in sociology from the University of Minnesota and has held research fellowships with the Public Religion Research Institute and the Social Science Research Council ().

References

Ackermann

Marsha

. 2002. Cool Comfort: America’s Romance with Air-Conditioning. Washington, DC: Smithsonian Books.

Adams

Jeffrey A.

Carley

Sanya

Konisky

David M.

2024. “Utility Assistance and Pricing Structures for Energy Impoverished Households: A Review of the Literature.” The Electricity Journal 37(2):107368. doi:10.1016/j.tej.2024.107368.

Ahn

Yoonjung

Uejio

Christopher K.

2022. “Modeling Air Conditioning Ownership and Availability.” Urban Climate 46:101322. doi:10.1016/j.uclim.2022.101322.

Alahmad

Barrak

Khraishah

Haitham

Royé

Dominic

Vicedo-Cabrera

Ana Maria

Guo

Yuming

Papatheodorou

Stefania I.

Achilleos

Souzana

, et al. 2023. “Associations between Extreme Temperatures and Cardiovascular Cause-Specific Mortality: Results from 27 Countries.” Circulation 147(1):35–46. doi:10.1161/CIRCULATIONAHA.122.061832.

Barlett

Christopher P.

DeWitt

Caroline C.

Madison

Cory S.

Heath

J.Bailey

Maronna

Brittany

Kirkpatrick

Sarah M.

2020. “Hot Temperatures and Even Hotter Tempers: Sociological Mediators in the Relationship between Global Climate Change and Homicide.” Psychology of Violence 10(1):1–7. doi:10.1037/vio0000235.

Berchin

Issa Ibrahim

Valduga

Isabela Blasi

Garcia

Jéssica

de Andrade Guerra

José Baltazar Salgueirinho Osório

. 2017. “Climate Change and Forced Migrations: An Effort towards Recognizing Climate Refugees.” Geoforum 84:147–50. doi:10.1016/j.geoforum.2017.06.022.

Berko

Jeffrey

Ingram

Deborah

Saha

Shubhayu

Parker

Jennifer

. 2014. “Deaths Attributed to Heat, Cold, and Other Weather Events in the United States, 2006– 2010.” National Health Statistics Reports; No. 76. Hyattsville, MD: National Center for Health Statistics.

Biddle

Jeff E.

2011. “Making Consumers Comfortable: The Early Decades of Air Conditioning in the United States.” The Journal of Economic History 71(4):1078–94.

Brooks

Clem

Manza

Jeff

. 2006. “Social Policy Responsiveness in Developed Democracies.” American Sociological Review 71(3):474–94.

10.

Brooks

Clem

Manza

Jeff

. 2007. Why Welfare States Persist: The Importance of Public Opinion in Democracies. Chicago, IL: University of Chicago Press.

11.

Brown

Keith R.

2011. “Interaction Ritual Chains and the Mobilization of Conscientious Consumers.” Qualitative Sociology 34(1):121–41. doi:10.1007/s11133-010-9188-3.

12.

Burke

Marshall

Hsiang

Solomon M.

Miguel

Edward

. 2015. “Climate and Conflict.” Annual Review of Economics 7(1):577–617. doi:10.1146/annurev-economics-080614-115430.

13.

Cloud

David H.

Williams

Brie

Haardörfer

Regine

Brinkley-Rubinstein

Lauren

Cooper

Hannah L. F.

2023. “Extreme Heat and Suicide Watch Incidents among Incarcerated Men.” JAMA Network Open 6(8):e2328380. doi:10.1001/jamanetworkopen.2023.28380.

14.

Cook

John T.

Frank

Deborah A.

Casey

Patrick H.

Rose-Jacobs

Ruth

Black

Maureen M.

Mariana

Chilton

deCuba

Stephanie Ettinger

, et al. 2008. “A Brief Indicator of Household Energy Security: Associations with Food Security, Child Health, and Child Development in US Infants and Toddlers.” Pediatrics 122(4):e867–75. doi:10.1542/peds.2008-0286.

15.

Costello

Kenneth W.

2021. “Bettering Utility Assistance to Low-Income Households.” Climate and Energy 38(5):1–9. doi:10.1002/gas.22260.

16.

Dacey

Timothy

Stewart

Evan

. 2023. “The Green Elephants in the Room: Perceived Environmental Harm and Support for Regulation among Republicans.” Sociological Inquiry 93(2):250–72. doi:10.1111/soin.12511.

