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Record W2097777098 · doi:10.5539/eer.v5n2p16

A Sustainable Approach to the Harmonization of Electric Power Availability with the Mining Industry in Africa: a Case Study of Mozambique

2015· article· en· W2097777098 on OpenAlexvenueno aff
Ryunosuke Kikuchi

Bibliographic record

VenueEnergy and Environment Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentHarmonizationBusinessCoalEnergy supplySustainabilityInvestment (military)Natural resource economicsCoal miningEnvironmental economicsPoliticsEconomicsEnergy (signal processing)EngineeringWaste managementPolitical science

Abstract

fetched live from OpenAlex

It remains for a sustainability study to consider how to meet social needs for energy services in the realization of sustainable development, because globally about 2 billion people still have no acess to modern energy. It is therefore necessary to draw attention to energy access - w million deaths annually are associated with the indoor burning of solid fuels. Energy-supply infrastructure globally needs a cumulative investment of US$25.6 trillion for the period 2008-2030, and African portion of this investment is estimated at US$454 billion. Power generation that is dependent on the mining industry seems to be a worthwhile subject for considering the combination of energy availability with national development, so this strategy is discussed, with the focus on a case study of Mozambique: it can be estimated that self-supply system in the mining industry actually export a certain amount of power to national and regional markets, and they may be an opportunity to generate low-cost power through the use of discard coal from coking coal export operations. However, Mozambique should prevent discard coal from becoming a hazardosu load; mercury is on of the most toxic element in coal and its by-products. Political strategies that solove a socio-economic problem but cause an eneviornmental one should be avoided if sustainable development is to be properly realized.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.053
GPT teacher head0.267
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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