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dc.contributor.authorNeto-Bradley, André P
dc.contributor.authorRangarajan, Rishika
dc.contributor.authorChoudhary, Ruchi
dc.contributor.authorBazaz, Amir B
dc.date.accessioned2021-08-17T23:30:16Z
dc.date.available2021-08-17T23:30:16Z
dc.date.issued2021
dc.identifier.issn2215-0161
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/326879
dc.description.abstractStudies on clean energy transition amongst low-income urban households in the Global South use an array of qualitative and quantitative methods. However, attempts to combine qualitative and quantitative methods are rare and there are a lack of systematic approaches to this. This paper demonstrates a two stage approach using clustering methods to analyse a mixed dataset containing quantitative household survey data and qualitative interview data. By clustering the quantitative and qualitative data separately, latent groups with common characteristics and narratives arising from each of the two analyses are identified. A second stage of clustering identifies links between these qualitative and quantitative clusters and enables inference of energy transition pathways followed by low-income urban households defined by both quantitative characteristics and qualitative narratives. This approach can support interdisciplinary collaboration in energy research, providing a systematic approach to comparing and identifying links between quantitative and qualitative findings.•A mixed dataset comprising of quantitative survey data and qualitative interview data on low-income household energy use is analysed using hierarchical clustering to detect communities within each dataset.•Interviewees are matched to quantitative survey clusters and a second stage of clustering is performed using cluster membership as variables.•Second stage clusters identify common pairs of survey and interview clusters which define energy transition pathways based on socio-economic characteristics, energy use patterns, and narratives for decision making and practices.
dc.languageeng
dc.publisherElsevier BV
dc.rightsAll rights reserved
dc.subjectClustering
dc.subjectData science
dc.subjectEnergy access
dc.subjectMixed methods
dc.titleEnergy transition pathways amongst low-income urban households: A mixed method clustering approach.
dc.typeArticle
prism.publicationDate2021
prism.publicationNameMethodsX
prism.startingPage101491
prism.volume8
dc.identifier.doi10.17863/CAM.74328
dcterms.dateAccepted2021-08-13
rioxxterms.versionofrecord10.1016/j.mex.2021.101491
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021
dc.contributor.orcidNeto-Bradley, Andre [0000-0001-8142-4451]
dc.identifier.eissn2215-0161
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEPSRC (1817347)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N021614/1)
pubs.funder-project-idTechnology Strategy Board (920035)
pubs.funder-project-idAlan Turing Institute (TUR-000232)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L016095/1)
pubs.funder-project-idEPSRC (via Alan Turing Institute) (EP/T001569/1)
cam.issuedOnline2021-08-14


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