var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://raw.githubusercontent.com/EECi/home/main/docs/publications/APN_041021.bib\",\"commas\":\"true\",\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cambridge\",\"title\":\"Tailoring Residential Energy Provision Strategies in Fast-Growing Cities using Targeted Data Collection\",\"url\":\"https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151\",\"doi\":\"10.1680/icsic.64669.151\",\"abstract\":\"Understanding the factors that influence energy transitions at a household level, is essential for designing and implementing successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits in the rapidly growing cities of the Global South. In India over 30 percent of urban households are still reliant on traditional fuels for some portion of their energy needs. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city wards and districts. Intelligent use of data can play an important role in addressing this spatial inequality. Energy transitions are often conditioned by a complex interaction of economic and social factors. Analysis of targeted locally collected data in combination with secondary data sources can provide a means of identifying appropriate strategies and incentives for specific wards and communities that policy makers and planners can enact. In this paper we will use the results of a survey of 420 households in 7 city wards in Bangalore, India and show how this micro-scale survey data can be leveraged using a novel conceptual framework. The high resolution offered by the micro-scale dataset was used to identify 5 different clusters of households as a result of energy use patterns and associated non-income characteristics. These typologies may be used to inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition for different types of low-income communities.\",\"booktitle\":\"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)\",\"publisher\":\"ICE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Neto-Bradley\"],\"firstnames\":[\"A.\",\"P\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Choudhary, R\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"Bazaz, A. B\"],\"suffixes\":[]}],\"year\":\"2019\",\"pages\":\"151–160\",\"bibtex\":\"@inproceedings{neto-bradley_a._p_tailoring_2019,\\n\\taddress = {Cambridge},\\n\\ttitle = {Tailoring {Residential} {Energy} {Provision} {Strategies} in {Fast}-{Growing} {Cities} using {Targeted} {Data} {Collection}},\\n\\turl = {https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151},\\n\\tdoi = {10.1680/icsic.64669.151},\\n\\tabstract = {Understanding the factors that influence energy transitions at a household level, is essential for designing and implementing successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits in the rapidly growing cities of the Global South. In India over 30 percent of urban households are still reliant on traditional fuels for some portion of their energy needs. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city wards and districts. Intelligent use of data can play an important role in addressing this spatial inequality. Energy transitions are often conditioned by a complex interaction of economic and social factors. Analysis of targeted locally collected data in combination with secondary data sources can provide a means of identifying appropriate strategies and incentives for specific wards and communities that policy makers and planners can enact. In this paper we will use the results of a survey of 420 households in 7 city wards in Bangalore, India and show how this micro-scale survey data can be leveraged using a novel conceptual framework. The high resolution offered by the micro-scale dataset was used to identify 5 different clusters of households as a result of energy use patterns and associated non-income characteristics. These typologies may be used to inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition for different types of low-income communities.},\\n\\tbooktitle = {International {Conference} on {Smart} {Infrastructure} and {Construction} 2019 ({ICSIC})},\\n\\tpublisher = {ICE},\\n\\tauthor = {Neto-Bradley, A. P and {Choudhary, R} and {Bazaz, A. B}},\\n\\tyear = {2019},\\n\\tdoi = {10.1680/icsic.64669.151},\\n\\tpages = {151--160},\\n}\\n\\n\",\"author_short\":[\"Neto-Bradley, A. P\",\"Choudhary, R\",\"Bazaz, A. B\"],\"key\":\"neto-bradley_a._p_tailoring_2019\",\"id\":\"neto-bradley_a._p_tailoring_2019\",\"bibbaseid\":\"netobradley-choudhary-bazaz-tailoringresidentialenergyprovisionstrategiesinfastgrowingcitiesusingtargeteddatacollection-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Tokyo\",\"title\":\"Applicability of an ‘uptake wave’ energy transition concept in Indian households\",\"volume\":\"294\",\"url\":\"http://dx.doi.org/10.1088/1755-1315/294/1/012091\",\"doi\":\"10.1088/1755-1315/294/1/012091\",\"abstract\":\"Reliable, secure, and affordable energy services are essential to ensuring sustainable economic and social development in the rapidly growing cities of the Global South, yet in India over 30 percent of urban households are still reliant on traditional fuels such as biomass and kerosene for some portion of their energy needs. Understanding the factors that influence energy transitions at a household level, is essential for successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city districts. In this paper we will use the results of a survey of 500 households in Bangalore, India and explore how this data compares with the ‘wave concept’ model of energy transition. This ‘wave concept’ view of energy transitions focuses on appliance ownership as a proxy for energy services and conceptualises the uptake of appliances as a wave with early and late adopters rather than an income-based step change, and as a result better accounts for the role of non-income factors. The wards targeted by the survey cover a range of low-income ward typologies characterised by factors including income, livelihoods, building construction, socio-cultural factors, access to fuels, and reliability of supply. Validating an appropriate model for the uptake of new energy technologies and fuels in households, can better inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition across different districts of a city. By understanding how households use energy, and what limits the adoption of more efficient technologies at a local level, city planners and engineers can develop targeted sustainable strategies for adoption of cleaner more efficient fuels and appliances in households.\",\"booktitle\":\"IOP Conference Series: Earth and Environmental Science\",\"publisher\":\"IOP\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Neto-Bradley\"],\"firstnames\":[\"A\",\"P\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"R\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bazaz\"],\"firstnames\":[\"A\",\"B\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2019\",\"pages\":\"012091\",\"bibtex\":\"@inproceedings{neto-bradley_applicability_2019,\\n\\taddress = {Tokyo},\\n\\ttitle = {Applicability of an ‘uptake wave’ energy transition concept in {Indian} households},\\n\\tvolume = {294},\\n\\turl = {http://dx.doi.org/10.1088/1755-1315/294/1/012091},\\n\\tdoi = {10.1088/1755-1315/294/1/012091},\\n\\tabstract = {Reliable, secure, and affordable energy services are essential to ensuring sustainable economic and social development in the rapidly growing cities of the Global South, yet in India over 30 percent of urban households are still reliant on traditional fuels such as biomass and kerosene for some portion of their energy needs. Understanding the factors that influence energy transitions at a household level, is essential for successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city districts. In this paper we will use the results of a survey of 500 households in Bangalore, India and explore how this data compares with the ‘wave concept’ model of energy transition. This ‘wave concept’ view of energy transitions focuses on appliance ownership as a proxy for energy services and conceptualises the uptake of appliances as a wave with early and late adopters rather than an income-based step change, and as a result better accounts for the role of non-income factors. The wards targeted by the survey cover a range of low-income ward typologies characterised by factors including income, livelihoods, building construction, socio-cultural factors, access to fuels, and reliability of supply. Validating an appropriate model for the uptake of new energy technologies and fuels in households, can better inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition across different districts of a city. By understanding how households use energy, and what limits the adoption of more efficient technologies at a local level, city planners and engineers can develop targeted sustainable strategies for adoption of cleaner more efficient fuels and appliances in households.