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Record W4394931741 · doi:10.6007/ijarems/v13-i2/21244

Waste Management System- A Comparative Study of Waste Management Systems in Malaysia and Canada

2024· article· en· W4394931741 on OpenAlexaboutno aff
Syed Raziq Kamal Syed Nasir, Yusnita Yusof

Bibliographic record

VenueInternational Journal of Academic Research in Economics and Management Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
FundersUniversiti Sultan Zainal Abidin
KeywordsBusinessManagement systemEnvironmental planningWaste managementEnvironmental resource managementOperations managementEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Minimizing waste production is a top priority in waste management, as it has the least adverse impact on environmental sustainability by reducing materials entering solid waste or recycling streams. This study offers a comprehensive overview of Canada's waste management landscape, serving as a model for Malaysia. The analysis covers policies, waste diversion programs, reduction initiatives, disposal practices, and energy-from-waste projects. Many Canadian regions show promising possibilities in waste policy frameworks, suggesting the need for explicit waste disposal targets to enhance action on reduction and diversion. For construction, renovation, and demolition (CRD), individual producer responsibility is advocated over collective Extended Producer Responsibility (EPR) due to industry distinctiveness. In Malaysia, dwindling disposal sites prompt the study, aiming to overcome challenges by assessing government recycling initiatives and addressing public indifference. While Malaysia faces disposal site shortages, the study recommends the adoption of successful Canadian waste management strategies. The article provides an insightful analysis of Municipal Waste Management (MWM) issues in Malaysia, offering a roadmap for both countries to navigate challenges and implement effective waste management practices.

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)0.005
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
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 head0.064
GPT teacher head0.360
Teacher spread0.296 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Academic Research in Economics and Management SciencesSame topicMunicipal Solid Waste ManagementFrench-language works237,207