2025

  1. Hong, J. Y., Kim, Y. H., Zhang, H., & Qin, T. (2025). Hug fans or follow celebrities? How nationalism is reinforced on Chinese social media. Science Advances, 11(45), eadu8241. https://www.science.org/doi/10.1126/sciadv.adu8241
  2. Sovacool, B. K., Fritz, L., Baum, C. M., Losi, L., Debnath, R., Walnum, H. J., Müller-Hansen, F & Brutschin, E. (2025). Social attitudes towards climate interventions: Are European publics uninformed about carbon removal and solar radiation management?. Environmental Science & Policy, 174, 104287. https://doi.org/10.1016/j.envsci.2025.104287
  3. Roozenbeek, J., Lasser, J., Marks, M., Qin, T., Garcia, D., Goldberg, B., Debnath, R., van der Linden, S., Lewandowsky, S. and Graz, T.U., (2025). Misinformation interventions and online sharing behavior: Lessons learned from two preregistered field studies. Royal Society Open Science, 12(11), 251377. https://doi.org/10.1098/rsos.251377
  4. Debnath, R. , Chandel, T., Han, F., and Bardhan, R. (2025) Heatwave increases nighttime light intensity in hyperdense cities of the Global South: A double machine learning study. Philosophical Transactions of the Royal Society Part A, The Royal Society https://doi.org/10.1098/rsta.2024.0568
  5. Cao, C., Debnath, R. and Alvarez, R.M. (2025) Interaction between Climate Factors and Air Quality in Three Norwegian Cities: A machine learning analysis. Atmospheric Environment: X https://doi.org/10.1016/j.aeaoa.2025.100366
  6. Han, P., Kocielnik, R., Song, P., Debnath, R., Mobbs, D., Anandkumar, A., and Alvarez, R. M. (2025) The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs. 2026 International Conference on Learning Representations (ICLR) https://arxiv.org/abs/2509.03730 (Accepted)
  7. Ashraf, W.M., Chen, P., Hasan., T., Dua, V., Abdulelah, S.A., and Debnath, R. (2025) Neural Network–enabled Domain-consistent Robust Optimisation for Global CO2 Reduction Potential of Gas Power Plants. Tackling Climate Change with Machine Learning workshop at NeurIPS 2025 (Accepted)
  8. Han, P., Kocielnik, R., Song, P., Debnath, R., Mobbs, D., Anandkumar, A., and Alvarez, R. M. (2025) Tracing Human-like Traits in LLMs: Origins, Real-World Manifestation, and Controllability. 2nd Workshop on Models of Human Feedback for AI Alignment (MoFA) at ICML 2025 https://openreview.net/forum?id=pdLNGgdO1A (Accepted)
  9. Mede, N., et al. (2025) Public communication about science in 68 countries: Global evidence on how people encounter and engage with information about science. Science Communication. https://doi.org/10.1177/10755470251376
  10. Xia, Z., Ye, J., Debnath, R. , et al. (2025). Growing climate change risk concerns with rising regional disparities in China . npj Climate Action (Nature), https://doi.org/10.1038/s44168-025-00272-z
  11. Chen, T., and Debnath, R. (2025). The need of explainability in low-carbon urban system design using AI: A systematic review. Machine Learning: Earth, https://doi.org/10.1088/3049-4753/adde60
  12. Debnath, R. , Alvarez, R.M., and Ebanks, D. (2025). Why publishing referee reports could backfire on public trust. Nature, https://doi.org/10.1038/d41586-025-02317-z
  13. Cologna, V. et al. and TISP Consortium (2025) Extreme weather event attribution predicts climate policy support across the world. Nature Climate Change https://doi.org/10.1038/s41558-025-02372-4
  14. Safir, A.H. , McInerney, K., Blackwell, A.F. and Debnath, R. (2025). Distributive Epistemic Injustice in AI Ethics: A Co-productionist Account of Global North-South Politics in Knowledge Production. FAccT '25: Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, https://doi.org/10.1145/3715275.3732136
  15. agistro, B., Debnath, R. , Ebanks, D., Wennberg, P., and Alvarez, R.M., (2025). Political ideology and views toward solar geoengineering in the United States. PLOS Climate, https://doi.org/10.1371/journal.pclm.0000643
