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Map · Jun 18, 2019

Support for Making Fossil Fuel Companies Pay for Climate Damages


This tool maps variations in Americans’ opinions about existing or potential lawsuits against fossil fuel companies. Climate scientists say the burning of fossil fuels (coal, oil, and natural gas) is causing global warming, which results in more extreme weather, droughts, wildfires, and flooding from sea level rise. We asked over 5,000 Americans who is responsible for the damages caused by global warming. The maps combine this nationally representative survey data with additional census and geographic data and modeling to depict the percentage of Americans in each state, congressional district, metro area, and county who hold fossil fuel companies responsible for the local damages of global warming.

 

About Downscaling Climate Opinions

These maps are based on data obtained using the KnowledgePanel ™ – a national online omnibus service of Ipsos Public Affairs. The KnowledgePanel™ is the largest commercially available online probability panel in the marketplace; making the sample truly projectable to the US population, which sets it apart from traditional “opt-in” or “convenience” panels. The data were obtained in early 2019.

Public opinion about global warming and its damages is an important influence on decision making about policies to reduce global warming and prepare for the impacts, as well as who should pay for the damages from those impacts. However, American opinions vary widely depending on where people live. Rather than relying on just one national number to understand public responses to climate change we have developed a geographic and statistical model to “downscale” national public opinion results to the state, congressional district, and county levels. We can now estimate public opinion across the country to create a rich picture of the diversity of Americans’ beliefs, attitudes, and policy support.

Explore the maps by clicking on a state, congressional district, or county and compare the results across questions and with other geographic areas. Beneath each map are bar charts displaying the results for every question at whichever geographic scale is currently selected.

See the methods page for more information about uncertainty estimates. In some cases, numbers that should sum to 100% or differences from the national average that should sum to zero do not; these effects are due to rounding errors and should be off by no more than one percentage point.

This research was funded by the Union of Concerned Scientists, the Energy Foundation, the 11th Hour Project, the Grantham Foundation for the Protection of the Environment, the MacArthur Foundation, the Overlook Foundation and the Endeavor Foundation.

Methodology

This site provides estimates of U.S. beliefs, risk perceptions, and policy preferences regarding pending and proposed lawsuits against fossil fuel companies at the state and local levels – a new source of high-resolution data on public opinions that can inform state and local decision-making and communication. The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on a large national survey dataset (n>5,000), along with demographic and geographic population characteristics.

The model for producing these estimates was validated on a large suite of questions related to global warming using three different methods. First, cross-validation analyses were conducted within the dataset. The dataset was divided into two sets of respondents, with one part used to run the model and the other kept aside for validation. The model estimates were then compared to the results of the set aside respondents to directly quantify the percentage of correct answers the model predicted. These cross-validation tests were repeated multiple times using different sample sizes and dividing the data in different ways. Second, the model estimates derived from the full dataset were compared to the results of independent, representative state- and city-level surveys conducted in California, Colorado, Ohio, Texas, San Francisco, and Columbus, Ohio in 2013. The mean absolute difference between model estimates and validation survey results was 2.9 percentage points (SD = 1.5) among the four states (CA, TX, OH, CO) and 3.6 percentage points (SD = 2.9) among the two metropolitan areas (Columbus, OH, and San Francisco, CA), well within the margins of error for the survey results alone (at a 95% confidence level). Estimates have also been validated internally through a series of technical simulations. Third, some model estimates were compared with third-party survey data collected by other researchers in previous years.

For the specific questions displayed here, which are based on 2019 survey data, confidence intervals indicate that the model is accurate to approximately ±10 percentage points at the state and county levels, and ±11 percentage points at the metro and congressional district levels. Such error ranges include the error inherent in the original national surveys themselves, which is ±3 percentage points. These uncertainty ranges are based on 95% confidence intervals using 999 bootstrap simulations. As modeled estimates, the results in the maps may vary slightly from the national survey results.

Survey Question Wording

Model estimates in the maps were derived from public responses to the following survey questions. The response categories for many questions were collapsed into a single variable for mapping. For example, for the question measuring whether respondents trust fossil fuel companies, “Strongly trust” and “Somewhat trust” were combined into a single measure of “Trust fossil fuel companies.” Likewise “Somewhat distrust” and “Strongly distrust” were combined into a single measure of “Distrust fossil fuel companies.” The responses below are color coded to indicate how they were grouped into the variables shown on the maps. Individuals who responded “Don’t know” or who did not answer the question were not modeled separately and appear as gray segments within the bar charts.

