Global Matrix 4.0 is the fourth round of international, intersectoral collaboration for the development of Report Cards on physical activity among children and adolescents, a comprehensive examination of 10 physical activity-related indicators, in 57 countries/jurisdictions around the world.1 The Global Matrix initiative contains an evaluation of behavioral/individual indicators as well as the factors that influence physical activity-related behaviors, including indicators for family and peers, schools, community and built environment factors, and government strategies and initiatives. Although currently existing benchmarks do not directly address macrolevel factors related to climatic features per se (eg, weather, climate change indicators), previous rounds of Global Matrix data2,3 as well as the data derived from Global Matrix 4.01,4 offer an opportunity to investigate some multilevel factors that could inform both climate change and public health benefits (noted as health cobenefits).5
Climate change refers to long-term shifts in temperatures and weather patterns, mainly driven by human activities for economic development such as burning fossil fuels.6 Though climate change related indicators were not part of the past Global Matrix initiatives,2,3,7 geographical, geopolitical, and country development differences in Report Card grades on physical activity for children and adolescents across the world were highlighted in the past reports.2,3,7 Furthermore, climatic features based on geographic location (eg, temperature, extreme heat days, extreme cold days) are suspected to influence physical activity among children and adolescents around the globe.2,3 Two recent reviews8,9 suggested that climate change has an unfavorable impact on physical activity. Given these findings, physical activity may be a double-edged sword where it could mitigate (eg, active transport) or amplify (eg, sport traveling and reliance on fossil fuels) climate change in coming years.8–10 In addition to physical activity, there are other behaviors and factors that may impact (or be impacted by) climatic features. For example, global trends in increased carbon energy-intensive screen time activities may accelerate the progression toward global warming11 in addition to habitual physical activity behaviors.12 Furthermore, extreme weather events may act as a barrier to outdoor activities such as active outdoor play or active transportation. On the contrary, active outdoor play, active transportation, and implemented strategies to promote these behaviors can also provide benefits to the environment and sustainability.10,13,14
Along with climate change, another macrolevel factor such as a country’s political ideology (ie, a system of ideas, one which forms the basis of economic or political theory and policy) influences physical activity and, ultimately, human health and well-being.15 In particular, neoliberal capitalist ideology, an ideology that promotes neoliberal capitalism such as deregulation of the financial system, economic austerity, and the proliferation of free trade agreements,16 is one ideology that is suggested to fuel both climate change and rising health inequities. A recent ecological study on 124 countries16 reported that neoliberal capitalist ideology, indicated by the measure of economic freedom,17 was a strong predictor of mortality due to non-communicable diseases (NCD). Furthermore, neoliberal capitalist ideology interacts with climate change factors to influence human behavior and health.16 Specifically, mortality due to NCD was higher in countries that are less culpable (ie, less responsible for climate change as they produced less greenhouse gases per capita) for global climate change compared to the countries that were highly culpable (ie, more responsible for climate change as they produced more greenhouse gases per capita) for climate change measured by country-specific CO2 emissions per capita. This indicates that the health of the global population is affected disproportionately by a combination of macrolevel factors, in this case, an economic ideology and subsequent climate change. Also, such impact exacerbates already existing inequities whereby those that suffer the most are not those responsible for climate change.16
Though it is acknowledged that improvements in the grades of the common indicators of the Global Matrix initiative are not as impactful as addressing the highly culpable structures of economic and political power16 directly, engaging in a physically active lifestyle and influencing factors that are conducive to both the environment and human health may provide some benefits to climate change mitigation as well as global population health.10,16 This is particularly relevant to young generations, as they are born into the world with a climate crisis and will experience a harsher climate (eg, extreme weather events, air pollution) and are more vulnerable to secondary impacts such as cardiorespiratory diseases, allergies, mental health issues, poverty, and lack of goods and resources.18 By better understanding the associations between macrolevel factors and physical activity patterns in children and adolescents worldwide, important insights into developing multilevel strategies may be uncovered that could maximize the dual benefits of climate change mitigation and physical activity promotion.10,19 For instance, reducing carbon-intensive behaviors (eg, screen-based activities, live streaming) and engaging in more carbon-free activities, such as physical activity and outdoor time, could help young individuals to decarbonize their daily lives and perhaps collectively mitigate climate change.11,13 Though individual-level behavioral changes alone may only make small contribution to climate change mitigation, the impact could be substantial if accompanied by macrolevel changes such as transforming the global economic landscape.16 Therefore, the objective of this paper was to explore the patterns and relationships between economic freedom, climate culpability, and Report Card grades among 57 countries/jurisdictions.
Methods
Data Source
The Global Matrix initiative of the Active Health Kids Global Alliance (AHKGA) has involved 4 cohorts of Report Cards on physical activity and relevant indicators (2014, 2016, 2018, and 2022).4 Global Matrix 4.0 involved Report Card development from 57 countries and jurisdictions. The Report Card creation and development process and protocols are described in detail elsewhere.1,20 Briefly, each participating country assigned a grade to the following 10 common indicators: Overall Physical Activity, Organized Sport and Physical Activity, Active Play, Active Transportation, Sedentary Behavior, Physical Fitness, Family and Peers, School, Community and the Environment, and Government. The grade assignment was based on harmonized benchmarks (Supplementary Table S1 [available online]) and grading criteria (Supplementary Table S2 [available online]) proposed by the AHKGA.
Measures
Report Card Grades
Each participating country/jurisdiction in the Global Matrix 4.0 provided their Report Card grades on 10 common indicators based on the best available evidence. They were also allowed to assign a grade on additional indicators of their interest such as the weight status, sleep, among others.1 Report Card grades used in the Global Matrix were focused on children and adolescents aged 5–17 years to ensure consistency across countries/jurisdictions. All submitted grades and justifications were reviewed by a scientific committee of the AHKGA to ensure consistency and accuracy of the grade assignment process, with changes requested in cases where an assigned grade was inconsistent with the data/justification provided. The letter grades ranged between “A+” and “F,” with “INC” indicating an incomplete grade given when data were not available or deemed insufficient to assign a letter grade.
