Title · Web view45 SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR 1 SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR

SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR

Environmentally active people: The role of autonomy, relatedness, competence and self-determined motivation

Anna N. Cookea*, Kelly S. Fieldingb and Winnifred R. Louisa

a School of Psychology, The University of Queensland, Brisbane, Australia.

b Institute for Social Science Research, The University of Queensland, Brisbane, Australia.

* corresponding author. Email [email protected], Telephone +61 7 3346 7282. McElwain

Psychology Building, The University of Queensland, St Lucia, Brisbane, Australia 4072.

Anna Cooke is an environmental psychology PhD student in the School of Psychology and Institute for Social Science Research at the University of Queensland. She researches people’s motivation for pro-environmental behaviour. In particular, her research examines the advantages of internalised motivation, and how information about climate change can be communicated in ways that support self-determined motivation.

Kelly S Fielding is a social and environmental psychologist in the Institute for Social Science Research at The University of Queensland. Her research focuses on understanding people’s environmental decisions and behaviour—with a particular focus on identity and norms—and identifying ways to promote more sustainable behaviour in households, organisations and communities.

Winnifred R. Louis (PhD McGill, 2001) is an Associate Professor in Psychology at the University of Queensland. Her research interests focus on the influence of identity and norms on social decision-making. She has studied this broad topic in contexts from political activism to peace psychology to health and the environment.

Acknowledgments

The authors would like to acknowledge helpful comments from four anonymous reviewers,

and research assistance from Stephanie Power.

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mailto:[email protected]


Environmentally active people: Autonomy, relatedness, competence and self-determined

motivation

To identify pathways to lower environmental impacts, this research examined the motivation

and antecedents of motivation (autonomy, relatedness, competence), of environmentally active

people. Previous research suggests that people with more self-determined motivation for pro-

environmental behavior (PEB) should carry out more PEBs, and have lower environmental

impacts, than people whose motivation is more externally regulated. Path analysis in Sample

1 (N=261) confirmed that self-determined motivation was positively related to both easy and

difficult PEB. The more participants judged that their needs for autonomy and relatedness

were met in relation to performing pro-environmental behaviour, the more self-determined

their motivation. Higher perceived relatedness was also directly related to reporting more

engagement in difficult PEB. Perceived competence was not related to self-determined

motivation or PEB. The pattern of results was largely supported when re-tested with a sample

(N=320) who completed a ‘carbon footprint’ measure of environmental impact as well as the

questionnaire completed by Sample 1. In this sample, autonomy, relatedness, and competence

were related to self-determined motivation. The research is the first to our knowledge to

examine and find a relationship between higher self-determined motivation and lower self-

reported environmental impact. These findings point to new approaches to increasing PEB.

Keywords: self-determination, autonomy, relatedness, competence, pro-environmental,

behaviour.

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Environmentally active people: Autonomy, relatedness, competence and self-determined

motivation

Governments, scientists and non-governmental organizations worldwide recognise the

need to lower humanity’s environmental impact, to reduce the severity of climate change,

ocean acidification, and other environmental threats that we, the global community, currently

face. Developed countries have a higher environmental impact per person than developing

countries, making up 15% of global population, yet causing the release of 45% of global

greenhouse gas emissions (UN 2007). Americans, Canadians and Australians are responsible

for the highest emissions per head of population globally. One aim of the United Nation’s

Decade of Education for Sustainable Development initiative (2005 – 2014) and of education

for sustainable development (ESD) generally, is to ‘encourage changes in behaviour that allow

for a more sustainable and just society for all’ (UNESCO 2006). Environmental education

(EE) also aims to endow students with the skills, knowledge and motivation to take action to

reduce environmental damage, to ‘produce scientifically literate citizens who make informed

decisions, especially when those decisions have environmental consequences’ (Darner 2014,

21).

This paper adds to this research area by looking at the desire for environmentally

active agents from a different angle: examining people who are already engaging in pro-

environmental behaviours and living lower impact lives. It asks: What is different about these

people? What are their reasons for living low-impact lives? And, of equal importance, what

situationally-influenced variables predict their behaviour? The present study draws on self-

determination theory (Deci and Ryan 1985, 2000), a theory widely used in education settings

and relevant to environmental education in particular (Darner 2009, 2012, 2014; Karaaslan,

Ertepınar and Sungur 2013). The purpose of the current research was to use self-determination

theory to examine motivation as a psychological predictor of pro-environmental behaviour,

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and perceived autonomy, competence and relatedness as predictors of motivation, for people

who are successfully engaging in pro-environmental behaviour. It is the first research, to our

knowledge, to fully test the self-determination theoretical model of predictors for motivation

and pro-environmental behaviour. It is also the first to test the applicability of this full model

to a range of different pro-environmental behaviours, as well as an estimate of environmental

impact. Through this approach, we aim to provide important information to environmental

education practitioners, policymakers and program developers, by identifying key predictors

of this desirable, but infrequently seen, pattern of behaviour.

1.1. Self-determination theory

Self-determination theory is a theory of human motivation in which self-determined

motivation is associated with higher rates of relevant target behaviours along with other

positive outcomes such as psychological well-being (Deci and Ryan 2000, 2008). The theory

proposes a continuum of motivation types according to the level of internalization (self-

determination) of motivation. Self-determined motivation is related to higher engagement in

pro-environmental behaviour (Lavergne et al. 2010; Pelletier et al. 1999; Pelletier et al. 1998;

Taberno and Hernandez 2010), a wider range of pro-environmental behaviour (Pelletier et al.

1998; Villacorta, Koestner, and Lekes 2003), a higher intensity of engagement in newly

adopted pro-environmental behaviour (Osbaldiston and Sheldon 2003), and more persistence

at engaging in difficult or inconvenient pro-environmental behaviours (Green-Demers,

Pelletier, and Menard 1997).

The continuum of motivation ranges from amotivation, to externally regulated

(controlled) motivation, to internally regulated (self-determined) motivation. Amotivation

differs from all other types of motivation in that there is no perceived link between behaviour

and the subsequent outcomes of behaviour (Deci and Ryan 1985). An amotivated person may

not engage in pro-environmental behaviour because they think that their behaviour would not

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contribute to reducing environmental problems. Externally regulated motivation is when

people engage in behaviour to gain a reward or to avoid a punishment, and represents the most

externally controlled type of motivation. If students participate in an after school tree planting

session solely to gain course credit, this is an example of externally regulated motivation.

Introjected motivation, the next motivation type on the continuum, is defined as behaviour

driven by internal, self-esteem-based contingencies whereby people do things to feel

worthwhile and to avoid feeling guilty. An example of introjected motivation would be when

wanting to gain the approval of a parent (or child) motivates someone to put cans in the

recycling bin. In a number of empirical studies, non-internalised motivations are related to

less target behaviour than internalised motivations across a range of life domains, such as

educational and health behaviours (see Deci and Ryan 2000, for a review).

