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by Sylvain Ganter, Todd Crawford, Christine Irwin, Vanessa Robichaud and Alejandro DeMaio-Sukic (Fisheries and Oceans Canada) and Jennie Wang, Jessica Andrews and Hugo Larocque (Statistics Canada) Show
Release date: July 19, 2021
More information PDF version Skip to text On this page Text begins HighlightsThis article examines the economic contributions provided by Canada’s marine sector, many of which depend on ocean ecosystems. The main findings include:
IntroductionActivities dependent on the ocean make a substantial contribution to the Canadian economy. Fisheries and naval installations provided a rationale for the first European settlement. Fish processing, shipbuilding, and marine transportation followed, providing a basis for economic development and growth on all three of Canada's coasts. These ocean activities defined settlement patterns that continue to this day. New marine economic activities emerged over the years including tourism, aquaculture, bio-technologies, specialized manufacturing, and offshore oil and gas exploration and development. A wide range of service industries support these activities. Together, they create substantial opportunities as well as challenges, emerging from increased and oftentimes competing uses of ocean space, including the need to protect and conserve Canada’s ecosystems and biodiversity. Start of text box
This article provides estimates of the economic contribution of marine sectors in Canada and describes some of the environmental challenges faced by these sectors. The article is based in part on a report prepared for Fisheries and Oceans Canada by Gardner Pinfold, "Economic Impact of Marine Related Activities in Canada". This information is also available in the Marine sectors in Canada summary tables (https://www.dfo-mpo.gc.ca/stats/maritime-eng.htm). Economic estimates are for the years 2014 to 2018, the most recent years for which source data is available.Note 3 These estimates cover all major private sector industries with a direct dependence on the oceans (extractive and non-extractive uses), as well as activities of public sectorNote 4 organizations with responsibilities for safety, managing ocean activities and research. Statistics Canada's Inter-provincial Input-Output Model (IO model)Note 5 was used to estimate the economic contribution of marine industries to the Canadian economy as measured by gross domestic product (GDP) and employment. This enables a meaningful comparison across industries and geographies. The selection of marine industries was based on previous work by Gardner Pinfold.Note 6 Gross value of output and/or expenditures data were collected for each marine industry, to which the corresponding IO multipliers were applied.Note 7 An exception to this approach was made for marine tourism and recreation, National Defence, and Fisheries and Oceans, where commodity-level expenditures were provided to Statistics Canada for a customized run of the IO model to obtain the related economic impact. GDP impacts represent an industry's contribution to Canada’s GDP. More specifically, the GDP of an industry consists of the value it adds to production of output by applying labour and capital to purchased inputs. GDP is calculated by subtracting from total revenues (or output) of a given industry, the costs of material, energy, and purchased services (e.g. accounting and legal services retained from outside the given industry). Employment is measured in terms of total number of full-time, part-time and seasonal jobs. Economic impacts are driven by direct, indirect and induced demand, expressed in terms of industry and consumer purchases of goods and services. The sum of impacts flowing from each level of demand gives the overall economic impact of marine sectors in Canada:
When two marine industries are linked by a supply chain, such as commercial fishing and fish and seafood processing or marine transportation and support activities for marine transportation, there is a risk of double counting economic impacts, as one industry generates demand for the outputs of the linked industry. For example, fish and seafood processing generates demand for the outputs of the commercial fishing industry, causing the indirect impacts of the fish and seafood processing industry to double count at least a portion of the direct and indirect impacts corresponding to the commercial fishing industry. The existence of double counting between marine industries was assessed using the IO Supply and Use Tables.Note 8 Double counting of economic impacts between commercial fishing and fish and seafood processing in the seafood sector, and between marine transportation and support activities for marine transportation in the transportation sector were removed in proportion to their respective IO linkages. Measuring the economic contribution of marine sectors presents many challenges. The standard classification of industries (NAICS – North American Industry Classification System) does not separate out the marine component for many industries, such as tourism. In such cases, the marine component was extracted by focusing on coastal provinces and territories, which could result in some inaccuracies. Suppressed data due to confidentiality constraints was remedied by employing reasonable proxies to distribute national estimates among provinces and territories or by applying annual growth rates. The latter were also used in cases where data was not readily available or had been discontinued. Canada has the longest coastline in the world and its exclusive economic zone (EEZ) extends across 5.75 million km2 of the Pacific, Arctic and Atlantic oceans. In 2016, 4.8 million Canadians, 13.5% of the population, lived within 10 km of the coast and a further 4.0% lived within 100 km of the coast (Table 1). The coastal share of population was highest in Prince Edward Island, Nunavut, Newfoundland and Labrador and Nova Scotia. People living near the coast are most able to benefit from the ocean and its resources, through employment and participation in recreational activities. However, they, and many others, enjoy the ecosystem services provided by the ocean including fish and seafood, climate regulation, carbon storage services, as well as opportunities for tourism and recreation.