},\\n\\tbooktitle = {{IOP} {Conference} {Series}: {Earth} and {Environmental} {Science}},\\n\\tpublisher = {IOP},\\n\\tauthor = {Neto-Bradley, A P and Choudhary, R and Bazaz, A B},\\n\\tmonth = aug,\\n\\tyear = {2019},\\n\\tpages = {012091},\\n}\\n\\n\",\"author_short\":[\"Neto-Bradley, A P\",\"Choudhary, R\",\"Bazaz, A B\"],\"key\":\"neto-bradley_applicability_2019\",\"id\":\"neto-bradley_applicability_2019\",\"bibbaseid\":\"netobradley-choudhary-bazaz-applicabilityofanuptakewaveenergytransitionconceptinindianhouseholds-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1088/1755-1315/294/1/012091\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Slipping through the net: Can data science approaches help target clean cooking policy interventions?\",\"volume\":\"144\",\"issn\":\"0301-4215\",\"shorttitle\":\"Slipping through the net\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S0301421520303815\",\"doi\":\"10.1016/j.enpol.2020.111650\",\"abstract\":\"Reliance on solid biomass cooking fuels in India has negative health and socio-economic consequences for households, yet policies aimed at promoting uptake of LPG for cooking have not always been effective at promoting sustained transition to cleaner cooking amongst intended beneficiaries. This paper uses a two step approach combining predictive and descriptive analyses of the IHDS panel dataset to identify different groups of households that switched stove between 2004/5 and 2011/12. A tree-based ensemble machine learning predictive analysis identifies key determinants of a switch from biomass to non-biomass stoves. A descriptive clustering analysis is used to identify groups of stove-switching households that follow different transition pathways. There are three key findings of this study: firstly non-income determinants of stove switching do not have a linear effect on stove switching, in particular variables on time of use and appliance ownership which offer a proxy for household energy practices; secondly location specific factors including region, infrastructure availability, and dwelling quality are found to be key determinants and as a result policies must be tailored to take into account local variations; thirdly some groups of households that adopt non-biomass stoves continue using biomass and interventions should be targeted to reduce their biomass use.\",\"language\":\"en\",\"urldate\":\"2020-10-16\",\"journal\":\"Energy Policy\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Neto-Bradley\"],\"firstnames\":[\"André\",\"Paul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"Ruchi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bazaz\"],\"firstnames\":[\"Amir\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2020\",\"keywords\":\"Energy access, Energy poverty, India, Cooking fuel, Urban analytics\",\"pages\":\"111650\",\"file\":\"ScienceDirect Full Text PDF:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\I92JBXQ5\\\\\\\\Neto-Bradley et al. - 2020 - Slipping through the net Can data science approac.pdf:application/pdf;ScienceDirect Snapshot:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\42E6B97Q\\\\§0301421520303815.html:text/html\",\"bibtex\":\"@article{neto-bradley_slipping_2020,\\n\\ttitle = {Slipping through the net: {Can} data science approaches help target clean cooking policy interventions?},\\n\\tvolume = {144},\\n\\tissn = {0301-4215},\\n\\tshorttitle = {Slipping through the net},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S0301421520303815},\\n\\tdoi = {10.1016/j.enpol.2020.111650},\\n\\tabstract = {Reliance on solid biomass cooking fuels in India has negative health and socio-economic consequences for households, yet policies aimed at promoting uptake of LPG for cooking have not always been effective at promoting sustained transition to cleaner cooking amongst intended beneficiaries. This paper uses a two step approach combining predictive and descriptive analyses of the IHDS panel dataset to identify different groups of households that switched stove between 2004/5 and 2011/12. A tree-based ensemble machine learning predictive analysis identifies key determinants of a switch from biomass to non-biomass stoves. A descriptive clustering analysis is used to identify groups of stove-switching households that follow different transition pathways. There are three key findings of this study: firstly non-income determinants of stove switching do not have a linear effect on stove switching, in particular variables on time of use and appliance ownership which offer a proxy for household energy practices; secondly location specific factors including region, infrastructure availability, and dwelling quality are found to be key determinants and as a result policies must be tailored to take into account local variations; thirdly some groups of households that adopt non-biomass stoves continue using biomass and interventions should be targeted to reduce their biomass use.},\\n\\tlanguage = {en},\\n\\turldate = {2020-10-16},\\n\\tjournal = {Energy Policy},\\n\\tauthor = {Neto-Bradley, André Paul and Choudhary, Ruchi and Bazaz, Amir},\\n\\tmonth = sep,\\n\\tyear = {2020},\\n\\tkeywords = {Energy access, Energy poverty, India, Cooking fuel, Urban analytics},\\n\\tpages = {111650},\\n\\tfile = {ScienceDirect Full Text PDF:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\I92JBXQ5\\\\\\\\Neto-Bradley et al. - 2020 - Slipping through the net Can data science approac.pdf:application/pdf;ScienceDirect Snapshot:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\42E6B97Q\\\\\\\\S0301421520303815.html:text/html},\\n}\\n\\n\",\"author_short\":[\"Neto-Bradley, A. P.\",\"Choudhary, R.\",\"Bazaz, A.\"],\"key\":\"neto-bradley_slipping_2020\",\"id\":\"neto-bradley_slipping_2020\",\"bibbaseid\":\"netobradley-choudhary-bazaz-slippingthroughthenetcandatascienceapproacheshelptargetcleancookingpolicyinterventions-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S0301421520303815\"},\"keyword\":[\"Energy access\",\"Energy poverty\",\"India\",\"Cooking fuel\",\"Urban analytics\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A clustering approach to clean cooking transition pathways for low-income households in Bangalore\",\"volume\":\"66\",\"copyright\":\"All rights reserved\",\"issn\":\"2210-6707\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S2210670720309112\",\"doi\":\"10.1016/j.scs.2020.102697\",\"abstract\":\"Improving access to clean cooking is a key part of India’s strategy to reduce energy poverty and tackle the health impacts of solid biomass fuel use. Currently policies to promote uptake of clean cooking fuels do not account for local socio-economic and cultural context. However, lack of access to clean cooking is a multi-dimensional problem that requires an understanding of both socio-economic macro-scale trends, as well as household and community behaviour at a micro-scale. This study uses data science approaches to integrate quantitative and qualitative data from a survey of low-income households in Bangalore, to identify dominant socio-economic characteristics, behaviours, and decision-making that act as barriers to clean cooking across a community. Key barriers identified include awareness and access to subsidy programmes, safety concerns, as well as weak community networks. Low income households can also be adversely affected by kerosene restrictions intended to promote LPG uptake. The clean cooking transition pathways identified can support targeting of local policy interventions to address barriers to clean cooking faced by different groups of households.