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M. M. Isac et al. (eds.), Knowledge and Willingness to Act Pro-Environmentally,
IEA Research for Education 16, https://doi.org/10.1007/978-3-031-76033-4_5
Chapter 5
Inequalities onEnvironmental Knowledge
AndrésStrello, RolfStrietholt, Yuan-LingLiaw, PuryaBaghaei,
andSabineMeinck
5.1 Introduction
The previous chapters conceptualized environmental knowledge and explained the
construction of the environmental knowledge scale and its psychometric properties.
In this chapter, using the subscore provided by the Trends in International
Mathematics and Science Study (TIMSS) 2019 international study center, we report
on the distributions of the environmental knowledge scale within each educational
system from an equity perspective.
Inequality in education is not a new topic. Since the Coleman report in the 1960s
(Coleman etal., 1966), there has been an important focus on the extent to which
educational systems are capable of reaching all students, regardless of their origin.
In the context of environmental knowledge, the discussion reaches new dimensions.
In the world-threatening context of climate change, the understanding of
environmental- related phenomena acquires greater importance because it builds a
foundation for developing pro-environmental attitudes and behavior (Steg &
Vlek, 2009).
A. Strello (*)
International Association for the Evaluation of Educational Achievement (IEA),
Hamburg, Germany
R. Strietholt · Y.-L. Liaw · P. Baghaei · S. Meinck
Department of Education, University of Bath, Bath, UK
KU Leuven, Leuven, Belgium
58
5.1.1 Environmental Knowledge Score
In Chap. 4, we introduced environmental knowledge and its distribution across
countries1 (see Fig. 4.9 in Chap. 4 or see Appendix in this chapter for the tabular
format). As with other educational outcomes, environmental knowledge varies dras-
tically among the participating countries. The TIMSS 2019 encyclopedia (Kelly
etal., 2020) shows the diversity of the educational systems that participated in the
study. Different educational systems have different curriculums, levels of profes-
sional development among their teachers, accountability systems, levels of school
stratication, economic resources and cultural aspects. The variation of scores
across countries reected the diversity in educational contexts.
The ve highest achieving countries average well above 550 points, i.e., more
than half a standard deviation above the scale center point. Meanwhile, the four
lower achieving countries do not even reach a mean of 400 points (i.e., one standard
deviation below the scale center), and three other countries do not reach at least 450
points (0.5 standard deviation below). In context, 1 year of schooling is associated
with a range from 18 to 60 score points (Luyten, 2006; Steinmann & Olsen, 2022),
meaning that the high achieving countries present, on average, gains equivalent to
three to 5 years of schooling. In equally striking inequalities, the 75th percentile of
students in the four lowest achieving countries have roughly similar performances
than the students in the 10th percentile of the high achieving countries. In addition,
the score distribution also varies across countries. Eleven participants have an inter-
quartile range of more than 150 points, meaning large differences between low and
high achieving students. In all countries the interquartile range is of at least 100
points, with just eight countries having a range of fewer than 120 points of differ-
ence. These differences become more obvious when looking at the standard devia-
tion (see Appendix A), that ranges from 78 points (below the international standard
deviation of 100 points) to 139 points. This variation can be partially explained in
the following results on achievement gaps.
Scores on environmental knowledge vary according to each country’s geographi-
cal region and economic development. Figure5.1 suggests that the highest scores
are concentrated in East Asia, Europe, and North America, while the lowest scores
are concentrated in Africa, West Asia, and South America. In other words, we can
observe a gap between the so-called Global North and the Global South on environ-
mental knowledge (United Nations Conference on Trade and Development
[UNCTAD], 2022).
1 TIMSS participating educational systems can consist of countries (e.g., Russian Federation) and
regions as benchmarking participants (e.g., Moscow City in the Russian Federation). For the sake
of simplicity, in this chapter, we sometimes refer to both as “countries.”
A. Strello et al.
59
Fig. 5.1 Mean environmental knowledge achievement and gross domestic product (GDP) per
capita in 2019
Source: GDP data for 2019 from the International Monetary Fund (2023)
In addition, achievement is associated with the economic wealth of each country.
