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Do environmental concern and future orientation predict metered household electricity use?

Authors:
  • University of Leipzig and ETH Zurich
Bruderer Enzler, Diekmann & Liebe 2019, p. 1
Accepted for publication as:
Bruderer Enzler, H., Diekmann, A., & Liebe, U. (2019). Do environmental concern and future orientation predict metered
household electricity use? Journal of Environmental Psychology, 62, 22-29.
doi:https://doi.org/10.1016/j.jenvp.2019.02.004
Do Environmental Concern and Future Orientation
Predict Metered Household Electricity Use?
Heidi Bruderer Enzler1,2 *, Andreas Diekmann1,3 and Ulf Liebe2,4 *
1 ETH Zurich, Environmental Research Group, WEP, 8092 Zurich, Switzerland.
2 University of Bern, Institute of Sociology, Fabrikstrasse 8, 3012 Bern, Switzerland.
3 Institute for Advanced Study Berlin, Wallotstraße 19, 14193 Berlin, Germany.
4 Department of Sociology, Social Sciences Building, University of Warwick, Coventry CV4 7AL, UK.
* Corresponding authors. e-mail addresses: bruderer@soz.gess.ethz.ch; Ulf.Liebe@warwick.ac.uk
Highlights
o More environmentally concerned and future-oriented persons use less electricity
o Consideration of Future Consequences is correlated to electricity use
o Subjective discount rates do not predict metered electricity use
o Metered electricity use is 23% higher for men than for women
o Results are in line with earlier research relying on self-reported behaviour
Abstract. Do individuals' environmental attitudes and future orientation predict actual energy
consumption? Little is known about the answer to this fundamental question because previous
research has relied on self-reported behaviour, which might be prone to social desirability. Therefore,
the present study combines survey data with metered data on actual electricity use. Environmental
concern is measured by attitudinal items, future orientation by a short version of the Consideration of
Future Consequences (CFC) scale as well as a behaviour-based subjective discount rate. Results did not
reveal any direct correlations between discount rates and electricity use but mediation analyses
suggested small indirect effects. Environmental concern and CFC, however, was positively and
considerably related to electricity use. Furthermore, there was a large gender difference, with women
using about 23% less electricity than men. This study provides evidence that households'
environmental attitudes and future orientation are correlated with actual energy consumption levels,
and thus lends support to corresponding educational programmes.
Keywords: Environmental concern; future orientation; subjective discount rate; electricity use; pro-
environmental behaviour; consideration of future consequences
Bruderer Enzler, Diekmann & Liebe 2019, p. 2
1 Introduction
Since reducing electricity consumption plays an important role in mitigating climate change, the
question as to its drivers is of utmost importance. The majority of studies looking into this topic report
correlations with a number of factors, including the local climate, the type of building, floor area,
appliance ownership and use, and disposable income (e.g. Bedir, Hasselaar, & Itard, 2013; Huebner,
Shipworth, Hamilton, Chalabi, & Oreszczyn, 2016; Jones, Fuertes, & Lomas, 2015; Kavousian, Rajagopal,
& Fischer, 2013; McLoughlin, Duffy, & Conlon, 2012). Increases in incomes have mostly been found to
be related to higher home energy use (Brandon & Lewis, 1999; Brounen, Kok, & Quigley, 2012;
Druckman & Jackson, 2008; Vringer & Blok, 1995). However, income elasticity is generally low (Alberini,
Gans, & Velez-Lopez, 2011; Sanquist, Orr, Shui, & Bittner, 2012) and the effect may be mediated by
other factors such as appliance ownership (Kavousian et al., 2013). Overall, these studies often explain
around 3040% of the variance in electricity use. Thus, although a broad range of predictors is
considered, there is still a considerable amount of variance that needs explaining. A closer look at these
studies reveals that they mostly neglect psychological factors such as environmental concern, locus of
control and future orientation.
Studies that do focus on such factors repeatedly point to their importance in explaining
consumer behaviour (for overviews see Bamberg & Möser, 2007; Hines, Hungerford, & Tomera, 1987;
Liebe, 2010; Steg & Vlek, 2009; Stern, 2000). However, few of these studies analyse actual electricity
use as opposed to measures of self-reported energy-saving behaviour such as frequency of curtailment
behaviour (e.g. switching off lights or using a lid when cooking), or efficiency-increasing investments
(e.g. buying efficient light bulbs or installing thermal insulation, Gardner & Stern, 1996). It has been
argued that the relationship between psychological factors and behaviour might be weaker or even
non-existent for measures of "actual" behaviour, such as metered electricity use (Van Beek, Handgraaf,
& Antonides, 2017). Therefore, the present study analyses metered consumption data and takes two
psychological factors into account: future orientation, i.e. the extent to which people value future
benefits as opposed to present benefits, and environmental concern.
So far, o nly a few studies have combined measures of general environmental concern with
metered electricity use. The results have been mixed: two studies report negative correlations (Cramer
et al., 1985; Sapci & Considine, 2014), while most do not find any support for a relationship between
environmental concern and electricity use (Huebner et al., 2016; Ohler & Billger, 2014) or combined
measures of gas and electricity use (Abrahamse & Steg, 2011; Brandon & Lewis, 1999; Huebner,
Hamilton, Chalabi, Shipworth, & Oreszczyn, 2015), respectively. Survey studies that run life cycle
analyses to estimate (home) energy use or greenhouse gas emissions report mixed results for
environmental concern (e.g. Bruderer Enzler & Diekmann, 2019; Diekmann & Jann, 2000; Gatersleben,
Steg, & Vlek, 2002; Kennedy, Krahn, & Krogman, 2015; Nässén, Andersson, Larsson, & Holmberg, 2015;
Poortinga, Steg, & Vlek, 2004).
