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ORIGINAL RESEARCH
published: 02 April 2019
doi: 10.3389/fpsyg.2019.00718
Edited by:
Giuseppe Carrus,
Università degli Studi Roma Tre, Italy
Reviewed by:
Paola Passafaro,
Sapienza University of Rome, Italy
Stefano Mastandrea,
Università degli Studi Roma Tre, Italy
*Correspondence:
Oliver Wilhelm
oliver[email protected]
Specialty section:
This article was submitted to
Environmental Psychology,
a section of the journal
Frontiers in Psychology
Received: 06 June 2018
Accepted: 15 March 2019
Published: 02 April 2019
Citation:
Geiger SM, Geiger M and
Wilhelm O (2019)
Environment-Specific vs. General
Knowledge and Their Role
in Pro-environmental Behavior.
Front. Psychol. 10:718.
doi: 10.3389/fpsyg.2019.00718
Environment-Specific vs. General
Knowledge and Their Role in
Pro-environmental Behavior
Sonja Maria Geiger1, Mattis Geiger2and Oliver Wilhelm2*
1Institute for Vocational Training and Work Studies, Technische Universität Berlin, Berlin, Germany, 2Psychological Institute,
University of Ulm, Ulm, Germany
Environmental knowledge has been established as a behavior-distal, but necessary
antecedent of pro-environmental behavior. The magnitude of its effect is difficult
to estimate due to methodological deficits and variability of measures proposed in
the literature. This paper addresses these methodological issues with an updated,
comprehensive and objective test of environmental knowledge spanning a broad
variety of current environment related topics. In a multivariate study (n= 214), latent
data modeling was employed to explore the internal factor structure of environmental
knowledge, its relationship with general knowledge and explanatory power on pro-
environmental behavior. We tested competing factor models and uncovered a general
factor of environmental knowledge. The main novel finding of the study concerns
its relationship with general knowledge. Employing an established test of general
knowledge to measure crystallized intelligence revealed a near perfect relationship
between environmental and general knowledge. This general knowledge (including the
environmental domain) accounted for 7% of the variance in environmentally significant
behavior. Age, additionally to acquired education, emerged as a common predictor
for both general knowledge and environmentally significant behavior. We discuss the
consequences of the strong relation between general and environmental knowledge
and provide a possible explanation for the positive age-environmental conservation
relationship reported in the literature.
Keywords: environmental knowledge, general knowledge, domain-specificity, environmentally significant
behavior, structural equation modeling
INTRODUCTION: THE ROLE OF KNOWLEDGE IN
PRO-ENVIRONMENTAL BEHAVIOR
Knowledge about environmental issues is thought to be a precondition for meaningful pro-
environmental behavior and its transmission is considered a key component and criterion for
successful implementation of environmental education programs (UNESCO, 2005;Heimlich and
Ardoin, 2008;Kaiser et al., 2008). A moderate, positive influence of domain-specific environmental
knowledge has been reported in early research on pro-environmental behavior (Hines et al., 1987)
and was replicated in various studies since (Kaiser and Frick, 2002;Frick et al., 2004;Meinhold
and Malkus, 2005;Geiger et al., 2014, 2018). Other studies have found very small cross-sectional
relationships (Roczen et al., 2013;Braun and Dierkes, 2017;Otto and Pensini, 2017) and the limited
use of knowledge interventions on behavior change has been discussed since the seminal paper of
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Geiger et al. General Knowledge and Environmental Behavior
Kollmuss and Agyeman (2002).Kaiser and Fuhrer (2003)
propose that environmental knowledge, as a distal predictor of
environmental behavior, is systematically underestimated and
therefore not as prominently researched in environmental
behavior studies as normative or attitudinal aspects. If
environmental knowledge is studied, it is often restricted to
subjective self-report measures of ability (e.g., Duerden and Witt,
2010;Milfont, 2012) or conflated with subjective evaluations
of environmental issues as so-called problem awareness (e.g.,
Bamberg and Möser, 2007). Due to these methodological issues,
it is impossible to derive sound conclusions about the influence
of actual environmental knowledge on pro-environmental
behavior. In cases where it is assessed objectively, different
knowledge types have been suggested with insufficient testing of
this claim (e.g., Roczen et al., 2013;Geiger et al., 2014;Liefländer
et al., 2015) and the relationship to an overarching general
knowledge factor has not been tested up to date, although
general knowledge is a widely researched topic in psychological
research as an individual differences with far-reaching practical
implications. The main aim of this paper is to explore the relation
of environmental knowledge to general knowledge and the
predictive power that each has on environmentally significant
behavior. Toward this end we present an updated, objective
environmental knowledge test, and investigate its measurement
properties that challenge the distinction of different knowledge
types in existing models of environmental competence.
Methodological Issues of Knowledge
Assessment
Without a precise definition of knowledge, it is impossible
to meaningfully investigate the structure of environmental
knowledge and its predictive power on corresponding behavior.
