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Record W3112844264 · doi:10.1177/2455747120923557

Cities and Greenhouse Gas Emissions: Moving Forward

2020· article· en· W3112844264 on OpenAlexaffabout
Daniel Hoornweg, Lorraine Sugar, Claudia Lorena Trejos Gómez

Bibliographic record

VenueUrbanisation · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGreenhouse gasPer capitaMetropolitan areaCarbon dioxide equivalentAgricultural economicsConsumption (sociology)Natural resource economicsTonneGeographyEnvironmental scienceBusinessEnvironmental protectionEconomic growthEconomicsPopulation

Abstract

fetched live from OpenAlex

Cities are blamed for the majority of greenhouse gas (GHG) emissions. So too are more affluent, highly urbanised countries. If all production- and consumption-based emissions that result from lifestyle and purchasing habits are included, urban residents and their associated affluence likely account for more than 80 per cent of the world’s GHG emissions. Attribution of GHG emissions should be refined. Apportioning responsibility can be misguided, as recent literature demonstrates that residents of denser city centres can emit half the GHG emissions of their suburban neighbours. It also fails to capture the enormous disparities within and across cities as emissions are lowest for poor cities and particularly low for the urban poor. This article presents a detailed analysis of per capita GHG emissions for several large cities and a review of per capita emissions for 100 cities for which peer-reviewed studies are available. This highlights how average per capita GHG emissions for cities vary from more than 15 tonnes of carbon dioxide equivalent (tCO 2 e) (Sydney, Calgary, Stuttgart and several major US cities) to less than half a tonne (various cities in Nepal, India and Bangladesh). The article discusses where GHG emissions arise and where mitigation efforts may be most effective. It illustrates the need to obtain comparable estimates at city level and the importance of defining the scope of the analysis. Emissions for Toronto are presented at a neighbourhood level, city core level and metropolitan area level, and these are compared with provincial and national per capita totals. This shows that GHG emissions can vary noticeably for the same resident of a city or country, depending on whether these are production- or consumption-based values. The methodologies and results presented form important inputs for policy development across urban sectors. The article highlights the benefits and drawbacks of apportioning GHG emissions (and solid waste generation) per person. A strong correlation between high rates of GHG emissions and solid waste generation is presented. Policies that address both in concert may be more effective as they are both largely by-products of lifestyles.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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

Citations30
Published2020
Admission routes2
Has abstractyes

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