Vulnerability to climate change and the desire for

Vulnerability to climate change and the desire for mitigation

Ahmad Saleh Safi, William James Smith & Zhongwei Liu

Journal of Environmental Studies and Sciences ISSN 2190-6483 J Environ Stud Sci DOI 10.1007/s13412-016-0384-7

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Author's personal copy J Environ Stud Sci DOI 10.1007/s13412-016-0384-7

Vulnerability to climate change and the desire for mitigation Ahmad Saleh Safi 1 & William James Smith Jr 2 & Zhongwei Liu 3

# AESS 2016

Abstract The interrelationship between vulnerability and climate change is understudied. Through this research, we fill this relative gap using rural Nevada as a case study. In 2009/ 2010, we surveyed 1872 ranchers and farmers, investigating their climate change-related assumptions, experiences, knowledge bases, and policy preferences. Almost 26 % responded to our mail-based survey. We created a climate change vulnerability index as a function of physical vulnerability, sensitivity, and adaptive capacity and applied it for every respondent using demographic data from the survey and secondary Geographical Information System-based data on water availability and use, and population. In our research, we investigate the influence of vulnerability and its components on climate change mitigation policy support. The results show that vulnerability is an insignificant determinant of supporting climate change mitigation policies. Both physical vulnerability and adaptive capacity play no role in determining climate change mitigation policy support. However, sensitivity to climate change decreases support of stringent policies (i.e., taxation policies), but does not influence support of non-stringent policies (i.e., technological fixes). The most prominent determinants of climate change mitigation policy support are beliefs regarding the anthropogenic causes of climate change, beliefs

* Ahmad Saleh Safi [email protected]

1

Al Azhar University, Gaza, Egypt

2

University of Nevada, Las Vegas Department of Anthropology, Box 455003, 4505 S. Maryland Parkway, Las Vegas, NV 89154-5003, USA

3

Department of Geography and Regional Planning, Indiana University of Pennsylvania, Indiana PA 15705, USA

regarding the causal relationship between drought and climate change in Nevada, and political orientations. Keywords Climate change policy support . Vulnerability . Ranchers and farmers Although the profile of climate change impacts has risen dramatically since the years leading up to the 1992 Rio Earth Summit, the U.S. public support of mitigation strategies, many of which have been perceived as being costly, and in some cases unwarranted, has been limited (Leiserowitz 2003; Leiserowitz et al. 2010). Accordingly, our research adds to the literature investigating the determinants of climate change mitigation policy support in order to help nurture productive communication among citizens, scientists, and policy makers on the path to establishment of comprehensive climate change mitigation plans. Our interdisciplinary research draws on the literature of three knowledge domains: (1) risk/hazard, (2) environmental politics, and (3) social psychology—all of which intersect with climate change policy discourse. Our major research question is, Bdoes vulnerability to climate change boost support of climate change mitigation policies?^ We understand mitigation policies as those policies that aim at reducing the source, or enhancing the sink, of greenhouse gases (IPCC 2014). We employ vulnerability as a function of three major components: (1) physical vulnerability, (2) sensitivity, and (3) adaptive capacity. Additionally, we investigate whether any or all the three components of vulnerability play a role(s) in determining rural Nevadans’ support of climate change mitigation policies. Specifically, we study the influence of vulnerability to drought as the major climate change impact in Nevada on ranchers’ and farmers’ mitigation policy support. We selected Nevada ranchers and farmers because of their dependence on

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scarce water resources and daily interaction with the physical environment, and because they are an understudied group in the discourse of climate change impacts and policy. Nevada is expected to suffer increased water stress, extended and more frequent droughts, and rising temperatures as a result of climate change (Kim et al. 2002; Piechota et al. 2004; Leung et al. 2004; Barnett et al. 2004; CIER 2008; Miller and Piechota 2008; Barnett and Pierce 2008; USGCRP 2009; U.S. Bureau of Reclamation 2011). Nevada is already facing one of the most severe droughts in its history dating back to the end of the last century (Piechota et al. 2004; SNWA 2007). In this paper, we first discuss the concept of vulnerability to set the stage for discussing its relationships with climate change mitigation policy support and the researcher’s methodology. Then we introduce literature concerning the determinants of climate change policy support, including vulnerability, in order to reveal gaps in this literature and situate our research questions. Then we introduce our hypotheses in relation to the literature. Afterwards, we describe our research methodology, present and discuss our findings, and finally, present our conclusions.

