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Tracing Climate Policy Linkages using Text-as-Data: New Perspectives

Methods
Climate Change
P030
Frank Wendler
Universität Hamburg
Karina Shyrokykh
Stockholm University
Energy Politics, Policy, and Governance
Monday 09:00 – Thursday 17:00 (25/03/2024 – 28/03/2024)
Climate governance frameworks are expanding and diversifying through the launch of green deal agendas, mainstreaming of zero-carbon targets, and green recovery programs. As a consequence, policy linkages are key for understanding the scope and density of climate agendas and symmetry of competing policy targets. Quantitative analysis of large bodies of text including policy documents, news and online media and documentation of political speech and contestation is a powerful resource in this regard. The workshop will invite contributions evaluating extant concepts of policy linkage using text-as-data approaches to discuss methodical challenges and perspectives for integrating insights across sub-disciplines of political science.
The workshop will initiate a conversation between two evolving research debates around the expansion of climate politics and governance. First, it will integrate and compare perspectives from various sub-disciplines of political science on climate policy linkages on a conceptual and theoretical level. These include concepts of climate policy integration, mainstreaming, and diffusion (or ‘climatization’) and their application to inter- or transnational, domestic and multi-level governance settings (Aykut et al. 2021, Laurens et al. 2022, Tosun & Lang 2017). The workshop will invite contributions providing innovative perspectives on the merit and applicability of extant concepts in this regard and their embedding in broader theoretical approaches. Second, it will stimulate a cross-disciplinary discussion on quantitative text-analysis methods, their main challenges and most recent innovations. A surge of research has applied text-as-data approaches to explore climate policy linkages, using dictionary-based methods, topic modelling, word-embedding analyses and machine learning methods (Macanovic 2022, Grundmann 2022). These contributions cover fields including party politics and political communication, the inclusion of new policy areas into climate action and green finance, legislative politics and newly emerging inter-departmental and inter-agency links. We welcome papers that cover these thematic fields while addressing challenges for methods of text-as-data analysis, including: construction and validation of dictionaries; combination of inductive and deductive approaches for mapping topics, linkages and tonality; recurrent problems of validity and reliability in large-N text bodies; questions of cultural and cross-case transferability; new approaches to including audiovisual material in surveys; and evaluating the qualitative-quantitative boundary in case studies and small-N comparisons.
Adam, S., Reber, U., Häussler, T., & Schmid-Petri, H. (2020). How climate change skeptics (try to) spread their ideas: Using computational methods to assess the resonance among skeptics’ and legacy media. PLOS ONE, 15(10), e0240089. https://doi.org/10.1371/journal.pone.0240089 Adelle, C., & Russel, D. (2013). Climate Policy Integration: A Case of Déjà Vu? Environmental Policy and Governance, 23(1), 1–12. https://doi.org/10.1002/eet.1601 Aykut, S. C., & Maertens, L. (2021). The climatization of global politics: Introduction to the special issue. International Politics, 58(4), 501–518. https://doi.org/10.1057/s41311-021-00325-0 Baden, C., Pipal, C., Schoonvelde, M., & van der Velden, M. A. C. G. (2022). Three Gaps in Computational Text Analysis Methods for Social Sciences: A Research Agenda. Communication Methods and Measures, 16(1), 1–18. https://doi.org/10.1080/19312458.2021.2015574 Biesbroek, R., Badloe, S., & Athanasiadis, I. N. (2020). Machine learning for research on climate change adaptation policy integration: An exploratory UK case study. Regional Environmental Change, 20(3), 1–13. https://doi.org/10.1007/s10113-020-01677-8 Boussalis, C., & Coan, T. G. (2016). Text-mining the signals of climate change doubt. Global Environmental Change, 36, 89–100. https://doi.org/10.1016/j.gloenvcha.2015.12.001 Cann, T. J. B., Weaver, I. S., & Williams, H. T. P. (2021). Ideological biases in social sharing of online information about climate change. PLOS ONE, 16(4), e0250656. https://doi.org/10.1371/journal.pone.0250656 Dellmuth, L., & Shyrokykh, K. (2023). Climate change on Twitter: Implications for climate governance research. WIREs Climate Change, n/a(n/a), e848. https://doi.org/10.1002/wcc.848 Faghmous, J. H., & Kumar, V. (2014). A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science. Big Data, 2(3), 155–163. https://doi.org/10.1089/big.2014.0026 Fownes, J. R., Yu, C., & Margolin, D. B. (2018). Twitter