Yale Program on Climate Change Communication

The data in this report are based on a nationally representative survey of 1,011 American adults, aged 18 and older. Results in Sections 3 and 4 are reported for the subset of 861 registered voters who participated in the survey. The survey was conducted April 16 –May 1, 2023. All questionnaires were self-administered by respondents in a web-based environment. The median completion time for the survey was 22 minutes.

The sample was drawn from the Ipsos KnowledgePanel®, an online panel of members drawn using probability sampling methods. Prospective members are recruited using a combination of random digit dial and address-based sampling techniques that cover virtually all (non-institutional) residential phone numbers and addresses in the United States. Those contacted who would choose to join the panel but do not have access to the Internet are loaned computers and given Internet access so they may participate.

The sample therefore includes a representative cross-section of American adults – irrespective of whether they have Internet access, use only a cell phone, etc. The sample was weighted, post survey, to match key US Census Bureau demographic norms.

From November 2008 to December 2018, no KnowledgePanel® member participated in more than one Climate Change in the American Mind (CCAM) survey. Beginning with the April 2019 survey, panel members who have participated in CCAM surveys in the past, excluding the most recent two surveys, may be randomly selected for participation. In the current survey, 267 respondents, 232 of whom are registered voters included in this report, participated in a previous CCAM survey.

The survey instrument was designed by Anthony Leiserowitz, Seth Rosenthal, Jennifer Carman, Matthew Ballew, Danning Lu, Marija Verner, Sanguk Lee, Matthew Goldberg, Jennifer Marlon, Joshua Low, Kristin Barendregt-Ludwig, Michel Gelobter, and Gerald Torres of Yale University; Edward Maibach, John Kotcher, Teresa Myers, and Nicholas Badullovich of George Mason University; Andrea Aguilar, Sha Merirei Ongelungel, Cristian Sanchez, and Karina Sahlin of the Digital Climate Coalition; Irene Burga and Mark Magan ̃a of Green Latinos; Saad Amer of Justice Environment; Romona Taylor Williams of Mississippi Citizens United for Prosperity; Montana Burgess of Neighbours United; Grace McRae and Makeda Fakede of the Sierra Club; and Manuel Salgado and Annika Larson of WE ACT for Environmental Justice. The categories for the content analysis of the open-ended responses about groups vulnerable to global warming were developed by John Kotcher of George Mason University, and open-ended responses were coded by Patrick Ansah, Tracy Mason, and Nicholas Badullovich of George Mason University. The categories for the content analysis of the open-ended responses about climate justice were developed by Jennifer Carman of Yale University, and the open-ended responses were coded by Matthew Ballew and Danning (Leilani) Lu of Yale University. The figures and tables were constructed by Emily Goddard of Yale University.

Margins of error

All samples are subject to some degree of sampling error—that is, statistical results obtained from a sample can be expected to dier somewhat from results that would be obtained if every member of the target population was interviewed. Average margins of error, at the 95% confidence level, are as follows:

• Americans ages 18+ (n = 1,011): Plus or minus 3 percentage points.
  • Democrats (total; n = 423): Plus or minus 5 percentage points.
  • Liberal Democrats (n = 244): Plus or minus 6 percentage points.
  • Moderate/conservative Democrats (n = 177): Plus or minus 7 percentage points.
  • Independents (n = 131): Plus or minus 9 percentage points.
  • Republicans (total; n = 365): Plus or minus 5 percentage points.
  • Liberal/moderate Republicans (n = 127): Plus or minus 9 percentage points.
  • Conservative Republicans (n = 235): Plus or minus 6 points.
• All Registered Voters (n = 861): Plus or minus 3 percentage points.
  • Democrats (total; n = 392): Plus or minus 5 percentage points.
  • Liberal Democrats (n = 230): Plus or minus 7 percentage points.
  • Moderate/conservative Democrats (n = 160): Plus or minus 8 percentage points.
  • Independents (n = 101): Plus or minus 10 percentage points.
  • Republicans (total; n = 334): Plus or minus 5 percentage points.
  • Liberal/moderate Republicans (n = 112): Plus or minus 9 percentage points.
  • Conservative Republicans (n = 220): Plus or minus 7 points.

Rounding error and tabulation

In data tables, bases specified are unweighted, while percentages are weighted to match national population parameters.