We are not aware of any study on electricity use and future orientation. Yet , more often than
not, energy saving involves a trade-off between short- and long-term outcomes. For example,
purchasing an energy-efficient refrigerator or lowering thermostat settings in winter results in lower
energy costs and helps protect the environment in the long run, while in the short term it leads to a
loss of comfort and/or higher initial costs. We believe that future orientation may have an impact on
all those environmentally relevant decisions where costs (monetary and otherwise) are more
immediate while benefits are more delayed. We broadly understand future orientation as the extent
to which an individual values future outcomes. The more value is placed on future outcomes, the more
future-oriented a person is.
Bruderer Enzler, Diekmann & Liebe 2019, p. 3
In previous research, this individual difference variable has been conceptualised either as a
psychological orientation or as a subjective discount rate. While both strands of research refer to the
same notion of a person (de-)valuing future outcomes as opposed to present outcomes, their empirical
approaches differ. Discount rates are typically assessed by incentivised decisions between smaller
sooner rewards and larger later rewards (Frederick, Loewenstein, & O'Donoghue, 2002). They have
been shown to be related to various activities with long-term consequences, such as smoking, drug
addiction, fitness training and educational decisions (Barlow, McKee, Reeves, Galea, & Stuckler, 2016;
Golsteyn, Gronqvist, & Lindahl, 2014; Heutel, Bardford, Courtemanche, McAlvanah, & Ruhm, 2014;
Lawless, Drichoutis, & Nayga, 2013). Regarding environmentally friendly behaviour in general, ho w e v e r,
there is little research and what there is shows mixed results, both for studies in Western countries
that did at best incentivise a few of their respondents (selected by means of lotteries; Bruderer Enzler,
Diekmann, & Meyer, 2014; Franzen & Vogl, 2013a; Heutel et al., 2014) and in developing countries
where studies have sometimes been able to incentivise all of their respondents (Clot & Stanton, 2014;
Fehr & Leibbrandt, 2008; Javaid, Kulesz, Schluter, Ghosh, & Jiddawi, 2016).
In contrast, other studies measure attitudes towards the future on multiple items using rating
scales. One of the most commonly used measures is the "Consideration of Future Consequences" (CFC)
scale (Strathman, Gleicher, Boninger, & Edwards, 1994). This scale captures the extent to which a
person is driven by short-term rewards or orients him- or herself towards long-term goals. Previous
research indicates that CFC may be relevant in various areas of everyday life (Joireman & King, 2016),
including environmental behaviour (Bruderer Enzler, 2015; Doran, Hanss, & Larsen, 2017; Khachatryan,
Joireman, & Casavant, 2013; Milfont, Wilson, & Diniz, 2012). However, most of the reported findings
pertaining to either the CFC scale or discount rates are based on self-reported behaviour (with few
exceptions; Fehr & Leibbrandt, 2008; Joireman, Posey, Truelove, & Parks, 2009; Khachatryan et al.,
2013). Furthermore, in most studies all data were collected within a single questionnaire or
experimental session. Respondents' tendency to reduce cognitive dissonance may thus have inflated
the relationships found, in particular regarding attitudinal measures, i.e. environmental concern and
CFC. Accordingly, Bruderer Enzler (2015) found systematic evidence for a relation of the CFC measure
and various kinds of environmentally responsible activities. No such relation was found for a measure
of the subjective discount rate (Bruderer Enzler et al., 2014). However, these findings may have suffered
from a self-reporting bias.
In the present study, these potential sources of bias are circumvented both by separating the
measurement of environmental concern and future orientation from collecting information on
behaviour, and by relying on an objective measure of behaviour, i.e. metered electricity use. This is
done by combining questionnaire data with electricity usage data provided by a utility company.
Furthermore, two measures of future orientation are used, i.e. the subjective discount rate and the
CFC scale, thus allowing for comparison. It is commonly expected that the correlation between the
discount rate and electricity use is positive (Hypothesis 1) while the correlation between CFC and
electricity use is negative (Hypothesis 2), i.e. persons who devalue the future to a lesser degree also
use less electricity. Finally, more environmentally concerned persons are also expected to use less
electricity (Hypothesis 3). We believe that by shifting from self-reported to metered behaviour and by
not assessing behaviour and individual characteristics within a single questionnaire, our hypothesis
tests are less prone to bias. In this way, our analyses shed light on the longstanding debate on the use
of behavioural vs. attitudinal measures. Furthermore, this paper is complemented by a series of
exploratory analyses: we examine mediation and moderation models and whether households are
interested in receiving further information on how they might save electricity.
Bruderer Enzler, Diekmann & Liebe 2019, p. 4
2 Materials and Methods
2.1 Study Design and Participant Recruitment
A brief online survey was carried out in cooperation with a local energy supplier in the German-
speaking part of Switzerland in 2016. A letter of invitation with a link to an online questionnaire was
sent to 10,000 customers. As 1,392 persons participated in the survey, the resulting response rate was
approximately 14%. In the following, we refer to those 723 respondents who provided answers to all
variables considered in this study.
The study was announced as a scientific study by ETH Zurich and the University of Bern on the
topic of energy use in Swiss households. The questionnaire did not include any sensitive questions and
anonymity was ensured. The utility company linked the survey data to the households' electricity
consumption during the previous year and made the then completely anonymised data available for
analysis. At no point in time did the researchers know the identities of the participants. The
respondents were not aware of the data linkage. In so doing, a possible bias was avoided (see Schwartz,
Fischhoff, Krishnamurti, & Sowell, 2013 for "Hawthorne effects" in non-covert energy studies).