Knowledge is the result of a person’s lifelong learning process,
i.e., the voluntarily accessible and organized accumulation of
veridical information (facts, rules, etc.). Veridical means that
the information is unequivocally true or false. To evaluate an
individual’s performance in a knowledge test, her responses
must be compared to the veridical answer of a given item
or question, and consequently be rated as either “correct”
or “wrong” (Cronbach, 1949). In contrast, many studies in
environmental research use confidence or agreement ratings
that assess self-concepts of one’s own knowledge, i.e., “I can
explain what the term ecology means,” (Duerden and Witt,
2010), “How well-informed do you consider yourself to be
on global warming and climate change?” (Milfont, 2012), or
“How would you rate your knowledge/ability/awareness about
composting/recycling/sustainability. . .?” (Redman and Redman,
2014). These tests do not measure actual knowledge, but
a meta-representation of subjective knowledge (Metcalfe and
Shimamura, 1996). Basic research on self-reports of cognitive
abilities shows weak to zero relations with objective ability
measures (Mabe and West, 1982;Jacobs and Roodenburg,
2014), corroborated recently for sustainability related knowledge
(Effeney and Davis, 2013). These low correlations are suggestive
of that the so-called Dunning-Kruger effect also exists in the
environmental domain, where especially novices in a certain
domain grossly overestimate their own expertise (Kruger and
Dunning, 1999;Sanchez and Dunning, 2018).
Another prominent approach in environmental research is the
subjective evaluation of the severity of a certain environmental
issue on again, a Likert-type agreement rating scale, e.g., “Air
pollution from private car use is a threat to plants and animals
in the world” (e.g., Eriksson et al., 2006). This approach is
problematic when it is conflated with objective knowledge
measures, as in the meta-analysis undertaken by Bamberg and
Möser (2007), for example. Their conclusion of an indirect
influence on behavior via normative variables and feelings of guilt
cannot be definitely attributed to objective, verifiable knowledge.
Likewise, the meta-analysis’s results could also reflect effects of
subjective feelings about whether an environmental issue is a
problem. As with confidence ratings, a recent study by Ünal et al.
(2017) could show that problem awareness correlated only weakly
with general knowledge on climate change consequences and not
at all with more specific knowledge on the topic.
These examples show that subjective measures of
environmental “informedness” and problem awareness are
not the same as objective knowledge. Despite recent evidence,
that these two types of knowledge are subject two different
psychological processes (Dunning and Helzer, 2014) and
might contribute to behavior in different ways, there is not
much systematic investigation into these differences in the
environmental domain. One notable exception is a recent
study by Passafaro and Livi (2017) that also evidenced an only
moderate correlation between perceived and actual recycling
skills (r= 0.33). More interestingly, they found that perceived
skills are indicative of if people are likely to act at all (motivation),
and actual skills relate to how well they will perform (accuracy).
To advance systematic research on the role and structure
of environmental knowledge in according behavior, objective
instruments are indispensable, even if their development is
more complex, as they have to be validated for veridicality,
content, uniqueness of correct answers, difficulty levels of
adequate distractors (false answer options) or are based on
highly ecologically valid simulation tasks (Passafaro et al., 2016).
This study contributes to this endeavor with the actualization
and advancement of such an instrument based on existing
instruments (Kaiser and Frick, 2002;Geiger et al., 2014;
Braun and Dierkes, 2017).
Specific Types of Environmental
Knowledge?
The environmental-competence model by Kaiser and colleagues
(Frick et al., 2004;Kaiser et al., 2008;Roczen et al., 2013) is
based on an objective knowledge test that advocates domain-
specific knowledge in environmental education. It specifies
three different knowledge types that have shown to affect
environmental behavior to different degrees. According to
this model, system related knowledge comprises declarative
knowledge about the ecological system and natural laws
(e.g., “What are constituents of biodiversity?”). Action-related,
procedural knowledge about possible actions for environmental
conservation (e.g., “Does buying organic food help to conserve
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biodiversity?”) is expected to exert a strong direct effect on
behavior. Effectiveness knowledge relates to information about
relative effects of different behaviors (e.g., “Is buying organic food
more effective to conserve biodiversity than giving up certain
products?”) and is assumed to have a motivational function on
engaging in relevant behaviors.
The assumed structure of three facets of environmental
knowledge – system, action, effectiveness – has rarely been
empirically tested against a parsimonious one factor model,
where all items are modeled onto a single dimension. When it
has, only one study found marginally better fit parameters for
the three factor model (Frick et al., 2004). One study found
no difference (Kaiser and Frick, 2002) and further studies did
not explicitly test different alternatives (Roczen et al., 2013;
Geiger et al., 2014;Liefländer et al., 2015). Taken this sparse
evidence, it remains unclear whether a multifaceted structure
of environmental knowledge, although theoretically plausible,
is an empirically warranted, appropriate model of people’s
actual knowledge.
General Versus Domain Specific
(Environmental) Knowledge
Unlike research on environmental knowledge, investigating the
structure and influence of general knowledge on behavioral
outcomes has a long tradition in psychological research on
individual differences (Cattell, 1971). In intelligence research,
general knowledge is a well-established construct subsumed
under crystallized intelligence (“gc”). It denominates the corpus
of acquired factual knowledge and a person’s experience over
a lifetime which predicts important outcomes in everyday
life, such as academic achievement (Ones et al., 2005) or
job performance (Hunter, 1986;Lievens and Patterson, 2011).
While most cognitive abilities decline during adulthood, general
knowledge grows with increasing age, up until late adulthood
(Schroeders et al., 2015). In contrast, fluid intelligence (“gf”)
comprises abilities such as reasoning, working memory capacity,
etc., which are independent of a specific learning history (for a
detailed description of both, see Carroll, 1993).
