# data[].attributes.body
Though I have a background in ecology I actually really enjoy taking surveys, social science is cool! I found this one to be super easy to use, I had my husband take the survey as well. I was in the alarmed category where he was in the concerned category. Makes sense for our personalities.

# data[].attributes.body
Andrea Portier also mentioned this tool, and I found it interesting to animate. I zoomed way in, to get better resolution for my area of interest. It's clunky, but once you figure it out, it has a good, simple picture of flood events.

# data[].attributes.attachment_link.readable_text
Contact: Dr. Huan Wu huanwu@umd.edu

Huffman, G.J., R.F. Adler, D.T. Bolvin, E.J. Nelkin, 2010: The TRMM Multi-satellite Precipitation Analysis (TMPA). Chapter 1 in Satellite Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds. Springer Verlag, ISBN: 978-90-481-2914-0, 3-22.

Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428.

Wu, H., R. F. Adler, Y. Tian, G. J. Huffman, H. Li, and J. Wang (2014), Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model, Water Resour. Res., 50, 2693.2717, doi:10.1002/2013WR014710.

Wu H., R. F. Adler, Y. Hong, Y. Tian, and F. Policelli (2012), Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model. J. Hydrometeor, 13, 1268.1284.

Wu H., J. S. Kimball, H. Li, M. Huang, L. R. Leung, R. F. Adler (2012), A new global river network database for macroscale hydrologic modeling, Water Resour. Res., 48, W09701, doi:10.1029/2012WR012313.

Wu, H., J. S. Kimball, N. Mantua, and J. Stanford (2011), Automated upscaling of river networks for macroscale hydrological modeling, Water Resour. Res., 47, W03517, doi:10.1029/2009WR008871.

# data[].attributes.attachment_link.description
Contact: Dr. Huan Wu huanwu@umd.edu Huffman, G.J., R.F. Adler, D.T. Bolvin, E.J. Nelkin, 2010: The TRMM Multi-satellite Precipitation Analysis (TMPA). Chapter 1 in Satellite Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds. Springer Verlag, ISBN: 978-90-481-2914-0, 3-22. Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J.

# data[].attributes.body
"Roughly one-third of the world’s food is never eaten. By reducing loss and waste, we can reduce the need for land and resources used to produce food as well as the greenhouse gases released in the process."

I think this is such an important message to share with all audiences. First of all, people may not have thought about it yet, and when they do realize the complexity of the food production and distribution system, it's a big lightbulb moment for understanding carbon emissions. Second, it's something you can think about on a global level, as well as a personal level (your very own alimentary canal!). Third, the concept of reducing food waste it is very acceptable across the opinion spectrum.

# data[].attributes.attachment_link.readable_text
Introduction

Roughly one-third of all food produced worldwide is wasted. Where income is low, waste is generally unintentional and occurs on farms or during storage or distribution. In regions of higher income, food waste dominates further along the supply chain. Retailers and consumers reject food based on bumps, bruises, and coloring, or simply order, buy, and serve too much.

When food is wasted, all the energy, resources, and money that went into producing, processing, packaging, and transporting it are wasted, too. Producing uneaten food squanders a whole host of resources-seeds, water, energy, land, fertilizer, hours of labor, financial capital-and generates greenhouse gases at every stage. The food we waste is responsible for roughly 8 percent of global emissions.

Project Drawdown's Reduced Food Waste solution involves minimizing food loss and wastage from all stages of production, distribution, retail, and consumption. It replaces current reported trends in food waste by region.

We can reduce waste in many ways. In lower-income countries, improving infrastructure for storage, processing, and transportation is essential. In higher-income regions, major interventions are needed at the retail and consumer levels. National food-waste targets and policies can encourage widespread change. Beyond addressing emissions, these efforts can also help to meet future food demand.

If measures are taken to reduce food waste by improving storage and transport systems, generating public awareness, and changing consumer behavior, this solution could lead to substantial reductions in waste and carbon emissions.

Methodology

To evaluate the impact of reduced food waste, we created an independent model outside Project Drawdown's core model framework that projects food consumption and waste from 2020 to 2050. This was required due to the complexity of estimating country- and regional-scale food consumption and waste trends.

