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Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research

Author

Listed:
  • Ramit Debnath

    (EPRG, CJBS, University of Cambridge)

  • Sarah Darby

    (University of Oxford)

  • Ronita Bardhan

    (Department of Architecture, University of Cambridge)

  • Kamiar Mohaddes

    (EPRG, CJBS, University of Cambridge)

  • Minna Sunikka-Blank

    (Department of Architecture, University of Cambridge)

Abstract
Text-based data sources like narratives and stories have become increasingly popular as critical insight generator in energy research and social science. However, their implications in policy application usually remain superficial and fail to fully exploit state-of-the-art resources which digital era holds for text analysis. This paper illustrates the potential of deep-narrative analysis in energy policy research using text analysis tools from the cutting-edge domain of computational social sciences, notably topic modelling. We argue that a nested application of topic modelling and grounded theory in narrative analysis promises advances in areas where manual-coding driven narrative analysis has traditionally struggled with directionality biases, scaling, systematisation and repeatability. The nested application of the topic model and the grounded theory goes beyond the frequentist approach of narrative analysis and introduces insight generation capabilities based on the probability distribution of words and topics in a text corpus. In this manner, our proposed methodology deconstructs the corpus and enables the analyst to answer research questions based on the foundational element of the text data structure. We verify theoretical compatibility through a meta-analysis of a state-of-the-art bibliographic database on energy policy, narratives and computational social science. Furthermore, we establish a proof-ofconcept using a narrative-based case study on energy externalities in slum rehabilitation housing in Mumbai, India. We find that the nested application contributes to the literature gap on the need for multidisciplinary methodologies that can systematically include qualitative evidence into policymaking.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ramit Debnath & Sarah Darby & Ronita Bardhan & Kamiar Mohaddes & Minna Sunikka-Blank, 2020. "Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research," Working Papers EPRG2019, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg2019
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    References listed on IDEAS

    as
    1. Debnath, Ramit & Bardhan, Ronita & Sunikka-Blank, Minna, 2019. "How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers," Energy Policy, Elsevier, vol. 132(C), pages 418-428.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. Jiang, Hanchen & Qiang, Maoshan & Lin, Peng, 2016. "A topic modeling based bibliometric exploration of hydropower research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 226-237.
    4. Ramit Debnath & Gianna Monteiro Farias Simoes & Ronita Bardhan & Solange Maria Leder & Roberto Lamberts & Minna Sunikka-Blank, 2020. "Energy Justice in Slum Rehabilitation Housing: An Empirical Exploration of Built Environment Effects on Socio-Cultural Energy Demand," Sustainability, MDPI, vol. 12(7), pages 1-27, April.
    5. Ramit Debnath & Ronita Bardhan, 2020. "India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-25, September.
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    Cited by:

    1. Muza, Olivia & Debnath, Ramit, 2021. "Disruptive innovation for inclusive renewable policy in sub-Saharan Africa: A social shaping of technology analysis of appliance uptake in Rwanda," Renewable Energy, Elsevier, vol. 168(C), pages 896-912.
    2. Debnath, Ramit & Bardhan, Ronita & Reiner, David M. & Miller, J.R., 2021. "Political, economic, social, technological, legal and environmental dimensions of electric vehicle adoption in the United States: A social-media interaction analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    3. Ramit Debnath & Ronita Bardhan & Sarah Darby & Kamiar Mohaddes & Minna Sunikka-Blank, 2020. "A deep-narrative analysis of energy cultures in slum rehabilitation housing of Abuja, Mumbai and Rio de Janeiro for just policy design," Working Papers EPRG2030, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Ramit Debnath & Ronita Bardhan, 2020. "India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-25, September.
    5. Gema Hernández-Moral & Sofía Mulero-Palencia & Víctor Iván Serna-González & Carla Rodríguez-Alonso & Roberto Sanz-Jimeno & Vangelis Marinakis & Nikos Dimitropoulos & Zoi Mylona & Daniele Antonucci & H, 2021. "Big Data Value Chain: Multiple Perspectives for the Built Environment," Energies, MDPI, vol. 14(15), pages 1-21, July.
    6. Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
    7. Dania Ortiz & Vera Migueis & Vitor Leal & Janelle Knox-Hayes & Jungwoo Chun, 2022. "Analysis of Renewable Energy Policies through Decision Trees," Sustainability, MDPI, vol. 14(13), pages 1-31, June.

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    More about this item

    Keywords

    energy policy; narratives; topic modelling; computational social science; text analysis; methodological framework;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • R28 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Government Policy

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