An investigation into climate change scepticism among farmers

Journal of Environmental Psychology 34 (2013) 137e150

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Journal of Environmental Psychology journal homepage: www.elsevier.com/locate/jep

An investigation into climate change scepticism among farmers Md. Mofakkarul Islam*, Andrew Barnes 1, Luiza Toma 2 Scotland's Rural College (SRUC) [formerly, Scottish Agricultural College (SAC)], West Mains Road, Edinburgh EH9 3JG, UK

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 6 March 2013

Although climate change is a major challenge facing the world today, a considerable proportion of the general public in the UK and other Western countries have been found to be sceptical of the issue. Given that livestock farming is a major contributor to climate change, this study explored the extent to which climate change scepticism prevailed among Scottish dairy farmers, the factors that affected their scepticism, and the lessons that could be derived for dealing with this challenge. According to Rahmstorf’s (2004) typology of trend, risk and attribution scepticism, appropriate statements were developed and measured on Likert-type scales. The factors that affected these three categories of scepticism were identified by using a Structural Equation Modelling approach. The prevalence of trend and attribution scepticism was quite low among the farmers, but the prevalence of risk scepticism was considerably high. The extent of these scepticisms was significantly affected by farmers’ age, economic status, education, experience with disease and pest infestations, use of media, contacts with agricultural extension consultants, environmental values, and economic values. The effects of these factors on scepticism and the directions of these effects were however different for the three categories of scepticism proposed by Rahmstorf. The theoretical and policy implications of these findings are discussed. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Climate change Scepticism Dairy farmer Scotland Structural Equation Modelling

1. Introduction Climate change is one of the most crucial challenges facing the world today. However, recent studies (Poortinga, Spence, Whitmarsh, Capstick, & Pidgeon, 2011; Whitmarsh, 2011) carried out in the UK indicate that, unlike government policy makers (see BBC, 2006; HM Government, 2008), international bodies (e.g. IPCC, 2007) and the mainstream scientific community (Doran & Zimmerman, 2009), a considerable proportion of the general public have remained sceptical about climate change. Studies carried out in Europe and USA provide a similar picture and report that such incongruent perceptions have increased during the last couple of years (Eurobarometer, 2009; Leiserowitz, Maibach, & Roser-Renouf, 2010). Such observations deserve attention since the public’s attitudes towards climate change have been found to affect their willingness to support mitigation and adaptation initiatives in various developed countries (e.g. see Akter & Bennett, 2011; Akter, Bennett, & Ward, 2012; O’Connor, Bord, & Fisher, 1999; Pidgeon, Lorenzoni, & Poortinga, 2008). However, although several studies have

* Corresponding author. Tel.: þ44 (0)131 535 4418. E-mail addresses: [email protected], [email protected] (Md.M. Islam), [email protected] (A. Barnes), [email protected] (L. Toma). 1 Tel.: þ44 (0)131 535 4042. 2 Tel.: þ44 (0)131 535 4394. 0272-4944/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvp.2013.02.002

investigated climate change scepticism among the “general public” of the UK and other developed countries, none has focused on the “farmers” of these regions. This is despite the recognition in important policy documents, such as the Stern Report and the IPCC report, that agriculture accounts for some 14% of the global Green House Gas (GHG) emissions, that agriculture provides important opportunities to mitigate climate change, and that a failure to tackle climate change will adversely affect global agricultural production and food security (see House of Lords, 2010; IPCC, 2007; Stern et al., 2006). Within agriculture, livestock farming emits significant quantities of GHGs through the production and use of fertilisers, energy use, release of gases from cultivated soil, land use change, and from ruminant digestive processes (FAO, 2010). In 2007, for instance, the global dairy sector alone contributed to 4% of the total global anthropogenic GHG emissions (FAO, 2010). An investigation into livestock farmers’ climate change scepticism is particularly vital for Scotland, where livestock farming is considered to be a key source of GHG emissions and the Scottish government (devolved) has set an ambitious target of reducing the overall GHG emissions by 80% by 2050 through innovations and behavioural change, e.g. by promoting the uptake of low-carbon and energy-saving farming practices (see Pareto Consulting, 2008; Scottish Government, 2009a, 2009b). In this paper, we report the results obtained from a study of climate change scepticism among Scottish dairy farmers. The specific objectives of this work were to: (1) determine the extent

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of climate change scepticism prevailing among the dairy farmers within Scotland, and (2) identify, using a modelling approach, the factors that affected their scepticism. Following this introduction section, in the next section, we have reviewed the literature in order to develop a conceptual framework and research hypotheses for investigating Scottish dairy farmers’ climate change scepticism. In Section 3, we have described the research methods and, in Section 4, provided and discussed the results of our study. In Section 5, we have drawn the key study conclusions and discussed their implications.

2.2. Determinants of climate change scepticism

2. Conceptual overview and research hypotheses

2.2.1. Demographic factors Some of the commonly identified factors affecting people’s environmental psychology are age, education, and economic status. As regards age, it is argued that younger people are more likely to hold pro-environmental attitudes. This may be because of the so called birth cohort effect, which suggests that a particular age group experiences specific historical events in unique ways that affect their attitudes collectively. Thus, adults who grow in an era when environmental issues are readily discussed and debated are more likely to be inclined towards environmentalism (Buttel, 1979). As the recognition and debates about climate change are of recent origin (e.g. IPCC, 2007; Stern et al., 2006), older people are less likely to hold a friendly attitude towards this issue. Another interpretation, called the life cycle effect, maintains that, since younger people are less integrated into society, they tend to be more liberal in their attitudes to new ideas, and therefore are more likely to “support environmental reform and accept pro-environmental ideologies” (Van Liere & Dunlap, 1980, p. 183). Reversely, as older people are more integrated into society and, since solutions to environmental problems threaten the existing social order (Van Liere & Dunlap, 1980, p. 183), older people are less likely to support such changes. However, although some recent studies on climate change scepticism tend to support this premise, others do not. For example, whilst Poortinga et al. (2011) found that older individuals were more likely to be climate sceptics, another study (COI, 2008) found the prevalence of climate scepticism among people as young as 11e17 years old. Studies on farmers’ climate change perceptions (e.g. Deressa, Hassan, & Ringler, 2011; Maddison, 2006) found positive effects of age on climate change perceptions. While, this potentially implies that older people are more likely to perceive climate change and therefore are less likely to deny its occurrence, such an explanation is difficult to hold as the studies did not explicitly focus on scepticism. It is argued that formal education should have a positive effect on environmental attitudes, since education is likely to promote awareness and understanding of environmental issues, which are often complex (Van Liere & Dunlap, 1980). It is observed that environmental studies and environmental science are increasingly being incorporated into school curricula (Rickinson, 2001). Therefore, people who have more formal education are more likely to be aware of and knowledgeable about the scientific consensus on climate change. However, empirical evidence in this regard has been inconsistent. For instance, in a 2008 survey carried out in the UK, Whitmarsh (2011) found scepticism to decline with higher educational levels, but the same author found an opposite result in an earlier survey carried out in 2003. Moreover, studies on farmers’ climate change perceptions (Deressa et al., 2011; Maddison, 2006) found no significant effect of formal education. With regard to economic status, the so called economic contingency hypothesis (Buttel, 1975) suggests that, when economic conditions worsen or at least perceived as worsening, those who are economically disadvantaged will bother less about environmental issues and give priority to economic goals. A similar argument can also be found in Maslow’s (1970) theory of need

