This index is an ongoing study, updated and refined quarterly. Many of the cities studied are now approaching their data collection limits, which brings us to the next phase of development: identifying and collecting data that measures bio-capacity and human anthropogenic footprints in greater depth. This is an iterative process that requires collaboration with all levels of government, academia and industry. The goal is to create tools that give people greater visibility into human impact on the biosphere and identify areas where solutions can have substantial impacts.
All formulas and equations used are published at the bottom of this article and they are further described in detail spreadsheets for each city.
One indicator was removed an one indicator was added.
- Economic Activity was removed due to insufficient data relevance
- Data Freshness was added with all scores set to zero
In this edition the updates listed above were included. Where possible, links to the data updates were added to the downloadable spreadsheets.
- 23 indicator data sets plus 3 calculated indicators were published
- 1250 data points were scored
- 95% data collection rate
- 133 data points were updated since the last release
- 913 source data web links were published
- 50 detailed spreadsheets with score details, data sources, formulae, etc...
- 64 data points were floor scored and 11 were ceiling scored
- Normalized Values are comparative values between zero and one which are ideal for indicator data points. Cities are split into "City Size Categories" then maximum and minimum's are selected from cities within those categories. This allows for the comparison of cities in a meaningful way.
- Floor Scores use the minimum value for indicator data points within a City Size Category. When data is missing for an indicator it is assigned zero, but if that indicator is needed in the calculation of another indicator the floor score is used; Our tests show this method is fair and more accurate.
- Ceiling Scores are used when a city data point is extremely large and if used it would dwarf all other city scores. The next highest score is used to calculate the maximum, then, the city is awarded the maximum score within its City Size Category. Ceilings are caused by unusual circumstances that go beyond the current scoring method and warrant further study.
- Missing data points are replaced with a provincial default where possible. If no provincial statistic exists then the data point is zeroed.
- Indicator weighting is now used for temperature extremes. In future editions weighting will be applied to other indicators so as to reflect their anthropogenic impact more accurately.
- Data Freshness is not scored yet. It is the average date of the newest 50% of data points collected. The format is "YYYY.N" where "N" is in tenths of a year.
Our research shows that smaller cities have smaller eco-footprints which is an inescapable fact due to scale! Larger cities have mass transit infrastructure which is not viable in smaller cities, so classifying cities by size makes comparative analysis possible and fair to all cities studied.
Smaller populations have smaller footprints. They require less space for residential, commercial, industrial, recreation and other human activities; all of which make footprints smaller. Lower is better.
Measured in square km. Larger municipal boundaries create larger city footprints. Annexation and amalgamation are how cities grow in size and this is always done for economic reasons. The exceptions to this rule are Halifax and Saguenay; both of these cities have extensive wilderness reserves inside their boundaries which skews both Municipal Area and Population Density indicators. For this reason they are given ceiling scores for both indicators. Smaller is better.
Protected greenspace areas are subtracted from Municipal Area because it reflects a more realistic density calculation. Higher population densities mean less space is used for more human activity. It is a measurement of footprint efficiency thus making higher density scores more desirable. It is also an indirect measurement of urban sprawl which has become the largest cause of greenspace loss inside city limits. Wilderness Reserve Area's are also included in this calculation. Higher is better.
The percentage of population growth. Slower population growth requires less space to grow into which means smaller footprint growth. City's with negative growth are assigned zero growth because negative growth is currently beyond the scope of this study. Lower is better.
This indicator measures a population's uptake of public transit, cycling and walking commuting habits. It is a percentile measurement where higher scores are better. In this edition no footprint weighting factors were used.
- Personal Automobile weighting factor is ZERO (excluded currently)
- Public Transit weighting factor is 1 (Buses, Trains, Infrastructure Footprints)
- Cycling weighting factor is 1 (Bike Paths, Parking, Infrastructure Footprints)
- Walking weighting factor is 1 (No infrastructure required)
This indicator scores the median driving distance in kilometers for solo automobile commuters travelling to work. Lower is better.
Measures the percentage of the workforce traveling outside the city for employment. This causes heavy traffic and poor air quality. Lower is better.
Measures economic activity in cities and how much it disturbs the ecosystems inside and surrounding cities is subjective. For this reason we removed it. We are considering add a green economic activity indicator in the future.
