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Record W2612750720

Multiple Benefits of Improving Energy Efficiency

2017· article· en· W2612750720 on OpenAlexaboutno aff
Arti Sawant

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2017
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEfficient energy useElectricityNatural resource economicsPaceEconomicsBusinessEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

The Intergovernmental Panel on Climate Change (IPCC) report states that buildings account 32 percent of the energy use and 19 per cent of the global emissions out of which almost a third can be attributed to the cooling sector. The aim of this paper is to establish the energy efficiency benefits and discuss health benefits from energy efficiency improvements in the emerging economies such as India. Air conditioners are important in targeting the improvement of energy efficiency policies and are the low-hanging fruit of climate policy in India because of its size and rapidly increasing market due to the aspirations of people in the country to improve their standard of living. Montreal Protocol’s latest Amendment, that limits the use of a supergreenhouse gas Hydrofluorocarbons (HFCs) will provide an impetus to the energy efficiency innovations, and has historically catalyzed energy efficiency improvements of up to 35 per cent. Projected growth in power demand for cooling could have a crippling effect on India’s fiscal status and prospects for economic growth to the extent it becomes necessary to further increase fossil fuel imports to maintain pace with high demand for electricity in the cooling sector. Modest energy efficiency improvements saves India billions of US dollars annually, several scores of Gigawatts (GW) of electricity, improved health outcomes in terms of reduced mortality and respiratory diseases and also indirect social impacts from improved health. Thus this paper sheds light on these benefits and establishing a clear unmistakable link between these energy efficiency improvements and health.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.227
Teacher spread0.204 · 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 teacher head, not a consensus.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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