\",\"journal\":\"Sustainable Cities and Society\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Neto-Bradley\"],\"firstnames\":[\"André\",\"Paul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rangarajan\"],\"firstnames\":[\"Rishika\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"Ruchi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bazaz\"],\"firstnames\":[\"Amir\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2021\",\"keywords\":\"Energy policy, Clustering, Energy transition, India, Urban analytics, Cleaning cooking\",\"pages\":\"102697\",\"bibtex\":\"@article{neto-bradley_clustering_2021,\\n\\ttitle = {A clustering approach to clean cooking transition pathways for low-income households in {Bangalore}},\\n\\tvolume = {66},\\n\\tcopyright = {All rights reserved},\\n\\tissn = {2210-6707},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S2210670720309112},\\n\\tdoi = {10.1016/j.scs.2020.102697},\\n\\tabstract = {Improving access to clean cooking is a key part of India’s strategy to reduce energy poverty and tackle the health impacts of solid biomass fuel use. Currently policies to promote uptake of clean cooking fuels do not account for local socio-economic and cultural context. However, lack of access to clean cooking is a multi-dimensional problem that requires an understanding of both socio-economic macro-scale trends, as well as household and community behaviour at a micro-scale. This study uses data science approaches to integrate quantitative and qualitative data from a survey of low-income households in Bangalore, to identify dominant socio-economic characteristics, behaviours, and decision-making that act as barriers to clean cooking across a community. Key barriers identified include awareness and access to subsidy programmes, safety concerns, as well as weak community networks. Low income households can also be adversely affected by kerosene restrictions intended to promote LPG uptake. The clean cooking transition pathways identified can support targeting of local policy interventions to address barriers to clean cooking faced by different groups of households.},\\n\\tjournal = {Sustainable Cities and Society},\\n\\tauthor = {Neto-Bradley, André Paul and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir},\\n\\tmonth = mar,\\n\\tyear = {2021},\\n\\tkeywords = {Energy policy, Clustering, Energy transition, India, Urban analytics, Cleaning cooking},\\n\\tpages = {102697},\\n}\\n\\n\",\"author_short\":[\"Neto-Bradley, A. P.\",\"Rangarajan, R.\",\"Choudhary, R.\",\"Bazaz, A.\"],\"key\":\"neto-bradley_clustering_2021\",\"id\":\"neto-bradley_clustering_2021\",\"bibbaseid\":\"netobradley-rangarajan-choudhary-bazaz-aclusteringapproachtocleancookingtransitionpathwaysforlowincomehouseholdsinbangalore-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S2210670720309112\"},\"keyword\":[\"Energy policy\",\"Clustering\",\"Energy transition\",\"India\",\"Urban analytics\",\"Cleaning cooking\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Energy transition pathways amongst low-income urban households: A mixed method clustering approach\",\"copyright\":\"All rights reserved\",\"issn\":\"2215-0161\",\"shorttitle\":\"Energy transition pathways amongst low-income urban households\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S2215016121002843\",\"doi\":\"10.1016/j.mex.2021.101491\",\"abstract\":\"Studies 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.\",\"language\":\"en\",\"urldate\":\"2021-08-19\",\"journal\":\"MethodsX\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Neto-Bradley\"],\"firstnames\":[\"André\",\"P.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rangarajan\"],\"firstnames\":[\"Rishika\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"Ruchi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bazaz\"],\"firstnames\":[\"Amir\",\"B.\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2021\",\"keywords\":\"Clustering, Data Science, Energy Access, Mixed methods\",\"pages\":\"101491\",\"file\":\"ScienceDirect Full Text PDF:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\UVMM463B\\\\\\\\Neto-Bradley et al. - 2021 - Energy transition pathways amongst low-income urba.pdf:application/pdf;ScienceDirect Snapshot:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\7̋VHKRZ2\\\\§2215016121002843.html:text/html\",\"bibtex\":\"@article{neto-bradley_energy_2021,\\n\\ttitle = {Energy transition pathways amongst low-income urban households: {A} mixed method clustering approach},\\n\\tcopyright = {All rights reserved},\\n\\tissn = {2215-0161},\\n\\tshorttitle = {Energy transition pathways amongst low-income urban households},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S2215016121002843},\\n\\tdoi = {10.1016/j.mex.2021.101491},\\n\\tabstract = {Studies 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.},\\n\\tlanguage = {en},\\n\\turldate = {2021-08-19},\\n\\tjournal = {MethodsX},\\n\\tauthor = {Neto-Bradley, André P. and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir B.},\\n\\tmonth = aug,\\n\\tyear = {2021},\\n\\tkeywords = {Clustering, Data Science, Energy Access, Mixed methods},\\n\\tpages = {101491},\\n\\tfile = {ScienceDirect Full Text PDF:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\UVMM463B\\\\\\\\Neto-Bradley et al. - 2021 - Energy transition pathways amongst low-income urba.pdf:application/pdf;ScienceDirect Snapshot:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\H7VHKRZ2\\\\\\\\S2215016121002843.html:text/html},\\n}\\n\\n\",\"author_short\":[\"Neto-Bradley, A. P.\",\"Rangarajan, R.\",\"Choudhary, R.\",\"Bazaz, A. B.\"],\"key\":\"neto-bradley_energy_2021\",\"id\":\"neto-bradley_energy_2021\",\"bibbaseid\":\"netobradley-rangarajan-choudhary-bazaz-energytransitionpathwaysamongstlowincomeurbanhouseholdsamixedmethodclusteringapproach-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S2215016121002843\"},\"keyword\":[\"Clustering\",\"Data Science\",\"Energy Access\",\"Mixed methods\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A microsimulation of spatial inequality in energy access: A Bayesian multi-level modelling approach for urban India\",\"copyright\":\"All rights reserved\",\"shorttitle\":\"A microsimulation of spatial inequality in energy access\",\"url\":\"http://arxiv.org/abs/2109.08577\",\"abstract\":\"Access 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, local socio-cultural context, and spatial effects. This approach offers previously unavailable insight into the spatial distribution of fuel use and residential energy transition within Indian cities. Uncertainty in the modelled effects is propagated through to fuel use estimates. 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.\",\"urldate\":\"2021-09-22\",\"journal\":\"arXiv:2109.08577 [physics]\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Neto-Bradley\"],\"firstnames\":[\"A.\",\"P.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhary\"],\"firstnames\":[\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Challenor\"],\"firstnames\":[\"P.\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2021\",\"note\":\"arXiv: 2109.08577\",\"keywords\":\"Physics - Physics and Society\",\"file\":\"arXiv Fulltext PDF:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\7AG6V3PB\\\\\\\\Neto-Bradley et al. - 2021 - A microsimulation of spatial inequality in energy .pdf:application/pdf;arXiv.org Snapshot:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\¶3NHKQWJ\\\\\\\\2109.html:text/html\",\"bibtex\":\"@article{neto-bradley_microsimulation_2021,\\n\\ttitle = {A microsimulation of spatial inequality in energy access: {A} {Bayesian} multi-level modelling approach for urban {India}},\\n\\tcopyright = {All rights reserved},\\n\\tshorttitle = {A microsimulation of spatial inequality in energy access},\\n\\turl = {http://arxiv.org/abs/2109.08577},\\n\\tabstract = {Access 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, local socio-cultural context, and spatial effects. This approach offers previously unavailable insight into the spatial distribution of fuel use and residential energy transition within Indian cities. Uncertainty in the modelled effects is propagated through to fuel use estimates. 