“We showed that we are united and that we, young people, are unstoppable.” - Greta Thurnberg, UN Youth Climate Summit (2019)
Climate change is one of the more serious challenges of our time, threatening humanity’s livelihood, habitat, safety, and well-being[1,2] . To preserve the world as we know it, a switch towards more pro-environmental behavior is required. Youth are seen as ‘agents of change’ on climate action, and their generation indeed exhibits more concern about climate change and its consequences than older generations[5-7]. Still, what actually moves young people to behave more pro-environmentally? Climate change awareness, concern, and knowledge were shown to differ between youth who do or do not engage in climate-friendly behavior, making them potential routes to stimulating such behavior. Doing so may engage the targeted youth (i.e, the policy makers of tomorrow) as well as peers and family members through what is called ‘multiplicative action’, something frequently observed in youths[8,35]. The current project therefore aims to (1) identify central constructs related to pro-environmental behavior in young people, and (2) test two interventions based on these central constructs and aimed at increasing pro-environmental behavior. Prolific provides a unique opportunity to examine this in relevant samples with respect to background variables (e.g., gender, socioeconomic background) and offers the possibility to study whether interventions need to be adapted to the country of residence. The proposed study is an essential step towards designing an evidence-based intervention for stimulating pro-environmental behavior among young adults.
In Study 1, we examine different constructs that have been shown to stimulate pro-environmental behavior either directly (hope that climate change may be remedied and concern over climate change consequences[9-13], climate change distress, humanity’s perceived control over climate change, and biospheric values) or indirectly (factual knowledge of climate change causes and consequences[12,13], the trust one has in the influence of designated people and organizations (e.g., the government) over climate change[17,18], social influences, specifically the acceptance of climate change by family and friends, science and climate change interest[20,21]). Also included is climate change skepticism, which weakens the relationship between concern/distress and pro-environmental behavior, and may even negatively impact behavior itself[9,14,22].
The interplay of these constructs will be studied using a complex system approach – psychometric network analysis – which allows for the inclusion of many different constructs while controlling for their mutual interactions (i.e., hope influences behavior and vice versa). Using this approach, we can identify efficient targets for intervention[e.g., 24-26] – the construct(s) closely related to behavior and most central within the network. Previously, such central construct(s) have been related with future behavior and intervening upon a central construct has been shown to increase donation behavior. Pro-environmental behaviors such as the willingness to pay for bio plastics and energy preferences in adult populations have been studied using this approach.
When designing interventions targeting pro-environmental behavior, taking into account the country of residence appears of importance[30-32]. Differences have already been found in networks modeling preventative behavior responses to COVID-19 in the Netherlands and the UK . In the current study, we model pro-environmental behavior in young adults in two neighboring high GDP countries in the European Union: The Netherlands and Germany. Differences (or lack thereof) provide insights about the intervention’s general applicability.
The design of the study is displayed in Figure 1. In Study 1, the networks of climate change cognitions, affects, and behavior in young Dutch and German adults are estimated, including the constructs described above. First, the two country networks will be compared: are the same constructs central across networks? Based on this comparison, the intervention targets will either be based on the centrality (i.e., influence) of constructs of the full sample network (scenario B: networks are similar) or per country (scenario A: networks differ).
In Study 2, we will intervene upon the most influential construct(s) which is (are) also directly connected with behavior measures. This is done by running a randomized controlled trial experimental study with 3 conditions (intervention 1, intervention 2, control) targeting young adults in the two countries. The aim of the intervention (i.e., a short video) is to increase pro-environmental behavior of young adults, specifically: (1) donating to a pro-environmental organization (e.g., Greenpeace) and (2) sharing the video via social media (i.e., multiplicative action).
Figure 1. Overview of the studies.
Given that with a sample size of 600 participants, centrality measures may be confidentially interpreted, a sample of at least 600 (+10% accounting for potential exclusion) participants per country will be recruited. In total, 1320 participants will therefore be recruited (660 per country). This will amount to costs of 3960 Pounds for Study 1 (7.5 hourly pay for 18 minutes plus 33% percent service costs).
Assuming the default small effect size (f2 = 0.01) for a MANOVA with a power of .8 and 𝛼 = .05, and the effect of interest being the interaction, Gpower recommends a minimum required sample size of 600 participants. This matches the recommended sample size for the mediation analyses according to the Zwicker study (i.e., 100 participants per intervention condition). Thus, 300 participants per country are recruited (+10% accounting for potential exclusion), or 660 participants in total. This accumulates to 1655 Pounds for Study 2 (7.5 hourly pay for 15 minutes plus 33% percent service costs).
The materials, design and analysis steps for both studies have been pre-registered on osf: OSF | Climate action now: Developing interventions to stimulate pro-environmental behavior in young adults. We also included the R-code and results of a pilot study (N = 434).
The anonymized datasets and analysis code will be made publicly available on osf after publication of the article. The preprint of the article will be published on https://psyarxiv.com/ and the article will be published open access (sponsored by Dutch Universities).
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Jacqueline N. Zadelaar
Maien S.M. Sachisthal
Maartje E.J. Raijmakers