1 00:00:00,060 --> 00:00:02,500 The following content is provided under a Creative 2 00:00:02,500 --> 00:00:04,019 Commons license. 3 00:00:04,019 --> 00:00:06,360 Your support will help MIT OpenCourseWare 4 00:00:06,360 --> 00:00:10,730 continue to offer high quality educational resources for free. 5 00:00:10,730 --> 00:00:13,330 To make a donation or view additional materials 6 00:00:13,330 --> 00:00:17,236 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:17,236 --> 00:00:17,861 at ocw.mit.edu. 8 00:00:30,762 --> 00:00:33,560 PROFESSOR: OK, let's go ahead and get started. 9 00:00:33,560 --> 00:00:36,080 We're actually not going to be joined 10 00:00:36,080 --> 00:00:38,269 by our friends in Zaragoza today, 11 00:00:38,269 --> 00:00:39,810 but we're still going to be recorded. 12 00:00:39,810 --> 00:00:44,270 So still have to stay on our best behavior, I guess. 13 00:00:44,270 --> 00:00:47,410 Today we're going to go through life cycle assessment, 14 00:00:47,410 --> 00:00:48,880 and talk about that and how we use 15 00:00:48,880 --> 00:00:53,400 it to quantify environmental impacts of products. 16 00:00:53,400 --> 00:00:55,670 The goal today is pretty simple. 17 00:00:55,670 --> 00:00:57,650 First is to lay understand the steps 18 00:00:57,650 --> 00:01:02,740 in the LCA, exactly what is the methodology, how it's used, 19 00:01:02,740 --> 00:01:05,620 the specific steps in it and what they mean, 20 00:01:05,620 --> 00:01:08,157 what goes into each of them. 21 00:01:08,157 --> 00:01:10,240 But really, what we're also going to spend time on 22 00:01:10,240 --> 00:01:11,698 is understanding the decisions that 23 00:01:11,698 --> 00:01:16,980 go into that, using the hand dryer study as an example 24 00:01:16,980 --> 00:01:19,240 to go through and understand how you link all 25 00:01:19,240 --> 00:01:22,520 these different steps together, what it means in understanding 26 00:01:22,520 --> 00:01:25,320 the decisions that you have to make as a person doing this 27 00:01:25,320 --> 00:01:28,700 or as a company, in terms of setting the boundary, 28 00:01:28,700 --> 00:01:33,020 understanding what your goal is, and all the uncertainty that 29 00:01:33,020 --> 00:01:34,770 goes along with all the decisions 30 00:01:34,770 --> 00:01:38,985 and assumptions that you have to make along the way. 31 00:01:38,985 --> 00:01:41,360 Last time when we were talking about carbon footprinting, 32 00:01:41,360 --> 00:01:43,070 we were talking really about things that 33 00:01:43,070 --> 00:01:46,630 go on at the corporate level. 34 00:01:46,630 --> 00:01:49,170 Within the boundary is essentially drawn around 35 00:01:49,170 --> 00:01:50,120 your organization. 36 00:01:50,120 --> 00:01:52,750 We talked about how you could use those internally, 37 00:01:52,750 --> 00:01:55,790 doing things like trying to evaluate different departments 38 00:01:55,790 --> 00:01:58,400 in your organization, tracking your performance, 39 00:01:58,400 --> 00:02:00,800 setting up some kind of internal metrics. 40 00:02:00,800 --> 00:02:02,450 All the standard sort of things you 41 00:02:02,450 --> 00:02:07,540 would do for financial cost, quality, sustainability, really 42 00:02:07,540 --> 00:02:09,259 just one extension of that. 43 00:02:09,259 --> 00:02:11,900 But also talking about its relation to external, 44 00:02:11,900 --> 00:02:14,640 you're publishing a corporate CSR report, 45 00:02:14,640 --> 00:02:17,770 participating in programs like the Carbon Disclosure Project, 46 00:02:17,770 --> 00:02:20,164 reporting to regulators, investors, et cetera. 47 00:02:20,164 --> 00:02:21,830 And one of the things we've talked about 48 00:02:21,830 --> 00:02:25,220 was the difficulty in comparing between organizations 49 00:02:25,220 --> 00:02:27,100 that might be doing different activities. 