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dc.contributor.authorNeto-Bradley, Andre
dc.contributor.authorChoudhary, Ruchi
dc.contributor.authorChallenor, Peter
dc.date.accessioned2021-12-23T00:31:22Z
dc.date.available2021-12-23T00:31:22Z
dc.date.issued2022-02-24
dc.identifier.issn2399-8083
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331741
dc.description.abstractAccess to sustained clean cooking in India is essential to addressing the health burden of indoor air pollution from biomass fuels, but spatial inequality in cities can adversely affect uptake and effectiveness of policies amongst low-income households. Limited data exists on the spatial distribution of energy use in Indian cities, particularly amongst low-income households, and most quantitative studies focus primarily on the effect of economic determinants. A microsimulation approach is proposed, using publicly available data and a Bayesian multi-level model to account for effects of current cooking practices (at a household scale), local socio-cultural context, and spatial effects (at a city ward scale). This approach offers previously unavailable insight into the spatial distribution of fuel use and residential energy transition within Indian cities. Uncertainty arising from heterogeneity in the population is factored into fuel use estimates through use of Markov Chain Monte Carlo (MCMC) sampling. The model is applied to four cities in the south Indian states of Kerala and Tamil Nadu, and comparison against ward-level survey data shows consistency with the model estimates. Ward-level effects exemplify how wards compare to the city average and to other urban area in the state, which can help stakeholders design and implement clean cooking interventions tailored to the needs of households.
dc.description.sponsorshipFitzwilliam College
dc.publisherSAGE Publications
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSouth Asia
dc.subjectUrban analytics
dc.subjectSpatial Modelling
dc.subjectBig Data
dc.subjectUncertainty
dc.titleA microsimulation of spatial inequality in energy access: A Bayesian multi-level modelling approach for urban India
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2021-12-21T20:23:09Z
prism.publicationNameEnvironment and Planning B: Urban Analytics and City Science
dc.identifier.doi10.17863/CAM.79190
dcterms.dateAccepted2021-12-01
rioxxterms.versionofrecord10.1177/23998083211073140
rioxxterms.versionVoR
dc.contributor.orcidNeto-Bradley, Andre [0000-0001-8142-4451]
dc.identifier.eissn2399-8091
dc.publisher.urlhttps://journals.sagepub.com/doi/full/10.1177/23998083211073140
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEPSRC (1817347)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L016095/1)
cam.issuedOnline2022-02-24
datacite.issupplementedby.urlhttps://doi.org/10.17863/CAM.66449
cam.orpheus.counter2
cam.depositDate2021-12-21
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
datacite.isderivedfrom.doi10.17863/CAM.66449
rioxxterms.freetoread.startdate2024-12-22


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International