Isobel Wild [00:00:07]:
I'm Isobel Wild. Welcome to the State of Sustainability podcast, a show for professionals transforming corporate sustainability strategies, brought to you by Altruistiq. The question I'm posing to our listeners today is, are you making the wrong sustainability decisions? We're going to use this episode to highlight the common mistakes that sustainability sustainability professionals make and sound out some of the solutions. But before we dig into that, Saif, how are you?
Saif Hameed [00:00:33]:
I'm good. I'm good, Izzy. It's an interesting time. There's a lot happening in the world today. I'm following along. I think a number of sustainability professionals are kind of also watching the news to see which way the us election goes. And that's expected to potentially cause some swings, I think, in our world, but generally speaking, pretty excited.
Isobel Wild [00:00:52]:
What are the big swings that you are? Maybe should we have a bit of optimism? Most excited about and most nervous about.
Saif Hameed [00:00:58]:
I think that a lot of sustainability momentum is now being converted to business as usual, which is interesting. So I think that to some extent, whatever happens in regulation, unless it's a massive swing, and it may be a massive swing, but unless it is, I think that a lot of the sort of practices that are now becoming corporate norms in terms of how they manage data, how they go about setting targets, how they go about tracking, I think a lot of that is becoming business as usual. So just to give an example of a similar shift that I'm seeing, which is I'm seeing a lot of companies start to question SBTI and SBTI targets and whether they participate in SBTI, but that doesn't affect the fact that they're still doing something about their emissions. And I think in the same way, we're becoming somewhat insulated to minor shifts in regulation where industry norms are kind of establishing their own momentum, which I think is quite cool.
Isobel Wild [00:01:54]:
And on this sBTI point, do you think organizations should be questioning sBTI? And, I mean, from my point of view, there seems to be a maelstrom of news headlines and arguments going on around carbon credits. And it's almost, this argument is discrediting SBTI as a whole. And for the listeners who maybe haven't kept up to date with the issue, is that SBTI, in April of this year, SBTI came out and said that they would start considering carbon offsets to actually be able for companies to meet those SPTI targets. Whereas previously they had said that it was only residual or hard to abate emissions, which can make up five to 10% of your corporate baseline, could be used by offsets. This opened up a whole can of worms. And people are coming at SBTI saying that this isn't the right thing to do. We need to prioritize reduction first. And then others have said, actually, this can actually make it a bit more achievable for some organizations.
Isobel Wild [00:02:54]:
I think we've actually mentioned this in a previous podcast where we dug into this argument in a bit more detail. But as a result, Saif, what you're saying is that maybe some organizations at the moment are moving away from SBTI. But if they are, where are they going? And is there a better place or a better standard to align to? Or is this just a phase?
Saif Hameed [00:03:14]:
Yeah, so just a small correction, I guess, Izzy, which is the. I don't even know what to call it, but the, let's say, pro offset statement was not an official SBTI position. It was a statement released by the board of trustees, effectively like the board of directors of SBTI. And it was very quickly qualified where SBTI immediately came out and said, look, actually, this is just a statement. This has no binding authority with respect to SPTI targets. That was, in itself, quite confusing. And SBTI then said, we're gonna revisit this whole thing later on in the year. I think that July, basically, is what they said.
Saif Hameed [00:03:55]:
A lot has happened since then. I think one of the things that's happened is that the CEO of SPGI stepped down. He cited personal reasons. But, of course, the timing is very coincidental. Let's say a second thing that's happened is, I think a number of companies that we're speaking with behind closed doors are saying they don't think SPGI is going to be around very long in the same way, way, shape or form. Whether that means that SBTI, as an organization, doesn't exist in itself four or five years from now, or whether it exists but it's a fringe organization, or it exists, but it's doing something slightly different. And jury is out. But I am hearing some large organizations saying internally and to their close circle that they've lost faith in SBTI in a way that, at least to me, conveys a sense that the damage is.
Isobel Wild [00:04:48]:
Done and what will fill the void.