50 00:02:27,100 --> 00:02:30,380 Even though they operate in the same business space, 51 00:02:30,380 --> 00:02:32,430 they might produce some of the same products, 52 00:02:32,430 --> 00:02:34,470 it's very difficult to compare two organizations 53 00:02:34,470 --> 00:02:36,714 based on their organizational level reporting, 54 00:02:36,714 --> 00:02:39,130 because they might control different aspects of the supply 55 00:02:39,130 --> 00:02:39,629 chain. 56 00:02:39,629 --> 00:02:41,720 They may do different things, even if they're 57 00:02:41,720 --> 00:02:43,120 producing the same products. 58 00:02:43,120 --> 00:02:46,600 Today we're going to talk about the lower section here, 59 00:02:46,600 --> 00:02:49,030 which is how we do a product or a supply chain level 60 00:02:49,030 --> 00:02:50,260 comparison. 61 00:02:50,260 --> 00:02:51,890 And some of the same ideas still apply, 62 00:02:51,890 --> 00:02:53,500 that there might be reasons to do this 63 00:02:53,500 --> 00:02:54,760 both internally and externally. 64 00:02:54,760 --> 00:02:56,385 Internally, right, trying to understand 65 00:02:56,385 --> 00:02:59,310 where in the supply chain your environmental impact is. 66 00:02:59,310 --> 00:03:01,526 What are the things you might have control over 67 00:03:01,526 --> 00:03:03,150 that you might be willing to-- that you 68 00:03:03,150 --> 00:03:04,960 might be looking to change. 69 00:03:04,960 --> 00:03:07,110 Exploring, especially differences 70 00:03:07,110 --> 00:03:08,790 in the design or changes that you might 71 00:03:08,790 --> 00:03:10,800 make to reduce those impacts. 72 00:03:10,800 --> 00:03:13,256 But also it has an external components as well, 73 00:03:13,256 --> 00:03:14,630 namely around things like product 74 00:03:14,630 --> 00:03:17,840 labeling or making environmental claims about your product. 75 00:03:17,840 --> 00:03:21,080 Trying to convince consumers or your customers 76 00:03:21,080 --> 00:03:23,622 that your product is better than ones that are on the market. 77 00:03:23,622 --> 00:03:26,079 And again, there's going to be some slight differences that 78 00:03:26,079 --> 00:03:27,810 go on between what you're trying to do 79 00:03:27,810 --> 00:03:29,490 and how you might go about it. 80 00:03:32,070 --> 00:03:36,290 So with that, we'll start off talking about this hand dryer 81 00:03:36,290 --> 00:03:39,440 study, and then go through the bits and pieces of it 82 00:03:39,440 --> 00:03:41,320 individually. 83 00:03:41,320 --> 00:03:43,765 This really is getting into-- it's 84 00:03:43,765 --> 00:03:45,390 a pretty good example of something that 85 00:03:45,390 --> 00:03:47,270 gets at the fundamental questions of how 86 00:03:47,270 --> 00:03:50,970 you compare different products and different things. 87 00:03:50,970 --> 00:03:52,590 In this case, we don't really care 88 00:03:52,590 --> 00:03:55,710 much about who owns which piece of each of these products. 89 00:03:55,710 --> 00:03:58,970 We're looking at the product as a whole from beginning to end 90 00:03:58,970 --> 00:04:02,560 and trying to come up with a way of comparing them. 91 00:04:02,560 --> 00:04:06,810 This is the result from some of the material that 92 00:04:06,810 --> 00:04:08,860 came out of the publication of this study. 93 00:04:08,860 --> 00:04:12,240 But it requires a different type of analysis and sort 94 00:04:12,240 --> 00:04:13,700 of a different level of work. 95 00:04:13,700 --> 00:04:17,899 Has anybody in here done a life cycle assessment before? 96 00:04:17,899 --> 00:04:20,310 What kind of product, or what did you look at? 97 00:04:20,310 --> 00:04:22,060 AUDIENCE: I used to work at a company that 98 00:04:22,060 --> 00:04:26,878 made seafood products, and we looked at three of our SKUs 99 00:04:26,878 --> 00:04:28,640 that we made for Walmart. 100 00:04:28,640 --> 00:04:32,040 They asked us to specifically do a life cycle assessment 101 00:04:32,040 --> 00:04:33,720 on three different products of theirs. 102 00:04:33,720 --> 00:04:35,390 PROFESSOR: Just to understand what the-- 103 00:04:35,390 --> 00:04:37,147 AUDIENCE: Yeah, not of a larger, they 104 00:04:37,147 --> 00:04:39,230 were working with us and numerous other companies, 105 00:04:39,230 --> 00:04:42,800 but they were trying to get a sense of what the larger 106 00:04:42,800 --> 00:04:46,305 impact of individual items that they carried 107 00:04:46,305 --> 00:04:48,721 was in different departments over the course of the weeks. 