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.},\\n\\turldate = {2021-09-22},\\n\\tjournal = {arXiv:2109.08577 [physics]},\\n\\tauthor = {Neto-Bradley, A. P. and Choudhary, R. and Challenor, P.},\\n\\tmonth = sep,\\n\\tyear = {2021},\\n\\tnote = {arXiv: 2109.08577},\\n\\tkeywords = {Physics - Physics and Society},\\n\\tfile = {arXiv Fulltext PDF:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\7AG6V3PB\\\\\\\\Neto-Bradley et al. - 2021 - A microsimulation of spatial inequality in energy .pdf:application/pdf;arXiv.org Snapshot:C\\\\:\\\\\\\\Users\\\\\\\\apn30\\\\\\\\Zotero\\\\\\\\storage\\\\\\\\P3NHKQWJ\\\\\\\\2109.html:text/html},\\n}\\n\",\"author_short\":[\"Neto-Bradley, A. P.\",\"Choudhary, R.\",\"Challenor, P.\"],\"key\":\"neto-bradley_microsimulation_2021\",\"id\":\"neto-bradley_microsimulation_2021\",\"bibbaseid\":\"netobradley-choudhary-challenor-amicrosimulationofspatialinequalityinenergyaccessabayesianmultilevelmodellingapproachforurbanindia-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/2109.08577\"},\"keyword\":[\"Physics - Physics and Society\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"APN_041021.bib\" href=\"data:text/bibtex;base64,@inproceedings{neto-bradley_a._p_tailoring_2019,
	address = {Cambridge},
	title = {Tailoring {Residential} {Energy} {Provision} {Strategies} in {Fast}-{Growing} {Cities} using {Targeted} {Data} {Collection}},
	url = {https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151},
	doi = {10.1680/icsic.64669.151},
	abstract = {Understanding the factors that influence energy transitions at a household level, is essential for designing and implementing successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits in the rapidly growing cities of the Global South. In India over 30 percent of urban households are still reliant on traditional fuels for some portion of their energy needs. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city wards and districts. Intelligent use of data can play an important role in addressing this spatial inequality. Energy transitions are often conditioned by a complex interaction of economic and social factors. Analysis of targeted locally collected data in combination with secondary data sources can provide a means of identifying appropriate strategies and incentives for specific wards and communities that policy makers and planners can enact. In this paper we will use the results of a survey of 420 households in 7 city wards in Bangalore, India and show how this micro-scale survey data can be leveraged using a novel conceptual framework. The high resolution offered by the micro-scale dataset was used to identify 5 different clusters of households as a result of energy use patterns and associated non-income characteristics. These typologies may be used to inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition for different types of low-income communities.},
	booktitle = {International {Conference} on {Smart} {Infrastructure} and {Construction} 2019 ({ICSIC})},
	publisher = {ICE},
	author = {Neto-Bradley, A. P and {Choudhary, R} and {Bazaz, A. B}},
	year = {2019},
	doi = {10.1680/icsic.64669.151},
	pages = {151--160},
}



@inproceedings{neto-bradley_applicability_2019,
	address = {Tokyo},
	title = {Applicability of an ‘uptake wave’ energy transition concept in {Indian} households},
	volume = {294},
	url = {http://dx.doi.org/10.1088/1755-1315/294/1/012091},
	doi = {10.1088/1755-1315/294/1/012091},
	abstract = {Reliable, secure, and affordable energy services are essential to ensuring sustainable economic and social development in the rapidly growing cities of the Global South, yet in India over 30 percent of urban households are still reliant on traditional fuels such as biomass and kerosene for some portion of their energy needs. Understanding the factors that influence energy transitions at a household level, is essential for successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city districts. In this paper we will use the results of a survey of 500 households in Bangalore, India and explore how this data compares with the ‘wave concept’ model of energy transition. This ‘wave concept’ view of energy transitions focuses on appliance ownership as a proxy for energy services and conceptualises the uptake of appliances as a wave with early and late adopters rather than an income-based step change, and as a result better accounts for the role of non-income factors. The wards targeted by the survey cover a range of low-income ward typologies characterised by factors including income, livelihoods, building construction, socio-cultural factors, access to fuels, and reliability of supply. Validating an appropriate model for the uptake of new energy technologies and fuels in households, can better inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition across different districts of a city. By understanding how households use energy, and what limits the adoption of more efficient technologies at a local level, city planners and engineers can develop targeted sustainable strategies for adoption of cleaner more efficient fuels and appliances in households.},
	booktitle = {{IOP} {Conference} {Series}: {Earth} and {Environmental} {Science}},
	publisher = {IOP},
	author = {Neto-Bradley, A P and Choudhary, R and Bazaz, A B},
	month = aug,
	year = {2019},
	pages = {012091},
}



@article{neto-bradley_slipping_2020,
	title = {Slipping through the net: {Can} data science approaches help target clean cooking policy interventions?},
	volume = {144},
	issn = {0301-4215},
	shorttitle = {Slipping through the net},
	url = {http://www.sciencedirect.com/science/article/pii/S0301421520303815},
	doi = {10.1016/j.enpol.2020.111650},
	abstract = {Reliance on solid biomass cooking fuels in India has negative health and socio-economic consequences for households, yet policies aimed at promoting uptake of LPG for cooking have not always been effective at promoting sustained transition to cleaner cooking amongst intended beneficiaries. This paper uses a two step approach combining predictive and descriptive analyses of the IHDS panel dataset to identify different groups of households that switched stove between 2004/5 and 2011/12. A tree-based ensemble machine learning predictive analysis identifies key determinants of a switch from biomass to non-biomass stoves. A descriptive clustering analysis is used to identify groups of stove-switching households that follow different transition pathways. There are three key findings of this study: firstly non-income determinants of stove switching do not have a linear effect on stove switching, in particular variables on time of use and appliance ownership which offer a proxy for household energy practices; secondly location specific factors including region, infrastructure availability, and dwelling quality are found to be key determinants and as a result policies must be tailored to take into account local variations; thirdly some groups of households that adopt non-biomass stoves continue using biomass and interventions should be targeted to reduce their biomass use.},
	language = {en},
	urldate = {2020-10-16},
	journal = {Energy Policy},
	author = {Neto-Bradley, André Paul and Choudhary, Ruchi and Bazaz, Amir},
	month = sep,
	year = {2020},
	keywords = {Energy access, Energy poverty, India, Cooking fuel, Urban analytics},
	pages = {111650},
	file = {ScienceDirect Full Text PDF:C\:\\Users\\apn30\\Zotero\\storage\\I92JBXQ5\\Neto-Bradley et al. - 2020 - Slipping through the net Can data science approac.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\apn30\\Zotero\\storage\\42E6B97Q\\S0301421520303815.html:text/html},
}



@article{neto-bradley_clustering_2021,
	title = {A clustering approach to clean cooking transition pathways for low-income households in {Bangalore}},
	volume = {66},
	copyright = {All rights reserved},