Saif Hameed [00:04:50]:
I don't think anyone has a view. I don't think anyone is passing judgment. Right. So this is currently, this is sort of chatter I'm hearing, basically. I think that there's this one movement which is people moving away intentionally and trying to decouple a bit because they think SBTI is unreliable. And I think that's this fluctuation over the last few months has emphasized that there's another group that are being dropped by SBTI because the targets are no longer SBTI lined. And so, like, Unilever, for example, falls into that camp and a number of other organizations. And as we start to reconcile the fact that many companies over committed a few years ago and are now realizing that over commitment, I expect more companies to be dropped or to exit SPTI.
Saif Hameed [00:05:31]:
So you have these two sort of opposed, two forces, in some ways quite different, but both sort of pulling SBTI apart the seams. You then have this weird conflict that SPTI created for itself where they have this consulting arm or this advisory arm, and they have the target validation arm, and they're trying to keep them both separate. And that's a bit confusing. And so I think, all in all, it's like, it's a weird place for SBTI to be. At the same time, they've now come out in July and said, actually, we're sticking more or less with our original position on offsets. So I suspect they've probably pissed off their funders and the other stakeholders that the word on the street is the funders and a few other government officials and other related stakeholders were the ones applying pressure to push SBTI towards a pro offset position. And so I feel like they've now just annoyed everyone, basically. Sorry.
Saif Hameed [00:06:25]:
Long story short, I feel like they've annoyed everyone somehow.
Isobel Wild [00:06:29]:
Yeah. I think this also just reflects how volatile the whole sustainability space is in general. Like, we've seen so many headlines throughout this last year, even where companies are backsliding on corporate targets and they've hit the news for that, and then suddenly everybody turns around, they're like, oh, what have you done? And I just think that it's so highly scrutinized and so much exposure is on all kind of businesses working, that it's actually very hard to be a sustainability professional. Put your neck on the line and say, let's externalize this. Let's talk about this. Let's go with bold claims in this way. It's a tricky space to be in.
Saif Hameed [00:07:09]:
Yeah, for sure. I mean, I think it is tricky. I think we're also all grappling with an absence of proper regulation in this space. And so all these things that we're discussing, like SBTI, CDP ratings of various shapes and sizes, these are all in some way filling a bit of a regulatory void. And I don't think the regulatory void will exist in the same way in two or three years. But right now it's very messy. I don't know if you've seen the news recently about in the UK, there's been now a sort of a regulatory backlash against the Virgin. Is it Virgin Airways or Virgin Atlantic, which is claiming 100% sustainable aviation fuel and are now getting scrutinized for that being a greenwashy claim, whereas that's like a program that has presumably run through all the right sort of checks and approvals and, and certifications in its own way, but is now on the wrong side of regulation.
Saif Hameed [00:08:07]:
So I think there's a lot of just uncertainty in this space that'll even out a little.
Isobel Wild [00:08:10]:
I think this leads me quite nicely onto the main topic of this conversation, is that in a lot of the conversations we're having, we're seeing a lot of mistakes that are unfolding within organizations that professionals are making, mostly unintentionally. But I would love to just get your thoughts on what those key ones are and maybe shout out to create some watch outs to avoid.
Saif Hameed [00:08:34]:
I think the list is long, a few that I'm noticing these days. There was a lot of momentum a couple of years ago towards gathering supply chain data and gathering greenhouse gas inventories from suppliers. And this more or less started a year, two years ago, where companies would be gathering, asking their suppliers for this data. I think what companies are finding now is that the data they're gathering is not hugely useful for them in calculating their scope three, because they're shifting now towards getting more activity based, more weight based data for their scope three calculations. And when they're gathering this greenhouse gas inventory data from a supplier, they have no way to apportion the share of that emissions that they are responsible for. This was a little easier when you were looking at spend based data gathering, because if you were looking at spend based data gathering, you'd basically try and use this greenhouse gas inventory data to compute a new economic intensity based emissions factor for this supplier. Whereas if it's activities and weights, you need data that represents the impact of what you're buying from a certain supplier, and that tends to be more product centric data. And so I'm seeing this shift now where a bunch of companies that have run their supply chain engagement programs, trying to gather greenhouse gas inventory data are now trying to shift towards product relevant data.
