Yale Program on Climate Change Communication

About Downscaling Climate Opinions

Public opinion on climate change is an important influence on the decision-making process for the development of policies to reduce and prepare for climate change impacts. Yet opinions can vary widely depending on where people live. So why rely on just a single national number to understand public responses to climate change at local levels?

Public opinion polling is generally done at the national level because local-level polling is very costly and time-intensive. Our team, however, has developed a geographic and statistical model to downscale national public opinion results to the country, region, and local authority levels. We can now estimate and visualize differences in opinion across the country, revealing a clearer picture of the diversity of British perceptions, attitudes, and support for policies. For instance, we estimate that nationally, 85% of Britons think global warming is happening. However, our model shows that 92% of people in the Cambridge local authority share this view, compared to only 78% in nearby South Holland.

Explore the maps by clicking on a country, region, or local authority and compare the results across questions and geographic areas. The maps contain results for England, Wales, Scotland, and Northern Ireland. Downscaled maps for The Republic of Ireland can be found here. Beneath each map are bar charts displaying the results for every question at whichever geographic scale is currently selected. For the bar charts and the maps alike, multiple responses to each survey question are grouped into positive and negative categories as defined on the Survey Question Wording tab. See the Methodology tab 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 are off by one percentage point; these effects are due to rounding errors.

See our Climate Note for more details.

Methodology

This tool provides estimates of climate change beliefs, risk perceptions, policy support, experience, and attribution perception among people aged 16 and older in the United Kingdom for all countries, regions, and lower-tier local authorities – a new source of high-resolution data on public opinion that can inform decision-making, policy, and education initiatives in the UK. The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on a large national survey dataset, along with demographic and geographic population characteristics.

The underlying survey was administered by Ipsos from 7 November to 13 November 2024 and included 10,660 British adults (16+). Respondents came from 12 regions and from 360 of 361 local authorities. Sample weights for all respondents were calculated by Ipsos to be nationally representative and to reflect the large diversity of the British population across sex, age, and socio-economic dimensions. 582 responses were removed due to missing data, resulting in a total sample size of n=10,078. Model development follows the methods described in Howe et al. (2015) to estimate local-scale US public opinion. 

Multilevel modeling with poststratification (MRP) was used to estimate the spatial distribution of climate opinions at country, region, and lower-tier local authority levels (Howe et al. 2015; Mildenberger et al. 2016, 2017). Dependent variables were first recoded into binary format, with positive response values grouped and coded as “1” (e.g., “Somewhat favor” and “Strongly favor”) and non-positive values coded as “0” (“Somewhat oppose”, “Strongly oppose”, “Don’t know”, Refused); see “Survey Question Wording” on the interactive map tool tab for a full list of questions and how each was recoded to binary. MRP modeling then proceeded in two phases. First, the multi-level model was constructed by predicting individual survey responses as a function of both individual-level demographics (sex, age, and socio-economic classification, as well as a three-way interaction term among these variables) and one geography-level covariate (voting patterns on leaving the European Union). Second, “post-stratification”, or spatial weighting, was performed using the fitted model, where population-weighted opinion estimates for each demographic-geographic subtype were aggregated based on the subtype population distribution within each geographic subunit. 

Voting data for leaving the European Union for each country, region, and lower-tier local authority for England, Wales, and Scotland were obtained from the UK Electoral Commission. These data were then crosswalked from the 2016 local authorities to the 2023 shapefiles used in this study. However, lower-tier local authority data were not available in this report for Northern Ireland. Therefore, we used the constituency-level data for Northern Ireland published by the Electoral Office for Northern Ireland (House of Commons Library), rasterized it in ArcGIS Pro, and then took an average for each 2023 lower-tier local authority to get an approximation of the voting patterns by local authority. The model configuration was selected after testing model fit using a variety of demographic and aggregate variables, including sex, age, socio-economic classification, voting patterns, and percentage of people over 65 years of age in each geography, as well as interaction terms.

The model was validated first by comparing modeled estimates with weighted survey averages at the national level, country level, and for the five most populous regions. The mean absolute error between modeled estimates and weighted survey averages across all variables was 0.92 percentage points at the national level, 1.39 percentage points at the country level, and 1.28 percentage points at the region level. The model was then subsequently validated by comparing two YPCCC results with externally and independently modeled data from More In Common, E3G, and Persuasion UK. Climate change worry was compared with a similar question on climate change worry from More In Common and E3G, and support for renewable energy was compared with a question about support for net zero from Persuasion UK. While the results differed slightly, likely because the questions and response options were not identical, trends across regions and constituencies were similar with both the YPCCC model and the external data. 