BELIEFS

Fossil fuel companies are responsible for global warming damages
Because the impacts of global warming are costly for local communities, some cities and states have filed lawsuits against fossil fuel companies to make them pay for protective measures and/or a share of the damages. How much responsibility do you think fossil fuel companies have for the damages caused by global warming?

  • A great deal
  • A moderate amount
  • Only a little
  • None at all
  • Don’t know

Trust fossil fuel companies
In general, how much do you trust or distrust fossil fuel companies?

  • Strongly trust
  • Somewhat trust
  • Somewhat distrust
  • Strongly distrust

RISK PERCEPTIONS

Global warming is harming my local community
How much do you think global warming is harming your local community?

  • A great deal
  • A moderate amount
  • Only a little
  • Not at all
  • Don’t know

POLICY SUPPORT

Support fossil fuel companies paying for global warming damages

How much do you support or oppose making fossil fuel companies pay for a portion of the damages to local communities caused by global warming?

  • Strongly support
  • Somewhat support
  • Somewhat oppose
  • Strongly oppose
  • Don’t know

Support for local lawsuits against fossil fuel companies

How much do you support or oppose your local officials filing a lawsuit to make fossil fuel companies pay for a portion of damages in your area that are caused by global warming?

  • Strongly support
  • Somewhat support
  • Somewhat oppose
  • Strongly oppose
  • Don’t know

 

The YPCCC is pleased to offer our downscaled climate change opinion estimates to the public. These data are distributed under the following terms of use. This is a legal agreement between you, the end-user (“User”) and Yale University on behalf of the Yale Program on Climate Change Communication (the “YPCCC”).  By downloading the survey data made available on this web site (“Data”) you are agreeing to be bound by the terms and conditions of this agreement.  If you do not agree to be bound by these terms, do not download or use the Data. The YPCCC hereby grants to the User a non-exclusive, revocable, limited, non-transferable license to use the Data solely for (1) research, scholarly or academic purposes, (2) the internal use of your business, or (3) your own personal non-commercial use.  You may not reproduce, sell, rent, lease, loan, distribute or sublicense or otherwise transfer any Data, in whole or in part, to any other party, or use the Data to create any derived product for resale, lease or license.  Notwithstanding the foregoing, you may incorporate limited portions of the Data in scholarly, research or academic publications or for the purposes of news reporting, provided you acknowledge the source of the Data (with express references to the YPCCC, as well as the complete title of the report) and include the following legend: The YPCCC bears no responsibility for the analyses or interpretations of the data presented here.

THE DATA IS PROVIDED “AS IS” WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, ARISING BY LAW OR OTHERWISE, INCLUDING BUT NOT LIMITED TO WARRANTIES OF COMPLETENESS,  NON-INFRINGEMENT, ACCURACY, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.  THE USER ASSUMES ALL RISK ASSOCIATED WITH USE OF THE DATA AND AGREES THAT IN NO EVENT SHALL YALE BE LIABLE TO YOU OR ANY THIRD PARTY FOR ANY INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES FOR THE INABILITY TO USE EQUIPMENT OR ACCESS DATA, LOSS OF BUSINESS, LOSS OF REVENUE OR PROFITS, BUSINESS INTERRUPTIONS, LOSS OF INFORMATION OR DATA, OR OTHER FINANCIAL LOSS, ARISING OUT OF THE USE OF, OR INABILITY TO USE, THE DATA BASED ON ANY THEORY OF LIABILITY INCLUDING, BUT NOT LIMITED TO, BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), OR OTHERWISE, EVEN IF USER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