Saif Hameed [00:09:56]:
So that's kind of one mistake that I see companies making and trying to correct for. And some are still building up momentum gathering greenhouse gas inventory data, while others are already trying to make the switch. Another one that I'm seeing is that companies start asking for product carbon footprint data, but start asking for that from all of their suppliers, whereas actually many of their suppliers are not really relevant or not really material for their environmental impact. And I do see this creating a lot of noise. A lot of these suppliers then start running around trying to figure out how to do this, and maybe they're like a service provider or the impact is quite small, but the effort now involved for them to figure this out ends up being quite high. So those are two things that I'm noticing coming up. And then there's a general question around data granularity and how you get to granularity. And I'm seeing a lot of companies set up the wrong way on that front as well.
Isobel Wild [00:10:47]:
So it sounds like the root cause to a lot of these mistakes is that they are gathering or they are collecting the wrong data. Are there any mitigation tools or alternative approaches that you would take to kind of solve for those?
Saif Hameed [00:11:02]:
I would firstly start with strategy, as in I would think about what are you planning to do with your sustainability program? Forget the data for a moment. What are the actual types of initiatives? What are the parts of your environmentalist state that you are looking to make big change in? So let's say it's emissions, okay? Within emissions, is it going to be packaging? Is it going to be the agricultural upstream? Is it something in logistics? Is it something in use of products? What are the big categories where you want to make big change? And within those categories, what types of data are you likely to need? And at what granularity are you likely to need it? And you may well find that actually you don't need any granularity in some areas, and you need a lot of granularity in other areas. Then you start to think about how do you set up your data infrastructure to be able to support that, rather than running headfirst into data gathering and then trying to figure out what value a lot of this data brings, because you're probably going to end up quite lumpy where you've indexed very heavily on something that's useless and you've indexed very little on something that ends up being quite important.
Isobel Wild [00:12:08]:
And are there any broad stroke categories that are useful to have high granularity in? And as maybe if I was starting out in the industry, coming to the job as a data analyst, and I was like, right, okay, I need to make inroads here. Where to start? Do you have any broad stroke suggestions?
Saif Hameed [00:12:26]:
Yeah, I'm seeing two categories where I'm seeing a lot of activity right now, and there are a couple of categories where I maybe expect activity later. Right now, I'm seeing a lot of activity on packaging and logistics, and I expect a lot of activity to come on dairy and livestock and meat, and let's unpick a bit of that. Right. So on packaging and logistics, we're seeing a lot of companies come to us and they're in the packaging space. They're packaging companies or their logistics companies, and they're coming to us asking us to help them generate product carbon footprints at scale. If they're packaging and if they're logistics, they want us to generate key account or customer based allocations of logistics. Trips run for that customer based on the vehicle type, fuel type, route, et cetera. And both of those demands reflect that their customers are coming to them, asking them for this sort of data and or they see commercial opportunity from having this data available, and they want to bring this data to their customers because they think they can monetize it in some way, shape or form.
Saif Hameed [00:13:29]:
And that, I think, is really interesting. I'm also seeing that from the brand side, where I'm seeing a lot of focus on these categories of data and getting granular. And I think that's because it is easy to change these two categories relatively quickly. And while they may not be 40, 50, 60% of your emissions baseline, they still add up to pretty reasonable numbers.
Isobel Wild [00:13:52]:
And are there any other considerations as well? Easy to change, but I know, like packaging, obviously, that has a lot of, that's like a big marketing real estate piece which comes front of hand to the consumer. Are there any other considerations that are woven into that answer?
Saif Hameed [00:14:10]:
Yeah, I mean, let's think about this. You've told me off easy in the past for starting with three things, and then I speak for like ten minutes and we don't get to the third thing. So I'm going to try and find a way to rework my instinctive response here. But I'd say let's take packaging now. Let's say that you're an FMCG or you're a brand or you're an apparel company, and you know you're going to need packaging data. But I think there's a number of interesting questions for you to think through before you start going out there and hunting for data. So the first is, how are you going to use this data? Are you going to use it for reporting and compliance and so on? Great. And are you also going to use it maybe for actually, like, redesigning the stock, keeping unit, redesigning the format and your R and D teams or someone else is going use that to play around with.