Defining Vulnerability In this study, we recognize vulnerability as a function of three components: (1) physical vulnerability, (2) sensitivity, and (3) adaptive capacity. The first component represents the exposure to climate change impacts, which is represented by the severity and probability of those impacts. Whereas, the other two components represent two different aspects of potential socioeconomic vulnerability to such impacts (Hahn et al. 2009). We adopt the definition of vulnerability stated by the IPCC (2001, p. 995) which is: …the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate v ariability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation [physical vulnerability] to which a system is exposed, its sensitivity, and its adaptive capacity (IPCC 2001, p. 995). Sensitivity is a measure of the extent to which a system can be harmed or benefit due to a given hazard. This aspect of socioeconomic vulnerability relates to characteristics of the system of concern that decide the output of its interaction with a specific hazard. For example, the level of reliance on natural resources such as water and forests determines communities’ and individuals’ sensitivity to climate change impacts such as drought, soil erosion, and wildfire (Scheraga and Grambsch 1998; Luers 2005; Fussel and Klein 2006; O’Brien et al. 2006).

Adaptive capacity reflects the capacity of a system to ameliorate, survive, adjust, take advantage of, and adapt to climate change impacts. It pertains to resources available to the system of concern (community, individual, etc.) and its accessibility to these resources when hazards take place. Individuals with higher income, greater wealth, better connected family, higher education, and thus, higher social status are usually more able to protect themselves from or adapt to hazards than others. Ranchers with better social status have better access to information regarding potentially upcoming droughts and protective measures and technologies. They also possess stronger connections to local politicians and institutions which may fasten and facilitate their access to governmental subsidies, compensations, and assistance. They also own more financial resources, which translates into more options that help alleviate the impact of droughts on their businesses, such as accessing more diverse land to graze, growing more drought resilient plants, installing, and utilizing more efficient irrigation networks and technologies. Ranchers and farmers with better adaptive capacity are usually entitled to better and more flexible finance and insurance (Metzger et al. 2005; Adger 2006; Fussel and Klein 2006; Smit and Wandal 2006; Gallopin 2006). Smith et al. (2011) offer an interesting view regarding how rural communities, such as the ones they study in New Zealand, can be Bhollowed out^ by changes relatively external to their communities. The authors note how rural communities have changed to be less collaborative and self-supporting, and how the health of social systems that contribute to resilience has been undermined by the urbanization of the population, Bhobby farmers,^ and the decline of social glues such as cultural homogeneity and government services. Some drivers of such changes have been technological and economic, including mobility and the momentum towards larger farms and less laborers. Their point that hazards, such as the catastrophic flooding experienced in their study area, can expose cracks in community and environmental structures, and that lessons descending from such events must be integrated to improve policy through supporting community health and lines of communication, is well taken. This work links to our own research in at least one important way. If the authors are correct that to combat vulnerability people should implement a range of actions that prepare themselves for hazards, yet preparedness is impacted by risk awareness, past experience (observations), evaluation of likelihood of a disaster occurring, as well as Bsocioeconomic criteria,^ then a clear understanding of drivers of perception and the health of networks and attending lines of communication is vital. However, we do not find urbanization to be undermining the values of ranchers and farmers, especially with regards to Bhobby farmers,^ as is the case in the New Zealand case. The main tension between rural and urban areas is a desire for scarce water, and to some extent, for political clout in a state with over 80 % of its

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population in urban areas, despite a large rural geographic space. And, the tension between Native Americans and both rural and urban populations is omnipresent over place and time.

Climate change mitigation policy support In the following section, we discuss the literature regarding the determinants of climate change mitigation policy support within three major categories: attitudinal, personal capacity, and vulnerability motives. Attitudinal motives include norms, beliefs, values, ecological worldviews, and risk perceptions (Stern 2000). Throughout this discussion, we will highlight the gaps in the literature and define the research questions of this research. Personal capacity attributes include knowledge regarding the environmental issue of concern, age, gender, educational level, and income (Stern 2000). Vulnerability as explained above is a function of exposure, sensitivity, and adaptive capacity in relation to a specific harm or climate change impact (IPCC 2001). Attitudinal motives The scientific community has conducted numerous studies scrutinizing the effect of attitudinal motives on public attitudes toward climate change. Many studies found that individuals who are biospherists (value nature), altruists (value community), liberals, democrats, egalitarians, and endorsers of the belief that nature is fragile and limited in its carrying capacity (New Ecological Paradigm, or NEP) are generally more concerned about the impacts of climate change (higher risk perception) and more supportive of climate change mitigation policies than those who are conservatives, egoists (value self), republicans, hierarchists, individualists, and NEP opponents (O’Connor et al. 1999, 2002; Leiserowitz 2003, 2005, 2006; Steg et al. 2005; Slimak and Dietz 2006; Dietz et al. 2007; Shwom et al. 2010; Steg et al. 2011). Additionally, some studies found that beliefs regarding climate change causes and consequences are strong determinants of concern about climate change impacts and levels of mitigation policy support (Krosnick et al. 2006; Jenkins-Smith et al. 2010; Kallbekken and Saelen 2011). Pidgeon and Fischhoff (2011) highlighted the importance of emotions in shaping climate change risk perception and decision making. Within the same category, Kellstedt et al. (2008) showed that perceived self-efficacy (perceived capability of causing change or imposing impact on the progress of a certain problem or issue) sometimes results in more support for climate change policies. But, some other times it results in more trust in the capacity of technology and scientists to ameliorate climate change in the future, and thus, defeats the need for supporting perceived costly climate change mitigation

