Contextualising energy justice in low-income built environment: Towards data-driven policy interventions for addressing distributive injustices in slum rehabilitation housing of the Global South

Around a billion people live in slums today globally, and rehabilitating them to formal housing is a significant challenge. Slum rehabilitation housing is a policy effort to solve this crisis and alleviate urban poverty. However, the question of whether slum rehabilitation programmes are accomplishing more good than harm or whether they are creating a whole host of new problems remains unexplored in the literature. This thesis investigates the effect of slum rehabilitation on household energy demand in Brazil, India and Nigeria through the lens of distributive energy justice. Furthermore, this thesis makes methodological innovation to aid in just policy design by improving the objectivity of including local and contextual knowledge on how poor households live and use energy. Doing so makes novel theoretical and methodological contributions: a theoretical contribution to temporality and spatial energy justice studies on how to offer cross-sectional depictions of energy demand within the slum rehabilitation housing, which was evaluated through structural equation modelling, and a methodological contribution in developing a deep-narrative analysis framework using natural language processing and machine learning-based Latent Dirichlet Allocation algorithm to capture the grounded narratives of distributive injustices objectively. This research highlighted the significance of contextualisation in planning for energy justice in slum communities and the role of digital tools like natural language processing in objectively integrating grounded narratives in just policy design. The contextualisation was done through zoom-in and zoom-out of the grounded narratives enabled through the multi-method approach. Zooming-out view of distributed injustices in the study areas of Mumbai (India), Rio de Janeiro (Brazil) and Abuja (Nigeria) revealed inefficiencies in the administration of electricity distribution companies, lumped billing periods and lack of people-centric built environment design considerations. Similarly, zooming-in the case studies revealed that the poor design of the slum rehabilitation-built environment influenced the increase in energy intensity in the Mumbai case, leading to energy poverty. Whereas created distinct poverty traps in the Brazilian and Nigerian cases through frequent power cuts, high cost of appliance repair, and poor housing design. Finally, policy implications were drawn as per the policy actors across municipal, state and national levels that suggested leveraging digital tools like the deep-narrative analysis and the heavy penetration of Information and Communication Technology devices in such low-income communities. Such tools can improve accountability in decision-making and improve the representation of the occupants through their narratives of injustices associated with living in such communities. Thus, this thesis uniquely forwarded a data-driven pathway for integrating local collective intelligence in just policy design.Bill and Melinda Gates Foundation through the Gates Cambridge Scholarship under the Grant Number OPP1144

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

India locked down 1.3 billion people on March 25, 2020, in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning

Building Science for low-income habitat

Lack of standardized sustainable habitat design guidelines for low-income housing plays an important role in determining the poor quality of life in these settlements, particularly in the slums. My work investigates process-driven pathways for developing and delivering sustainable habitat design guidelines using socio-technical frameworks. I employ mixed-mode research methods to understand low-income habitat from the perspective of people, places and practices. I combine urban experimentation with robust simulation techniques to derive practical solutions for improving the quality of life (QoL) of the urban poor. Urban experimentation includes data acquisition through in-situ environmental sensing of the low-income habitations, modelling of the houses, calibration of the sensed data, and its urban scale building energy calculations using state-of-the-art building energy simulation techniques. I integrate the socio-cultural stochastics in the building simulation framework to derive empirical evidence of the urban QoL in these settlements. There are three cohorts of my research: 1) Investigation of building performance; 2) Spatial analytics for urban sustainability and policy analysis; 3) Data-driven simulation and modelling techniques for derivation of low-income sustainability heuristics.Ministry of Human Resource Development, Government of Indi

Disruptive innovation for inclusive renewable policy in sub-Saharan Africa: A social shaping of technology analysis of appliance uptake in Rwanda.