The questionnaire included an item battery on environmental concern (Diekmann &
Preisendörfer, 2003) and a shortened version of the CFC scale. The latter scale was reduced to six items
due to restrictions of survey space (imposed by the utility company). Thus, three items pertaining to
an orientation towards the future and three items referring to immediate benefits were selected based
on explorative analyses of data from other studies available to the authors. In order for the scale to go
well with the remainder of the survey, a five-point response scale with verbal labels ranging from "does
not apply at all" to "applies fully" was used. The item wordings of the two scales can be found in Table
1 and descriptive statistics for all variables can be found in the online appendix. For both environmental
concern (Cronbach's α = .876) and the shortened CFC scale (α = .750), indexes were computed with
higher values indicating a higher concern for the environment and the future, respectively. 1 The
resulting scores theoretically range from 1 to 5 (i.e. the sum of response scores divided by the number
of items).
To assess subjective discount rates, four questions were used requiring a choice between CHF
1,000 in one year's time and CHF 2,000, 1,500, 1,200 and 1,100 in two years' time respectively.2
Responses were incentivised by a lottery: the participants were informed that three persons would be
drawn at random to receive one of the payments they had opted for. After the field time ended, three
lucky winners were selected and paid according to their decisions.
Based on the amount needed for a person to switch from the smaller sooner to the larger later
reward, discount rates were inferred. For example, in theory, waiting for the later payment of CHF 2,000
(or CHF 1,500) suggests a discount rate of 100% or less (or 50% or less). For our analyses, patience for
CHF 2,000 but not for CHF 1,500 was represented by 75%, i.e. the midpoint between 50% and 100%.
1 There is an ongoing debate (c.f. Joireman & King, 2016) as to whether CFC should be understood as one-dimensional (as
did the original authors, Strathman et al., 1994) or two-dimensional (e.g. Joireman, Shaffer, Balliet, & Strathman, 2012).
It has also been suggested that the frequently reported two-factorial structure might be the result of a method effect due
to the wording of the scale (see Hevey et al., 2010; McKay, Morgan, van Exel, & Worrell, 2015). In line with the latter
reasoning, we opted for the more parsimonious one-dimensional understanding of our six-item instrument. An initial
confirmatory factor analysis without correlated error terms led to a poor fit, χ2(9) = 184.23, p < .001, RMSEA = .164 with
90% CI [.144, .185], pclose < .001, CFI = .818, BIC = 10,269. However, after introducing error correlations (based on
modification indices) between the three positively worded items, between the negatively worded items cfc03 and cfc11
and between cfc07 and cfc10 (both pertaining to "negative" or "problematic consequences"), the resulting fit was good,
χ2(4) = 7.55, p = .110, RMSEA = .035 with 90% CI [.000, .073], pclose = .693, CFI = .996, BIC = 10,125. Of course, this is not
a unique solution.
2 CHF 1,000 equalled roughly USD 1,000 in 2016. The wording and framing of the discounting items can be found in the
online appendix.
Bruderer Enzler, Diekmann & Liebe 2019, p. 5
Being willing to wait for all larger later rewards was interpreted as a discount rate of 5% (the midpoint
between 0% and 10%), while no patience at all was represented as 100%. Incomplete but consistent
patterns were interpreted if either two consecutive decisions indicate a tipping point from the larger
later to the smaller sooner reward or the responses suggest one of the extreme categories.
Table 1. Item wording for the Consideration of Future Consequences (CFC) and environmental concern scales
Item text
Shortened CFC scale
a
I consider how things might be in the future. (cfc01)
I am willing to sacrifice now in order to achieve future outcomes. (cfc06)
I think it is important to take warnings about negative outcomes seriously even if the negative outcome will
not occur for many years. (cfc07)
I mainly act to satisfy my immediate concerns, figuring the future will take care of itself. (cfc03) *
I think that sacrificing now is usually unnecessary since problematic future outcomes can be dealt with at a
later time. (cfc10) *
I only act to satisfy immediate concerns, figuring that I will take care of future problems that may occur at a
later date. (cfc11) *
Environmental concern
b
It bothers me when I think about the environmental conditions in which our children and grandchildren will
probably have to live.
If we continue down the same path, we are heading toward an environmental catastrophe.
If I read news or watch TV news reports about environmental problems, I often become outraged and angry.
There are limits on growth that our industrialized world has already exceeded or will soon reach.
Most people in this country still do not act in an environmentally conscious way.
In my opinion, many environmentalists exaggerate claims about environmental threats. *
Politicians still do not do enough to protect the environment.
In order to protect the environment, we should all be willing to reduce our current standard of living.
Actions to protect the environment should be implemented even if they cause job losses.
Notes: * Items with stars were reverse-scored before creating the indexes.
a Items and translations were adopted from Bruderer Enzler (2015). For the current study, the scale was shortened
from the original 12 items to 6 items as well as slightly simplified to increase readability. Item names in brackets use
the original numbering by Strathman et al. (1994).
b Items taken from Diekmann and Preisendörfer (2001, 2003)
Furthermore, the survey included socio-demographic variables (year of birth, gender, highest level of
education attained, household income in categories of CHF 2,000 up to more than CHF 12,000) as well
as the number of rooms in the apartment, a binary question whether the respondent is a tenant or
owns their apartment, and a series of five binary items indicating whether each of the following devices
is present in the household: a television set, a dishwashe r, a washing machine and a tumble drier (both
exclusively accessed by the household), an electric stove (with or without oven). In addition,
participants were asked whether their building used an electric heating system and whether they
would be interested in receiving further information on energy-saving measures or not.
