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Record W2162664006 · doi:10.1111/gcbb.12063

Implications of emissions timing on the cost‐effectiveness of greenhouse gas mitigation strategies: application to forest bioenergy systems

2013· article· en· W2162664006 on OpenAlexafffund
Jon McKechnie, Heather L. MacLean

Bibliographic record

VenueGCB Bioenergy · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoMinistry of Natural Resources
KeywordsGreenhouse gasEnvironmental scienceCarbon neutralityClimate changeBioenergyBiomass (ecology)Climate change mitigationTime horizonNatural resource economicsEnvironmental economicsBiofuelEconomicsEcologyEngineeringWaste management

Abstract

fetched live from OpenAlex

Abstract Conventional cost‐effectiveness calculations ignore the implications of greenhouse gas (GHG) emissions timing and thus may not properly inform decision‐makers in the efficient allocation of resources to mitigate climate change. To begin to address this disconnect with climate change science, we modify the conventional cost‐effectiveness approach to account for emissions timing. GHG emissions flows occurring over time are translated into an ‘Equivalent Present Emission’ based on radiative forcing, enabling a comparison of system costs and emissions on a consistent present time basis. We apply this ‘Present Cost‐Effectiveness’ method to case studies of biomass‐based electricity generation (biomass co‐firing with coal, biomass cogeneration) to evaluate implications of forest carbon trade‐offs on the cost‐effectiveness of emission reductions. Bioenergy production from forest biomass can reduce forest carbon stocks, an immediate emissions source that contributes to atmospheric greenhouse gases. Forest carbon impacts thereby lessen emission reductions in the near‐term relative to the assumption of biomass ‘carbon neutrality’, resulting in higher costs of emission reductions when emissions timing is considered. In contrast, conventional cost‐effectiveness approaches implicitly evaluate strategies over an infinite analytical time horizon, underestimating nearer term emissions reduction costs and failing to identify pathways that can most efficiently contribute to climate change mitigation objectives over shorter time spans (e.g. up to 100 years). While providing only a simple representation of the climate change implications of emissions timing, the Present Cost‐Effectiveness method provides a straightforward approach to assessing the cost‐effectiveness of emission reductions associated with any climate change mitigation strategy where future GHG reductions require significant initial capital investment or increase near‐term emissions. Timing is a critical factor in determining the attractiveness of any investment; accounting for emissions timing can better inform decisions related to the merit of alternative resource uses to meet near‐, mid‐, and long‐term climate change mitigation objectives.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.275
Teacher spread0.190 · 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 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

Citations8
Published2013
Admission routes2
Has abstractyes

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