Saif Hameed [00:14:55]:
Because if that's the case, then you're going to need more than what your purchasing data can provide you with your purchasing system. Whatever you're using is going to provide you with how much you've spent. Maybe if you're lucky, it also has some weights, maybe it has some other characteristics and those characteristics set in purchasing and you're going to use that. But it is going to be missing a lot of other characteristics that you're R and D teams are going to need for them to derive value from calculated packaging emissions data. And those other characteristics might be what is the blend of recycled or non recycled material. Now, maybe that's available because the material that you bought from a supplier has something in the category, the label, the title, like you've said, hey, I've bought like a 30 70 mix of plastic and recycle plastic pet, for instance. Maybe that's the actual category title. You still need some way to convert that, by the way, into a tag that can be used by your R and D team.
Saif Hameed [00:15:56]:
But that's like one type of data that will usually be missing maybe from this purchasing data set. So you're now going to need to find another data set. Maybe it's the result of interviews that you've run with some suppliers, maybe it's stored somewhere else, maybe there's a supply chain engagement program run elsewhere. But it's like another dataset somewhere that captures this detail of what share of this particular packaging item that you bought is recycled or not. And there will be other categories similarly to this. And you're actually going to need to combine them probably or ideally before you calculate the stuff into emissions content. But then you also want to preserve that tagging structure so that it can be used by the R and D team and the R and D team can say, hey, I want to change up this packaging format to more recycled material and I want to reference recycled material that I'm already buying because I want to work with my existing suppliers. Can I actually play around with this data in whatever application I'm using? And at Altruistiq, for example, you can do this because we preserve the tagging structure.
Saif Hameed [00:16:56]:
And so you can use that tagging as like a filter and you can play around with it within the platform environment. You can actually reconstruct products and materials, but that is designed for outcome that we wanted to have available going in and we also know that our customers want to have that available going in and that's why we've sold for it. It's much harder to do that retrospectively. Once you've already gathered data, published it, you're now trying to work with it.
Isobel Wild [00:17:25]:
Interesting. And can we talk for a minute about aggregate data? And maybe actually should we start off with what that actually means?
Saif Hameed [00:17:33]:
Aggregate data makes life easy and then makes life hard later. So if you think about in its simplest form, most companies that were doing carbon calculations five years ago, and there weren't that many of them, but those that there were were using some form of aggregate data. They were saying, okay, how much packaging have I bought? That's one line item in an Excel spreadsheet, or maybe it's five line items. How much have I spent on logistics? Or how much diesel has been used? That's maybe one line item or five line items. But this is all aggregation. You're basically summing up a lot of stuff into a higher level category. The reason you're summing it up is because that just makes data much easier to work with. Excel doesn't work at all for over a million rows, but it starts to seriously have issues and crash at like 5100 thousand rows.
Saif Hameed [00:18:23]:
So actually no one wants to be working, no analyst really wants to be working with huge data sets in Excel, for example. And so you start to. Most of the carbon calculating or carbon accounting world has been used to working with aggregate data because this used to be a very small scale side of desktop sort of activity in the past. However, aggregate data also makes it really hard to identify change opportunities at any level of specificity. So if you've aggregated all of your logistics trips into one line item, and you basically know you used 100 liters of diesel last week or whatever, good luck figuring out where you change that. You're going to have to gather a whole lot more information and run a bunch of conversations to figure that out. And that's also a bit harder to do once you've set up and you're running. Whereas the way that we think about it, you actually want to gather the granular data at the start of the process and then aggregate up from there.
Saif Hameed [00:19:23]:
Within our platform environment, for instance, you can have all the granularity, which means you maybe have like a billion rows of data available in our largest customer cases, but you can also play around with aggregates in the form of tags, filters, et cetera. And that means you can drill super deep, but you can also work super high level. So I think that's another design error that a lot of teams make is they start with the aggregate, they set their targets on the aggregate data and they run their program of the aggregate data, and as a result, once they start digging into more granularity, the whole world changes because they get a lot more nuance.
Isobel Wild [00:19:58]:
This makes me think of the conversation that you had with Charles Kahn from Patagonia, who said that they're in their third or fourth iteration of their data infrastructure, and each time they're like, this is the one. This is the one we're going for. It's going to be brilliant. And then you get six months down the line and you realize actually something's breaking here. Something's not working here from what it sounds here, is that actually getting that granularity, first off is amazing, and that is where you want to start off and build up from. But if you are at a stage where you have got the aggregate data and now actually need to work backwards, what advice would you give from that starting point? Are there any best practices?