108 00:04:48,721 --> 00:04:51,095 PROFESSOR: Did you actually do the-- were you responsible 109 00:04:51,095 --> 00:04:51,640 for sort of-- 110 00:04:51,640 --> 00:04:53,889 AUDIENCE: I did a large portion of the data collection 111 00:04:53,889 --> 00:04:56,600 for the stuff we were most directly responsible for. 112 00:04:56,600 --> 00:04:58,120 It was kind of broken into chunks, 113 00:04:58,120 --> 00:05:00,404 so a major part was packaging, then 114 00:05:00,404 --> 00:05:02,320 there was the food that went in the packaging. 115 00:05:02,320 --> 00:05:05,730 And so I was working with all the different groups who 116 00:05:05,730 --> 00:05:09,230 knew the most about those respective portions 117 00:05:09,230 --> 00:05:13,380 to then draw the data from them and put it together cohesively. 118 00:05:13,380 --> 00:05:14,870 PROFESSOR: How about anybody else? 119 00:05:14,870 --> 00:05:17,590 Anybody read previous studies like this before, 120 00:05:17,590 --> 00:05:21,510 or seen anything like this before? 121 00:05:21,510 --> 00:05:25,310 AUDIENCE: I actually read this one for when we were working 122 00:05:25,310 --> 00:05:29,600 on the [? S-Lab ?] project. 123 00:05:29,600 --> 00:05:31,540 And I really wanted some guidance 124 00:05:31,540 --> 00:05:33,200 on the choice of materials. 125 00:05:33,200 --> 00:05:38,170 So they said LCA, but when we got down to it, 126 00:05:38,170 --> 00:05:39,652 it was actually just the materials, 127 00:05:39,652 --> 00:05:42,035 so it was not the whole assessment. 128 00:05:42,035 --> 00:05:43,910 PROFESSOR: What were some of your impressions 129 00:05:43,910 --> 00:05:46,252 about it after reading through this? 130 00:05:49,556 --> 00:05:52,647 AUDIENCE: I thought it was strange of them 131 00:05:52,647 --> 00:05:54,480 in the beginning to say that they were going 132 00:05:54,480 --> 00:05:56,580 to do a full life cycle assessment including 133 00:05:56,580 --> 00:06:00,010 transportation costs, and then two sentences later they 134 00:06:00,010 --> 00:06:02,850 said they were going to ignore the entire supply chain 135 00:06:02,850 --> 00:06:05,690 and just focus on doing an apples to apples 136 00:06:05,690 --> 00:06:07,610 comparison of the performance. 137 00:06:07,610 --> 00:06:10,540 And they sort of black box all of the dryers 138 00:06:10,540 --> 00:06:14,880 into the same sort of process in China. 139 00:06:14,880 --> 00:06:19,160 And I thought we weren't necessarily getting 140 00:06:19,160 --> 00:06:21,740 who, maybe, was the real champion between Dyson 141 00:06:21,740 --> 00:06:22,400 and Excel. 142 00:06:22,400 --> 00:06:24,430 Because if perhaps one of them happens 143 00:06:24,430 --> 00:06:27,090 to have manufacturing in the United States, 144 00:06:27,090 --> 00:06:29,280 then they're getting a large transportation 145 00:06:29,280 --> 00:06:32,590 cost lumped into the cycle that maybe doesn't exist. 146 00:06:32,590 --> 00:06:35,540 So I don't really know why they decided to go that route. 147 00:06:35,540 --> 00:06:38,210 It seems like they went through all the machinations 148 00:06:38,210 --> 00:06:41,720 for the paper towels, so it wouldn't have been maybe 149 00:06:41,720 --> 00:06:44,610 that difficult to get it for the other companies 150 00:06:44,610 --> 00:06:48,029 as well, so that they actually did the real full life cycle. 151 00:06:48,029 --> 00:06:49,570 Because otherwise, it seems like they 152 00:06:49,570 --> 00:06:51,990 should have collapsed the scope if they only 153 00:06:51,990 --> 00:06:55,110 wanted to do performance, and leave the upstream part 154 00:06:55,110 --> 00:06:56,822 of the supply chain out of it. 155 00:06:56,822 --> 00:07:00,100 PROFESSOR: So right, they didn't really ignore the supply chain, 156 00:07:00,100 --> 00:07:03,137 they more just standardized it, in a sense. 