WEBVTT 1 00:00:00.000 --> 00:00:02.583 (upbeat instrumental music) 2 00:00:04.617 --> 00:00:05.450 This is our fourth year 3 00:00:05.450 --> 00:00:07.114 of, um, running a sustainability report 4 00:00:10.440 --> 00:00:11.561 or a carbon emissions report, um, 5 00:00:11.561 --> 00:00:12.394 that looks at corporates, about 2,000 of them. 6 00:00:12.394 --> 00:00:13.227 I'll go through the details in a second. 7 00:00:13.227 --> 00:00:14.186 We do it in partnership with CO2 AI, um, and I'll let Dmitry perhaps introduce CO2 AI a little bit 8 00:00:14.186 --> 00:00:15.019 before we get into the details of the report. 9 00:00:15.019 --> 00:00:15.852 So, ah, CO2 AI is a carbon management emission platform 10 00:00:15.852 --> 00:00:16.685 that helps you to compute your emissions. 11 00:00:16.685 --> 00:00:17.518 Actually, you did a great job explaining what we do. 12 00:00:17.518 --> 00:00:18.351 We focus, not on the specific problem like you, 13 00:00:18.351 --> 00:00:19.184 but we're rather trying to help big corporations 14 00:00:19.184 --> 00:00:20.017 to measure their emissions across their - all scopes, 15 00:00:20.017 --> 00:00:20.850 across all their, uh, data, and to then drive the carbonization, 16 00:00:20.850 --> 00:00:21.683 try to find, uh, best levers that can help you 17 00:00:21.683 --> 00:00:22.516 to reduce your emissions. 18 00:00:22.516 --> 00:00:23.349 Ah, we're a very young startup that was originated inside BCG 19 00:00:23.349 --> 00:00:24.182 and was created by BCG originally. 20 00:00:24.182 --> 00:00:25.015 Now, we're an independent startup, 21 00:00:25.015 --> 00:00:25.848 but we already have quite cool logos, 22 00:00:25.848 --> 00:00:26.681 that's companies that's working with us. 23 00:00:26.681 --> 00:00:27.514 Ah, you know some of them - some of them are less maybe public known, 24 00:00:27.514 --> 00:00:29.423 but they're huge corporations, like Cisco, Reckitt, 25 00:00:29.423 --> 00:00:30.256 which has challenges especially in the scope 3.1 26 00:00:30.256 --> 00:00:31.089 with a lot of a lot of supplies that you need to help 27 00:00:31.089 --> 00:00:31.922 to decarbonize. 28 00:00:31.922 --> 00:00:32.755 Um, the carbon emission survey, ah, this year 29 00:00:32.755 --> 00:00:33.588 roughly covered, um, 2000 organizations. 30 00:00:33.588 --> 00:00:34.421 Um, I think the interesting, um, factor here, 31 00:00:34.421 --> 00:00:35.254 it's about 40% of global emissions. 32 00:00:35.254 --> 00:00:36.087 So the data we're seeing is representative, ah, 33 00:00:36.087 --> 00:00:36.920 of what's out there. 34 00:00:36.920 --> 00:00:38.087 And then you can see the spread across geographies 35 00:00:38.087 --> 00:00:38.920 and industries. 36 00:00:38.920 --> 00:00:39.753 Um, if you have an interest in geography 37 00:00:39.753 --> 00:00:40.586 there's some really interesting insights about countries 38 00:00:40.586 --> 00:00:41.419 that are actually leading on, um, measurement and reduction. 39 00:00:41.419 --> 00:00:42.252 and it is not the countries you expect, um, which I think is an interesting, um, set of, ah, results anyway, um... 40 00:00:42.252 --> 00:00:43.085 Ah, it's a bit of a sad picture... so, um, 41 00:00:43.085 --> 00:00:43.918 and I think this is where the power 42 00:00:43.918 --> 00:00:44.751 of the year-on-year comes. 43 00:00:44.751 --> 00:00:45.584 Um, only 9% of organizations 44 00:00:45.584 --> 00:00:46.597 are comprehensively measuring scope one, two, 45 00:00:46.597 --> 00:00:47.430 and three, right? 46 00:00:47.430 --> 00:00:48.263 That is scope three of stream and scope three downstream. 47 00:00:48.263 --> 00:00:49.096 But that's eroding. 48 00:00:49.096 --> 00:00:50.102 Um, that's not the one that scares me the most. 49 00:00:50.102 --> 00:00:51.102 Ah, the middle and the last one are perhaps the problem. 