Modeling household cooking fuel choice: A panel multinomial logit approach

Alem, Yonas; Beyene, Abebe D.; Köhlin, Gunnar; Mekonnen, Alemmu

doi:10.1016/j.eneco.2016.06.025

Cited by 197 publications

(116 citation statements)

References 29 publications

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“…There are relatively high implicit discount rates associated with electricity and oil based heating systems compared to district heating, geothermal or wood-based systems. Fuel prices are certainly important considerations in household fuel choice decisions in developing countries (Alem et al, 2016;Mensah and Adu, 2015;Zhang and Hassen, 2017) but there is mixed evidence in developed countries. In the stated-preference studies fuel prices have a significant impact (Rouvinen and Matero, 2013;Scarpa and Willis, 2010) but only a small number of other empirical studies include fuel prices as a potential determinant of fuel choice.…”

Section: Related Literaturementioning

confidence: 99%

“…As households will continue to use traditional fuels such as firewood along with modern fuels, switching back in response to relative prices and other factors (Wickramasinghe, 2011;Van der Kroon et al, 2013) some have argued that a multiple fuel model is more appropriate (Masera et al, 2000). Among the key determinants of fuel choice among households in developing countries are fuel prices, income, and education, as well as security of supply considerations for fuels such as gas and electricity (Alem et al, 2016;Behera et al, 2016;Mensah and Adu, 2015;Zhang and Hassen, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Exploring the spatial and temporal determinants of gas central heating adoption

McCoy

Curtis

2018

Resource and Energy Economics

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a b s t r a c tIn order to better understand the potential for both policy and technological improvements to aid carbon abatement, long-term historical information on the time-path of transition from more traditional to cleaner fuels is useful. This is a relatively understudied element of the fuel switching literature in both developed and emerging economies. This research adds to this literature by examining the adoption time-path of network gas as a heating fuel. We merge a unique dataset on gas network roll-out over time, with other geo-coded data and employ an instrumental variables technique in order to simultaneously model supply and demand. Results indicate a non-linear relationship between the proportion of households using gas as their primary means of central heating and the length of time the network has been in place in each area. Proximity to the gas network, peat bogs, and areas which have banned the consumption of bituminous coal also affect gas connections. Variations in socioeconomic and dwelling characteristics at area level can also help explain connections to the gas network. A better understanding of this variation is crucial in designing targeted policies and can aid network expansion decisions.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the spatial and temporal determinants of gas central heating adoption

McCoy

Curtis

2018

Resource and Energy Economics

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“…According to previous studies, the MNL model can overcome the shortcomings of traditional linear models when studying individual heterogeneity and social psychology. The MNL model has advantages in terms of its clear conceptual basis and its simple calculation, and it has been used widely in economic, transportation, and other studies [30,31]. Therefore, we employed the MNL model to study the factors that influence the carbon capability.…”

Section: Mathematical Modelmentioning

confidence: 99%

Measurements and Factors That Influence the Carbon Capability of Urban Residents in China

Long

Chen

2018

Sustainability

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Due to the rapid growth in residential energy consumption, there is an urgent need to reduce carbon emissions from the consumer side, which requires improvements in the carbon capability of urban residents. In this study, previous investigations of carbon capability were analyzed and classified into four dimensions: carbon knowledge capability, carbon motivation capability, carbon behavior capability, and carbon management capability. According to grounded theory, a quantitative research model was constructed of the carbon capability of urban residents in Jiangsu, which was used to conduct a questionnaire survey. SPSS 19.0 and LatentGOLD were employed to process the questionnaire data and the carbon capability of the residents was evaluated. The results showed that the residents of Jiangsu Province could be divided into six groups based on their different carbon capabilities, where these six major groups accounted for 28.19%, 21.21%, 18.33%, 15.84%, 9.88%, and 6.55% of the total sample. Gender, age, occupation, and educational level had significant effects on the carbon capabilities of residents, whereas the annual household income and household population had no significant effects. According to the characteristics of each cluster based on the four carbon capability dimensions, the six clusters were designated as "balanced steady cluster", "self-restraint cluster", "fully backward cluster", "comprehensive leading cluster", "slightly cognitive cluster", and "restrain others cluster". Quantitative analysis showed that 61.93% of the residents of Jiangsu reached the qualified rate for the carbon capability but the excellent rate was only 15.84%. Relevant policy implications are suggested based on these conclusions.

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“…Energy ladder model states that the households would move along the energy ladder when they receive higher income or social status. However, households are usually unable to get rid of traditional fuels completely because of cost consideration, culture preference, or supply side considerations (Masera et al, 2000;Alem et al, 2016). It has been noted that multiple fuel use constitutes the rule rather than the exception in many 1 https://www.who.int/en/news-room/fact-sheets/detail/household-air-pollution-and-health 2 Cooking fuels also have attracted increasing interest over the years because fuel wood harvesting has caused extensive deforestation.…”

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confidence: 99%

“…It is worth noting that the dynamic fuel stacking behavior cannot be observed in crosssection data (Alem et al, 2016). Moreover, the covariates available in the NSS data, that most of Indian literature on fuel choices is based on, are limited.…”

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confidence: 99%

Household Cooking Fuel Choice in India, 2004-2012: A Panel Multinomial Analysis

Kuo

Azam

2018

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.We use two waves of nationally representative India Human Development Survey to examine factors driving the cooking fuel choice in urban and rural India, separately. We utilize a random effects multinomial logit model that controls for unobserved household heterogeneity. We find that a clean-break with the use of traditional fuels is less likely in rural areas, but more probable in urban areas. The household characteristics (e.g. income, education) that are positively correlated with use of clean fuel also increases the probability of fuel stacking for rural households. We also find that access to paved road is an important determinant for rural household adopting clean fuel, and there exists evidence of social spillover effects in rural areas. Moreover, the bargaining power of women that is associated with economic status (e.g. education or economic freedom) is positively associated with the use of clean fuel. Finally, we find considerable impact of liquefied petroleum gas prices on the probability of use of clean fuel for urban households, but no significant impact for rural households. JEL Classification:Q42, Q35, O12, O13, C25

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Modeling household cooking fuel choice: A panel multinomial logit approach

Cited by 197 publications

References 29 publications

Exploring the spatial and temporal determinants of gas central heating adoption

Exploring the spatial and temporal determinants of gas central heating adoption

Measurements and Factors That Influence the Carbon Capability of Urban Residents in China

Household Cooking Fuel Choice in India, 2004-2012: A Panel Multinomial Analysis

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