Theoretically, general knowledge might cover any domain,
from knowledge about current events (e.g., celebrities) to detailed
knowledge about decimals of π. The so-called “knowledge-is-
power” hypothesis assumes that independent, domain-specific
factors of knowledge are shaped by individual learning history
in a certain domain, i.e., gaining expertise (Hambrick and Engle,
2002;Hambrick, 2003;Hambrick and Oswald, 2005) and that
this expertise explains domain specific performance. However,
factor analytic research puts the domain specificity into question
(Hambrick and Meinz, 2011). In several large and broadly varying
samples, a carefully developed gc test with multiple domains (e.g.,
history, medicine, literature, and many more distinct disciplines)
always points to a general factor of gc with no evidence of domain
specificity (Schroeders et al., 2013;Schipolowski et al., 2014a,b;
Wilhelm et al., 2014).
Environmental knowledge might be considered another
knowledge domain – a domain of specific interest in research
concerning pro-environmental behavior or reaching sustainable
development goals in a wider scope. Whether environmental
knowledge is embedded in the general accumulated knowledge
base of people has not yet been tested. The relationship
between environmental and general knowledge is important for
practitioners, as it will add empirical evidence to the discussion
on appropriate approaches to environmental education. While
an environmental-specific ability approach is advocated by
Kaiser et al. (2008), recent literature on education for
sustainable development advocates the concept of broad,
content-independent competencies, such as perspective taking,
participation, or handling of complex information (Haan et al.,
2008;Michelsen and Fischer, 2017). If environmental knowledge
shows systematic variation between participants after controlling
for general knowledge, and if this residual variation predicts
environmental behavior, the knowledge-is-power hypothesis will
be supported. On the other hand, if environmental knowledge
is empirically indistinguishable from general knowledge or does
not account for environmental behavior and we will interpret
this as evidence for a general knowledge model in explaining
domain-specific behavior.
The Predictive Power of Knowledge in
Environmental Behavior
It has been convincingly shown that a simple information deficit
model, where the provision of information is deemed sufficient
to change the relevant behavior, is inappropriate (Kollmuss and
Agyeman, 2002;Schultz, 2002). However, this evidence should
not dismiss knowledge as a meaningful predictor of behavior
altogether. Theoretically, knowledge can be considered a limiting
factor for environmental behavior: if people do not know about
environmental effects of their behavior, they cannot intentionally
adjust their behavior toward generating less environmental
impact. Kaiser (1998);Kaiser and Wilson (2000) was one of the
first authors to present a comprehensive measure of ecological
behavior, spanning behaviors from different areas of life as
mobility, waste management, consumerism, energy consumption
and vicarious behavior. Critique on their intention-based
approach, where the intention to seek to minimize environmental
damage solely defines pro-environmental behavior was currently
raised (Steg and Vlek, 2009;Huddart Kennedy et al., 2013;
Geiger et al., 2017). These authors call for inclusion of actual
impact criteria in the investigation of environmental behaviors
in the social sciences, such as e.g., the ecological or carbon
footprint of the services and products consumed by people.
The current paper applies such an impact-based idea, where
environmental behaviors are selected for their actual high impact
on the environment.
With regards to the role of knowledge for environmental
behavior, two extensive meta-analyses on the explanation of
pro-environmental behavior (Hines et al., 1987;Bamberg and
Möser, 2007) have supported an indirect role mediated via
moral norms, feelings of guilt, and/or intentions to act more
environmentally friendly. Various empirical papers show that
when knowledge is measured objectively, direct relationships
can be observed, explaining anywhere between 3 and 24% of
behavioral variance (Kaiser and Frick, 2002;Frick et al., 2004;
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Meinhold and Malkus, 2005;Roczen et al., 2013;Geiger et al.,
2014, 2018;Braun and Dierkes, 2017). The notion of an
indirect, necessary, yet insufficient role of knowledge to bring
about informed behavior is also brought forward by Kaiser
and Fuhrer (2003), along with three arguments on why the
influence of knowledge on behavior has been systematically
underestimated: (a) the existence of overruling more direct
influences as situational restrictions, (b) inadequate assumptions
about the structure of knowledge, and (c) the use of inadequate
statistical procedures that do not account for measurement
errors. In the current paper, we will scrutinize the latter two
obstacles: we investigate the adequate knowledge structure using
latent modeling techniques to see if an influence of knowledge on
behavior is replicated under such circumstances.
RESEARCH GOALS
Responding to the methodological problems outlined in the
introduction, the first goal of this study is to present an
updated and expanded measurement instrument for objective
assessment of environmental knowledge and to test its internal
structure concerning content subdomains and knowledge types
(cf. Kaiser et al., 2008). As an improvement compared to
existing scales, which focus on natural science aspects of
ecology, the new test includes current issues in sustainability
related social, political, and economic topics (see Leach
et al., 2013). As basic intelligence research has called into
question the “knowledge-is-power” hypothesis, the second goal
is to investigate whether environmental knowledge should be
regarded an independent knowledge domain or a subdomain
of general knowledge. Our third goal is to investigate whether
environmental knowledge predicts pro-environmental behaviors
under best of measurement conditions over and beyond the
influence of general knowledge.
MATERIALS AND METHODS
Procedure
An online survey in German language was conducted with the
survey tool Unipark and published online from July to September
2015. The link was advertised to a larger German-speaking online
community sample and additionally advertised on German social
media webpages. The survey included a short sociodemographic
questionnaire followed by three scales in the following order:
general knowledge (Berliner Test zur Erfassung Kristalliner
Intelligenz, BEFKI), the updated environmental knowledge test
(EKT) and a short impact based scale of environmental behavior
(SIBS). The study was conducted according the ethical guidelines
for online studies of the German Society for Online Research
(Deutsche Gesellschaft für Online-Forschung, 2007). Consent of
each participant was requested in digital form on the first page
of the survey and anonymity of participants was guaranteed.