Total Addressable Market

We defined the global market for reduced food waste as the total demand for food, based on estimated kilocalories supplied per year for consumption by all of the people in the world. We projected the baseline food consumption for all countries up to 2060 in kilocalories per capita per year, using data compiled by the Food and Agriculture Organization (FAO) for 2013. We forecasted future consumption using growth factors from Alexandratos and Bruinsma (2012), which reflect projected dietary changes. The projected dietary changes show significant impacts for countries like India and China, whose populations are expected to demand more carbon-intensive foods over the next 30 years.

We calculated total food loss and wastage according to regional estimates of waste generated at each supply chain stage (agricultural production, post-harvest handling and storage, processing, packaging, distribution, and consumption; FAO, 2011), applied to aggregated country-level food demand by commodity type. Current adoption of reduced food waste (defined as the amount of functional demand supplied in 2014) is considered to be 0 percent.

Adoption Scenarios

We calculated impacts of increased adoption of reduced food waste from 2020 to 2050 by comparing two growth scenarios with a reference scenario, in which the market share was fixed at current levels.

Adoption scenarios in this model grow linearly over time starting from the base year of 2014, and are considered complete in 2050. We chose linear growth trends because of the lack of country or regional data; additional behavioral research at more granular scales can reveal more representative adoption estimates.

Scenario 1: Total global food loss and wastage is reduced 50 percent by 2050.

Scenario 2: Total global food loss and wastage is reduced 75 percent by 2050.

Emissions Model

To estimate emissions, we drew commodity-specific carbon dioxide equivalent per-calorie values from several sources (see Audsley et al., 2010; Heller and Keoleian, 2014; Hoolohan et al., 2013; Tilman and Clark, 2014; Vieux et al., 2012) to estimate minimum, average, and maximum emissions factors per commodity. We multiplied the emissions factors by the baseline annual food demand by country and commodity to get the carbon dioxide equivalent values for food items over time. We aggregated emissions estimates by commodity type (cereals, roots and tubers, oilseeds and pulses, fruits and vegetables, meat, fish and seafood, milk) and regions used by Project Drawdown.

We applied FAO regional estimates of food loss and wastage by commodity types across the supply chain stages to aggregated emissions values associated with regional food demand. We calculated emissions reductions based on the incremental adoption of reduced food waste, which was applied across all stages of the supply chain. We then aggregated resulting emissions reductions to represent the total global reduction from avoided agricultural production.

Integration

We calculated the total reduction in food loss and wastage by weight and commodity type. We assumed that reduced loss and wastage can be diverted to feed current and future undernourished populations. We used diverted tonnage of food as an input in our Integrated Yield Model, which combines all agricultural production models to determine the annual yield required to meet the estimated food and bio-based product demand. Results from both demand-side solutions (Reduced Food Waste and Plant-Rich Diets), determine the need to convert land to cropland and grassland to meet future food demand. We applied emissions reductions associated with land conversion to Reduced Food Waste and Plant-Rich Diets solutions according to the proportion of their contribution to diverted food supply. We also applied the results from Reduced Food Waste to solution models in the Industry sector that use organic municipal solid waste.

Results

Between 2020 and 2050, Scenario 1 projects the reduction of 88.50 gigatons of carbon dioxide equivalent emissions: 32.98 gigatons due to diverted agricultural production, 55.20 gigatons from avoided land conversion, and 0.32 gigatons from ecosystem protection.

Scenario 2 projects the reduction of 102.20 gigatons of carbon dioxide equivalent emissions: 47.16 gigatons due to diverted agricultural production, 54.71 gigatons from avoided land conversion, and 0.32 gigatons from sequestration from ecosystem protection.

Discussion

Food loss and waste has many negative economic and environmental impacts. The global economic, environmental, and social cost of food wastage is estimated at US$2.6 trillion, which is nearly equal to the GDP of France (FAO, 2014). Food waste generates unnecessary greenhouse gas emissions and wastes both water and land, harming ecosystems.

Reducing food loss and waste can help close the over 60 percent gap between food available today and food needed in 2050, thereby working toward eliminating hunger. Although solutions at consumer level are difficult to implement and hard to measure, we must pursue them wherever consumer food waste is high. We must develop tools to standardize food loss and waste measurement and reporting. We must set food waste reduction targets at country levels and broken down to corporate, supplier, and consumer levels. We need to create incentives that change food waste behavior. By doing these things, we can reap the benefits of reducing food waste for the economy, the environment, society, and human health.