157 00:07:03,137 --> 00:07:05,470 Because they did include all of those things that go on, 158 00:07:05,470 --> 00:07:07,830 but they didn't model the exact supply chain 159 00:07:07,830 --> 00:07:11,790 that each specific product might have taken. 160 00:07:11,790 --> 00:07:13,550 So what was the reason for that? 161 00:07:13,550 --> 00:07:15,560 They mentioned why they did it in there. 162 00:07:15,560 --> 00:07:17,351 AUDIENCE: They said they wanted to put them 163 00:07:17,351 --> 00:07:19,540 all on equal footing as part of the supply chain, 164 00:07:19,540 --> 00:07:21,580 but it seems like not putting them 165 00:07:21,580 --> 00:07:25,080 on equal footing is sort of the purpose of the life cycle, 166 00:07:25,080 --> 00:07:29,436 to see who actually has the better process. 167 00:07:29,436 --> 00:07:31,220 AUDIENCE: It actually didn't matter. 168 00:07:31,220 --> 00:07:35,210 They had a figure that explained if all these different devices 169 00:07:35,210 --> 00:07:37,620 were manufactured in Malaysia, US, or China, 170 00:07:37,620 --> 00:07:40,790 there's very little difference across them. 171 00:07:40,790 --> 00:07:41,520 PROFESSOR: Right. 172 00:07:41,520 --> 00:07:43,853 We'll talk about that, because as we read through there, 173 00:07:43,853 --> 00:07:46,580 there's a lot of assumptions that they had to make going on. 174 00:07:46,580 --> 00:07:48,670 So what might have been one of the reasons 175 00:07:48,670 --> 00:07:51,630 they had to make that assumption about the supply chain, 176 00:07:51,630 --> 00:07:52,450 about taking this. 177 00:07:52,450 --> 00:07:56,190 AUDIENCE: They just might not have had the data available 178 00:07:56,190 --> 00:07:58,790 and they determined that they could spend a year trying 179 00:07:58,790 --> 00:08:00,680 to find the data, but in the end, 180 00:08:00,680 --> 00:08:01,100 it wouldn't have made a difference 181 00:08:01,100 --> 00:08:02,733 and they wanted to go through the study 182 00:08:02,733 --> 00:08:03,722 and finish it anyways. 183 00:08:03,722 --> 00:08:04,430 PROFESSOR: Right. 184 00:08:04,430 --> 00:08:07,690 In essence, that if you didn't have all the information 185 00:08:07,690 --> 00:08:10,500 for all of the products, then you really 186 00:08:10,500 --> 00:08:13,170 had two choices which was to perhaps go 187 00:08:13,170 --> 00:08:15,572 spend the time trying to identify everything, 188 00:08:15,572 --> 00:08:17,280 the exact path that all of those products 189 00:08:17,280 --> 00:08:18,560 took in their supply chain. 190 00:08:18,560 --> 00:08:21,290 The other would be to try to ignore that piece 191 00:08:21,290 --> 00:08:23,920 and put them on a sort of standardized setting 192 00:08:23,920 --> 00:08:26,080 to do the comparison with, and then come back later 193 00:08:26,080 --> 00:08:27,538 and test some of those assumptions. 194 00:08:27,538 --> 00:08:30,300 And that was sort of the approach 195 00:08:30,300 --> 00:08:31,980 that they took with this one. 196 00:08:31,980 --> 00:08:35,324 What were some of the other areas that stood out? 197 00:08:35,324 --> 00:08:37,449 AUDIENCE: The sensitivity analyses 198 00:08:37,449 --> 00:08:40,289 were the majority of the paper. 199 00:08:40,289 --> 00:08:43,179 PROFESSOR: The paper itself was like 25 pages, 200 00:08:43,179 --> 00:08:45,805 and then there was like 80 pages of sensitivity analysis 201 00:08:45,805 --> 00:08:46,280 and whatnot at the end. 202 00:08:46,280 --> 00:08:47,779 AUDIENCE: Yeah, and there were a lot 203 00:08:47,779 --> 00:08:50,780 of assumptions in understanding which of the assumptions 204 00:08:50,780 --> 00:08:51,685 had more risk. 205 00:08:54,064 --> 00:08:55,980 What I was really struck by, too, was the fact 206 00:08:55,980 --> 00:08:57,610 that so much of the data was coming 207 00:08:57,610 --> 00:09:00,700 from either the companies themselves 208 00:09:00,700 --> 00:09:03,500 or from other studies. 