Economic contribution of marine sectorsIn 2018, marine sectors in Canada generated 298,333 jobs and contributed $36.1 billion in GDP to Canada’s economy (Charts 1 and 2). A significant proportion of the employment and GDP was created in industries that are directly dedicated to the use or extraction of marine resources in Canada (direct impacts): 143,608 jobs and $20.0 billion in GDP. An additional 88,859 jobs and $9.1 billion in GDP were created in upstream industries that supply those directly involved in using and extracting marine resources (indirect impacts). Induced impacts, those corresponding to economic activity triggered by the expenditure of labour incomes generated by marine industries, contributed to creating 65,867 jobs and $7.0 billion in GDP 
Charts 3 and 4 provide breakdowns of total marine sector employment and GDP into its component industries. The economic activity of marine sectors was led by private sector industries, which contributed 79.6% of jobs (237,482) and 83.1% of GDP ($30.0 billion). The industries that generated the most employment were transportation (68,762), fishing and seafood (64,996), and tourism and recreation (63,587). The industries that generated the most GDP were fishing and seafood ($7.6 billion), oil and gas ($7.5 billion), and transportation ($7.5 billion). Canada’s public sectorNote 9 contributed the remaining 20.4% of employment (60,851) and 16.9% of GDP ($6.1 billion, Chart 4). The federal departments of National Defence and Fisheries and Oceans Canada (including the Canadian Coast Guard) contributed most of the jobs (26,054 and 21,476 respectively) and of the GDP ($2.6 billion and $2.2 billion respectively) generated by the public sector (Annex Table 1 and Annex Table 2).
The overall contribution of marine sectors to the Canadian economy represented 1.6% of national employment and GDP (Annex Table 3 and Annex Table 4). The influence and significance of marine sectors on the economy of coastal provinces and territories is much larger, particularly in Atlantic Canada where marine sectors accounted for substantial shares of total provincial employment in Newfoundland and Labrador (16.8%), Nova Scotia (13.3%), and Prince Edward Island (9.3%). The contribution of marine sectors to total provincial GDP was also particularly high in Newfoundland and Labrador (30.0%), Nova Scotia (13.5%), and Prince Edward Island (10.3%). Overall, marine sectors contributed 3.8% of Canada’s marine regions’ employment and 4.1% of the country’s marine regions’ GDP (Annex Table 3 and Annex Table 4). Between 2014 and 2018, employment grew by 11.6% from 267,278 to 298,333 (Annex Table 5, Chart 5), compared to an increase in GDP of 12.3%, from $32.1 billion in 2014 to $36.1 billion in 2018 (Annex Table 6). The manufacturing and construction and the transportation industries exhibited the strongest employment growth over the period, increasing by 21.9% and 20.9% respectively. In terms of GDP, the strongest growth was in the manufacturing and construction industry (39.4%), followed by fishing and seafood (32.2%), and tourism and recreation (29.1%). Among the major industry groupings, only offshore oil and gas posted a decline in GDP (-21.5%). This substantial decline in GDP was caused in part by a significant drop (-28.3%) in oil prices, from an average of US$99.02 in 2014 to $US 71.06 in 2018.Note 10
Measured as a share of total employment and GDP, the contribution of marine sectors to the Canadian economy remained relatively stable from 2014 to 2018. The share of employment increased slightly (6.5%) from 1.5% in 2014 to 1.6% in 2018, while the share of total GDP, meanwhile, remained stable at 1.6% in both years. The main driver of this trend was the offshore oil and gas sector, which experienced a substantial drop in GDP, while employment dropped to a lesser extent. Ocean ecosystemsIn addition to the economic contributions provided by the marine sector, consideration must be given to the importance of protecting the ocean environment and biodiversity. In 2010, Canada agreed to meet 20 global biodiversity targets by 2020, including Aichi Target 11, to conserve at least 10% of coastal and marine areasNote 11 and has now set a target of reaching 30% by 2030.Note 12 Protecting Canada’s ocean territory will contribute to species resilience and will help support sustainable industries and coastal communities and adaptation to future pressures.Note 13 In 2019, 13.8% or 795,000 km2 of Canada’s marine areas was conserved through a variety of measures including marine protected areas (6.1%), other effective marine conserved areas (4.9%), national marine conservation areas (2.0%) and national parks (0.2%) (Table 2).Note 14 The newest addition, Tuvaijuituk Marine Protected Area, is also the largest, covering 319,411 km2 of the Arctic Ocean off the coast of Ellesmere Island.