This indicator scores residential housing footprints. Until cities start publishing their housing statistics we are stuck using Municipal Census data as a proxy. We use a weighting factor to approximate the land use areas. There is a tab in each cities detail spreadsheet listing building type weights. Smaller is better.
It is a measure of Total Particulate Matter (TPM) The substances measured are: Particulate Matter 10 Microns or less, Particulate Matter 2.5 Microns or less, Sulfur Oxides, Nitrogen Oxides, Volatile Organic Compounds and Carbon Monoxide. Lower is better.
Total tonnage of garbage before recycling redirect percentage. Lower is better
Percentage of waste recycled and redirected away from landfills. Higher is better
Total organic matter tonnage collected by city from all sources. Higher is better
Provinces track Greenhouse Gas (GHG) emissions and publish data for the whole province. Cities are now expected to report GHG emissions to provincial and/or federal government agencies and we will use that data as it becomes available. Lower is better.
This is a percentage calculation. Some cities measure their own capacity but most rely on Provincial statistics. Higher is better.
We do not score content and only the existence of an ecological initiatives landing page with links to recycling, green space initiatives, protection programs, etc.. We maintain links to it for all cities in the index. A clear landing page scores higher.
The Actuaries Climate Index™ is an objective measure of observed changes in extreme weather and sea level changes in coastal cities. It is intended to provide a useful monitoring tool of climate trends and will be updated quarterly as data for each meteorological season becomes available. Lower is better.
Parkland connects us to nature and helps build ecological empathy. Parks are also habitats for nature, so the more park space a city has, the more eco-friendly it is. Higher area is better.
More parks mean greater residential accessibility. The closer the proximity to parks, the more likely we are to walk in them and experience nature. Also, they can become wildlife corridors for insects, birds, etc..
Higher is better.
This is what nature is all about! Ecological empathy is cultivated when we regularly visit nature reserves and experience the wildlife and fauna. Higher is better.
It uses the Plant Hardiness Index of each city. The higher the number the more biologically friendly the year round climate is. Higher is better.
The total number of indicators data points collected including unreleased indicator data points. When cities measure and track things, they become visible and gain value which can be included in planning decisions.
Higher is better.
It measures of how current the data is. This is accomplished by averaging the year of data collection dates for each indicator. All scores are ZERO in this edition. Higher is better.
Formulas are further refined in city spreadsheet details
Click scoreboard then click a city name then click download: Detail Spreadsheet
City Size Categories
Population Growth Pressure
Travel to Work by
Driving Distance For Solo Commutes
Workforce Commuting Outside City
(Removed) Economic Activity
Air Pollution Emissions
Solid Waste Tonnage
Organic Waste Tonnage
Domestic Water Usage
Renewable Electrical Capacity
Green Initiatives On Website
(Merged) Temperature Extremes Winter
(Merged) Temperature Extremes Summer
Biological Temperate Zone
(New) Data Freshness
Determines grouping for normalization max / min calculations
Score = 1 - Normalized Population
Score = 1 - Normalized Area
Score = Normalized(Pop. / (Municipal Area - Greenspace Area))
Score = Normalized percentage growth, if < 0 use 0
Score = Sum of Normalized(Percentages x Weighting Factor)
Score = 1 - Normalized Median Driving Distance
Score = 1 - Normalized(Population x Percentage)
Score = Sum of Normalized(GDP,GDP Growth, etc..)
Score = 1 - Normalized sum of (Structure Percentile x Weighting)
Score = 1 - Normalized(sum of Air Pollutants)
Score = 1 - Normalized Garbage Tonnage
Score = Normalized Percentage of Garbage Recycled
Score = Normalized Curbside Composte Tonnage
Score = 1 - Normalized Water Usage
Score = 1 - Normalized City GHG
Score = Renewable Capacity / Total Capacity
Score = 1 for a clear landing page, Otherwise score = 0
Score = | Normalized (Winter Min Monthly Avg) | x 40%
Score = 1 - | Normalized (Summer Max Monthly Avg) | x 60%
Score = 1 - normalized (Actuaries Climate Index)
Score = Normalized (Sum of Parkland Area)
Score = Normalized (Park Count)
Score = Normalized (Sum of Wilderness Area)
Score = (Plant Hardiness Index) / 100
Score = (Indicator Count - Missing Data Points) / 10
Score = ZERO; average(data collection dates)
Score = (Sum of Indicator Scores) x 10