policies. However, Aitken et al. (2011) found negative correlations between perceived powerlessness (people believing that their behavior will not impact the discourse of climate change), and the commons dilemma (feeling that others will not change their behavior regarding a shared problem, even if they do), and taking action to mitigate climate change. Climate change discourse has been politicized in many countries, most notably in the USA (Borick et al. 2011; Poortinga et al. 2011; Dunpal and McCright 2010; Jamison 2010; McCright and Dunlap 2011a, b; Tobler et al. 2012). Such extreme politicization of the climate change discourse results in Brisk attenuation,^ and thus a Bparalysis^ of the U.S. climate policy over the last two decades (Kasperson and Kasperson 1996; Pielke 2007; Brewer and Pease 2010). Many studies found that political orientation is a strong determinant of believing in the reality of climate change and its anthropogenic causes (Kahan et al. 2011; Jenkins-Smith et al. 2010). The gap between democrats and republicans in terms of believing in climate change reality increased from 4 % in 1997 to 28 % in 2010 (Dunlap and McCright 2008; Borick et al. 2011; Hamilton 2010). Safi et al. (2012) and Zia and Todd (2010) found that political orientation is a strong determinant of risk perception. In this research, we examine the influence of both political orientation and beliefs regarding the anthropogenic causes of climate change on mitigation policy support. Personal capacity motives With regards to the personal capacity category, several studies concluded that knowledge of climate change causes and impacts influences both risk perception and climate change mitigation policy support (Jaeger et al. 1993; O’Connor et al. 1999, 2002). However, different studies reached conflicting findings regarding the influence of demographic attributes such as gender, age, income, and race on supporting climate change mitigation policies, as can be seen in Table 1 (O’Connor et al. 1999, 2002; Zahran et al. 2006; Leiserowitz 2006; Dietz et al. 2007; Semenza et al. 2008; Smith et al. 2014). Davidson and Haan (2011) explained that the role of gender is usually mediated by other factors such as political orientation and beliefs. In our research, we investigate the impact of income, marital status, and education on policy support, albeit as factors that shape adaptive capacity and thus vulnerability to climate change. Vulnerability Few studies have focused on the interrelationship between vulnerability to climate change and mitigation policies support (Weber 2011; Messner and Mayer 2006). The lack of such studies can be attributed to a disciplinary divide that acts as a barrier to communication and integration between

Author's personal copy J Environ Stud Sci Table 1

The influence of demographic variables on mitigation policy support in the literature

Publication

Research population

Significant determinants

Insignificant determinants

O’Connor et al. (1999)

723 Americans

Female (−), education (+), Age (+)

O’Connor et al. (2002)

623 central Pennsylvanians

Education (+)

Age, gender, income

Leiserowitz (2006) Zahran et al. (2006)

673 Americans 1093 Americans

Female (+), non-white (+), education (+) Non-white, education (+), and income (+)

Gender

Dietz et al. (2007) Semenza et al. (2008)

316 respondents in Michigan and Virginia 1202 respondents from Portland, Oregon, and Houston Texas 316 respondents from Michigan and Virginia

Older (+), non-white (+) Younger (+), education (+)