Climate change poses a particular challenge to Canada’s ocean ecosystems. As ocean waters warm,Note 15 fish populations are expected to migrate northwardsNote 16 and new spawning grounds may need protected status. The abundance and mix of species is expected to change, affecting fisheries. Canadian waters are also experiencing changes in ocean chemistry. The ocean has absorbed more than a quarter of the carbon dioxide produced by human activities, increasing the acidity of ocean water.Note 17 This increased acidity corrodes the shells and exoskeletons of molluscs and crustaceans, may impact mortality rates of young fish, and may increase the impact of harmful algal blooms.Note 18 Climate change will potentially also result in increased storm events and larger waves, impacting many marine industries. Arctic waters in particular are experiencing larger waves as sea ice melts which in turn is helping to speed up the retreat of sea ice.Note 19 Annex A: Data sourcesFishing and seafoodCommercial fishing: Atlantic and Pacific Regions: Fisheries and Oceans Canada (DFO), commercial sea fisheries landings, Canada Provincial-Values, (http://www.dfo-mpo.gc.ca/stats/commercial/sea-maritimes-eng.htm). Arctic Region: Pacific Region Integrated Fisheries Management Plans (http://www.pac.dfo-mpo.gc.ca/fm-gp/ifmp-eng.html) and DFO Central and Arctic region internal catch data. Aquaculture: Statistics Canada Table 36-10-0488-01, Output, by sector and industry, provincial and territorial, Aquaculture [BS112500]. 2018 extrapolated from 2017 using Statistics Canada Table 32-10-0108-01, Aquaculture economic statistics, value added account, gross output. Fish processing: Statistics Canada Table 36-10-0488-01, Output, by sector and industry, provincial and territorial, [BS311700], seafood preparation and packaging, 2018 extrapolated from 2017 using Statistics Canada Table 36-10-0402-01,Gross domestic product (GDP) at basic prices, by industry, provinces and territories, adjusted using Statistics Canada Table 18-10-0030-01, Industrial product price index, by product, NAPCS 171. Offshore oil and gasOil and gas exploration/extraction: Statistics Canada Table 36-10-0488-01, Output, by sector and industry, provincial and territorial, [BS21100], oil and gas extraction, 2018 extrapolated from 2017 using Statistics Canada Table 36-10-0402-01 (Gross domestic product (GDP) at basic prices, by industry, provinces and territories) adjusted using Statistics Canada Table 18-10-0268-01 (Raw materials price index), NAPCS14111 for crude oil and NAPCS 142 for natural gas. TransportationMarine transportation: Statistics Canada Table 36-10-0488-01, Output, by sector and industry, provincial and territorial, Water transportation [BS483000]. 2018 extrapolated from 2017 using Statistics Canada Table 36-10-0402-01, Gross domestic product (GDP) at basic prices, by industry, provinces and territories; adjusted using Statistics Canada Table 18-10-0005-01 Consumer Price Index, annual average, not seasonally adjusted, Services Support activities: Statistics Canada, Table 36-10-0478-01 Supply and use tables, detail level, provincial and territorial, Water transportation support, maintenance and repair services [MPS488004] products supplied by Support activities for transportation [BS488000] industry at basic prices. 2018 extrapolated from 2017 using Marine transportation growth rate. Tourism and recreationRecreational fishing: Fisheries and Oceans Canada 2015 Survey of Recreational Fishing data on expenditures, (http://www.dfo-mpo.gc.ca/stats/recreational-eng.htm), adjusted for saltwater expenditures only, and extrapolated forward using average growth rate. Recreational boating: 2016 estimates on expenditures by type taking from 2018 National Marine Manufacturer Association (NMMA) Canadian Recreational Boating Statistical Abstract. Values back casted and extrapolated using new boat sales. Cruise ships: 2012 and 2016 Business Research and Economic Advisors (BREA) reports: “The Economic Contribution of the International Cruise Industry in Canada” (interpolated for 2013 to 2015), total annual expenditures. Values for 2017 and 2018 extrapolated using number of cruise visitors sourced from Transport Canada annual reports and provincial government tourism ministries. Coastal tourism: 2006 coastal tourism spending (calculated by Gardner Pinfold) extrapolated using reallocated expenditures by province/territory from Statistics Canada Table 24-10-0013-01 (2006-2010) and Table 24-10-0027-01 (2011-2017) and Table 24-10-0045-01 (2018). Manufacturing and constructionShipbuilding and boat building: Statistics Canada Table 36-10-0488-01, Output, by sector and industry, provincial and territorial, Ship and boat building [BS336600]. 2018 extrapolated from 2017 using Statistics Canada Table 36-10-0402-01, Gross domestic product (GDP) at basic prices, by industry, provinces and territories, Ship and boat building [3366]; adjusted using Table 18 10 0030 01, Industrial product price index, by product, monthly, Ships [44111] and Boats and personal watercraft [44211]. Ports and harbours construction: Atlantic and Pacific Regions:
Arctic Region: Statistics Canada Table 34-10-0063-01, Capital expenditures, non-residential tangible assets, by type of asset and geography, plus Pangnirtung harbour expenditures (DFO internal data). Public sector, universities and environmental non-governmental organizationsDepartment of National Defence (DND): Data on defence services operations and maintenance (O&M) and capital expenditures for coastal provinces and territories were obtained from DND. The data was derived from DND Estimated Expenditures by Electoral District and Province. Fisheries and Oceans Canada (DFO): Expenditures were obtained by using DFO expenditures data sourced from the internally available Multi Year Financial Planning System. Other federal departments: Total spending on marine-related activities from Departmental Performance Reports and Reports on Plans and Priorities for Canadian Food Inspection Agency (CFIA), Environment and Climate Change Canada (ECCC), Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC), Parks Canada (PCA), and Transport Canada (TC). Provincial/territorial government departments: Provincial and territorial expenditures associated with the ocean economy were obtained from the Main Estimates and Public Accounts for each respective province and territory. An effort was made to exclude data otherwise counted in the National Accounts including ferry transportation, services to water transportation and marine-related construction. Universities: Estimates of university ocean-related expenditures are based on a two-stage approach. The first stage is compiling all ocean-related grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the federal Council for Innovation (CFI). In the case of the Territories, as there are no universities located there, they are allocated a portion of any grant relating to the Arctic Ocean. The second stage involved grossing up estimated annual expenditures for coastal universities (based on marine expenditures estimated from university budgets) using the increase in total university budgets from the Canadian Association of University Business Officers (CAUBO). Environmental non-governmental organizations (ENGOs): 2008 expenditures (calculated by Acton White) grossed up using the growth rate of financial data of representative ENGOs (taken from the CRA Registered Charity Information Return). Annex B: Summary tables
Greenan, B.J.W. et al., 2018, "Chapter 7: Changes in oceans surrounding Canada," Canada’s Changing Climate Report, Bush and Lemmen (Eds.), Government of Canada, Ottawa, Ontario, p. 343-423. Return to note 1 referrer Footnote 2.Morley, J. et al., 2018, "Projecting shifts in thermal habitat for 686 species on the North American continental shelf," PLoS ONE, 13(5): e0196127.(https://doi.org/10.1371/journal.pone.0196127) (accessed July 20, 2020). Return to note 2 referrer The 2018 data presented in this report are preliminary and are subject to revision once updated data sources become available. Estimates of economic impact for the year 2018 relied on the 2017 version of Statistics Canada’s IO model. The data source for industry output for several industries—Statistics Canada’s Table 36-10-0488-01, Output, by sector and industry, provincial and territorial—was only available until 2017 and other sources of data were used to estimates changes in output between 2017 and 2018 (see Annex A). Return to note 3 referrer Footnote 4.The public sector category here includes federal and provincial governments, universities, and environmental non-governmental organizations. Return to note 4 referrer Footnote 5.Statistics Canada, 2019, Input-Output Model Simulations (Interprovincial Model), (https://www150.statcan.gc.ca/n1/en/catalogue/36230002) (accessed April 7, 2021). Return to note 5 referrer Footnote 6.Gardner Pinfold, 2009, "Economic Impact of Marine Related Activities in Canada", Statistical and Economic Analysis Series, Publication 1-1, Fisheries and Oceans Canada, Economic Analysis and Statistics Branch. Return to note 6 referrer Footnote 7.Statistics Canada, Table 36-10-0595-01 Input—output multipliers, provincial and territorial, detail level, (https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3610059501) (accessed April 7, 2021). Note that for the year 2018, the 2017 IO multipliers were applied. Return to note 7 referrer Footnote 8.Statistics Canada, Table 36-10-0478-01 Supply and use tables, detail level, provincial and territorial. (https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3610047801) (accessed April 7, 2020). Return to note 8 referrer Footnote 9.The public sector category here includes federal and provincial governments, universities, and environmental non-governmental organizations. Return to note 9 referrer Footnote 10.U.S. Energy Information Administration, 2019, Europe Brent Spot Price FOB, (https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=M) (accessed April 8, 2021). Return to note 10 referrer Footnote 11.Fisheries and Oceans Canada, 2018, Oceans collaboration, (https://www.dfo-mpo.gc.ca/oceans/collaboration/international-eng.html) (accessed March 23, 2020). Return to note 11 referrer Footnote 12.Fisheries and Oceans Canada, 2020, Canada joins Global Ocean Alliance: Advocates for protecting 30 per cent of the world’s ocean by 2030, News release, July 9, 2020, (https://www.canada.ca/en/fisheries-oceans/news/2020/07/canada-joins-global-ocean-alliance-advocates-for-protecting-30-per-cent-of-the-worlds-ocean-by-2030.html) (accessed July 21, 2020). Return to note 12 referrer Footnote 13.Fisheries and Oceans Canada, 2020, About Marine Protected Areas, (https://www.dfo-mpo.gc.ca/oceans/mpa-zpm/info-eng.html) (accessed July 21, 2020). Return to note 13 referrer Footnote 14.Fisheries and Oceans Canada, 2019, Canada’s marine protected and conserved areas, (https://www.dfo-mpo.gc.ca/oceans/conservation/areas-zones/index-eng.html) (accessed March 16, 2020). Return to note 14 referrer Footnote 15.Greenan, B.J.W. et al. 2018. Return to note 15 referrer Footnote 16.Morley, J. et al. 2018. Return to note 16 referrer Footnote 17.Greenan, B.J.W. et al. 2018. Return to note 17 referrer Footnote 18.Haigh, R. et al., 2015, "Effects of ocean acidification on temperate coastal marine ecosystems and fisheries in the north east Pacific," PLoS ONE, 10(2): e0117533. (https://doi.org/10.1371/journal.pone.0117533) (accessed July 20th, 2020) Return to note 18 referrer Footnote 19.Greenan, B.J.W. et al. 2018. Return to note 19 referrer