Economic Factors and Economic Freedom
Economic variables for each country/jurisdiction included income levels and Gross Domestic Product (GDP) per capita, provided by the World Bank,21 and the Index of Economic Freedom, provided by the Heritage Foundation.22 The Index of Economic Freedom is a composite measure developed based on 12 indicators categorized into 4 broad pillars of economic freedom on a scale between 0 (least free) and 100 (most free). These include rules of law (property rights, government integrity, and judicial effectiveness), government size (government spending, tax burden, and fiscal health), regulatory efficiency (business freedom, labor freedom, and monetary freedom), and open markets (trade freedom, investment freedom, and financial freedom).22 More information about the Index is available at https://www.heritage.org/index/about, with customizable visualization of the data by country and year. The Index of Economic freedom scores were categorized into tertiles for evidence synthesis: highly free (74.3–90.2 [highest value]), moderately free (65.0–74.2), and less free (44.0−64.9 [lowest value]). The Index of Economic Freedom was used to indicate the economic structure of a country/jurisdiction as used in previous work.16
Climate Change and Culpability Indicators
Climate variables included mean annual temperature and precipitation between 1991 and 2020 from the Climate Change Knowledge Portal for Development Practitioners and Policy Makers.23 Data on air pollutants were for the year 2018 (most up-to-date), total greenhouse gas (GHG) emissions, CO2 emissions per capita, particulate matter (PM2.5), nitric oxide (NO), and methane (CH4), were collected from the World Bank21 to indicate climate culpability of each country. CO2 emissions was per capita values; however, GHG emissions, PM2.5, NO, and CH4 data were only available in total values. Therefore, total population data for the year 2018 from each country were also collected from the World Bank21 to calculate the per capita value of each air pollutant. Climate culpability was calculated by obtaining the summative value of all air pollutants per capita for each country: climate culpability = CO2 emissions per capita + ([total GHG emissions + PM2.5 + NO + CH4]/total population × 1000). Per capita values for total GHG emissions, PM2.5, NO, and CH4 were also multiplied by 1000 for statistical purposes. From the climate justice angle, a cumulative per capita air pollutants score was used to normalize each country’s contributions to emissions according to their relative populations. This allows making direct comparisons between countries without misleading due to their varying levels of environmental output when cumulative values are used. For analyses, climate culpability scores were categorized into tertiles for evidence synthesis: highly culpable (10,961.9–33,598.0), moderately culpable (7767.1–10,961.8), and less culpable (2942.3–7767.0).
Climate data were not available in some countries/jurisdictions (eg, Hong Kong,24 Taiwan, Jersey, Guernsey), and, for these cases, corresponding data were obtained from their respective government webpages or reports. If not available, data were treated as missing in data analyses.
Statistical Analysis
Statistical analyses were conducted separately by income groups (low- and middle-income countries [LMIC] and high-income countries [HIC]), given the inherent disparities across regions based on Report Card indicator grades,2 the contribution to and impact of climate change, as well as economic freedom. Descriptive statistics were calculated to obtain mean values or percentages for the key variables. Pearson correlation coefficients between Report Card grades, economic factors, and climate change and culpability indicators were determined stratified by income groups. Continuous variables were used for economic freedom and climate culpability.
For all analyses, Report Card letter grades from the 2022 Global Matrix 4.01 were converted to numerical values using the standardized approach.2 The conversion is provided in the Supplementary Table S2 (available online). The average scores were then calculated for behavioral/individual indicators (ie, Overall Physical Activity, Organized Sport and Physical Activity, Active Play, Active Transportation, Sedentary Behavior, and Physical Fitness) and the sources of influence (ie, Family and Peers, School, Community and the Environment, and Government), separately. Overall average of the scores by country was also calculated. Pairwise deletion was used to treat missing data (ie, INC grades) to minimize the number of cases excluded from the analysis. Cohen benchmarks were used to interpret the effect size of correlation coefficients (.10–.29 = small correlation, .30–.49 = moderate correlation, and ≥ .50 = strong correlation).25
Countries were categorized based on their economic freedom and climate culpability into tertiles, and heat maps were created to visually represent relationships between these variables (ie, each plotted on X and Y axes) and the grades for each indicator. By observing how cell colors change across each axis, any patterns in Report Card grades in relation to these variables were observed and synthesized. All statistical analyses were performed in SPSS (version 29.0, IBM Corp).
Results
A total of 57 countries developed their country/jurisdiction’s Report Card with the relevant data sued in this study. Nineteen countries were classified as LMIC, and 38 countries/jurisdictions were classified as HIC (Table 1). Scores for Report Card indicators varied between LMIC and HIC. Specifically, compared to LMIC, HIC scored higher in Overall Physical Activity, Organized Sport and Physical Activity, and Physical Fitness for behavioral/individual indicators and School, Community and the Environment, and Government for the sources of influence; while LMIC, compared to HIC, scored higher in Active Transportation and Sedentary Behavior. No apparent differences were observed by income groups in Active Play and Family and Peers indicators. The overall average score was also higher in HIC compared to LMIC attributable to the higher score in the sources of influence observed in HIC. The difference in the average of behavioral scores was trivial.