Saif Hameed [00:20:40]:
Yeah, I think the same principle, Izzy, which is I would think about where you need granularity and where you don't, and where granularity will give you meaningful incremental accuracy. So, for example, logistics is a great one with logistics, because the emissions calculation is very straightforward. And what I mean by this is that there is a predictable chemical formula for whatever fuel you're using that can give you a reasonably reliable conversion rate. Like if it's a liter of diesel of a certain spec, it will convert into a certain amount of emissions in a standard vehicle. And there's some variation based on the type of vehicle, like is it an old engine or a new engine or whatever, but the parameters are quite narrow. And so in this case, actually getting granularity can be quite helpful, reasonably easy, and you can actually start chipping away and making some fairly rapid improvements. And so that, I think is a great example of getting more accuracy and more granularity. And I would probably start with that.
Saif Hameed [00:21:39]:
I think packaging is another great area. I think there are some places where actually it's probably not very useful, like in most of our customers, which are food, apparel, personal care, these sorts of companies, the agency providing you with, providing you with stationary supplies, like these things are very small elements, and so maybe you never need to get granularity. There actually, like a lot of these, like professional services companies as well, could just be a lump sum aggregate for a long time yet, and you don't need to think about breaking that out. A surprising category is always financed emissions by companies just parking their deposits with a bank that finances fossil fuel companies. And then theres a massive line item on financed emissions as well.
Isobel Wild [00:22:24]:
But yeah, I think even from a personal point of view, when I was first coming into the world of sustainability, I came across the make my money matter scheme, which was saying how moving your pension to the right pension provider can be the biggest area that you can reduce your impact, which blew my marbles away. I was so shocked by that.
Saif Hameed [00:22:44]:
But hey, just to jump in, actually, sorry, just to build on that, I did a strategy for a big retail bank four or five years ago when I was at McKinsey. It was a sustainability strategy. They had a new CEO, and the CEO wanted to make a big wave on sustainability. And the whole argument was retail deposits. Actually, the CEO's argument was, we want to capture a market of retail depositors, like people like you and me, who just want to have a more sustainable bank account. And that's the reason. And for that reason, we're looking at the portfolio of lending that we do to corporations, and we're segmenting oil and gas, coal based industries, et cetera, et cetera. And what we concluded was actually the downside of just saying no to coal based lending altogether and to downsizing oil and gas progressively over the next several years to zero.
Saif Hameed [00:23:38]:
The downside impact of that was a very easy yes versus the upside of getting a few incremental percentage points of market share with retail investors that were concerned about where they deposit their money, really.
Isobel Wild [00:23:51]:
And I guess I wonder how that's also changed now as well. With all the news headlines around renewables taking over fossil fuels. I can imagine actually gap has kind of gone even further.
Saif Hameed [00:24:03]:
This is a fun one, because actually I think it's changed in unpredictable ways. So a few things happened since then. One is interest rates shot up. And so what I'm describing was still a low interest rate environment, which means that the money that you lent out to these companies, you didn't actually make maybe as much of a spread on it as you would now. And so banking generally can be a much more profitable business lending to those sectors now than I imagine it could before. Another thing has been that renewable energy has actually become less clearly cheaper now than it was four or five years ago. And the economics have shifted a bit. So you see a lot of big renewable energy champions like Oersted were doing much better than and are not doing so well now.
Saif Hameed [00:24:48]:
So actually, I think the dynamic has probably changed a fair bit in somewhat negative ways. But at the same time, I think a lot of consumers, if you just look at generational transition, I would actually fact that if you reran the numbers on what consumer share cares about this now versus five years ago, I would expect that that share has increased.
Isobel Wild [00:25:08]:
Wow, that's really interesting and surprising. I didn't expect those insights on the renewable energy market to be quite like that. To take us back a few steps. You were talking about data accuracy and data granularity. I think those terms usually come hand in hand, but I just wanted to ask, do they or are there trade offs that you have to make to if you're opting for granularity? Actually, does that sometimes come at the expense of accuracy and vice versa?