This is shown in Fig.5.1, which plots each country’s environmental knowledge
mean achievement against their respective gross domestic product (GDP) per capita
in 2019 (International Monetary Fund, 2023).2 We excluded benchmarking partici-
pants from the analyses due to the difculty of estimating GDP for them. The hori-
zontal axis of the gure shows the heterogeneity of the economic wealth of the
participating countries, with few countries from different continents reaching well
over US$ 60,000 per capita and many others not reaching the US$ 10,000 per capita
mark. The blue line represents the linear correlation between GDP per capita and
the mean environmental knowledge achievement, showing a strong correlation
(r=0.56), i.e., countries with higher GDP per capita tend to perform better in envi-
ronmental knowledge. The line also enables us to compare how much some coun-
tries are under- or overperforming in relation to their economic wealth. Many East
Asian countries seem to outperform given their economic status, while several
African countries appear to underperform. This suggests that the economy of each
country can explain some of this variation, but there are also some unique factors
within each educational system that affect the environmental knowledge perfor-
mance, unrelated to the country’s economic wealth.
2 GDP per capita is a common indicator of the economic wealth of each country and consists of the
total monetary value of all the nished goods and services within a country, divided by the popula-
tion size.
5 Inequalities onEnvironmental Knowledge
60
5.2 Social Inequality Within Countries
In the following sections, we investigate within-country variation of environmental
knowledge, with a specic focus on social inequality. Social inequality refers to the
equalitarian ideal where differences on student performance should not be related to
their family background or other deterministic characteristics they cannot control.
In each of these sections we test the achievement gaps between groups determined
by students’ socioeconomic status (SES), gender, and the urbanicity of their
schools.3
5.3 Socioeconomic Inequality inEnvironmental Knowledge
5.3.1 Background
SES may be referred to as the individual’s position within a society, understanding
that within societies people or groups are marked by their access to or control over
wealth, power, or status (Mueller & Parcel, 1981). The differences in achievement
between different SES groups has been an important topic of research in education
for decades. Evidence of signicant achievement gaps by SES groups have
been present since the very rst international large-scale assessments (see
Chmielewski, 2019).
The difference in achievement between students from different SES families can
be explained by the fewer resources for education that students of lower SES back-
grounds may receive. This difference in resources accumulates along students’
development trajectory, explaining the disparities in achievement between them.
Following Bourdieu’s theory, these resources are manifested as economic resources
(e.g., families with higher incomes can send their children to private schools or
afford private tutoring) and in the families’ cultural and social capital (Bourdieu,
1986; Broer etal., 2019; Coleman, 1988, 1990).
In addition, educational systems also play a role in the generation of disparities.
Besides what Boudon (1974) named as primary effects, the differentials in achieve-
ment due to SES, these differences may be exacerbated during educational
transitions, i.e., secondary effects (Jackson etal., 2007). This means that in addition
to the presence or absence of achievement gaps between SES groups, the features
of educational systems have an inuence on the magnitude and importance
of these gaps (Strietholt et al. 2019; Van de Werfhorst & Mijs 2010; Volante
etal. 2019).
3 For information on how the achievement gap was calculated, see Chap. 4.
A. Strello et al.
61
5.3.2 Variables
Indicators such as parental income, occupation or educational level are seen as
proxies of students’ SES (Duncan et al., 1972; Gottfried, 1985; Hauser, 1994;
Mueller & Parcel, 1981; Sirin, 2005; White, 1982). Due to the difculties in obtain-
ing reliable information from students and the difculties in constructing interna-
tionally comparable indicators (see Jerrim et al., 2019; Jerrim & Micklewright,
2014), other indicators such as the number of books at home have been used, where
evidence has shown its association with the achievement level of students (Brese &
Mirazchiyski, 2013; Hanushek & Woessmann, 2011). However, this indicator also
has its own share of problems, as it may suffer from endogeneity bias and be con-
founded with achievement itself—that is, low achievers accrue fewer books and are
more likely to underestimate the number of books reported (Engzell, 2019). On
more conceptual terms, parental education and number of books at home also may
represent different constructs. While both are related to Bourdieu’s concept of cul-
tural capital, parental education is an institutionalized capital that can further be
transferred into economic capital, while the number of books would be a rough
representation of the embodied culture of home. Moreover, previous analyses have
shown that SES based on parental education and on number of books at home are
highly correlated, but not a perfect correlation (Strietholt & Strello, 2021). Therefore,
as proxy of student SES, we decided to use two different measures: parental educa-
tion and number of books at home, both measures as reported by the students.