Characteristics of the 57 Countries/Jurisdictions That Participated in the Global Matrix 4.0
| Key variables (N = 57) | Income groups | |
|---|---|---|
| LMICa | HICa | |
| n = 19 | n = 38 | |
| Report card grades scores (2–15; mean) | ||
| Overall Physical Activity | 5.4 (D) | 6.1 (D+) |
| Organized Sport and Physical Activity | 6.2 (D+) | 8.4 (C) |
| Active Play | 7.3 (C−) | 7.4 (C−) |
| Active Transportation | 8.0 (C) | 7.7 (C−) |
| Sedentary Behavior | 7.1 (C−) | 5.8 (D) |
| Physical Fitness | 7.1 (C−) | 7.8 (C−) |
| Family and Peers | 7.8 (C−) | 7.8 (C−) |
| School | 8.5 (C) | 10.6 (B−) |
| Community and the Environment | 7.1 (C−) | 10.3 (B−) |
| Government | 7.5 (C−) | 9.7 (C+) |
| Overall scores | ||
| Behavioral/Individual averageb | 6.3 (D+) | 6.5 (D+) |
| Sources of influence averagec | 7.5 (C−) | 9.0 (C+) |
| Overall averaged | 6.7 (D+) | 7.7 (C−) |
| Economic factors | ||
| GDP per capita, USD | 6290.8 | 39,259.4 |
| Index of Economic Freedom (0–100)e | 59.5 | 73.0 |
| Highly free (74.3–90.2), % | 5.6 | 47.2 |
| Moderately free (65.0–74.2), % | 16.7 | 41.7 |
| Less free (44.0–64.9), % | 77.8 | 11.1 |
| Climate change factors | ||
| Total population (average) | 622,227 | 34,361,477 |
| Temperature, °C (annual mean 1990–2020) | 20.2 | 11.4 |
| Precipitation, mm (annual mean 1990–2020) | 1342.5 | 843.3 |
| CO2 emissions per capita, Kt (2018) | 3.4 | 7.7 |
| Total greenhouse gas emissions per capita (tonnes of carbon dioxide equivalent; 2018) (× million) | 5300.5 | 10,439.0 |
| PM2.5 per capita, μg/m3 (2018) (× million) | 3.2 | 7.4 |
| NO per capita (1000 metric tons of CO2 equivalent; 2018) (× million) | 452.2 | 828.3 |
| CH4 per capita (Kt of CO2 equivalent; 2018) (× million) | 1223.7 | 1627.1 |
| Climate culpabilityf | 6982.9 | 12,903.8 |
| Highly culpable (10,961.9–33,598.0), % | 15.8 | 42.4 |
| Moderately culpable (7767.1–10,961.8), % | 21.1 | 42.4 |
| Less culpable (2942.3–7767.0), % | 63.2 | 15.2 |
Abbreviations: CH4, methane; CO2, carbon dioxide; GDP, gross domestic product; HIC = high-income countries; LMIC, low- and middle-income countries; NO, nitrogen oxide; PM2.5, particulate matter 2.5 (atmospheric particulate matter that have a diameter of less than 3.5 μm);
aLMIC (Argentina, Botswana, Brazil, China, Colombia, Ethiopia, India, Indonesia, Lebanon, Malaysia, Mexico, Montenegro, Nepal, Philippines, Serbia, South Africa, Thailand, Vietnam, and Zimbabwe); HIC (Australia, Canada, Chile, Chinese Taipei, Croatia, Czech Republic, Denmark, England, Estonia, Finland, France, Germany, Greenland, Guernsey, Hong Kong, Hungary, Ireland, Israel, Japan, Jersey, Lithuania, New Zealand, Poland, Portugal, Scotland, Singapore, Slovakia, Slovenia, South Korea, Spain, Spain [Basque County], Spain [Extremadura], Spain [Region of Murcia], Sweden, United Arab Emirates, United States, Uruguay, and Wales). bBehavioral/individual indicators included Overall Physical Activity, Organized Sport and Physical Activity Participation, Active Play, Active Transportation, Sedentary Behavior, and Physical Fitness. cSources of influence indicators included Family and Peers, School, Community and the Environment, and Government. dOverall Indicators included all 10 behavioral and sources of influence indicators listed above. eIndex of Economic Freedom from the Heritage Foundation measured based on 12 factors in 4 broad pillars, that is, rule of law, government size, regulatory efficiency, and open markets. fClimate culpability = CO2 emissions per capita + ([total greenhouse gas emissions + PM2.5 + NO + CH4]/total population × 1000).
As shown in Table 1, mean GDP per capita was 6 times greater in HIC compared to LMIC, and HIC showed more neoliberal economic structures than LMIC. In terms of proportions, 47.2% of HIC had highly free economies compared to 5.6% in LIMC, while 11.1% of HIC and 77.8% of LIMC had less free economies. For the climate change and culpability indicators, per capita values for air pollutants were higher in HIC than LMIC. For climate culpability, which is calculated by adding per capita values for all air pollutants, 42.4% of HIC were classified as highly culpable to climate change, while the corresponding percentage for LMIC was 15.8%. Also, 42.4% of HIC and 21.1% of LIMC were classified as moderately culpable, while 15.2% of HIC and 63.2% of LMIC were classified as less culpable to climate change.
The results of correlation analysis between the key variables by income groups are described in Table 2. In LMIC, a strong, positive, correlation was observed between Overall Physical Activity and Active Transportation (r = .58, P = .011), Organized Sport and Physical Activity and School (r = .63, P = .021), Physical Fitness and climate culpability (r = .93, P = .002), School and Community and the environment (r = .61, P = .017), Government with the index of economic freedom (r = .46, P = .047), annual temperature (r = .61, P = .005), and annual precipitation (r = .65, P = .003), GDP per capita and climate culpability (r = .84, P < .001), and between annual temperature and precipitation (r = .56, P = .013). A strong, negative correlation was observed between Overall Physical Activity and annual precipitation (r = −.55, P = .015), Active Play with annual temperature (r = −.62, P = .044) and annual precipitation (r = −.68, P = .022), and between Active Transportation with Sedentary Behavior (r = −.53, P = .025) and annual precipitation (r = −.56, P = .015).
