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-of-concept 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.
Highlights
The energy policymaking needs in today’s world cannot be met alone by the existing methods of quantification due to the multi-agent nature of energy justice and its associated challenges [1]
The meta-theoretical fit was used as a line of argument for evaluating the theoretical compatibility of the nested application of topic modelling (TM) and grounded theory (GT) for narrative analysis
This study presented a novel nested deep-narrative analysis approach using topic modelling and grounded theory for energy policy application
Summary
The energy policymaking needs in today’s world cannot be met alone by the existing methods of quantification due to the multi-agent nature of energy justice and its associated challenges [1]. The current regime of energy policymaking by scientists and technologists is guided by the promotion of preferred technologies with theoretical properties that complements the objectives of the leading political forces They justify their proposition based on quantitative models of energy and climate systems that graft directional biases in the policymaking, often overlooking the strengths of qualitative stakeholder deliberations. Even the work of the Intergovernmental Panel on Climate Change (IPCC) has been characterised as significantly directional and ‘unidisciplinary’ as it is based on a clear separation between the natural sciences and social sciences, and an understanding that social sciences are based on natural sciences [3] The problem with such technocratic directionality bias is that it leads to an inherent limitation in the definition of policy goals, which become defined as ‘reasonable’ or ‘rational’ according to technical parameters. A methodological advancement to a similar discourse-analysis through topic modelling was proposed by Jacobs & Tschötschel [25] which provides a critical epistemological background for this study
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