For tabulation purposes, percentage points are rounded to the nearest whole number. As a result, percentages in a given chart may total slightly higher or lower than 100%. Summed response categories (e.g., “strongly support” + “somewhat support”) are rounded after sums are calculated. For example, in some cases, the sum of 25% + 25% might be reported as 51% (e.g., 25.3% + 25.3% = 50.6%, which, after rounding, would be reported as 25% + 25% = 51%).

Instructions for coding Section 1.1: Open-ended responses about groups perceived to be most harmed by global warming

A doctoral student and a postdoctoral fellow coded the open-ended responses using instructions and categories developed by one of the Primary Investigators. Percent agreement ranged from 93% —99% for the categories coded. Dierences between the two coders were resolved via discussion between them and the Primary Investigator. “Not asked” classification was determined by a “No” or “Not sure” response to the preceding question, “Do you think that global warming harms some groups of people in the United States more than others?” Participants who provided that response were not shown this open-ended question. Definitions of the other categories used by the coders are listed below.

For the following variables, we code each survey response for the presence or absence (0=absent; present=1) of the following categories listed below. The order in which the categories are mentioned in the survey response does not matter for the purposes of coding, simply the presence or absence of a particular category.

• Areas Prone to Extreme Weather —This category represents any reference to people who live in areas prone to extreme weather (e.g., wildfires, tornadoes, hurricanes, drought, extreme heat). This DOES NOT INCLUDE NON-SPECIFIC references to people who live in cities or rural areas, coastal areas, certain areas with specific climatic conditions, or references to SPECIFIC regions. Examples include: ”People in hurricane and tornado belts.” ”people living in areas prone to wildfires” ”people in areas prone to extreme weather”
• Areas With Specific Climatic Conditions —This category represents any reference to people who live in areas with specific climatic conditions (e.g., especially dry/wet areas, especially hot/cold areas). This DOES NOT INCLUDE NON-SPECIFIC references to people who live in cities or rural areas, coastal areas, areas prone to certain types of extreme weather, or references to SPECIFIC regions. Examples include: ”Dry and desert areas” ”Hotter weather states” ”Those who reside in parts of the country where the climate is colder during the winter months.”
• Children/Young People —This category represents any reference to children or young people. Examples include: “young children” “young” “kids” “Children”
• Coastal Residents —This category represents any reference to people living near the coast, water, or in low-lying areas. Examples include: ”Coastal residents” ”people living in low lying coastal areas.” ”people living near water”
• Disenfranchised —This category represents any general reference to people who are underprivileged, disadvantaged, marginalized, or disenfranchised. Examples include: “Underprivileged” ”marginalized people”
• Everyone —This category represents any response that indicates everyone or all people are vulnerable to harm from climate change. Examples include: “All of us” “Everyone”
• Farmers —This category represents any reference to farmers or those who work in agriculture. This DOES NOT INCLUDE any NON-SPECIFIC reference to people who live or work in polluted areas. Examples include: “those involved in agriculture” “Farmers”
• Non-US Region —This category represents any EXPLICIT reference to people who live in a particular geographic region outside the United States. Examples include: “Other countries besides usa” “3rd world countries”
• Older Adults —This category represents any reference to seniors, the elderly, or older adults. Examples include: “Elderly” “older” “the very old” “senior citizens”
• People of Color —This category represents any general reference to racial or ethnic minorities, people of color (sometimes abbreviated as POC), or specific references to certain groups including African Americans, Asians, Hispanics/Latinos, and American Indians. Examples include: “POCs” “asian american” “African Americans” “minorities” “Native Americans/Indians”
• People with Medical Conditions —This category represents any general reference to people with pre-existing health conditions and the unhealthy, OR a more specific reference to people with illnesses such as lung disease, or the disabled. Examples include: ”unhealthy people” ”people with breathing problems” ”those with medical issues”
• Poor/Low Income —This category represents any reference to poor people, members of low-income households, or the homeless. Examples include: “Lower class” “Less fortunate” “homeless” “Poor” “Low-income individuals”
• Rural Residents —This category represents any reference to people who live in rural or less populated areas. Examples include: ”rural areas” “in less populated places”
• Specific Region —This category represents any reference to people who live in a particular geographic region. This DOES NOT INCLUDE NON-SPECIFIC references to people who live in cities or rural areas, areas prone to certain types of extreme weather, or certain areas with specific climatic conditions. Examples include: “northern people” “southern states” “Those living below the 45th parallel” “California”
• Urban Residents —This category represents any reference to people who live in cities, urban areas, and references to those in highly populated areas. Examples include “Those in highly populated areas” “City” “urban residents”
• Women —This category represents any reference to women. Examples include: “women” “pregnant women”
• Don’t Know —This category includes any response that expresses a lack of sucient knowledge to provide an answer. Examples include: “I don’t know” “not sure”
• Other —This category includes any responses that are intelligible, but that don’t fit any of the other categories.

Instructions for coding Section 2.2: Open-ended responses about the term “climate justice”

The three lead authors at Yale Program on Climate Change Communication first conducted independent coding of the open-ended responses. The first author developed a codebook based on all three raters’ categories and the other authors coded the responses again following the final codebook. Dierences in the coding were resolved via discussion between the three researchers. Percent agreement ranged from 81% —98% for the categories coded. The “haven’t heard of climate justice” classification was determined by a “nothing at all” response to the preceding question, “How much, if anything, have you heard or read about the concept of climate justice?” Participants who provided that response were not shown this open-ended question. Definitions of the other categories used by the coders are listed

below.

For the following variables, we code each survey response for the presence or absence (0 = absent; 1 = present) of the following categories listed below. The order in which the categories are mentioned in the survey response does not matter for the purposes of coding, simply the presence or absence of a particular category.

A survey response can be coded positive for multiple content variables. For example, the response, “Holding corporations accountable for pollution” was coded as “present” for both accountability and reparations (for the reference to accountability) and corporations (for mentioning corporations). Definitions for each content variable are provided below.

• Accountability and Reparations (including law enforcement) —This category includes any reference to groups who bear greater responsibility for causing global warming and should act (or be forced to act) to address its harms. Examples include: ”Assuring that those who are causing climate change take responsibility for those subject to the eects of climate change” “Reparations for communities disproportionately impacted by climate change” “Take oenders to court”
• Action, Activism, and Social Change —This category inludes any reference to social movements, protests, and other collective action to address global warming and other social problems, including specific activist organizations or activists. Examples include: ”Creating a plan to combat climate change” “Grassroots organizations advocating for disadvantaged populations who are disproportionately aected by global warming” “Greta” ”Greenpeace” ”Activists”
• Corporations —This category includes any references to corporations, corporate responsibility, a specific corporation, or a type of corporation. Examples include: “Coal factories” “Holding big companies accountable for pollution” “Big corporations”
• Disproportionate Harm —This category includes any reference to global warming harming some groups of people more than others, and may or may not name specific groups who are harmed. Examples include: ”Some being aected greatly, others not at all” “The greater impact of climate change on low income and minority communities”
• Environmental/Climate Issues or Protection (general) —This category includes any response that is not clearly dierent from climate/environmental issues more broadly. These responses refer to general causes (such as fossil fuels), impacts (such as extreme weather, harms to plants, animals, or natural spaces), or solutions (mitigation or adaptation) related to global warming or environmental problems without any mention of social justice-related concepts. Examples include: ”Being a steward for nature and future generations” “Finding ways to combat global warming” “Reduce Pollution”

• Equitable Benefits/Solutions —This category includes any reference to global warming solutions that promote social equity or protect people from global warming impacts, or references to social equity generally. Examples include: ”Assuring that exploited communities who are most aected by climate change are supported more” “Protecting the vulnerable from the impacts of climate change” “Equity”
• Government and Politics —This category includes any reference to government actors, legislative action, or specific political parties/ideologies. They may also include references to government or political parties in a negative way (and are also coded as Naysayers in those cases). Examples include: ”Crafting law to support climate policy” “Another SCAM perpetrated on the American public by crooked Politicians” “Politics” “Socialists / Communists”
• Naysayers —This category includes any response that indicates a dismissive attitude toward climate justice (or global warming). Examples include: “A bunch of nut jobs wanting justice because they believe in global warming” “A waste of time” “hoax” ”Lies”
• Social Inequalities (including inequality, racial disparity, economic disparity) —This category includes any reference to social factors that oer certain groups advantages or disadvantages in terms of resources, power, etc. These factors are mentioned on their own or as a cause of disproportionate harms of global warming. Examples include: “Poor people and neighborhoods” ”Environmental racism” “The intersectionality of race/class and climate change/global warming”
• Don’t Know —This category includes any response that expresses a lack of sucient knowledge to provide an answer. Examples include: “I do not know” ”Not sure” ”no idea” ”Don’t know”
• Other —This category includes any response that does not clearly fit into any of the other categories, or require extensive subjective interpretations to be categorized. Examples include: “how little power we have as citizens” “Charging users” “Standing on your beliefs” “Money” “Misunderstood”
• No Response —This category includes any response that is left blank with a value of -1.