• Parental education If the response for both parents is available, we kept the
value of highest education. Parental education in TIMSS is reported by students
and later standardized in ISCED levels (UNESCO Institute for Statistics, 2012)
within the international dataset to keep the categories comparable across coun-
tries. For the sake of simplicity, we dichotomized parental education into two
values: parents without a university degree (that is, lower than ISCED 6) and
parents with a university degree (that is, ISCED 6 or higher).
• Number of books at home We categorized the values into two groups: fewer
than 100 books and 100 books or more.
Table 5.1 shows the percentage of students whose parents have a university degree
and percentage of students reporting more than 100 books at home. As can be seen,
countries show similar patterns for both variables (i.e., countries with higher per-
centages of students with highly educated parents also tend to have more students
reporting more than 100 books at home). The correlation between those variables is
strong (r=0.60).
In addition, the variation in SES is evident across the participating educational
systems in TIMSS 2019, demonstrating the heterogeneity of the countries partici-
pating in this study. Ten participating countries, from a wide range of geographical
contexts, had 50% of students with parents holding a university degree. On the other
hand, the lower levels of parental education are concentrated in the African, West
Asian and even some European countries. Regarding the number of books, this
5 Inequalities onEnvironmental Knowledge
Table 5.1 Size of sociodemographic groups (percent of students)
Country
Parental education
(% university)
Number of books
(% >100 books)
Gender
(% girls)
Urbanicity
(% urban area)
Australia 49 41 50 84
Bahrain 50 17 49 86
Chile 30 12 49 88
Chinese Taipei 43 28 50 84
Cyprus 59 30 49 78
Egypt 36 10 55 51
England 49 29 53 69
Finland 54 39 48 53
France 34 26 49 70
Georgia 36 37 48 74
Hong Kong SAR 33 26 46 97
Hungary 36 39 50 59
Islamic Republic of Iran 23 15 47 74
Ireland 45 33 49 59
Israel 62 32 52 78
Italy 28 34 50 38
Japan 49 31 52 87
Jordan 33 9 48 80
Kazakhstan 43 13 49 55
Republic of Korea 65 59 48 87
Lebanon 38 13 49 59
Lithuania 46 26 50 74
Malaysia 15 12 51 70
Morocco 14 6 50 70
New Zealand 45 33 49 81
Norway 69 40 49 50
Oman 36 16 48 72
Portugal 33 25 50 82
Qatar 62 16 50 89
Romania 27 23 51 51
Russian Federation 50 20 48 74
Singapore 51 27 49 100
South Africa 23 6 52 38
Sweden 59 32 49 55
Türkiye 16 23 50 83
United Arab Emirates 58 21 48 89
United States 56 29 49 73
Benchmarking participants
Ontario (Canada) 52 40 50 87
Quebec (Canada) 53 26 51 78
Moscow City
(Russian Federation)
76 43 48 99
Gauteng (South Africa) 32 8 55 78
Western Cape (South Africa) 23 11 55 76
Abu Dhabi (United Arab
Emirates)
55 19 47 81
Dubai (United Arab Emirates) 68 28 50 98
63
indicator seems to be more distinctive, as some countries show particularly low
levels: in three countries, less than 10% of the students are categorized into the high
SES group, based on the number of books in their home. Other countries with rela-
tively few such students are concentrated in the Middle East, Africa, and South
America. In most countries, between 20% and 40% of the students report having
more than 100 books at home.
5.3.3 Results withParental Education
The socioeconomic achievement gap based on parental education, i.e., the mean
difference between students with parents with university degree and without univer-
sity degree, is reported in Fig.5.2. The gure shows that in all countries there are
signicant knowledge gaps between socioeconomic groups based on parental
education and in all countries the higher socioeconomic groups have advantages.
However, across countries there is large variation in the size of these gaps. Some
countries, such as the Republic of Korea or Kazakhstan, present less than 40 points
in mean difference between the different socioeconomic groups. In contrast, for
participants such as Abu Dhabi or Western Cape the gap reaches 100 points, and in
countries such as Lebanon, United Arab Emirates, Hungary and Qatar this achieve-
ment gap is close to 90 points, i.e., differences of around one standard deviation
between socioeconomic groups.
In addition, there are some geographic patterns related to the magnitude of the
achievement gap. Many of the countries with the highest gap between socioeco-
nomic groups by parental education seem to be from the Middle East, plus countries
such as Hungary and New Zealand. In contrast, many of the countries with the
lowest gaps between socioeconomic groups by parental education seem to be from
East Asia, plus countries such as Kazakhstan, the Russian Federation, and Norway.
