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Record W1983446435 · doi:10.4155/cmt.10.36

Cutting CO<sub>2</sub>emissions from the US energy sector: meeting a 50% target by 2030

2011· article· en· W1983446435 on OpenAlexaboutno aff
Erika A. Warnatzsch, David Reay

Bibliographic record

VenueCarbon Management · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasClimate change mitigationClimate changeEnergy sectorNatural resource economicsRange (aeronautics)Environmental scienceLegislatureEnergy mixScale (ratio)Business as usualEfficient energy useEnvironmental economicsEconomicsElectricity generationEngineeringPower (physics)Geography

Abstract

fetched live from OpenAlex

to be addressed on a global scale in order to reduce emissions.These target areas are power generation, industry and manufacturing, mobility, buildings and consumer choices [104].In addition to looking at these global sectors, the report also looks at regional trends in four of the world's large economic regions: the USA and Canada, EU-25, China and Japan.WBCSD outlines many potential mitigation and abatement strategies as well as the possible barriers that may arise.The key indicators that this report uses for Canada and the USA are: growth (of energy, GDP and emissions), sources of power generation (i.e., fossil fuel, nuclear and renewable) and mobility (i.e., distance traveled, energy efficiency and type of fuel).WBCSD conclude that by controlling and changing these three indicators, combined USA and Canada carbon (C) emissions would drop to 0.9 Gt C in 2050 compared with the 2002 emissions of 1.76 Gt C, a 50% decrease.This study has two central objectives: to examine the main sources of CO 2 in the US energy sector and to put forward potential mitigation strategies for these emissions.More specifically, this study aims to determine strategies that will result in a reduction of cumulative CO 2 emissions from the US energy sector by at least 50% below 2005-levels by 2030.These strategies must not only be currently technologically viable but also economically, socially and politically acceptable. Experimental Current CO 2 emissions from the US energy sectorDefinition of the emissions from the energy sector Before the emissions from the energy sector can be analyzed, it is first necessary to define what the energy sector consists of and which emissions will be included.Hereafter, the 'energy sector' includes all activities requiring the use of any form of fuel to produce heat or work.Fuel sources include: coal, petroleum, natural gas, nuclear power and renewable energy sources.This study only considers CO 2 emissions and not the emissions from all GHGs or other agents of climate forcing.However, this particular GHG makes up 96% of the total emissions from the topic sector -the energy sector -and therefore mitigation strategies targeting CO 2 have the potential to achieve a large impact on overall emissions [1]. Analysis of current CO 2 emissions from the US energy sector Key termsKyoto agreement: International agreement to reduce GHG emissions from developed world nations under the United Nations Framework Convention on Climate Change, introduced in 1997 with a compliance period of 2008-2012.Energy sector: The US energy sector includes all energy production for consumption in the transportation, residential, commercial, industrial and electric utilities sectors.Renewable energy: Energy from a source that replenishes through natural phenomena on the scale of a human lifetime and is virtually inexhaustible.Direct emissions: Emissions that result from owned or leased sources.End-user: Sector, industry or individual that purchases or produces energy for its own consumption and not for resale.Census region and division: Groupings of states based on geographical, historical or cultural similarities for the purpose of data presentation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.963

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 studies0.0000.000
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 head0.061
GPT teacher head0.212
Teacher spread0.151 · 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 designNot applicable
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
Published2011
Admission routes1
Has abstractyes

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