Challenges in Measuring Sustainability and the Quality of Life –The Case of a Small Canadian City
Bibliographic record
Abstract
Many set of indicators are currently available to measure sustainability and the quality of life or well-being of the people. However, choice of the set of indicators can widely affect the ranking of a place in terms of the quality of life of the people. For example, Costa Rica ranks 1stin terms of the 2012 Happy Planet Index, but it ranks 69th in terms of the Human Development Index. The goal of this paper is to design a robust mechanism by combining several methodologies to assess the quality of life in a place. We used a small Canadian city, Kamloops, for our case study. This 311 km 2 city in Interior British Columbia has a population of about 87 000. MoneySense ranked it 44th and 64th best places to live in Canada in 2012 and 2013 respectively. The index that is too sensitive to annual fluctuations of a few indicators could be good for understanding short-run quality of life changes, but might not necessarily reflect the level of well-being and its sustainability in a place. In this paper, we have defined a methodology to select an appropriate set of indicators for Kamloops that would measure the average quality of life and its sustainability. With hundreds of global and regional quality of life and sustainability indicator initiatives, and many available methodologies, it is important for any city to choose the appropriate indicators and evaluation methods. Economists often use estimated monetary values of desired indicators to compute Genuine Progress Indicator, or such other quality of life or composite sustainability indices. Natural scientists, on the other hand, use only a few physical indicators for environmental sustainability assessment. We believe that both monetary and physical indicators are important components of any quality of life index, and therefore, have to be part of a comprehensive sustainability plan. A data aggregation method has been suggested in this paper for computing relatively more composite indices from the large number of quality of life and sustainability indicators. The absence of reliable and adequate data is a serious challenge in measuring the desired indicators. Due to data constraints, a complete assessment of the average quality of life and its sustainability in Kamloops is not possible at this time using our suggested methodology. However, the proposed methodology and the data compiled for this study are steps forward to a complete and systematic accounting of well-being, happiness, income, wealth, and sustainability indices for Kamloops. We have assessed the current Kamloops Sustainability Plan based on our proposed criteria. It is expected that this study will make different stakeholders in the City of Kamloops re-think about their sustainability plans, and will help contribute to make Kamloops a better place to live. The methodology recommended in this paper is general enough to be used for quality of life and sustainability assessment in any place.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".