5.3.4 Results withNumber ofBooks atHome
Figure 5.3 reports the SES achievement gap based on the number of books at home,
i.e., the achievement gaps between students with 100 or more books versus equal or
lower than 100 books at home. While similar to the results based on parental educa-
tion, the gaps are not identical, although it is difcult to compare as the differences
may be due to the cutoff point used. Sweden and the United States present an exam-
ple of these differences, with the second and third highest gaps, respectively. In
addition, in Jordan the achievement gap is not statistically signicant, while Egypt
even presents a negative direction in achievement gap (i.e., students with fewer
books have higher scores); however, it is important to remember that in these two
countries most of the student population reported a very low number of books.
5 Inequalities onEnvironmental Knowledge
64
Fig. 5.2 Environmental knowledge gap between students with parents with university
degree versus without university degree
Notes: Benchmarking participants are marked in blue. RSA = South Africa, UAE = United Arab
Emirates. (*) Population coverages notes, see Appendix B.7in Mullis etal. (2020). (†) Sampling
participation rates notes, see Appendix B.10in Mullis et al. (2020). (9) Country deviated from
international dened population and surveyed adjacent upper grade
5.4 Gender Achievement Gap
5.4.1 Background
Gender inequality in education has been a historically contentious topic. Until
recently, there were important differences in school enrollment, that have now been
neutralized in the Western world and reduced in most other countries (Steinmann &
Rutkowski, 2023). Current gender inequalities studies are centered around career
A. Strello et al.
65
Fig. 5.3 Environmental knowledge gap between students reporting more than 100 books versus
equal or less than 100 books at home
Notes: Benchmarking participants are marked in blue. RSA = South Africa, UAE = United Arab
Emirates. (*) Population coverages notes, see Appendix B.7in Mullis etal. (2020). (†) Sampling
participation rates notes, see Appendix B.10in Mullis et al. (2020). (9) Country deviated from
international dened population and surveyed adjacent upper grade
preferences, such as the underrepresentation of women in STEM4 careers, and
still show systematic differences in performance between boys and girls that are
differentiated between domains (Leder, 2019). Based on international evidence, in
the reading domain girls have outperformed boys since the very rst studies in the
1970s until the latest editions of PIRLS and PISA,5 with variations in the magnitude
4 STEM=science, technology, engineering and mathematics.
5 PIRLS=Progress in International Reading Literacy Study; PISA=Programme for International
Student Assessment.
5 Inequalities onEnvironmental Knowledge
66
between countries and a slight decrease from 2001 onwards (see Steinmann
etal., 2023). In mathematics and science domains, the gender gap has been less
clear, as there is variation in the size and direction of the gender achievement
gap between countries (Leder, 2019; Rosén et al., 2022; Steinmann &
Rutkowski, 2023).
The likely causes of the gender gap can be divided into two broad explanations:
nature and nurture (see overviews by Halpern, 2012; Hyde, 2014). The nature theo-
ries assume innate differences between boys and girls on their learning process;
however, the evidence has shown that boys and girls score mostly the same on cog-
nitive ability tests (see gender similarity hypothesis in Hyde, 2014; Zell etal., 2015).
The nurture theories focus on the environmental inuences that differ between boys
and girls. Nurture-related theoretical perspectives all suggest that societal gender
norms and existing gender differences in education transmit to students, perpetuat-
ing educational gender inequalities, such as the overrepresentation of men in STEM
careers (Eccles etal., 1990; Halpern, 2012; Neuville & Croizet, 2007). In the pres-
ent study, we cannot distinguish between nature and nurture but rather measure the
accumulated differences of sex and gender.
5.4.2 Results
The gender achievement gap, i.e., the mean difference between girls and boys, is
illustrated in Fig.5.4. There are signicant gaps between girls and boys in less than
half of the countries. In addition, in countries where the achievement gap is signi-
cant, roughly half of the participants present achievement gaps in favor of boys and
roughly half in favor of girls.
There are some clear geographic patterns regarding the magnitude and direction
of the achievement gap. Most countries with positive gaps, i.e., where girls have an
advantage, are in the Middle East or Africa, with Finland being the only exception.
