Yale Program on Climate Change Communication

About Downscaling Climate Opinion Maps

Public opinion on climate change is an important influence on the decision-making process for the development of policies to reduce climate change impacts or prepare for the 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 the state and district 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 state and district levels. We can now estimate and visualize differences in opinion across the country, revealing a clearer picture of the diversity of Indian perceptions, attitudes, and support for policies. 

Our national survey shows that  96% of adults in India think global warming is happening. However, there is variation from state to state and district to district. In Odisha, 94% of people share this view, for example, compared to 81% in neighboring Andhra Pradesh. 

Explore the maps by clicking on a state or district and comparing the results across questions and geographic areas. Beneath each map are bar charts displaying the results for every question at the currently selected geographic scale. 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 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.

Methodology

This site provides estimates of the Indian adult population’s climate change beliefs, experiences, risk perceptions, and policy preferences at state and district levels – a new source of high-resolution data on public opinion that can inform Indian decision-making, policy, and education initiatives. This version of the India Climate Opinion Maps is based on data from 2022 through 2025. The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on four large national survey datasets (combined n = 19,644), along with demographic and geographic population characteristics.

The surveys were implemented by CVoter and were translated into 12 different languages. Respondents came from 34 of India’s 36 states and Union territories and from 575 of 634 modeled districts. No respondents came from the Lakshadweep or Andaman and Nicobar Islands, so estimates are not provided for these Union territories on the maps. Sample weights for all respondents were calculated by CVoter to be nationally representative and to reflect the large diversity of the Indian population across gender, language, education, caste, age, and income dimensions.

To allow modeling, individuals from the survey were matched with both their 2011 administrative states and districts and their current states and districts. Many new districts (and one state) have been created since 2011, and a few districts have merged. In most cases, whether areas were carved out of others or combined, the outer boundaries tended to remain stable. As a result, we successfully matched all but eleven new districts to the census counts for analogous districts from 2011. Districts identified in the new Indian government map that had changes to their borders and could not be matched to other data sources were assigned to one of the overlapping districts from the 2011 Indian census based on maps and information from government websites. Seven districts in the survey data could not be matched to any other data source, and respondents assigned to those districts were assigned to overlapping modern and census districts using the same method. Multilevel modeling with poststratification (MRP) was used to estimate the spatial distribution of India’s climate opinions at state and district 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 is constructed by predicting individual survey responses as a function of both individual-level demographics (gender, age, caste, and urbanicity, as well as a four-way interaction term among these variables) and geography-level covariates (i.e., education and climate vulnerability). Second, “post-stratification”, or spatial weighting, is performed using the fitted model, where population-weighted opinion estimates for each demographic-geographic subtype are aggregated based on the subtype population distribution within each geographic subunit. Climate vulnerability data for each state and district were obtained from the report Mapping India’s Climate Vulnerability (Mohanty and Wadhawan, 2021), and education data came from the Government of India for each 2011 state and district. The model configuration was selected after testing model fit using a variety of potential sociodemographic, environmental, and contextual predictors (e.g., climate vulnerability rating, percent of population with a secondary education or higher, total fertility rate, primary language, PM2.5, and percent tree cover) and interaction terms. 

For the 2025 India model estimates, 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 state level, and ±8 percentage points at the district level. Such error ranges include the error inherent in the original national surveys themselves, which is typically ±3 percentage points.

Survey Questions

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.

Experience

Personally experienced severe heat waves

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced severe heat waves?”

• Yes
• No
• Don’t know
• Refused

Personally experienced agricultural pests and disease

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced agricultural pests and disease?”

• Yes
• No
• Don’t know
• Refused

Personally experienced electricity power outages

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced electricity power outages?”

• Yes
• No
• Don’t know
• Refused

Personally experienced water pollution

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced water pollution?”

• Yes
• No
• Don’t know
• Refused

Personally experienced droughts and water shortages

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced droughts and water shortages?”

• Yes
• No
• Don’t know
• Refused

Personally experienced severe air pollution

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced severe air pollution?”

