MODELING CLIMATE POLICY: ADDRESSING THE CHALLENGES OF POLICY EFFECTIVENESS AND POLITICAL ACCEPTABILITY
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».