2.2 Analytical Approach
All analyses were carried out using Stata 15 (StataCorp., 2017). The three hypotheses of this study were
analysed by means of Ordinary Least Squares (OLS) regression models. The results are presented both
for all households and restricted to one-person households (Ta b le 3). While the analyses using the full
Bruderer Enzler, Diekmann & Liebe 2019, p. 6
sample have more power, they assume that the respondent's characteristics are representative of the
whole household. The analyses of one-person households only, in contrast, allow for more strict tests
of our hypotheses as only with this restriction, all variables refer to the same unit of analysis.
The hypothesis on environmental concern was tested separately from future orientation, thus
not controlling for future orientation. The rationale is that these two concepts are theoretically and
empirically related. Therefore, it would be difficult to disentangle the separate contributions of future
orientation and environmental concern in explaining electricity consumption.
3 Results
3.1 Descriptive Results
While average household electricity consumption was 250 kWh per month, this figure was closely
related to household size (mean consumption of one- to four-person households: 147, 265, 281 and
356 kWh/month, respectively). Overall, the participants indicated a relatively high degree of
environmental concern and future orientation as assessed by the CFC scale (see Table 2). Furthermore,
the two measures of future orientation, CFC and the discount rate, were correlated with one another
(same as in previous research, e.g. Van Beek et al., 2017) as well as with environmental concern. The
average discount rate was 38% and when excluding the most extreme cases (≥ 100%) it dropped to
29%. Previous research typically reports similar estimates, such as one study reporting 40% and 27%
for Switzerland (the latter number was computed excluding cases with discount rates 100%; Bruderer
Enzler et al., 2014) or another study reporting 28% for Denmark (Harrison, Lau, & Williams, 2002).
Table 2. Descriptive statistics and Pearson correlations for key independent variables (all households, n = 723)
Mean
Cronbach's
alpha
Pearson correlations
CFC
Discount rate (in %)
37.98
n/a
CFC
4.18
.750
Environmental concern
3.86
.876
.556***
Notes: n/a means "not available". *** p < .001
Median age of respondents was 54 years and the proportion of males was 63%. Thus, on average, the
respondents were older than the general adult population, for which the median is 49 years, and males
were overrepresented (compared to 49% males in the adult population; Swiss Federal Statistical Office,
2018). As in most surveys, there was an upward bias in education: 64% of respondents had earned a
college (university of applied sciences) or university degree. For all further analyses, these two
categories were used to form an indicator for "high education". Median household income fell into the
category of CHF 8,000 to CHF 9,999 per month.
3.2 Who Uses More Electricity?
As can be seen in Table 3, there were positive effects of household size, apartment size, the number of
electrical devices and having an electric heating system on electricity consumption. For one-person
households, however, the effects for apartment size and heating system did not reach statistical
significance. All models suggested gender differences (that are discussed further below). The effects of
the remaining socio-demographic variables, including income, were not statistically significant.
Bruderer Enzler, Diekmann & Liebe 2019, p. 7
Regarding our hypotheses, the results revealed that for all households, electricity use was
negatively and statistically significantly related to both environmental concern (Hypothesis 3) and CFC
(Hypothesis 2). The coefficients indicated that a one-unit increase of these variables was associated
with a decrease in electricity use by 8.1 and 11.8%, respectively. In contrast, no significant relationship
was observed for the subjective discount rate (Hypothesis 1). For one-person households, the
significant effect of CFC remained but the effect of environmental concern lost significance. Yet a
significant effect of environmental concern was present in a bivariate regression (β = -.160, t =-2.55, p
= .012), while the discount rate showed no significant effect at all, either in multivariate or in bivariate
analyses (bivariate regression: β = -.0001, t = -.16, p = .876 for all households and β = .0014, t = 1.09, p
= .275 for one-person households).
Table 3. OLS regression of electricity consumption (in kWh, logarithmised) for all households and for one-person
households
All households
One-person households
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Discount rate (in %)
-.000
(-.10)
.001
(.51)
CFC
-.126**
(-3.12)
-.158*
(-2.05)
Environmental concern
-.084**
(-2.63)
-.081
(-1.20)
Number of persons in household
.192***
(6.60)
.202***
(6.96)
.200***
(6.89)
Number of rooms in apartment
.088***
(4.09)
.087***
(4.03)
.091***
(4.21)
.099+
(1.67)
.094
(1.60)
.103+
(1.74)
Electrical devicesa
.118***
(5.79)
.109***
(5.37)
.107***
(5.19)
.130**
(2.91)
.120**
(2.68)
.123**
(2.73)
Electric heating system (0 = no, 1 = yes)
.193**
(2.82)
.188**
(2.77)
.191**
(2.81)
.127
(.92)
.132
(.97)
.120
(.88)
Owns apartment (0 = no, 1 = yes)
-.071
(-1.25)
-.065
(-1.16)
-.066
(-1.17)
-.073
(-.59)
-.060
(-.49)
-.064
(-.52)
Female (0 = no, 1 = yes)
-.129**
(-2.61)
-.108*
(-2.16)
-.099+
(-1.95)
-.282**
(-2.89)
-.255**
(-2.62)
-.256*
(-2.56)
Age (in years)
.001
(.39)
.001
(.51)
.001
(.45)
.001
(.35)
.002
(.73)
.002
(.45)
High education (0 = no, 1 = yes)
.017
(.35)
.030
(.62)
.024
(.49)
.137
(1.31)
.148
(1.44)
.135
(1.31)
Household income (in CHF/month)
.000
(.97)
.000
(1.05)
.000
(.78)
-.000
(-1.30)
-.000
(-1.53)
-.000
(-1.60)
Constant
4.078***
(31.32)
4.580***
(22.16)
4.402***
(24.60)
4.333***
(19.67)
4.996***
(12.95)
4.697***
(12.75)
Observations
723
723
723
188
188
188
R2adj.