• Yes
• No
• Don’t know
• Refused

Personally experienced severe storms

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced severe storms?”

• Yes
• No
• Don’t know
• Refused

Personally experienced famines and food shortages

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced famines and food shortages?”

• Yes
• No
• Don’t know
• Refused

Personally experienced severe cyclones

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced severe cyclones?”

• Yes
• No
• Don’t know
• Refused

Personally experienced severe floods

“I am going to read you a list of hazards and ask if you have personally experienced them in the past 12 months. In the past 12 months, have you personally experienced severe floods?”

• Yes
• No
• Don’t know
• Refused

Has personally experienced the effects of global warming

“I will now read you a statement. Please tell me how much you agree or disagree with it. I have personally experienced the effects of global warming.”

• Strongly agree 
• Somewhat agree 
• Somewhat disagree 
• Strongly disagree 
• Refused
• Don’t know

Global warming is important to me personally

“How important is the issue of global warming to you personally? Would you say it is very important, somewhat important, not very important, or not at all important?”

• Extremely important
• Very important
• Somewhat important
• Not very important
• Not at all important
• Refused
• Don’t know

Beliefs

Know a lot or something about global warming

“How much do you know about global warming? Do you know a lot about it, something about it, just a little about it, or have you never heard of it?”

• I know a lot about it
• I know something about it
• I know just a little about it
• I have never heard of it
• Refused
• Don’t know

Global warming is happening

“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 and weather patterns may change as a result. What do you think? Do you think that global warming is happening? Would you say ‘yes’, ‘no’, or ‘I don’t know’?”

• Yes
• No
• Don’t know 
• Refused

Global warming is caused mostly by human activities

“If global warming is happening, do you think that it is caused mostly by human activities, by natural changes in the environment, some other cause, or none of these because it is not happening?”

• Caused mostly by human activities
• Caused mostly by natural changes in the environment
• Some other cause (Please specify )
• None of these because global warming is not happening 
• Don’t know
• Refused

Global warming is affecting extinctions of plant and animal species

“How much do you think global warming is affecting extinctions of plant and animal species in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting severe heat waves

“How much do you think global warming is affecting severe heat waves in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting agricultural pests and diseases

“How much do you think global warming is affecting agricultural pests and diseases in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting droughts and water shortages

“How much do you think global warming is affecting droughts and water shortages in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting severe air pollution

“How much do you think global warming is affecting severe air pollution in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting water pollution

“How much do you think global warming is affecting water pollution in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting famines and food shortages

“How much do you think global warming is affecting famines and food shortages in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting severe cyclones

“How much do you think global warming is affecting severe cyclones in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting monsoons

“How much do you think global warming is affecting monsoons in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting severe floods

“How much do you think global warming is affecting severe floods in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting severe storms

“How much do you think global warming is affecting severe storms in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Global warming is affecting electricity power outages

“How much do you think global warming is affecting electricity power outages in India?”

• A lot
• Some
• Only a little
• Not at all
• Don’t know
• Refused

Risk Perception

Worried about global warming

“How worried are you about global warming? Would you say you are very worried, somewhat worried, not very worried, or not at all worried?”

• Very worried 
• Somewhat worried
• Not very worried
• Not at all worried
• Don’t know
• Refused

Global warming will harm me and my family

How much do you think global warming will harm you and your family? Would you say a great deal, a moderate amount, only a little, not at all, or do you not know?

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

Global warming will harm people in my community

How much do you think global warming will harm people in your community? Would you say a great deal, a moderate amount, only a little, not at all, or do you not know?

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

Global warming will harm people in India

How much do you think global warming will harm people in India? Would you say a great deal, a moderate amount, only a little, not at all, or do you not know?

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

Global warming will harm future generations

How much do you think global warming will harm future generations of people? Would you say a great deal, a moderate amount, only a little, not at all, or do you not know?

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

Global warming will harm plant and animal species

How much do you think global warming will harm plant and animal species? Would you say a great deal, a moderate amount, only a little, not at all, or do you not know?