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by Nicholas Lantz, Marcelle Grenier and Jennie Wang
Release date: August 17, 2021
More information PDF version Skip to text On this page Text begins Green spaces are essential to building the resilience and liveability of cities through the ecosystem goods and services they provide. For example, trees and other vegetation can improve urban air quality, mitigate urban heat island effects, reduce or delay storm water runoff, provide wildlife habitat and provide recreational opportunities and aesthetic benefits.Note Urban greenness reflects the presence and health of vegetation in urban areas and is a measure of urban ecosystem condition. This study uses data from satellite imagery to track greenness across cities at three points in time. These data broadly represent vegetation across the whole of the city, reflecting parks and other publicly and privately owned green spaces and features. The level of urban greenness will depend on natural environmental conditions, for example climate, as well as differences in local land use.Note Urbanization processes such as densification and urban expansion can result in significant reductions in the quantity and quality of ‘green' areas and related increases in ‘grey’ areas that consist of buildings, impervious surfaces, bare soil and low density vegetation. Long-term and temporary changes in greenness can be linked to these urbanization processes, as well as the addition or maturing of urban vegetation and changes in vegetation condition related to natural factors such as drought, insects or disease. Start of text box
This analysis provides a synoptic view of urban greenness in Canada for three reference years over an 18-year period as a measure of urban condition. For more information on ecosystem accounts, see Canadian System of Environmental-Economic Accounting – Ecosystem Accounts. This assessment provides a consistent approach for measuring urban greenness across the country, which can be used to help measure progress towards the United Nations Sustainable Development Goal 11 target 11.7 “By 2030, provide universal access to safe, inclusive and accessible, green and public spaces.”Note Monitoring greenness over time can help inform decision making on greening policies. This analysis used the normalized difference vegetation index (NDVI) generated from moderate resolution imaging spectroradiometer (MODIS)Note to estimate average urban greenness for 996 of 1,010 population centres (i.e., those located south of 60° latitude) in summer.Note In short, NDVI was used to measure the overall greenness of cities and towns in Canada. NDVI captured by remote sensors is an indicator of vegetation presence and quantity—it provides a relative measure of photosynthetic activity. The results of NDVI calculation range from -1 to +1 and these values vary depending on the type of satellite images, season, study area, atmospheric effects, soil type, humidity, etc. Generally, high NDVI values correspond to healthier vegetation while low NDVI values indicate less or no vegetation. NDVI values close to +1 should represent dense green leaves, whereas very low values (0.1 and below) correspond to barren rock, sand, snow, water or impervious surfaces (e.g., roads and buildings). The urban green class defined in this analysis corresponds to areas with an NDVI greater than or equal to 0.5, representing areas that are predominantly vegetated (Figure 1). Areas with lower values are considered ‘grey’ and are largely non-vegetated, though patches of grass, shrubs or crops, or other unhealthy/poor condition vegetation will be included. The selection of the 0.5 cut-off for identifying green and grey areas was determined after analysis of more than 50 sites using high resolution imagery available in Google Earth Pro and ESRI imagery basemaps and the application of the NDVI trends and vegetation change tools available in Google Earth Engine. The greenness layers and changes were also compared visually to the urban greenness score assigned by Czekajlo et al. for 10 sites.Note The areas showing decrease of greenness were similar on both products. Water areas were excluded from the analysis. The title of this image is “Examples of urban pixels classed as green or grey.” The purpose of this image is to visually display the levels of greenness between varying land covers. The green or grey class is based on the MODIS NDVI value. The MODIS pixels are represented by a box with a white outline and are overlaid on high resolution imagery provided by Google to visualize what is present on the ground. The image compares twelve MODIS pixels organized into two horizontal rows. The top row displays decreasing greenness levels in green pixels and the bottom row displays decreasing greenness levels in grey pixels. In between the two rows there is an arrow spanning across the page from left to right, shaded in a gradient of green to white to illustrate decreasing greenness. The first six MODIS pixel images are classed as green because their MODIS NDVI value is greater than or equal to 0.5, with the greenest pixel on the left and the least green pixel on the right. The last six MODIS pixel images are classed as grey because their MODIS NDVI value is less than 0.5, with the least grey pixel on the left and the greyest pixel on the right. From left to the right, the first green pixel represents mostly treed area with a few buildings present and a stream and road passing through it, the second represents a golf course with mostly grass, the third represents an agricultural