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Record W7028356975

Financial Comparison of Incineration and Landfill Projects in Canada

2020· dissertation· en· W7028356975 on OpenAlexaboutno aff

Bibliographic record

VenueEastern Mediterranean University Institutional Repository (Eastern Mediterranean University) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsIncinerationMunicipal solid wasteLoanPer capitaPopulationInvestment (military)DebtMobile incinerator
DOInot available

Abstract

fetched live from OpenAlex

How everyday generated waste is managed has become a crucial part of municipal management. In many parts of the world, waste generation is reaching unprecedented levels as population and urbanization increases. How to manage municipal solid waste is even more of a problem in a country like Canada which ranks second in per capita waste generation, only behind the United States. Residual municipal solid waste mostly goes either directly to landfill or to the incineration plant. These waste management practices have generated debates from various perspectives. Most available literature scrutinize the competing waste management practices from economic, sustainability, and environmental perspectives.\nThis thesis aims to compare these competing waste management practices in Canada as business from the perspective of a private investor. It employs a cash-based benefit-cost analysis to analyze the financial return to the owner. It also analyzes the projects from the perspective of the bank that will be approached for an investment loan.\nThe landfill project returned an NPV of C$ 23.62 million to the project owner while the incineration project resulted in a negative NPV, causing a loss of C$141.1 million below the prevailing 8% discount rate. Also, with the banker’s analysis of both projects, the bank will be reluctant to give a loan to the incineration project due to the negative NPV return and inability to repay its debt unlike the landfill project.\nKeywords: financial analysis, waste management, landfill, incineration

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.211
Teacher spread0.189 · 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.

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
Published2020
Admission routes1
Has abstractyes

Explore more

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