.286
.295
.293
.118
.137
.124
Notes: t statistics in brackets; + p < .10, * p < .05, ** p < .01, *** p < .001.
a Sum scale of five binary items indicating whether each of the following devices is available to the household:
television set, dishwasher, washing machine and tumble drier (both exclusively accessed by the household), electric
stove (with or without oven).
Bruderer Enzler, Diekmann & Liebe 2019, p. 8
3.3 Exploratory Analyses Regarding Future Orientation
This section presents two sets of exploratory analyses: First, we explored whether the effects of future
orientation on electricity use might be mediated by environmental concern. We argue that more
future-oriented individuals might develop a more pronounced environmental concern that in turn
influences their behaviour. This line of thought corresponds to the "awareness model" put forth by
Joireman, Strathman, and Balliet (2006). Previous research provides support for this or very similar
hypotheses (e.g. Bruderer Enzler, 2015; Joireman, Van Lange, & Van Vugt, 2004; Joireman et al., 2001).
In our analyses, possible indirect effects for the discount rate were also explored even though there
was no evidence for any direct effects (see Table 3). In recent literature on mediation, there is
agreement that even in the absence of direct effects it is useful to model indirect effects (Hayes, 2014;
Zhao, Lynch, & Chen, 2010).
Second, as an alternative to this awareness hypothesis, we analysed whether future orientation
moderates the relationship between environmental concern and electricity use. This type of interaction
hypothesis has been termed "concern model" by Joireman et al. (2006). Our proposition is that the
more future-oriented a person is, the more their environmental concern influences their behaviour.
However, previous research has yielded mixed results (e.g. Collins & Chambers, 2005; Joireman et al.,
2004).
3.3.1 Mediation by Environmental Concern
To test whether the effect of future orientation on behaviour is mediated by environmental concern,
indirect effects were estimated following the product of coefficients approach using the Stata
command -sgmediation- written by Ender (2012). To determine the significance of the indirect effects,
bias-corrected 95% confidence intervals were computed by bootstrapping (1,000 iterations; Hayes,
2009; Zhao et al., 2010). The upper section of Table 4 summarises the results of these analyses while
the underlying models can be found in the online appendix.
Table 4. Summary of mediation analyses with electricity use as the dependent variable (in kWh, logarithmised)
Independent variable
Mediator
Samplea
Covariatesb
Indirect effect
95% CI [LL, UL]c
CFC
Environmental
concern
All households
Without
-.0874d
[-.1481, -.0251]
All households
With
-.0291
[-.0804, .0210]
One-person households
Without
-.0575
[-.1778, .0723]
One-person households
With
.0054
[-.1002, .1335]
Discount rate
Environmental
concern
All households
Without
.0004d
[.0002, .0009]
All households
With
.0002d
[.0001, .0005]
One-person households
Without
.0005d
[.0000, .0013]
One-person households
With
.0002
[-.0001, .0009]
Female
CFC
All households
Without
-.0190d
[-.0402, -.0048]
All households
With
-.0217d
[-.0436, -.0084]
One-person households
Without
-.0436d
[-.1097, -.0084]
One-person households
With
-.0302d
[-.0883, -.0015]
Female
Discount rate
All households
Without
.0001
[-.0038, .0055]
All households
With
-.0001
[-.0054, .0032]
One-person households
Without
-.0067
[-.0470, .0052]
One-person households
With
-.0038
[-.0454, .0076]
Female
Environmental
concern
All households
Without
-.0421d
[-.0771, -.0149]
All households
With
-.0305d
[-.0581, -.0058]
One-person households
Without
-.0566
[-.1421, .0023]
One-person households
With
-.0292
[-.0941, .0142]
Bruderer Enzler, Diekmann & Liebe 2019, p. 9
Notes:
a All households: n = 723. One-person households: n = 188.
b The same variables are used as covariates as in Tab l e 3.
c CI = confidence interval; LL = lower limit; UL = upper limit. All CIs from bootstrapping are bias-corrected.
d 95% CI does not include zero.
The results did not provide much support for mediation of the effect of CFC on behaviour: Only an
analysis without covariates and relying on the full sample lent support to the hypothesis. Here, a one-
unit increase in CFC was associated with a decrease of 8.4% in electricity use due to the indirect effect
(in addition to a remaining direct effect that signified a reduction of 6.2% per one-unit increase in CFC).
Thus, while there were direct effects (not controlling for environmental concern, cf. Table 3), our results
did not consistently support mediation of these effects by environmental concern.
For the discount rate on the other hand, there were not any direct effects (cf. Table 3) but there
were indirect effects. This held for all households and when not controlling for further covariates
also for one-person households. The indirect effects suggested, depending on the model, that if the
discount rate increased by 10 units (e.g. from 10% to 20%), the associated electricity use would increase
by 0.2 to 0.5%.
3.3.2 Future Orientation as a Moderator
To explore whether future orientation acts as a moderator of the relationship between environmental
concern and behaviour, models 3 and 6 in Table 3 were rerun twice once including the interaction of
environmental concern with CFC, once including the interaction with the subjective discount rate.
Neither interaction term was significant neither for the full sample nor for one-person households.3
3.4 Exploratory Analyses Regarding Gender
As can be inferred from Table 3, women in one-person households used about 22.524.6% less
electricity than men. Even when controlling for other variables such as income, apartment size or
electrical devices, the gender difference remained. In this section, these differences are explored in
more detail.