17.

Dixon

P. Grady

Brommer

David M.

Hedquist

Brent C.

Kalkstein

A. J.

Gregory

Goodrich

J. C.

Dickerson

Walter C. C.

, et al. 2005. “Heat Mortality versus Cold Mortality: A Study of Conflicting Databases in the United States.” Bulletin of the American Meteorological Society 86(7):937–43. doi:10.1175/BAMS-86-7-937.

18.

Douglas

Ellen

Kirshen

Paul

. 2022. Climate Change Impacts and Projections for the Greater Boston Area. Boston, MA: University of Massachusetts Boston, School for the Environment.

19.

Dwyer

Ryan

Palepu

Anita

Williams

Claire

Daly-Grafstein

Daniel

Zhao

Jiaying

. 2023. “Unconditional Cash Transfers Reduce Homelessness.” Proceedings of the National Academy of Sciences 120(36):e2222103120. doi:10.1073/pnas.2222103120.

20.

Edgell

Penny

Stewart

Evan

Billups

Sarah Catherine

Larson

Ryan

. 2020. “The Stakes of Symbolic Boundaries.” The Sociological Quarterly 61(2):309–33. doi:10.1080/00380253.2019.1625736.

21.

Finnigan

Ryan

Meagher

Kelsey D.

2019. “Past Due: Combinations of Utility and Housing Hardship in the United States.” Sociological Perspectives 62(1):96–119. doi:10.1177/0731121418782927.

22.

Foster

Carly Hayden

. 2008. “The Welfare Queen: Race, Gender, Class, and Public Opinion.” Race, Gender & Class 15(3/4):162–79.

23.

Frank

Deborah A.

Neault

Nicole B.

Skalicky

Anne

Cook

John T.

Wilson

Jacqueline D.

Levenson

Suzette

Meyers

Alan F.

, et al. 2006. “Heat or Eat: The Low Income Home Energy Assistance Program and Nutritional and Health Risks among Children Less Than 3 Years of Age.” Pediatrics 118(5):e1293–302. doi:10.1542/peds.2005-2943.

24.

Franzese

Paula A.

Gorin

Abbott

Guzik

David

. 2016. “The Implied Warranty of Habitability Lives: Making Real the Promise of Landlord Tenant Reform.” Rutgers Law Review 69:1–45.

25.

Fraser

Andrew M.

Chester

Mikhail V.

Eisenman

David

Hondula

David M.

Pincetl

Stephanie S.

English

Paul

Bondank

Emily

. 2017. “Household Accessibility to Heat Refuges: Residential Air Conditioning, Public Cooled Space, and Walkability.” Environment and Planning B: Urban Analytics and City Science 44(6):1036–55. doi:10.1177/0265813516657342.

26.

Gilens

Martin

. 2009. Why Americans Hate Welfare: Race, Media, and the Politics of Antipoverty Policy. Chicago, IL: University of Chicago Press.

27.

Graff

Michelle

Pirog

Maureen

. 2019. “Red Tape Is Not So Hot: Asset Tests Impact Participation in the Low-Income Home Energy Assistance Program.” Energy Policy 129:749–64. doi:10.1016/j.enpol.2019.02.042.

28.

Harlan

Sharon L.

Brazel

Anthony J.

Prashad

Lela

Stefanov

William L.

Larsen

Larissa

. 2006. “Neighborhood Microclimates and Vulnerability to Heat Stress.” Social Science & Medicine 63(11):2847–63. doi:10.1016/j.socscimed.2006.07.030.

29.

Hazlett

Chad

Mildenberger

Matto

. 2020. “Wildfire Exposure Increases Pro-environment Voting within Democratic but Not Republican Areas.” American Political Science Review 114(4):1359–65. doi:10.1017/S0003055420000441.

30.

Hernández

Diana

. 2016. “Understanding ‘Energy Insecurity’ and Why It Matters to Health.” Social Science & Medicine 167:1–10. doi:10.1016/j.socscimed.2016.08.029.

31.

Heyward

Giulia

. 2024. “NYC Lawmaker Wants to Require Landlords to Provide Air Conditioning during the Summer.” Gothamist. https://gothamist.com/news/nyc-lawmaker-wants-to-require-landlords-to-provide-air-conditioning-during-the-summer.

32.

Katz

Michael B.

2008. The Price of Citizenship: Redefining the American Welfare State. Philadelphia: University of Pennsylvania Press.

33.