Saif Hameed [00:25:38]:
Yeah, I think the terms are, can be a bit confusing. So when I think about accuracy, I think about, is this data what I think it is? Basically, the extent to which this is what I think it is, or what I expect it to be, is accuracy. And so if you tell me that this is a spend based emissions calculation, and I'm using a spend based emissions factor, and I have some spend based business data that I'm using against it, then for me accuracy is okay. Did the multiplication work all right? Was there an error in data entry otherwise, like, did the formula spit out the result that it should have? Because for that classification, as long as the multiplication worked properly, that is an accurate outcome. It is not a granular outcome by any measure, and its not a super usable outcome. But I wouldnt say it is necessarily inaccurate. Its a lower bar, basically a lower quality number in some ways. I think that what you get with granularity is that you get the ability to be more precise.
Saif Hameed [00:26:43]:
And so to that extent, I think from a usability standpoint, granularity equates to accuracy. When most people, like an executive on the board, for instance, is looking at, or non executive on the board for that matter, is looking at emissions data, and you're just telling them, look, this is the emissions of our business, and you're not having lots of additional qualifiers and saying they're spend based elements and activity and weight based elements, and you're just saying this is the emissions footprint of our business, then that data is probably highly inaccurate because it will contain lots of high level estimations and approximations, and then the accuracy of that goes down. So I would think about what is the expectation that you're creating as driving your understanding of accuracy?
Isobel Wild [00:27:23]:
Okay, that's a clearer way to think about it than I was thinking about originally. One of the mistakes we mentioned at the start was it's actually not worth going out to all your hundreds or tens of thousands of suppliers asking them for data. But for the suppliers that you do need to get data from, what's the best approach to get that data in maybe a quite non painful way, which has been cited as like a big pain point for suppliers at the moment, being inundated with lots of surveys. Are there any examples that you've seen out there of a company that's really nailing it and actually getting it right?
Saif Hameed [00:27:59]:
Yeah. So, like, as I kind of think about this, the mental model that I would go with. So let's say I am running a supply chain engagement program. I'm responsible in a business for gathering this sort of data. I would try and think about what are the categories of data where incremental granularity matters to me or is useful for me. And I would kind of almost, I'm thinking of like a two by two, that's one axis on my two by two matrix. How important is this data to me? And then I think the other one is, to what extent do I need to go it alone on this if I want to get it? And that's the other axis. And so where you end up with is you say, okay, look, actually there is some data that is super important for me, and I will probably need to go it alone.
Saif Hameed [00:28:41]:
Let me give you an example there. Let's say that you are Starbucks and you're looking at better coffee data, better data around the coffee beans. This is super important for you. Obviously, the odds are that you are going to, in some way, shape or form, need to go it alone or need to set the standard, etcetera, because you are the dominant buyer of that product in the value chain. And if you're not moving, actually the whole industry is probably not moving. That's one area where you probably do need to go it alone, and it is super material. There will be lots of other great examples of this. And in some cases it's by virtue of being dominant, in some cases, probably just, you may be the only one that cares enough about it to move the needle.
Saif Hameed [00:29:20]:
There's another category which is, this is super important to me. And actually, I don't need to go it alone because everyone else is pushing in the same direction. Packaging data, great example. Every FMCG right now is looking at their packaging data, is speaking with their packaging supplier and trying to figure out how to get more accurate packaging data at the product level. You don't need to go it alone. You should join up, join forces with, whether it's the WBCSD pact framework or coalition, whether it's some other program, just be a free rider on the momentum that's already running and tap into that infrastructure and ecosystem. Maybe it means you need to wait a year before you get great data, but it doesn't make sense for you to invest in setting up a whole parallel infrastructure when that's an industry wide problem that others are going to help you solve.
Isobel Wild [00:30:06]:
Interesting. And so maybe to just mention what you started with, which was around gathering data for product carbon footprints versus GHG scope one to three, do you essentially think that and this, I might be putting you into a corner here, so get ready to break free if you don't agree to. But do you essentially think that maybe the GHC protocol scope one to three is actually going to become a bit obsolete and won't be used in the future? And actually we're going to be moving more towards a standard where we'll look at product carbon footprints and have catalogs of product carbon footprints as an identifying factor. And the corporate footprint, the corporate baseline, will perhaps come as like an afterthought instead.
Saif Hameed [00:30:47]:
The short answer is probably no. It's a reasonably certain no on the first one and a qualified no on the second. And let me unpick both of those on the first one. The greenhouse gas protocol has something like an over 90% market share in the carbon accounting world. So right now, the majority of any kind of carbon calculations, whether product level or corporate wide, are greenhouse gas protocol based calculations. And scope one, two, three is hugely dominant. So I don't think it's going to change. I think most other structures and regulations, et cetera, et cetera, are going to reference it, and I think that's going to make it even more entrenched.