Bivariate tests suggested that women (M = 4.11, n = 271) were not only more concerned with
the environment than men (M = 3.71, n = 452), t(721) = -7.18, p < .001, but also more future-oriented
as measured by CFC, t(721) = -3.61, p < .001 (Mwomen = 4.28, Mmen = 4.13). However, there were no
differences regarding discount rates, t(721) = .14, p = .891 (Mwomen = 37.6, Mmen = 38.0).
To examine whether there were indirect effects of gender, mediation analyses were carried out
applying the procedure described in section 3.3.1. The results (see lower section of Table 4) suggested
that the effect of gender might indeed be mediated by CFC, but not by the discount rate. For example,
for females in one-person households, the indirect effect through CFC accounted for a 3.0% lower
electricity use (controlling for covariates). For environmental concern as a mediator, the evidence was
mixed: While there were significant indirect effects for the full sample, the effects for one-person
households were not significant.
To explore whether gender acts as a moderator of the relationship between behaviour and CFC
(as suggested by Joireman & Liu, 2014), discount rates and environmental concern, respectively,
regression models 3 and 6 in Table 3 were rerun each with the interaction of the variable of interest
with gender. Howeve r, n one of the added interaction terms were significant neither for the full
sample nor for one-person households.
3 Results for all moderation analyses can be found in the online appendix.
Bruderer Enzler, Diekmann & Liebe 2019, p. 10
3.5 Expressed Interest in Information on Energy-saving Measures
Finally, the effects of the discount rate, CFC and environmental concern on the interest in receiving
information on energy-saving measures were analysed. The results are presented in Table 5.
Table 5. Binary logit models of interest in information on energy saving, all households and one-person
households
All households:
wants information
One-person households:
wants information
Discount rate (in %)
-.0001
(-.07)
.001
(.19)
CFC
.117
(.70)
.051
(.15)
Environmental concern
.529***
(3.93)
.793*
(2.56)
Electricity use (log(kWh/month))
.228+
(1.74)
.787**
(2.72)
Number of rooms in apartment
-.042
(-.56)
-.373+
(-1.73)
Electrical devicesa
.128+
(1.77)
.068
(.44)
Electric heating system (0 = no, 1 = yes)
-.218
(-.92)
-.460
(-.94)
Owns apartment (0 = no, 1 = yes)
.270
(1.40)
.087
(.21)
Female (0 = no, 1 = yes)
-.125
(-.72)
-.131
(-.38)
Age (in years)
-.009
(-1.39)
.015
(1.23)
High education (0 = no, 1 = yes)
.249
(1.48)
.741*
(2.02)
Household income (in CHF/month)
-.0001
(-1.56)
-.0001
(-.86)
Constant
-3.442
(-3.51)
-7.252
(-3.49)
Observationsb
721
188
McFadden R2
.040
.102
Notes: z statistics in brackets; + p < .10, * p < .05, ** p < .01, *** p < .001.
a Sum scale of five binary items indicating whether each of the following devices is available to the household:
television set, dishwasher, washing machine and tumble drier (both exclusively accessed by the household), electric
stove (with or without oven).
b n is lower than in Table 2 since two persons responded to all items used in the hypothesis tests but did not indicate
whether they were interested in receiving further information.
There were positive and statistically significant effects of electricity use and environmental concern on
the expressed interest. In contrast, neither the effect of the discount rate nor CFC was statistically
significant. Yet in bivariate logit models, CFC had a positive effect (β = .424, z = 3.15, p = .002 for all
households and β = .511, z = 2.04, p = .042 for one-person households). The discount rate, in contrast,
did not have any significant effect at the bivariate level (β = -.002, z = -1.08, p = .281 and β = -.0005, z
= -.13, p = .895).
Bruderer Enzler, Diekmann & Liebe 2019, p. 11
4 Discussion
In contrast to the majority of previous studies on future orientation, environmental concern and (self-
reported) behaviour, we were able to base our analyses on subjective survey data as well as on
objective data on electricity consumption supplied by a utility compa ny. To assess the valuation of
immediate versus future benefits, the survey included both a measure from economics, i.e. the
subjective discount rate, and a psychological multi-item scale, i.e. a shortened version of the CFC scale.
The results indicated that CFC (Hypothesis 2) and environmental concern (Hypothesis 3) were
both significantly and negatively related to electricity consumption. Increases in these variables by one
unit were associated with decreases in electricity consumption by 8.1% and 11.8%, respectively.
However, contrary to expectations, there were not any direct effects of discounting on electricity use
(Hypothesis 1).
To analyse these relationships in more depth, we explored (in a post hoc fashion) alternative (or
complementary) models of how environmental concern and future orientation might be interrelated
with energy use. There was not any support for a moderation hypothesis that argued that a higher
degree of future orientation would amplify the effect of environmental concern on behaviour. However,
there was evidence that the discount rate may yet be related to behaviour, i.e. our analyses revealed
an indirect effect mediated by environmental concern. While this is noteworthy in terms of theory, the
absolute effects were small depending on model specification, they indicated that an increase in the
discount rate by 10 units (that is 10%) was associated with 0.5% increase in electricity use at most.
Analogous mediation analyses for CFC did not suggest any indirect effects. However, this might be due
to the high correlation between CFC and environmental concern which results in larger standard errors
(c.f. Zhao et al., 2010). Overall, these additional results suggest it may be advisable for future research
to look into such mediation models.
