Nova Scotia Energy Poverty Data Visualization Tool Developed by EfficiencyOneWebinar on December 9, 2022
Energy poverty is prevalent across Canada with the rising costs of energy. Nova Scotia experiences significant levels of energy poverty due to the rising costs of energy, and as such many are struggling to pay their bills.
EfficiencyOne has created its own data visualization tool to help understand the causes of energy poverty, the geographic differences in where there are higher and lower levels of energy poverty, and how we can use the tool and the data to target areas that are most in need. The tool has mapping capability to present overall where energy poverty is more prevalent, it analyzes the impact of energy costs, income levels, and incorporates the use of energy efficiency programs on reducing the impact of energy poverty.
John Esaiw, Chief Strategy and Technical Officer, EfficiencyOne
Ryan McGibbon, Data Scientist, EfficiencyOne
Allison Mostowich, Director of Engagement, Efficiency Canada
John Esaiw: Just quick introductions. I’m John Esaiw, chief Strategy and Technical Officer here at Efficiency One. And for those of you who aren’t familiar with us we operate our energy efficiency programs through efficiency Nova Scotia, which is the first energy efficiency utility in Canada.
And so we are actually regulated and we deliver programs through that brand, efficiency Nova Scotia, to all of Nova Scotians. And then just as a matter of quick introduction, so Ryan McGibbon is the brains behind what we’re gonna present today. I didn’t honestly push any buttons to get this thing to become real.
But what we’re intending to show is our beta version of our energy poverty data visualization tool, and it’s really for us to start showing what we’re working on here. What’s causing energy poverty here in the province? The drivers of it geographical areas where it’s higher and lower as far as the rates of energy poverty, and and some of the things we’re working on to address energy poverty through our programs.
So this is the first as far as an overview goes of everything that’s going on with the data we’ve used. And this is the first time we’ve actually taken all of the different sources of data. We have the new census data, we have our own proprietary that we’ve used for this plus other sources of data and things like the commodity costs that we’ve got here, and we’ve brought it all together so we can start to visualize and understand what are the effects of energy poverty here as far as levels go.
And you can see, starting with so the top here, this is all homes in Nova Scotia. There’s about 435,000 households in Nova Scotia that are part of the data we have, and based on the parameters we’re able to set for this tool, we can see the split between homes in energy poverty and homes not in energy poverty.
And so that’s the very top, call it orange and green that’s displayed. And then below we have charts that show the split of energy poverty. And then next to those, there are two other pie charts that show the split between electric homes and non-electric homes. And so you can see there’s a great divergence between the electric and non-electric side of the equation here.
Off to the left. These are all the parameters that we can set for the tool. And oil is still used in significant volumes here in Nova Scotia. Whether it’s residential, commercial industrial. Across the board, oil is still quite a bit of the story here as far as the energy that’s used here.
We currently have the cost of electricity here. It’s a little over 16 cents kilowatt hour. We’re in the middle of a proceeding right now as far as the go forward rates of electricity, but we can adjust it to reflect what that change might do, and then all the other fuel types that we have here in Nova Scotia for energy.
So wood, propane, natural gas. Again, we can change any of these to show how it might impact our numbers and then we’ve used 6% as the energy poverty threshold. So what we’re working on is 6% of after tax income is the calculation that’s being done here. And that’s based on the most recent census the day that we have.
But we know that data is already a little bit old. So we know incomes have changed a little bit since the last census data. So we can use this slider, we call it, to move up the median income levels if we want to. So the median income change here, for example, I can make a zero, which means we haven’t changed it from the latest set of data. It does take a bit to refresh here, but then you could see on the screen things, do change virtually, instantaneously, and create a different sense of numbers. The one that we really pay attention to here a lot is oil. And so oil at the bottom of the pandemic was 60 cents. And so if we refresh that, you can see the numbers dramatically changed from the cost of oil and at the high in the pandemic it was two to two and a quarter. So you can see, dramatic change here from highs and lows in, and people just simply using oil here in the province.
Right now it’s probably somewhere around a dollar 50. Again, this moves around quite a bit here just because we’re exposed to the market on the price of oil that, that we pay for here in Nova Scotia, it’s not produced here. And then, so at the bottom here we’ve got the metrics. So based on meeting income, 6% of after tax income creates a threshold. So if you’re spending more than $3,153 in this example on energy, that would put you technically into energy poverty in our calculations here.
And then the annual home energy costs. So this is the estimate based on the split, using our data of the primary sources of heating that are used here in Nova Scotia.