area with part of the pixel overlaid on residential area, the fourth represents a city park with a baseball diamond, grass, and a parking lot, the fifth represents a residential area with large lots and trees, and the sixth represents a residential area with smaller lots and some trees. From left to right, the first grey pixel represents a residential area with few trees and grass and mostly artificial surfaces, the second grey pixel represents a residential area with no trees, some grass and mostly artificial surfaces, the third pixel represents an area with some large buildings, mostly paved surfaces and some grass, the fourth pixel represents an area with mostly large buildings, paved surfaces and some grass, the fifth pixel represents a new residential development with individual houses, paved surfaces, bare ground and no vegetation, and the sixth pixel represents an entirely paved surface. Greenness was assessed for nine weeks from June 25 to August 26 for the reference years 2001, 2011 and 2019 for the same physical area using the 2016 population centre boundary to ensure consistency. This geography was developed by Statistics Canada in 2011 to replace the ‘urban area’ geography and delineation rules were revised for the 2016 Census. Consequently, in this study, the 2001 and 2011 assessments may capture peri-urban areas that were subsequently developed, while 2019 may exclude urban expansion that occurred post 2016. Using the 2016 boundary may therefore bias results towards a higher proportion of green area in 2001 and 2011 and less in 2019. The MODIS data used in this study has a spatial resolution of 230 m, which corresponds to an area of 0.05 km2 (i.e., a 52,900 m2 footprint) and the resolution of the pixel (230 m x 230 m) is a limitation of this data set. However, MODIS vegetation indices have demonstrated the capacity to identify spatial and temporal patterns of human growth in urban areas.Note MODIS NDVI is often used in epidemiological investigations of greenness and health, Note and some benefits include its higher temporal resolution compared to the higher spatial resolution sensors, such as Landsat. Similar results have been obtained from MODIS and Landsat NDVI, demonstrating the validity of the MODIS dataset in greenness-health studies.Note As well, use of MODIS data makes it feasible to collect and process at the continental level. This assessment of greenness has several limitations associated with the use of NDVI to represent greenness, including the coarse resolution of the MODIS data and the selection of the 0.5 NDVI cut-off as a threshold to classify green or grey pixels. As well, no distinction was made between greenness resulting from publicly accessible parks and private inaccessible spaces. For trend analysis, assessment of additional time series data is required, while higher resolution data is needed for the identification of detailed urban green spaces. A next step for this work will be the assessment of green space extent and greenness condition using more spatially-detailed datasets and additional time periods. In 2019, 76% of the area in 996 population centres in southern Canada could be classed as green (Table 1). This percentage varied based on city size and regional differences. In large urban population centres, an average of 70% of the total land area was classed as green, with the share ranging from 38% in Winnipeg to 94% in Kanata (Chart 1). These values reflect peak summer greenness and can vary greatly depending on local climate conditions. A comparison of the top five large urban population centres shows that 65% of Toronto, 70% of Montreal, 68% of Vancouver, 42% of Calgary and 60% of Edmonton were classed as green in 2019 (Figure 2). Average urban greenness was 78% for medium population centres and 87% in small population centres. In over one-third (35%) of small population centres the entire area was classed as green. Population centres across the Prairies had the lowest greenness on average. Greenness was highest in the Atlantic provinces.
The title of this figure is “Urban Greenness, top 5 large urban population centres.” It provides a visual representation of urban greenness for three years (2001, 2011 and 2019) in Toronto, Montreal, Vancouver, Calgary and Edmonton. This figure includes a legend and 20 images. The legend contains four categories: Grey pixel = NDVI < 0.5, Green pixel = NDVI ≥ 0.5, Yellow border = population centre limits (2016), Blue pixel = Water. Images are organized in five rows, one row per population centre, and four columns. The first row shows images for Toronto, the second row shows images for Montreal, the third shows images for Vancouver, the fourth shows images for Calgary, and the fifth shows images for Edmonton. The first three columns organize images for 2001, 2011 and 2019. These images display pixels that are classified as green or grey based on the MODIS NDVI cutoff of 0.5. The final column displays a true colour image from Sentinel-2 from 2019, with a scale and compass arrow indicating north as reference information. A yellow line outlines the population centre on all images. This figure shows the share of each population centre that was classified as green was lowest in 2019. Sources: Statistics Canada, Environment and Energy Statistics Division, special tabulation from Statistics Canada, 2020, Corrected representation of the NDVI using historical MODIS satellite images (250 m resolution) from 2000 to 2019, https://open.canada.ca/data/en/dataset/dc700f75-19d8-4913-9846-78615ca93784 (accessed April 29, 2020); contains modified Copernicus Sentinel data 2019 processed by Sentinel Hub, https://www.sentinel-hub.com/explore/eobrowser/ (accessed January 15, 2021).