	issn = {2210-6707},
	url = {http://www.sciencedirect.com/science/article/pii/S2210670720309112},
	doi = {10.1016/j.scs.2020.102697},
	abstract = {Improving access to clean cooking is a key part of India’s strategy to reduce energy poverty and tackle the health impacts of solid biomass fuel use. Currently policies to promote uptake of clean cooking fuels do not account for local socio-economic and cultural context. However, lack of access to clean cooking is a multi-dimensional problem that requires an understanding of both socio-economic macro-scale trends, as well as household and community behaviour at a micro-scale. This study uses data science approaches to integrate quantitative and qualitative data from a survey of low-income households in Bangalore, to identify dominant socio-economic characteristics, behaviours, and decision-making that act as barriers to clean cooking across a community. Key barriers identified include awareness and access to subsidy programmes, safety concerns, as well as weak community networks. Low income households can also be adversely affected by kerosene restrictions intended to promote LPG uptake. The clean cooking transition pathways identified can support targeting of local policy interventions to address barriers to clean cooking faced by different groups of households.},
	journal = {Sustainable Cities and Society},
	author = {Neto-Bradley, André Paul and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir},
	month = mar,
	year = {2021},
	keywords = {Energy policy, Clustering, Energy transition, India, Urban analytics, Cleaning cooking},
	pages = {102697},
}



@article{neto-bradley_energy_2021,
	title = {Energy transition pathways amongst low-income urban households: {A} mixed method clustering approach},
	copyright = {All rights reserved},
	issn = {2215-0161},
	shorttitle = {Energy transition pathways amongst low-income urban households},
	url = {https://www.sciencedirect.com/science/article/pii/S2215016121002843},
	doi = {10.1016/j.mex.2021.101491},
	abstract = {Studies 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.},
	language = {en},
	urldate = {2021-08-19},
	journal = {MethodsX},
	author = {Neto-Bradley, André P. and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir B.},
	month = aug,
	year = {2021},
	keywords = {Clustering, Data Science, Energy Access, Mixed methods},
	pages = {101491},
	file = {ScienceDirect Full Text PDF:C\:\\Users\\apn30\\Zotero\\storage\\UVMM463B\\Neto-Bradley et al. - 2021 - Energy transition pathways amongst low-income urba.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\apn30\\Zotero\\storage\\H7VHKRZ2\\S2215016121002843.html:text/html},
}



@article{neto-bradley_microsimulation_2021,
	title = {A microsimulation of spatial inequality in energy access: {A} {Bayesian} multi-level modelling approach for urban {India}},
	copyright = {All rights reserved},
	shorttitle = {A microsimulation of spatial inequality in energy access},
	url = {http://arxiv.org/abs/2109.08577},
	abstract = {Access 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, local socio-cultural context, and spatial effects. This approach offers previously unavailable insight into the spatial distribution of fuel use and residential energy transition within Indian cities. Uncertainty in the modelled effects is propagated through to fuel use estimates. 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.},
	urldate = {2021-09-22},
	journal = {arXiv:2109.08577 [physics]},
	author = {Neto-Bradley, A. P. and Choudhary, R. and Challenor, P.},
	month = sep,
	year = {2021},
	note = {arXiv: 2109.08577},
	keywords = {Physics - Physics and Society},
	file = {arXiv Fulltext PDF:C\:\\Users\\apn30\\Zotero\\storage\\7AG6V3PB\\Neto-Bradley et al. - 2021 - A microsimulation of spatial inequality in energy .pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\apn30\\Zotero\\storage\\P3NHKQWJ\\2109.html:text/html},
}
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In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"netobradley-rangarajan-choudhary-bazaz-aclusteringapproachtocleancookingtransitionpathwaysforlowincomehouseholdsinbangalore-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S2210670720309112\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'netobradley-rangarajan-choudhary-bazaz-aclusteringapproachtocleancookingtransitionpathwaysforlowincomehouseholdsinbangalore-2021', 'http://www.sciencedirect.com/science/article/pii/S2210670720309112', event);\">\n    A clustering approach to clean cooking transition pathways for low-income households in Bangalore.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Neto-Bradley, A. P., Rangarajan, R., Choudhary, R.,  &amp; Bazaz, A.\n</span>\n\n  \n\n</span>\n\n  <i>Sustainable Cities and Society</i>, 66: 102697. March 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S2210670720309112\"\n     onclick=\"log_download('netobradley-rangarajan-choudhary-bazaz-aclusteringapproachtocleancookingtransitionpathwaysforlowincomehouseholdsinbangalore-2021', 'http://www.sciencedirect.com/science/article/pii/S2210670720309112', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A clustering approach to clean cooking transition pathways for low-income households in Bangalore [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.scs.2020.102697\"\n     onclick=\"log_download('netobradley-rangarajan-choudhary-bazaz-aclusteringapproachtocleancookingtransitionpathwaysforlowincomehouseholdsinbangalore-2021', 'http://doi.org/10.1016/j.scs.2020.102697', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/netobradley-rangarajan-choudhary-bazaz-aclusteringapproachtocleancookingtransitionpathwaysforlowincomehouseholdsinbangalore-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('netobradley-rangarajan-choudhary-bazaz-aclusteringapproachtocleancookingtransitionpathwaysforlowincomehouseholdsinbangalore-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{neto-bradley_clustering_2021,\n\ttitle = {A clustering approach to clean cooking transition pathways for low-income households in {Bangalore}},\n\tvolume = {66},\n\tcopyright = {All rights reserved},\n\tissn = {2210-6707},\n\turl = {http://www.sciencedirect.com/science/article/pii/S2210670720309112},\n\tdoi = {10.1016/j.scs.2020.102697},\n\tabstract = {Improving access to clean cooking is a key part of India’s strategy to reduce energy poverty and tackle the health impacts of solid biomass fuel use. Currently policies to promote uptake of clean cooking fuels do not account for local socio-economic and cultural context. However, lack of access to clean cooking is a multi-dimensional problem that requires an understanding of both socio-economic macro-scale trends, as well as household and community behaviour at a micro-scale. This study uses data science approaches to integrate quantitative and qualitative data from a survey of low-income households in Bangalore, to identify dominant socio-economic characteristics, behaviours, and decision-making that act as barriers to clean cooking across a community. Key barriers identified include awareness and access to subsidy programmes, safety concerns, as well as weak community networks. Low income households can also be adversely affected by kerosene restrictions intended to promote LPG uptake. The clean cooking transition pathways identified can support targeting of local policy interventions to address barriers to clean cooking faced by different groups of households.