Education, gender, income Gender, race,

Income (+),

Age, education, gender, white

Shwom et al. (2008)

vulnerability and climate policy research domains (Satterfield et al. 2004). The study of vulnerability is prominent within the disciplines of geography, development studies, disaster and climate change research, and environmental sciences; whereas, climate change mitigation policy support research is rooted in psychology, sociology, and political science (Blake 2001; Messner and Mayer 2006, Brikmann et al. 2013). Zahran et al. (2006) investigated the influence of physical vulnerability to a set of climate change impacts on Americans’ support of climate change mitigation policies. The authors noted mixed findings. They reported that people living in areas experiencing significant increases in summer temperatures are more supportive of climate change mitigation policies. However, those who live closer to the coastline (1.6 km or 1 mile radius) and inhabit lower elevation buildings, and thus, are more susceptible to the risk of inundation, are less supportive of climate change mitigation policies. Residing in areas with increased natural hazard casualties does not impact climate change mitigation policy support. In Britain, Spence et al. (2011) and Whitmarsh (2009) studied the impact of physical conditions on attitudes towards climate change. They reached conflicting results. Whitmarsh (2009) found that among the population of Southern England, those who experienced floods are not significantly more involved in activities that mitigate climate change. Spence et al. (2011) found that experiencing floods is a significant determinant of stated willingness to decrease energy use as a climate change mitigation strategy. Spence et al. (2011) partially attributed the difference between their findings and the findings of Whitmarsh to the fact that the UK witnessed many extremely large-scale flooding events in the period that separated the survey work of Whitmarsh in 2003 and their own work in 2010. According to Spence et al. (2011), those events combined with the increased prominence of climate change in the mass media, and the publication of the IPCC Fourth Assessment Report in 2007 may have enhanced a perceived causality between locally experienced flooding and climate change.

As can be seen above, there are arguably a few studies that have investigated the influence of vulnerability on climate change mitigation policy support. This limited number focused only on the physical vulnerability to certain climate change impacts and neglected the other two components of sensitivity and adaptive capacity. Accordingly, our research fills this gap in the literature through investigating the impact of vulnerability as a function of physical vulnerability, adaptive capacity, and sensitivity on climate change mitigation policy support. We also investigate if believing in climate change as a cause of experienced hazards (i.e., Nevada drought) affects public support of climate change mitigation policies. Our research is informed by the Protection Motivation Theory. The Protection Motivation Theory connects the characteristics of hazards and change in affected people’s protective behavior in relation to such hazards. Rogers (1975), the founder of this theory, stipulated that intent to avert certain fear stimuli (hazards) is positively connected to the attributes of this hazard, including its severity and probability (physical vulnerability), in addition to the efficacy of potential aversion strategies. However, this connection is mediated by cognitive processes that formulate both risk and vulnerability appraisals. This means, the more relatively severe and/or of higher probability the hazard, the higher the perceived risk of being affected by this hazard, and thus the higher the chance that affected people will adopt preventive measures at the individual level. In this research, we investigate the impact of physical vulnerability to hazard (climate change imposed drought) as a part of a more the comprehensive vulnerability (as defined above) to that hazard, and how this may or may not translate to climate change mitigation policy support.

Study hypotheses We have hypothesized that increased vulnerability to climate change in terms of physical vulnerability, sensitivity, and adaptive capacity would enhance the acceptance of climate

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change mitigation policies. We have also predicted that both physical vulnerability and sensitivity would increase support of climate change mitigation policies, while adaptive capacity would suppresses such support. The Protection Motivation Theory partially supports our hypothesis. As mentioned above, Rogers (1975) argued that the more severe and/or more frequent the hazard (more physical vulnerability), the more the affected people would be willing to adopt protective behavior. The findings of Spence et al. (2011) and some of the findings of Zahran et al. (2006) also partially support our hypotheses. Observations, as well as sociopolitical and cultural influences on perception explored in our paper, help shape views regarding hazards, and thus, behavior. Based on the literature investigating the influence of values, beliefs, and political orientations on support of climate change mitigation policies, we have hypothesized that being conservative would significantly decrease climate change mitigation policy support; whereas, believing in the anthropogenic causes of climate change would enhance such support. We have also expected that connecting climate change to a locally experienced potential impact (in this case drought) would enhance support of climate change mitigation policies.