In contrast, the countries presenting negative gaps, i.e., where boys have an advan-
tage, seem to be a heterogeneous group of educational systems. South Africa pres-
ents an interesting case, as the results in Western Cape show a gap favoring boys,
while we nd a non-signicant gap favoring girls in Gauteng and in the full-country
sample. In addition, in the countries where boys have an advantage, the achievement
gaps are signicant but relatively small, around 20 points; in contrast, in Oman,
Bahrain, and Jordan the achievement gaps in favor of girls are around 40 points.
Since all these countries are from the Arab world, the clear gap in favor of girls in
these countries may be explained with factors such as the predominance of gender
segregated schools and the disparity in school enrollment by gender (Fryer & Levitt,
2010; Smits & Huisman, 2013).
A. Strello et al.
67
Fig. 5.4 Environmental knowledge gap between female and male students
Notes: Benchmarking participants are marked in blue. RSA = South Africa, UAE = United Arab
Emirates. (*) Population coverages notes, see Appendix B.7in Mullis etal. (2020). (†) Sampling
participation rates notes, see Appendix B.10in Mullis et al. (2020). (9) Country deviated from
international dened population and surveyed adjacent upper grade
5.4.3 Variables
We used the variable reported by students as the indicator of gender. In TIMSS
2019, two genders are available in the international dataset: boy and girl.
In Table5.1 we reported the percentage of girls by country. Most participants
report around 50% of girls, with a minimum of 46% and maximum of 55%, mean-
ing that there are no notable differences between countries in the enrollment of
each gender.
5 Inequalities onEnvironmental Knowledge
68
5.5 Urbanicity Achievement Gap
5.5.1 Background
School location has been part of the school contextual questionnaires since the earli-
est international large-scale assessments, such as the rst International Association
for the Evaluation of Educational Achievement (IEA) International Mathematics
Study in 1964 and its following second edition in 1980–1982 (Noonan, 1976;
Westbury & Travers, 1990). The results of the rst edition of TIMSS in 1995 (Martin
etal., 2000) showed that the urbanicity of the school location was a relevant predic-
tor in several educational systems.
In the context of climate change and the challenges it brings to the population,
discussion of levels of environmental knowledge by urbanicity acquires new mean-
ings. We advocate for a renewed focus on the urban–rural divide within the context
of environmental concerns. Evidently, the global climate crisis affects rural and
urban areas differently, as urban areas are more likely to suffer higher temperatures
as a consequence of global warming (Chapman etal., 2017). On the other hand,
rural areas may be affected in aspects such as agricultural production, with the
added consequences on their economy and demographic changes within a popula-
tion that, in many cases, is already more vulnerable (due to lower SES and access to
services) (Bi & Parton, 2008; Fahad & Wang, 2020; Lal et al., 2011; Olesen &
Bindi, 2002). In this context, it becomes important to investigate whether levels of
environmental knowledge vary between urban and rural areas. In this section,
we explore how environmental knowledge scores distribute across groups of
urbanicity.
We found very few comparative studies that measured the gap between urban
and rural areas and none which focused on environmental knowledge. The only
exception we found was Echazarra and Radinger’s (2019) working paper. Using
TALIS6 2013 and PISA 2015 data, the authors found that rural schools tended to
perform worse in science than urban schools in most countries, with rural students
having a lower socioeconomic background in most cases. In addition, students from
rural areas had lower further education expectations than students from urban areas
even after controlling by SES, but with notorious heterogeneity between countries
on the resources rural schools receive. Reviewing further non-comparative evi-
dence, the urban and rural disparity seems to heavily depend on the context of each
country. For example, it has been found that in many countries there are wide gaps
between urban and rural areas, since the latter receive less resources than their urban
counterparts; these countries include Nigeria (Alordiah et al., 2015; Owoeye &
Yara, 2011), Australia (Murphy, 2019), China (Wang etal., 2014), and Thailand
(Lounkaew, 2013), as well as countries within sub-Saharan Africa (Zhang, 2006)
6 TALIS=Teaching and Learning International Survey.
A. Strello et al.
69
and several ex-soviet countries (Kryst etal., 2015). In contrast, in countries such as
the United States, it has been found that students in urban areas do worse than their
rural and suburban counterparts, possibly due to the segregation logics that are
found in large urban areas (Lankford etal., 2002; Miller & Votruba-Drzal, 2015;
Sirin, 2005).
5.5.2 Variables
For the indicator of urbanicity, we used the information reported by school princi-
pals in the school questionnaire. The principals were asked to categorize their school
location by type of area: from remote rural area to a densely populated urban area.