},\n\tjournal = {Sustainable Cities and Society},\n\tauthor = {Neto-Bradley, André Paul and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir},\n\tmonth = mar,\n\tyear = {2021},\n\tkeywords = {Energy policy, Clustering, Energy transition, India, Urban analytics, Cleaning cooking},\n\tpages = {102697},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Improving access to clean cooking is a key part of India’s strategy to reduce energy poverty and tackle the health impacts of solid biomass fuel use. Currently policies to promote uptake of clean cooking fuels do not account for local socio-economic and cultural context. However, lack of access to clean cooking is a multi-dimensional problem that requires an understanding of both socio-economic macro-scale trends, as well as household and community behaviour at a micro-scale. This study uses data science approaches to integrate quantitative and qualitative data from a survey of low-income households in Bangalore, to identify dominant socio-economic characteristics, behaviours, and decision-making that act as barriers to clean cooking across a community. Key barriers identified include awareness and access to subsidy programmes, safety concerns, as well as weak community networks. Low income households can also be adversely affected by kerosene restrictions intended to promote LPG uptake. The clean cooking transition pathways identified can support targeting of local policy interventions to address barriers to clean cooking faced by different groups of households.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"netobradley-rangarajan-choudhary-bazaz-energytransitionpathwaysamongstlowincomeurbanhouseholdsamixedmethodclusteringapproach-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2215016121002843\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'netobradley-rangarajan-choudhary-bazaz-energytransitionpathwaysamongstlowincomeurbanhouseholdsamixedmethodclusteringapproach-2021', 'https://www.sciencedirect.com/science/article/pii/S2215016121002843', event);\">\n    Energy transition pathways amongst low-income urban households: A mixed method clustering approach.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Neto-Bradley, A. P., Rangarajan, R., Choudhary, R.,  &amp; Bazaz, A. B.\n</span>\n\n  \n\n</span>\n\n  <i>MethodsX</i>,101491. August 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2215016121002843\"\n     onclick=\"log_download('netobradley-rangarajan-choudhary-bazaz-energytransitionpathwaysamongstlowincomeurbanhouseholdsamixedmethodclusteringapproach-2021', 'https://www.sciencedirect.com/science/article/pii/S2215016121002843', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Energy transition pathways amongst low-income urban households: A mixed method clustering approach [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.mex.2021.101491\"\n     onclick=\"log_download('netobradley-rangarajan-choudhary-bazaz-energytransitionpathwaysamongstlowincomeurbanhouseholdsamixedmethodclusteringapproach-2021', 'http://doi.org/10.1016/j.mex.2021.101491', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/netobradley-rangarajan-choudhary-bazaz-energytransitionpathwaysamongstlowincomeurbanhouseholdsamixedmethodclusteringapproach-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('netobradley-rangarajan-choudhary-bazaz-energytransitionpathwaysamongstlowincomeurbanhouseholdsamixedmethodclusteringapproach-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{neto-bradley_energy_2021,\n\ttitle = {Energy transition pathways amongst low-income urban households: {A} mixed method clustering approach},\n\tcopyright = {All rights reserved},\n\tissn = {2215-0161},\n\tshorttitle = {Energy transition pathways amongst low-income urban households},\n\turl = {https://www.sciencedirect.com/science/article/pii/S2215016121002843},\n\tdoi = {10.1016/j.mex.2021.101491},\n\tabstract = {Studies 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.},\n\tlanguage = {en},\n\turldate = {2021-08-19},\n\tjournal = {MethodsX},\n\tauthor = {Neto-Bradley, André P. and Rangarajan, Rishika and Choudhary, Ruchi and Bazaz, Amir B.},\n\tmonth = aug,\n\tyear = {2021},\n\tkeywords = {Clustering, Data Science, Energy Access, Mixed methods},\n\tpages = {101491},\n\tfile = {ScienceDirect Full Text PDF:C\\:\\\\Users\\\\apn30\\\\Zotero\\\\storage\\\\UVMM463B\\\\Neto-Bradley et al. - 2021 - Energy transition pathways amongst low-income urba.pdf:application/pdf;ScienceDirect Snapshot:C\\:\\\\Users\\\\apn30\\\\Zotero\\\\storage\\\\H7VHKRZ2\\\\S2215016121002843.html:text/html},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Studies 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.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"netobradley-choudhary-challenor-amicrosimulationofspatialinequalityinenergyaccessabayesianmultilevelmodellingapproachforurbanindia-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/2109.08577\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'netobradley-choudhary-challenor-amicrosimulationofspatialinequalityinenergyaccessabayesianmultilevelmodellingapproachforurbanindia-2021', 'http://arxiv.org/abs/2109.08577', event);\">\n    A microsimulation of spatial inequality in energy access: A Bayesian multi-level modelling approach for urban India.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Neto-Bradley, A. P., Choudhary, R.,  &amp; Challenor, P.\n</span>\n\n  \n\n</span>\n\n  <i>arXiv:2109.08577 [physics]</i>. September 2021.\n  <span class=\"note\">arXiv: 2109.08577</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/2109.08577\"\n     onclick=\"log_download('netobradley-choudhary-challenor-amicrosimulationofspatialinequalityinenergyaccessabayesianmultilevelmodellingapproachforurbanindia-2021', 'http://arxiv.org/abs/2109.08577', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A microsimulation of spatial inequality in energy access: A Bayesian multi-level modelling approach for urban India [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/netobradley-choudhary-challenor-amicrosimulationofspatialinequalityinenergyaccessabayesianmultilevelmodellingapproachforurbanindia-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('netobradley-choudhary-challenor-amicrosimulationofspatialinequalityinenergyaccessabayesianmultilevelmodellingapproachforurbanindia-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{neto-bradley_microsimulation_2021,\n\ttitle = {A microsimulation of spatial inequality in energy access: {A} {Bayesian} multi-level modelling approach for urban {India}},\n\tcopyright = {All rights reserved},\n\tshorttitle = {A microsimulation of spatial inequality in energy access},\n\turl = {http://arxiv.org/abs/2109.08577},\n\tabstract = {Access 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, local socio-cultural context, and spatial effects. This approach offers previously unavailable insight into the spatial distribution of fuel use and residential energy transition within Indian cities. Uncertainty in the modelled effects is propagated through to fuel use estimates. 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.},\n\turldate = {2021-09-22},\n\tjournal = {arXiv:2109.08577 [physics]},\n\tauthor = {Neto-Bradley, A. P. and Choudhary, R. and Challenor, P.},\n\tmonth = sep,\n\tyear = {2021},\n\tnote = {arXiv: 2109.08577},\n\tkeywords = {Physics - Physics and Society},\n\tfile = {arXiv Fulltext PDF:C\\:\\\\Users\\\\apn30\\\\Zotero\\\\storage\\\\7AG6V3PB\\\\Neto-Bradley et al. - 2021 - A microsimulation of spatial inequality in energy .pdf:application/pdf;arXiv.org Snapshot:C\\:\\\\Users\\\\apn30\\\\Zotero\\\\storage\\\\P3NHKQWJ\\\\2109.html:text/html},\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Access 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, local socio-cultural context, and spatial effects. This approach offers previously unavailable insight into the spatial distribution of fuel use and residential energy transition within Indian cities. Uncertainty in the modelled effects is propagated through to fuel use estimates. 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.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"netobradley-choudhary-bazaz-slippingthroughthenetcandatascienceapproacheshelptargetcleancookingpolicyinterventions-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0301421520303815\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'netobradley-choudhary-bazaz-slippingthroughthenetcandatascienceapproacheshelptargetcleancookingpolicyinterventions-2020', 'http://www.sciencedirect.com/science/article/pii/S0301421520303815', event);\">\n    Slipping through the net: Can data science approaches help target clean cooking policy interventions?.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Neto-Bradley, A. P., Choudhary, R.,  &amp; Bazaz, A.