Pearson Correlations Between the Key Variables by Income Groups
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PA | SP | AP | AT | SB | PF | FAM | SCH | COM | GOV | GDP | IEF | CC | TEM | PRE | ||
| 1 | PA | — | .33 | .15 | .19 | .33 | .37 | .09 | −.27 | .25 | −.06 | .01 | −.09 | −.13 | −.22 | .29 |
| 2 | SP | .11 | — | .56* | .34 | −.11 | −.03 | .25 | −.19 | .42* | .03 | .29 | .22 | .01 | −.05 | .08 |
| 3 | AP | .30 | .50 | — | .29 | .07 | −.06 | .31 | −.31 | −.43 | −.47* | −.03 | −.38 | −.65** | −.07 | .22 |
| 4 | AT | .58* | .45 | .45 | — | .11 | .03 | .22 | .30 | .06 | −.03 | −.04 | −.06 | −.43* | −.09 | .07 |
| 5 | SB | −.33 | −.08 | .36 | −.53* | — | .33 | .07 | .14 | .14 | −.03 | −.01 | −.29 | −.20 | .25 | .12 |
| 6 | PF | .60 | .40 | .77 | .12 | .08 | — | .35 | .45 | .61** | .07 | −.13 | −.23 | −.21 | −.36 | .64** |
| 7 | FAM | .35 | .40 | .03 | .15 | −.49 | .19 | — | .11 | .43* | −.28 | −.01 | −.40 | −.28 | −.27 | −.08 |
| 8 | SCH | −.22 | .63* | .44 | −.02 | .01 | .36 | .12 | — | .28 | .11 | −.33 | −.22 | −.17 | .11 | .05 |
| 9 | COM | −.13 | .45 | .32 | −.08 | .37 | −.03 | .19 | .61* | — | .10 | .25 | .23 | .13 | .23 | .21 |
| 10 | GOV | −.43 | −.23 | −.38 | −.32 | .02 | −.26 | −.27 | .17 | .13 | — | .21 | .37* | .18 | −.12 | .08 |
| 11 | GDP | .17 | −.17 | −.21 | −.20 | −.28 | .70 | .12 | −.01 | .01 | −.01 | — | .61*** | .37* | −.02 | .16 |
| 12 | IEF | −.17 | −.44 | −.25 | −.25 | −.04 | .31 | .06 | −.15 | −.13 | .46* | .39 | — | .52** | .13 | .13 |
| 13 | CC | .30 | −.26 | −.07 | −.03 | −.23 | .93** | −.02 | .09 | −.11 | −.11 | .84*** | .40 | — | .31 | .63 |
| 14 | TEM | −.45 | .45 | −.62* | −.12 | −.16 | −.37 | −.22 | −.01 | −.21 | .61** | −.29 | .26 | −.28 | — | −.03 |
| 15 | PRE | −.55* | −.29 | −.68* | −.56* | .27 | −.30 | −.13 | .08 | .38 | .65** | −.05 | .45 | −.22 | .56* | — |
Note: Results from low- and middle-income countries are in bold. Abbreviations: AP, active play; AT, active transportation; CC, climate culpability; COM, community and the environment; FAM, family and peers; GDP, gross domestic product per capita; GOV, government; IEF, Index of Economic Freedom; PA, overall physical activity; PF, physical fitness; PRE, annual mean precipitation 1990–2020; SB, sedentary behavior; SCH, school; SP, organized physical activity and sport participation; TEM, annual mean temperature 1990–2020.
*P < .05. **P < .01. ***P < .001.
In HIC, a moderate, positive correlation was observed between Organized Sport and Physical Activity and Community and the Environment (r = .42, P = .020), Family and Peers and Community and the Environment (r = .43, P = .027), Government and economic freedom (r = .37, P = .034), and GDP per capita and climate culpability (r = .37, P = .033). A strong, positive correlation was observed between Organized Sport and Physical Activity and Active Play (r = .56, P = .012), Physical Fitness with Community and the Environment (r = .61, P = .005) and annual precipitation (r = .64, P = .003), GDP per capita and economic freedom (r = .61, P < .001), and economic freedom and climate culpability (r = .52, P = .002). A moderate, negative correlation was observed between Active Play and Government (r = −.47, P = .048) as well as Active Transportation and climate culpability (r = −.43, P = .014). Finally, a strong, negative correlation was observed between Active Play and climate culpability (r = −.65, P = .005).
Economic Freedom, Climate Culpability, and Behavioral/Individual Indicators
Figure 1 illustrates the Report Card scores for each behavioral/individual indicator across 2 axis variables, economic freedom and climate culpability categorized into tertiles, as a grid of colored squares (see Supplementary Table S3 [available online] for classifications). For Overall Physical Activity (Figure 1A), the highest score was observed in less free and moderately culpable countries/jurisdictions. The lowest score for Overall Physical Activity was observed in moderately free and less countries/jurisdictions. For Organized Sport and Physical Activity (Figure 1B), highly free countries/jurisdictions, regardless of the variations in climate culpability within, generally showed higher scores than those that are moderately or less free. For Active Play, the lowest score was observed in highly free and less culpable countries/jurisdictions. The highest score was observed in less free and highly culpable countries/jurisdictions. For Active Transportation (Figure 1D), moderately free countries/jurisdictions, regardless of the variations in climate culpability within, generally showed the highest scores as well as less free and highly culpable countries/jurisdictions. In highly free countries/jurisdictions, highly culpable countries/jurisdictions showed the lowest score for Active Transportation. Less free and less culpable countries/jurisdictions showed the best scores for Sedentary Behavior, while the worst was observed in more free but less/moderately culpable countries/jurisdictions (Figure 1E). Lastly, for Physical Fitness (Figure 1F), scores were higher in highly free but less culpable countries/jurisdictions and less free as well as less free and moderately culpable countries/jurisdictions.
