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Record W4289985716 · doi:10.5539/mas.v16n4p1

Climate Change Mitigation Technologies: Prospects and Challenges

2022· article· en· W4289985716 on OpenAlexvenueno aff
Bernard Arogyaswamy

Bibliographic record

VenueModern Applied Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyFossil fuelEnvironmental economicsGreenhouse gasClimate change mitigationClimate changeNatural resource economicsBusinessEnvironmental scienceEmerging technologiesEnvironmental resource managementComputer scienceEconomicsEngineeringEcologyWaste management

Abstract

fetched live from OpenAlex

One of the significant, perhaps momentous, developments in the effort to mitigate climate change has been the convergence around the target of Net Zero Emissions (NZE). The path to NZE envisions a massive increase in the installed capacity of renewable energies (REs)accompanied by a radical reduction in fossil energies. The paper outlines the main technologies required to make renewables a reliable alternative. Storage mechanisms such as batteries, hydrogen, and pumped storage are reviewed. Major impediments to the replacement of fossil fuels with renewable sources include technological, political, and social lock ins. Since NZE emissions are unlikely to be realized without installing negative emission devices, carbon dioxide removal methods and their concomitant challenges are discussed. Demand side mitigation in the transport, buildings, and industry sectors, relevant technological solutions, and adjustments to social preferences are briefly addressed. In addition to analyzing the challenges to realize an NZE world, the paper cautions against a single metric focus while ignoring concerns over adaptation, rising inequalities, water and food availability, and biodiversity. The financial implications of the various mitigation technologies are highlighted along with the need to transfer technologies and funds to developing nations to avoid and/or reduce emissions.   

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.027
GPT teacher head0.208
Teacher spread0.181 · 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 designOther design
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

Citations1
Published2022
Admission routes1
Has abstractyes

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