Rural off-grid renewable energy solutions often fail due to uncertainties in household energy demand, insufficient community engagement, inappropriate financial models and policy inconsistency. Social shaping of technology (SST) of household appliances provides a critical lens of understanding the involved socio-technical drivers behind these constraints. This study employs an SST lens to investigate appliance uptake drivers in 14,580 households in Rwanda, such that these drivers can aid in policy design for green growth at the grassroots level. The methodology includes an epistemological review of non-income drivers of appliance uptake. Empirical analysis using a binary logistic regression, based on which disruptive innovation pathways were derived for fostering green growth. Results showed that appliance uptake was highly gendered and skewed across the Ubudehe (social welfare) categories. ICT-devices like mobile phones and radios had a higher likelihood of ownership than welfare appliances like refrigerator and laundry machines. Fans and cookers also demonstrated a greater probability of ownership. Disruptive innovation pathways were derived from leveraging the ICT-driven wave of appliance ownership, creation of service sectors through off-grid renewable solutions and promoting cleaner fuel-switching of cooking energy at the household level. Further policy implications were drawn to support the creation of consumption identities for green growth.Bill & Melinda Gates Foundation [OPP1144

How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers.

This study explores the effect of slum rehabilitation on appliance ownership and its implications on residential electricity demand. The low-income scenario makes it unique because the entire proposition is based on the importance of non-income drivers of appliance ownership that includes effects of changing the built environment (BE), household practices (HP) and appliances characteristics (AC). This study demonstrates quantitatively that non-income factors around energy practices influence appliance ownership, and therefore electricity consumption. The methodology consists of questionnaire design across the dimension of BE, HP and AC based on social practice theory, surveying of 1224 households and empirical analysis using covariance-based structural equation modelling. Results show that higher appliance ownership in the slum rehabilitation housing is due to change in household practice, built environment and affordability criteria of the appliances. Change in HP shifts necessary activities like cooking, washing and cleaning from outdoor to indoor spaces that positively and significantly influences higher appliance ownership. Poor BE conditions about indoor air quality, thermal comfort and hygiene; and product cost, discounts and ease of use of the appliances also triggers higher appliance ownership. The findings of this study can aid in designing better regulatory and energy efficiency policies for low-income settlements.RD is supported by the Bill & Melinda Gates Foundation (Grant no. OPP1144) through the Gates-Cambridge Scholarship. This study is in parts funded by Ministry of Human Resource Development, Government of India (Grant no. 14MHRD005) under the Frontier Areas in Science and Technology grant awarded to RB

Discomfort and distress in slum rehabilitation: Investigating a rebound phenomenon using a backcasting approach.

Slum rehabilitation policies in India is observed to have a rebound effect on the occupants, where rehabilitated occupants move back to the horizontal slums. In this study, we investigate the cause behind this rebound phenomenon based on a theory of homeostasis, where the loss of homeostasis refers to occupants' heightened discomfort and distress in their built environment. A novel methodological framework was developed to investigate it based on the principles of participatory backcasting approach and the theory of homeostasis. Thirty households in Mumbai's slum rehabilitation housing were interviewed to determine the social, economic and environmental cause of distress and discomfort. Granular information was obtained by further investigating the factors that influence occupants' attitude, emotions, health, control and habits in their built environment that regulates their holistic comfort and lack of stress. The causal linkages among these factors were established using a qualitative fault tree. Results show two primary cause of distress and discomfort in the study area owing to economic distress and built environment related discomfort. Economic distress was from low-income and high electricity bills due to higher household appliance ownership, and built environment discomfort was due to lack of social spaces and poor design of the slum rehabilitation housing. This study showed that mitigating such non-income drivers of distress and discomfort can prevent rebound phenomenon and improve the sustainability of the slum rehabilitation process.RD would like to thank the Commonwealth Scholarship Commission and the Cambridge Trust for support through the Commonwealth Shared Scholarship 2017-18 (INSS-2017-339) and Bill & Melinda Gates Foundation for support through the Gates-Cambridge Scholarship 2018-21 (OPP1144). RB would like to thank Charles Wallace India Trust for supporting her as a CWIT Fellow- 2018 at CRASSH, University of Cambridge. Part of this study is supported by the Ministry of Human Resource Development, Government of India project ‘FAST’ (Grant No. 14MHRD005) and IRCC-IIT Bombay Fund (Grant No. 16IRCC561015) and the British Academy Knowledge Frontiers: International Interdisciplinary Research Projects titled ‘Gender and household energy: female participation in designing domestic energy in India's slum rehabilitation housing’ (Grant No. KF1\100033). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding bodies and supporting organisations

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models.

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking