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Each Arctic summer since 2008, the Sea Ice Outlook (SIO) has invited researchers and the engaged public to contribute predictions regarding the September extent of Arctic sea ice. Then, each September, we see the accuracy or inaccuracy of those predictions. More than 1,000 individual predictions, based on many different methods, were contributed from 2008 to 2020. Earlier papers analyzed the ensemble skill of the first few hundred SIO contributions through 2013 ( Stroeve et al. 2014) and through 2015 ( Hamilton & Stroeve 2016). Here, I bring those analyses up to date with data through 2020. The main conclusions from earlier papers have proven to be robust, but unexpected new insights emerged as well. The long term downward trend in ice extent is reasonably well described as linear (R2 = 0.79) or quadratic (R2 = 0.81). Very large changes from the previous year’s extent in 2012 and 2013 resulted in the largest prediction errors. Both errors reflect one 2012 cyclone. For reasons not yet understood, SIO predictions especially those from dynamic modeling predict the previous year’s extent rather than the current year. 

Outreach and communication with the public have substantial value in polar research, in which studies often find changes of global importance that are happening far out of sight from the majority of people living at lower latitudes. Seeking evidence on the effectiveness of outreach programs, the U.S. National Science Foundation sponsored large-scale survey assessments before and after the International Polar Year in 2007/2008. Polar-knowledge questions have subsequently been tested and refined through other nationwide and regional surveys. More than a decade of such work has established that basic but fairly specific knowledge questions, with all answer choices sounding plausible but one being uniquely correct, can yield highly replicable results. Those results, however, paint a mixed picture of knowledge. Some factual questions seem to be interpreted by many respondents as if they had been asked for their personal beliefs about climate change, so their responses reflect sociopolitical identity rather than physical-world knowledge. Other factual questions, by design, do not link in obvious ways to climate-change beliefs—so responses have simpler interpretations in terms of knowledge gaps, and education needs. 

How much does the US public know about polar regions? Researchers exploring this topic have occasionally mixed factual questions in among the more typical opinion queries on general-public surveys. A recent article in the Journal of Geoscience Education (Hamilton 2020) describes a key finding from these surveys: there are "two kinds" of polar knowledge. One kind is evoked by questions like this: Which of the following three statements do you think is more accurate? Over the past few years, the ice on the Arctic Ocean in late summer... - Covers less area than it did 30 years ago (correct) - Declined but then recovered to about the same area it had 30 years ago - Covers more area than it did 30 years ago The declining area of late-summer Arctic sea ice, tracked by satellites over the past 40 years, is a basic and widely reported scientific fact. On surveys, however, many people do not recognize this fact, but answer instead based on their opinion about global warming. Similar results occur if we ask whether, in recent decades, CO2 concentrations in the atmosphere have increased: again, many people give answers contrary to science, but reflecting instead their beliefs or political identity. Although these questions involve important and well-established facts, survey responses defy simple interpretation as indicators of knowledge. 

Research on the social bases of environmental concern has established robust findings across various sociodemographic characteristics. This includes interaction effects between education and political identity, as well as particularly low concern among supporters of President Trump.

Using 2016 survey data, we extend such research to examine U.S. public support for four climate‐change mitigation strategies: investment in renewable energy, lifestyle changes, a revenue‐neutral carbon tax, and cap‐and‐trade.

We perform ordered logit regression of belief in anthropogenic climate change and support for these strategies on several key independent variables.

Support follows some of the patterns expected for environmental concern generally but with new details. Trump support is a dominant predictor, and education × party interactions show significant variations in levels of support.

This provides important insights for public policy decision making related to climate change by considering which characteristics are most predictive of support for specific strategies.

 

Transportation infrastructure such as highways and bridges requires upgrades and maintenance. In many U.S. regions, these requirements have surpassed current funding, so new solutions are needed. One obvious though imperfect source is gasoline taxes, but raising these is politically risky, regardless of need. To illuminate this conflict, we analyze data from four random-sample telephone surveys (2016–2018, n = 2,035) that asked residents in the U.S. state of New Hampshire about their perceptions of highway and bridge conditions, and support for gas tax increases. About one third of the respondents counterfactually reported that highway and bridge conditions had improved compared with 10 or 20 years ago. At the county level, subjective perceptions correlate well with actual pavement and bridge conditions. Majorities of respondents also said they would support tax increases of 5 of 10 cents, although support falls off at higher amounts. Support for a tax increase varies not only with the proposed amount, but also with individual characteristics—especially political identity. In a structural equation model, infrastructure perceptions serve as an intervening variable between ideology and tax support: if infrastructure is falsely seen as improving, that supports an ideologically favored rejection of taxes. Partisan differences in perceptions of physical conditions, noted previously in other domains such as climate change, pose an unexpected challenge in building public support for transportation infrastructure.