Since discount rates and CFC both assess the valuation of future versus immediate consumption,
similar results for the two measures were expected. Yet, our analyses offer consistent support for
effects of CFC while the evidence for discounting was mixed. Although this is in line with previous
research, the question remains as to why this is the case. One possible reason is the discounting
measure used in this study. Future research may want to compare alternative measures within the
same study. These could be longer series of choice tasks (leading to more fine-grained measures and
more variance), tasks involving lower monetary amounts or tasks with different incentives (certain
payoffs, lotteries or purely hypothetical choices).
Furthermore, doubts have been raised whether discount rates do assess a trait and it has been
noted that the measurement of temporal preferences may be confounded by other factors such
transaction costs or risk preferences (cf. Frederick et al., 2002). In addition, some researchers argue in
favour of domain-specific measures of discounting. While following the so-called "correspondence
principle" (Ajzen, 1991), that is measuring concepts at the same level of specificity, may indeed increase
correlations, this is also problematic as it may lead to an overestimation of the relevance of discount
rates.
Hence, from the point of view of our results, the multi-item measurement of future orientation
using a shortened version of the CFC scale seems to be of higher predictive validity than the discount
rate. Our results did not only suggest that it may be advisable to assess future orientation by means of
attitudinal scales such as the CFC scale instead of discounting measures, but also support the validity
of studies relying on self-reported measures of pro-environmental behaviour. Even when analysing
metered electricity consumption and thus ruling out the potential bias introduced by self-reported
behaviour, our results were still in line with previous research. This is encouraging. However, earlier
studies have shown that while self-reported and actual behaviour are correlated there is still a lot of
Bruderer Enzler, Diekmann & Liebe 2019, p. 12
unexplained variance (Kormos & Gifford, 2014), which suggests researcher should whenever possible
base their work on actual behaviour instead.
In addition, it should be noted that besides studies on future orientation, there is a wide range
of psychological publications dealing with temporal aspects of conservation, see for example Gifford et
al. (2009) for a study on temporal pessimism or McDonald, Chai, and Newell (2015) for a review on
psychological distance. While these topics are outside of the scope of the present article, they may
provide interesting avenues for future research. Furthermore, future studies may also want to analyse
the relationship between temporal orientations and the choice of "green" energy contracts by
customers. Previous research has shown that, in line with a status quo bias in decision making
(Samuelson & Zeckhauser, 1988), the definition of green defaults is very effective in promoting the
uptake of green tariffs (e.g. Ebeling & Lotz, 2015) but not much is known about individual differences
relating to these choices.
An interesting by-product of our analyses was an unexpectedly large gender difference: In our
study, women used roughly 23% less electricity than men even after controlling for relevant factors
such as apartment size or income and when restricting the analyses to one-person households. This is
in line with many previous studies on environmental behaviour that have reported that females were
consistently behaving in a more environmentally friendly way (Kollmuss & Agyeman, 2000; Stern, Dietz,
& Kalof, 1993; Zelezny, Chua, & Aldrich, 2000). However, these studies mostly relied on self-reported
behaviour. Consequently and in contrast to our study it is not clear whether the reported gender
differences are real or an artefact of more optimistic self-reporting by women than by men. Out of
three earlier studies on actual electricity use and gender, one reported a lower per capita electricity
use for households with a higher proportion of females (Brounen et al., 2012), whereas the other two
indicated that a higher share of females in households or ZIP code areas was associated with higher
electricity use (Elnakat & Gomez, 2015; Elnakat, Gomez, & Booth, 2016). However, the latter two
studies reported only bivariate results, which is problematic since gender is associated with other
determinants of electricity use, such as income and home size. Hence, our results lend further support
to actual gender differences.
Additional exploratory analyses suggested that the effect of gender may be mediated by
environmental concern and CFC but not by the discount rate. Furthermore, there was no interaction
between gender and CFC and environmental concern, respectively, which suggests that the effects of
these variables are not gender-specific. Further studies are needed to analyse the possible causes of
the reported gender difference.
A major limitation of the present study is potential self-selection. As the response rate was low
as was to be expected based on earlier studies inviting customers of energy suppliers (Abrahamse &
Steg, 2011; Ohler & Billger, 2014) the question remains whether the resulting sample differs
meaningfully from the general population for the purposes of the present study and thus whether the
results hold for other segments of the population as well. For example, there are differences between
sample and population in terms of gender and age. This might be due to the fact that the invitation
letter likely went to the household head, who in larger households is often one of the older residents
and is often male. Despite the introduction of the study as a study on the topic of "energy use" (not
"energy saving"), it is possible that a sample of environmentally motivated households took part. While
other studies have shown that environmental concern is comparatively high in Switzerland (Franzen &
Vogl, 2013b), caution in generalising our results is warranted. Furthermore, since our study is cross-
sectional, the results remain correlational in nature. Future (longitudinal) studies would be needed to
analyse causal relationships.
Another limiting factor is our treatment of the CFC scale. We used a six-item scale and a
confirmatory factor analysis only led to a good fit when introducing several correlated error terms.
Bruderer Enzler, Diekmann & Liebe 2019, p. 13
While introducing such correlations based on empirical results and discussing them post hoc appears
to be rather the rule than an exception, caution is warranted and future studies may consider using the
14-item version of the CFC scale put forth by Joireman et al. (2012). A carefully developed,
methodologically tested and agreed upon short version of the CFC scale would be useful to future
research in fields where questionnaire space is a particularly scarce resource as was the case for this
study.