Admittedly, the time gap represents a caveat to our data collection methodology. Nonetheless, we used the Student t test to compare the means of participants’ responses and characteristics from the two waves. We found no significant differences between the two waves in terms of age, gender, political orientation, and climate change policy support. Variables Dependent variables The survey contained 45 questions, 11 of which were used for the sake of this research. In one question (Q. 39), we asked the respondents to check whatever number of policies they support from a list of eight climate change mitigation policies and initiatives based on the work of Leiserowitz (2006) and Leiserowitz et al. (2010). The choices included & & &

Methodology Participants We surveyed 1872 ranchers and farmers in Nevada in two waves in December 2009 and August 2010. We collected a list representing the majority of Nevada ranchers and farmers from a partner university program (USDA 2010). In the two waves, we sent mail-out-mail-back survey packets to all ranchers and farmers on the list. Each packet included a survey (same questions and wording in the two phases), a cover letter personally signed by Smith, Jr. being the principal investigator and a prepaid return envelope. In the first wave, the packets included additional holiday cards because it was a holiday season. This analysis is part of a far larger multidisciplinary project that also included the general public and Native American tribes, as well as the creation of a short documentary; to learn more, see Smith Jr. et al (2014) for free access to the article. In total, 479 (321 in the 1st wave and 158 in the 2nd wave) surveys were completed and returned, to make a response rate of 25.6 %. While this method achieved a fair response rate for a notoriously difficult target group, it raised the possibility of having biased results. Such biases stem from the 8-month gap between the two waves and a lack of mechanism to check if some highly motivated participants responded to the survey twice. We find the 2nd reason of bias very unlikely because of the nature of our respected target group combined with the length of the survey (needs 25 min to complete).

& & & & &

Developing renewable energy resources such as wind, solar, and geothermal; Educating the public, including through schools, on human causes of climate change; Taxing corporations/industries that contribute to climate change; Taxing fossil fuels; Taxing citizens for climate change mitigation; Pressuring car companies to produce more fuel-efficient vehicles; Pressuring the U.S. government to ratify international protocols which commit the U.S. to a fast reduction of its emission; and Using market incentives and pollution trading mechanisms to reduce industrial emissions.

To reduce the number of variables, we performed Principal Component Analysis with Varimax rotation. Two factors had eigenvalues more than 1, explaining 59.4 of the variance (Table 2). The first factor represents the policies of developing renewable energy resources, educating the public regarding climate change, pressuring car companies to produce more fuel efficient vehicles, and using market incentives and pollution trading mechanisms to reduce industrial emissions (Cronbach’s α = 0.753; M = 0.43; SD = 0.36). These policies do not include taxation or legal commitments to mitigate climate change; thus, we averaged and combined them in one index called the non-stringent policy support index. The second factor includes the policies of taxing industries, taxing gasoline, taxing citizens, and pressuring the U.S. government to ratify international protocol which commit the USA to a fast reduction of its emission (Cronbach’s α = 0.756; M = 0.11; SD = 0.23). We combined those variables in one index through averaging them and called it the stringent policy support index.

Author's personal copy J Environ Stud Sci Table 2 Factor loadings of the climate change mitigation policy support variables

Variable

Factor 1

Developing renewable energy resources such as wind, solar and geothermal Educating the public, including through schools, on human causes of climate change

0.766 0.665

Factor 2

Taxing corporations/industries that contribute to climate change

0.709

Taxing fossil fuels Taxing citizens for climate change mitigation

0.803 0.688

Pressuring car companies to produce more fuel-efficient vehicles Pressuring the U.S. government to ratify international protocols which commit the U.S. to a fast reduction of its emission Using market incentives and pollution trading mechanisms to reduce industrial emissions

0.765 0.627 0.675

Note: Only loadings higher than 0.50 are displayed

Independent variables For assessing physical vulnerabilities of Nevada ranchers and farmers, we used water resource vulnerability as our indicator. We assessed existing water resource vulnerability in Nevada utilizing Kelly and Adger’ (2000, p. 328) Bwounded soldier^ approach. Unlike other approaches that focus on determining the severity and probability of hazards to take place in the future, the wounded soldier approach focuses on assessing the impact of the existing conditions (injury) on the capacity of systems (individuals or groups) to avoid, cope with and adapt to expected hazards or risks. An area suffering more water stress now most probably will suffer more than other areas when a drought hits their region. We used ArcGIS 9.3 geographic information system software to calculate and map both the inverse Falkenmark Index (population/natural surface water availability) and criticality ratio (water use/natural surface water availability water availability) in Nevada by zip code (Falkenmark and Widstrand 1992; Kulshreshtha 1998; Perveen 2008). We normalized the two indices using the following equation taken from Hahn et al. (2009). Then, we averaged them as an estimate of the water resource vulnerability in Nevada (for more discussion on the maps resulted from this work please see Safi et al. 2012).