Klinenberg

Eric

. 2002. Heat Wave: A Social Autopsy of Disaster in Chicago. Chicago, IL: University of Chicago Press.

34.

Klinenberg

Eric

Araos

Malcolm

Koslov

Liz

. 2020. “Sociology and the Climate Crisis.” Annual Review of Sociology 46:649–69. doi:10.1146/annurev-soc-121919-054750.

35.

Kovats

R. Sari

Hajat

Shakoor

. 2008. “Heat Stress and Public Health: A Critical Review.” Annual Review of Public Health 29(1):41–55. doi:10.1146/annurev.publhealth.29.020907.090843.

36.

Kuras

E. R.

Hondula

D. M.

Brown-Saracino

2015. “Heterogeneity in Individually Experienced Temperatures (IETs) within an Urban Neighborhood: Insights from a New Approach to Measuring Heat Exposure.” International Journal of Biometeorology 59(10):1363–72. doi:10.1007/s00484-014-0946-x.

37.

Lamont

Michèle

. 2018. “Addressing Recognition Gaps: Destigmatization and the Reduction of Inequality.” American Sociological Review 83(3):419–44. doi:10.1177/0003122418773775.

38.

Lamont

Michele

. 2023. Seeing Others. New York, NY: Simon & Schuster.

39.

Lamont

Michèle

Molnar

Virag

. 2002. “The Study of Boundaries in the Social Sciences.” Annual Review of Sociology 28:167–95.

40.

Leiserowitz

Anthony A.

2005. “American Risk Perceptions: Is Climate Change Dangerous?” Risk Analysis 25(6):1433–42. doi:10.1111/j.1540-6261.2005.00690.x.

41.

Masselot

Pierre

Mistry

Malcolm

Vanoli

Jacopo

Schneider

Rochelle

Iungman

Tamara

Garcia-Leon

David

Ciscar

Juan-Carlos

, et al. 2023. “Excess Mortality Attributed to Heat and Cold: A Health Impact Assessment Study in 854 Cities in Europe.” The Lancet Planetary Health 7(4):e271–81. doi:10.1016/S2542-5196(23)00023-2.

42.

McCright

Aaron M.

Dunlap

Riley E.

2011. “Cool Dudes: The Denial of Climate Change among Conservative White Males in the United States.” Global Environmental Change 21(4):1163–72.

43.

McCright

Aaron M.

Dunlap

Riley E.

Xiao

Chenyang

. 2013. “Perceived Scientific Agreement and Support for Government Action on Climate Change in the USA.” Climatic Change 119(2):511–18.

44.

Mezey

Nancy J.

2020. “Presidential Address: Start Spreading the News: Illuminating the Effects of Climate Change as a Social Problem.” Social Problems 67(4):605–15. doi:10.1093/socpro/spaa009.

45.

Mikhitarian

Sarah

. 2018. “Homes with Central Air Command $5,486 More on Average.” Zillow, Inc. https://www.zillow.com/research/central-air-home-rent-premium-20436/.

46.

Mildenberger

Matto

Howe

Peter D.

Trachtman

Samuel

Stokes

Leah C.

Lubell

Mark

. 2022. “The Effect of Public Safety Power Shut-offs on Climate Change Attitudes and Behavioural Intentions.” Nature Energy 7(8):736–43. doi:10.1038/s41560-022-01071-0.

47.

Mildenberger

Matto

Lachapelle

Erick

Harrison

Kathryn

Stadelmann-Steffen

Isabelle

. 2022. “Limited Impacts of Carbon Tax Rebate Programmes on Public Support for Carbon Pricing.” Nature Climate Change 12(2):141–47. doi:10.1038/s41558-021-01268-3.

48.

Mohr

John W.

1994. “Soldiers, Mothers, Tramps and Others: Discourse Roles in the 1907 New York City Charity Directory.” Poetics 22(4):327–57. doi:10.1016/0304-422X(94)90013-2.

49.

Murphy

Sean

. 2024. “It’s So Hot. Are Landlords Legally Obliged to Provide Air Conditioning?” The Boston Globe, July 11. https://www.bostonglobe.com/2024/07/11/business/heat-air-conditioning-weather-apartments/.

50.

Noonan

Douglas S.