\n</span>\n\n  \n\n</span>\n\n  <i>Energy Policy</i>, 144: 111650. September 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0301421520303815\"\n     onclick=\"log_download('netobradley-choudhary-bazaz-slippingthroughthenetcandatascienceapproacheshelptargetcleancookingpolicyinterventions-2020', 'http://www.sciencedirect.com/science/article/pii/S0301421520303815', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Slipping through the net: Can data science approaches help target clean cooking policy interventions? [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.enpol.2020.111650\"\n     onclick=\"log_download('netobradley-choudhary-bazaz-slippingthroughthenetcandatascienceapproacheshelptargetcleancookingpolicyinterventions-2020', 'http://doi.org/10.1016/j.enpol.2020.111650', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/netobradley-choudhary-bazaz-slippingthroughthenetcandatascienceapproacheshelptargetcleancookingpolicyinterventions-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('netobradley-choudhary-bazaz-slippingthroughthenetcandatascienceapproacheshelptargetcleancookingpolicyinterventions-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{neto-bradley_slipping_2020,\n\ttitle = {Slipping through the net: {Can} data science approaches help target clean cooking policy interventions?},\n\tvolume = {144},\n\tissn = {0301-4215},\n\tshorttitle = {Slipping through the net},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0301421520303815},\n\tdoi = {10.1016/j.enpol.2020.111650},\n\tabstract = {Reliance on solid biomass cooking fuels in India has negative health and socio-economic consequences for households, yet policies aimed at promoting uptake of LPG for cooking have not always been effective at promoting sustained transition to cleaner cooking amongst intended beneficiaries. This paper uses a two step approach combining predictive and descriptive analyses of the IHDS panel dataset to identify different groups of households that switched stove between 2004/5 and 2011/12. A tree-based ensemble machine learning predictive analysis identifies key determinants of a switch from biomass to non-biomass stoves. A descriptive clustering analysis is used to identify groups of stove-switching households that follow different transition pathways. There are three key findings of this study: firstly non-income determinants of stove switching do not have a linear effect on stove switching, in particular variables on time of use and appliance ownership which offer a proxy for household energy practices; secondly location specific factors including region, infrastructure availability, and dwelling quality are found to be key determinants and as a result policies must be tailored to take into account local variations; thirdly some groups of households that adopt non-biomass stoves continue using biomass and interventions should be targeted to reduce their biomass use.},\n\tlanguage = {en},\n\turldate = {2020-10-16},\n\tjournal = {Energy Policy},\n\tauthor = {Neto-Bradley, André Paul and Choudhary, Ruchi and Bazaz, Amir},\n\tmonth = sep,\n\tyear = {2020},\n\tkeywords = {Energy access, Energy poverty, India, Cooking fuel, Urban analytics},\n\tpages = {111650},\n\tfile = {ScienceDirect Full Text PDF:C\\:\\\\Users\\\\apn30\\\\Zotero\\\\storage\\\\I92JBXQ5\\\\Neto-Bradley et al. - 2020 - Slipping through the net Can data science approac.pdf:application/pdf;ScienceDirect Snapshot:C\\:\\\\Users\\\\apn30\\\\Zotero\\\\storage\\\\42E6B97Q\\\\S0301421520303815.html:text/html},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Reliance on solid biomass cooking fuels in India has negative health and socio-economic consequences for households, yet policies aimed at promoting uptake of LPG for cooking have not always been effective at promoting sustained transition to cleaner cooking amongst intended beneficiaries. This paper uses a two step approach combining predictive and descriptive analyses of the IHDS panel dataset to identify different groups of households that switched stove between 2004/5 and 2011/12. A tree-based ensemble machine learning predictive analysis identifies key determinants of a switch from biomass to non-biomass stoves. A descriptive clustering analysis is used to identify groups of stove-switching households that follow different transition pathways. There are three key findings of this study: firstly non-income determinants of stove switching do not have a linear effect on stove switching, in particular variables on time of use and appliance ownership which offer a proxy for household energy practices; secondly location specific factors including region, infrastructure availability, and dwelling quality are found to be key determinants and as a result policies must be tailored to take into account local variations; thirdly some groups of households that adopt non-biomass stoves continue using biomass and interventions should be targeted to reduce their biomass use.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"netobradley-choudhary-bazaz-tailoringresidentialenergyprovisionstrategiesinfastgrowingcitiesusingtargeteddatacollection-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'netobradley-choudhary-bazaz-tailoringresidentialenergyprovisionstrategiesinfastgrowingcitiesusingtargeteddatacollection-2019', 'https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151', event);\">\n    Tailoring Residential Energy Provision Strategies in Fast-Growing Cities using Targeted Data Collection.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Neto-Bradley, A. P, Choudhary, R,  &amp; Bazaz, A. B\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Smart Infrastructure and Construction 2019 (ICSIC)</i>, pages 151–160, Cambridge,  2019. ICE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151\"\n     onclick=\"log_download('netobradley-choudhary-bazaz-tailoringresidentialenergyprovisionstrategiesinfastgrowingcitiesusingtargeteddatacollection-2019', 'https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Tailoring Residential Energy Provision Strategies in Fast-Growing Cities using Targeted Data Collection [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1680/icsic.64669.151\"\n     onclick=\"log_download('netobradley-choudhary-bazaz-tailoringresidentialenergyprovisionstrategiesinfastgrowingcitiesusingtargeteddatacollection-2019', 'http://doi.org/10.1680/icsic.64669.151', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/netobradley-choudhary-bazaz-tailoringresidentialenergyprovisionstrategiesinfastgrowingcitiesusingtargeteddatacollection-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('netobradley-choudhary-bazaz-tailoringresidentialenergyprovisionstrategiesinfastgrowingcitiesusingtargeteddatacollection-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{neto-bradley_a._p_tailoring_2019,\n\taddress = {Cambridge},\n\ttitle = {Tailoring {Residential} {Energy} {Provision} {Strategies} in {Fast}-{Growing} {Cities} using {Targeted} {Data} {Collection}},\n\turl = {https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.151},\n\tdoi = {10.1680/icsic.64669.151},\n\tabstract = {Understanding the factors that influence energy transitions at a household level, is essential for designing and implementing successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits in the rapidly growing cities of the Global South. In India over 30 percent of urban households are still reliant on traditional fuels for some portion of their energy needs. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city wards and districts. Intelligent use of data can play an important role in addressing this spatial inequality. Energy transitions are often conditioned by a complex interaction of economic and social factors. Analysis of targeted locally collected data in combination with secondary data sources can provide a means of identifying appropriate strategies and incentives for specific wards and communities that policy makers and planners can enact. In this paper we will use the results of a survey of 420 households in 7 city wards in Bangalore, India and show how this micro-scale survey data can be leveraged using a novel conceptual framework. The high resolution offered by the micro-scale dataset was used to identify 5 different clusters of households as a result of energy use patterns and associated non-income characteristics. These typologies may be used to inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition for different types of low-income communities.