2.1. Defining climate change scepticism In the context of climate change, the term sceptic is used to refer to people who deny climate change and whose views are incongruent with scientific consensus on climate change. In the literature, the term scepticism is used synonymously with denialism, contrarianism, and cynicism (Poortinga et al., 2011). In recent times, however, there has been recognition that the term scepticism is not a unitary construct (Akter et al., 2012; Poortinga et al., 2011; Rahmstorf, 2004). Rahmstorf (2004), for example, identifies three types of climate scepticism found among the general public: trend scepticism, attribution scepticism, and impact scepticism. Trend sceptics are people who deny the very existence of climate change. Attribution sceptics, although acknowledge the occurrence of climate change, do not believe that the reasons are anthropogenic. Impact sceptics, on the other hand, agree with the idea that the world’s climate is changing because of anthropogenic factors, but decline to accept that such changes pose significant risks. Such a typology indicates that a person may not be sceptical about all aspects of climate change. For instance, studies on climate change scepticism among the British public (BBC World Service, 2007; DEFRA, 2002; Downing & Ballantyne, 2007; Poortinga et al., 2011; Whitmarsh, 2011) more or less consistently found a small minority (15e18%) as trend and attribution sceptics, but a much higher proportion (around 40e50%) as risk sceptics (Whitmarsh, 2009). These studies have also shown that the prevalence of risk scepticism was higher in the UK compared to other European countries, although not as high as in the USA (Leiserowitz et al., 2010). This suggests the possibility that people may not have coherent beliefs about climate change generally but may instead pick and choose the various dimensions of climate change. However, it is yet to be known how this manifests in the case of farmers. It is also recognised in the relevant literature that people’s disagreements with climate change may have different levels. For instance, it is argued that, whilst the term scepticism refers to strongly held disbelief about climate change, another term uncertainty refers to a lower subjective sense of conviction regarding the trend, attribution and risks of climate change (Poortinga et al., 2011). Similarly, another concept e ambivalence e is used to refer to feelings, attitudes or beliefs about climate change that are in tension with one another, that is, people who are ambivalent tend to evaluate the same issue both positively and negatively at the same time (Poortinga et al., 2011). However, it is recognised that clear distinctions between the different attitudinal terms are difficult to draw, both in their everyday expressions and in empirical studies (Poortinga et al., 2011). This implies that the same individual may have different levels of disagreement with climate change, including uncertainty, ambivalence and scepticism. This again raises the complexity of labelling someone plainly as climate sceptic or non-sceptic e an issue that very few studies have addressed.

We are not aware of any theory that has been developed specifically to explain the determinants of farmers’ climate change scepticism. Neither are we aware of any empirical study on farmers’ climate change scepticism. However, a number of theories in the broader discipline of environmental psychology and the relevant literature on climate change suggest that the factors that could potentially determine farmers’ climate change scepticism could be grouped under the following categories.

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hierarchy, which considers economic needs as more powerful drivers than aesthetic (higher level) needs, such as concerns for environmental issues. A recent study confirmed that a decline in beliefs about climate change in developed countries was driven by economic insecurity caused by the Great Recession (Scruggs & Benegal, 2012). Other studies on climate change scepticism however have produced inconsistent results. For instance, whilst Poortinga et al. (2011) found that British people who belonged to the working class and at the lowest level of subsistence were more likely to be climate sceptics, Whitmarsh (2011) found that people with higher household incomes were far more likely to be sceptical. Similarly, in agriculture, while Maddison (2006) found that subsistence farmers (implies a lower economic status) were more likely to perceive climate changes, Deressa et al. (2011) found that farmers with higher income were more likely to do so. A possible reason why richer people might deny climate change is that, because they can afford more goods and services, and lead a luxury lifestyle based on high-energy consumptions, they are likely to lose more from changes to low-carbon lifestyles. A Swedish study found the anxiety to change material comfort and high-energy dependent lifestyle as a reason for climate change denial (Stoll-Kleemann, O’Riordan, & Jaeger, 2001). Moreover, wealthier individuals are better able to insulate themselves from the effects of environmental risks due to their ability to afford insurance premiums and the costs of moving to, and living in, low-risk areas (Pryce, Chen, & Galster, 2011). 2.2.2. Direct personal experience Learning theories (Chawla, 1999; Kolb & Fry, 1975) suggest that attitudes develop (change) not only from formal education but also from people’s direct personal experiences. There is a substantial body of literature that supports this proposition, especially in the context of risk perceptions. For instance, the theories of availability and affect heuristics posit that the perceived likelihood of a risk increases if it can be readily imagined or has been experienced in the past (Slovic, Finucane, Peters, & MacGregor, 2002; Slovic, Finucane, Peters, & MacGregor, 2004; Tversky & Kahneman, 1973). Individuals use these memories and experiences as filters or heuristics to evaluate and prioritise everyday risks (Whitmarsh, 2008). For the same reason, people who have directly experienced flood will be more likely to perceive it as a risk (Hansson, Noulles, & Bellovich, 1982; Payne & Pigram, 1981). Experiment-based studies using outdoor temperature and heat primes as treatments found positive effects of these experiences on people’s beliefs in global warming (Joireman, Truelove, & Duell, 2010). An English study (Whitmarsh, 2008) found that public’s experience with air pollution significantly affected their perceptions of the risks of climate change. However, the same study did not find any such effect on flood victims. This implies that the experiences that invoke risk perceptions are likely to be different for different groups. The risks for agriculture that are commonly ascribed to climate change include: decreased productivity, increased disease and pests, degradation of key ecosystem services (e.g. nutrient balance, water quality, biodiversity, etc.), and depletion of animal stocks (FAO, 2007; Ziervogel & Ericksen, 2010). This, in theory, suggests that the farmers who have experienced these adverse conditions will less likely to be climate sceptics. 2.2.3. Information and communication The primacy of information and communication on people’s attitudes are widely recognised. In Beck’s theory of risk society, for instance, mass media is viewed as playing a crucial role in the processes of risk revelation, the social contestation that surrounds scientific knowledge of risk, and processes of social challenge to risk society (Cottle, 1998). Such a role of mass media is also widely

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recognised in the climate change literature (American Psychological Association, 2010; Antilla, 2005; Gavin & Marshall, 2011; Weber, 2010). This is primarily because, as Weber (2010, p. 333) argues, climate change is largely a “statistical phenomenon” that describes changes in aggregate weather conditions over a long period of time. This makes it difficult for people to comprehend climate change from experience and observation, since observation is time-bound and memory of past events can be faulty (Weber, 2010). However, mass media e including, newspapers, television, and web e have been found to represent climate sceptic views to a great extent in the UK as well as other developed countries (Antilla, 2005; Gavin & Marshall, 2011; Poortinga et al., 2011). This means that people who have higher contacts with mass media are more likely to be climate sceptics. A recent experimental study in the UK (Corner, Whitmarsh, & Xenias, 2012) found subjects becoming more sceptical after reading two newspaper editorials that made opposing claims about climate change. The effects of information and communication on farmers’ attitudes and behaviour are also widely recognised in the literature. In addition to mass media, however, agricultural extension3 agents or consultants are key information sources for farmers in Europe and USA (Demiryurek, Erdem, Ceyhan, Atasever, & Uysal, 2008; Jensen, English, & Menard, 2009; Rolls, Slavik, & Miller, 1999). These sources have been found to have positive effects on farmers’ climate change perceptions in developing countries (Deressa et al., 2011; Maddison, 2006). These findings potentially imply that farmers who have higher contacts with agricultural extension or consultancy service providers are less likely to be climate sceptics. The literature also suggests that farmers’ personal and demographic characteristics often affect their use of various types of information and communication sources. For instance, studies in USA (Jensen et al., 2009) and Turkey (Demiryurek et al., 2008) found livestock farmers’ age to have negative effects on their likelihood of using mass media, Internet, and extension agents, whilst formal education and income to have positive effects. This suggests the possibility that although there may be many sources through which dairy farmers could receive information about climate change; these sources might have little effects on older, less educated, and poorer farmers because of their limited use of the sources. 2.2.4. Personal values According to Schwartz (1999) values are ‘‘conceptions of the desirable that guide the way social actors (e.g., organisational leaders, policy makers, individual persons) select actions, evaluate people and events, and explain their actions and evaluations’’ (p. 24). They are considered to be the antecedents of environmental beliefs, as espoused in the values-beliefs-norms theory (Stern, Dietz, Abel, Guagnano, & Kalof, 1999). The relationships between values and environmental concerns, attitudes, and behaviour are well-studied. According to Stern and colleagues (Stern & Dietz, 1994; Stern, Dietz, & Kalof, 1993) and Merchant (1992), in general, there are three types of values that affect people’s environmental psychology and behaviour: self-interest, humanistic or social altruism (e.g. caring for the larger community, possibly the humanity as a whole), and biospheric altruism (e.g. caring for other species). The latter two types can be merged as “altruism” since many empirical studies did not find respondents

3 The terms “Extension Services” and “Advisory Services” are used synonymously to refer to the provisions of information, advice, and training for farmers. The former term is used by USA and Canada and the latter by UK and other European countries. Extension or advisory services are provided either by the state or the private sector or a combination of both (see Swanson & Rajalahti, 2010 for details).