Participation was voluntary and 10 online vouchers worth 20€
were raffled among the participants who completed all measures
and opted in for the raffle. Ethical approval was not required as
per local legislation.
Sample
The website of online study was accessed 443 times. The dataset
was cleaned by excluding participants who dropped out before
the end (n= 228, including instant drop-outs, not accepting
study terms and conditions, study restarts, etc.), because study
aborts must be treated as a reversal of participation consent.
Furthermore, participants that responded to at least one out of
four attention check questions erroneously (n= 1; an example
item reads “This is an Attention Check, please respond with B”),
or showed clear response patterns (n= 0, i.e., with little-to-no
within person variance across response options) were excluded.
Overall, 214 subjects remained and are included in all subsequent
analyses. Mean age is 32.4 years (SD = 11.7 years, ranging from 18
to 92 years of age, German population median 18+: 50 years) and
131 participants are female. With respect to education (German
population in parentheses, Statista, 2014), 5.9% (32.9%) have a
basic school education, 16.8 % (29,4%) have a simple high school
diploma, 39.2 % (29,5%) have passed high school with a university
entrance diploma or comparable, and 38.3% possess a university
degree. Our sample is thus younger and better educated than
the German average.
Measures
Environmental Knowledge Test (EKT)
The environmental knowledge test was constructed to cover
relevant core areas of ecology, climate, resources, environmental
contaminants/health, and consumption behaviors. Based on a
wider understanding of sustainability explicitly endorsing a
socio-economic dimension (Farley and Smith, 2013;Leach et al.,
2013), we additionally included items measuring knowledge
about sustainability-related societal and economic issues. The test
was constructed in a multiple-choice format with one correct
answer and three distractors (see Appendix A) to be comparable
to the validated test of general knowledge in terms of form,
language, and difficulty (see below and Appendix B).
In the first round, items from existing scales were screened
for relevant content and compliance with test construction
standards such as veridicality and comparability of distractors
based on their wording. This step yielded 7 suitable items from
the environmental knowledge scale by Kaiser and Frick (2002):
items 1, 2, 3, 7, 13, 23, 32) 7 items from the EKLA scale
(Geiger et al., 2014: items 6, 8, 15, 17, 18, 20, 21) and 3 items
from Schahn’s scale (Schahn, 1999: items 24, 33, 34). Nineteen
additional items were constructed on the basis of ecology and
environmental study books, course curricula of German schools,
and webpages of official environmental institutions (e.g., German
federal environmental agency1), German federal ministry for
the environmental, nature conservation, building and nuclear
safety (BMU2).
The resulting 36 items can also be classified according to
their knowledge type (cf. Frick et al., 2004), yielding 21 system
1www.umweltbundesamt.de
2www.bmub.bund.de
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knowledge items, 7 action-related knowledge items, and 8
effectiveness knowledge items.
Response format of all items was multiple choice and
constructed or adapted to have one correct answer and three
distractor answers. For a list of all German items included in
the final form (n= 35, 1 item excluded and 3 more items
adapted) with their according thematic clusters, see Appendix A
in Supplementary Material. The English translation has not been
validated and is only displayed for illustrative purposes.
General Knowledge (BEFKI gc)
One extensively validated instrument for the assessment of
general knowledge is the Berlin Test of Fluid and Crystallized
Intelligence (BEFKI gc, Schipolowski et al., 2013;Wilhelm
et al., 2014). The crystallized intelligence section of this test
comprises 64 items that capture general knowledge in a multiple
choice form. The 16 content domains reflect the German
national curriculum and comprise subjects from natural sciences
(chemistry, physics, geography etc.) humanities (music, arts,
history, etc.), and social sciences (finance, economics, religion,
etc.). Confirmatory factor analysis (CFA) has repeatedly revealed
a strong general factor with high reliability (ω= 0.88) and little
domain specificity (Wilhelm et al., 2014). It is available in three
versions for different education levels (until grade 8, grade 8 to
10, grade 11+), as well as in parallel versions for applied and
research purposes. As we aimed for a wide age range (18+), and
as gc increases until late adulthood, we used the version for grade
11+. For three example items of the domain music, history, and
chemistry, see Appendix B in Supplementary Material.
Short Impact Based Pro-environmental Behavior
Scale (SIBS)
The SIBS comprises five of the six behavioral areas mobility,
private energy consumption, waste management, consumption
choices, and social behaviors assessed by Kaiser (1998), plus
nutrition, which was added because of the high environmental
impact of nutrition-related behaviors alongside the consumption
fields of housing and mobility (EEA, 2013;Thøgersen, 2014;
Geiger et al., 2017). The items were selected according to their
environmental impact in terms of greenhouse gas emission or
ecological footprint. Within each domain, so-called “big points”
(Bilharz and Schmitt, 2011) were addressed, as e.g., heating
or solar energy production for private energy consumption, or
airplane and individual motor travels for the mobility sector
and meat consumption and regional food for nutrition (Lorek
and Spangenberg, 2001;Tukker et al., 2010), with the exception
of three general consumption and two social behaviors that
do not have a quantifiable direct environmental impact (items
11–15). Response format was a 5-point Likert scale ranging
from 0 = “never” to 4= “always.” Two dichotomous items
on electricity-provision were combined into one 5-point scale
-item (neither item affirmed = 0, renewable energy provider
affirmed = 2, own solar panel = 3, both items affirmed = 4).