In contrast to controlled motivations, self-determined motivations are reasons for acting

that come from within the self. The first self-determined, or autonomous, form of motivation

is identified motivation, where the behaviour is freely chosen by the individual and when

people identify a behaviour as important and valuable, and as part of a solution to a recognised

problem. An example of an identified motivation for behaviour is when people sign up for an

energy audit of their house because they think that lowering their energy use is an important

action to take. Integrated motivation for behaviour is when a behaviour has become

integrated with a person’s sense of self. In this way, behaviours are undertaken because they

are valued, but also because it reinforces how individuals see themselves. Hence, if

individuals identify themselves as someone who looks after the environment, switching all

light bulbs from incandescent (high energy use) to compact fluorescent (low energy, efficient)

bulbs, is rewarding, as it reinforces a valued part of their identity. At the extreme end of the

continuum of self-determination is intrinsic motivation, defined as behaviours that do not need

external consequences, as they are interesting or enjoyable in themselves (Deci and Ryan

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2000). For example, some people ride bicycles to school or work every day because they

enjoy it, with the health and environmental benefits of cycling as secondary, or

supplementary, to their enjoyment.

1.2. Antecedents of internalised, self-determined motivation

Self-determination theory proposes that internalised motivation develops because

humans have an innate tendency to integrate rationales for behaviour into their self-identity

(Deci and Ryan 2000). This integration does not happen automatically, as internalization of

motivation can be supported or thwarted by the social environment. The situation impacts on

the internalization of rationales and motivation to the extent that it satisfies people’s needs for

autonomy, relatedness, and competence (Deci and Ryan 2000). Briefly, autonomy is defined

as volition and choice. For example, if students perceive pressure from other people such as

teachers to engage in pro-environmental behaviour this would be a controlled situation that

would result in low perceived autonomy, whereas a lack of pressure from outside sources

would result in high perceived autonomy arising from a sense of personal choice. Relatedness

represents the level of connectedness to others. A low relatedness support situation would be

one where school or university friends denigrate efforts to lower environmental impact, while

high relatedness support would be reflected in situations where these friends engage in pro-

environmental behaviour and discuss it positively. Competence refers to being effective

within an environment and able to obtain valued outcomes from it. As an example, high

perceived competence may be the feeling that one has the skills, or access to skills and

information, to e.g., write a letter to a politician about an environmental problem as part of

course assessment. The experience of low competence would be not having these skills, and

not knowing where to find information or help. In this way competence is similar to the

concept of perceived self-efficacy that refers to beliefs about whether a person can achieve

certain outcomes (Bandura 1977,1997).

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1.3. Self-determination theory and pro-environmental behaviour

The positive relationships between perceiving autonomy, relatedness and competence

and having internalised motivation have been found in various behavioural domains such as

education, health behaviours and sporting achievement (e.g., Deci and Ryan 2000; Reis et al.

2000; Sheldon and Filak 2008). However, with regard to pro-environmental behaviour, it is

mainly the relationship between autonomy and self-determined motivation that has been tested

empirically. Greater autonomy has been shown to correlate with more self-determined

motivation for pro-environmental behaviour (Osbaldistan and Sheldon 2003; Lavergne et al.

2010). Experimental research in an education setting has shown that the use of more

autonomy supportive language in relation to recycling (for example ‘could’ instead of ‘must),

led to increased internalised motivation, which was related to better test scores on a quiz about

recycling, deeper processing of the topic, and free choice of further learning (Vansteenkiste et

al. 2004). These findings are also in synchrony with movements in environmental education

and service learning in the US, where guidelines for excellence in environmental education by

the North American Association for Environmental Education incorporate several autonomy

supportive goals for EE programs. These include being fair and open to student inquiry,

focusing on concepts, and connecting the content to learners’ everyday lives (NAAEE 2010),

thus providing a rationale that students can internalise, a key way of supporting autonomy

(Deci et al., 1994). The National Youth Leadership Council Service Learning standards

provide an even more obvious example of autonomy support in education, with standards

including ‘Youth Voice’ and ‘Meaningful Service’ (NYLC 2008).

To our knowledge, very little research examining potential links between relatedness

or competence and self-determined motivation for pro-environmental behaviour has been

published. In an EE setting, Darner (2007) found that relatedness support and perceived

relatedness predicted increased self-determined motivation, but competence variables did not,

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potentially because of the measures used to estimate competence support and perception.

Research by Darner (2012) also compared an EE college course that was modified with the

aim of supporting all three psychological needs to an unaltered course, and showed that in the

supportive condition students had lower amotivation than in the control course. However

student’s perceived relatedness, competence and autonomy were not measured, so

relationships between competence and relatedness and motivation were not explicitly

assessed.

From self-determination theory research in other domains, such as education

behaviours (e.g., Filak and Sheldon 2003), we expect that positive relationships will be found;

perceiving competence and relatedness for pro-environmental behaviour will be related to

more internalised motivation for the behaviour. However, pro-environmental behaviour

differs from education and health behaviours in ways that raise intriguing questions, and do

not assure the replication of past results in the pro-environmental domain. The differences

between environmental and health or academic behaviours relate to, for example, the

individual versus collective costs and benefits of these different behaviours. Self-

determination of motivation may be particularly important in environmental education as there

are potentially fewer individual extrinsic rewards for environmental behaviour than for

academic or health-related behaviours.

Previously examined behaviours, such as studying, have clear individual-level costs

and benefits (i.e., spending more time studying a text, and greater understanding of that text);

benefits that are closely linked in time, depend only on the behaviour of the individual, and

benefit the individual directly. Pro-environmental behaviour also has a cost to the individual,

ranging from small (time to sort recycling) to large (investment in solar panels for a house,

deciding not to travel overseas for holidays). However, the benefits of many pro-

environmental behaviours can be uncertain and diffuse, could occur far into the future, or

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mainly benefit people in other countries or non-human life. Moreover, the success of the

behaviour depends on the behaviour of other people. For example, taking a bus instead of

driving has the potential costs of decreased convenience and longer travel time, while benefits

such as reduced greenhouse gas emissions are not observable or will not happen unless the

behaviour is enacted collectively.

When considering that pro-environmental behaviours are undertaken partly for the

benefit of others (other people, other species, or nature at large; de Groot and Steg 2010), and

depend on the participation of others for large scale success, we propose that the experience of

relatedness could help to promote them. The impact of other people on environmental

behaviour is well established; for example, in one study where householders gathered to learn

about pro-environmental behaviour, behaviour change was predicted by how stimulated by

and obligated to their group people felt (Staats, Harland, and Wilke 2004). Social group

norms have been linked to environmental behaviour in several studies (e.g., McDonald,

Fielding, and Louis 2012; Smith et al. 2012) and in one study, energy reduction in the

workplace was greater when information about energy reduction was communicated by a peer

(workmate) instead of someone unknown to participants (Carrico and Riemer 2011). It is

possible that these effects would be influenced by how related people felt to the other people

involved in the actions, and thus how self-determined their motivation for action was.