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always making a distinction between them (Dietz, Fitzgerald, & Shwom, 2005). This categorisation of values is very similar to that of self-enhancement (self-interest) and self-transcendence (altruism) values in Schwartz’s Value Inventory, which, according to Dietz et al. (2005) is the “most commonly used” tool to measure values (p. 347). Although the actual statements and measures vary across studies, the key themes underpinning self-enhancement values are wealth, material, money, authority and influence, etc., whilst those characterising self-transcendence values include protecting the environment, preserving nature, respecting the earth, harmony with other species, social justice, equality, and so on (Dietz et al., 2005, p. 351). Regardless of the various categorisations and measures, there is an apparent agreement in the literature that selfinterest (self-transcendence) and altruism (social or biospheric) are incompatible and contrasting values. A similar dichotomy of values is also found in the theories of Dominant Social Paradigm (DSP) (Pirages & Ehrlich, 1974) and New Environmental Paradigm (NEP) (Dunlap & Van Liere, 1978). Whilst, the former is underpinned by the core values of economic growth and domination of nature, the latter incorporates values of environmental conservation (Beus & Dunlap, 1990). Beus and Dunlap (1990) applied the DSP and NEP lenses to analyse the values or worldviews underpinning contemporary agriculture, which, according to the authors, reflect two “opposing” paradigms e conventional agricultural paradigm and alternative or ecologically sustainable agricultural paradigm. Whilst conventional agriculture reflects values such as “competition and self-interest”, sustainable agriculture incorporates values of “cooperation and community”. Similarly, whilst conventional agriculture is based on “domination of nature”, sustainable agriculture is underpinned by “harmony with nature” (Beus & Dunlap, 1990, p. 598). Empirically, in general, altruism (social and/or biospheric), selftranscendence, and NEP values are consistently found as significant predictors of pro-environmental concerns, attitudes, and behaviour (Karp, 1996; Nilsson, von Borgstede, & Biel, 2004; Schultz & Zelezny, 1998; Slimak & Dietz, 2006). On the other hand, self-interest is found to have negative or no effects (Karp, 1996; Poortinga, Steg, & Vlek, 2004; Slimak & Dietz, 2006). Similar results were found in recent studies of climate change scepticism in the UK that indicate that people with lowest environmental and self-transcendence values were likely to be the most sceptical of climate change, and vice-versa (Poortinga et al., 2011; Whitmarsh, 2011). Although the influence of these apparently conflicting values on farmers’ climate change attitudes is yet to be investigated, it is reasonable to hypothesise that, since climate change is largely an environmental issue, farmers having higher environmental values will be less sceptical of climate change, whilst farmers with higher economic values will be more likely to deny climate change. The literature also indicates that values may act as mediators between people’s demographic and social-structural characteristics e such as age, education, and income e and their environmental psychology and behaviour (Arcury & Christianson, 1990; Dunlap, Van Liere, Mertig, & Jones, 2000; Slimak & Dietz, 2006). For example, in general, it is observed that people with proenvironmental values are likely to be wealthier, younger, and more educated (Arcury & Christianson, 1990; Dunlap & Van Liere, 1978). It is also implied in the literature that values may mediate the relationships between mass media use and people’s environmental psychology and behaviour. This is because mass media influence is seen by some (e.g. Inglehart, 1977) as a major cause of the emergency of post-materialist values (e.g. caring about clean and healthy environment, freedom of speech, national order, etc.) in developed countries, although other authors using the cultivation theory find mass media to promote selfish materialist or consumerist values that undermine people’s environmental concerns

(Gerbner, Gross, Morgan, & Signorielli, 1986; Good, 2007; Paek & Zhongdang, 2004). In the context of this study, the above arguments imply that, farmers who are older, less educated, and belong to lower income groups will have lower environmental value orientations (or higher economic value orientations) and, because of this, they will be more sceptical about climate change. Moreover, farmers’ mass media exposure will have either negative or positive effects on their personal values, which, in turn, will affect their climate change scepticism. 2.3. Research hypotheses The hypotheses tested in this study are shown in Table 1. It was hypothesised that farmers’ age would have a positive effect (þ) on their scepticism, education would have a negative () effect and economic status would have either a positive or a negative effect (þ/). It was also hypothesised that farmers’ firsthand experience with the adverse effects of climate change, such as increased disease and pest infestations, would have a negative effect on their scepticism. As regards information and communication, we hypothesised that farmers’ extent of mass media use would have a positive effect on scepticism, but farmers’ extent of contact with agricultural extension service providers would have an opposite effect. Moreover, according to the value-based theories of environmental concerns and attitudes, we expected farmers’ environmental values to have a negative effect on scepticism, but their self-interest or economic values to have a positive effect. We also hypothesised that farmers’ demographic factors will act as background variables and affect scepticism indirectly via their effects on farmers’ values and the use of information and communication sources. Specifically, we expected that farmers’ education and economic status would have positive effects on environmental values but negative effects on economic values,

Table 1 A hypothesised model of the factors affecting dairy farmers’ climate change scepticism. Variables

Directions

Variables

Expected effects

Age Age Age Age

/ / / /

Positive Negative Positive Negative

Age Education Education Education Education

/ / / / /

Education Economic status

/ /

Scepticism Environmental values Economic values Contact with extension agents Use of mass media Scepticism Environmental values Economic values Contact with extension workers Use of mass media Scepticism

Economic status Economic status Economic status

/ / /

Economic status Personal experience with disease & pests Use of mass media Use of mass media

/ /

Contact with extension workers Environmental values Economic values

Negative Negative Positive Negative Positive Positive Positive/ negative Positive Negative Positive

Environmental values Economic values Contact with extension workers Use of mass media Scepticism

Positive Negative

/

Scepticism Environmental values Economic values Scepticism

Positive Positive/ negative Negative

/ /

Scepticism Scepticism

Negative Positive

/ /

Note: italicised paths refer to expected indirect effects.