Car use was assessed with three questions on ownership, gasoline
usage and fuel type, that were combined to an overall 5-point
index of car use. After excluding two items on food waste and use
of deposit bottle due to negative loadings, the final form included
18 items (see Appendix C in Supplementary Material). With
only two to four items per subdomain, it is a short scale for the
assessment of environmentally relevant behaviors.
Methodological Framework and
Statistical Analysis
This study uses structural equation modeling as a framework, a
modeling technique that allows to combine factor analytic with
path analytic research questions (for an introduction see Kline,
2011). The factor analytic approach allows to test measurement
models, i.e., if latent constructs (as e.g., environmental knowledge
in our case) are measured appropriately with items that
adequately represent the latent factor. The path or regressional
analytic approach reveals underlying relationships between these
latent factors in a structural model (e.g., how much behavioral
variance is explained by knowledge). Model fit parameters are
used to indicate the overall quality of a specified model including
both aspects of the model, the measurement and structure
model(s). Therefore, the measurement model of a latent variable,
especially when using a new test, has to be tested separately before
including it in an overall structural model, this was done here for
environmental knowledge as well as for behavior.
The statistical analyses to this end were conducted using
R 3.4.1 (R Core Team, 2014), setting the threshold of any
statistical significance testing in this manuscript to α= 0.05.
We calculated McDonald’s (1999) with the package semTools
(version 0.4-13; semTools Contributors, 2016) as an adequate
index of reliability for latent variable modeling (Sijtsma, 2009).
Item analysis was conducted using the package psychometrics
(version 2.2., Fletcher, 2010). To investigate the factor structure
of the EKT, we tested nested confirmatory factor models using
the DIFFTEST option in Mplus (Muthén and Muthén, 2010) due
the dichotomous nature of our data (correct/wrong), requiring
the use of Weighted Least Squares Mean and Variance estimation
(WLSMV, Muthen et al., 1997). Other latent analyses were
conducted using the package lavaan (Rosseel, 2012) in R with
Maximum Likelihood (ML) estimation.
The CFA and structural equation models (SEM) in section
“Relationship of Environmental Knowledge and General
Knowledge” and “The Role of General Knowledge and
Environmental Knowledge in Environmental Behavior” were
conducted with parcels as indicators, as our sample was to small
to estimate these models on an item level, which would have
required 97 indicators (Little et al., 2002). Parcels as proportion of
correct scores were built for all knowledge and behavioral items
belonging to a content domain. For example, all knowledge items
related to financial issues were combined to a finance parcel and
all knowledge items related to climate change were combined to
a climate parcel. We proceeded likewise with behavioral items.
For thematic classification of environmental knowledge items,
see Appendix A and for behavioral items, see Appendix C.
To evaluate the goodness of fit of our models, we use root
mean square error approximation (RMSEA) and comparative fit
index (CFI). RMSEA is an absolute index of model fit reflecting
the discrepancy between observed and postulated model, and
therefore should approximate 0 (“perfect fit”). The CFI, on the
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other hand, is an incremental index that compares the postulated
model to a base model with uncorrelated factors and thus should
approximate 1 for perfect fit. According to statistical conventions,
a model reflects the correlational structure of the empiric data
well (“good model fit”) if CFI ≥0.95 and RMSEA <0.06 (Hu
and Bentler, 1999) and reasonable well (“acceptable model fit”), if
CFI ≥0.90 and RMSEA <0.08 (Bentler, 1990;Steiger, 1990).
RESULTS
We will first present the results on the psychometric properties
and descriptive results of the environmental knowledge test
[see section “Psychometric Properties and Descriptive Results
of the Test of Environmental Knowledge (EKT)”], followed
by the results on the relationship between environmental
knowledge and general knowledge (see section “Relationship
of Environmental Knowledge and General Knowledge”),
and end with the analysis on the predictive power of
both on environmental behavior (see section “The Role
of General Knowledge and Environmental Knowledge in
Environmental Behavior”).
Psychometric Properties and Descriptive
Results of the Test of Environmental
Knowledge (EKT)
We computed three nested confirmatory factor analyses to
evaluate the factor structure of the newly developed EKT. Three
items (numbers 14, 28, and 33) were excluded from all further
reported analyses due to negative loadings in all tested models.
On the remaining 33 items, we tested: (1) a g-factor model with all
items loading on a single dimension; (2) a three factor model with
knowledge type: system, action and effectiveness knowledge as
factors (Frick et al., 2004;Kaiser et al., 2008;Roczen et al., 2013);
and (3) a seven factor model with content domains as factors (c.f.
Appendix A). In case of multiple factors, these were allowed to
correlate. Results of the three models are summarized in Table 1.
Fit indices, as well as the χ2-Difference Test, indicate that no
model is clearly superior to any other model. The more complex
models 2 and 3 revealed serious computation issues, namely a
non-positive definite ψmatrix, Heywood cases, and correlations
TABLE 1 | Model comparisons of competing factor structures of the EKT (item
n= 33) ordered by declining parsimony.
#Model type χ2-Model test RMSEA CFI χ2-Difference
test to
previous #
1 g-factor χ2(495) = 562,
p= 0.020
0.025 0.895 −
2 Three factors:
knowledge types
χ2(492) = 558,
p= 0.021
0.025 0.896 χ2(3) = 55.0,
p= 0.174
3 Seven factors:
content domains
χ2(474) = 539,
p= 0.021
0.025 0.898 χ2(18) = 20.1,
p= 0.325
χ2-Difference tests were always conducted in contrast to the previous model. As
the estimator was WLSMV, χ2-Difference tests were conducted using the MPLUS
DIFFTEST option (Muthén and Muthén, 2010).
r>1 between latent factors. In model 2, two of the three
factor correlations were estimated larger than unity, whereas
in model 3, 16 out of 21 factor correlations were larger than
unity. The computational issues can be interpreted as indicators
of over-factoring. Therefore, and for reasons of parsimony, we
conclude that a g-factor solution is the appropriate representation
of factor structure of the EKT. The final g-factor has acceptable
reliability of ω= 0.737.