—Heat maps of Report Card grades for behavioral/individual indicators by economic freedom and climate culpability. (A) Overall physical activity. (B) Organized sport and physical activity. (C) Active play. (D) Active transportation. (E) Sedentary behavior. (F) Physical fitness. Note: White boxes (eg, moderately free and less culpable countries/jurisdictions for Active Play) indicate that there was no available data. Only 51 countries/jurisdictions had full data on both economic freedom and climate culpability; the 6 countries with missing data were Chinese Taipei, Greenland, Guernsey, Hong Kong, Jersey, and Montenegro. Less free and less culpable countries (n = 11): Croatia, Ethiopia, France, India, Indonesia, Lebanon, Mexico, Nepal, Philippines, Vietnam, and Zimbabwe; less free and moderately culpable countries (n = 5): Brazil, China, South Africa, Portugal, and Slovenia; less free and highly culpable countries (n = 2): Argentina and Serbia; moderately free and less culpable countries (n = 3): Colombia, Hungary, and Thailand; moderately free and moderately culpable countries (n = 8): Botswana, Germany, Japan, Slovakia, Spain, Spain (Basque County), Spain (Extremadura), and Spain (region of Murcia); moderately free and highly culpable countries/jurisdictions (n = 6): Czech Republic, Israel, Finland, Poland, South Korea, and Uruguay; highly free and less culpable (n = 2): Chile and Sweden; highly free and moderately culpable (n = 5): Denmark, England, Lithuania, Scotland, and Wales; highly free and highly culpable (n = 9): Australia, Canada, Estonia, Ireland, Malaysia, New Zealand, Singapore, United Arab Emirates, and United States (classification also available in Supplementary Table S3 [available online]).
Citation: Journal of Physical Activity and Health 19, 11; 10.1123/jpah.2022-0342
Economic Freedom, Climate Culpability, and Sources of Influence
Figure 2 illustrates the Report Card scores for each of the sources of influence indicators across 2 axis variables, economic freedom and climate culpability categorized into tertiles, as a grid of colored squares (see Supplementary Table S3 [available online] for classifications). For Family and Peers (Figure 2A), the highest score was observed in moderately free and less culpable countries/jurisdictions. The lowest score was observed in highly free and highly culpable countries. For the School indicator (Figure 2B), less or moderately free and highly culpable countries/jurisdictions showed the highest score, while countries/jurisdictions that are less free and less culpable showed the lowest score. Patterns were similar for Community and the Environment (Figure 2C) and Government (Figure 2D) indicators. Specifically, highly free countries/jurisdictions generally scored better than their moderately and less free counterparts. For Community and the Environment (Figure 2D), the score was the highest in highly free highly culpable countries, and lower scores were observed with lowering economic freedom in a linear manner per each climate culpability stratum.
—Heat maps of Report Card grades for sources of influence by economic freedom and climate culpability. (A) Family and peers. (B) School. (C) Community and the environment. (D) Government. Note: Only 51 countries/jurisdictions had full data on both economic freedom and climate culpability; the 6 countries with missing data were Chinese Taipei, Greenland, Guernsey, Hong Kong, Jersey, and Montenegro. Less free and less culpable countries (n = 11): Croatia, Ethiopia, France, India, Indonesia, Lebanon, Mexico, Nepal, Philippines, Vietnam, and Zimbabwe; less free and moderately culpable countries (n = 5): Brazil, China, South Africa, Portugal, and Slovenia; less free and highly culpable countries (n = 2): Argentina and Serbia; moderately free and less culpable countries (n = 3): Colombia, Hungary, and Thailand; moderately free and moderately culpable countries (n = 8): Botswana, Germany, Japan, Slovakia, Spain, Spain (Basque County), Spain (Extremadura), and Spain (region of Murcia); moderately free and highly culpable countries/jurisdictions (n = 6): Czech Republic, Israel, Finland, Poland, South Korea, and Uruguay; highly free and less culpable (n = 2): Chile and Sweden; highly free and moderately culpable (n = 5): Denmark, England, Lithuania, Scotland, and Wales; highly free and highly culpable (n = 9): Australia, Canada, Estonia, Ireland, Malaysia, New Zealand, Singapore, United Arab Emirates, and United States (classification also available in Supplementary Table S3 [available online]).
Citation: Journal of Physical Activity and Health 19, 11; 10.1123/jpah.2022-0342
Economic Freedom, Climate Culpability, and Overall Scores
Figure 3 illustrates the Report Card scores for average scores for behaviors and sources of influence, and the overall average score across 2 axis variables, economic freedom and climate culpability categorized into tertiles, as a grid of colored squares (see Supplementary Table S3 [available online] for classifications). When scores for each indicator were averaged, moderately free and moderately culpable countries/jurisdictions showed the best average score for behavioral/individual indicators, while the worst score was observed in highly free and highly culpable countries as well as moderately free and less culpable countries. For the sources of influence, the average score was generally high in highly free countries with the lowest climate culpability. For the overall score, in the highly free stratum, scores were higher with lower climate culpability. In the moderately free group, the scores were higher with higher climate culpability. Finally, in the less free group, the highest score was observed in moderately culpable countries/jurisdictions, while the lowest score was observed in less culpable countries/jurisdictions.