Some pro-environmental behaviours are very easy, low skill threshold behaviours

which have few barriers to their success, such as turning off lights or televisions when leaving

rooms. There are also difficult, complex behaviours with numerous barriers, including

composting and installing solar panels. In other domains where competence has been

measured, such as studying new and difficult subjects at school, competence was positively

associated with self-determined motivation (e.g., Deci et al. 1981). Thinking of the more

common and highly endorsed pro-environmental behaviours, such as saving energy by

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switching off lights, we could hypothesise high perceived competence for individual

behaviours, and a lower association between competence and self-determined motivation and

action due to restriction of range (Azjen 1991). Conversely, people could experience greater

variability in competence for the more challenging behaviours, and therefore we might expect

to replicate previous findings of a relationship between perceived competence and self-

determined motivation. These theoretically interesting hypotheses regarding relatedness and

competence are addressed in the present research.

1.4. The present study

The aim of the current study was to explore the motivation of environmentally active

people, using the framework of self-determination theory. By environmentally active we

mean those who are currently engaging in a range of environmental actions including actions

that address private sphere actions such as saving energy and water as well as those that are

collective in nature such as being a member of an environmental group (Stern, 2000).

Consistent with self-determination theory, it was hypothesised that when people have more

self-determined motivation they are more likely to report engaging in a broad range of pro-

environmental behaviours. Importantly we also draw on self-determination theory to

investigate antecedents of pro-environmental behaviour that are influenced by the individuals’

situation. Considerable self-determination theory research shows that when people’s needs for

autonomy, relatedness and competence are met, they are more likely to have self-determined

motivation (Deci and Ryan 2000). In light of this, even considering the important differences

between pro-environmental behaviour and behaviours where the relationship between

relatedness and competence and self-determined motivation has been previously studied, we

hypothesise that autonomy, relatedness and competence in relation to proenvironmental

behaviour will all be positively related to self-determined motivation for proenvironmental

behaviour.

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Self-determination theory offers an important extension to current and past research on

pro-environmental behaviour. In reviews of research, it is shown that both historically (Hines,

Hungerford, and Tomera 1986/87) and more recently (Kollmuss and Agyeman 2002) research

on pro-environmental behaviour has tended to focus on the psychological antecedents of

behaviour such as environmental attitudes and concern (Van Liere and Dunlap 1980;

Hawcroft and Milfont 2010), knowledge and literacy (Maloney and Ward 1973; Hollweg et al.

2011), moral norms, intention to act (Fishbein and Ajzen 1975; Schwenk and Moser 2009),

and consequences of behaviour for the individual that could reinforce or undermine future

action (Cook and Berrenberg 1981; Lipsey 1977). Although these factors are important

determinants of pro-environmental behaviour (e.g., Bamberg and Moser 2007; Kaiser and

Gutscher 2003), the advantage of focussing on the role of situationally-affected factors is that

they are potentially easier to influence through behaviour change interventions than

psychological variables (Cooke and Fielding 2010). This is supported by Kaplan and Kaplan

(2009, 338) when they say that an essential tenet of their Reasonable Person Model is that to

bring out the best in people often ‘requires addressing problematic aspects of the

environment’.

1.4.1. Measurement of pro-environmental behaviour and impact

In this research we were interested in sampling a wide variety of pro-environmental

behaviours including public activism and citizenship behaviours (e.g., attending rallies, Stern

2000), and a range of private sphere resource conserving behaviours (e.g., electricity use

minimization, buying secondhand clothes, eating less meat, Tobler, Visschers, and Siegrist

2012). In developing a set of behaviours that were appropriate to our Australian samples, we

diverged from previously validated scales. As there are many taxonomies of pro-

environmental behaviour and our items comprised a new set of of behaviours, we took an

empirical approach, using an exploratory factor analysis to categorise behaviours. We

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expected that this analysis would tap into one of the established taxonomies, however, none of

these were confirmed. Instead, two sets of behaviours emerged. As one set of behaviours were

simple with few barriers, and the other set included behaviours that were more complex, and

with more barriers, we named the factors easy and difficult behaviours. We expand on our

classification of behaviours in sections 2.3.3. and 4.4.

In the present study we also included a carbon footprint estimate of environmental

impact. Carbon footprint measures are an estimate of the greenhouse gas emissions that are

released due to all the actions that make up someone’s lifestyle, and thus take into account

many influences on environmental impacts that are not usually included in research examining

people’s pro-environmental behaviour, such as distance to workplaces and house size. For

example, if two people say they try to combine trips and reduce driving as much as possible,

and always turn the lights and television off when they leave the room, they could have

similar scores on traditional self-report pro-environmental behaviour scales. However, if one

commutes by car to work and lives in a large air-conditioned house, and the other walks to

work and lives in a small apartment with no air-conditioning, their carbon footprints would be

very different. Hence, a carbon footprint measure that includes information about driving

distance and energy use can provide important novel information about a person’s

contribution to environmental problems.

As an extension of our hypotheses that self-determination of motivation will be related

to higher levels of pro-environmental behaviour, we also hypothesise that self-determined

motivation will be related to lower environmental impacts. If self-determined motivation for

pro-environmental behaviour does predict different types of pro-environmental behaviour, and

critically, if it predicts environmental impact, this finding would have important implications

for programs aimed at changing people’s environmental behaviour, including environmental

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education and education for sustainable development from early childhood and throughout the

lifespan.

2. Method

2.1. Sampling and procedure

We relied on a convenience sample that was recruited via email and Facebook.

Participants were invited to complete an online survey entitled ‘Environmental attitudes and

motivation for action’, by following a link in an email or on the social networking site

Facebook. The email was forwarded through various environmental and climate change

action group email lists, to allow oversampling of people who were interested in and active

about environmental problems. However, it was emphasised in the email that everyone was

invited to participate, regardless of their opinions about or level of interest in environmental

issues or action, to obtain a range of responses. No incentive was offered for participation and

participants were assured of the anonymity of their responses.

2.1.1. Identifying environmentally-active respondents

To test our hypotheses of the relationships between self-determined motivation,

situational antecedents of motivation, pro-environmental behaviours and environmental

impact, we sought two samples of environmentally active people. Our target participants were

people such as ‘voluntary simplifiers’, who lead lives with limited spending on consumer

goods, and with a focus on non-materialistic sources of satisfaction and meaning (Etzioni

1998; Huneke 2005). The samples self-selected into an online survey about environmental

behaviour, some of whom further self-selected to also complete a carbon footprint measure.

Many participants heard about the research through climate change action email lists. The

present research thus examined motivation and perceived autonomy, relatedness and

competence, as well as a range of pro-environmental behaviours, for highly engaged

respondents. By starting with people who are likely already engaging in the desired

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behaviour, we aimed to identify the key factors and situationally influenced antecedents

associated with their behaviour.