In terms of policy, the results presented in this paper are encouraging as they indicate that
environmental concern and CFC are both related to electricity use. In addition, both more
environmentally concerned persons and persons living in households with higher electricity
requirements indicated more interest in information on energy saving. This is encouraging since
particularly large consumers in tendency will have a greater potential for actual savings. Therefore,
information provision, educational campaigns, etc. may well be beneficial. Due to higher consumption
levels, policy measures regarding one-person households could focus more strongly on men as a target
group. Well-planned communication of environmental and other long-term consequences of
behaviour (e.g. financial savings) may have an impact on energy use.
Acknowledgements: This work was funded by the Swiss National Science Foundation within the
National Research Programme 71 (grant number: 407140_153715).
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... Hierarchical regression analysis and the bootstrap method were used to test the hypotheses mentioned in the model. Hierarchical regression analysis is often used to test the relationships between variables in empirical research [3,6]. It has the merits of simple operation and easy interpretation. ...
With the continuous prominence of environmental problems, some online environmental platforms have been built in China. Such platforms provide an important carrier for public to learn environmental knowledge and participate in environmental protection. However, whether such platforms can play a substantive role in promoting users' green consumption behaviors is still unclear. Focusing on this question, the influence of online environmental platform services on public green consumption behaviors is explored. A model based on the theory of stimulus-organism-response is established to analyze the influential mechanism, using the online environmental platform services as the independent variable, users' green consumption behaviors as the dependent variable, environmental attitude as the mediator, and users' price sensitivity as the moderator. Survey data are used to test the model. The empirical results show that online environmental platform services have a significant positive impact on users' green consumption behaviors. Environmental attitude plays a partial mediating role and price sensitivity negatively moderates the mediating role of environmental attitude. Suggestions are given from the perspectives of platform operators and government. This paper provides both theoretical and practical implications for sustainable consumption.
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Background Tobacco smoking harms health, so why do people smoke and fail to quit? An explanation originating in behavioural economics suggests a role for time-discounting, which describes how the value of a reward, such as better health, decreases with delay to its receipt. A large number of studies test the relationship of time-discounting with tobacco outcomes but the temporal pattern of this relationship and its variation according to measurement methods remain unclear. We review the association between time-discounting and smoking across (i) the life course, from initiation to cessation, and (ii) diverse discount measures. Methods We identified 69 relevant studies in Web of Science and PubMed. We synthesized findings across methodologies and evaluated discount measures, study quality and cross-disciplinary fertilization. Results In 44 out of 54 studies, smokers more greatly discounted the future than non-smokers and, in longitudinal studies, higher discounting predicted future smoking. Smokers with lower time-discount rates achieved higher quit rates. Findings were consistent across studies measuring discount rates using hypothetical monetary or cigarette reward scenarios. The methodological quality of the majority of studies was rated as ‘moderate’ and co-citation analysis revealed an isolation of economics journals and a dearth of studies in public health. Conclusion There is moderate yet consistent evidence that high time-discounting is a risk factor for smoking and unsuccessful cessation. Policy scenarios assuming a flat rate of population discounting may inadequately capture smokers’ perceptions of costs and benefits.
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There is a growing literature addressing psychological variables that can be associated with choices of environmentally sustainable tourism alternatives. This paper contributes to this literature by focusing on individual differences in value orientations (i.e. egoistic values, altruistic values, and biospheric values), time perspective (i.e. consideration of immediate consequences and consideration of future consequences), and efficacy beliefs (i.e. self-efficacy and collective efficacy). A cross-sectional survey (N = 385) was carried out to investigate the role of each of these psychological variables in explaining intentions to choose environmentally sustainable travel options. Overall results showed that value orientations, time perspective, and efficacy beliefs together contributed to explaining about 53% of the variance in behavioural intentions. Consideration of future consequences and collective efficacy showed the strongest associations with behavioural intentions. Implications of these findings for research and managerial practice are noted.
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Chapter
Many everyday decisions have an intertemporal character and are consequently influenced by an individual’s time orientation. In Section 1, we discuss and compare various conceptualizations of time orientation, such as discounting, time perspective and consideration of future consequences, and ways to measure these concepts. In addition, we provide an overview of studies on the relations between various time orientation measures. In Section 2, we discuss the extent to which time orientation is a domain-general or domain-specific construct, by reviewing studies that compare time orientation across and within various domains, such as money and health. In Section 3, we discuss the relationship between time orientation and health behavior. We conclude with a general discussion of the different constructs and measures.
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A variety of theoretical approaches have been taken in an attempt to understand, explain, and promote pro-environmental behavior. The present article gives an overview, including specific applications, and identifies and discusses various strategies used by researchers to deal with the availability of different approaches. The overview includes elementary rational choice theory, the theory of planned behavior, norm-activation theory, theories of habitual behavior, and theories within a social dilemma framework. Strategies identified are ‘extending existing theories by single explanatory factors’, ‘comparing theories’ in a competitive manner, and ‘combining theories’ in an integrative manner. It is argued that research would benefit from more standardization in empirical applications, from more competitive theory testing as opposed to integrative theory testing, and from an evaluation of approaches on theoretical grounds as opposed to focusing solely on empirical performance.
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In 1994, Strathman, Gleicher, Boninger, and Edwards (1994) introduced the 12-item consideration of future consequences scale to measure individual differences in the extent to which people base their decisions on the immediate vs. future consequences of their actions. After tracing the origin and development of the CFC construct, theoretical models, including the awareness and concern models, buffering and susceptibility models, and an integrative model of CFC are outlined. Next, a narrative review of work examining the role of CFC within several outcome domains is presented, including research in the areas of health, financial, work, ethical, and environmental behavior and decision-making. Measurement issues including the dimensionality of the scale and adaptation to specific domains (e.g., healthy eating) are subsequently considered, and eight directions for future research are discussed.