'National Differences in Environmental Indicators' (AsPredicted #147485)
Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 2 authors.
Pre-registered on 10/17/2023 06:30 AM (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? Previous investigations (Hershfield et al, 2014) found that national differences in country age predict differences in environmental concern and performance. We theorize that rather than country age, it is a country's Longterm Orientation (LTO), Intergenerational Solidarity (ISI) and tendency to Overcome Temporal Discounting (OTD) that predicts differences in environmental performance (H1-H3).
We will examine these associations controlling for country age, GDP (in millions of USD) and the average of six World Governance Indicators (i.e., using the identical predictors that Hershfield et al., 2014 used). We will examine these associations for the following outcomes: the Environmental Performance Index (EPI; H1a-H3a), changes in the EPI over 10 years (H1b-H3b), the Ecological Threat Index (ETI; H1c-H3c), Greenhouse Gas Emissions (GHG; H1d-H3d), and the percentage of the population that is alarmed (H1e-H3e) and concerned (H1f-H3f) based on the international SASSY survey from the Yale Program for Climate Change Communication (YPCCC).
3) Describe the key dependent variable(s) specifying how they will be measured. For all measures, the most recent indicators were obtained.
Predictors
1. Country Age: We will subtract a nation's year of creation from the current year (i.e., 2023) to estimate a country's age. This information was obtained from the CIA Factbook and Wikipedia.
2. LTO: We will use the LTO index created by Hofstede, available here: https://www.hofstede-insights.com/country-comparison-tool
3. ISI: We will use the national scores for the ISI index from the McQuilkin (2018) paper titled "Doing Justice to the Future: A global index of intergenerational solidarity derived from national statistic"
4. OTD: We will use the national scores for temporal discounting obtained form the Ruggeri et al., (2022) paper titled: "The Globalizability of Temporal Discounting".
Outcomes
1. EPI and EPI Change: We will use the total score of the EPI index, and the 10-year change score, available here: https://epi.yale.edu/.
2. GHG: We will use the Historical GHG Emissions data collected by Climate Watch, available here: https://www.climatewatchdata.org/ghg-emissions?end_year=2021&source=GCP&start_year=1960
3. ETI: The Ecological Threat Index was created by the Institute for Economics and Peace, and it's part of an annual report on the subject. Scores are available here, https://www.visionofhumanity.org/maps/ecological-threat-report/#/.
4. The percentage of the alarmed and concerned groups in each country are obtained from the YPCCC report on the subject, https://climatecommunication.yale.edu/publications/global-warmings-six-audiences-a-cross-national-comparison/?utm_source=Yale+Program+on+Climate+Change+Communication&utm_campaign=6a2288c25a-EMAIL_CAMPAIGN_2023_07_29_12_39&utm_medium=email&utm_term=0_-6a2288c25a-%5BLIST_EMAIL_ID%5D
Covariates
1. GDP: The Gross Domestic Product in millions of USD will be used. Data were obtained from the World Bank.
2. WGI: The average of six world governance indexes will be used to account for differences in government across nations. Scores were obtained from the World Bank.
4) How many and which conditions will participants be assigned to? We are using a cross-sectional design, so no random assignment will take place.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. All analyses will be conducted in any of the following software: SAS, Rstudio and Jamovi.
To evaluate hypotheses H1a-H1f, H2a-H2f, and H3a-H3f we will estimate linear regression models. Importantly, we will also estimate a second set of models accounting for the non-independence of nations. Recent findings suggest that accounting for geographical and cultural similarities can help eliminate bias in national-level analyses (Claessens et al., 2023). Specifically we will account for geographic and cultural phylogenetic similarity across nations through the use of a geographic proximity matrix and a linguistic proximity matrix.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. No data exclusion will occur, as we are relying on national level indicators for our analyses. However, the sample size will vary for each analysis, as not all countries have data for each respective indicator.
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. A total of 204 countries had data relevant to our hypotheses. A total of 61 countries have scores for the temporal discounting indicator; a total of 101 for the LTO index; and a total of 118 for the ISI. Sensitivity analyses suggest that with an alpha of .05, power set to .80, and four predictors (main predictor, country age, GDP, and WGI average) we can meaningfully detect effect sizes (eta square) as small as: .117, .073, and .063 respectively for each sample size (smallest to largest).
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) Response to question 1: Data have been collated by the research team (since each index was publicly available and a national statistic). However, no analyses have been conducted prior to this pre-registration.
Exploratory analyses
1. We will report correlations between all indexes in the supplementary materials.
2. We will report analyses only for age as a predictor (controlling for GDP and the WGI average) to replicate the results of Hershfield et al. (2014) and extend those findings to other indicators.
3. We will examine whether our main predictors, and a country's age also predict increased national endorsement of the following item obtained from the European Social Survey and the World Values Survey: "It is important to care for nature and environment (valuing nature)". This item is part of the Schwartz Human Values Inventory, and is reflective of benevolence, however it is also reflective of concern for nature. Scores for this item are averaged across waves of the ESS and WVS at the national level.
