Slipping through the net: Can data science approaches help target clean cooking policy interventions?

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

Applicability of an 'uptake wave' energy transition concept in Indian households

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.</jats:p

The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems

In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries at the global and national levels and show that these networks are significantly nested. This means that the less filled rows and columns of these networks' adjacency matrices tend to be subsets of the fuller rows and columns. Moreover, we show that nestedness remains relatively stable as the matrices become more filled over time and that this occurs because of a bias for industries that deviate from the networks' nestedness to disappear, and a bias for the missing industries that reduce nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. We interpret the high level of nestedness observed in these networks in the context of the neutral model of development introduced by Hidalgo and Hausmann (2009). We show that, for the observed fills, the model can reproduce the high level of nestedness observed in these networks only when we assume a high level of heterogeneity in the distribution of capabilities available in countries and required by products. In the context of the neutral model, this implies that the high level of nestedness observed in these economic networks emerges as a combination of both, the complementarity of inputs and heterogeneity in the number of capabilities available in countries and required by products. The stability of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of the study of network properties in the evolution of economic networks.Comment: 26 page

ICAR: endoscopic skull‐base surgery

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Applicability of an 'uptake wave' energy transition concept in Indian households

© Published under licence by IOP Publishing Ltd. 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

Slipping through the net: Can data science approaches help target clean cooking policy interventions?

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

Tailoring residential energy provision strategies in fast-growing cities using targeted data collection

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

Energy transition pathways amongst low-income urban households: A mixed method clustering approach

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