MétaCan
Menu
← Back to cohort
Record W6922236412 · doi:10.11575/prism/37694

Alberta in the Age of Renewable Power: Policy Lessons from Germany and Sweden

2019· other· en· W6922236412 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Renewable energyMains electricityWork (physics)Electricity

Abstract

fetched live from OpenAlex

In an increasingly carbon-constrained society, governments across the world have designed policies to support the development of renewable electricity. In particular, Germany and Sweden are world leaders in the development of renewable electricity. In contrast, the province of Alberta has limited experience creating a policy environment that encourages renewable electricity generation. This capstone project explores the policy lessons that Alberta can take from Germany and Sweden to foster the development of renewable electricity. By incorporating lessons learned from Germany and Sweden, the Alberta government could adopt new policies that increase the proportion of electricity derived from renewable sources. This paper is arranged into four chapters. The first chapter provides an overview of Alberta, Sweden, and Germany’s past and present renewable electricity policies. The second chapter analyzes each jurisdiction’s current policy according to four criteria: 1) effectiveness, as quantified through the compound annual growth rate in renewable electricity capacity or generation; 2) diversity of actors, as evaluated through any special provisions that promote the participation of companies of varying sizes; 3) diversity of technologies, through an analysis of the number of renewable technologies able to secure support under each program; and 4) each program’s impact on household electricity costs, as measured by the compound annual growth rate in the size of the electricity surcharge as a share of household electricity costs/kWh. The third chapter compares public acceptance of renewable energy in each region through an analysis of public opinion polls. Finally, the fourth chapter summarizes the policy lessons Alberta can take from Germany and Sweden to foster the development of renewable electricity. There are four lessons Alberta can take from Germany and Sweden. First, as seen in Germany, the government's ability to anticipate changes required to integrate renewables into the electricity grid may limit the effectiveness of Alberta's future renewable policy. Second, the Alberta government could improve future policy by making special provisions to promote a diversity of actors; however, Alberta can learn from the overwhelming participation of small actors in Germany’s auctions by limiting their future provisions to those that provide a level playing field for all actors. Third, for Alberta to encourage a diverse range of technologies while still promoting the most cost-effective electricity production, the province could implement a technology-neutral policy first (as seen in Sweden), followed by a transition to a technology-specific policy (as seen in Germany). Lastly, if Alberta strives to become a large-scale producer of renewable electricity, it may have to impose an electricity surcharge on consumers; however, it is likely the surcharge will stabilize as Alberta’s renewable sector matures, as seen in Germany and Sweden. In brief, this capstone provides the foundational knowledge required to understand renewable electricity policy in Alberta, Sweden, and Germany. This paper also offers specific policy lessons that Alberta may apply to keep pace with the global push towards a clean and renewable power sector.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.112
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0030.002
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 head0.040
GPT teacher head0.344
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueOpen MIND→French-language works237,207→