—Heat maps of Report Card grades for overall scores by economic freedom and climate culpability. (A) Average of the behavioral/individual Indicators. (B) Average of the sources of influence indicators. (C) Average of the overall indicators. (A) Behavioral/individual indicators included Overall Physical Activity, Organized Sport and Physical Activity Participation, Active Play, Active Transportation, Sedentary Behavior, and Physical Fitness. (B) Sources of influence indicators included Family and Peers, School, Community and the Environment, and Government. (C) Overall Indicators included all 10 behavioral and sources of influence indicators listed above. Note: Only 51 countries/jurisdictions had full data on both economic freedom and climate culpability; the 6 countries with missing data were Chinese Taipei, Greenland, Guernsey, Hong Kong, Jersey, and Montenegro. Less free and less culpable countries (n = 11): Croatia, Ethiopia, France, India, Indonesia, Lebanon, Mexico, Nepal, Philippines, Vietnam, and Zimbabwe; less free and moderately culpable countries (n = 5): Brazil, China, South Africa, Portugal, and Slovenia; less free and highly culpable countries (n = 2): Argentina and Serbia; moderately free and less culpable countries (n = 3): Colombia, Hungary, and Thailand; moderately free and moderately culpable countries (n = 8): Botswana, Germany, Japan, Slovakia, Spain, Spain (Basque County), Spain (Extremadura), and Spain (region of Murcia); moderately free and highly culpable countries/jurisdictions (n = 6): Czech Republic, Israel, Finland, Poland, South Korea, and Uruguay; highly free and less culpable (n = 2): Chile and Sweden; highly free and moderately culpable (n = 5): Denmark, England, Lithuania, Scotland, and Wales; highly free and highly culpable (n = 9): Australia, Canada, Estonia, Ireland, Malaysia, New Zealand, Singapore, United Arab Emirates, and United States (classification also available in Supplementary Table S3 [available online]).
Citation: Journal of Physical Activity and Health 19, 11; 10.1123/jpah.2022-0342
Discussion
This study examined the Report Card grades from 57 countries/jurisdictions in relation to economic freedom and climate culpability. Specifically, we were interested in examining whether patterns existed in the Report Card grades based on how neoliberal and/or how culpable to climate change the country/jurisdiction was. Findings indicated that, overall, Report Card grades were higher in HIC compared to LIMC, particularly for the sources of influence on physical activity among children and adolescents. Also, HICs showed higher levels of economic freedom based on a neoliberal ideology and that they are more responsible for climate change. When economic freedom and climate culpability were considered for individual and overall indicators, variations existed, which indicates that different countries/jurisdictions by economic freedom and climate culpability experience unique challenges. However, in general, moderately free and moderately culpable countries/jurisdictions performed better for the behavioral/individual indicators, while highly free but less culpable countries/jurisdictions performed better for the sources of influence. For overall indicators, moderately free and moderately or highly culpable countries/jurisdictions generally showed higher Report Card grades than the countries/jurisdictions in other strata.
Results From LMIC
The correlations between behavioral/individual indicators varied by country income group. In the LMIC group, Overall Physical Activity and Active Transportation were positively correlated. This supports the previous findings from Global Matrix 3.026 that active transportation is a more achievable form of physical activity and necessary to get to places in countries with low and medium Human Development Index.27 These suggest that perhaps, active transportation is a main source for overall physical activity in LMIC. A strong, positive correlation was also observed between Organized Sport and Physical Activity and School. Organized activities may likely occur in the school setting in LMIC as observed in the previous round of Global Matrix.2 In previous work comparing physical activity participation among adolescents in Canada and Guatemala,28 more Guatemalan students participated in organized sport and physical activity in school than Canadian students (51.0% vs 38.1%), while more Canadian students participated in league or team sports outside of school than their Guatemalan counterparts (49.2% vs 41.8%). HIC such as Canada may have more private or out-of-school organizations that offer physical activity opportunities for adolescents, and parents may be able to afford these expenses; however, in LMIC, such as Guatemala, more school-based opportunities may be built into their education system compared to costly, out-of-school programs. Recent evidence from Thailand also indicated that, though physical activity policies did not translate into students’ physical activity levels except for active transportation, most schools (87.5% of 136 schools) reported having several physical activity-related policies and practices.29
In LIC, behavioral/individual indicators were correlated with some climate change indicators but not with the 2 economic factors. Specifically, annual temperature and/or precipitation were negatively correlated with Overall Physical Activity, Active Play, and Active Transportation in LMIC. In particular, Active Play was strongly negatively correlated with both annual temperature and precipitation. These results indicate that for LMIC, climate change indicators such as ambient temperatures and precipitation may play an important role in determining children’s ability to engage in active play. Certainly, ambient temperature is an important correlate of outdoor play,30 and given that LMIC generally showed higher mean ambient temperature and precipitation values, it remains prudent to emphasize how the effects of climate change and global warming will inequitably affect regions already observing elevated environmental pressures. One counterintuitive finding is the almost perfect, positive correlation between Physical Fitness and climate culpability in LMIC. Typically, air pollution is a major factor for cardiorespiratory illnesses and life expectancy in general.31,32 Perhaps this is, in part, driven by the fact that more culpable countries have also higher GDP per capita and, thus, may have more resources for preventative health promotion initiatives, such as promoting fitness. In previous studies, the effect of GDP per capita on health was more profound in LMIC than HIC,33 and the effect of air pollution on health is rather nonlinear in LMIC, while, in HIC, the relationship is linear.32 That said, given that only a few LMIC (n = 7, compared to 19 in HIC) provided data on Physical Fitness, this is highly speculative and even could be spurious, thus, more studies should be conducted to confirm the findings.
For the sources of influence, Government was moderately positively correlated with economic freedom, while GDP per capita was positively and strongly correlated with climate culpability in LMICs. Though it is difficult to confirm the significance of these correlations due to the limitation associated with the statistical analysis, it may be that economically more free countries/jurisdictions have more resources at the government level that supports children’s physical activity in LMIC. Also, it is plausible that countries/jurisdictions with higher GDP per capita in LMIC are likely more culpable to climate change due to investment in industry production.16,32 In our data, Argentina, China, and Malaysia were the top 3 countries for GDP per capita and more culpable than other LMICs (Supplementary Table S4 [available online]).
