Adequate energy services in the home, such as heating, cooling, lighting, cooking and more, are important to providing a comfortable and healthy environment. Energy poverty refers to a situation when a household experiences a lack of sufficient energy services. There are several factors that can contribute to energy poverty, from household factors such as low income and the need and behavior of household members, to energy inefficiency of a dwelling or high energy tariffs. It has been estimated that between 6% to 19% of households in Canada experience energy poverty (Riva et al., 2021). Previous research has explored the regional patterning of energy poverty in Canada, with findings that indicate a higher incidence of energy insecurity in rural areas (Das et al., 2022; Grubbs, 2022). To encompass large geographic areas, these previous works used large units of analysis, such as the provincial or census subdivision level, and in turn lacked insight into the spatial distribution of energy poverty within urban areas and communities. This ARIA project aimed to explore the spatial patterning of energy poverty within major census metropolitan areas in Canada to build on literature investigating the spatial variation of energy poverty in the country.
There are two main metrics for assessing household energy poverty. The 10% indicator defines a household as energy poor if 10% of household income after tax is dedicated to energy
expenses. Alternatively, the 2M indicator considers a household to be energy poor if it spends twice the national median share of its income on energy expenses. Both the 10% and 2M indicators were used during analysis in this ARIA project. Analysis focussed on sixteen census metropolitan areas (CMAs): Victoria, Vancouver, Edmonton, Calgary, Saskatoon, Regina, Winnipeg, Toronto, Ottawa/Gatineau, Montr茅al, Qu茅bec, Fredericton, Moncton, Charlottetown, Halifax and St. John鈥檚. These CMAs were chosen to include at least one census metropolitan area per province, two for provinces that have a significant urban centre aside from the capital city. Data was obtained from the 2016 Canadian census while calculations of energy poverty were provided by Statistics Canada. Analyses were conducted at the dissemination area level, the smallest standard geographic area for which all census data are disseminated. This scale was selected to best highlight the spatial distribution of energy poverty at a small scale. A local indicator of spatial association (LISA) analysis was conducted for energy poverty computed for all tenure-type households, for home owners, and for renters. A LISA analysis identifies spatial clustering by testing data against a null hypothesis of complete spatial randomness. Results indicate where there is a clustering of values, in this case prevalence of energy poverty.
The produced maps returned many interesting results. Across major census metropolitan areas in Canada, there is a clear spatial patterning of energy poverty when considering total tenure, owner and renter households. At a glance, energy poverty tends to be concentrated in known lower-income neighbourhoods in the city. In addition to this, areas on the outskirts of the census metropolitan area tend to see greater incidence of energy poverty clusters.
I was interested in an ARIA internship as it provided a unique opportunity to conduct an independent research project. Such a long-term, self-guided project requires specific skills that are more difficult to develop in a traditional class setting. My ARIA project granted me the chance to learn valuable academic skills in addition to habits related to time management, independence and personal accountability. My ARIA learning objectives were therefore twofold: those associated with hard research skills and those associated with personal growth. In terms of research experience, I succeeded in learning a great amount about the quantitative research process, including data management and organization, data exploration and the use of geospatial analysis software QGIS and GeoDa.
My ARIA internship comprised of many highs and lows. The most frustrating moments were, however, consistently accompanied by the most gratifying ones. Learning to work with a large dataset and manage it with new software was an unsurprisingly challenging process. Specifically, transferring information between programs and ensuring the data remained uncorrupted proved a significant task. Overcoming this required a lot of patience and critical thinking. One highlight from the ARIA experience was succeeding in finally being able to transfer and manage the data properly, and the confidence that came with gaining a strong understanding of the software. Closer to the end of the internship, producing my final maps and seeing some really interesting results also stands out as a highlight.
I believe my ARIA internship will influence my future career and education path. Over this summer I greatly enjoyed the independent research experience. In the short-term, I hope to build on the work that was completed this summer and continue to work on this project with my supervisor. Looking further ahead in the future, I hope to apply the skills I learned during my ARIA internship in future postgraduate studies.