Hsieh

Lin-Han Chiang

Matisoff

Daniel

. 2015. “Economic, Sociological, and Neighbor Dimensions of Energy Efficiency Adoption Behaviors: Evidence from the U.S Residential Heating and Air Conditioning Market.” Energy Research & Social Science 10:102–13. doi:10.1016/j.erss.2015.07.009.

51.

O’Brien

Daniel T.

Gridley

Brian

Trlica

Andrew

Wang

Jonathan A.

Shrivastava

Aatmesh

. 2020. “Urban Heat Islets: Street Segments, Land Surface Temperatures, and Medical Emergencies during Heat Advisories.” American Journal of Public Health 110(7):994–1001. doi:10.2105/AJPH.2020.305636.

52.

Olofsson

Anna

Öhman

Susanna

Nygren

Katarina Giritli

. 2016. “An Intersectional Risk Approach for Environmental Sociology.” Environmental Sociology 2(4):346–54. doi:10.1080/23251042.2016.1246086.

53.

Ostro

Bart

Rauch

Stephen

Green

Rochelle

Malig

Brian

Basu

Rupa

. 2010. “The Effects of Temperature and Use of Air Conditioning on Hospitalizations.” American Journal of Epidemiology 172(9):1053–61. doi:10.1093/aje/kwq231.

54.

Palan

Stefan

Schitter

Christian

. 2018. “Prolific.aca Subject Pool for Online Experiments.” Journal of Behavioral and Experimental Finance 17:22–27. doi:10.1016/j.jbef.2017.12.004.

55.

Park

R. Jisung

Goodman

Joshua

Hurwitz

Michael

Smith

Jonathan

. 2020. “Heat and Learning.” American Economic Journal: Economic Policy 12(2):306–39. doi:10.1257/pol.20180612.

56.

Peer

Eyal

Brandimarte

Laura

Samat

Sonam

Acquisti

Alessandro

. 2017. “Beyond the Turk: Alternative Platforms for Crowdsourcing Behavioral Research.” Journal of Experimental Social Psychology 70:153–63. doi:10.1016/j.jesp.2017.01.006.

57.

Pellow

David N.

Brehm

Hollie Nyseth

. 2013. “An Environmental Sociology for the Twenty-First Century.” Annual Review of Sociology 39:229–50. doi:10.1146/annurev-soc-071312-145558.

58.

Petersen

Michael Bang

Sznycer

Daniel

Cosmides

Leda

Tooby

John

. 2012. “Who Deserves Help? Evolutionary Psychology, Social Emotions, and Public Opinion about Welfare.” Political Psychology 33(3):395–418. doi:10.1111/j.1467-9221.2012.00883.x.

59.

Pew Research Center for the People & the Press/Pew Social & Demographic Trends. 2006. “Pew Research Center Poll # 2006-SDT04: Pew Social Trend—Personal Finance/Social Trust.” Question 21: USPSRA.121406A.R39E. Cornell University, Ithaca, NY: Roper Center for Public Opinion Research. https://doi.org/10.25940/ROPER-31095895.

60.

Pew Research Center for the People & the Press/Pew Social & Demographic Trends. 2009. “Pew Research Center Poll: Pew Social Trends–April 2009-Fertility Survey.” Question 56: USPSRA.042309S.R20D. Cornell University, Ithaca, NY: Roper Center for Public Opinion Research. https://doi.org/10.25940/ROPER-31095959.

61.

Pew Research Center for the People & the Press/Pew Social & Demographic Trends. 2010. “Pew Social and Demographic Trends Poll: Impact of the Recession.” Question 116: USPSRA.081910S.R10B. Cornell University, Ithaca, NY: Roper Center for Public Opinion Research. https://doi.org/10.25940/ROPER-31095990.

62.

Portner

Hans-O.

Roberts

Debra

Trisos

Christopher

Simpson

Nicholas

. 2022. Summary for Policymakers: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. doi:10.1017/9781009325844.001.

63.

Reid

Megan

. 2013. “Social Policy, ‘Deservingness,’ and Sociotemporal Marginalization: Katrina Survivors and FEMA.” Sociological Forum 28(4):742–63. doi:10.1111/socf.12051.

64.

Scampini

Renee

. 2021. “The Food Police: Food Stamps, Surveillance, and the Construction of the Undeserving Hungry.” PhD disseration. University of Wisconsin-Milwaukee, Milwaukee, WI.

65.

Schor

Juliet B.

2007. “In Defense of Consumer Critique: Revisiting the Consumption Debates of the Twentieth Century.” The ANNALS of the American Academy of Political and Social Science 611(1):16–30. doi:10.1177/0002716206299145.

