MétaCan
Menu
Back to cohort
Record W2305317181 · doi:10.31357/fesympo.v20i0.2583

Urban Growth and Climate Change Strategies for Effective Mitigation and Adaptation

2015· article· en· W2305317181 on OpenAlexaffabout
H. Ranasinghe, H.W. Gammanpila

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.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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

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 designTheoretical or conceptual
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
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of International Forestry and Environment SymposiumSame topicScience and Climate StudiesFrench-language works237,207