Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions.

Research shows that people's wait-and-see preferences for actions against climate change are a result of several factors, including cognitive misconceptions. The use of simulation tools could help reduce these misconceptions concerning Earth's climate. However, it is still unclear whether the learning in these tools is of the problem's surface features (dimensions of emissions and absorptions and cover-story used) or of the problem's structural features (how emissions and absorptions cause a change in CO2 concentration under different CO2 concentration scenarios). Also, little is known on how problem's difficulty in these tools (the shape of CO2 concentration trajectory), as well as the use of these tools as a decision aid influences performance. The primary objective of this paper was to investigate how learning about Earth's climate via simulation tools is influenced by problem's surface and structural features, problem's difficulty, and decision aids. In experiment 1, we tested the influence of problem's surface and structural features in a simulation called Dynamic Climate Change Simulator (DCCS) on subsequent performance in a paper-and-pencil Climate Stabilization (CS) task (N = 100 across four between-subject conditions). In experiment 2, we tested the effects of problem's difficulty in DCCS on subsequent performance in the CS task (N = 90 across three between-subject conditions). In experiment 3, we tested the influence of DCCS as a decision aid on subsequent performance in the CS task (N = 60 across two between-subject conditions). Results revealed a significant reduction in people's misconceptions in the CS task after performing in DCCS compared to when performing in CS task in the absence of DCCS. The decrease in misconceptions in the CS task was similar for both problems' surface and structural features, showing both structure and surface learning in DCCS. However, the proportion of misconceptions was similar across both simple and difficult problems, indicating the role of cognitive load to hamper learning. Finally, misconceptions were reduced when DCCS was used as a decision aid. Overall, these results highlight the role of simulation tools in alleviating climate misconceptions. We discuss the implication of using simulation tools for climate education and policymaking.

Australia's Health Star Rating policy process: Lessons for global policy-making in front-of-pack nutrition labelling.

AIM: This study explored factors that shaped the development of Australia's Health Star Rating system for front-of-pack labelling (FoPL) on packaged foods and whether insights could be drawn from this experience to inform the development of global FoPL standards. METHODS: Ten individual semi-structured interviews were conducted with public health or consumer advocates, academics in the field of nutrition labelling and policy, a food industry employee, and Australian public servants. Thematic analysis was undertaken, guided by Kingdon's Multiple Streams Framework, to identify factors which shaped Australian and international FoPL policy processes. RESULTS: Senior Australian bureaucrats played the policy entrepreneur role to facilitate the development of the Health Star Rating system. The public health and consumer advocacy groups formed an alliance to counter-balance the influence of the food industry in the Health Star Rating development process. Public health and consumer groups have less influence at Codex Alimentarius, where policy-making is constrained by political alliances and consensus voting structures. CONCLUSIONS: Strong leadership, policy entrepreneurship and a coherent alliance between public health and consumer groups enabled the development of a FoPL system in Australia and could contribute to advancing FoPL standards at the international level.