In early 2024 we published a paper looking at the climate impacts of different land-use scenarios (e.g. greenfield sprawl versus intensification) in south-central Ontario. The findings were what most urban planners would expect: if Ontario continues to build lower-density greenfield sprawl it will have worse climate impacts than higher-density intensification. The paper was mostly a regional story, summarized by municipality – and the findings were contextualized relative to current and future regional policy.
As part of this research, there was an interesting data piece on mapping out household-level greenhouse gas (GHG) emissions that didn’t see too much light of day at the time. It was used more as an intermediary step for scenario modelling and for some contextual framing. But we did create the following two graphics from this data. These graphics tell an important story – that residents in denser, centralized, urban areas have far less of an impact on emissions than in more suburban, exurban, and rural areas.
Specifically, to create this map and chart, we compiled and combined data at small geographic units (Census Dissemination Areas) on household energy use (mostly a function of dwelling type) and transportation use (mostly a function based on how far, on average, households drove motor vehicles).
Generally, the larger your dwelling and the further you drive day-to-day, the greater your carbon footprint. Suburban and exurban areas tend to have larger homes, are more car-dependent, and residents tend to work further away from where they live. Similar findings have been observed across U.S. cities.
Recently, I have been interested in the local, nuanced, geographic patterns of this data, especially since I’ve been going back to this work from time to time to support other projects. So I thought it would be worthwhile to create a zoomed-in map showcasing this data, as it directly shows how much better dense, transit-oriented, and walkable neighbourhoods are for the environment compared to low-density, car-centric counterparts within Toronto and neighbouring municipalities.
In the peripheral suburbs, commute distances tend to be longer and there are more large, single-detached homes. But in the outer areas, we do see pockets of lower emissions. These tend to be in older walkable neighbourhoods, such as in central Markham, Brampton, or Oshawa. In these neighbourhoods, even if a number of residents have longer commutes, the overall GHG emissions impact is less due to a greater mix in housing (i.e. not all larger single-family dwellings), shorter distances to non-work destinations, and the availability of other travel modes.
Within Toronto, lower emissions are also correlated with areas with high walkability, fewer detached single-family homes, and good transit accessibility (i.e. proximity to TTC subway routes). The few areas within Toronto that have high emissions are some of the wealthiest in the city, made up of large detached homes and where the vast majority of trips are by car.
Interestingly, there are also relatively well-defined differences between the City of Toronto and adjacent municipalities, and even between neighbourhoods divided by the municipal boundary. This could be partly due to limited transit connections between municipalities, as each operates their own transit agency. Generally, within the City of Toronto transit service is more frequent and better connected to the rapid transit network, whereas in adjacent municipalities routes are less frequent. These differences mirror patterns seen in transit mode share maps, highlighting how service levels and network design can influence travel behaviour and resulting emissions.
It is important to note that this data is based on household energy and daily travel patterns, and does not take into account other factors such as increased material consumption of household goods and furniture associated with dwelling size and wealth, the embodied carbon in building materials used to construct homes, or variations in emissions from nearby retail and commercial development – all of which would increase the numbers in the charts and likely impact spatial patterns on the maps. That said, the urban-suburban patterns would likely persist if we included data on these factors
That’s it for now. If you’re interested in this data and research, check out our paper and feel free to reach out directly by email if you have any questions.