50 00:00:51.102 --> 00:00:51.935 Um, targets... so about 3% of organizations walk back their targets. 51 00:00:51.935 --> 00:00:52.768 Um, we see this, right? 52 00:00:52.768 --> 00:00:53.601 Many of our clients have come to us and said, 53 00:00:53.601 --> 00:00:54.434 the target's too difficult, I can't reach it. 54 00:00:54.434 --> 00:00:55.267 Um, if I keep growing the way I do, 55 00:00:55.267 --> 00:00:56.100 it's, I'm really gonna struggle. 56 00:00:56.100 --> 00:00:56.933 And so they're walking back their targets. 57 00:00:56.933 --> 00:00:57.766 Um, so I think it's an unfortunate, ah, state of play 58 00:00:57.766 --> 00:00:58.599 and then, um, only 11% are reducing in line 59 00:00:58.599 --> 00:00:59.432 with their ambitions, right? 60 00:00:59.432 --> 00:01:00.265 So that's... I think where, ah, hopefully technology, 61 00:01:00.265 --> 00:01:01.098 as we'll talk a little later, green technology can help us. 62 00:01:01.098 --> 00:01:01.931 Now, the bright spot... there are companies 63 00:01:01.931 --> 00:01:02.764 that actually are decarbonizing. Great. 64 00:01:02.764 --> 00:01:03.597 And the leaders of them, so 25% of the companies 65 00:01:03.597 --> 00:01:04.430 that are decarbonizing are getting 7% or more 66 00:01:04.430 --> 00:01:05.263 of their revenue and financial benefits - 67 00:01:05.263 --> 00:01:06.096 and I'll say net financial benefits, 68 00:01:06.096 --> 00:01:06.929 so after their investments. 69 00:01:06.929 --> 00:01:07.762 So we're talking about 200 million bucks to the bottom line. 70 00:01:07.762 --> 00:01:08.595 Okay, finally, a sigh of relief. 71 00:01:08.595 --> 00:01:09.428 Like you can actually make money 72 00:01:09.428 --> 00:01:10.261 and decarbonize at the same time. 73 00:01:10.261 --> 00:01:11.094 So it's not just, you know, moral high grounds. 74 00:01:11.094 --> 00:01:11.927 Um, the main way they do it is they save cost at the same time as they decarbonize. 75 00:01:11.927 --> 00:01:12.760 And and many of us will hear this with our clients, 76 00:01:12.760 --> 00:01:13.593 whether you swap, um, old boilers for new ones, 77 00:01:13.593 --> 00:01:14.426 whether you reduce your footprint. 78 00:01:14.426 --> 00:01:15.259 Um, a lot of it, and we see this in the MACC curves. 79 00:01:15.259 --> 00:01:16.092 I don't know if all of you know what a MACC curve is, 80 00:01:16.092 --> 00:01:16.925 but we generally see the first 20 to 30% 81 00:01:16.925 --> 00:01:17.758 of initiatives you have be cost - cost negative. 82 00:01:17.758 --> 00:01:18.591 Um, so they are really, really impactful. 83 00:01:18.591 --> 00:01:19.424 Um, so this data just essentially prove 84 00:01:19.424 --> 00:01:20.257 that, that it's happening on mass scale, um, 85 00:01:20.257 --> 00:01:21.090 and we are hoping more folks hear this message 86 00:01:21.090 --> 00:01:21.923 and more leaders hear this message, um, so they understand 87 00:01:21.923 --> 00:01:22.756 that there's a really positive ROI on the money 88 00:01:22.756 --> 00:01:23.589 being put forward to decarbonize. 89 00:01:23.589 --> 00:01:24.422 Um, I'll hand over to Dmitry in a second, 90 00:01:24.422 --> 00:01:25.255 but what are companies that lead do? 91 00:01:25.255 --> 00:01:26.088 Um, and this is where you'll see Sylvain's famous number 92 00:01:26.088 --> 00:01:26.921 that he, he suggested earlier. 93 00:01:26.921 --> 00:01:27.754 They do a bunch of basic things, right? 94 00:01:27.754 --> 00:01:28.587 Like, okay, you need to measure, um, 95 00:01:28.587 --> 00:01:29.420 you should report and have a target. 96 00:01:29.420 --> 00:01:30.253 But where things get really interesting 97 00:01:30.253 --> 00:01:31.086 is if you're gonna lead in this space, you need a plan. 98 00:01:31.086 --> 00:01:31.919 Um, and, and this comes up more and more now 99 00:01:31.919 --> 00:01:32.752 and more of the statistically significant data indicates 100 00:01:32.752 --> 00:01:33.585 the climate transition plans and their importance. 