Considering overall model fit, RMSEA was good, but CFI was
not, which might be due to five items (12, 18, 20, 22, 35) revealing
low and non-significant loadings due to extreme item difficulties
(very hard/very easy). However, our sample is not sufficiently
representative (i.e., sample size, distribution of education, age) to
conclude with certainty that these items should be removed as of
yet. Therefore we suggest slightly modified versions of these items
with easier i.e., harder distractors to be tested in future studies, as
presented in Appendix A.Table 2 shows the descriptive results
for the seven different content domains. Whereas our sample is
very knowledgeable about issues on climate and environmental
contamination, knowledge on resources and their limitations,
as well as according consumption behaviors conserving them,
are less prevalent.
The final EKT (without items 14, 28 and 33) showed no
evidence of floor or ceiling effects. Mean item difficulty (i.e.,
solving probability) was at M= 0.686, with a standard deviation
of SD = 0.131. Mean item discrimination (i.e., the factor loading,
which is the biserial item-factor relation in the model with
WLSMV estimation) was good with M= 0.443 and a standard
deviation of SD = 0.217. Consequently, we consider the item
discrimination of the test to be acceptable overall.
Relationship of Environmental
Knowledge and General Knowledge
Next, we evaluated the relation of our newly developed EKT
with the established BEFKI gc measure of general knowledge,
specifying one latent factor for each construct. Based on
modification indices we accepted one error correlation between
indicators of general knowledge (parcels finance and medical
knowledge). The model had acceptable to good fit [χ2(228) = 348,
p<0.001; CFI = 0.926; RMSEA = 0.049]. The two latent factors
were extremely highly correlated (r= 0.930).
TABLE 2 | Descriptive results on knowledge in different content domains.
Thematic domain Mean SD Min Max
1 Basic ecology 66.7% 16.8 20.0% 100%
2 Climate 82.0% 16.4 20.0% 100%
3 Resources 34.0% 25.0 0% 100%
4 Consumption behavior 59.6% 18.1 0% 100%
5 Society/politics 74.9% 24.4 0% 100%
6 Economy 80.1% 25.2 0% 100%
7 Environmental contamination 86.0% 19.4 0% 100%
Environmental knowledge overall 68.3% 13.1 18.2% 97%
The descriptive results are displayed for applied contexts, factor-analytic results
show that they all belong to a general knowledge factor gc.
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TABLE 3 | Model comparisons of general knowledge structure (indicators are
parcels; parcel n= 16+7), including vs. excluding environmental knowledge as
specific factor.
#Model type χ2-Model test RMSEA CFI χ2-Difference
test to
previous#
1 g-factor χ2(229) = 355.4,
p<0.001
0.051 0.922
2 Bifactor: general
factor +nested
environmental
knowledge factor
χ2(222) = 339.4,
p<0.001
0.050 0.927 χ2(7) = 16.0,
p= 0.025
χ2-Difference test was conducted using the MPLUS DIFFTEST option (Muthén and
Muthén, 2010).
As this correlation indicates very high redundancy of
environmental knowledge to the broader theoretically underlying
construct of general knowledge, we further investigated the
specificity of environmental knowledge. Therefore, we estimated
and compared two inferentially nested models: (1) a g-factor
model with only one factor of general knowledge predicting
all parcels and (2) a bifactor model with the g-factor as in
model 1 and an additional orthogonal factor with loadings only
from the EKT-Parcels. The results of this model comparison are
presented in Table 3.
Both models had very similar and acceptable to good fit.
The ordinary χ2-difference test comparing both models was
significant with p= 0.025. However, because the χ2-difference
test has high power to detect negligible effects with larger sample
sizes we also investigated the nested factor’s loadings and variance
(Brannick, 1995;Kelloway, 1995). The mean factor loading was
low with M= 0.210 and none of the loadings were significant.
Consequently, the factor’s variance neither was significant, with
p= 0.230 indicating that there is no specificity of environmental
knowledge in general knowledge.
The Role of General Knowledge and
Environmental Knowledge in
Environmental Behavior
Table 4 summarizes the descriptive results on all three test
variables. The overall mean of correct answers was very high
for general knowledge items (79.3%) and higher than that
of environmental knowledge items (68.6%). Environmental
behavior was on a moderate level, close to the verbal
anchor “occasionally,” with highest prevalence for recycling
and frugal behaviors and lowest prevalence for high cost
behaviors such as owning a solar panel or donating money to
environmental organizations. For the prevalence data of all 18
behaviors, see Appendix C.
We initially planned to compare the prediction of general
knowledge and environmental knowledge on environmentally
significant behavior. Due to the above described findings
of very strong relations between general knowledge and
ecological knowledge and the non-significant variance of the
environment specific knowledge, we take both constructs
to be redundant; i.e., we assume that the EKT items are
actually indicators of general knowledge, just as the BEFKI
items are. Therefore, we modeled the EKT parcels as further
indicators of a single general knowledge factor and studied
the extent that such a global knowledge factor predicts
environmentally significant behavior. To guarantee symmetry
between measures, the SIBS scale was also modeled on the
parcel level showing a good fit: χ2(9) = 15, p= 0.090;
RMSEA = 0.057; CFI = 0.976.