References

Alexandratos, N. and Bruinsma, J.. (2012). World agriculture towards 2030/2050: The 2012 revision. ESA Work. Pap, 3. Agricultural Development and Economics Division of the FAO http://large.stanford.edu/courses/2014/ph240/yuan2/docs/ap106e.pdf

Audsley, E., Brander, M., Chatterton, J. C., Murphy-Bokern, D., Webster, C., and Williams, A. G. (2010). How low can we go? An assessment of greenhouse gas emissions from the UK food system and the scope reduction by 2050. World Wildlife Fund and Food Climate Research Network. https://dspace.lib.cranfield.ac.uk/handle/1826/6503

UN Food and Agriculture Organization (FAO). (2011). Food Wastage Footprint & Climate Change. https://www.fao.org/3/bb144e/bb144e.pdf

UN Food and Agriculture Organization (FAO) (2014). Food wastage footprint full-cost accounting: final report. https://www.fao.org/3/bb144e/bb144e.pdf

Heller, M. C. and Keoleian, G. A. (2014). Greenhouse Gas Emission Estimates of U.S. Dietary Choices and Food Loss: GHG Emissions of U.S. Dietary Choices and Food Loss. Journal of Industrial Ecology, pp 391-401. https://css.umich.edu/sites/default/files/publication/CSS14-23.pdf

Hoolohan, C., Berners-Lee, M., McKinstry-West, J., and Hewitt, C. N. (2013). Mitigating the greenhouse gas emissions embodied in food through realistic consumer choices. Energy Policy, 63 pp 1065-1074. http://linkinghub.elsevier.com/retrieve/pii/S0301421513009701

Tilman, D. and Clark, M. (2014). Global diets link environmental sustainability and human health. Nature, 515 (7528) pp 518-522. http://www.nature.com/articles/nature13959

Vieux, F., Darmon, N., Touazi, D., Soler, L. G. (2012). Greenhouse gas emissions of self-selected individual diets in France: Changing the diet structure or consuming less?. Ecological Economics, 75 pp 91-101. http://linkinghub.elsevier.com/retrieve/pii/S0921800912000043

# data[].attributes.attachment_link.description
Roughly one-third of the world's food is never eaten. By reducing loss and waste, we can reduce the need for land and resources used to produce food as well as the greenhouse gases released in the process.

# data[].attributes.body
I love drilling down to the county level with these maps

# data[].attributes.attachment_link.provider_name
Yale Program on Climate Change Communication

# data[].attributes.attachment_link.readable_text
These maps show how Americans' climate change beliefs, risk perceptions, and policy support vary at the state, congressional district, metro area, and county levels. NOTE: The Congressional District data are based on the 116th Congress. Recent and ongoing redistricting means that some of these data are not current (e.g. North Carolina) for the Congressional District level.

About Downscaling Climate Opinions

This version of the Yale Climate Opinion Maps is based on data through fall 2021. Public opinion about global warming is an important influence on decision making about policies to reduce global warming or prepare for the impacts, but American opinions vary widely depending on where people live. So why would we rely on just one national number to understand public responses to climate change at the state and local levels? Public opinion polling is generally done at the national level, because local level polling is very costly and time intensive. Our team of scientists, however, has 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 and a rich picture of the diversity of Americans' beliefs, attitudes, and policy support is revealed.

Our national surveys show that 72% of Americans think global warming is happening. Our new YCOM model estimates, however, show that only 48% of people in Emery County, Utah agree. Meanwhile 71% in neighboring Grand County, Utah believe global warming is happening.

Explore the maps by clicking on your 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. Note that the congressional districts for North Carolina are based on the 2019 (116th Congress) district boundaries and so do not reflect the latest redistricting there.

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 are off by one percentage point; these effects are due to rounding errors.

This research and website are funded by the Skoll Global Threats Fund, 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. We are very grateful to Connie Roser-Renouf, Ed Maibach, Lisa Fernandez, Eric Fine, Bessie Schwarz, Mike Slattery, and Seth Rosenthal for their assistance with and support of the project. For further questions about these maps or what they mean, please see our Frequently Asked Questions tab (above).