If your primary source of heating is oil, you would still be using electricity, for example. But in combination with oil and your electric use, you’re spending approximately $3,926 annually on energy if you’re primarily heated by oil. And the oil here is used for heating and for hot water. Those are both still significant uses of oil here in the province.
So that’s the first dashboard we’ve got. I’m gonna skip over the initiatives and come back to it after I’ve explained how these are all working. What we’ve done here is also mapped out what it looks like across the province. And so if any of you are familiar with the CUSP tool, Canadian urban sustainability practitioners, they have a tool that everyone I think is fairly familiar with.
So this is similar concept here that we’ve taken from that where we can zero in, so the darker colors here basically represent the higher levels of energy poverty in the province. So again, this is so we can visualize using our data where energy poverty exists in higher percentages versus lower percentages.
So I’m not gonna spend too much time on the map. I just wanted to show the example of what it might look like on the map. The data we’re using here is fairly granular. Ryan has and through our external third party, we get data at a postal code level, basically. And so we’re able to view a lot all of this data at a very granular level at a postal code there, here across the province.
So again, we can really zero in on where the energy poverty is occurring, the number of homes that are experiencing energy poverty. And then we can break it down by the age of the homes as well. We have some of the oldest housing stock in Canada here, and so we’re able to see what this looks like based on the age of the homes that we have here.
And then these are all just views of across the province. If I jump back up to the overview here for a minute what we can do, so these FSA codes are essentially postal codes. We can change this and so if I take it off, select all, and I just wanna focus in on, let’s say if I wanted to understand what’s going on in Dartmouth, I can just click Dartmouth, Northwest, Energy, everything will refresh.
And then I can see exactly what’s going on in that one specific area in Nova Scotia. It’s the same data being represented here, but we can again, zero in very closely on individual areas across the province or the entire province as a whole.
There’s a lot of data here, so it does take a minute to refresh on your screens. It’s not the lag in the webinar, it’s actually just the amount of data that’s it’s grinding through essentially to do the calculations. And so this is just a distribution for all Nova Scotia households. Again, it’s just representing it in a different way based on the 435,000 estimated homes here in Nova Scotia. And then we’ve got energy usage stats here on the annual cost of energy and the average annual kilowatt hour usage of energy. So we actually have the units here so we can see how much energy on average is being used, and then a distribution here between the number of households and the annual cost of energy.
The one neat thing that Ryan built in here is we can now exclude, based on our data. Again, not perfect, but we can exclude mes and renters from all of this. And so again, it’s just another feature that we’re able to use in the tool. So you can see the estimated number of homes comes down significantly to little over 306,000.
And that, that’s just eliminating urbs and renters from the equation so we can see the difference between.
Again, on the energy consumption level, we’ve got it broken out for non-electric homes as well as electric homes. So this is the non-electric homes. Once again, the number of homes in energy poverty, 60%.
This is showing based on all the parameters we’ve got on that left side the annual average energy costs that are broken up by the fuel type usages, this data is a little harder to get because in many cases we know that there are households using multiple forms of energy. So there’s ability that a home could have primary source of oil, but they could have also a natural gas or a propane backup. They could have a wood backup or any combination of those. And so that data is a little harder to get. So like I said this isn’t necessarily perfect, but it’s, I think, closely representing what’s going on here across the province based on the data that we have.
And so the final one here is the same view, but for specifically for electric homes. So you can see the annual energy cost is quite significant if you’re primarily using electric versus non-electric heat. Now the thing I wanted to show here, and this is again, this is something unique to what we’re trying to do here and represent is that we are showing here ” what if.”
And so this is what if we installed a heat pump on let’s say 10% of all homes in Nova Scotia? And that’s a one head heat pump. And we can go to two heads, three heads.
But this is just showing, for example, what if we did a one head heat pump on the homes in energy poverty in Nova Scotia? How would that reduce the levels of energy poverty in the province? So you can see just from doing the one head heat pump where we’re taking people off of commodity whether it would be oil or gas and primarily switching them over to a heat pump for space heating. This would reduce energy poverty by 10% across the province. Then we can say, okay, what if they participated in A home energy assessment. And so if 10% of those participated in a home energy assessment and we get savings from those, so that changes up the equation again to lower energy poverty because we’re actually lowering their energy use at that point. So that takes it down, roughly 18.8%. And then if we did efficient product installation. So again, these are just products that would lower energy use or manage energy use in a household. And so again, it’s just recalculating to show that program and its impact.