  16. Cao, C., Debnath, R. , and Alvarez, R.M., (2025). Physics-based machine learning for predicting urban air pollution using decadal time series data. Environmental Research Communications, https://iopscience.iop.org/article/10.1088/2515-7620/add795
  17. Safir, A.H., and Sharma, S., (2025). Networks, narratives and neocoloniality of AI for Climate Action. Big Data & Society, https://doi.org/10.1177/2053951725133409
  18. Cologna, V., (...), Debnath, R. , (...), Zwann, RA., (2025). Trust in scientists and their role in society across 68 countries. Nature Human Behaviour. https://doi.org/10.1038/s41562-024-02090-5
  19. Mede, N., (...), Debnath, R. , (...), Zwann, RA., (2025). Perceptions of science, science communication, and climate change attitudes in 68 countries – the TISP dataset. Nature Scientific Data. https://doi.org/10.1038/s41562-024-02090-5
  20. Debnath, R. , Tkachenko, N and Bhattacharyya, M., (2025). Enabling people-centric climate action using human-in-the-loop AI: A review. Current Opinion in Behavioural Sciences. https://doi.org/10.1016/j.cobeha.2025.101482
  21. Amazeen, M., Sovacool, B.K., Krishna, A., Debnath, R. , and Wells, C., (2025). The Future of Energy? Building Resilience to ExxonMobil’s Disinformation through Disclosures and Inoculation . npj Climate Action (Nature). https://doi.org/10.1038/s44168-025-00209-6
  22. He, Y., (...), Debnath, R., and Parry, I., (2025). Energy mapping of existing building stock in Cambridge using energy performance certificates and thermal infrared imagery. Environmental Data Science. https://doi.org/10.1017/eds.2024.2
  23. Ansar, T. and Ashraf, W.M. (2025). Comparison of Kolmogorov–Arnold Networks and Multi-Layer Perceptron for modelling and optimisation analysis of energy systems. Energy and AI . https://doi.org/10.1016/j.egyai.2025.100473
  24. Seshadri, A., Gambhir, A., and Debnath, R. (2025). Navigating Systemic Risks in Low-Carbon Energy Transitions in an Era of Global Polycrisis. Global Sustainability. https://doi.org/10.1017/sus.2025.7
  25. Magistro, B., Debnath, R. ,, Wennberg, P., and Alvarez, R.M., (2024). Partisanship overcomes framing in shaping solar geoengineering perceptions: Evidence from a conjoint experiment. npj Climate Action (Nature) https://www.nature.com/articles/s44168-025-00236-3
  26. Xia, Z., Ye, J.and Debnath, R. (2025). A Comment on "A Systematic Review of Worldwide Causal and Correlational Evidence on Digital Media and Democracy" by Lorenz-Spreen et al., (2023).I4R Discussion Paper Series, The Institute for Replication (I4R). https://hdl.handle.net/10419/312257 (Preprint)
  27. Ashraf, W.M., Dua. V and Debnath, R. (2025) Domain Consistent Industrial Decarbonisation of Global Coal Power Plants. Preprint https://arxiv.org/abs/2503.03571
  28. Kyrychenko, Y., Roozenbeek, J., Davidson, B., van der Linden, S., and Debnath, R. (2025) Human Preferences for Constructive Interactions in Language Model Alignment. Preprint https://arxiv.org/abs/2503.16480
  29. Zhang, C., Cao, C., Zhang, P., Alvarez, R. M., and Debnath, R. (2025) Measuring the Interaction of Sociodemographic and Building Characteristics on Energy Efficiency and Environmental Impact in London Using Machine Learning. Preprint https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5192239
  30. Maiya, S., Liu, Y., Debnath, R. and Korhonen, A. (2025) Improving Preference Extraction In LLMs By Identifying Latent Knowledge Through Classifying Probes. Preprint https://arxiv.org/abs/2503.17755
  31. Xia, Z., (...), and Debnath, R. (2025) Government Efforts in Environmental Improvement Affect Individual Policy Support in China. Preprint https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5114702
  32. Schoenegger, P. et al. (2025) Large Language Models Are More Persuasive Than Incentivized Human Persuaders. Preprint https://arxiv.org/abs/2505.09662
  33. Xia et al. (2025) Predicting Climate Policy Support with Large Language Models: Assessing Accuracy and Bias in AI-Simulated Policy Support. Preprint https://osf.io/preprints/osf/7kmsj_v1