66.

Schram

Sanford F.

Soss

Joe

Fording

Richard C.

Houser

Linda

. 2009. “Deciding to Discipline: Race, Choice, and Punishment at the Frontlines of Welfare Reform.” American Sociological Review 74(3):398–422. doi:10.1177/000312240907400304.

67.

Shove

Elizabeth

. 2003. Comfort, Cleanliness and Convenience: The Social Organization of Normality. Oxford, UK: Berg.

68.

Skarha

Julianne

Spangler

Keith

Dosa

David

Rich

Josiah D.

Savitz

David A.

Zanobetti

Antonella

. 2023. “Heat-Related Mortality in U.S. State and Private Prisons: A Case-Crossover Analysis.” PLoS One 18(3):e0281389. doi:10.1371/journal.pone.0281389.

69.

Skocpol

Theda

. 1996. Protecting Soldiers and Mothers: The Political Origins of Social Policy in the United States. Cambridge, MA: Harvard University Press.

70.

Soss

Joe

Fording

Richard C.

Schram

Sanford

. 2011. Disciplining the Poor Neoliberal Paternalism and the Persistent Power of Race. Chicago, IL: University of Chicago Press.

71.

Spangler

Keith R.

Liang

Shixin

Wellenius

Gregory A.

2022. “Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000–2020.” Scientific Data 9(1):326. doi:10.1038/s41597-022-01405-3.

72.

Sparkman

Gregg

Geiger

Nathan

Weber

Elke U.

2022. “Americans Experience a False Social Reality by Underestimating Popular Climate Policy Support by Nearly Half.” Nature Communications 13(1):4779. doi:10.1038/s41467-022-32412-y.

73.

Steensland

Brian

. 2006. “Cultural Categories and the American Welfare State: The Case of Guaranteed Income Policy.” American Journal of Sociology 111(5):1273–326. doi:10.1086/499508.

74.

Steensland

Brian

. 2010. “Moral Classification and Social Policy.” Pp. 455–68 in Handbooks of Sociology and Social Research, edited by Hitlin

Vaisey

New York, NY: Springer.

75.

Stokes

Leah C.

2016. “Electoral Backlash against Climate Policy: A Natural Experiment on Retrospective Voting and Local Resistance to Public Policy.” American Journal of Political Science 60(4):958–74. doi:10.1111/ajps.12220.

76.

Stokes

Leah C.

Warshaw

Christopher

. 2017. “Renewable Energy Policy Design and Framing Influence Public Support in the United States.” Nature Energy 2(8):1–6. doi:10.1038/nenergy.2017.107.

77.

The Roper Organization. 1973. “Roper Reports #1974-01: Consumerism/Gasoline and Fuel Shortage. Question 81: USROPER.74-1.R33B.” Cornell University, Ithaca, NY: Roper Center for Public Opinion Research. https://doi.org/10.25940/ROPER-31097297.

78.

Veblen

Thorstein

. 1899. The Theory of the Leisure Class: An Economic Study in the Evolution of Institutions. New York: Macmillan.

79.

Vesentini

Andrea

. 2017. “It’s Cool Inside: Advertising Air Conditioning to Postwar Suburbia.” American Studies 55/56:91–117.

80.

Warde

Alan

. 2015. “The Sociology of Consumption: Its Recent Development.” Annual Review of Sociology 41:117–34. doi:10.1146/annurev-soc-071913-043208.

81.

Wilk

Richard

. 2001. “Consuming Morality.” Journal of Consumer Culture 1(2):245–60. doi:10.1177/146954050100100211.

82.

Zhang

Baobao

van der Linden

Sander

Mildenberger

Matto

Marlon

Jennifer R.

Howe

Peter D.

Leiserowitz

Anthony

. 2018. “Experimental Effects of Climate Messages Vary Geographically.” Nature Climate Change 8(5):370–74. doi:10.1038/s41558-018-0122-0.

83.

Zhao

Qunshan

Dickson

Chelsea

Thornton

Jowan

Solís

Patricia

Wentz

Elizabeth A.

2020. “Articulating Strategies to Address Heat Resilience Using Spatial Optimization and Temporal Analysis of Utility Assistance Data of the Salvation Army Metro Phoenix.” Applied Geography 122:102241. doi:10.1016/j.apgeog.2020.102241.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.37 MB