Results From HIC
In HIC, Organized Sport and Physical Activity was correlated positively with Active Play. This may indicate that those who are physically active in one domain are also physically active in other domains among children and adolescents in HICs. Given that Organized Sport and Physical Activity was also positively correlated with Community and the Environment, the surrounding environment, either natural or built, may be important in providing more opportunities for structured (eg, organized sports) and perhaps unstructured (eg, active play) activities among children and adolescents.34 Community and the Environment was also positively correlated with Family and Peers, indicating that the surrounding environment may provide more opportunities for the social support system that is conducive to physical activity. This may be because micro (eg, peers, parents), meso (eg, interaction between micro and exosystems), and exosystems (eg, extended family, neighbors, neighborhood) interact and create a macrosystem-level cultural climate within the neighborhood/community. Such potential interactions as a facilitator to promote physical activity are well noted in previous work.35
A moderate negative correlation found in HIC was between Active Play and Government. The most recent definition of active play is that it is “a form of play, that is, voluntary engagement in activity that is fun and/or regarding and usually driven by intrinsic motivation, that involves physical activity of any intensity.”13 Given its definition, it is possible that active play is unlikely impacted by the Government indicator such as having more physical activity policies (see Supplementary Table S1 [available online]) as opposed to, for instance, Organized Sport and Physical Activity; however, the negative correlation shown may very well could be spurious. Government indicator was not correlated with any other behavioral/individual indicators in HIC. This may indicate that governmental leadership and commitment are not being translated into children and adolescents’ physical activity, despite a relatively higher score (9.7, corresponding grade: C+) observed in HICs compared to other indicators as well as that of LMICs (7.5, corresponding grade: C−) (see Table 1). This should be further investigated. To note, Government was positively correlated with economic freedom, indicating that governmental support for physical activity among children and adolescents is generally better in more free countries/jurisdictions.
Unlike LMIC, climate culpability was negatively correlated with Active Play and Active Transportation. This result may indicate that children and adolescents living in the countries/jurisdictions that are more responsible for climate change may have lower levels of engagement in active play and active transportation. Ambient air pollution, defined as air pollution in outdoor environments, is becoming an increasing concern globally and known to influence both physical activity and health.8,9,36 Though mechanisms are to be explored, perhaps in HIC and more culpable countries/jurisdictions, the engagement in active play and active transportation among children and adolescents may be somewhat linked with climate culpability. Given that climate culpability was also positively correlated with economic freedom and GDP per capita in HIC, economic and climatic factors could be more relevant to active play and active transportation in generating disproportionate impact of climate change on these behavioral indicators across countries/jurisdictions within HIC.
Another notable finding in HIC was that annual temperature and precipitation were not correlated as they were in LMIC with behavioral/individual indicators. This may be because climate is overall milder in HIC compared to LMIC (see Table 1). Having more infrastructure to engage in physical activity indoors as shown in previous rounds of Global Matrix2,3,37 may be another explanation.
Economic Freedom, Climate Culpability, and Report Card Grades
Overall, moderately free and moderately culpable countries/jurisdictions (ie, Botswana, Germany, Japan, Slovakia, Spain, Basque County, Extremadura, Region of Murcia) showed the highest score for the overall behavioral/individual grade. For the sources of influence, highly free and less culpable (ie, Chile, Sweden) showed the highest score. The countries/jurisdictions lagging, based on the overall average of the Report Card grades, were moderately free and highly or moderately culpable countries/jurisdictions (see Supplementary Table S3 [available online] for a list of countries/jurisdictions in these classifications). These findings indicate that, in general, the association between climate culpability and Report Card grades of countries/jurisdictions is unequal across the countries/jurisdictions with different economic freedom strata. Within highly free countries/jurisdictions, the negative impact of more neoliberal economies may be most disadvantageous to the countries/jurisdictions that are highly responsible for climate change (ie, meaning producing more emissions than other countries/jurisdictions with the same grouping of economic freedom), while in less or moderately free countries, the countries/jurisdictions with the lowest Report Card grades are those that are less culpable to climate change (ie, meaning producing fewer emissions than other countries/jurisdictions with the same grouping of economic freedom). Such global inequities that economic freedom and climate change have simultaneously created are highlighted in a recent study that examined the associations between neoliberal capitalist ideology, climate culpability, physical inactivity, and mortality due to NCD in 124 countries.16 Briefly, neoliberal capitalist ideology indicated by economic freedom measures was a strong predictor of NCD-related death, and this relationship was strongest among countries that are less culpable to climate change and the weakest among the countries that are most culpable to climate change.