},\n\tbooktitle = {International {Conference} on {Smart} {Infrastructure} and {Construction} 2019 ({ICSIC})},\n\tpublisher = {ICE},\n\tauthor = {Neto-Bradley, A. P and {Choudhary, R} and {Bazaz, A. B}},\n\tyear = {2019},\n\tdoi = {10.1680/icsic.64669.151},\n\tpages = {151--160},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Understanding the factors that influence energy transitions at a household level, is essential for designing and implementing successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits in the rapidly growing cities of the Global South. In India over 30 percent of urban households are still reliant on traditional fuels for some portion of their energy needs. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city wards and districts. Intelligent use of data can play an important role in addressing this spatial inequality. Energy transitions are often conditioned by a complex interaction of economic and social factors. Analysis of targeted locally collected data in combination with secondary data sources can provide a means of identifying appropriate strategies and incentives for specific wards and communities that policy makers and planners can enact. In this paper we will use the results of a survey of 420 households in 7 city wards in Bangalore, India and show how this micro-scale survey data can be leveraged using a novel conceptual framework. The high resolution offered by the micro-scale dataset was used to identify 5 different clusters of households as a result of energy use patterns and associated non-income characteristics. These typologies may be used to inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition for different types of low-income communities.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"netobradley-choudhary-bazaz-applicabilityofanuptakewaveenergytransitionconceptinindianhouseholds-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1088/1755-1315/294/1/012091\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'netobradley-choudhary-bazaz-applicabilityofanuptakewaveenergytransitionconceptinindianhouseholds-2019', 'http://dx.doi.org/10.1088/1755-1315/294/1/012091', event);\">\n    Applicability of an ‘uptake wave’ energy transition concept in Indian households.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Neto-Bradley, A P, Choudhary, R,  &amp; Bazaz, A B\n</span>\n\n  \n\n</span>\n\n  In <i>IOP Conference Series: Earth and Environmental Science</i>, volume 294, pages 012091, Tokyo, August 2019. IOP\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1088/1755-1315/294/1/012091\"\n     onclick=\"log_download('netobradley-choudhary-bazaz-applicabilityofanuptakewaveenergytransitionconceptinindianhouseholds-2019', 'http://dx.doi.org/10.1088/1755-1315/294/1/012091', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Applicability of an ‘uptake wave’ energy transition concept in Indian households [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1088/1755-1315/294/1/012091\"\n     onclick=\"log_download('netobradley-choudhary-bazaz-applicabilityofanuptakewaveenergytransitionconceptinindianhouseholds-2019', 'http://doi.org/10.1088/1755-1315/294/1/012091', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/netobradley-choudhary-bazaz-applicabilityofanuptakewaveenergytransitionconceptinindianhouseholds-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('netobradley-choudhary-bazaz-applicabilityofanuptakewaveenergytransitionconceptinindianhouseholds-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{neto-bradley_applicability_2019,\n\taddress = {Tokyo},\n\ttitle = {Applicability of an ‘uptake wave’ energy transition concept in {Indian} households},\n\tvolume = {294},\n\turl = {http://dx.doi.org/10.1088/1755-1315/294/1/012091},\n\tdoi = {10.1088/1755-1315/294/1/012091},\n\tabstract = {Reliable, secure, and affordable energy services are essential to ensuring sustainable economic and social development in the rapidly growing cities of the Global South, yet in India over 30 percent of urban households are still reliant on traditional fuels such as biomass and kerosene for some portion of their energy needs. Understanding the factors that influence energy transitions at a household level, is essential for successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city districts. In this paper we will use the results of a survey of 500 households in Bangalore, India and explore how this data compares with the ‘wave concept’ model of energy transition. This ‘wave concept’ view of energy transitions focuses on appliance ownership as a proxy for energy services and conceptualises the uptake of appliances as a wave with early and late adopters rather than an income-based step change, and as a result better accounts for the role of non-income factors. The wards targeted by the survey cover a range of low-income ward typologies characterised by factors including income, livelihoods, building construction, socio-cultural factors, access to fuels, and reliability of supply. Validating an appropriate model for the uptake of new energy technologies and fuels in households, can better inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition across different districts of a city. By understanding how households use energy, and what limits the adoption of more efficient technologies at a local level, city planners and engineers can develop targeted sustainable strategies for adoption of cleaner more efficient fuels and appliances in households.},\n\tbooktitle = {{IOP} {Conference} {Series}: {Earth} and {Environmental} {Science}},\n\tpublisher = {IOP},\n\tauthor = {Neto-Bradley, A P and Choudhary, R and Bazaz, A B},\n\tmonth = aug,\n\tyear = {2019},\n\tpages = {012091},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Reliable, secure, and affordable energy services are essential to ensuring sustainable economic and social development in the rapidly growing cities of the Global South, yet in India over 30 percent of urban households are still reliant on traditional fuels such as biomass and kerosene for some portion of their energy needs. Understanding the factors that influence energy transitions at a household level, is essential for successful strategies to promote the uptake of cleaner fuels and deliver associated socio-economic benefits. Such fast-growing cities often display intra-urban inequalities of considerable magnitude which can condition individual access to resources and impact the effectiveness of energy provision strategies for individual city districts. In this paper we will use the results of a survey of 500 households in Bangalore, India and explore how this data compares with the ‘wave concept’ model of energy transition. This ‘wave concept’ view of energy transitions focuses on appliance ownership as a proxy for energy services and conceptualises the uptake of appliances as a wave with early and late adopters rather than an income-based step change, and as a result better accounts for the role of non-income factors. The wards targeted by the survey cover a range of low-income ward typologies characterised by factors including income, livelihoods, building construction, socio-cultural factors, access to fuels, and reliability of supply. Validating an appropriate model for the uptake of new energy technologies and fuels in households, can better inform policy makers, entrepreneurs, and engineers on the influence of non-income barriers to energy transition across different districts of a city. By understanding how households use energy, and what limits the adoption of more efficient technologies at a local level, city planners and engineers can develop targeted sustainable strategies for adoption of cleaner more efficient fuels and appliances in households.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);