And so you can see another uptick in reduction in the levels of energy poverty based. And so this is showing all of those programs all together. If there was roughly about 10% of household in Nova Scotia that were using these three programs so we can really see the impact of our programs here and how we can work towards reducing energy poverty.
And so that’s how we’re managing it. This is really letting us understand what the impact of programs is through the use of the mapping tool and the other data we have, we can see where energy poverty exists. We can understand the impact of our programs and putting those two together, we can say, “okay, here’s where energy poverty exists in greater numbers. We should develop programs that address those geographic areas,” and we can understand what the primary source of heating is in those areas. So maybe it’s more oil, maybe it’s more natural gas, maybe it’s more combinations of them. So we can address that energy use and the one thing that’s coming along that we don’t yet have that we’re really gonna be able to understand especially the electrical side of energy use is we’re getting a much granular level of data.
Nova Scotia power is installing smart meters essentially across the province. And so we’re gonna start getting access to a much grant. We get the rights to access Nova Scotia Power customer data as part of our franchise agreement for Efficiency Nova Scotia. And currently the level of granularity of the data is monthly.
But then with the a m i data, once it starts coming in, we’ll have 15 minute data. So we’ll really be able to understand energy use in a very different way once we have that very granular level of data. And I know Ryan’s very excited to get 15 minute data introduced into here. I think we almost blew up Power bi just putting the data we had in here, let alone 15 minute data. So anyway, we’ll have to figure that out. But we are using Power BI for this. It’s a switch over for us. We were using different product before, but this has really allowed us to really I would say get to the next level with our data visualization tools and how we’re using data here for our programs, program delivery, program design.
And so I think I think that’s the tour through the tool, essentially. That’s what I wanted to show and give everyone a good feeling of what we’re doing here and how we’re doing it and what’s going on specifically in Nova Scotia. And so with that, certainly happy to start taking any questions or comments on what we’re doing here.
Allison Mostowich: Awesome. Thanks John. So while you’ve got the tool up Brendan was curious Brendan from Efficiency Canada was curious about different scenarios. So he was talking about the anticipated electric rate increase. I’m wondering if you could demonstrate some of the changes with that.
John Esaiw: Yeah. So we’ve roughly estimated that those energy poverty rates shift roughly by about two to 3% for every 1 cent increase in rates here. That’s what the tool’s telling us at least.
Allison Mostowich: You can see how dramatically the shift in oil prices affects that too. That was pretty intense. And so I had a quick question about some of the different scenarios and I think people in the background were really impressed with the different scenarios especially considering the programming you have.
So it looked like you had a couple of your different programs on there, including heat pumps. So this is interesting. Is this just existing programming and is this kind of where you see the transition really happening is through these three programs that you’re delivering right now?
John Esaiw: Yeah. So these are all things we’re doing today. And so in many ways the tough part to represent here is the homes that have undertaken energy efficiency programs and how many, we’ve installed, across Nova Scotia as a province, a lot of heat pumps have been installed here and we continue to do that work.
So the tool is, the reduction in energy use is being taken into account because we’ve got our data on energy use here at a household level. But it’s applying the program to all the homes. So it’s not a perfect science, as far as applying the programs to it.
Something we could think about refining, but it’s it’s giving us a good feel for the impact we can have through our programs for sure.
Allison Mostowich: Okay. Yeah. And it sounds like you can do some integration of programs as they come up. We have somebody asking in the background about deep retrofits, like how you feel deep retrofits would affect this.
John Esaiw: It’s a good question. We’re trying to define what deep retrofits are. Everyone is working on this. It’s a significant issue here. And it’s more so because of our older housing stock here that we have in Nova Scotia. So deep energy retrofits at a residential. With the types of housing we have here. Certainly I couldn’t say we could integrate that into the tool today. That would be challenging, I’m gonna say. But it’s not to say we can’t it’s just that we haven’t looked at, we have to define what would deep retrofits be.
The work we’re doing right now deep retrofit definition would be achieving at least a 50% reduction up to a 70% reduction. So what measures, what would need to be deployed in that home, in that specific kind of home, because of our variation of housing stock here. So how do we get that reduction down by 50%? That’s a starting point for a deep retrofit. I think in my mind, at least, if we’re gonna call it deep, and then, that energy reduction, the reduction in energy poverty, that will be the knock on effect of a deep retrofit. And then of course, funding is always going to be the challenge there, because these are expensive projects. And so that’s something we’re considering as well as we developed the retrofit scenarios here.
Allison Mostowich: Great. So Matt is asking if these scenarios can show items such as cost for example dollars per kilowatt of output of an installed heat pump capacity and dollar per ton of GHGs reduced.