M.M. Islam et al. / Journal of Environmental Psychology 34 (2013) 137e150

whilst age would have the reverse effects. Furthermore, farmers’ age would have negative and education and economic status would have positive effects on mass media use and extension contacts. Similarly, we hypothesised that the effects of mass media use on scepticism would be indirect and mediated by farmers’ values. In this connection, we expected either a positive or a negative effect of mass media use on farmers’ environmental and economic value orientations. 3. Research methods 3.1. Data and sample This study is based on the data collected during a 2009 telephone survey of 533 “specialist dairy” farmers in Scotland drawn from a population of 1651 farmers from the June Agricultural Census database. A specialist dairy was the one with two thirds of its income coming from dairy production. Some other selected characteristics of the sample are provided in Table 2. The majority of the farmers were located in the South-West of the country due to naturally conducive biophysical conditions. Although this survey did not cover non-specialist holdings, such as on beef enterprises, specialist dairying captures the majority of dairy cows under management in Scotland and, provides the basis for targeting policy interventions at an industry level. 3.2. Instrument and variables A questionnaire was constructed that aimed to gather information on farmers’ climate change scepticism, demographic characteristics, personal experiences, contact with information and communication sources, and personal values. Responses to the questions were measured using appropriate statements and scales (Tables 3 and 4). These statements were compiled from discussion with farmers, environmental farm advisors, and dairy farming experts operating within the then Scottish Agricultural College (SAC). The survey also included questions related to future insurance requirements, adoption of technologies, responsibility towards GHG emissions as well as resource use efficiency and general business planning that are not reported in this paper. In line with Rahmstorf’s (2004) framework, three types of scepticism were assessed. Farmers’ trend scepticism was measured by their degree of agreement and disagreement with two statements and for risk scepticism with three statements (see Table 3). The responses were recorded on a five point Likert-type scale, ranging from Strongly Agree (1), Agree (2), Unsure (3), Disagree (4), and Strongly Disagree (5). The scale was considered to be ordinal, moving from non-scepticism, ambivalence, to scepticism. This means that the farmer who “strongly agreed or agreed” with say, the likely rise in global temperature, was considered to be a nonsceptic, whilst one who “strongly disagreed or disagreed” with such a statement was considered to be a sceptic (or denialist). In between these two extremes were the ambivalent individuals who were unsure about their responses. Farmers’ attribution scepticism was assessed by asking their views on eight of the commonly identified sources of GHG emissions, including those associated with the dairy industry (see Table 3). The views of the respondents on these causes were measured on a four point scale, ranging from Don’t Know (0), Major Cause (1), Minor Cause (2), and Not a Cause At All (3). This scale was also considered to be ordinal. A respondent who perceived the sources as “major causes” was considered to be a non-sceptic and the person who perceived that the sources were “not a cause at all” was considered to be a sceptic. The ambivalent (somewhat sceptic) farmers were considered to fall between these two extremes who

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Table 2 Selected demographic profiles of the dairy farmers. Demographic characteristics

Frequency

Percentage

Age in years Up to 25 26e35 36e50 51e65 Over 65

2 34 251 194 52

0.4 6.4 47.1 36.4 9.8

Education Primary Secondary College University or higher

7 212 258 56

1.3 39.8 48.4 10.5

Farm size in hectare Up to 80 81e120 121e160 161e200 >200

152 139 96 54 87

28.5 26.1 18.0 10.1 16.3

Gross margin Up to £50,000 £50,001e100,000 £10,001e150,000 £150,001e200,000 >£200,000

39 133 167 104 90

7.3 25.0 31.3 19.5 16.9

Total cattle number Up to 85 86e170 171e340 341e425 >425

18 71 231 100 113

3.4 13.3 43.3 18.8 21.2

considered the sources as “minor causes”. The attribution scale differed from the trend and risk scales because initial discussions with relevant stakeholders4 led to a realisation that many farmers might not be aware of the anthropogenic sources of GHG emissions commonly identified in the scientific literature. If this was the case, then it would have been meaningless to inquire whether or not the farmers concerned were sceptical of the sources. Accordingly, a “don’t know” response category was included in the scale and all these responses were dropped from subsequent analyses (Table 3). The initial discussions also led to a decision that a major cause to not a cause at all scale, rather than a strongly agree to strongly disagree scale, would be more suitable for capturing farmers’ responses to the attribution items. The statements and scales used in measuring the predictors of scepticism e including, farmers’ demographic characteristics, personal experience with disease and pest infestations, use of information and communication sources, environmental values, and economic values e are provided in Table 4. 3.3. Data analysis For research objective 1, that is, to determine the extent of scepticism prevailing among the dairy farmers, the data were analysed in terms of frequency counts and percentages, and for objective 2, that is, to identify the factors affecting farmers’ scepticism, a Structural Equation Modelling (SEM) approach was used. A SEM model depicts directional (regression paths) and non-directional

4 As already mentioned earlier, the statements and scales were developed in consultation with farmers, environmental farm advisors, and dairy farming experts operating within the Scottish Agricultural College (SAC).

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Table 3 Descriptive statistics of the scepticism-related latent variables and their validity and reliability estimates. Latent variables

Indicators (codes)

Scale (scores)

Mean score

Std. dev.

Factor loadings

Variance explained

Alpha reliability

Trendscep

Climate change is an important environmental issue (Trend 1). It is likely that average annual temperatures will increase in the future (Trend 2). Climate change will lead to increasing productivity losses due to disease and pests (Risk1). Uncertainty due to climate change seriously affects my ability to invest in my business (Risk2). The threats from climate change forces me to reassess my business objectives (Risk3). Causes of GHG emissions/climate change: pollution from other industries (Attrib1) Pollution from cars (Attrib2) Pollution from gas/electric generators (Attrib3) Pollution from consumer activities (travel etc.) (Attrib4) Destruction of tropical forests (Attrib5) Manufacturing and use of fertilisers (Attrib6) Methane from cows and storage (Attrib7) Manufacturing and use of dairy feeds (Attrib8)

Strongly disagree (5), disagree (4), unsure (3), agree (2), strong agree (1) Ditto

2.364

1.169

0.853

72.76%

0.624

2.554

1.065

0.853

Ditto

2.638

1.072

0.581

60.11%

0.660

Ditto

3.569

1.161

0.860

Ditto

3.480

1.161

0.853

Major cause (1), minor cause (2), not a cause at all (3), don’t know (0)a Ditto Ditto Ditto

1.396

0.755

0.747

56.07%

0.886

1.372 1.549 1.415

0.708 0.813 0.714

0.765 0.770 0.791

Ditto Ditto Ditto Ditto

1.167 1.709 1.835 2.013

0.669 0.802 0.831 0.805

0.743 0.754 0.701 0.716

Riskscep

Attribscep

a All “don’t know” responses (Attrib1e5: 5.4e7.1%; Attrib6: 11.8%; Attrib7: 18.9%; Attrib8: 24%) were dropped from subsequent analyses as a “don’t know” response means that the person has not formed an opinion yet.

Table 4 Descriptive statistics of the predictor latent variables and their validity and reliability estimates. Latent variables

Indicators (codes)

Scale (scores)

Mean score

Std. dev.

Factor loadings

Variance explained

Alpha reliability

Ages

Age in years

3.49

0.772

1.00

100%

1.00

Educs

Education

2.68

0.674

1.00

100%

1.00

Econstat

Standard gross margin (Econ1)

18e25 (1), 26e35 (2), 36e50 (3), 51e65 (4), Over 65 (5) Primary (1), secondary (2), college (3), univ./higher (4) Up to £50,000 (1); £50,001e100,000 (2); £100,001e150,000 (3); £150,001e 200,000 (4); >£200,000 (5) Up to 80 ha (1), 81e120 ha (2), 121e160 ha (3), 161e200 ha (4), >200 ha (5) Up to 85 (1), 86e170 (2), 171e340 (3), 341e425 (4), >425 (5) Refused (0), £1e60,000 (1), £60,001e 129,000 (2), £130,00 & above (3) Not affected (1); slightly affected (2); much affected (3) Ditto Never (1); occasionally (2); regularly (3); very frequently (4) Ditto Ditto

3.14

1.182

0.925

2.59

1.419

0.751

3.41

1.066

0.889

1.9662

0.7576

0.755

1.5216

0.6268

1.6679 1.82

Land area in hectare (Econ2)

Herd size, i.e. total animals (Econ3) Quota sizea (Econ4) Persexp

Extcont (agricultural consultants)

Media (agricultural press, radio, TV, etc.)

Envval

Econval

a

Increase in pests in past 10 years (Persexp1) Increase in diseases in past 10 years (Persexp2) Contact for info on environmental issues (Ext1) Contact for info on dairy policy (Ext2) Contact for day-to-day management decisions (Ext3) Contact for medium-long-term business strategy (Ext4) Consult for info on environmental issues (Media1) Consult for info on dairy policy (Media2) Consult for day-to-day management decisions (Media3) Consult for medium to long-term business strategy (Media4) It is important for me to farm as environmentally as possible (Envval1). It is important for me to leave the land as good as or better than I received it (Envval2). Encouraging wildlife, planting trees and protecting the water supply is important to me (Envval3). I aim to get the best market prices for my milk (Econval1). It is important for me to keep my debt as low as possible (Econval2). For me it is important to make the largest possible profit (Econval3).