Saif Hameed [00:31:30]:
Maybe a decade or two from now, we're having a different conversation. Izzy, if you and I are still around, still running this podcast, but for now, like, I don't see that changing. From a reporting landscape perspective, I think that product carbon footprints will rapidly become the means through which companies structure and run their decarbonization programs and their broader environmental programs as those pcfs become more like pefs and start covering other elements as well. At the same time, my qualified note is I don't think you're going to have this catalog of PCs that anyone can browse, because from the conversations we have with companies, very few B two b organizations or B two c organizations want that data to be publicly available and accessible. This data tends to be exchanged between two organizations in a somewhat closed circuit. It is much less likely to be easily referenceable because of both the risk and liability that comes with, and also the potential for IP leakage that that comes with. So I think there's going to be a much slower opening up there.
Isobel Wild [00:32:37]:
I think we've mentioned it before, but the secret sauce that people perhaps don't want to reveal en masse, but all these mistakes and challenges that we've just spoken to and rooted in the issue of having the wrong data, collecting the wrong data, or managing the wrong data, what one piece of advice would you have for sustainability professionals to stay forward leaning on it?
Saif Hameed [00:33:01]:
I think the best advice I can give now, in 2024, is that these problems are being solved by other companies. And so it's not the daunting task it was two, three years ago. There are patterns, there's momentum, there are tools, there are consultants. This problem is being solved. I think the best thing you can do for yourself, to be kind to your future self, is to just think through what you want to achieve with your data before you run around gathering it. That's probably still the best advice that I would give anyone.
Isobel Wild [00:33:33]:
A strong note to end on. Saif, are there any other points that you want to flag or emphasize size that we've spoken about today?
Saif Hameed [00:33:40]:
I think a lot of companies are worrying about things that may not be super important for them. So, you know, I was speaking with a beverage company earlier today, and they were asking me about Flag. They were also quite clear that they don't intend to set an SBTI target, and so they're wondering whether they should, they should have flag. Flag conformant scope, three calculations, and in an environment where the guidance is still famously draft, I've now lost track of whether it's still draft or whether anything changed last week, etcetera. And you're not setting an SBTI target. Flag is probably not a thing that you need to worry about right now. And this business is a mid sized business. There's a lot of other stuff they could be doing do focus on reducing their emissions, but also communication with customers, et cetera.
Saif Hameed [00:34:28]:
There's no need, really to get bogged down in making life overly complicated for its own sake. You might end up still doing a lot of the stuff that you do to be flag conformant, because it makes sense for you in running your program. But flag as a reporting standard is probably overkill. So I think a lot of companies are maybe overcomplicating their lives and should focus on what moves the needle in the way that you need it to.
Isobel Wild [00:34:53]:
Yeah, definitely. And we actually have an upcoming event on maximizing and managing your sustainability budget. And as a teaser, because I've had the pleasure of speaking to lots of the panelists. Well, a few of the panelists who are speaking on that, they've mentioned that aligning with standards and regulations are actually like a huge, huge budget sink, which can end up being way more than you anticipated, and you actually end up spending a lot more money on reporting than actually on making the action happen.
Saif Hameed [00:35:25]:
You see, the interesting thing is that this should not be a surprise for most companies, at least those in Europe, because the EU CSRD actually commissioned a study to gauge how much companies would spend, on average, on meeting CSRD requirements. And I forget the exact number, but for the roughly 50,000 companies in scope, the average was expected to be well over a million euros per year, not including the setup cost, which was expected to be a bit higher. And 50,000 companies in Europe means you're well beyond the traditional large gaps into actually a lot of mid sized businesses as well. So this should not be that big of a. I'm not saying that's right or wrong, I'm just saying it shouldn't be a big surprise.
Isobel Wild [00:36:08]:
Saif, thank you so much for your insights today. It was great to discuss all these problems and solutions in more detail, and thank you everybody for listening. And please reach out with any feedback or content suggestions. As always, we're keen to link them in where we can.
Saif Hameed [00:36:24]:
Thanks, Izzy. Bye.