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

Global warming is harming people in India now or will within 10 years

When, if ever, do you think global warming will start to harm people in India? Would you say people in India are being harmed now by global warming, or people in India will start to be harmed by global warming in 10 years, in 25 years, in 50 years, in 100 years, or never?

• They are being harmed now
• In 10 years
• In 25 years
• In 50 years
• In 100 years 
• Never
• Don’t know
• Refused

Policy Support

Support for a national program to teach all Indians about global warming

“Next, please tell me how much you would favor or oppose India taking each of the following steps to help deal with environmental problems. Would you strongly favor, somewhat favor, somewhat oppose, or strongly oppose India taking this step? [A national program to teach all Indians about global warming]”

• Strongly favor 
• Somewhat favor 
• Somewhat oppose 
• Strongly oppose 
• Refused
• Don’t know

Support for a national program to train people for new jobs in the renewable energy industry

“Next, please tell me how much you would favor or oppose India taking each of the following steps to help deal with environmental problems. Would you strongly favor, somewhat favor, somewhat oppose, or strongly oppose India taking this step? [A national program to train people for new jobs in the renewable energy industry such as wind and solar]”

• Strongly favor 
• Somewhat favor 
• Somewhat oppose 
• Strongly oppose 
• Refused
• Don’t know

India should use less fossil fuels in the future

“Do you think that in the future India should use more, less, or about the same amount of fossil fuels, like coal, oil, and gas, as it does today?”

• Much less
• Somewhat less
• Same amount as today
• More
• Much more
• Don’t know
• Refused

India should use more renewable energy sources in the future

“Do you think that in the future India should use more, less, or about the same amount of renewable sources of energy, like solar panels and wind turbines, as it does today?”

• Much more
• More
• Same amount as today
• Somewhat less
• Much less
• Don’t know
• Refused

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.

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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 Indians (age 18 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 >19,000 individuals that have been collected since 2022. The actual survey responses were combined with demographic data from the 2011 Census of India to estimate opinions based on information such as gender, age, caste, and urbanicity.

Where do the survey data underlying the estimates come from?

The data underlying the maps come from a large national survey dataset ( >19,000 respondents) collected between December 2021 and April 2025 as part of the Climate Change in the Indian Mind project led by the Yale Program on Climate Change Communication in partnership with CVoter and SED. A nationally representative sample of respondents was contacted by mobile telephone using predictive dialing technology and computer-assisted telephone interviewing (CATI). Reports from the individual surveys are available here: Climate Change in the Indian Mind.

How many people did you survey in my state/district? / 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 India as a whole. Since our surveys are nationally representative, the number of respondents in each state and district is proportional to its share of the national population. In other words, larger states and districts will have more respondents than smaller states and districts. In some smaller districts, very few, if any, respondents may have been surveyed. In those cases, our model uses information about the population of the district and information about the characteristics of the district (such as education levels and vulnerability to extreme weather in the district) to make its estimates. While this may sound surprising, both education and extreme weather vulnerability have been shown to influence opinions about climate change. Knowing the levels of vulnerability and educational attainment of a district’s residents, along with the proportion of different demographic groups within that district, allows us to predict residents’ climate opinions very well. All of this information together allows us to produce reliable estimates (see next question) for all districts, even if the number of individuals sampled within a specific district 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 state level), and are larger at finer geographic scales (e.g., the district 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 state-level estimates and ±8 percentage points for the district-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.

Why aren’t there any data for Ladakh, Manipur, Mizoram, Lakshadweep, and the Andaman & Nicobar Islands?

The public opinion data we use to model the distribution of opinion across India excluded phone samples from people living in India’s islands and disputed territories. Unfortunately, it is not possible to accurately infer beliefs in islands and disputed territories from our current opinion dataset.

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:

Marlon, J., Leiserowitz, A., Thaker, J., Goddard, E., Neyens, L., Kolluri, S., Rosenthal, S., Carman, N. R. Modala (2024). Variations in Climate Opinions Across India. Yale University. New Haven, CT: Yale Program on Climate Change Communication.

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

When will the data be updated next?

The estimates will be updated periodically when new India climate opinion survey results are released.