69.51%

0.835

0.863

74.42%

0.655

0.6775 0.723

0.863 0.775

76.05%

0.894

1.90 1.80

0.769 0.766

0.904 0.878

Ditto

1.79

0.723

0.924

Ditto

2.72

1.018

0.843

82.80%

0.930

Ditto Ditto

2.87 2.69

0.946 1.013

0.925 0.931

Ditto

2.67

1.015

0.937

Strongly disagree (1), disagree (2), unsure (3), agree (4), strongly agree (5) Ditto

4.17

0.874

0.785

53.71%

0.551

4.71

0.508

0.660

Ditto

4.16

0.984

0.748

Strongly disagree (1), disagree (2), unsure (3), agree (4), strongly agree (5) Ditto

4.63

0.684

0.648

47.00%

0.435

4.57

0.821

0.739

Ditto

4.35

0.820

0.666

Milk quota allocated under the Common Agricultural Policy (CAP) of the European Union.

M.M. Islam et al. / Journal of Environmental Psychology 34 (2013) 137e150

The structural equation model : h ¼ Bh þ Gx þ z

(1)

The measurement model for y : y ¼ Ly h þ 3

(2)

The measurement model for x : x ¼ Lx x þ d

(3)

where: h is an m  1 random vector of endogenous latent variables; x is an n  1 random vector of exogenous latent variables; B is an m  m matrix of coefficients of the h variables in the structural model; G is an m  n matrix of coefficients of the x variables in the structural model; z is an m  1 vector of equation errors (random disturbances) in the structural model; y is a p  1 vector of endogenous variables; x is a q  1 vector of predictors or exogenous variables; Ly is a p  m matrix of coefficients of the regression of y on h; Lx is a q  n matrix of coefficients of the regression of x on x ; 3 is a p  1 vector of measurement errors in y; d is a q  1 vector of measurement errors in x. The estimation of the measurement model involved confirmatory factor analysis, where latent variables represented shared variance. The results are presented in Tables 3 and 4 and, as can be seen, all of the latent variables extracted have acceptable validity and reliability estimates. Following the construction of the measurement models, three structural models were developed using the Maximum Likelihood (ML) method with the statistical package Lisrel 8.80 (Jöreskog & Sörbom, 2007). The structural models tested the direct and indirect (mediated) causal relationships between the three scepticismrelated latent variables and their predictors. According to Joreskog’s (1993) suggestion, a flexible model-generating approach, rather than a strictly confirmatory approach, was adopted. Accordingly, a few new regression paths were added to the initial model proposed in Table 1 in order to improve model fit. The overall fit of the models were assessed based on the Normed Chi-Square (chi-square/degree of freedom), Root Mean Square Error Approximation (RMSEA), Goodness of Fit Index (GFI), Incremental Fit Index (IFI), Comparative Fit Index (CFI), and Relative Fit Index (RFI), as suggested by various authors (Bollen, 1989; Kaplan, 2009). A value of 3.0 for Normed Chi-Square, a value of 0.08 for RMSEA, and a value of 90% for GFI, IFI, CFI, and RFI were considered as good or adequate fits (Kaplan, 2009). 4. Results and discussion 4.1. Climate change scepticism The results presented in Fig. 1 indicate that only a small proportion (about 16%) of the farmers were sceptical, over 47e58% were non-sceptical and over a quarter to one-third were ambivalent (unsure) about the two trend items.

Climate change is important issue

Annual temp is likely to rise

40 35

Percentage

30 25 20 15 10 5 0 strongly agree

agree

unsure

disagree

strongly disagree

Fig. 1. Distribution of farmers according to trend scepticism.

As regards risk scepticism, over 17% of the farmers were sceptical of the likely increase in disease and pest infestations due to climate change and the figures were well over 50% about the likely risks of climate change on farmers’ businesses (Fig. 2). This implies that a considerable majority of the farmers do not believe that climate change poses any direct risk to their livelihood. Similar to trend scepticism, however, around one-fifth to one-third of the farmers were ambivalent about climate change risks. Concerning attribution scepticism, we see that the proportion of farmers who denied the various anthropogenic sources of GHG emissions ranged from over 5% to 24% (Fig. 3). However, while the proportion of climate sceptics ranged from 5.4% to 8.6% for emissions from other industries, cars, gas/electric generators, consumer activities, and destruction of tropical forests, the proportion of sceptics ranged from 11.8% to 24% for manufacturing and use of fertilisers, methane from cows and manure storage, and manufacturing and use of dairy feeds. It is noteworthy that the latter three sources of emissions are directly related to dairy production activities (FAO, 2010). This means that the proportion of dairy farmers who were sceptical in attributing their own industry activities to climate change vis-à-vis the other sources of emissions was higher. Although this study is based on a subset of the British population, the results are more or less consistent with previous studies on climate change scepticism among the British public (BBC World Service, 2007; DEFRA, 2002; Downing & Ballantyne, 2007; Poortinga et al., 2011; Upham et al., 2009; Whitmarsh, 2011) that found a small minority (15e18%) as trend and attribution sceptics, but a much higher proportion (around 40e50%) as risk sceptics (Whitmarsh, 2009). This means that risk scepticism is the most prevalent form of scepticism in the UK. The above findings also indicate the need for treating climate change scepticism as a multi-dimensional construct. As we can see, the dairy farmers were not equally sceptical about all aspects of

Causes productivity loses due to disease & pests Affects ability to invest in business Forces me to reasses business 40 35 30

Percentage

(correlations) linear relationships among a set of indicator and latent variables (Bollen, 1989). Unlike conventional multiple regression methods, SEM is a procedure of “simultaneous regressions” in which a variable that is considered “dependent” in one equation can be considered an “independent” variable in another equation. The method is widely used in deductive or hypothesis-testing research, including the psychological and behavioural aspects of climate change (e.g. Tikir & Lehmann, 2011). The SEM involves two steps, namely the estimation of the measurement model, which specifies how the observed variables (indicators) are dependent on their corresponding latent variables, and the estimation of the structural model, which specifies the causal relationships between the latent variables. The models are defined by the following system of equations in matrix terms (Jöreskog & Sörbom, 2007):

143

25 20 15 10 5 0 strongly agree

agree

unsure

disagree

strongly disagree

Fig. 2. Distribution of farmers according to risk scepticism.

144

M.M. Islam et al. / Journal of Environmental Psychology 34 (2013) 137e150 Pollution-other industries

Pollution-cars

Pollution-gas/electric generators

Pollution-consumer activites

Tropical forest destruction

Fertiliser manufacturing/use

Methane from dairy

Dairy feed manufacturing/use

80 70

Percentage

60 50 40 30 20 10 0

major cause

minor cause

not a cause at all

Fig. 3. Distribution of farmers according to attribution scepticism (causes of GHG emission/climate change).