In the final SEM, shown in Figure 1, we tested a
regression model with the general knowledge factor – now
representing BEFKI and EKT parcels – predicting the latent
sustainable behavior (SIBS) factor. The model had acceptable
to good fit: χ2(375) = 537, p<0.001; RMSEA = 0.045;
CFI = 0.914. The standardized regression weight between
sustainable behavior and general knowledge was small (γ= 0.261)
but significant (p= 0.020).
In a last step, considering that our sample was not
representative in age and education, we controlled the previous
model for age and education. In a common cause model, we
let age and education predict both sustainable behavior and
general knowledge. The model fit was of mixed quality, with
χ2(429) = 638, p<0.001; RMSEA = 0.048; CFI = 0.896. Age and
education were unrelated in our sample (r= 0.003, p= 0.964).
Age had a significant positive effect on both, sustainable behavior
(γ= 0.289, p= 0.017) and general knowledge (γ= 0.396,
p<0.001), where older participants showed more sustainable
behavior and more general knowledge. Education had no effect
on sustainable behavior (γ=−0.061, p= 0.445), but a significant
positive effect on gc (γ= 0.317, p<0.001). After controlling
for age and education, the prediction of general knowledge on
sustainable behavior dropped to γ= 0.166, which did not differ
significantly from the weight obtained by the model without age
and education [χ2(1) = 0.880; p= 0.348]. Additionally, entering
TABLE 4 | Zero order bivariate correlations and descriptive results for environmental and general knowledge and environmental behavior.
GK EK EB Age Sex (female) Edu Mean SD Min Max
General
knowledge
−0.37∗∗ −0.19∗∗ 0.31∗∗ 79.3% 13.1 31% 100%
Environmental
knowledge
0.77∗∗ −0.32∗∗ −0.20∗∗ 0.26∗∗ 68.6% 14.4 18% 97%
Environmental
behavior
0.22∗∗ 0.22∗−0.26∗∗ 0.22∗∗ 0.02 2.15 0.90 0.41 3.40
For the knowledge scales, mean is presented in % correct answers, the behavior scale spans from 0 (“never”) to 4 (“always”), ∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001.
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Geiger et al. General Knowledge and Environmental Behavior
FIGURE 1 | SEM model prediction of environmentally significant behavior through general knowledge and age as common cause. The γof general knowledge
predicting environmentally significant behavior in brackets is the value without including age and education. The value without brackets is the value when including
age and education.
gender into the structural model yielded a positive effect for
female (γ= 0.608, p<0.012) on standardized behavior.
DISCUSSION
Recent work has called for the construction of objective
evaluation instruments to assess progress in sustainability
education (Shephard et al., 2013;Décamps et al., 2017). We
developed an environmental knowledge test from existing
German tests to relate it to established measures of general
knowledge. The test includes questions on a wide range of topics
relevant to sustainability issues: basic concepts from ecology,
climate, resources, consumption behavior, environmental
pollution, economy, and society. The psychometric evaluation
shows solid evidence for a unidimensional measure with
acceptable reliability. Regarding the internal structure of
environmental knowledge, neither different subdomains nor
knowledge types according to Kaiser and colleagues (Kaiser and
Frick, 2002;Frick et al., 2004) emerged as factors. This calls
into question the usefulness of further distinguishing between
knowledge forms in environmental education.
For our highly educated – and therefore somewhat variability
restricted – sample, the mean probability of solving an item was
68.6%, allowing for discrimination concerning knowledge in less
selected samples. The descriptive results hint on underexposed
knowledge domains: whereas knowledge on climate issues
and environmental deterioration is quite widespread in our
educated sample, basic knowledge about issues on natural
resources is least prevalent. Likewise knowledge on according
consumption behaviors, i.e., action-related knowledge to
preserve resources and protect the climate are not as widespread
as desirable. We encourage use of the EKT for the evaluation
of environmental education measures that aim at conveying
environmentally significant knowledge in these different
domains (see Appendix A for the full 35 item version, including
seven improved items).
Our main theoretical finding, however, lies in the
indistinguishability of environmental and general knowledge.
The current data shows that in our age-heterogeneous
sample, environmental knowledge is inseparable from general
knowledge. With this, environmental knowledge is no different
from other knowledge domains, such as medical or history
knowledge, etc., and adds more evidence to refuting the
“knowledge-is-power” hypothesis. Surprisingly, in broad
samples, knowledge shows much less domain specificity than one
would expect. The convergence of different academic knowledge
domains to a general knowledge factor was shown in various
studies (Schipolowski et al., 2014a;Schroeders et al., 2015), but is
a novel finding in the area of environmental research.
General knowledge, including the environmental domain,
predicted around 7% of variance in pro-environmental behavior.
Using latent data modeling techniques, this (small) effect size is
attenuated for measurement error and thus should be closer to a
true value than simple correlation or regression coefficients based
on manifest variables often used (for a critique see also Kaiser and
Fuhrer, 2003). Albeit a small effect, this finding is noteworthy
because it questions the claim that only behavioral-proximal
knowledge (e.g., action knowledge) is relevant for corresponding
behaviors. Our general knowledge test comprised very diverse
and sustainability-unrelated subjects, ranging from humanities
(e.g., history) to social sciences (e.g., religion). Providing
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Geiger et al. General Knowledge and Environmental Behavior
evidence for the relationship between such a basic construct
(general knowledge) and a very specific outcome (environmental
conservation behavior) constitutes an example for a theoretically
interesting and non-trivial finding, as opposed to correlational
findings between similarly operationalized variables often
presented in social science research (Fiedler, 2014).