We employed an approach in which a theoretical model of pro-environmental

behaviour (see Figure 1) is tested and developed in one environmentally active sample, and

then re-tested on a second sample. The second sample comprised those participants who

chose to complete the carbon footprint measure, allowing us to extend previous research by

including this estimate of environmental impact as an additional outcome measure.

2.2. Participants

The carbon footprint measure added significant time to an already long questionnaire

and therefore was optional (from 20 minutes on average to 25-30 minutes). People who did

not complete the measure were placed in Sample 1, in which the hypothesised model was

tested. Participants who completed the carbon footprint measure were included in Sample 2.

2.2.1. Sample 1

A convenience sample of 261 participants (173 female, 71 male, 17 not indicated)

completed the survey. The ages of participants ranged from 15 to 76 (M = 36.1 years, SD =

13.9). This sample included 221 people who declined to complete the carbon footprint

measure, and 40 people who started, but did not finish, the carbon footprint. Some of these

people closed the survey window instead of clicking through to the final demographic items,

resulting in more missing demographic data in this sample than in Sample 2. The subsample

of 40 people who started but did not finish the carbon footprint did not differ from the main

group of Sample 1 (N=221) on any variables (all ts(259)<2.84 ns), although they differed

from Sample 2 on participants’ competence (but no other variables) (t(358)=2.86, p<.05/10

Bonferroni adjustment for multiple comparisons utilised). Therefore, we placed the sub-

sample of 40 participants in Sample 1. Preliminary analyses were also conducted to ensure

that inclusion of these participants did not change results. As there were no substantive

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changes to the path analysis results, when analyses were conducted with or without these

participants, they were retained in Sample 1 to increase the stability of the analysis.

2.2.2. Sample 2

Three hundred and twenty participants completed the survey and the additional carbon

footprint measure (200 female, 119 male, 1 not indicated). The ages of participants ranged

from 18 to 76 (M=35.9, SD= 13.8). Means and standard deviations for focal variables and

demographic variables for each sample are compared in Table 1. Scale reliabilities are

provided in the diagonal of Table 2, which also provides intercorrelations between model

variables in both samples.

2.3. Measures for Samples 1 and 2

2.3.1. Perceived autonomy, relatedness and competence

The perceived autonomy, relatedness and competence scales were adapted from items

developed by Sheldon et al. (2001) to measure these constructs in relation to satisfying

experiences. In the current research the items began with the stem “When I am engaging in

behaviour that lowers my environmental impact I feel …”, and were completed with three

items measuring perceived autonomy, (e.g., “… I feel that my choices are based on my true

interests and values”), three items measuring relatedness (e.g., “… I feel close and connected

with other people who are important to me”), and three items measuring competence (e.g., “…

I feel that I am taking on and mastering hard challenges”). Participants rated their agreement

on a 7-point Likert scale from 1 - strongly disagree to 7 - strongly agree.

2.3.2. Self-determination of motivation towards the environment.

The Motivation Towards the Environment Scale (MTES; Pelletier et al. 1998) was

adapted to measure people’s motivations for lowering their environmental impact.

Participants read the question “Why are you lowering your environmental impact?”, then

various responses to the question, and rated how well the responses corresponded to their own

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motivations, on a 7-point scale from 1 - does not correspond at all to 7 - corresponds

completely. There are six subscales measuring the types of motivation in the scale. Of the

controlled or external motivations there were four amotivation items (e.g., “I don’t know why

I do it, I have the feeling I’m wasting time”), four externally regulated motivation items (e.g.,

“to avoid being criticised”), and three introjected motivation items (e.g., “because I’d regret

not doing something”). The self-determined motivation subscales included four identified

motivation items (e.g., “because helping the environment is a reasonable thing to do”), four

integrated motivation items (e.g., “because looking after the environment is an integral part of

my life”), and four intrinsic motivation items (e.g., “because I like the feelings I have when I

am doing things for the environment”). In accordance with standard procedures (Green-

Demers, Pelletier, and Menard 1997), a global self-determination index was created from

these items. The subscale scores were weighted according to their theoretical position on the

self-determination continuum (intrinsic motivations are weighted +3, integrated motivations

+2, identified motivations +1, introjected motivations -1, externally regulated motivations -2,

and amotivation items -3) and the overall mean of these weighted scores became a person’s

self-determination index of motivation towards the environment, with higher scores indicating

more self-determined motivation.

2.3.3. Easy and difficult pro-environmental behaviour scales

Participants self-reported their engagement in 21 pro-environmental behaviours. We

conducted an Exploratory Factor Analysis (EFA) of these items in SPSS, using Principle

Components Extraction with an oblique (Oblimin) rotation. The EFA identified two

components on the basis of the Scree plot. This model was clean and interpretable, without

cross-loading items. Appendix A includes a table of all items, with factor loadings.

The items comprising the first cluster were five energy saving behaviours (e.g.,

switching off the television when you are not watching it), three computer energy saving

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behaviours (e.g., switching off the computer when it is not in use), and one behaviour which

minimises packaging waste (using a re-usable water bottle instead of buying plastic water

bottles). While perceptions of difficulty were not collected, we have named this cluster of

items the easy pro-environmental behaviour scale, as these behaviours were relatively simple,

with few steps and few obvious barriers to prevent people from carrying them out.

The second scale included two ecological eating items (e.g., minimizing the amount of

meat you eat), four public or citizenship pro-environmental behaviours (e.g., participating in

marches or protests about environmental issues) and five items classified as comfort related

behaviours, as they involved either giving up luxuries or engaging in a pro-environmental

behaviour when there is a more convenient or pleasant option available (e.g., avoiding using

air-conditioning, composting food scraps). We named this factor difficult pro-environmental

behaviours, as the behaviours generally have more than one step and thus involve more effort,

and/or have more barriers to completing them (including psychological barriers to giving up

comfort).

The public environmental behaviours were originally scored on a 5-point scale (1,

never, occasionally, sometimes, often to 5, very often), consistent with Seguin, Pelletier, and

Hunsley (1998). All other behaviour items were measured with responses made on 6-point

scales (from 1, never, almost never, sometimes, most of the time, almost always, to 6, always),

consistent with Thogersen and Olander (2006). All items were standardised in the present

analyses to avoid biasing the factor analysis and to facilitate comparison between behaviours.

2.3.4. Demographic variables

Participants entered their age in years and indicated their gender, membership in

environmental groups, and whether they identified with a low consumption lifestyle at the end

of the survey. This item was phrased ‘Do you aim to live a simple, low consumption lifestyle,

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either as part of a movement such as voluntary simplifiers, or by yourself?’. They also

confirmed that they lived in Australia, although no ethnicity data was collected.

2.4. Measures for Sample 2

Sample 2 completed the above measures with the addition of a measure of

environmental impact.

