Postdoctoral scholar Ramit Debnath, along with Mike Alvarez, professor of Political and Computational Social Science at Caltech, are investigating how people's social networks can impact progress in reducing greenhouse gas emissions. Their recent paper explores how these networks might help spur action in the hard-to-decarbonize building sector. We asked Ramit about the project, and his ongoing interest in the field.
Tell us about the motivation for this project – why is it so hard to decarbonize the building sector and what does studying social media tell us about how to do it?
The motivation is two folded. First is through IPCC's latest AR6 Report, where climate experts emphasise enabling people-centric approaches for climate mitigation and adaptation. It is especially needed for hard-to-decarbonise sectors like the building sector, which contributes to around 39% of total carbon dioxide emissions globally. We are just beginning to understand how to systematically use large-scale behavioural datasets for climate action. It shaped the second motivational factor regarding using state-of-the-art computational social science approaches to capture public reactions (Twitter users) to global climate action over 13 years.
What does your research say about the possibility of addressing this difficult sector?
Our research shows that people (Twitter users) are reactive to high-level climate policy events in this challenging sector. We test, using network theory, the evolution of terms associated with climate change and environmental justice over 13 years. It is a good sign that online communication helps shape sensitivity towards climate action in such complex sectors. This sensitisation is essential for making people aware that individual action and collective decision-making can help address our climate change mitigation goals. This is especially important for achieving building operational energy efficiency goals but also critical in driving steps towards acknowledging ways to reduce embodied emissions and design of circular economy.
In dealing with climate change, we often have to balance creating a sense of urgency about the problem with a need to avoid hopelessness or despair. What did you learn about this tradeoff – are there ways to use social media to spread the "right" kind of message that might generate action rather than frustration?
This is always challenging with social media-driven climate communication, as echo chambers are designed to create polarisation and misinformation. Our study showed that reactiveness is driven mainly by a rise in negative sentiments (sadness, anger) following high-level policy events by IPCC and UNFCCC. However, social media can spread the right message in various ways. For example, greater emphasis on online climate science communication from research and governance institutions can help get the 'right' message out there. Emphasis should be placed on getting facts out while countering misinformation and designing pre-bunking strategies to immunise the public from misinformation. Moreover, sensitising people that climate change is real and caused by human activities is crucial. This must account for the diversity and inclusion criteria to enable broader communication.
Why did you get involved in this type of work? What type of impact do you hope to have with your research?
I am trained as an electrical and electronics engineer with graduate degrees in sustainable development and just transition. Throughout this interdisciplinary training, I realised the need to go beyond disciplinary silos to address our wicked global challenges. Computational social science is exciting as it provides the methodological bandwidth to tackle real-life problems associated with environmental and climate justice. Through my collaboration with Mike, I want my research to improve public understanding of climate change using data-driven evidence. This can effectively influence policymaking and place people at the centre of a just transition.
One of the key takeaways from this work is that, in addition to the importance of access to publicly available data about people's behavior on social media, we need computational social science tools to be able to process and understand that data in a way that can fight misinformation and polarization, to harness it for effective solutions. We have also just recently published a letter about this need, and hopefully this will spur action in making both the data and the tools more useful in the future (you can read the letter in Nature Climate Change).