Index Sd ¼ ðSd ‐Smin Þ=ðSmax ‐Smin Þ

ð1Þ

We utilized two GIS datasets to estimate the abovementioned indices: water availability and use and population. For water availability and use, we employed the latest version of Water-Global Assessment and Prognosis (WaterGAP 3.1) developed by the Center for Environmental Systems Research, Kassel University, Germany in cooperation with the National Institute of Public Health and the Environment. The spatial resolution of this dataset is 9 × 9 km. We collected the population database (LandScan 2008) from the Oak Ridge National Laboratory, Tennessee. The spatial resolution of this

dataset is 1 × 1 km. Those datasets were reconciled using the spatial join function of ARCGIS. For the sensitivity index, we used the external diversity index as a proxy. For every rancher and farmer, we calculated the proportion of her/his income originating from agricultural activities to the total household income (Hahn et al. 2009). The share of agricultural income to the total income reflects the level of dependence on the water resource for living and thus represents a good, but not all inclusive, proxy for sensitivity to drought. Then we normalized the index using equation (1). Admittedly, there are more factors that affect farmers’ sensitivity to climate change induced impacts drought including the level of diversification of crops and domestic animals (internal diversity index). Those farmers who practice more than one agricultural activity or produce diversified crops or products enjoy having more choices facing at times of droughts than those who rely entirely on one activity or product. Other sensitivity factors such as gender and age were tested as standalone independent predictors of climate change policy support as can be noticed later. For adaptive capacity, we calculated the social status index based on Hollingshead (1975). It ranges from 26 to 66. This index is derived from four factors: occupation, education, income, and marital status (Hollingshead 1975; Cirino et al. 2002). As discussed before, the social status index reflects the social status and resources available for farmers and ranchers that differentiate their capacities to ameliorate the impacts of drought on their families and businesses. We used the education and occupation scores developed by Holingshead (1975). For married ranchers/farmers, we averaged their and their spouses’ education scores. We used the annual income to assign occupation scores to ranchers and farmers, using ranching and farming as the career type. We summed the education and occupation scores for every rancher and farmer, and then we normalized those scores using equation (1) to get the adaptive capacity index. The adaptive capacity index calculated here is a proxy of adaptive capacity rather than being a definitive measure [for more discussion on this please look at Safi et al. (2012)].

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For vulnerability as a composite index, we used the approach of Hahn et al. (2009) in calculating the livelihood vulnerability index ranging from −1 (least vulnerable) to 1 (most vulnerable). This approach operationalizes the fact that both sensitivity and physical vulnerability increase vulnerability to hazards, while adaptive capacity decreases such vulnerability. We used the following equation improved from Hahn et al. (2009): Vulnerability ¼ ðP:V:−Ad:C: þ SeÞ=3

ð2Þ

Model parameters In addition to the vulnerability-related indices, our models included other variables that were measured through a set of survey questions. The variables are ideological orientation (i.e. conservative or not), beliefs regarding the anthropogenic causes of climate change ranging from strongly disagrees to strongly agree with the statement, BI believe that human activity has been playing a significant role in recent climate change,^ and beliefs regarding the possible causality relationship between climate change and the contemporary drought in Nevada (yes or no). Data analysis We performed two sets of hierarchical multiple regression analyses. The first set included the support of non-stringent policies index as a dependent variable and a set of models that hierarchically encompassed political orientations, beliefs regarding the anthropogenic causes of climate change, beliefs regarding the causes of the contemporary drought in Nevada, the composite vulnerability index, and the separate components of vulnerability as independents variables. The second set of regression analyses included the support of stringent policies index as a dependent variable over the same set of models used above.

Results and Discussion A solid majority of our respondents (69.1 %, N = 479) supported developing renewable energy resources (Fig. 1). Only 41.2 % of the respondents supported pressuring car companies to produce more fuel efficient vehicles; fewer respondents (33.2 %) supported educating the public about climate change and its human causes; however, 28.5 % supported using market incentives and pollution trading mechanisms to reduce industrial emissions of CO2. Fewer respondents (16.7 %) supported pressuring the government to ratify international climate change protocols which commit the USA to relatively fast reduction in its emissions. The least popular of all were

taxation policies. Only 15.0 % supported imposing taxes on corporations/industries that contribute to climate change for climate change mitigation. About 9.7 % supported imposing taxes on fossil fuels, and as few as 2.3 % supported taxing citizens. On the other hand, only 16.9 % of the respondents supported doing nothing as a governmental policy facing climate change.