2.4.1. Self reported environmental impact

The carbon footprint measure used in the present study was adapted from an online

carbon calculator (Carbon Neutral 2008). This particular calculator was chosen because it is

relatively simple and quick, but also measures lifestyle aspects which have the largest impact

on the environment in terms of greenhouse gases released. The measure included information

from electricity and gas bills, type of diet, approximate amount of waste produced, distances

travelled by car, train, bus, tram and ferry each week, to allow calculation of a yearly estimate,

and long distance travel (flights) taken in the past year (the complete list of carbon footprint

items is included in Appendix A). These figures were then entered into emissions equations,

with factors based on national greenhouse gas reporting figures, and summed, resulting in an

estimate of environmental impact, that is, a number of tonnes of greenhouse gases emitted as a

result of the behaviour reported. The mean carbon footprint in this sample (M = 15.62, SD =

8.94) is similar to, but smaller than, estimates of a mean Australian carbon footprint

(approximately 20.6 tonnes per person per year, Hertwich and Peters 2009), as would be

expected of an environmentally active sample.

2.5. Overview of Analyses

Sample 1 and 2 descriptive statistics were examined to assess the environmental

activities of participants, and means were compared between the samples. The hypothesised

path model (see Figure 1) was tested in Sample 1, using items parceled together into scale

scores (e.g., Little et al. 2002), as our sample was not large enough for an adequate assessment

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of a full structural equation measurement model (e.g., Bentler and Chou 1987). This approach

allowed us to test a path model with an appropriate number of parameters to be estimated for

our sample sizes. Modification indices greater than 10 were requested. From the test of the

hypothesised model, one direct structural path between an antecedent and a behaviour variable

was identified as significant through modification indices, in addition to the indirect paths

hypothesised. This path was added, resulting in a model with improved fit (see Figure 2).

The developed path model was then re-tested in Sample 2. This re-test was primarily to

protect against post-hoc model adjustments that can over-capitalise on chance, although it also

allowed us to explore whether there were any differences between models, in this more

environmental sample. In Sample 2 the model was also tested without, and then with, the

addition of the carbon footprint. Data screening, initial sample descriptive statistics, sample

comparisons, variable intercorrelations and the EFA of pro-environmental behaviours were

conducted using SPSS Version 18.0. For testing the path models, AMOS Version 18.0 was

used.

Maximum likelihood estimation was employed to estimate all path models reported.

Several indicators of fit between the model and the data are reported. The value is a

statistical measure of overall fit that measures the closeness of fit between the sample

covariance matrix and the fitted covariance matrix. Thus, a non-significant statistic is

desirable, indicating no significant difference between matrices. However, with moderate to

large sample sizes (N>100) the statistic is usually significant (Bentler 1992). This is because

with large samples, any divergence from the model, for example due to departures from

normality, will be significant, resulting in otherwise well-fitting models being rejected.

Therefore, we present two other statistics of practical fit used to assess our models. The root

mean square error of approximation (RMSEA) is a measure of absolute model fit, reflecting

the size of the residuals when the model is used to predict the data, adjusting for model

19


complexity. An RMSEA below .06 represents good fit and below .08 acceptable (Hu and

Bentler 1999). However, when sample sizes are small (N<250), the RMSEA can be very

sensitive to outliers and other departures from normality, and can reject otherwise well-fitting

models (Hu and Bentler 1999). The comparative fit index (CFI) is also reported. CFI is a

measure of difference between the hypothesised model and an independence model, where the

variables are assumed to be uncorrelated. Values that approach 1, with a cut off of .95, have

acceptable fit (Kim, 2005).

3. Results

3.1. Preliminary analyses

3.1.1 Environmental activity of samples

We examined the environmental activity of the samples to determine whether

environmentally active people were oversampled, as intended. Fifty-five people (33%) in

Sample 1 and 81 people (43%) in Sample 2 stated that they were members of one or more

environmental groups (22 people in Sample 1 and one person in Sample 2 did not answer this

question). This is a much higher level of environmental group membership than the

Australian population in which 7% of the broad population are members of an environmental

group. Even when considering only younger Australians, membership levels are still only 9%

(ABS 2003). In addition, one hundred and fifty one people (58%) in Sample 1 and 192 people

(60%) in Sample 2 stated that they ‘aimed to live a simple, low consumption lifestyle, either

as part of a movement such as voluntary simplifiers, or by themselves’ (22 people in Sample 1

and three people in Sample 2 did not answer this question). Previous research has estimated

that one fifth to a quarter of the population could be classified as voluntary simplifiers, as

defined above (23% in an Australian study by Hamilton and Mail 2003; 18 – 30%, in Chhetri,

Stimpson, and Western 2009). The high proportion of environmental group membership and

20


identification with ‘low consumption living’ in our samples suggests that the samples included

a high proportion of environmentally active participants. For Sample 2, the mean carbon

footprint estimated of our sample was lower than other estimates of an average Australian

carbon footprint, also indicating that we did sample environmentally active people.

3.1.2. Sample differences

Means, standard deviations and statistics of comparison between samples for all model

and demographic variables are displayed in Table 1. On demographic variables, there were no

significant differences between the samples (s(1, N =581) <8.06, ns). However, Sample 2

had higher mean scores on four model variables; autonomy (t(581) =3.13, p <.05/10,

Bonferroni adjustment for multiple comparisons utilised), self-determination of motivation

(t(581)=2.83, p<.05/10), easy (t(581)=2.61, p<.05/10) and difficult pro-environmental

behaviour.(t(581)=3.18, p<.01/10). Participants who completed the carbon footprint measure

(Sample 2), scored higher than participants who did not complete the measure (Sample 1) on

the perceived autonomy scale, had more self-determined motivation towards the environment,

and reported engaging in more of both easy and difficult pro-environmental behaviour.

Sample 2 was also trending higher on relatedness, competence, and environmental group

membership, although these differences were not statistically significant. In short, consistent

with the fact that participants in Sample 2 had expended extra effort to complete the optional

carbon footprint measure, the comparisons indicate that this sample is generally more pro-

environmental than Sample 1.

3.2. Sample 1 Analyses

3.2.1. Descriptives and intercorrelations

Scale means and standard deviations are provided in Table 1, and scale reliabilities and

intercorrelations in Table 2. All scales are of acceptable reliability, and correlations are

significant and in the expected direction. As expected, perceived autonomy, relatedness, and

21


competence were significantly and positively correlated with self-determination of motivation

and motivation was positively correlated with easy and difficult pro-environmental behaviour

and negatively correlated with the carbon footprint (i.e., more self-determined motivation, less

environmental impact). There were some deviations from normality, with perceived autonomy

and easy pro-environmental behaviour in particular negatively skewed (Sk =-.57, SE = .15,

and Sk =-.58, SE =.15), as could be expected from an environmentally active sample.

However, all scales are within the acceptable range for skewness and kurtosis for use in Path

Analyses (<3 and <10, Kline 2011), with skewness and kurtosis statistics for all variables

being smaller than 1.