and climate change. Sociology Compass, 12(6), e12587. https://doi.org/10.1111/soc4.12587 Grundmann, R. (2022). Using large text news archives for the analysis of climate change discourse: Some methodological observations. Journal of Risk Research, 25(3), 395–406. https://doi.org/10.1080/13669877.2021.1894471 Guber, D. L., Bohr, J., & Dunlap, R. E. (2021). ‘Time to Wake Up’: Climate change advocacy in a polarized Congress, 1996-2015. Environmental Politics, 30(4), 538–558. https://doi.org/10.1080/09644016.2020.1786333 Hase, V., Mahl, D., Schäfer, M. S., & Keller, T. R. (2021). Climate change in news media across the globe: An automated analysis of issue attention and themes in climate change coverage in 10 countries (2006–2018). Global Environmental Change, 70, 102353. https://doi.org/10.1016/j.gloenvcha.2021.102353 Kettner, C., & Kletzan-Slamanig, D. (2020). Is there climate policy integration in European Union energy efficiency and renewable energy policies? Yes, no, maybe. Environmental Policy and Governance, 30(3), 141–150. https://doi.org/10.1002/eet.1880 Kukkonen, A., Ylä-Anttila, T., & Broadbent, J. (2017). Advocacy coalitions, beliefs and climate change policy in the United States. Public Administration, 95(3), 713–729. https://doi.org/10.1111/padm.12321 Laurens, N., Brandi, C., & Morin, J.-F. (2022). Climate and trade policies: From silos to integration. Climate Policy, 22(2), 248–253. https://doi.org/10.1080/14693062.2021.2009433 Macanovic, A. (2022). Text mining for social science – The state and the future of computational text analysis in sociology. Social Science Research, 108, 102784. https://doi.org/10.1016/j.ssresearch.2022.102784 Majdik, Z. P. (2019). A Computational Approach to Assessing Rhetorical Effectiveness: Agentic Framing of Climate Change in the Congressional Record, 1994–2016. Technical Communication Quarterly, 28(3), 207–222. https://doi.org/10.1080/10572252.2019.1601774 Matti, S., Petersson, C., & Söderberg, C. (2021). The Swedish climate policy framework as a means for climate policy integration: An assessment. Climate Policy, 21(9), 1146–1158. https://doi.org/10.1080/14693062.2021.1930510 Moser, S. C. (2016). Reflections on climate change communication research and practice in the second decade of the 21st century: What more is there to say? WIREs Climate Change, 7(3), 345–369. https://doi.org/10.1002/wcc.403 Palmer, J. R. (2015). How do policy entrepreneurs influence policy change? Framing and boundary work in EU transport biofuels policy. Environmental Politics, 24(2), 270–287. https://doi.org/10.1080/09644016.2015.976465 Pearce, W., Holmberg, K., Hellsten, I., & Nerlich, B. (2014). Climate Change on Twitter: Topics, Communities and Conversations about the 2013 IPCC Working Group 1 Report. PLOS ONE, 9(4), e94785. https://doi.org/10.1371/journal.pone.0094785 Radford, J., & Joseph, K. (2020). Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science. Frontiers in Big Data, 3. https://www.frontiersin.org/articles/10.3389/fdata.2020.00018 Reber, U. (2019). Overcoming Language Barriers: Assessing the Potential of Machine Translation and Topic Modeling for the Comparative Analysis of Multilingual Text Corpora. Communication Methods and Measures, 13(2), 102–125. https://doi.org/10.1080/19312458.2018.1555798 Rietig, K. (2013). Sustainable Climate Policy Integration in the European Union. Environmental Policy and Governance, 23(5), 297–310. https://doi.org/10.1002/eet.1616 Schäfer, M. S., & Hase, V. (2023). Computational methods for the analysis of climate change communication: Towards an integrative and reflexive approach. WIREs Climate Change, 14(2), e806. https://doi.org/10.1002/wcc.806 Stede, M., & Patz, R. (2021). The Climate Change Debate and Natural Language Processing. 8–18. https://doi.org/10.18653/v1/2021.nlp4posimpact-1.2 Thorsen, S., & Astrupgaard, C. (2021). Bridging the computational and visual turn: Re-tooling visual studies with image recognition and network analysis to study online climate images. Nordic Journal of Media Studies, 3(1), 141–163. https://doi.org/10.2478/njms-2021-0008 Tosun, J., & Lang, A. (2017). Policy integration: Mapping the different concepts. Policy Studies, 38(6), 553–570. https://doi.org/10.1080/01442872.2017.1339239 von Lüpke, H. (2023). Climate Policy Integration: A Comparative Analysis of Land Use Change and Energy Sectors in Indonesia and Mexico. Springer Climate.
1: How are climate policy linkages created and how can they be detected using text data?
2: How can variants of policy linkages be systematized and compared?
3: How can text-as-data approaches cover aspects of scope and density of climate action targets?
4: How can text-as-data approaches be used to model coalitions, agency and political conflict?
5: How can inductive and deductive approaches to quantitative text analysis be combined?
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