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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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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
18
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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
SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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.
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SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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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.
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SELF-DETERMINED ENVIRONMENTAL BEHAVIOUR
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%).
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