To facilitate the analyses, we dichotomized the urbanicity into two categories: urban
areas (densely populated urban areas plus medium sized cities and suburban areas)
and rural areas (small towns and rural areas).
Table 5.1 shows the percentage of students who study in urban areas. In some
countries, for example, Chile, Türkiye, the Republic of Korea and Israel, a consider-
able majority of students attend schools located in urban areas. In Singapore and
Hong Kong SAR most or almost all students go to schools in urban areas. However,
other countries show very different patterns; for example, the percentage of students
attending schools in urban areas is approximately 40% in Norway, South Africa
and Italy.
5.5.3 Results
The urbanicity achievement gap, i.e., the mean score difference between students
living in urban areas (medium cities or larger) versus those living in rural areas
(small towns or smaller), are shown in Fig.5.5. In 26 countries there is a positive
achievement gap, i.e., students in urban areas perform better than their rural coun-
terparts. In most countries, the magnitude of the achievement gap is not as high as
the socioeconomic gap seen above, but there are outliers such as United Arab
Emirates and Abu Dhabi with over 100 points of difference between urban and rural
areas, and other countries such as South Africa, Türkiye, and the Islamic Republic
of Iran whose gaps reach over 60 points. In most other countries, the gap is lower
than 40 score points. In contrast, in France and Bahrain, the achievement gap is
negative. This means that students in rural areas outperform their counterparts, scor-
ing approximately 15 points higher on average than students studying in urban
areas. Lastly, in 13 countries there are no statistically signicant differences between
urban and rural areas.
5 Inequalities onEnvironmental Knowledge
70
Fig. 5.5 Environmental knowledge gap between urban (medium cities or larger) and rural (small
town or smaller) areas
Notes: Benchmarking participants are marked in blue. RSA = South Africa, UAE = United Arab
Emirates. Moscow City (Russian Federation) is omitted due the low number of respondents in
rural areas; Singapore is omitted due all respondents being located in urban areas. (*) Population
coverages notes, see Appendix B.7in Mullis etal. (2020). (†) Sampling participation rates notes,
see Appendix B.10in Mullis etal. (2020). (9) Country deviated from international dened popula-
tion and surveyed adjacent upper grade
5.6 Discussion andConclusion
In this chapter, we studied the variation in environmental knowledge from an equity
perspective. The overall results indicate important variation in the levels of environ-
mental knowledge among the TIMSS 2019 participating educational systems and
students.
A. Strello et al.
71
We further discussed in this chapter the results shown in the previous chapter,
where we found that there are important differences in the levels of mean achieve-
ment in environmental knowledge between countries, and that the high- achieving
students in lower performing countries reached similar levels of performance com-
pared to the low-achieving students in the highest performing countries. In the same
vein, there were important differences in the distribution of environmental knowl-
edge across countries, with some presenting large gaps between high and low per-
formers while others had reduced differences. It is worrying, but sadly not surprising,
to observe a strong correlation between countries’ wealth (measured as GDP) and
the performance level of their students. Richer countries tended to perform signi-
cantly better than poorer countries.
Additionally, we studied the within-country variation on environmental knowl-
edge through measuring the achievement gap between sociodemographic groups as
a measure of social inequality. We found that environmental knowledge is very
highly associated with the SES of students’ families, both by parental education and
by number of books at home. In the context of climate change, students who have a
poorer understanding of how the environment functions and how it is affected by
human actions are likely to have fewer resources in their future lives. In contrast, in
almost half of the countries, the gender achievement gap was non-signicant, and in
the other half, there were small gender achievement gaps. Finally, many countries
presented important gaps by levels of urbanicity, showing that students from big or
mid-sized urban areas systematically perform better on environmental knowledge
than their rural or suburban counterparts, while for many others this gap was
non-signicant.
Overall, this chapter has shown that there is important work to do in terms of
environmental knowledge. Environmental knowledge has been conspicuously
under-researched to date. To ll this research gap, our research offers new insights
on which groups of students have not yet obtained the necessary knowledge to
understand, evaluate and act upon the challenges to come. Environmental crises,
including climate change, biodiversity loss, pollution, water scarcity, and more, are
all global phenomena, but currently young people in the TIMSS participating coun-
tries show vastly differing, and often low, levels of knowledge on environmental
topics. Moreover, within countries there are also important differences on how well
their future citizens understand the phenomena. Considering the pressing nature of
ongoing environmental challenges, this nding is cause for concern, and highlights
the urgent need for action. It is imperative for policymakers and educators to equip
our students for the future, enabling them to contribute to a more sustainable
environment.
