Energy justice, built environment and poverty: A deep-narrative analysis of energy cultures in Brazil, India and Nigeria for policy modelling

Abstract

Distributive energy justice as a policy instrument is crucial to meet UN-SDG 7 targets at the poorest section of rapidly urbanising Global South. This study mobilised this policy instrument to derive poverty alleviation focal points across slum rehabilitation housing in Brazil, India and Nigeria. It emphasises on the social dimensions of energy services which currently remains a literature gap. The core methodological innovation lies in the nested deep narrative analysis approach using topic modelling and grounded theory, developed in Debnath et. al. (2020). This paper presents the application of this methodology through the analysis of energy culture and built environment in poverty. Narratives of poverty and energy services were collected through focus group discussions that served as the primary data object. Topic modelling was performed to remove directionality biases in the narratives using a Latent Dirichlet Allocation (LDA)-based algorithm, which was then strengthen using grounded theory. Cross-validation of the topic models was performed using science-mapping of state-of-the-art literature in ‘energy justice’ and ‘poverty’ with an LDA-tuning algorithm. Results converged around seven key topics that probabilistically highlighted the grounded reality of complex interactions between poverty, built environment and human-scale energy services. The derived policy-focal points illustrated that built environment design, land use planning and governance are crucial to the success of SDG-7 in poverty. It showed how utilities and distribution companies can strengthen their energy governance to support social policies and improve the quality and reliability of electric power. The high diffusion of ICTs in the study area can act as a platform of change to bring distributive justice.

India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling

“…The objective of topic modelling is to extract latent semantic topics from large volumes of textual documents (i.e., corpora). LDA is a widely used topic modelling (TM) technique, with recent applications spanning across political science and rhetoric analysis (8)(9)(10)15,16), disaster management (12,17,18) and public policy (13,14,19). The analysis consisted of three main steps.…”

“…This study aided in understanding the polarising shifts in policy interventions that modulated the political narratives in Germany. More recent applications of TM includes crisis identification in urban areas for evidence-based policymaking (12), deep narrative analysis for deriving intervention points for distributive energy justice in poverty (13) and informed public policy design in public administration (14).…”

A nested computational social science approach for deep-narrative analysis in energy policy research

“…Sensitive analysis of a big data driven versus a micro-data driven narrative analysis and policy interpretation remains a gap in the literature. While this study did not intend to address this gap, however, we test the practical efficacy of the proposed micro-dataset driven deep-narrative analysis in Debnath et al, (2020). Our meta-analysis based on the bibliographic dataset of the state-of-the-art literature in energy policy, narrative analysis, computational social science and topic modelling have shown that our nested methodology potentially enables in the cross-fertilisation of topic modelling and grounded theory-aided narrative-analysis.…”

Mapping the Global Academic Support for Sustainable Development Goal 7: A Bibliometric Analysis and Topic Modelling Approach

“…Potential to identify key themes and topics that can inform policymakers' policies and decision making. [55]…”