Uncertainty ranges are based on 95% confidence intervals using bootstrap simulations. These confidence intervals indicate that the model is accurate to approximately ±6 percentage points at the country level, ±6 percentage points at the region level, and ±7 percentage points at the local authority district level. Such error ranges include the error inherent in many of our national surveys, which is typically ±3 percentage points.

The modeling approach used to develop the maps is adapted from Howe, P., Mildenberger, M., Marlon, J.R., and Leiserowitz, A., “Geographic variation in opinions on climate change at state and local scales in the USA,” Nature Climate Change. DOI: 10.1038/nclimate2583.

See the Climate Note for additional details about methods.

The YPCCC is pleased to offer our downscaled UK 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: Howe, Peter D., Matto Mildenberger, Jennifer R. Marlon, and Anthony Leiserowitz (2015). “Geographic variation in opinions on climate change at state and local scales in the USA.” Nature Climate Change, doi:10.1038/nclimate2583. Direct any questions to the YPCCC at climatechange@yale.edu.

Frequently Asked Questions

What do these maps depict?

The maps depict estimates of the percentage of adults in the United Kingdom (age 16 and over) who hold particular attitudes, perceptions, and policy preferences on the problem of climate change. The estimates were generated from a statistical model that incorporates actual survey responses from a large dataset of 10,078 individuals that was collected in November 2024. The actual survey responses were combined with demographic data from the 2021 England/Wales and Northern Ireland census and the 2022 Scotland census to estimate opinions based on information such as sex, age, and National Statistics Socio-economic classification (NSSEC).

Where do the survey data underlying the estimates come from?

The data underlying the maps come from a large national survey dataset (10,660 respondents) collected during November 7–13, 2024, as part of the Climate Change in the British Mind project led by the Yale Program on Climate Change Communication in partnership with Ipsos. A nationally representative sample of respondents was contacted, and all questionnaires were self-administered by respondents in a web-based environment. Reports from the individual surveys are available here: Climate Change in the British Mind.

How many people did you survey in my country/region/local authority? How can you say anything about opinions in my geography if you did not survey anyone there?

The number of respondents surveyed varies for each geographic unit, but is very large across the UK as a whole. Since our surveys are nationally representative, the number of respondents in each country, region, and local authority is proportional to its share of the national population. In other words, larger countries, regions, and local authorities will have more respondents than smaller countries, regions, and local authorities. In some smaller local authorities, very few, if any, respondents may have been surveyed. In those cases, our model uses information about the population of the local authority and information about the characteristics of the local authority (such as NSSEC and voting patterns on leaving the European Union in the local authority) to make its estimates. While this may sound surprising, both socio-economic status (here and here) and voting patterns on leaving the European Union (The Guardian or here) have been shown to influence opinions about climate change. Knowing the levels for each of these for a local authority’s residents, along with the proportion of different demographic groups within that local authority, allows us to predict the residents’ climate opinions very well. All of this information together allows us to produce reliable estimates (see next question) for all local authorities, even if the number of individuals sampled within a specific local authority is small.

How accurate are the estimates?

No model is perfect, and there are uncertainties in the model estimates. The model uncertainties are smaller at broad geographic scales (e.g., the country level) and are larger at finer geographic scales (e.g., the local authority level). The model estimates also tend to be conservative, so geographic areas with extremely high or low measures are not estimated as well as areas with values closer to the national average for each survey question. The average margin of error is ±6 percentage points for the country- and region-level estimates and ±7 percentage points for the local authority-level estimates (at the 95% confidence level).

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, while other geographic areas may be small, but have many residents. 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 to census and economic data (e.g., per capita income or unemployment rates).

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.

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 above on the “Data Download” tab 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:

Gillreath-Brown, A., Goddard, E., Jefferson, M., Richards, E., Carman, J., Rosenthal, S., Leiserowitz, A., Marlon, J.R., (2025). UK climate opinion maps. Yale Program on Climate Change Communication. New Haven, CT.

If you publish a news article, visualization, or blog post using these data, please include a link back to this website.