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Record W2295199635

Ontario’s Electricity Supply Industry After the Restructuring: An Economic and Environmental Impact Analysis

2015· dissertation· en· W2295199635 on OpenAlexaboutno aff
Lawrence Gluck

Bibliographic record

VenueYorkSpace (York University) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringElectricityMains electricityBusinessElectric power industryNatural resource economicsEngineeringEconomicsFinanceElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The Government of Ontario set out to restructure Ontario’s electricity industry in the late 1990s. Through the enactment of the Energy Competition Act, 1998 and the subsequent Electricity Restructuring Act, 2004, Ontario’s electricity sector changed from a traditional “public utility” model (i.e. a state-owned vertically-integrated utility) to a “hybrid model”, which includes both regulated and competitive aspects. 
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\nThis thesis paper seeks to answer the question: from an economic and environmental perspective, how have Ontario’s electricity consumers been impacted by changes resulting from the restructuring and post-restructuring policies of government? To answer this question, the prices paid for electricity service (commodity, transmission, and distribution) prior to the restructuring are compared to the prices paid for the same service after the restructuring. The analysis reveals that prices are rising more rapidly in the post-restructuring era. The question becomes what changes in the sector are driving the price increases and are consumers benefitting from these changes? This paper evaluates the changes to the sector resulting from the restructuring, and from other post-restructuring government policies, in a qualitative manner to determine whether consumers are receiving any benefit from these changes. The analysis highlights that some changes have impacted customers positively (i.e. shift to more environmentally-friendly energy sources, conservation, distributor amalgamation, etc.) and other changes simply added costs with no real benefits to consumers (i.e. facilitation of a competitive market for electricity supply, retail electricity markets, etc.).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.919

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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 designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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