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Record W2305317181 · doi:10.31357/fesympo.v20i0.2583

Urban Growth and Climate Change Strategies for Effective Mitigation and Adaptation

2015· article· en· W2305317181 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of International Forestry and Environment Symposium · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicScience and Climate Studies
Canadian institutionsToronto and Region Conservation Authority
Fundersnot available
KeywordsUrbanizationClimate changePopulationUrban climatePopulation growthFlood mythConsumption (sociology)BusinessEnvironmental planningUrban planningNatural resource economicsUrban resilienceGeographyEconomic growthEconomicsCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Between 1950 and 2030, the share of the world‟s population that lives in cities is predicted togrow from 30% to 60%. This urbanization has consequences for the likelihood of climatechange and for the social costs that climate change will impose on the world‟s quality of life.Cities are the engine of capitalist growth. Over time, people move from rural to urban areasas they seek a higher standard of living. In cities, people earn higher incomes and thus havethe financial resources to purchase more consumption products ranging from privatetransportation to larger homes. Urbanization increases the demand for residential andcommercial electricity consumption. Low and middle-income nations now have threequartersof the world‟s urban population. They also have most of the urban population atgreatest risk from the increased intensity and/or frequency of storms, flooding, landslides andheat waves that climate change is bringing or will bring.The need for action by Governments on climate-change adaptation is also urgent – andprobably more urgent than that suggested by the IPCC‟s Fourth Assessment. This paperdetails the high adaptive and mitigative capacities which are infused into urban planning inplanned cities using a case study from Toronto, Canada based on Toronto Green Standards.The main thrust areas highlighted in this paper are the development of innovative methods forreducing storm water flows thus reducing flood hazards, the use of advanced energy efficienttechnologies including renewable energies, development of innovative green spaces such asgreen roofs and designs that will reduce the urban heat island effect. The services provided bythe provincial/municipal governments aided by the private sector in ensuring the protection ofthe urban populations and ecosystems from the adverse consequences of climate change arephenomenal in bringing on success; early warning for hazardous climatic events, rapidemergency response from the police, health service and fire services, all buildingsconforming to building regulations and to health and safety regulations and served by pipedwater, sewers, all-weather roads, electricity and drains 24 hours a day. The cost of suchinfrastructure and services represents a small proportion of income for most citizens whetherpaid direct as service charges or within taxes. For the most part, most citizens engage verylittle in the management of these because it is assumed that government systems will ensureprovision. However there are channels for complaints if needed – for instance localpoliticians or lawyers, ombudsmen, consumer groups and watchdogs. Thus, the vast majorityof urban dwellers are protected from extreme weather without them having to engage in theinstitutions that ensure such protection. In addition to these there are other measures such ascarbon pricing/taxes, incentives for green lifestyles etc. adopted to motivate people to reduceglobal warming emissions.Keywords: Urban planning, Urban growth, Climate change mitigation, Climate change adaptation, Green standards

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.336

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 communication0.0000.001
Open science0.0000.000
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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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