Methodology

This site provides estimates of U.S. climate change beliefs, risk perceptions, and policy preferences at the state and local levels - a new source of high-resolution data on public opinion that can inform national, state and local decision-making, policy, and education initiatives. The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on a large national survey dataset (n>28,000), along with demographic and geographic population characteristics.

Validating models is essential for producing accurate results. Our estimates were validated 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 2021 model estimates, uncertainty ranges are based on 95% confidence intervals using 999 bootstrap simulations. These confidence intervals indicate that the 2020 model is accurate to approximately ±7 percentage points at the state and congressional district levels, and ±8 percentage points at the metro and county levels. Such error ranges include the error inherent in the original national surveys themselves, which is typically ±3 percentage points.

The availability of 2014, 2016, 2018, 2019, and 2020 estimates prompts questions about what has changed between these years in particular places. From our surveys, we know that public opinion on some questions has shifted over this period at the national level. However, our statistical model, as well as the census and election data that go into our model, have also all been updated for accuracy. Thus, estimated shifts in public opinion at the subnational level since 2014 may be due not only to opinion changes, but also changes in demographics and other variables that we are using to model opinions. In addition, our model tends to be conservative when making estimates, especially for places with smaller populations (where there are likely to be fewer survey respondents), so variations over time between states, counties, etc. may be underestimated. As a result, we recommend caution when interpreting any changes over time using different versions of our model, because we can't distinguish between what is a true shift in actual opinion between 2014, 2016, 2018, 2019, 2020, and 2021, and what reflects the model's improved ability to map climate opinions at the local scale. We are currently developing a new model explicitly for changes in opinions over time at the state level, so please join our mailing list if you would like to be alerted about these developments.

For more details on the model or methods, please see the peer-reviewed paper: 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.

Climate Change in the American Mind 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 how worried respondents are about global warming, "very worried" and "somewhat worried" were combined into a single measure of "worried." Likewise "Not very worried" and "Not at all worried" were combined into a single measure of "not worried." 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

Global warming is happening
Recently, you may have noticed that global warming has been getting some attention in the news. Global warming refers to the idea that the world's average temperature has been increasing over the past 150 years, may be increasing more in the future, and that the world's climate may change as a result. What do you think: Do you think that global warming is happening?

Global warming is caused mostly by human activities
Assuming global warming is happening, do you think it is... ?

Caused mostly by human activities

Caused mostly by natural changes in the environment

None of the above because global warming isn't happening

Other

Don't know

Most scientists think global warming is happening
Which comes closest to your own view?

Most scientists think global warming is happening

There is a lot of disagreement among scientists about whether or not global warming is happening

Most scientists think global warming is not happening

Don't know enough to say

Global warming is affecting the weather in the United States
How strongly do you agree or disagree with the statement below?

RISK PERCEPTIONS

Worried about global warming
How worried are you about global warming?

Global warming will harm plants and animals
How much do you think global warming will harm plants and animal species?

Global warming will harm future generations
How much do you think global warming will harm future generations of people?

Global warming will harm people in developing countries
How much do you think global warming will harm people in developing countries?

Global warming will harm people in the US
How much do you think global warming will harm people in the United States?

Global warming will harm me personally
How much do you think global warming will harm you personally?

Global warming is already harming people in the US
When do you think global warming will start to harm people in the United States?

Has personally experienced the effects of global warming
How much do you agree or disagree with the following statement: "I have personally experienced the effects of global warming"?

POLICY SUPPORT

Fund research into renewable energy sources

How much do you support or oppose the following policies?
Fund more research into renewable energy sources, such as solar and wind power

Regulate CO2 as a pollutant
How much do you support or oppose the following policies?
Regulate carbon dioxide (the primary greenhouse gas) as a pollutant

Set strict CO2 limits on existing coal-fired power plants
How much do you support or oppose the following policy?
Set strict carbon dioxide emission limits on existing coal-fired power plants to reduce global warming and improve public health. Power plants would have to reduce their emissions and/or invest in renewable energy and energy efficiency. The cost of electricity to consumers and companies would likely increase.

Require fossil fuel companies to pay a carbon tax
How much do you support or oppose the following policies?
Require fossil fuel companies to pay a carbon tax and use the money to reduce other taxes (such as income tax) by an equal amount.

Require utilities to produce 20% electricity from renewable sources
How much do you support or oppose the following policies?
Require electric utilities to produce at least 20% of their electricity from wind, solar, or other renewable energy sources, even if it costs the average household an extra $100 a year.