101 00:01:33.585 --> 00:01:34.418 You need product-level emissions 102 00:01:34.418 --> 00:01:35.251 'cause actually sentiment of what you buy in any, 103 00:01:35.251 --> 00:01:36.084 whether it's grocery store or vehicle 104 00:01:36.084 --> 00:01:36.917 or whatever, really matters. 105 00:01:36.917 --> 00:01:37.750 So you knowing what the carbon footprint of something is 106 00:01:37.750 --> 00:01:38.583 and then making an informed consumer decision 107 00:01:38.583 --> 00:01:39.437 makes all the difference. 108 00:01:39.437 --> 00:01:40.270 So that's the four times. 109 00:01:40.270 --> 00:01:41.103 So those that lead are four times more likely 110 00:01:41.103 --> 00:01:41.936 to measure product carbon footprints. 111 00:01:41.936 --> 00:01:42.769 It is far more difficult... I think Dmitry tried to code it... He'll tell you all about it if you wanted the break. 112 00:01:42.769 --> 00:01:43.602 Um, and then they leverage AI. 113 00:01:43.602 --> 00:01:44.435 Um, we gotta get some shortcuts in, right? 114 00:01:44.435 --> 00:01:45.268 People are trying and waiting for data to be perfect 115 00:01:45.268 --> 00:01:46.101 and they want the perfect measurement 116 00:01:46.101 --> 00:01:46.934 and, you know, it just doesn't exist. 117 00:01:46.934 --> 00:01:47.767 And AI helps them get there a bit faster 118 00:01:47.767 --> 00:01:48.600 and it helps them get going, um, and and that's a really big unlock. 119 00:01:48.600 --> 00:01:49.468 Um, and with that, I'll hand over to Dmitry 120 00:01:49.468 --> 00:01:50.301 to take us through why. 121 00:01:50.301 --> 00:01:51.134 So essentially, you saw on previous slide, 122 00:01:51.134 --> 00:01:51.974 there are two levers that helped a lot companies 123 00:01:51.974 --> 00:01:52.807 to decarbonize faster. 124 00:01:52.807 --> 00:01:53.640 It's AI and product carbon footprint. 125 00:01:53.640 --> 00:01:54.473 Essentially, the reason why AI is so helpful here, 126 00:01:54.473 --> 00:01:55.306 companies have millions of activities, 127 00:01:55.306 --> 00:01:56.139 you called it what you will multiply 128 00:01:56.139 --> 00:01:56.972 and impacts before... abservations... we call it activities, but it's essentially the same thing. 129 00:01:56.972 --> 00:01:57.805 And usually big corporation have millions of them. 130 00:01:57.805 --> 00:01:58.638 Oh, I bought this amount of collaborative electricity 131 00:01:58.638 --> 00:01:59.471 in this day and this month, or I purchased these items 132 00:01:59.471 --> 00:02:00.304 or I resolved these like thousands of items. 133 00:02:00.304 --> 00:02:01.137 And you can imagine, especially for retailers, 134 00:02:01.137 --> 00:02:01.970 it's very, very hard. 135 00:02:01.970 --> 00:02:02.995 They sell hundreds of thousands of items every day. 136 00:02:02.995 --> 00:02:03.828 Ah, they're different. 137 00:02:03.828 --> 00:02:04.661 And you need to understand where the hotspots are, 138 00:02:04.661 --> 00:02:05.494 where the problem is. 139 00:02:05.494 --> 00:02:06.327 You don't need ... you need to know where to focus. 140 00:02:06.327 --> 00:02:07.160 And AI gives you an ability 141 00:02:07.160 --> 00:02:07.993 to quickly match those activities, observations 142 00:02:07.993 --> 00:02:08.826 to emission factors, impact factors 143 00:02:08.826 --> 00:02:09.659 to understand your CO2 emissions. 144 00:02:09.659 --> 00:02:10.492 After that you can go into more deeper, you can try 145 00:02:10.492 --> 00:02:11.325 to maybe estimate a bit more accurate your hotspot, 146 00:02:11.325 --> 00:02:12.158 but you need to know where you need to estimate. 