climate change. This suggests that farmers may not have coherent beliefs about climate change generally but may instead pick and choose its various dimensions. 4.2. Determinants of climate change scepticism 4.2.1. Trend scepticism Model results as provided in Table 5 indicate that, among the three demographic variables entered into the model, farmers’ age and economic status had no significant effects on trend scepticism. However, as hypothesised, education had a small but significant (standardised Beta 0.09; p < 0.05) negative effect, which confirmed that educated farmers were less likely to be climate

sceptics. As argued in the literature (Van Liere & Dunlap, 1980) this might be because of education’s influence on farmers’ awareness and knowledge of environmental issues. As hypothesised, farmers’ personal experience with disease and pest infestations had a negative effect (standardised Beta 0.26; p < 0.001) on scepticism. Consistent with the literature on experiential learning and availability and affect heuristics (Chawla, 1999; Hansson et al., 1982; Joireman et al., 2010; Kolb & Fry, 1975; Payne & Pigram, 1981; Slovic et al., 2002, 2004; Tversky & Kahneman, 1973; Whitmarsh, 2008), this finding means that farmers who had experienced disease and pest infestations to higher extent were less likely to be climate sceptics. This finding suggests that the formation of people’s climate change beliefs may not always be “entirely indirect” or mediated by news coverage and film documentaries, as proposed in the literature (e.g. Weber, 2010). Rather, the biophysical contexts within which people are located can also have significant influences. Of the two variables relating to the use of information and communication sources, farmers’ contact with extension agents had no significant effect, which did not correspond with findings from developing countries (Deressa et al., 2011; Maddison, 2006) regarding the effects of extension contacts on farmers’ climate change perceptions. Moreover, although mass media have been found to represent contrarian views to a great extent (Antilla, 2005; Corner et al., 2012; Gavin & Marshall, 2011; Poortinga et al., 2011), farmers’ use of media had a negative rather than a positive effect (standardised Beta 0.12; p < 0.01) on scepticism. This contradiction may have various interpretations. For example, the farmers included in our study perhaps missed the media news that focused on climate controversies. Conversely, although they noticed the news concerned, they did not perhaps believe in the messages and were persuaded more by the news that promoted the scientific

Table 5 Parameter estimates, fit indices, and variance explained in the trend scepticism model. Latent variables

Regression paths

Latent variables

Total effects (std. beta)

t-Values

Indirect effect (std. beta)

t-Values

Ages Ages Ages Ages Ages Educ Educ Educ Educ Educ Econstat Econstat Econstat Econstat Econstat Persexp Persexp Persexp Persexp Media Media Media Media Extcont Extcont Extcont Envval Econval Econval

/ / / / / / / / / / / / / / / / / / / / / / / / / / / / /

Trend Envval Econval Extcont Media Trend Envval Econval Extcont Media Trend Envval Econval Extcont Media Trend Extcont Envval Econval Trend Envval Econval Extcont Trend Envval Econval Trend Trend Envval

0.05 0.23 0.20 0.08 0.05 L0.09 0.02 0.12 0.19 0.12 0.02 0.03 0.10 0.17 0.10 L0.26 0.14 0.00 0.00 L0.12 0.16 0.11 0.12 L0.09 0.00 0.02 L0.47 L0.24 0.61

0.99 n.s. 4.13*** 4.16*** 2.01* 1.30 n.s. L1.96* 2.74** 2.50* 4.36*** 2.74** 0.39 n.s. 0.62 n.s. 2.09* 3.75*** 2.20* L5.14*** 2.79** 0.02 n.s. 0.40 n.s. L2.59** 3.08** 2.28* 2.66** L1.87 n.s. 0.02 n.s. 0.41 n.s. L5.40*** L4.52*** 7.99***

L0.10

L3.54***

L0.04

L1.28 n.s.

L0.04

L1.41 n.s.

L0.01

L1.61 n.s.

L0.08

L3.12**

0.00

L0.05 n.s.

e L0.29

e L4.61***

Fit indices: Normed Chi-Square (chi-square/degrees of freedom) ¼ 3.25; RMSEA ¼ 0.065; NFI ¼ 0.91; NNFI ¼ 0.92; CFI ¼ 0.94; IFI ¼ 0.94; RFI ¼ 0.89; GFI ¼ 0.90 Total variance explained (R-square) ¼ 0.29 Note: n.s. ¼ non-significant; *p < 0.05; **p < 0.01; ***p < 0.001.

M.M. Islam et al. / Journal of Environmental Psychology 34 (2013) 137e150

consensus on climate change. This non-persuasion or persuasion, in turn, was perhaps affected by many other factors, including farmers’ psychological and personality traits, message framing, credibility of the sources, and various other contextual factors, as espoused in relevant theories (see Ajzen, 1992 for a review of key theories and empirical studies). Such interpretations can however be only tentative at the best, since there are very few, if any, empirical studies on the factors that affect the success or failure of climate change communications. As regards the effects of values, farmers’ environmental values had a strong negative effect on scepticism (standardised Beta 0.47; p < 0.001) which confirmed our hypothesis based on the values-based theories of environmental attitudes and concerns (Beus & Dunlap, 1990; Merchant, 1992; Stern & Dietz, 1994; Stern et al., 1993) and the recent empirical studies on climate change scepticism among the general public in the UK (Poortinga et al., 2011; Whitmarsh, 2011). However, contrary to our hypothesis, farmers’ economic values had a negative rather than a positive effect (standardised Beta 0.24; p < 0.001). It is also noteworthy that farmers’ economic values had a large positive effect (standardised Beta 0.61; p < 0.001) on environmental values. During the estimation process of the model, this regression path had to be introduced in order to improve model fit. This suggests that farmers with higher economic values also had higher environmental values, and therefore, unlike implied in the literature (Beus & Dunlap, 1990), environmental and economic values underpinning farming practices may not always be contrasting. An explanation might be that in Western countries like Scotland, it is increasingly becoming important for farm businesses to respect environmental integrity and uphold environmental values in order to be able to make profits. Therefore, environmental and economic values are perhaps becoming one integral component, leading to the emergence of a Third Paradigm. In our opinion, this new paradigm is reflected in recent discourses of “Sustainable Intensification” that intends to combine the values of conventional agriculture (e.g. intensification and profit maximisation) with the values of alternative agriculture (e.g. environmental sustainability and ethics) (see Garnett & Godfray, 2012; Pretty, Toulmin, & Williams, 2011). SEM results also indicate that farmers’ age had significant positive effects on environmental and economic values, both of which in turn had negative effects on scepticism. These results and the corresponding SEM estimates of “indirect effects” (standardised Beta 0.10; p < 0.001) confirmed that age had an indirect negative effect on farmers’ scepticism through its effects on farmers’ values. However, the positive effect of age on environmental values contradicts with the findings of others (Arcury & Christianson, 1990; Dunlap & Van Liere, 1978). This might be because of variations between our research and those studies in terms of sample characteristics and the indicators and measures of environmental values used. Concerning the indirect effect of education, as expected in line with the literature (Arcury & Christianson, 1990; Dunlap et al., 2000; Slimak & Dietz, 2006), farmers’ education had positive effects on environmental values, and negative effects on economic values. However, the estimate of indirect effect of education on scepticism was statistically insignificant. Although, as hypothesised, farmers’ economic status had a negative effect on economic values, the variable had no effect on environmental values. Moreover, the Beta coefficient of indirect effect between economic status and scepticism was statistically non-significant. These results ruled out the presence of indirect effects of education and economic status on farmers’ scepticism (via values). We also found that farmers’ education and economic status had significant positive effects on media use, which in turn, had negative effects on farmers’ scepticism. However, as already mentioned