3.2.2. Path model testing

In the analyses below, the antecedent variables (autonomy, relatedness, and

competence) were allowed to correlate with each other, as is standard with the needs variables

in self-determination theory (e.g., Gagne 2003; Reis et al. 2000). The error terms of the

outcome variables (pro-environmental behaviour scales and carbon footprint) were also

allowed to correlate, consistent with Tobler, Visschers, and Siegrist (2012), and as these sets

of variables have high bivariate correlations (see Table 2). To improve clarity these

correlations are not represented in the figures, but are reported in full in the text below.

The hypothesised model had poor fit to the data ( (6, 261) = 47.662, p < .01, CFI

= .91, RMSEA =.16, RMSEA 90% CIs = [.12, .21]). Modification indices (MIs) greater than

10 were requested. There was one modification index larger than 10, indicating that allowing

relatedness and the error term for the difficult behaviour scale to correlate would result in a

drop of chi-sq by 13.12. In other words, there was some unexplained variance in the difficult

behaviour scale that could be explained by the relatedness variable. Therefore, we added a

direct path from relatedness to difficult behaviour. When the model was re-run with this added

path, the path was shown to be significant (see Figure 2, p <.01), and the model fit the data

22


well ( (5, 261) = 17.80, p<.01, CFI = .97, RMSEA =.10, RMSEA 90% CIs [.05, .15]), with

the added path resulting in significantly improved model fit, as indicated by a significant drop

in (Δ (1) = 29.86, p <.01).

Although not hypothesised, this direct path between antecedent and behaviour is not

ruled out by the theoretical rationale for the hypothesised model, and suggests that in addition

to the hypothesised indirect path through self-determined motivation, there are other ways that

relatedness is positively associated with certain behaviours. This is further discussed in

Section 4.2. While the RMSEA was higher than ideal, this model was accepted, as the sample

size is only just over 250 and there were small departures from normality, and therefore a high

RMSEA could be expected. As expected, autonomy, relatedness and competence were

significantly intercorrelated (autonomy/relatedness r =.43, relatedness/competence r = .54,

autonomy/competence = .49), as were the error terms of the two behaviour scales (r =.37; all

ps < .001). Unexpectedly, the path between perceived competence and self-determination of

motivation was not significant. Significance of direct and indirect effects was estimated

using a bias corrected bootstrapping procedure (5000 samples). Bootstrapping is a resampling

statistical technique that can be used in the estimation of nearly any statistic, but that is

thought to provide more stability and power (lower Type II error) for analyses of subtle effects

(e.g., Hayes, 2009). In mediation models, bootstrapping repeatedly takes samples with

replacement from cases in the data, estimating coefficients in the model. The relative indirect

effects are calculated from these estimated coefficients. The summary of effects

decomposition for this model is presented in Table 3.

3.3. Sample 2 Analysis

Although the novel alteration to the hypothesised model was theoretically interesting

and interpretable, it is well known that post-hoc model changes based on modification indexes

may overcapitalise on chance. Therefore, the model was re-tested in the second sample,

23


which included people with higher engagement with environmental issues. We tested the

model twice in Sample 2, once without the carbon footprint variable, to allow direct

comparison to the model in Sample 1, and then with the carbon footprint variable included.

The model tested in Sample 2 without the carbon footprint measure is reported first.

3.3.1. Descriptive statistics and intercorrelations

Scale means and standard deviations are shown in the second column of Table 1, and

reliabilities and intercorrelations between model variables for Sample 2, including the carbon

footprint measure, are shown in Table 2. Again, there were some departures from normality

in the scales. In this sample, autonomy (Sk =-.77, SE =.14), easy pro-environmental behaviour

(Sk = -.47, SE = .14) and self-determination of motivation (Sk = -.63, SE = .14) were all

negatively skewed. The carbon footprint was positively skewed (Sk =1.25, SE = .14) and

kurtotic (K =2.10, SE = .27). All variables fell within acceptable bounds of normality for use

in path analyses, which state that skewness be lower than 3 and kurtosis below 10 (Kline

2011). The pattern of correlations between the variables was similar to that in Sample 1. The

carbon footprint was significantly correlated in the expected direction with every variable in

the model with the exception of perceived relatedness, with which there was no significant

correlation.

3.3.2. Path model re-test

The model developed with Sample 1 was re-tested with Sample 2, and fit the data well

( (5, 320) = 10.56, p = .06, CFI = .99, RMSEA =.06, RMSEA 90% CIs [.00, .11]). As in

Sample 1, autonomy, relatedness and competence were significantly intercorrelated

(autonomy/relatedness r = .39, relatedness/competence r = .42, autonomy/competence r

= .46), as were the error terms of the behaviour scales (r = .38 all ps < .01). In contrast to

Sample 1, the path from competence to self-determined motivation was significant in this

sample.

24


The developed model was then tested with the carbon footprint variable added, with a

path from self-determined motivation to carbon footprint. This model also fit the data well (

(8, 320) = 14.39, p = .07, CFI = .99, RMSEA =.05, RMSEA 90% CIs [.00, .09]) (see Figure

3). As expected, the error term of the carbon footprint variable was significantly correlated

with error terms of the easy/simple behaviour scale (r = -.20, p <.01) and with the

difficult/complex behaviour scale (r=-.30, p <.01). As the statistics for the model with and

without the carbon footprint are almost identical, only the path model including the carbon

footprint measure are presented, in Figure 3. The summary of effects decomposition is in

Table 3.

4. Discussion

This research examined self-determination of motivation and antecedents of

motivation as key variables in a model of pro-environmental behaviour and environmental

impact. Path analysis in two samples confirmed that the more participants judge that their

needs for autonomy and relatedness (Sample 1 & 2) and competence (Sample 2) were met in

relation to performing pro-environmental behaviour, the more self-determined their

motivation. In turn, higher self-determined motivation was related to reporting more

engagement in both easy and difficult pro-environmental behaviours. In both samples,

relatedness was directly linked to difficult pro-environmental behaviour, in addition to the

indirect relationship through self-determined motivation. In the second sample, higher self-

determined motivation was associated with lower environmental impact, as measured by a

self-reported carbon footprint.

4.1. Self-determination of motivation

The main hypothesis of the research was supported; in both samples, self-

determination of motivation was positively related to engagement in pro-environmental

behaviour. This replicates previous research (Lavergne et al. 2010; Pelletier et al. 1999;

25


Pelletier et al. 1998; Villecorta, Koestner, and Lekes 2003), and extends knowledge in this

area by showing that self-determination of motivation was associated with a range of both

easy and difficult self-reported pro-environmental behaviours. The current research is also the

first to our knowledge to examine (and find) an association between self-determination of

motivation and lower self-reported environmental impact. The finding suggests that self-

determined motivation should be a key aim of environmental education and other programs

aiming to move people to low impact lifestyles.