Provide tax rebates
Provide tax rebates for people who purchase energy-efficient vehicles or solar panels.
How much do you support or oppose the following policies?

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: 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 American adults (age 18 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 national survey dataset ( >28,000 respondents) collected between 2008 through 2021 as part of the Climate Change in the American Mind project led by the Yale Program on Climate Change Communication and the George Mason University Center for Climate Change Communication. Reports from the individual surveys are available here: CCAM Reports.

How many people did you survey in my state/county? / How can you say anything about opinions in my geography if you didn't survey anyone there?

The number of respondents surveyed varies for each geographic unit but is very large across the US as a whole. Since our surveys are nationally representative, the number of respondents in each state and county is proportional to its share of the national population. In other words, larger states and counties will have more respondents than smaller states and counties. In some smaller counties, very few, if any, respondents may have been surveyed. In those cases, our model uses information about the population of the county, information from other counties in the same state and region, and information about the characteristics of the county (such as how the county voted in previous elections) to make its estimates. While this may sound surprising, it makes sense when we consider the strongest influences on a person's individual opinions about climate change in the US - political views. Knowing the political leanings of a county's (or other unit's) residents, along with the proportion of different demographic groups within that county, allows us to predict resident's climate opinions very well. Adding additional information (e.g., about rural/urban context, carbon-intensive activities, etc.), further improves predictions. Moreover, for any given geographic unit, information is pooled from nearby units and from higher-level geographies (e.g. state level information is included for all counties within that state). All of this information together allows us to produce reliable estimates (see next question) for all counties even if the number of individuals sampled within a specific county is small.

How accurate are the estimates?

No model is perfect and there are uncertainties in the model estimates. To validate the model, we conducted independent surveys in four states (CA, TX, OH, CO) and two metropolitan areas (Columbus, OH and San Francisco, CA) and compared the survey results to our model estimates. On average, the model estimates differed from the survey results by 2.9 percentage points among the four states and 3.6 percentage points among the two metropolitan areas, within the survey margins of error. A series of technical simulations estimate that the model has an average margin of error of ±7 percentage points at the state and congressional district levels, ±8 percentage points at the metro and county levels. Such error ranges include the error inherent in the original national surveys themselves, which is typically ±3 percentage points. The model uncertainties are smaller at broad geographic scales (e.g., the state level), and are larger at finer geographic scales (e.g., at the county and city levels). 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.

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:

Howe, P., Mildenberger, M., Marlon, J., & Leiserowitz, A. (2015) "Geographic variation in opinions on climate change at state and local scales in the USA," Nature Climate Change. DOI: 10.1038/nclimate2583.

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

How do the YCOM maps compare from year to year?

We originally created YCOM in 2014 and then updated it in 2016, 2018, 2019, 2020, and 2021. Public concern about global warming has generally increased since 2014. However, improvements to our model, including new data that increase our ability to resolve differences between jurisdictions, and data for adults 18+ (prior estimates only included data for adults 25+) may have contributed to shifts in our current estimates as compared with those of 2014. As a result, we recommend caution interpreting any changes over time because we can't easily disaggregate what is a true shift between any particular year and what reflects the model's improved ability to map climate beliefs at the local scale. We are currently working on a new tool which will show changes in opinions over time for each state. Join our mailing list via the "Subscribe" button below on the right to be alerted when we release this new tool.

# data[].attributes.attachment_link.title
Yale Climate Opinion Maps 2021 - Yale Program on Climate Change Communication

# data[].attributes.attachment_link.description
Use the Yale Climate Opinion Maps to explore climate change beliefs, risk perceptions, and policy support at every geographic level in the United States.

# data[].attributes.body
I watched all of the videos as well, but did not have time to watch the interviews. I want to go back later and watch some of them. I manage our volunteer program where I work and I can see these being great resources to have our volunteers watch to give them more information and context about climate change. Spent some time looking at other resources at Project Drawdown as well and found it all really usefuf!

# data[].attributes.body
I enjoyed learning more and exploring the GPM website, but I had issues using giovanni. Working from home today on my laptop so not sure if the problem will still occur on my office computer. I will attempt tomorrow and fingers crossed it will work better!