The Determinants of support of climate change mitigation policies Support of non-stringent policies Regressing support of non-stringent mitigation policies on a model that includes political orientation, beliefs regarding the anthropogenic causes of climate change, and beliefs regarding the causal relationship between Nevada drought and climate change explains 45.3 % of variance in support of nonstringent policies (Table 3). The regression analysis shows that conservatives are less supportive of those policies than others; whereas, those who believe in the anthropogenic causes of climate change or connecting Nevada drought to climate change are more supportive of non-stringent climate change mitigation policies. Adding the composite vulnerability index to the model that includes beliefs variables does not add to the explanatory power of the model regarding the variance in support of non-stringent climate change policies. Vulnerability to climate change as a function of physical vulnerability, sensitivity (measured by the External Diversity Index described above), and adaptive capacity (measured as the Social Status Index described above) does not influence support of non-stringent climate change mitigation policies. Believing in the anthropogenic causes of climate change and connecting climate change to the drought in Nevada continued being a very strong determinant of policy support. On the other hand, adding the separate component of vulnerability to the model that includes political orientation, beliefs regarding the anthropogenic causes of climate change and beliefs regarding the causal relationship between Nevada drought and climate change adds 1.2 % to the explanation of variance in support of non-stringent climate change mitigation policies (R2 = 46.5). However, none of the vulnerability components, at least as measured in this research, appears to be a significant determinant of support of the non-stringent climate change mitigation policies. (There will likely be researchers who feel that a wider set of indicators is needed to more authoritatively illuminate sensitivity and adaptive capacity.) The other variables of the model kept their relationships with the non-stringent climate change mitigation policy support as in the previous models.

Author's personal copy J Environ Stud Sci Fig. 1 Nevada ranchers and farmers’ support for various climate change public policies. Blue denotes non-stringent policies and green denotes stringent policies

Support of stringent policies When it comes to support of stringent climate change mitigation policies, regression on political orientation, beliefs regarding the anthropogenic causes of climate change, and beliefs regarding the connection between Nevada drought and climate change explains 47.9 % of variance in support of stringent policies (Table 4). Being conservative decreases support of such policies; whereas, believing that climate change as partially anthropogenic and responsible for the current drought in Nevada enhances such support. Adding vulnerability to climate change to the model that includes political orientation, climate change beliefs, and beliefs regarding the relationship between Nevada drought and climate change does not add any explanatory power of variance. Vulnerability to climate change as a function of

Table 3 Hierarchical regression of support of non-stringent climate change mitigation policies

physical vulnerability, adaptive capacity (measured by the Social Status Index described above), and sensitivity (measured as the External Diversity Index described above) does not impact stringent climate change mitigation policy support. However, adding the separate component of vulnerability to the model that includes political orientation, beliefs regarding the anthropogenic causes of climate change, and beliefs regarding the causal relationship between Nevada drought and climate change adds 1.1 % to the explanation of variance (R2 = 49.0). Sensitivity to climate change or more reliance on agriculture of livelihood decreases support of stringent climate change policies. Both adaptive capacity (measured as the Social Status Index) and physical vulnerability are not significant determinants of stringent climate change mitigation policy support.

Variables

Model 1

Model 2

Model 3

Political orientation Beliefs regarding the anthropogenic causes of climate change Beliefs regarding the relationship between drought and climate change Vulnerability Physical vulnerability Sensitivity Adaptive capacity Variance explained (adjusted R2) N

−0.122*** 0.520*** 0.159***

−0.114*** 0.532*** 0.144***

−0.109** 0.522*** 0.162***

Note: Standardized β were reported in this table **Significant at the 5 % level ***Significant at the 1 % level

−0.028

45.3 % 442

45.2 % 417

0.036 −0.029 0.006 46.5 % 393

Author's personal copy J Environ Stud Sci Table 4 Hierarchical regression of support of stringent climate change mitigation policies

Variables

Model 1

Model 2

Model 3

Political orientation Beliefs regarding the anthropogenic causes of climate change

−0.239*** 0.369***

−0.239*** 0.360***

−0.206*** 0.361***

Beliefs regarding the relationship between drought and climate change Vulnerability

0.276***

0.276***

0.278***

−0.049

Physical vulnerability Sensitivity

0.049 −0.0109*** 47.9 %

47.7 %

−0.04 49.0 %

442

417

393

Adaptive capacity 2

Variance explained (adjusted R ) N Note: Standardized β were reported in this table **Significant at the 5 % level ***Significant at the 1 % level