4.2. Autonomy, relatedness and competence

Again replicating previous research (Lavergne et al. 2010; Osbaldistan and Sheldon

2003; Vansteenkiste et al. 2004), perceiving higher autonomy (choice and self-direction) for

pro-environmental behaviour was related to higher self-determined motivation for our

participants. In addition, in both samples higher perceptions of relatedness (i.e., feeling close

and connected to people while acting) were also associated with more self-determined

motivation for environmental behaviour. This finding is consistent with research in other

behavioural domains (e.g., health and education, Deci and Ryan 2000; Sheldon and Filak

2008), and other pro-social behaviours (Pavey, Greitemeyer, and Sparks 2011), and replicates

research by Darner (2007). Relatedness also had a direct association with difficult pro-

environmental behaviour in both samples. Competence was related to self-determined

motivation only in Sample 2.

The importance of experiencing autonomy for promoting self-determined motivation is

well established in self-determination research generally (Su and Reeve 2011). The current

research adds to findings that autonomy is highly relevant to motivation for pro-environmental

behaviour, and indirectly related to pro-environmental behaviour. In the current study

perceived autonomy was by far the most important predictor of self-determined motivation.

While research in general population samples is needed, these findings may have important

26


implications for encouraging generalised pro-environmental behaviour both in and out of the

classroom. Traditional approaches to increasing pro-environmental behaviour include

providing information about environmental problems and solutions, teaching that

environmentally damaging behaviour is wrong or immoral, and rewarding environmentally

friendly behaviour (e.g., refunds for returning glass bottles to the manufacturer), or punishing

damaging behaviour (e.g., fines for dumping rubbish). One of the founding principles of self-

determination theory is that coercion of behaviour through either rewards, punishments, or

other types of pressure reduces people’s experience of autonomy and undermines their self-

determined motivation. The current research supports suggestions from previous SDT

research, that these approaches may be harmful, if self-determined motivation is the aim.

Since knowledge about environmental problems and effective solutions are pre-requisites for

taking action (Attari et al. 2010), a key question is how to support people’s autonomy (their

self-direction and choice) while providing relevant information. Here many EE and ESD

programs have an advantage over traditional formal education, as participatory learning and

democratic decision making (where students are stakeholders in discussions of what and

sometimes how they are going to learn, Schusler et.al. 2009) have been recognised as essential

to meeting the goal of EE and ESD to ‘empower learners to change their behaviour and take

action for sustainable development’ (UNESCO 2012). These teaching approaches are

inherently autonomy supportive.

Relatedness emerged as a key variable in the model of motivation for pro-

environmental behaviour in our environmentally active samples, although it was not as strong

a predictor of self-determined motivation as autonomy. This finding supports the proposition

that experiencing relatedness could be valuable in promoting pro-social behaviour, such as

pro-environmental behaviours. Our finding of a direct relationship also suggests that

relatedness is associated with action via other psychological processes, as well as via

27


heightened self-determined motives. For example, perceiving that others support

environmental action may have triggered belongingness motives (Baumeister and Leary 1995;

Leary and Baumeister 2000), such that people engage in pro-environmental action to meet

needs for approval in social relationships. Similarly, perceived relatedness implies normative

support for the behaviour, which could invoke group identity motives (Smith et al. 2012;

McDonald, Fielding, and Louis 2012, Lindenberg and Steg 2007). Further research is needed

to test the mechanisms underlying the direct relatedness effect.

Finally, in our results, perceived competence was related to self-determined motivation

in Sample 2, but not in Sample 1. For the more environmentally active sample, feeling more

capable and successful at pro-environmental behaviour was generally related to higher

internalised motivation for the behaviour, while competence was not related to self-

determined motivation in the less environmental sample. One speculative explanation for the

difference in findings between the two samples is that there is a moderator of the relationship

which could explain why the relationship between competence and motivation is positive in

the more environmental Sample 2, but not in the less environmentally active Sample 1. One

moderator could be experience or history of pro-environmental behaviour. Sample 1 reported

engaging in significantly less pro-environmental behaviour than Sample 2. It is possible that

for people who have only engaged in beginner or moderate levels of pro-environmental

behaviour, autonomy and relatedness were the only important supports of pro-environmental

behaviour. Once people have the depth of experience of Sample 2 participants, perhaps any

increase in competence is related to higher self-determined motivation for pro-environmental

behaviour. Further research with a broader sample is needed to explore these potential

explanations.

4.4. Research contributions, limitations and future directions

28


Within the EE movement, there have been calls to address climate change as one of the

major challenges (and opportunities) that humanity faces, and prepare citizens to participate in

problem solving around this growing crisis (Marcinkowski 2010). Our results suggest that an

important next step is to examine the best ways to increase people’s experience of autonomy,

relatedness and competence in regard to pro-environmental behaviour, and to assess how these

interventions affect people’s motivation, behaviour, and environmental impact. Fortunately,

self-determination theorists have identified and established techniques for supporting people’s

needs (for example, the use of autonomy supportive language, avoidance of coercion, and

expressing interest and caring towards people), and these techniques can be adapted to the

pro-environmental behaviour domain. Autonomy supportive course elements can include

giving instructional guidance while students construct their own solutions to environmental

problems (instead of providing the solution) and having students guide parts of the lectures

and discussions (Darner 2014; Karaaslan, Ertepınar and Sungur 2013). Relatedness

supporting elements include having a consistent and cohesive group to work with, and linking

the study topics to people outside of the course by interviewing friends and family members of

the students about particular environmental problems, listening to guest speakers, reading

newspaper articles and going on field trips (Darner 2014). Competence supporting course

elements include posing questions to the class that are optimally challenging/complex (Darner

2009), that are an extension of previous knowledge and allow understanding gained while

analysing one problem to then be used on further problems (Darner 2014). Karaaslan,

Ertepınar and Sungur (2013) also proposed that when students become aware of their own role

in the system – as problem creator and solver – this increases their sense of competence

regarding those problems.

There are a number of limitations of the study that need to be acknowledged. The

choice of environmentally aware samples for the current research allowed a focus on the

29


motivation of an important and neglected target group. While this strategy was deliberate—to

explore the predictors of high levels of sustainable behaviour we needed to sample people

engaging in such—it also means that our results may not generalise to the broader community.

Replicating the model in a broader, less environmentally focused sample would be an

important complement to this research, and allow an exploration of the motivation and

situational variables of an equally important group of people: those who are decidedly un-or

anti-environmentally focused.

One concern about the use of carbon footprint measures in research about people’s

pro-environmental behaviour is that there are large influences from factors that are outside of

people’s control, such as the source of electricity available to people (e.g., from coal, nuclear

or renewable sources, which have different greenhouse gas emissions per unit of electricity

produced). However, if we are to identify ways to reduce our burden on ecosystems, then an

estimate of that impact is an important metric to be using, as Stern (2000) highlights when he

says that an impact oriented approach to studying environmentally significant behaviour is

critical for making research useful. Going further, identifying relationships between

individual variables and carbon footprints will provide important information for interventions

about factors that help people overcome outside forces to influence their own environmental

impact.