# data[].attributes.body
The LHASA model that Andrea Portier mentioned uses GPM data combined with slope maps. It feels even more powerful when you see the data validation with location of actual landslides and known fatalities.

# data[].attributes.attachment_link.readable_text
Collect Landslides and Advance NASA Science

Landslides cause billions of dollars in infrastructural damage and thousands of deaths every year worldwide. Data on past landslide events guides future disaster prevention, but to date we do not have a global picture of exactly when and where landslides occur. NASA is building the biggest open global landslide inventory to address this problem, and the only way we can do this is with the help of citizen scientists like you!

The Cooperative Open Online Landslide Repository, or COOLR, is an open platform where scientists and citizen scientists can share landslide reports. See all landslide data from COOLR with other scientific data using the Landslide Viewer application. Want to contribute? Add to COOLR using our citizen science application, Landslide Reporter.

With more hands and your help, we can complete the global picture of landslides to prepare for and protect against future disasters.

Learn More "

# data[].attributes.attachment_link.description
NASA scientists are building an open global inventory of landslides and we need your help! Become a citizen scientist and you can help inform decisions that could save lives and property today.

# data[].attributes.body
I really enjoyed it and the visuals so much I watched the whole series and many of the interviews. The visuals were great and clear. Having those are key to any type of communicating, but the big takeaway was about thinking in terms of a positive tomorrow while understanding the problem in a way that is communicable. There is a lot to digest in those videos that can spark some ideas.

# data[].attributes.headline
Differing views of 'Happening' vs Harm Me Personally

# data[].attributes.subject
Differing views of 'Happening' vs Harm Me Personally

# data[].attributes.body
I thought it was interesting that across all demographics, there was a significant decline in the % of americans who thought climate change would harm them personally vs. thought it is happening.

I think that by telling new stories and showing local impacts, we can help work to bridge this disconnect and demonstrate the how pervasive climate impacts really are.

# data[].attributes.body
I'm curious if Y3C is capturing all the SASSY data and integrating into their master mind of data for the 6 America's work

# data[].attributes.body
I really appreciate this GMP short video...

# data[].attributes.attachment_link.title
Water Falls: Getting the Big Picture | Precipitation Education

# data[].attributes.attachment_link.description
The second spinoff video for the Science on a Sphere film "Water Falls."

# data[].attributes.body
With my people, I have them sort the solutions by the 2 different scenarios and notice what is at the top of the list that is actually relevant to the landscape/geography where they live/work. Then we look closely at the NCA5 Key Messages for our region. Then we align the most effective solutions to the issues of our region. Finally we consider all the types of skills, vocations, professions, etc... that are needed to accomplish these solutions. Then we consider how to promote, elevate, encourage opportunities for people to train for these jobs... many jobs are climate green/climate jobs that most never consider as much.

# data[].attributes.body
After looking at the Education materials from GPM I'm curious to know when they'll start to integrate the new 'text book' water cycle schematic and focus on pools and fluxes from the USGS... it's much more realistic from a systems approach

# data[].attributes.attachment_link.readable_text
Skip to main content

Official websites use .gov
A.gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock () or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

The new Water Cycle Diagram is now available!

# data[].attributes.attachment_link.title
The Water Cycle | U.S. Geological Survey

# data[].attributes.attachment_link.description
The water cycle describes where water is on Earth and how it moves. Human water use, land use, and climate change all impact the water cycle. By understanding these impacts, we can work toward using water sustainably.

# data[].attributes.body
Project Drawdown is an incredible resource! I was surprised to see the potential impact of reducing food waste and moving towards plant-rich diets. My sibling works with a program that redistributes leftover food from college cafeterias to local food banks. This is a great way to get involved on a local level, both in reducing waste, and in finding ways to support those living with food insecurity in our own communities. I highly recommend reaching out to local college cafeterias and encouraging them to donate food that would otherwise be wasted.

# data[].attributes.body
I zoomed into the county level on the Yale Climate Opinion Map, and was excited to see that 81% of people in my county believe that climate change is happening! That's +9 above the national average, and higher than I expected. It was surprising to me that only 50% of my county believes that they have personally experienced the effects of global warming. We have been experiencing significantly less snow in Massachusetts than in previous years. (I haven't used my snow tires in years!) Storms have been more intense and more damaging in many areas. It feels impossible to ignore! This presents an opportunity to further educate the public on the effects of climate change on a local scale. As interpreters we know that if we can make the message personal, we can make a difference!