2001 drought impacted greenness measuresIn general, the proportion of green area in population centres in 2019 was lower than in 2001. Approximately three-quarters of large (77%) and medium (71%) population centres had lower levels of greenness over this period (Table 1). In comparison, 35% of small population centres experienced a drop in greenness while 33% saw no change in greenness levels. Between 2001 and 2011, the overall proportion of urban greenness remained the same. This finding can be largely explained by widespread drought conditions in 2001 across the Canadian south-west and an abnormally dry summer in Ontario and Quebec in the same year.Note The 2001 drought had a significant impact on the condition of urban forests,Note resulting in a lower proportion of green area in that year. In contrast, weather conditions during the growing season were more normal in 2011. Note An increase in greenness from 2001 to 2011 was observed for approximately half of large urban (55%) and medium (41%) population centres. A decrease was observed for 29% of large urban centres and 47% of medium population centres, which indicates that urbanization processes in these areas were likely significant enough to overcome the effect of weather conditions and contribute to the variation in the greenness measure. In 2011, urban greenness was stable or increased in the majority (74%) of small population centres relative to 2001. Urbanization processes linked to population change a factorDrought conditions monitoring indicates that the south of Canada experienced abnormally dry conditions to moderate drought in 2019.Note While these conditions may have contributed to lower levels of greenness, they were less severe than those experienced in 2001, which suggests that in general the drop in urban greenness from 2001 to 2019 may be explained by urbanization processes. Winnipeg, Milton, Kelowna, Windsor and Vancouver experienced some of the largest decreases in the share of green extent in 2019 compared to 2001 (Chart 2). These larger decreases in greenness are likely driven partly by the contributions of urbanization and the 2019 drought. For example, in Milton, the drop in greenness over the period coincided with a population increase of 350% from 2001 to 2016 (Figure 3 and Table 2). However, it is important to note that decreases in Winnipeg and Windsor may have been amplified by the effect of the emerald ash borer—an insect that has had a large impact on trees in some regions of the country.Note
The title of this figure is “Urban Greenness, Milton, Ontario.” This figure provides a visual representation of urban greenness for three years (2001, 2011 and 2019) in Milton, Ontario. This figure includes a legend and five images. The legend contains four categories: Yellow border = population centre limits (2016), Grey pixel = NDVI <0.5, Green pixel = NDVI ≥0.5, Blue pixel = Water. The top row organizes images for 2001, 2011 and 2019. These images display pixels that are classified as green or grey based on the MODIS NDVI cutoff of 0.5. A yellow line outlines the population centre. The second row includes reference info including a map of Canada indicating the location of Milton, Ontario on the left and a true colour image from Sentinel-2 from 2019 on the right. Between these images there is a legend, a scale and a compass. This figure shows a large increase in areas classified as grey in 2011 and 2019, compared to the 2001. Sources: Statistics Canada, Environment and Energy Statistics Division, special tabulation from Statistics Canada, 2020, Corrected representation of the NDVI using historical MODIS satellite images (250 m resolution) from 2000 to 2019, https://open.canada.ca/data/en/dataset/dc700f75-19d8-4913-9846-78615ca93784 (accessed April 29, 2020); contains modified Copernicus Sentinel data 2019 processed by Sentinel Hub, https://www.sentinel-hub.com/explore/eobrowser/ (accessed January 15, 2021).
European Commission, 2016, “Urban ecosystems,” Mapping and Assessment of Ecosystems and their Services, 4th report, https://ec.europa.eu/environment/nature/knowledge/ecosystem_assessment/pdf/102.pdf (accessed September 18, 2020); Heris, M., et al., 2021, “Piloting urban ecosystem accounting for the United States,” Ecosystem Services, Vol. 48, https://doi.org/10.1016/j.ecoser.2020.101226 (accessed February 1, 2021). Return to note 1 referrer Footnote 2.Nowak, D. J., et al., 1996, “Measuring and analyzing urban tree cover,” Landscape and Urban Planning, Vol. 36, no. 1, https://doi.org/10.1016/S0169-2046(96)00324-6 (accessed September 18, 2020). Return to note 2 referrer Footnote 3.Corbane et al. (2020) have proposed greenness, as measured using NDVI, as a proxy indicator to measure progress towards SDG target 11.7. For more information, see Corbane, C., et al., 2020, “The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL),” International Journal of Digital Earth, 2020, Vol. 13, no. 1, p. 101-118, https://doi.org/10.1080/17538947.2018.1530311 (accessed September 21, 2020). Return to note 3 referrer Footnote 4.Statistics Canada, 2019, Corrected representation of the NDVI using historical MODIS satellite images (250 m resolution) from 2000 to 2019 , (https://open.canada.ca/data/en/dataset/dc700f75-19d8-4913-9846-78615ca93784 (accessed September 18, 2020); Bédard, F., 2010, “Satellite image data processing at Statistics Canada for the Crop Condition Assessment Program (CCAP),” Methodology document for Statistics Canada Integrated Metadata base, https://www.statcan.gc.ca/eng/statistical-programs/document/5177_D1_T9_V1 (accessed September 18, 2020); Davidson, A., 2018, An Operational Canadian Ag-Land Monitoring System (CALMS): Near-real-time agricultural assessment from space, Agriculture and Agri-Food Canada, 100 pp. Return to note 4 referrer Footnote 5.Population centres have a population of at least 1,000 and a population density of 400 persons or more per square kilometre, based on population counts from the Census of Population. All areas outside population centres are classified as rural areas. Population centres are classified in three groups: small (population between 1,000 and 29,999), medium (population between 30,000 and 99,999) and large urban (100,000 or more). Ottawa-Gatineau, Lloydminster, Hawkesbury, Campbellton and Flin Flon have been split at the provincial boundaries. For more information see the Census Dictionary, https://www12.statcan.gc.ca/census-recensement/2016/ref/dict/index-eng.cfm (accessed November 12, 2020). Return to note 5 referrer Footnote 6.Czekajlo, A. et al., 2020, “The urban greenness score: A satellite-based metric for multi-decadal characterization of urban land dynamics,” International Journal of Applied Earth Observation and Geoinformation, Vol. 93, https://doi.org/10.1016/j.jag.2020.102210 (accessed November 12, 2020). Return to note 6 referrer Footnote 7.Hussein, S.O., F. Kovacs and Z. Tobak, 2017, “Spatiotemporal assessment of vegetation indices and land cover for Erbil city and its surrounding using MODIS imageries,” Journal of Environmental Geography, Vol. 10 (1-2), p. 31-39, https://doi.org/10.1515/jengeo-2017-0004 (accessed November 12, 2020). Return to note 7 referrer Footnote 8.Crouse, D.L. et al., 2017, “Urban greenness and mortality in Canada’s largest cities: a national cohort study,” The Lancet Planetary Health, Vol 1, no. 7, https://www.sciencedirect.com/science/article/pii/S2542519617301183?via%3Dihub#cesec20 (accessed November 12, 2020); James, P., J.E. Hart, R.F. Banay and F. Laden, 2016, “Exposure to greenness and mortality in a nationwide prospective cohort study of women,” Environmental Health Perspectives, Vol. 124, no. 9, p. 1344–1352; http://dx.doi.org/10.1289/ehp.1510363 (accessed November 12, 2020); Casey, J.A., et al., 2016,“Greenness and birth outcomes in a range of Pennsylvania communities,”International Journal of Environmental Research and Public Health, Vol. 13, no. 3, https://doi.org/10.3390/ijerph13030311 (accessed November 12, 2020); Cusack, L., A. Larkin, S. Carozza and P. Hystad, 2017, “Associations between residential greenness and birth outcomes across Texas,”Environmental Research, Vol. 152, p. 88–95, https://doi.org/10.1016/j.envres.2016.10.003 (accessed November 12, 2020). Return to note 8 referrer Footnote 9.Reid C.E., L.D. Kubzansky, J. Li, J.L. Shmool and J.E. Clougherty, 2018, “It’s not easy assessing greenness: A comparison of NDVI datasets and neighborhood types and their associations with self-rated health in New York City,” Health and Place, Vol. 54, p. 92-101, https://doi.org/10.1016/j.healthplace.2018.09.005 (accessed November 12, 2020). Return to note 9 referrer Footnote 10.Wheaton, E., et al., 2008, “Dry times: hard lessons from the Canadian drought of 2001 and 2002,” The Canadian Geographer, Vol. 52, no. 2, https://onlinelibrary.wiley.com/doi/full/10.1111/j.1541-0064.2008.00211.x (accessed September 18, 2020); Statistics Canada, 2002, “The western Canadian drought of 2001 – how dry was it?, Vista on the Agri-Food Industry and the Farm Community, Catalogue no. 21-004-XIE, http://publications.gc.ca/Collection/Statcan/21-004-X/21-004-XIE2002103.pdf (accessed September 18, 2018). Return to note 10 referrer Footnote 11.Hogg, E.H., J.P. Brandt and M. Michaelian, 2008, “Impacts of a regional drought on the productivity, dieback, and biomass of western Canadian aspen forests,” Canadian Journal of Forest Research, Vol. 38, no. 6, https://doi.org/10.1139/X08-001 (accessed September 18, 2020). Return to note 11 referrer Footnote 12.Agriculture and Agri-Food Canada, 2020, Canadian Drought Monitor, https://agriculture.canada.ca/en/agriculture-and-environment/drought-watch-and-agroclimate/canadian-drought-monitor (accessed September 18, 2020). Return to note 12 referrer Footnote 13.Agriculture and Agri-Food Canada, 2020. Return to note 13 referrer Footnote 14.Natural Resources Canada, 2020, Emerald Ash Borer, https://www.nrcan.gc.ca/our-natural-resources/forests-forestry/wildland-fires-insects-disturban/top-forest-insects-diseases-cana/emerald-ash-borer/13377 (accessed September 18, 2020); Epp, B., 2018, Emerald Ash Borer Management in Manitoba, Manitoba Sustainable Development, http://www.cif-ifc.org/wp-content/uploads/2018/10/ReducedFileSize-3-EAB-Manitoba-Brad-Epp.pdf (accessed September 21, 2020); City of Winnipeg, 2020, Emerald Ash Borer (EAB), https://www.winnipeg.ca/PublicWorks/parksOpenSpace/UrbanForestry/EmeraldAsh.stm (accessed September 21, 2020). Return to note 14 referrer
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