On the other hand, despite the high level of general and
environmental knowledge in our sample, pro-environmental
behavior was merely average. The large amount of behavioral
variance unaccounted for by knowledge backs up the notion that
the influence of knowledge is partially overridden by potential
intervening factors, such as normative influences (Bamberg and
Möser, 2007), situational restrictions (Geiger et al., 2018), old
behavior patterns (Klöckner, 2013) or simply net household
income (Kleinhückelkotten et al., 2016). More recent work has
called attention to the potential of environmental emotions, that
could serve as a mediator to make environmental knowledge
more relevant for actual environmental protection (Carmi et al.,
2015;Otto and Pensini, 2017). Thus, according educational
approaches seem more promising when taking into account a
variety of variables. This stance is advocated by recent educational
approaches on sustainable development (Michelsen and Fischer,
2017) that incorporate a wider range of general abilities to cope
with environmental challenges. Educational aims beyond the
transmission of knowledge comprise raising awareness, evolving
empathic and social skills, and facilitating participation processes
for sustainable solutions, for example. Such basic skills are
typically acquired early in life and therefore taught in school
and early education, as is general knowledge. Consequently,
we recommend the implementation of interventions early on
in school that facilitate sustainable behavior via basic abilities,
such as general knowledge. As our study was cross-sectional,
we cannot answer the questions whether conveying general
abilities is more effective than domain-specific knowledge in
environmental education, but our results do encourage further
comparative research toward this end.
Age, independently of education level, emerged as a relevant
predictor of both general knowledge and environmentally
significant behavior, thus we might have inadvertently discovered
an explanation for the positive age effect on environmental
behavior: it is partially mediated by general knowledge that
is accumulated over time (Horn, 2008). Although some
older studies failed to detect a positive influence of age on
environmental behavior (Hines et al., 1987;Ostman and Parker,
1987), other studies do (Gatersleben et al., 2002;Gilg et al.,
2005), and a recent meta-analysis on the topic states “small but
generalizable relationships [. . .] that older individuals appear to
be more likely to engage with nature, avoid environmental harm,
and conserve raw materials and natural resources” (Wiernik
et al., 2013, p. 826). Additional explanations for a direct age
effect range from cohort effects rooted in the frugal upbringing
of older generations (Olli et al., 2001;Wiernik et al., 2013),
increased conscientiousness of older adults (Roberts et al., 2006),
and/or an increased feeling of responsibility to leave an intact
environment for future generations, the so called “legacy-motive”
(Fox et al., 2010). Our findings suggest that the accumulation
of knowledge over time (the age-dependent share of general
knowledge) is a further possible explanation for the positive age-
effect. Education, on the other hand, showed no direct effect on
behavior. Accordingly, we take the level of education to be only
indirectly relevant for environmental behavior in contributing to
general knowledge of people.
A limitation of the present study is that it is confined
to the current epoch and not set up to disentangle age-
from cohort-effects. Over the last decades, environmental
knowledge in Germany has become canonized, academic
knowledge taught in schools and educational institutions.
This reality makes our study highly cohort-dependent and
studies from different decades or societies without a canonized
environmental education might yield different results. A further
limitation consists in the educated sample of the study.
The convergence of knowledge domains might have been
different in a less educated sample. Nevertheless, societal
groups with high formal education and mediocre environmental
behavior like our sample might be a relevant target group for
environmental communication. A recent representative study
from Germany showed, that overall energy consumption of
households hinges strongly on formal education level and related
net income (Kleinhückelkotten et al., 2016). Future research
needs to clarify the independent influences of age, cohort,
education and general knowledge on environmental behavior
with a representative sample. As environmental knowledge,
to a certain degree, depends on current developments and
refers to regional aspects, according assessment instruments
will have to be continuously updated and adapted. The
EKT is an instrument that provides a valid item pool for
such future work.
CONCLUSION
In our study we found knowledge on a wide range of
environmental topics to be a unidimensional factor inseparably
linked to the general knowledge base of individuals. This finding
challenges approaches that distinguish further between different
types or content domains of environmental knowledge. When
measured with an objective, reliable, and valid instrument,
general knowledge accounts for a small portion of variance in
environmentally significant behavior. The study adds evidence
for refuting the “knowledge-is-power” hypothesis on the
importance of domain-specific knowledge and supports an
educational approach that goes beyond the transmission of
environmental knowledge units aimed on specific environmental
issues. The positive relationships between age, accumulated
knowledge, and environmentally significant behavior yields an
unexpected explanation for positive age effects in environmental
conservation behavior.
AUTHOR CONTRIBUTIONS
SG performed the initial test construction assisted by MG and SG
wrote the manuscript. MG performed the most data processing
and statistical analyses and wrote part of the manuscript. OW
guided data analyses and manuscript development.
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Geiger et al. General Knowledge and Environmental Behavior
FUNDING
The reported researchwas supported by a grant from the Ministry
of Science, Research and Art of Baden-Württemberg in the
program “Science for Sustainability.”
ACKNOWLEDGMENTS
The authors want to thank Sandra Gerhardt from the University
of Ulm for her support in data collection and preparation and the
sustainability experts Daniel Fischer from Leuphana University,
Manuel Rivera from the IASS Potsdam and a Biology Teacher
from a public high school for helpful comments on an earlier
version of the test.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2019.00718/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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