# data[].attributes.body
I goofed around with Giovanni, trying out different variables and locations. Finally settled on plotting hydrology and precip for the state on my birthday. With more practice, I can get faster and decent enough at this to produce graphics ahead of presentations and maybe even use this live in small group settings.

I also enjoyed learned more about how GPM data is used in different sectors.

# data[].attributes.body
I've seen this series before and enjoyed revisiting it. I'm especially focused on food waster right now, as I'm helping plan two conferences and we're trying to be more aggressive about it. We're working with the caterers on prep and leftovers handling, and are going to design tabletop tent cards and QRs to deliver some educational content. At the Hollywood Climate Summit last month, they did all vegetarian food, in addition to encouraging people to bring their own utensils and having only biodegradable plates on hand. Veg-only catering may be a stretch goal for the events I'm working on, but I'm going to propose it!

# data[].attributes.body
I shared the survey with a friend and even though we were both "alarmed", we had a great discussion about why. He lives in rural Iowa and I'm here in suburban NC so we discussed our different climates/weather and specific impacts that were giving us heartburn. We also compared our county opinion data, which we both accurately guessed before looking. We both found it satisfying to have a deliberate discussion about these topics. I'm now thinking about hosting a climate café at a conference I'm working on next year where we can host attendees and locals in similar explorations.

# data[].attributes.body
I found the Yale Climate Opinion Map very interesting. The two rural counties that I live and work in, to my surprise, reflected higher levels of acknowledgement that "Global Warming is happening" than I expected. One country was even above the national average!

The SASSY questionnaire showed that both my supervisor and I are "alarmed". We both have struggled with offering our audiences messages of hope. I feel that the Project Drawdown Solutions site will be very helpful in offering our audience tangible solutions.

# data[].attributes.body
I found it fitting to utilize the GPM tool that Andrea showed us to look at precipitation models that are currently happening across the US. Besides Hurricane Debby making landfall in Florida, there is a ridge of heavy precipitation along the east coast of the US preceding the Hurricane. This tool is interesting to use to see not only the flow and pattern of rain anywhere but also the amount and intensity with which it occurs. It also goes to show how we can use it to map out hurricanes and see their eyewall. I believe that this tool should be utilized more to map precipitation trends at the local level which then helps to plan for extreme flood events and be better prepared.

# data[].attributes.body
Project Drawdown focuses on so many things Climate Related. It is a truly amazing resource to use. Dr. Bagley really got alot of us thinking about how every single job to ever exist is a "climate Job". And my takeaway from it all is that we can each do our part to help one of the biggest contributors to Climate Change - GHG Emissions. I love how much resources they have for "drawdown Solutions" specifically in relation to Emissions solutions. The Solutions library has a wide range of solutions for every sector.

# data[].attributes.attachment_link.title
Drawdown Solutions Library | Project Drawdown

# data[].attributes.attachment_link.description
Drawdown Solutions reduce greenhouse gases by avoiding emissions and/or sequestering carbon dioxide already in the atmosphere.

# data[].attributes.body
The survey I got alarmed which I know I was but it's very surprising to know that some persons aren't even concerned. To be aware of what is happening around you is very important which brought me tot he Opinion Maps of Florida, specifically Miami-Dad and Broward county. At the county level I expected a higher percentage of persons who think global warming is happening. For such a large county I expected at least 86-90% as much of the county is on the coastal region and is faced with alot of sea level rise. It is forecasted by 2060 that Miami will no longer be as the rising sea levels will take it over.

Excuse my blurry photo as my computer did not want to screenshot anything lol

# data[].attributes.headline
Project Drawdown Job Function Action Guides

# data[].attributes.subject
Project Drawdown Job Function Action Guides

# data[].attributes.body
When exploring Project Drawdown, i also came across their job function action guides, which I plan to share with our Green Business Partners program team. I feel like this gives Fairfax County businesses "practical and shareable resources that highlight specific, high-impact climate actions" that they can share with their employees, and really make being a green business a lifestyle for them and their employees.

# data[].attributes.attachment_link.title
Job Function Action Guides | Project Drawdown

# data[].attributes.attachment_link.description
Our mission is to help the world reach "Drawdown" as quickly, safely, and equitably as possible.