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Record W1951632615 · doi:10.1596/978-1-4648-0374-1

Surge in Solar-Powered Homes: Experience in Off-Grid Rural Bangladesh

2014· book· en· W1951632615 on OpenAlexaff
Shahidur R. Khandker, Hussain A. Samad, Zubair K.M. Sadeque, Mohammed Asaduzzaman, Mohammad Yunus, Ariful Haque

Bibliographic record

VenueThe World Bank eBooks · 2014
Typebook
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsMcMaster University
FundersMinisterie van Buitenlandse ZakenDepartment for International DevelopmentDepartment of Foreign Affairs and Trade, Australian GovernmentStyrelsen för Internationellt Utvecklingssamarbete
KeywordsGridArchitectural engineeringGeographyMeteorologyEnvironmental scienceEngineeringGeodesy

Abstract

fetched live from OpenAlex

Bangladesh has made remarkable progress in raising living standards and reducing poverty, particularly in previously lagging regions. Rapid solar home system (SHS) expansion in Bangladesh to some 3 million rural households by early 2014 has drawn the attention of donors and governments of other countries. The book s broad aim is twofold: (a) to assess the welfare impact of SHS on households, and (b) to evaluate the present institutional structure and financing mechanisms in place, noting that households want cheaper systems and good quality service while suppliers require a reasonable market-based profit to stay in business. The study entailed an intensive empirical investigation based on both primary and secondary data. The primary data consisted mainly of a large-scale, nationally representative household survey with appropriate geographic spread. Conducted in 2012 by the Bangladesh Institute of Development Studies (BIDS) and assisted by the World Bank, the household survey was designed to examine SHS benefits and costs. The book addresses a number of research issues, which are grouped according to general and gendered household impact, program delivery and monitoring of technical standards, market size and demand, and carbon emissions reduction. The book also analyzes household uses of solar-electric energy services. Typically, SHS models are used for lighting, powering fans and television sets, and charging mobile devices and other electrical equipment. Finally, the book evaluates the gender-disaggregated benefits and women's empowerment from SHS adoption. The gender analysis included two major research questions: (a) can the socioeconomic status of rural women be enhanced by increasing the opportunity to participate in alternative energy-service delivery, and (b) if SHS brings positive impacts in terms of social indicators, what additional efforts can supplement them to bring about a radical shift in gender roles and responsibilities. The book's findings show that better household lighting improves household welfare both directly and indirectly. The book has eight chapters. Chapter one is introduction. Chapter two describes the current status of Bangladesh's SHS expansion program, including salient features of system operation, as well as program delivery and financing. Chapter three reviews the role of electrification in rural development and international experience in using SHS as a complementary solution in remote off-grid areas. Based on the survey data findings, chapter four identifies the major drivers of SHS adoption and system capacity selection at the household and village level, while chapter five discusses and estimates the welfare benefits. Chapter six focuses on SHS market analysis and role of the subsidy, including consumers' willingness to pay and the potential impact of subsidy phase-out. Chapter seven turns to the quality of partner organization (PO) service and other supply-side issues, along with market constraints to meet future demand. Finally, chapter eight offers policy perspectives and a way forward.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.221
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2014
Admission routes1
Has abstractyes

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