Integrated Discussions
Overall, the correlations between economic and climatic factors and the Report Card indicators varied greatly by income groups; therefore, global strategies to promote physical activity for children and adolescents should be developed based on the needs of specific income groups. For LMIC, behavioral/individual grades were generally lower than HICs except for Active Transportation and Sedentary Behavior. Given that active transportation may contribute to overall physical activity in these countries, promoting other domains of physical activity, particularly organized sport and physical activity participation, may be a priority. This is also a feasible option in promoting overall physical activity given that climatic factors appear to influence different domains of physical activity (ie, active play, active transportation). In HIC, 2 behavioral/individual indicators were correlated with each other (ie, Organized Sport and Physical Activity, Active Transportation), which suggests that engagement in different physical activity indicators is not mutually exclusive. Climate culpability appears to be relevant to behavioral/individual indicators, suggesting that children and adolescents living in the countries/jurisdictions that are more responsible to climate change show lower levels of active play and active transportation. Given that both active play and active transportation have been mentioned as potential interventions for both climate change mitigation and adaptation,13,38 it is important to further investigate the mechanisms between climate change and these behaviors, particularly in HICs. On a related note, emerging evidence suggests that physical activity could help individuals in adapting to climate change. For instance, increased blood volume, better fluid balance, and improved thermoregulation gained from engaging in aerobic exercise are known to also increase tolerance to extreme heat events.39 Research examining the potential role of physical activity-related indicators in climate change adaptation is warranted given that climate change will likely continue.40
The patterns among economic freedom, climate culpability, and Report Card grades were neither clear nor consistent. The best score for behavioral/individual indicators was observed in moderately free and less culpable countries/jurisdictions; yet for the sources of influence, moderately free and less culpable countries showed the worst score along with less free and highly culpable countries/jurisdictions. When the patterns were observed by the economic freedom groups, the patterns were almost the opposite in relation to climate culpability between less free and highly free countries/jurisdictions for both behavioral/individual and the sources of influence indicators. Similarly, for overall scores, within the highly free group, highly culpable countries/jurisdictions were lagging, while within the moderately and less free groups, less culpable countries/jurisdictions were lagging. These findings may indicate that, unlike in economically highly free countries (where the overall Report Card grades are higher with lower climate culpability in a linear fashion), the impact of climate culpability is disproportionate in moderately or less free countries, meaning that the overall Report Card grades are higher with higher culpability. To confirm these exploratory findings, future studies should investigate on how economic freedom and climate culpability interact and influence different indicators of physical activity among children and adolescents. Furthermore, the unclear and inconsistent patterns between economic freedom, climate culpability, and Report Card grades may be attributable to the fact that this study did not include any other potential macrolevel confounders such as other dimensions of ideology than neoliberal capitalism. For instance, other political ideologies, including a range of ideals covering government, economics, education, healthcare, and foreign policy, social and cultural ideologies (eg, feminism, individualism, religion, human rights), may also be responsible for the associations examined and should be further explored in future studies.
Strengths and Limitations
The AHKGA’s most recent Report Card data used in this study is both a major strength and a limitation of the work produced. Specifically, data from 57 countries/jurisdictions, led by local country leaders and based on the use of best available evidence nationally, represent a comprehensive evaluation of physical activity related indicators based on the internationally harmonized development process. Local-level Report Card development was also done based on interdisciplinary and intersectoral collaboration; therefore, the evidence provided in the Report Card grades is likely to be relevant at the local and international levels and of high quality. However, because of the nature of ecological data and small sample size (N = 57), on top of treating incomplete grades as missing data, more robust statistical analyses were not possible. Also, on a related note, statistical inference or directionality cannot be made from our correlational analyses, and the interpretation of statistical results should be done with caution. In addition, the findings only reflect the evaluation of the country/jurisdiction level patterns and correlations, not at the individual levels for each country. More importantly, the findings are limited to the countries included in this study only; therefore, future work should include more countries for better global representation.
The incorporation of data on economic freedom and climate culpability is another strength. To the authors’ knowledge, this is the first work that explored the potential associations between economic freedom, climate culpability, and varying country-level physical activity related indicators using global data. This opens up a new avenue for physical activity researchers to investigate physical activity related indicators in relation to upstream factors. Nonetheless, the data on economic freedom and climate culpability were from 2018 (the most recent available data in the same year consistently across all countries/jurisdictions), and Report Card data are cross-sectional with the data collected during a various period for each country, so causality cannot be established. In addition, the interpretation of analysis involving climate indicators (ie, Annual Mean Temperature 1990–2020, Annual Mean Precipitation 1990–2020) at the country level are limited, particularly in large countries including several different climate zones. Therefore, the patterns and relationships shown in this study only provides proxy correlational results and should be further investigated with more accurate, longitudinal data to confirm our findings. Furthermore, the classification of countries/jurisdictions by economic freedom and climate culpability was done using tertiles, given the small sample size (ie, small number of countries included in the analysis), and for the ease of interpretation by grouping countries with similar characteristics; however, such classification differs from other studies16; therefore, the interpretation should be done in such context. Lastly, it is important to note that the statistical significance of the correlations described in our interpretation is largely driven by sample size. Specifically, in some cases a correlation around .40 was significant, and in other cases, a correlation around .70 was not significant because there were large differences in the sample sizes based on missing data for some of the indicators.
Conclusions
This is the first study to explore the patterns of AHKGA Report Card grades by factors including economic freedom and climate culpability using the most recent data from the Global Matrix 4.0. Our findings suggested that neoliberal capitalist ideology, as assessed by the Index of Economic Freedom, and climate culpability (ie, how responsible each country is for climate change), together, may be related to Report Card grades, by potentially acting as background forces in generating disproportionate outcomes in moderately and less free countries/jurisdictions. Specifically, unlike highly free countries where highly culpable countries show worse outcomes, the Report Card grades in moderately and less free countries/jurisdictions may be disproportionately affected by climate culpability. Future studies should build on these preliminary findings to clarify the relationships between economic and climatic factors and physical activity indicators among children and adolescents, particularly in LMIC given that the grades were generally lower in these countries/jurisdictions compared to HIC. Furthermore, global-level physical activity promotion strategies should closely evaluate different areas that need interventions tailored by country income groups, with careful considerations for inequities in global political economy and climate change.
Finally, the design, implementation, monitoring, and evaluation of future physical activity promotion policies, programs, and measures should explicitly incorporate consideration of the risks of climate change particularly for the current and future generations of children and adolescents. Specifically, currently existing initiatives aimed at promoting physical activity for young people (eg, AHKGA) and other population groups such as 8 to 80 cities (https://www.880cities.org/), GoPA!,41 and the Lancet Series on urban design, transport, and health.42 Together, these efforts will contribute to achieving United Nation’s Sustainable Development Goals 3 (Good Health and Well-Being), 11 (Sustainable Cities and Communities), 13 (Climate Action), and 17 (Partnerships for the Goals).43
Acknowledgments
The authors thank the country/jurisdiction Report Card leaders and their Leadership Group members participating in the Active Healthy Kids Global Alliance (AHKGA) Global Matrix 4.0. The authors also acknowledge the members of the AHKGA Executive Committee for their contribution to advancing the methodology for Report Card development.
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