Introduction to <i>Climate Change Adaptation in New York City: Building a Risk Management Response</i>
Bibliographic record
Abstract
Climate change has the potential to impact everyday life in New York City.Environmental conditions as we experience them today will shift, exposing the city and its residents to new hazards and heightened risks.We will be challenged by increasing temperatures, changes in precipitation patterns, rising sea levels, and more intense and frequent extreme events.While mitigation actions that reduce greenhouse gas emissions will help to decrease the magnitude and impact of future changes, they will not prevent climate change from occurring altogether.Given the impacts of climate change and the high costs and long-term planning needed to adapt effectively, investment can be considered today to begin the adaptation process.Taking action now will limit damages and costs through the coming decades and, in many cases, can provide near-term benefits and operational cost savings.By recognizing the risks posed by climate change early, in some cases decades before impacts are likely to be felt, the city will be able to develop incremental actions to increase its climate resilience.This report, which is part of the New York City Panel on Climate Change's (NPCC) overall effort to advise the City on climate change, outlines the need for early and ongoing adaptation actions in New York and suggests approaches to create a practical adaptation program.A report issued by the NPCC in February 2009 projected that New York City may face a 4-7.5 • F increase in its mean annual temperature, a 5-10% increase in its baseline rainfall, and at least 12-23 inches of sea level rise by the end of the century.Sea level rise may be much greater if accelerated ice melt continues in polar regions.These climate changes will affect many aspects of the city's infrastructure, such as increased strain on materials; heightened energy demand; more frequent and more extensive street, basement, and sewer flooding; and permanent inundation of low-lying areas and wetlands.At the same time, New York has opportunities to develop responsive climate change adaptation measures, both in the near-and long-term, that protect its citizens and the infrastructure on which they depend.The central question is, how can the city marshal its capacity to devise and implement a robust climate change adaptation strategy?To support these efforts, which are a part of New York City's long-term sustainability plan (PlaNYC), Mayor Michael Bloomberg convened a panel of climate change and impact scientists, university scholars, and private-sector experts to advise on issues related to climate change and adaptation.Funded through a grant from the Rockefeller Foundation and modeled on the Intergovernmental Panel on Climate Change (IPCC), the NPCC was launched in August 2008.As part of its activities, the NPCC has developed tools to assist the New York City Climate Change Adaptation Task Force, which was convened to develop a coordinated adaptation plan for the city's critical infrastructure and consists of over 40 public-and private-sector stakeholders.Development and implementation of the Task Force process was done in collaboration with the Boston Consulting Group.The City's overall adaptation plan is being developed by the Mayor's Office of Long-Term Planning and Sustainability. Climate change adaptation as a challenge and opportunity for citiesClimate change is now under way, and every day more people recognize it as a major concern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".