145

earlier, the SEM parameter estimates regarding “indirect effects” of education and economic status on scepticism were statistically non-significant. Therefore, the hypothesis regarding a mediatory role of mass media between education and economic status and scepticism could not be accepted. Regarding the indirect effect of media use, the results indicate that farmers’ media use had significant positive effects on both environmental and economic values. The value variables, in turn, had strong negative effects on scepticism. Moreover, SEM estimates of indirect effects of media use were statistically significant (standardised Beta 0.08; p < 0.01). This result confirms our hypothesis that the effects of mass media on scepticism may be indirect and mediated by values. However, the result indicates that mass media increases both post-materialist and materialist values, rather than increasing one value type or the other, as implied in the divergent premises of post-materialism (Inglehart, 1977) and cultivation theory (Gerbner et al., 1986; Good, 2007; Paek & Zhongdang, 2004). As a possible explanation for this finding, we would like to argue again that people’s environmental and economic values may not necessarily be always opposing. Rather, they might be complementary as well. The fit indices of the trend model indicated an “adequate” fit (Kaplan, 2009). Although the Normed Chi-Square was slightly above the cut-off point of 3, the other indices, such as RFI, GFI, CFI, etc. were within the range of 90. However, the model explained only 29% of the observed variance in scepticism. This is not surprising, given that the model did not include many other important variables associated with people’s environmental psychology, such as gender, race, ethnicity, religion, political party affiliation, use of Internet, and so on. 4.2.2. Risk scepticism Similar to the trend model, farmers’ age had no effect on risk scepticism (Table 6). However, quite contrary to the trend model and our hypotheses, farmers’ education had a significant positive effect (standardised Beta 0.14; p < 0.01) on scepticism. No suitable explanation was found in the literature, except that such a finding might be because of variations in the way the variable has been measured (Klineberg, Mckeever, & Rothenbach, 1998). A similar effect was observed for economic status which defied the contingency-based explanation (Buttel, 1975; Scruggs & Benegal, 2012) that because of economic contingencies, such as recent economic downturns, poor people will be more likely to deny climate change than the rich. Rather, the finding might support the premise (Pryce et al., 2011; Stoll-Kleemann et al., 2001) that because of the anxiety to lose a high-energy dependent lifestyle and the financial ability to offset risks, richer farmers will perhaps be more inclined to deny climate change. In the context of this study, this may imply purchasing crop protection insurance and affording disease and pest control measures. Therefore, although wealthier farmers may not necessarily deny the occurrence of climate change, they may disagree that such changes are likely to harm them personally. Similar to the trend model, farmers’ extent of experience with disease and pest infestations had a significant negative effect on risk scepticism, providing support for the explanation found in the risk perception literature (Hansson et al., 1982; Payne & Pigram, 1981; Slovic et al., 2002, 2004; Tversky & Kahneman, 1973; Whitmarsh, 2008) that people with firsthand experience of adverse natural events are less likely to be climate sceptics. Such experiences may be instrumental in invoking a feeling that the risks of climate change are not NIMBY (Not In My Backyard) in nature and this, in turn, can help reduce scepticism. Unlike in the trend model, the effects of media use and extension contacts on risk scepticism were non-significant. This was in

146

M.M. Islam et al. / Journal of Environmental Psychology 34 (2013) 137e150

Table 6 Parameter estimates, fit indices, and variance explained in the risk scepticism model. Latent variables

Regression paths

Latent variables

Total effects (std. beta)

t-Values

Indirect effect (std. beta)

t-Values

Ages Ages Ages Ages Ages Educ Educ Educ Educ Educ Econstat Econstat Econstat Econstat Econstat Persexp Persexp Persexp Persexp Media Media Media Media Extcont Extcont Extcont Envval Econval Econval

/ / / / / / / / / / / / / / / / / / / / / / / / / / / / /

Risk Envval Econval Extcont Media Risk Envval Econval Extcont Media Risk Envval Econval Extcont Media Risk Extcont Envval Econval Risk Envval Econval Extcont Risk Envval Econval Risk Risk Envval

0.00 0.24 0.07 0.10 0.07 0.14 0.01 0.19 0.2 0.12 0.13 0.01 0.05 0.16 0.1 L0.24 0.16 0.01 0 0 0.15 0.11 0.11 L0.02 0.04 0.02 0.05 L0.16 0.54

0.00 n.s. 4.48*** 0.87 n.s. 2.51* 1.76 n.s. 2.79** 0.23 n.s. 3.27** 4.46*** 2.67** 2.50* 0.31 n.s. 0.98 n.s. 3.62*** 2.25* L3.94*** 3.26** 0.87 n.s. 0.31 n.s. L0.09 n.s. 3.47*** 2.19* 2.63** L0.42 n.s. 0.91 n.s. 0.31 n.s. 0.79 n.s. L2.59** 7.13***

0.00

L0.21 n.s.

0.03

1.57 n.s.

0.01

0.56 n.s.

0.00

L0.42 n.s.

L0.02

L1.24 n.s.

0.00

0.12 n.s.

e 0.03

e 0.79 n.s.

Fit indices: Normed Chi-Square (chi-square/degrees of freedom) ¼ 3.14; RMSEA ¼ 0.063; NFI ¼ 0.91; NNFI ¼ 0.92; CFI ¼ 0.94; IFI ¼ 0.94; RFI ¼ 0.89; GFI ¼ 0.90 Total variance explained (R-square) ¼ 0.12 Note: n.s. ¼ non-significant; *p < 0.05; **p < 0.01; ***p < 0.001.

disagreement with the hypothesised effect that we proposed based on a review of the literature (American Psychological Association, 2010; Antilla, 2005; Deressa et al., 2011; Gavin & Marshall, 2011; Maddison, 2006; Weber, 2010). A possible explanation could be that the media and the extension agents perhaps could not provide adequate coverage and/or compelling evidence regarding the ways agricultural emissions (e.g. CH4 and N2O) could harm farmers personally. In addition to this, however, as we have discussed earlier, there may be many other intervening factors relating to farmers’ psychological and personality traits, message framing, credibility of the sources, and various types of contextual factors, as found in the communication literature (see Ajzen, 1992). With regard to the effects of values, farmers’ environmental values had no effects, which were in disagreement with our hypothesis and the findings of previous studies on publics’ climate change scepticism in the UK (Poortinga et al., 2011; Whitmarsh, 2011). Moreover, unlike the propositions underpinning the values-based theories of environmentalism (Beus & Dunlap, 1990; Stern & Dietz, 1994; Stern et al., 1993), farmers’ economic values had a negative rather than a positive effect on risk scepticism. Similar to the explanation provided for the trend model, it can be argued that since environmental and economic values underpinning agriculture are becoming one integral component (which we call the Third Paradigm), people with higher economic value orientations are more likely to be cautious about the risks of climate change. Regarding indirect effects of farmers’ demographic characteristics, it was found that although farmers’ age, education and economic status had significant effects on some of the variables relating to values and the use of media and extent agents, the SEM estimates of indirect effects were insignificant in all instances (Table 6). This ruled out the possibility of indirect effects of age, education, and economic status on farmers’ risk scepticism. A similar result was obtained for the indirect effect of media use.

The fit indices of the risk model were very similar to the trend model (Table 6), but it explained only 12% of the observed variance in scepticism, perhaps because of the non-inclusion of many other important variables, as mentioned in Section 4.2.1. 4.2.3. Attribution scepticism Unlike in the trend and risk models, farmers’ age had a significant and positive effect (standardised Beta 0.15; p < 0.001) on attribution scepticism (Table 7). The finding confirmed the hypothesis we derived based on the age cohort and life cycle theories (Buttel, 1979; Van Liere & Dunlap, 1980). Likewise, farmers’ education and economic status had negative effects on scepticism, which confirmed our hypotheses and corresponded with the propositions in the literature (Buttel, 1979; Van Liere & Dunlap, 1980). Consistent with the trend and risk models, farmers’ personal experience with disease and pest infestations was found to be a significant negative predictor of attribution scepticism. This finding suggests that, farmers who experience, or at least are able to perceive, the adverse effects of climate change e such as disease and pests e will be less likely to deny the anthropogenic causes of climate change. This may be because of observational and experiential learning effects or strong memories associated with disease and pest infestations, as explained in the literature (Hansson et al., 1982; Joireman et al., 2010; Payne & Pigram, 1981; Slovic et al., 2002, 2004; Tversky & Kahneman, 1973; Whitmarsh, 2008). As hypothesised, farmers’ extent of contact with extension agents had a significant negative effect (Beta 0.12; p < 0.01) on attribution scepticism. However, unlike the hypothesis proposed, farmers’ use of mass media had significant negative effects on scepticism. This was unexpected since mass media have been found to provide comprehensive coverage of contrarian views (Antilla, 2005; Gavin & Marshall, 2011; Poortinga et al., 2011) and has proved to be effective in increasing scepticism (Corner et al., 2012).