147 00:02:12.158 --> 00:02:12.991 That's probably the biggest unlock. 148 00:02:12.991 --> 00:02:13.824 Without it, you really cannot say what to do next. 149 00:02:13.824 --> 00:02:14.657 So you got your total emissions, but what to do with it? 150 00:02:14.657 --> 00:02:15.490 You really need to understand, 151 00:02:15.490 --> 00:02:16.323 oh, this item is my biggest hotspot. 152 00:02:16.323 --> 00:02:17.156 I want to work with supply of this item to decarbonize it. 153 00:02:17.156 --> 00:02:17.989 (Dmitry faintly speaking) 154 00:02:17.989 --> 00:02:18.822 And second thing is product carbon footprint. 155 00:02:18.822 --> 00:02:19.655 Essentially, you... there are different levers why it's important. 156 00:02:19.655 --> 00:02:20.488 For like producers of product, of course, it's important 157 00:02:20.488 --> 00:02:21.537 to understand what's coming inside of it. 158 00:02:21.537 --> 00:02:22.370 What can I reduce? 159 00:02:22.370 --> 00:02:23.203 What can I swap maybe potentially 160 00:02:23.203 --> 00:02:24.036 for less carbon-emissive item. 161 00:02:24.036 --> 00:02:24.869 And for example, you're, I don't know, a beer producer, 162 00:02:24.869 --> 00:02:25.702 you want to do it at scale. 163 00:02:25.702 --> 00:02:26.535 You have 20, 30 types of different base, maybe hundreds. 164 00:02:26.535 --> 00:02:27.368 So you want to have some methodology 165 00:02:27.368 --> 00:02:28.201 that helps you to do it at scale. 166 00:02:28.201 --> 00:02:29.034 So you blend AI and matching with different emission factors 167 00:02:29.034 --> 00:02:29.867 together with some structure on how to aggregate this data 168 00:02:29.867 --> 00:02:30.700 and to show transparency as you were presenting before, ah, 169 00:02:30.700 --> 00:02:31.533 to your customers in order to convince them 170 00:02:31.533 --> 00:02:32.366 that you actually have really great products 171 00:02:32.366 --> 00:02:33.199 in terms of the carbon. 172 00:02:33.199 --> 00:02:34.032 Um, can I go next, please? 173 00:02:34.032 --> 00:02:34.865 And one of the examples we worked with repeat-recently 174 00:02:34.865 --> 00:02:35.698 on, first, estimating their emissions 175 00:02:35.698 --> 00:02:36.531 and, ah, helping them... um... reduce them... ah... over time. 176 00:02:36.531 --> 00:02:37.364 So the problem is that they have 25,000 products 177 00:02:37.364 --> 00:02:38.197 and there is way more ingredients coming into creation 178 00:02:38.197 --> 00:02:39.030 as of those products. 179 00:02:39.030 --> 00:02:39.863 You need to estimate them to see where your hotspots are 180 00:02:39.863 --> 00:02:40.696 and create a plan that can be executed across the company. 181 00:02:40.696 --> 00:02:41.529 The last bit is actually also very, very complex 182 00:02:41.529 --> 00:02:42.362 because it's a big corporation. 183 00:02:42.362 --> 00:02:43.195 You have hundreds of users internally, 184 00:02:43.195 --> 00:02:44.028 which can affect, like, uh, uh, this equation. 185 00:02:44.028 --> 00:02:44.861 So you need a way to people - for people to communicate, 186 00:02:44.861 --> 00:02:45.694 to set targets, to involve your supply chain into it - 187 00:02:45.694 --> 00:02:46.527 to potentially, ah, talk with your echo design teams 188 00:02:46.527 --> 00:02:47.360 how to change this in the future. 189 00:02:47.360 --> 00:02:48.193 So all of this is a very, very complex part 190 00:02:48.193 --> 00:02:49.026 with a lot of data to play with. 191 00:02:49.026 --> 00:02:49.859 And it's data that, it's more granular than financial 192 00:02:49.859 --> 00:02:50.692 because you need to actual splits 193 00:02:50.692 --> 00:02:51.525 of what is inside your items. 194 00:02:51.525 --> 00:02:52.358 That is very hard to make, very easy surfaceable 195 00:02:52.358 --> 00:02:54.053 for also non-very experienced users.