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Record W7001036881

Identifying drivers of reactive nitrogen emissions and innovative nutrient policies using the Canadian geographic context

2023· dissertation· en· W7001036881 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContext (archaeology)NutrientProduction (economics)NitrogenClimate change
DOInot available

Abstract

fetched live from OpenAlex

Excess reactive nitrogen (Nr) in the environment results in serious negative impacts to society including air pollution, eutrophication, and climate change.To better manage Nr there is a need to examine the underlying drivers of Nr emissions and to identify potential areas for intervention.However, gaps remain in our understanding of the drivers of Nr emissions because they are diverse and often complex.In this thesis, I address three specific gaps: accounting for subnational geographic variation, understanding the effects of changes in economic structure on whole-economy Nr emissions, and integrating nutrient-related concerns comprehensively into policy.I undertake a series of analyses to inform these aspects of research into the drivers of Nr emissions and opportunities for policy integration in Canada.These three studies draw on multiple methodological and interdisciplinary approaches, including the development of a novel N footprint model, application of economic decomposition analysis methods, and a policy assessment that uses text and network analysis techniques.First, using my model of Canadian N footprints, I examine the patterns of provincial footprints and how consumption-oriented drivers of Nr emissions vary between provinces in relation to their diverse geographic contexts.I then expand on the footprint accounting perspective by exploring more detailed trends in Nr emissions across Canada over time.I do this by synthesizing data from a variety of sources to assess the role of socioeconomic drivers on whole-economy Nr emissions.My results from these two studies collectively show that provincial total and per capita N footprints and territorial emissions vary considerably based on population, affluence, and the relative economic importance of the fossil fuel and agriculture sectors across Canadian provinces.They also underscore the challenges of attributing Nr emissions for export-oriented economies like Canada's and emphasize the importance of improving emissions intensity and shifting economies toward less Nr intensive sectors.I then explore the nutrient-related policy environment in Canada (i.e., federal and provincial legislative Acts with relevance to nitrogen and phosphorus) using semantic network analysis.My findings from this analysis reveal potential areas for policy integration in nutrient-related legislation in Canada.Finally, I reflect on the results of my three studies from different emissions accounting perspectives, examine how Canadian legislation matches (or does not match) previously identified drivers and sources of Nr emissions, and remark on the overarching geographic and provincial trends identified throughout this dissertation.Despite the difficulties in sustainably managing nutrients at the national scale given conflicting or competing economic, social, and environmental goals, my research demonstrates how the multifaceted nature of nutrient issues provides opportunities for innovative policies to address these challenges.Résumé L'excès d'azote réactif (Ar) dans l'environnement entraîne de graves impacts négatifs sur la société y compris la pollution de l'air, l'eutrophisation et le changement climatique.Pour mieux gérer le Ar, il est nécessaire d'examiner les facteurs sous-jacents des émissions de Ar et d'identifier les domaines potentiels d'intervention.Cependant, il existe des lacunes dans notre compréhension des moteurs des émissions de Ar car ils sont divers et souvent complexes.Dans cette thèse j'aborde trois de ces lacunes: le rôle des variations géographiques infranationales, les effets des changements de structure économique, et l'intégration des politiques de pollution par les nutriments.J'utilise le Canada comme étude de cas pour éclairer ces trois domaines de recherche.Mes études utilisent plusieurs méthodologiques interdisciplinaires, notamment le développement d'un nouveau modèle d'empreinte, l'application de méthodes d'analyse de décomposition économique, et une évaluation des politiques qui utilise des techniques d'analyse de texte et de réseau.Tout d'abord, à l'aide de mon modèle d'empreintes canadiennes d'azote, j'examine les tendances des empreintes provinciales et la façon dont les facteurs d'émissions de Ar axés sur la consommation varient entre les provinces en fonction de leurs divers contextes géographiques.Je développe ensuite la perspective de comptabilisation de l'empreinte en explorant des tendances plus détaillées des émissions à travers le Canada au fil du temps.Je synthétise des données provenant de diverses sources pour évaluer le rôle des moteurs socio-économiques sur les émissions de Ar de l'ensemble de l'économie.Mes résultats de ces deux études montrent que les empreintes provinciales totales et par habitant, et les émissions territoriales varient considérablement en fonction de la population, de la richesse et de l'importance économique relative des secteurs des combustibles fossiles et de l'agriculture dans les provinces canadiennes.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.252
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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes2
Has abstractyes

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