Discussion Vulnerability and policy support Our analyses show that vulnerability to climate change and its components are insignificant, or at best, weak, determinants of support of climate change policies, whether they are stringent or not. To the authors’ best knowledge, there is no literature investigating the impact of vulnerability, sensitivity, and adaptive capacity on climate change mitigation policy support. However, it is worth mentioning that latent concepts such as vulnerability, adaptive capacity, and sensitivity are complex factors to measure accurately. Those factors were measured using proxies as described above. Accordingly, the generalizations made on those factors need to be used with caution, as being limited to the methods in which those factors were measured. Nonetheless, Zahran et al. (2006), Whitmarsh (2009), and Spence et al. (2011) investigated the impacts of different physical vulnerabilities on supporting climate change mitigation policies. Zahran et al. (2006) found that different types of physical vulnerabilities impact policy support in diverse ways. Our results indicate that living in a more water stressed area is not a determinant of support of climate change mitigation policies, which mimics findings related to living in an area with higher hazardous risk casualties investigated by Zahran et al. (2006). While sensitivity to climate change or reliance on agriculture for livelihood does not influence support of non-stringent climate change mitigation policies, it does decrease (contrary to our hypothesis) support of stringent climate change mitigation policies. It seems that the more the farmers and ranchers are dependent on agriculture for living, the more they are unwilling or unable to accept national policies that require financial sacrifice in the form of taxes or increased prices of goods. These taxes increase the cost of some agricultural

inputs such as fuel, insecticides, and fertilizers and thus may decrease ranchers’ and farmers’ profit and in more extreme cases they (the taxes) might cost them their livelihoods. This explanation agrees with O’Connor et al. (2002) finding that perceiving environmental policies as threatening to job security significantly suppresses climate change policy support. Beliefs Political orientation, beliefs regarding the anthropogenic causes of climate change, and connecting climate change to its potential local impacts (drought) greatly impact support of climate change mitigation polices, both stringent and nonstringent. This is evident in the high variance explanatory power of the models that include those variables and the constant significance of their impact on policy support. These findings agree with the literature on climate change mitigation policy support and beliefs (O’Connor et al. 1999, 2002; Leiserowitz 2003, 2005, 2006; Slimak and Dietz 2006; Dietz et al. 2007; Krosnick et al. 2006; Borick et al. 2011; Poortinga et al. 2011; Dunlap and McCright 2010; McCright and Dunlap 2011a,b; Tobler et al. 2012).

Conclusions Vulnerability to climate change as a function of physical vulnerability, sensitivity, and adaptive capacity (at least as measured in this research) is not a determinant of climate change policy support. Sensitivity or greater reliance on the waterdependent agriculture for living slightly decreases ranchers’ and farmers’ support of climate change mitigation policies that involve taxation and legal bindings and thus affect their livelihoods. Adaptive capacity (or the Social Status Index) and physical vulnerability are both insignificant determinants of climate change policy support. The most important factors

Author's personal copy J Environ Stud Sci

that determine peoples’ policy support within the climate change milieu are their beliefs concerning the causes and local impacts of climate change and their political orientations. Climate change is becoming more and more a value and beliefs laden issue uprooted from its scientific reality, or as called by Pielke (2007) Babortion politics.^ Perhaps this is because the issue has been too identified with a particular political ideology and specific political actors (i.e. Al Gore). Given this troubling momentum, it is difficult to expect effective mitigation policies any time soon. Hamilton (2010) supported this conclusion and stated that the gap between democrats and republicans regarding their beliefs about climate change has increased from 4 % in 1997 to 34 % in 2008. Avoiding a delayed and perhaps ineffective response to climate change demands restoring trust and credibility regarding science and its messengers and avoiding ‘on the hill’ politicization. Acknowledgments This research was generously funded by the National Science foundation (NSF) and the Nevada System of Higher Education (NSHE) (Agreement No. EPS-0814372). Many organizations provided assistance during the pursuit of this research project by either providing secondary data or precious technical support. Those organizations include the Dessert Research Institute (DRI), the University of Nevada Cooperative Extension, the Center for Environmental Systems Research, Kassel University, Germany, and the Oak Ridge National Laboratory in Tennessee. This research would have never been completed without the generosity of Nevada’s ranchers and farmers, who spent considerable time reading, contemplating and completing a survey that formed the backbone of this research project. The first author would like to thank his mother (Fatma Safi) who supported him restlessly in his career and scientific pursuits. He would also thank his wife Fatma and his children for providing love, patience and understanding. Dr. Smith would like to thank those at UNLV who provided a supportive environment and the creative space to complete this and many associated works. He also thanks Dr. Ross Guida and Dr. David Hassenzahl for their input, and his family for their support.

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