5 Inequalities onEnvironmental Knowledge
72
Appendix: Environmental Knowledge Score Mean
andDistribution Across Educational Systems
Country Mean Mean SE SD P10 P25 P75 P90
Australia 533.6 3.4 92.3 410.3 474.1 597.7 646.5
Bahrain 478.6 2.7 111.5 328.2 404.2 560.1 617.8
Chile 463.3 3.3 88.4 348.8 403.3 524.6 575.7
Chinese Taipei 581.5 2.4 84.8 469.2 529.6 640.4 682.7
Cyprus 474.6 2.8 101.4 339.8 408.8 545.4 598.5
Egypt 375.2 5.3 122.7 209.6 294.3 463.1 527.3
England 504 5.4 97.9 373.3 440.4 571.9 624.1
Finland 543.5 3.7 96.8 416.9 483.3 610.2 662
France 490 4.2 95 366.3 427.7 556.8 608.3
Georgia 418.4 4.3 106.8 278.8 348.8 493.1 551.8
Hong Kong SAR 505.1 6.2 106.3 358.5 436.4 579.8 637.4
Hungary 520.6 3.8 99.8 389.2 455 589.7 645.2
Islamic Republic of Iran 447 3.9 95.4 322.9 384 512.7 567.8
Ireland 524.2 3.8 89.4 405.4 467.9 585.8 634.1
Israel 505.6 4.6 104 367.7 436.1 580.1 633.2
Italy 492.8 3.4 91.4 375 432.2 555.3 607.5
Japan 562.8 3.2 82.8 453.3 510.1 618.7 664.5
Jordan 455.9 4.7 102.2 319.6 390.7 528.9 580.4
Kazakhstan 468.2 3.7 103.8 335.7 399.1 537.3 601.4
Republic of Korea 560.5 3 88.6 446.4 503.7 620.8 671.8
Lebanon 339.4 4.8 118.3 189 255.6 423.2 497.1
Lithuania 526.1 3.3 84.7 416.1 471.7 583.3 630.9
Malaysia 465.7 4 101.1 328.2 398.1 538.2 590.1
Morocco 383.6 3 98.5 254.4 318.3 451.7 508
New Zealand 503.2 3.8 100.3 369.5 437.3 572.6 626.7
Norway 503.4 3.8 103.2 366.6 438.1 575.3 632.8
Oman 452.6 3 118.6 291.6 371.8 540.5 598.5
Portugal 525.1 3.5 85.1 416.8 467.6 582.1 633.4
Qatar 467.3 5 106.4 326.4 392.1 544.2 605.7
Romania 459.9 4.7 104.6 323.1 391.6 533.8 589.9
Russian Federation 533.4 4.2 85 422.5 477.7 591.5 638.7
Singapore 590.7 4.4 95.2 459.6 535.1 658.6 701.5
South Africa 360.9 3.3 114.8 219 280.4 434.5 512.6
Sweden 525.2 3.3 106.5 383.9 457.2 599.4 656.6
Türkiye 515.2 3.4 95.5 389.8 452.2 582.5 633
United Arab Emirates 461.8 2.4 130.1 281.4 372.7 558 622.9
United States 525.1 5.3 113.8 370.8 452.2 606.5 663.1
(continued)
A. Strello et al.
73
Country Mean Mean SE SD P10 P25 P75 P90
Benchmarking participants
Ontario (Canada) 532.9 3.3 93.1 411.7 473.4 597.8 646.3
Quebec (Canada) 539.3 4.8 89.5 421.9 480.6 601.8 651.5
Moscow City (Russian Federation) 561.3 3.5 78.0 459.4 509.7 615.6 659.6
Gauteng (South Africa) 413.5 4.0 111.4 272.0 335.4 489.3 562.8
Western Cape (South Africa) 432.0 6.7 125.9 277.4 341.5 514.8 608.3
Abu Dhabi (United Arab Emirates) 407.3 4.0 139.1 221.6 299.1 515.1 588.2
Dubai (United Arab Emirates) 538.0 2.3 107.6 390.9 471.1 613.6 666.8
Notes: P = percentile, SD = standard deviation, SE = standard error
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