Ryan McGibbon: Yeah, so we could show perhaps homes on average that participate in the HA program, how much energy savings costs could be associated in a house by house basis. That might be a version two thing when we bring in AMI data because at the granular level it might be a little bit of, I won’t say guessing cuz it would be educated, but we’d want a more accurate estimate if we did that. But theoretically, yeah, if we know how much less kilowatt hours using or of an oil home switches to a heat pump, we know that they’re using this much less liters of oil, which we can transfer depending on the efficiency of their furnace to how many greenhouse gases we’re saving as well. So all those calculations could be in there.
John Esaiw: And the calculations we’re doing would also include not just removal of oil for instance, but then it would also include as the grid cleans up here, because we do have regulation that says our grid has to be 80% renewable by 2030. And so as the grid cleans up, the draw from a cleaner grid changes that G H G calculation as well.
Allison Mostowich: Okay. Thank you. So Nick is asking a question in the background and reading my mind. How has this data visualization tool helped in other ways? I think you mentioned that it’s helping you basically target programming to specific geographical areas. But how has it helped advance your low income or other EE programs? And he’s saying, obviously this could be summarized through reports and recommendations, but wanted to understand any benefits that you’ve seen through use of the visualization tool.
John Esaiw: Yeah. And so this is something we’ve very recently done, so I can’t claim that this tool has had a direct impact on programs yet. I would love to say that but that would be an end goal once we’re really starting to roll this out.
Like I said it’s beta, so we still have some work to do with this. We’re getting some great suggestions even here today, now. The more times we look at this, the more it becomes a, ” how much data can we jam into this thing?” And it really almost becomes a real estate thing.
Like we only have so much screen to work with here too. That’s part of it. But, certainly it would be great, once we start showcasing this tool more, the impacts of our programs and building those in more that, in my mind would certainly lead to program development whatever that looks like.
And then, Ryan’s also doing a lot of work with our marketing team. We’ve developed what we’ve called a playbook here. And so maybe Ryan, you can talk about that and how we could use that as well.
Ryan McGibbon: Yeah, so that’s exactly what I was gonna add. So I was behind the development of this tool, but I also use a lot of this data outside of the tool. I have access to this data myself, and I’m using that with marketing to help identify areas that are more so in need or, for example, for our E P I program. We will, one will identify areas that have customers that look like they fit that typical scenario of that program. So we can increase participation for that program.
And then we use this tool to also cross reference with the ideal customer scenario to say, where are the ideal customers in area where energy poverty is also high? So we did that in a couple areas in Nova Scotia new Waterford in Northern Nova Scotia. And several areas in mid and Southern Nova Scotia. And they were very successful when we did targeted campaigns based on this data and ideal customer scenario data. So that would be my answer for that.
Allison Mostowich: Yeah, that’s excellent. I think we are always interested from the policy perspective too, about which statistics certain people are going to be interested in, what resonates and how we can enhance the message about energy poverty at the federal or provincial level. I know there’s a lot of people interested in that right now, including us.
Okay, let’s move on to another question. So Brendan again is asking how do you account for renter populations that might not pay their own energy bill? So existing programs might target these postal codes based on low income, but they may not have significant energy poverty based on the cost income metric.
Ryan McGibbon: Yeah. That obviously that’s essentially one of the main reasons we entered that option at the very top right of that tool to exclude merges and renters, because it does get complicated. We don’t have data that says specifically which renter is paying their own bill versus which power bill is being paid by the owner. We only know the location or sort of specific unit or apartment and how much kilowatt hours they’re using. So we did a rough estimate based off every location in Nova Scotia that we had electricity or power data for. And then we also include that exclusion tool on the top right, so we can identify areas without having to worry about that factor.
Allison Mostowich: Thank you. I will ask the one question about survey data. Traditionally survey data is not necessarily scientifically accurate. Does this tool use any survey data? And if so, how do you confirm that data is accurate?
John Esaiw: So it does use survey data. Nova Scotia Power did a survey across the province, and it was gathering data on primary use for your home, primary source of energy. And so I think that data’s fairly accurate. And the sample size was large enough so that we could use it in this tool to predict the primary sources of heating across the province that we’ve built into this
Ryan McGibbon: Yeah, so for this tool and it’s, I guess it’s tricky to see that here, but there’s no one source of the truth for our data. So we have Nova Scotia Power Survey data that they sent out that helps us identify what percentage of homes that are not electric are, oil, heat and gas, et cetera.