Other limitations of our research include the use of self-reported data rather than

objective measures of behaviour, the less than optimal reliability of some of our scales (i.e.,

perceived autonomy and competence, and easy pro-environmental behaviour), and the use of a

purpose-built pro-environmental behaviour questionnaire rather than a validated measure.

Interestingly, the latter scale did not factor according to existing taxonomies (e.g., the

distinction between public and private sphere behaviours; Stern, 2000) and instead we labeled

our factors as easy and difficult behaviour, following Kaiser and Gutscher (2003), who

30


propose that previously identified low correlations between different pro-environmental

behaviours are due to the differential difficulty of the behaviours. Still, we acknowledge that

we did not measure the difficulty of our behaviours and our construct labels are therefore

based on our inferences about the complexity or simplicity of the behaviours. This

classification of pro-environmental behaviours into easy and difficult is just one of many ways

of categorising pro-environmental behaviours, and is not without limitations. For example,

people may vary in how difficult they find a particular behaviour. Future research should

examine why people find some behaviours more difficult than others, to identify the reasons

these categories emerge. Future research may also want to investigate how people’s different

motivations for different behaviours could influence the model. For example, some

environmental behaviours might be more self-determined than others, and if so, it would be

interesting to explore how and why such within-category differences arise.

Moreover, there is a lack of psychometric data to validate carbon footprint measures,

primarily because there are multiple ways to estimate carbon footprint and these vary

according to national legislation and commercial practices. There is a need, therefore, for

future research to replicate our findings with validated behavioural and environmental impact

scales.

5. Conclusion

The current research is the first study, to our knowledge, to find support for a full

theoretical model of motivation for pro-environmental behaviour including autonomy,

relatedness and competence as antecedents of motivation, and motivation as a predictor of

both easy and difficult pro-environmental behaviour, and a measure of environmental impact.

By identifying three situationally influenced variables that are important to motivation for pro-

environmental behaviour, these variables can be prioritised in future research, EE programs,

and public campaigns to promote behaviour to lower peoples’ environmental impact. Based

31


on the current results, it is argued that supporting the internalization of motivation could

increase a range of environmental behaviours, producing environmentally active citizens.

Further, interventions with these aims can draw on decades of self-determination research to

identify ways of supporting needs for autonomy, relatedness and competence in relation to

pro-environmental behaviour. This could be a key approach to supporting generalised

environmental behaviour, leading to lower environmental impacts.

32


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Appendix A

Pro-environmental behaviour questionnaire items

All behaviours were rated on a 6-point scale of frequency, from never to always, apart from the 4 public pro-environmental behaviours (marked with an *), which were rated on a 5-point scale of frequency from never to very often. (Standardised items were included in the factor analysis.)

Please rate how often you engage in the following behaviours.How often do you:

Mean (SD, Sample 1)

Factor loading

Easy pro-environmental behaviours

Switch off your computer when it's not in use 4.40 (1.31) .74Switch off your computer monitor when it's not in use 4.55 (1.44) .67Switch off lights when you leave the room for more than 5 minutes

4.64 (1.15) .67

Switch your computer off at the wall when not in use 2.79 (1.79) .54Switch off the television when you are not watching it 5.27 (.93) .50Rug up with clothes and blankets before using heaters in winter 5.18 (1.08) .49Use a re-usable water bottle instead of buying water in plastic bottles

5.05 (1.16) .41

Hang up washing on a clothes line instead of using a dryer 5.33 (1.01) .33Only use the washing machine when it is full 5.26 (.98) .29

Difficult pro-environmental behaviours

Participate in marches or protests about environmental issues* 1.85 (1.24) .88Sign petitions about environmental issues* 3.29 (1.26) .74Buy recycled toilet paper 3.95 (1.85) .69Donate money to environmental organizations* 2.55 (1.28) .67Talk to your friends and family about environmental issues* 3.59 (1.08) .67Buy second hand clothes and goods (e.g., furniture, appliances) instead of new when possible

2.97 (1.39) .66

Limit the amount of meat you eat 3.61 (1.66) .60Compost (or feed to worms) your food scraps 3.56 (2.11) .47Use durable items instead of disposable ones (i.e. use a thermal coffee cup instead of getting paper cups each time).

4.25 (1.45) .44

Limit the amount of dairy you eat 2.74 (1.55) .44Avoid using air-conditioning (e.g., use a fan instead) 4.95 (1.21) .38Minimise the number of car trips you take by walking, cycling or taking public transport for trips within the city you live in

4.09 (1.50) .28

Note. Extraction Method: Principal Component Analysis, rotation Oblimin with Kaiser Normalization. Standardised items were used in the Factor Analysis. Unstandardised means are reported here. Easy and difficult behaviour components correlated .44.

43

SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR 44


Carbon Footprint itemsThe next questions are about the biggest contributions to your carbon footprint - waste, transport, energy, food, and flights.

Some of the questions require details about your electricity and gas use. To help answer these questions, please take a minute to find your electricity and/or natural gas bills, if they are handy.

So that we can work out approximate environmental impact per person, please enter below the number of people who live in your household.

Waste - these questions are about your general waste bin (not your recycling bin).How big is your council garbage bin? (Standard garbage bins are 240L)How often is your general waste bin collected? (weekly, fortnightly, other)About how full is your bin when it gets collected, usually? (10 – 100%)

Short distance travel Do you own a car or other vehicle? If yes - What type of vehicle is it? What fuel does this vehicle take? (petrol, diesel, biodiesel, LPG)How many kilometers does it travel in a week? (please estimate)(repeated for second vehicles). Please estimate the distance (in kilometers) you travel by bus/train/tram/ferry or CityCat each week (separate questions).

The next questions are about your household energy use. Do you have your electricity bill, or do you know how much electricity you use per day, quarter or year? (If yes, quantity was entered, if no, participants entered the state where they lived, and a state mean was used).

Does your household use gas, and if so, do you have gas bottles/cylinders delivered, or is your house connected to gas pipes? if yes - natural gas by pipes

- average quantity per day (from latest bill) was entered, or state average used. natural gas by bottle or LP gas by the bottle

- reported size of bottle and approximately how often bottles are replaced.

Long distance travel For each flight taken in the past year, please enter the departure and arrival cities, what class of seat (economy, premium economy, business or first class), the type of aircraft (small 1-50 passengers, medium 51-200 passengers, or large 201+ passengers), and whether the trip was one way or return.

Diet Please choose the description that best fits your diet (vegan, vegetarian, mainly white meat, red and white meat, mainly red meat).

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If you do purchase renewable energy through GreenPower (Australian Government accredited renewable energy program), what percentage of the electricity you purchase comes from GreenPower (0 – 100%).

46