M.M. Islam et al. / Journal of Environmental Psychology 34 (2013) 137e150

147

Table 7 Parameter estimates, fit indices, and variance explained in the attribution scepticism model. Latent variables

Regression paths

Latent variables

Total effect (std. beta)

t-Values

Indirect effect (std. beta)

t-Values

Ages Ages Ages Ages Ages Educ Educ Educ Educ Educ Econ Econ Econ Econ Econ Persexp Persexp Persexp Persexp Media Media Media Media Extcont Extcont Extcont Envval Econval Econval

/ / / / / / / / / / / / / / / / / / / / / / / / / / / / /

Attrib Envval Econval Extcont Media Attrib Envval Econval Extcont Media Attrib Envval Econval Extcont Media Attrib Extcont Envval Econval Attrib Envval Econval Extcont Attrib Envval Econval Attrib Attrib Envval

0.15 0.17 0.24 0.09 0.14 L0.17 0.03 0.11 0.20 0.09 L0.09 0.01 0.00 0.18 0.08 L0.12 0.09 0.01 0.00 L0.14 0.11 0.10 0.10 L0.12 0.11 0.03 L0.22 L0.04 0.81

3.64*** 3.14** 4.93*** 2.14* 3.27** L3.76*** 0.56 n.s. 2.17* 4.52*** 1.95 n.s. L1.98* 0.12 n.s. 0.00 n.s. 4.07*** 1.72 n.s. L2.97** 2.25* 1.50 n.s. 0.67 n.s. L3.24** 2.20* 2.04* 2.28* L2.71** 2.13* 0.71 n.s. L1.83 n.s. L0.73 n.s. 9.70***

0.00

0.22 n.s.

L0.04

L2.23*

L0.03

L1.83 n.s.

L0.01

L1.74 n.s.

L0.02

L1.59 n.s.

L0.03

L1.51 n.s.

e L0.18

e L1.81 n.s.

Fit indices: Normed Chi-Square (chi-square/degrees of freedom) ¼ 3.99; RMSEA ¼ 0.075; NFI ¼ 0.89; NNFI ¼ 0.89; CFI ¼ 0.91; IFI ¼ 0.91; RFI ¼ 0.86; GFI ¼ 0.85 Total variance explained (R-square) ¼ 0.15 Note: n.s. ¼ non-significant; *p < 0.05; **p < 0.01; ***p < 0.001.

As already mentioned earlier, media use also had significant negative effects on trend scepticism. The reasons for this contradictory finding could have been better understood if studies (e.g. Antilla, 2005; Gavin & Marshall, 2011) on mass media coverage of climate change had categorised the climate-related information according to the three types e trend, risk, and attribution e used in this study. However, several tentative explanations can be forwarded. First, it is noteworthy that the use of media had positive effects on contact with extension agents, meaning that farmers who used media more frequently also had higher contacts with extension agents. This regression path had to be introduced in order to improve model fit. This suggests that contacts with extension agents might have countervailed the contrarian views published or discussed on mass media, since it is well-known that interpersonal contacts have more persuasive effects on farmers (see Rogers, 2003). Second, the persuasive effects of media do not depend on their degree of use alone, but also on other factors (Ajzen, 1992) mentioned earlier. The results also indicate that, although media use and extension contacts had significant effects on values, the value variables had no significant effect on farmers’ attribution scepticism. This indicates that, unlike what could be expected according to the propositions in the literature (Gerbner et al., 1986; Inglehart, 1977), the effects of media and extension contacts were not mediated by values. This might be because of the variations in the type of media investigated in this study. It is also apparent from the attribution model that, although farmers’ age had significant effects on values and media and extension contacts, the variable had no indirect effect on scepticism (Beta ¼ 0.00). As hypothesised, education had a negative effect on economic values and a positive effect on contact with extension agents. Contact with extension agents, on the other hand, had a

negative effect on scepticism. This and the SEM estimate of indirect effect (Table 7) confirmed that education had an indirect effect on scepticism mediated via contact with extension agents. However, similar to age, economic status had no indirect effect on scepticism. Overall, the attribution model had reasonable fits to the data. However, the model explained only 15% of the total variance in scepticism (Table 7). This was quite expected as only a limited number of predictor variables were entered into the model. 5. Conclusions and implications This study aimed to investigate the extent of climate change scepticism among Scottish dairy farmers and the factors that affected their scepticism. Using Rahmstorf’s (2004) typology, this study reveals that, similar to those observed among the general public in the UK, the prevalence of trend and attribution scepticism is not substantial among Scottish dairy farmers, but the prevalence of risk scepticism is. This calls for a greater emphasis on the risks of climate change in communication and engagement strategies. The use of Rahmstorf’s typology also indicates the difficulty of labelling someone plainly as “sceptic” or “non-sceptic”, since the same farmer who is sceptical of one aspect of climate change (e.g. trend) may not be sceptical of the other (e.g. risk). Therefore, treating scepticism as a multi-dimensional construct is crucial, both from academic (e.g. using multiple dimensions of scepticism in research) and policy perspectives (e.g. designing category-specific communication and engagement strategies). Drawing on a range of theoretical perspectives, this study provides evidence that farmers’ climate change scepticism are affected by their demographic characteristics, direct personal experiences with the adverse effects of climate change, use of information and communication sources, and personal values. The use of

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Rahmstorf’s typology, however, indicates that the influence of these factors and their direction of effects vary according to the type of scepticism investigated. Identification of these category-specific predictors of scepticism provides important guidelines for devising effective communication and engagement strategies. For instance, findings regarding the effects of demographic factors indicate that all types of interventions may not be suitable for addressing all types of scepticism. Whilst, raising the educational level of farmers though formal or non-formal means can be useful for reducing trend scepticism, the same intervention may not be suitable (or even necessary) for reducing risk scepticism, since risk scepticism was found to increase with higher educational level. Similarly, while older age (and corresponding consolidation of values) can help reduce trend scepticism, tackling attribution scepticism, in contrast, calls for younger people to be attracted in farming. Likewise, a better targeting of wealthier sections of the farming community may be required for addressing risk scepticism, but such a strategy may be irrelevant for trend and attribution scepticisms. The negative effects of farmers’ direct personal experiences on all three types of scepticism indicate how important it is for communication and engagement strategies to relate climate change with real-life and local farming problems, rather than with abstract and distant hazards, such as melting of glaciers in the Himalayas. Such a finding also indicates that it might be useful to engage the affected farmers in various communication campaigns, e.g. inviting them as guest speakers to share their real-life experiences with other farmers, since farmer-to-farmer communication has been found to influence farmers’ climate change perceptions (Deressa et al., 2011). The significant negative effects of mass media use and extension (advisory) contacts on farmers’ scepticism suggest that these channels could be used as strategic instruments to influence farmers’ climate change beliefs. Moreover, the analyses of “indirect effects” in this study show that, a combination of education and advisory-based interventions can be more effective. However, since raising the level of formal education requires a long-term approach, the use of non-formal education can be an option. An apt example of such a combination of non-formal education and advisory services is found in the widely known Farmer Field School (FFS) intervention championed in Asian countries by the UN-FAO (see Pontius, Dilts, & Bartlett, 2002). The FFS model has recently been adapted to address climate change in many developing countries (FAO, 2011). However, experience shows that the application of such innovative models at country-wide scales requires significant government funding support, which can be a challenge in the UK and other European countries where state advisory services have been underfunded, downsized, and/or privatised since late 1980s (see Rivera, 2008; Swanson & Rajalahti, 2010; Wallace, 1998). In our opinion, since tackling climate change can offer many public goods benefits, e.g. ensuring food security, government investments in media and extension (advisory) services may be justified. However, as this study shows, media and extension “contacts” alone may not be enough to deal with all types of scepticism (e.g. risk) and indepth studies are needed in order to identify other factors e such as farmers’ cognitive and personality traits, source credibility, message framing, and contextual factors e that can potentially affect the success of climate change communication. The application of Rahmstorf’s typology also indicates that, whilst emphasising farmers’ values can be effective for tackling trend and risk scepticisms, this may not be relevant for dealing with attribution scepticism. With regard to values, another important contribution of this study is that it indicates the co-effects of both environmental and economic values on farmers’ scepticism. This means that communication initiatives should go beyond emphasising the altruistic aspects of environmentally sustainable

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