But then we’re also mixing that with our H E A data, which we know going into the homes what heat source they are. So we’re using that to predict, using that to create models, to predict oil usage, gas usage, depending on the heat type. And then we can also look at Nova Scotia power patterns and use that to predict what the heating source is as well to a less granular level.
So we know that a home that’s using much more kilowatt hours in the winter than the summer is likely an electrically heated home. And then you can apply the opposite to assume for other heat sources. It’s not a perfect case scenario, it’s one of the things that make this report tough to put together, but we have a, I think, a pretty good estimation overall for that electric heat sources.
Allison Mostowich: Fantastic. Cindy’s wondering if this is going to be available publicly at some point. Is that part of the plan?
John Esaiw: We’re thinking about it. Not yet, though. There’s some sensitivities around the data here. I’m not sure yet, I guess is my answer. I wish I had a better answer than that. I’m not sure this is something we could just put on the website today. It’s specific to Nova Scotia. So there’s that constraint. If you are from somewhere else and wanted to use it for your own purposes, it wouldn’t work at all.
But it is specific to Nova Scotia, but there’s no plans yet to just, put a link to this on the website. Like cusp, has, for example just something we’re not quite ready for yet. If anyone listening in here does want to get ahold of us and has specific questions related to the tool or wants your own separate presentation for your own self or your own organization we’re super happy to do that. And if there’s anything specific you wanna see in that presentation for some of the work you might be doing. We’re happy to share and help you if we can.
Allison Mostowich: Fantastic. So Nick’s asking about saying this is a bit outside your mandate, but, I think some of the recent events can give this question context.
He’s interested in overlaying the flood risk to these homes. And if there is a relation between flood risk and energy poverty.
John Esaiw: I suppose I’ve never connected those dots in my head. But I think likely rising sea levels are going to impact those that are in residences that are closer to the shorelines. The hurricane in Newfoundland proved that fact out in a very dramatic way that anything near the shorelines is certainly highly exposed to weather and rising sea levels. And , I don’t know if we actually have data that we could correlate with, homes and energy poverty and, proximity to rising sea levels, shorelines, I’m gonna say probably don’t, I don’t know how we could get that. There could be other organizations out there that have data on proximity to shorelines, but we only have data at a postal cold level so we could get to that geographical area, put in the postal code and see number of homes in energy poverty, but we couldn’t really zero into that Google Maps kind of level. Because we can’t get incomes at a individual level. And so it’d be pretty difficult to put that together, I guess is what I’m saying as I think about it, it’s a pretty interesting idea. I’m not sure if we could get there yet.
Ryan McGibbon: Yeah. My only thought on that is we had a way to look at a postal code and see average distance to shoreline or average height or a mix of those two from sea level, and then compare that to energy poverty rates. But that’s not data I even know where that exists. But that would be the only way I would think about correlation and the way we’ve structured this data.
Allison Mostowich: And it sounds like that’s the answer to Brendan was asking about being able to show the rates of energy poverty in urban centers versus rural. So it sounds like it is based on the postal codes as well, or is there something a little bit more defined?
John Esaiw: Yeah, definitely the postal code level. So the map the map that I showed would show that the rates of energy poverty are higher, I would say relatively higher in rural areas here.
Simply because I think there’s a lot more oil usage in rural areas here versus urban areas that are Electric use. There’s some areas in Halifax for instance, the energy poverty levels are only, 10 or 12% because those are newer areas. They’re mostly all electric. But yeah, at the postcode level we do have that. It’s probably fair to say rural’s probably higher rates than urban.
Ryan McGibbon: If you could identify a postal code as mostly urban or percent wise, even if a postal code’s 80% urban or something like that’s an analysis that could probably be done, not something we’re doing currently, but that could be.
Allison Mostowich: Awesome. Okay, that’s really helpful.
And I’m just, I’m gonna take a moment to just draw everyone’s attention to our campaigning. So in Efficiency Canada, this is one of our big portfolios is low income. We are working on trying to get the Canadian government to commit 2 billion to this low income energy efficiency portfolio and create across Canada strategy. So if anyone is interested in speaking to their MP on this we have some information online. There’s lots of actions you can take to try to push this. We’re really trying to push this in the next federal budget. If this is something that is important to you, please go on our website and help us tell the Canadian government that this is very important. So with that, I just want to thank you John and Ryan for joining us today.
That was really awesome. That is an amazing tool, and I have absolutely no idea how you did it in the background. It just, it works like magic to me. Thank you again. And hopefully we will see everybody at the next edition of DiscoverEE. Take care, everyone.