Four or So Ultrabrief Interventions That Increase Public Acceptance Regarding Global Warming

• In Event: Promoting Public Understanding of Science: A Cornerstone of Diverse Democracies

Abstract

An element of democracy––albeit asserted by other forms of government––is that of epistemic/empirical accuracy and/or justified true beliefs. A government and its science(s) tend toward coherence, and democracies claim to be most sensitive to highly accurate theory-evidence couplings from trained scientists. Normative science is a “leading edge” of democracy––guiding the current (or perhaps next) generation’s understanding of astronomy, evolution, and climate, etc.––in contrast to astrology and denials of evolution and global warming. The slower this leading edge reaches diverse communities, the more their residents risk suffering socioeconomic and wellbeing injustices. Climate change arguably represents the greatest human public health danger, particularly for the poor.

Those denying global warming occasionally, but incorrectly, suggest that scientists are motivated to accept climate change largely due to funding or social pressures. However, scientists would overwhelmingly prefer global warming to not be occurring (e.g., for younger/future organisms’ sake); indeed, one who might disconfirm (anthropogenic) global warming would be maximally rewarded by both science and society (Denial101x, 2015)! Thus, as global warming seems empirically accurate and normative, Berkeley’s Reasoning Research Group has conducted nine studies (eight experiments, overwhelmingly randomized) and generated one website to help assess and/or promote the public understanding of global warming (e.g., Clark, Ranney, & Felipe, 2013; Ranney & Clark, submitted). Of these nine, four (italicized below) will be discussed—each representing a distinct intervention-type.

With adult and high-school participants, we have shown that almost no one knows global warming’s basic physical-chemical mechanism (e.g., Ranney et al., 2012). However, in one type of intervention (utilizing immediate and delayed posttests), 2-to-45 minutes’ mechanistic instruction dramatically increased such understandings. Furthermore, for liberals and conservatives alike, mechanistic learning––or estimating-and-then-learning just seven-to-ten highly germane global warming statistics (the second type of intervention)––increased climate change acceptance. A third type of intervention shows that even statistics relating indirectly to global warming––for instance, that inhibit an individual’s level of nationalism––also increases global warming acceptance; in particular, supra-nationalist statistics that reduce unrealistic beliefs about the US’s international standing in social and technical-educational realms heighten climate change acceptance. Even more recent data show that providing learners with data-averaging graphs––the fourth kind of intervention––also increases (e.g., anthropogenic) global warming acceptance; for example, one intervention compellingly helps learners appreciate that, over multi-averaged time-scales, the graph of Earth’s temperature for the past 130 years is remarkably similar to the graph of the Dow Jones Industrial Average (when adjusted for inflation).

Such “easy” science-driven attitudinal and cognitive changes disconfirm what we term “stasis theory,” which views information as largely futile or potentially counterproductive. (Stasis theory appears historically naïve, range-restricted, and misinterpretive of non-causal/correlational and “polarization” data; we find no polarizations.) To foster public climate wisdom, we introduced HowGlobalWarmingWorks.org, a now-popular website that includes five short physical-chemical mechanistic videos (from 0.9 to 4.7 minutes; e.g., Ranney et al., 2013) in multiple languages—along with multi-translated texts, the aforementioned graphs, and seven compelling global warming statistics (e.g., involving ice, heat, greenhouse gases, and scientific consensus).

Authors

• Michael Ranney, University of California - Berkeley

• Charles Chang, University of California - Berkeley

• Tommy Ng, University of California - Berkeley

• Justin Teicheira, University of California - Berkeley

• Tina Luong, University of California - Berkeley

• Lukas Gierth, University of California - Berkeley