Alternate energy sources for lighting among rural households in the Himalayan region of Pakistan: Access and impact

Ali, Akhter; Rahut, Dil Bahadur; Mottaleb, Khondoker Abdul; Aryal, Jeetendra Prakash

doi:10.1177/0958305x19842960

Cited by 13 publications

(5 citation statements)

References 70 publications

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“…Therefore, we estimate Multivariate Probit model analysis with four binary outcome choice variables: solar PVs, grid electricity, kerosene, and others (none of the mentioned three). The multivariate probit model has also been applied by Behera et al (2015), Ali et al (2019), and Wassie & Adaramola (2021) to analyze the determinants of household choices of energy fuels for lighting. Following Mullahy (2016), the multivariate probit model in this paper was formulated as follows:…”

Section: Model Specification and Econometric Methodologymentioning

confidence: 99%

Bringing Power to the People of Uganda: Determinants of Solar Photovoltaics Adoption in Uganda

Alinaitwe¹

2023

JSSM

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This paper examines the factors determining the adoption and use of solar photovoltaic (PVs) technologies in Uganda using detailed Uganda 2018/2019 Living Standards Measurement Survey (LSMS) household data. The data were analyzed with a Probit model and a Multivariate Probit model. We found that the major drivers of solar PVs use in Uganda are: savings, income, education, age of household head, and household size. However, households in urban areas, households with access to grid electricity, households with reliable grid electricity supply, and female-headed households are less likely to adopt solar PVs. The study recommends that the government should promote awareness of solar energy and establish credit schemes for solar provision to lessen the burden of upfront investment in solar.

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Section: Model Specification and Econometric Methodologymentioning

confidence: 99%

Bringing Power to the People of Uganda: Determinants of Solar Photovoltaics Adoption in Uganda

Alinaitwe¹

2023

JSSM

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“…More specifically, the model considers three dependent variables and takes the following form in our study (Behera et al, 2015;Ali et al, 2019). And…”

Section: Multivariate Probit (Mvp) Modelmentioning

confidence: 99%

Determinants of Adoption and the Type of Solar PV Technology Adopted in Rural Pakistan

Ahmar

Ali

Jiang

et al. 2022

Front. Environ. Sci.

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The electricity crisis in Pakistan has been triggering grid power outages (load shedding) for many decades, which has not only affected the commercial and industrial sectors but also the domestic sector, specifically the livelihood of rural areas of the country. However, the extant literature advocates that renewable energy technologies (RETs), such as solar photovoltaic (PV) can be the remedy. Given the abundant availability of solar energy in Pakistan that can be converted into electrical energy using a solar PV system, this study examines the determinants of solar PV adoption in rural areas of Pakistan. Our preliminary investigations—using government/official publications—indicate that despite the huge potential of solar energy in Pakistan, the usage of solar PV systems at the household level in rural areas is still untapped, which makes this research agenda more appealing and provocative. In doing so, this study first conducts surveys, face-to-face comprehensive interviews, and questionnaires in four different districts of Pakistan and then implements a stepwise two-stage novel approach on a sample of 1,140 selected rural households. The first stage focuses on the determinants of solar PV system adoption, whereas the second stage focuses on the determinants of the type of solar PV system adopted. Using logistic regression, this study finds that age, education, children in school, income level, access to credit, gender (female), and price of a solar PV system are the factors significantly affecting the solar PV system adoption. In the second stage, we use a multivariate probit model and find that among these significant factors, the former five are significantly positive for the uptake of solar home-system, whereas the latter two are significant for both solar shed-lighting and solar panel-kit systems. In addition to these factors, landholding and access-to-road are significant for solar home systems, whereas household size, distance-to-market, and access-to-grid-electricity are significant for both solar shed-lighting and solar panel-kit systems. Since burning fossil fuels and solid biomass fuels for domestic energy needs are common in rural areas globally and cause carbon emissions and several severe health issues, the findings of this study are useful in many ways. In specific, we contribute to the literature examining the determinants of RETs in rural communities in developing countries.

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“…The literature indicates different socioeconomic factors which influence the choices to use energy sources such as poverty, income differences, household size, education, and locational variables play significant role to determine the households' decision to use energy for the purpose of cooking, heating, and lightening (Ashagidigbi et al, 2020;Ali et al, 2019). The underlying study has bridged up the gap by classifying the energy sources into dirty and clean with respect to their ranking as given energy ladder hypothesis (Leach, 1992).…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing the Choice between Dirty and Clean Energy Sources: Evidence from Pakistan

Manzoor¹

2022

JDSS

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Energy Ladder Hypothesis (ELH) explains the relationship between income and households' decision to use dirty or clean energy sources for the purpose of cooking, heating, and lightening. Therefore, the underlying research paper aims to estimate the validation of the ELH in Pakistan, moreover, to estimate the other socioeconomic factors which influence the households' decision to use energy sources. We have used HIES . The findings identify that ELH holds in Pakistan, which explains that the increase in income level drives households to shift from dirty energy (firewood, dung cake, charcoal, agriculture waste) utilization to the clean energy sources (electricity, and gas). Moreover, higher quintiles of income are found using cleaner energy sources than the bottom income quintiles. ELH holds by using income quintiles. Other factors such as household characteristics (family size, dependency ratio, education, gender, and locational dwelling) are found influencing the decision to use dirty or clean energy sources.

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Alternate energy sources for lighting among rural households in the Himalayan region of Pakistan: Access and impact

Cited by 13 publications

References 70 publications

Bringing Power to the People of Uganda: Determinants of Solar Photovoltaics Adoption in Uganda

Bringing Power to the People of Uganda: Determinants of Solar Photovoltaics Adoption in Uganda

Determinants of Adoption and the Type of Solar PV Technology Adopted in Rural Pakistan

Factors Influencing the Choice between Dirty and Clean Energy Sources: Evidence from Pakistan

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