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Record W2125434571

MODELING CLIMATE POLICY: ADDRESSING THE CHALLENGES OF POLICY EFFECTIVENESS AND POLITICAL ACCEPTABILITY

2011· dissertation· en· W2125434571 on OpenAlexaboutno aff
Nicholas Rivers

Bibliographic record

VenueSummit (Simon Fraser University) · 2011
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeEconomicsComputable general equilibriumGreenhouse gasPublic economicsClimate change mitigationRevenuePolicy analysisEconomic impact analysisNatural resource economicsPolitical economy of climate changeEconomic policyMacroeconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Reducing greenhouse gas emissions by a substantial amount will require aggressive climate change policies.Policy makers and the public are concerned that such policies could be associated with negative economic impacts, such as reduction in the growth rate of economic output, loss of international competitiveness, and concentration of costs amongst vulnerable demographic groups, regions, or economic sectors.The aim of this thesis is to show that the design of climate change policy has a substantial bearing on such economic impacts, to the extent that policy makers can effectively choose many of the likely economic impacts of a particular climate change policy through careful design.Conversely, inattention during climate change policy design can lead to undesirable economic impacts.The analysis is conducted with a series of computable general equilibrium models as well as an econometric model.These models are applied to examine both proposed and existing climate change and energy efficiency policies.Several findings emerge from the analysis.First, so-called 'intensity-based' climate change policies, which have been proposed in Canada for nearly a decade but which have met with much criticism, may be useful in promoting economic growth and maintaining international competitiveness.Second, under unilateral application of climate change policy, the international competitiveness of energy-intensive industries in developed countries is likely to be worsened.However, several policy mechanisms are available that substantially mitigate this loss in international competitiveness.Third, climate change policies are unlikely to result in a more unequal distribution of income in society, unless revenues from the policy are allocated in a equalityworsening manner.And fourth, past energy efficiency subsidies on average do not appear to have been cost-effective in reducing energy consumption.iii Although writing a thesis is an individual (and sometimes isolating) activity, I have a number of people to thank.First, my thanks go to Mark, whose frequent encouragement over a few years finally led me to enroll in the PhD program at REM.Although I was uncertain at first, the decision was the right one in retrospect.So Mark, thanks for pushing me in this direction, and for the continual encouragement you've given me since.I have learned a lot from my time as your student.Next, thanks to the rest of the research group, and especially Jotham and Chris, who I know the best.I've enjoyed our conversations, many arguments, and especially the frequent laughs.I've also enjoyed a nice big desk downtown for the last few years.I can't imagine any office being quite as fun (or as loud) without you guys around.And finally, thanks to Simone.You supported my decision to come to school for a PhD without second thought, have listened to me give practice talks while pacing in our living room, and have read boring papers and chapters without complaint.But most of all, you've been happy to go on long walks and chat at the end of the day.Lots of love and many thanks

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)0.010
metaresearch head score (Gemma)0.034
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: none
Teacher disagreement score0.026
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.004
Scholarly communication0.0080.008
Open science0.0030.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.100
GPT teacher head0.280
Teacher spread0.180 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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