The YPCCC has taken measures to ensure that the Data is devoid of information that could be used to identify individuals (e.g., names, telephone numbers, email addresses, social security numbers) who participated in or who were the subject of any research surveys or studies used to collect the Data (“Personally Identifying Information”).  However, in the event that you discover any such Personally Identifying Information in the Data, you shall immediately notify the YPCCC and refrain from using any such Personally Identifying Information. This license will terminate (1) automatically without notice from the YPCCC if you fail to comply with the provisions of this agreement, or (2) upon written notice (by e-mail, U.S. or otherwise) from the YPCCC.  Upon termination of this agreement, you agree to destroy all copies of any Data, in whole or in part and in any and all media, in your custody and control. This agreement shall be governed by, construed and interpreted in accordance with the laws of the State of Connecticut. You further agree to submit to the jurisdiction and venue of the courts of the State of Connecticut for any dispute relating to this Agreement. Please use the following citation in any work that makes use of the data and documentation as follows: Marlon, J., Wang, X., Gustafson, A., Ballew, M., Goldberg, M., Rosenthal, S., Leiserowitz, A. (2019) Majority of Americans think fossil fuel companies are responsible for the damages caused by global warming. Yale University. New Haven, CT: Yale Program on Climate Change Communication, School of Forestry & Environmental Studies. Direct any questions to the YPCCC at climatechange@yale.edu.

To download the data, fill out your information and then click "Download data". You will then receive the data in an email.

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Frequently Asked Questions

What do these maps depict?
The maps depict estimates of the percentage of American adults (age 25 and over) who hold particular beliefs, attitudes, and policy preferences about global warming. The estimates were generated from a statistical model that incorporates actual survey responses, but combines these responses with demographic data from the U.S. Census to estimate opinions for different groups of people based on information such as their gender, race and ethnicity, and educational attainment; they also take into account changes in public opinion over time.
Where do the survey data underlying the estimates come from?
The data underlying the maps come from a large nationally-representative survey dataset ( >5,000 respondents) collected in early 2019. Americans were contacted via the KnowledgePanel ™ – a national online omnibus service of Ipsos Public Affairs. The KnowledgePanel™ is the largest commercially available online probability panel in the marketplace; making the sample truly projectable to the US population. This rigorous sampling method sets the resulting data apart from those obtained using traditional “opt-in” or “convenience” panels.
How accurate are the model estimates?
The technique used to develop estimates for every state, congressional district, metro area, and county are based on a widely-used and robust statistical technique that emerged from studies in political science called Multilevel Regression and Poststratification (MRP). Confidence intervals indicate that the model is accurate to approximately ±10 percentage points at the state and county levels, and ±11 percentage points at the metro and congressional district levels. Such error ranges include the error inherent in the original national surveys themselves, which is ±3 percentage points. These uncertainty ranges are based on 95% confidence intervals using 999 bootstrap simulations.
What does the gray color mean on some of the bars beneath the maps?
The gray area reflects people who refused to answer the question or said “don’t know”. We do not provide specific values for the gray areas because we did not develop estimates for these particular responses.
Do the maps account for differences in population density across the country?
No, the maps depict the estimated proportion of people within each geographic area who would answer each question as indicated. We have not adjusted the maps based on population density differences. It is important to keep in mind that some geographic areas may be large, but have few residents (e.g., Wyoming), while other geographic areas may be small, but have many residents (e.g., New Jersey). For reference, Wikipedia has a population density map here. The type of map used in this tool is called a choropleth map, which means the colors on the maps reflect the percentage of the population in a given geographic unit. These kinds of maps are used to represent everything from election results (e.g.,the red state / blue state maps common during presidential elections) to census and economic data (e.g., per capita income or unemployment rates).
Do these maps reflect changes in opinions due to recent extreme weather events like Superstorm Sandy?
Perhaps. The maps may reflect the impacts that specific extreme weather events had on public opinion in a given geographic unit. If public opinion in a particular area has been influenced by local events it is possible that the model would detect such an influence. However, data from specific events or types of events are not explicitly built into the model as predictor variables.
Can I use the data?
Yes. We encourage you to explore the maps and use the results in your own work. The data are available on our Data Download tab at the top of this page so that you can do your own analyses and create your own visualizations. If you publish an academic paper using these data please acknowledge the source by using the following citation:

Marlon, J., Wang, X., Gustafson, A., Ballew, M., Goldberg, M., Rosenthal, S., Leiserowitz, A. (2019) Majority of Americans think fossil fuel companies are responsible for the damages caused by global warming. Yale University. New Haven, CT: Yale Program on Climate Change Communication, School of Forestry & Environmental Studies.

If you publish a news article, visualization, blog post, or other publication using these data or maps, please include the link to this website and attribution to the Yale Program on Climate Change Communication.