209 00:09:03,500 --> 00:09:06,570 And there was a point at the end of the paper that 210 00:09:06,570 --> 00:09:08,610 said they didn't have access to the writers, 211 00:09:08,610 --> 00:09:11,140 authors, or practitioners who prepared those studies. 212 00:09:11,140 --> 00:09:15,759 So I think that the gap, they made assumptions 213 00:09:15,759 --> 00:09:18,717 made on gaps of information, but even the information 214 00:09:18,717 --> 00:09:21,182 that they do have I called into question. 215 00:09:21,182 --> 00:09:24,140 I [? didn't believe ?] the accuracy of it. 216 00:09:24,140 --> 00:09:26,480 PROFESSOR: So access to data was something 217 00:09:26,480 --> 00:09:27,814 that came up a number of times. 218 00:09:27,814 --> 00:09:29,730 You saw there are a number of previous studies 219 00:09:29,730 --> 00:09:31,190 that have been done, and you kept 220 00:09:31,190 --> 00:09:33,523 probably noticing the references throughout the document 221 00:09:33,523 --> 00:09:36,380 of every time that there's some assumption or some play made. 222 00:09:36,380 --> 00:09:38,110 And most of them were being traced back 223 00:09:38,110 --> 00:09:41,017 to previous studies that had been done before. 224 00:09:41,017 --> 00:09:44,000 AUDIENCE: Even if you had access to data, 225 00:09:44,000 --> 00:09:46,950 the thing also is that data changes and you get updates, 226 00:09:46,950 --> 00:09:48,730 and you get more data. 227 00:09:48,730 --> 00:09:53,100 So I thought that the LSA was true at some point, 228 00:09:53,100 --> 00:09:55,435 but it's also made very quickly. 229 00:09:58,676 --> 00:10:02,080 PROFESSOR: I'm going to say it's static. 230 00:10:02,080 --> 00:10:04,860 It's a snapshot of a point in time 231 00:10:04,860 --> 00:10:07,650 of a specific configuration of the supply chain analysis. 232 00:10:07,650 --> 00:10:11,070 AUDIENCE: My frustration with it was the single most important 233 00:10:11,070 --> 00:10:13,774 variable when it's how long people take to dry their hands 234 00:10:13,774 --> 00:10:15,190 or how many paper towels they use, 235 00:10:15,190 --> 00:10:17,830 and they had no real data on that. 236 00:10:17,830 --> 00:10:20,164 It was just completely made up, because you can't really 237 00:10:20,164 --> 00:10:22,329 get that kind of data, because you can't put cameras 238 00:10:22,329 --> 00:10:24,410 in a bathroom, and you can't put someone in there 239 00:10:24,410 --> 00:10:26,420 without affecting their behaviors. 240 00:10:26,420 --> 00:10:27,894 PROFESSOR: The joke that they had-- 241 00:10:27,894 --> 00:10:30,310 the study authors were here at MIT in the material systems 242 00:10:30,310 --> 00:10:31,790 labs-- is that they thought about going out 243 00:10:31,790 --> 00:10:33,456 to the airport and just sort of standing 244 00:10:33,456 --> 00:10:36,182 in the bathroom with a notepad tracking people, 245 00:10:36,182 --> 00:10:38,723 but figured that might get them thrown out by security pretty 246 00:10:38,723 --> 00:10:39,223 quickly. 247 00:10:42,709 --> 00:10:45,250 In some sense, this comes back to the idea of access to data. 248 00:10:48,660 --> 00:10:51,160 You're making these assumptions because you lack the data 249 00:10:51,160 --> 00:10:53,662 that, if you had gone through and done some of the studies 250 00:10:53,662 --> 00:10:56,120 and things like that-- actually observed people-- you might 251 00:10:56,120 --> 00:10:59,885 understand how exactly it is, what is the average hand drying 252 00:10:59,885 --> 00:11:01,060 time, things like that. 253 00:11:01,060 --> 00:11:04,750 AUDIENCE: They talked about the lifetime of the dryer, which 254 00:11:04,750 --> 00:11:07,410 they had as five years, and thought 255 00:11:07,410 --> 00:11:08,555 that seemed kind of low. 256 00:11:08,555 --> 00:11:09,930 It seems like five years would be 257 00:11:09,930 --> 00:11:14,390 pretty frequent for a building manager to change out dryers. 258 00:11:14,390 --> 00:11:16,460 Is that time until it breaks or is 259 00:11:16,460 --> 00:11:18,820 that the time until they recommended [INAUDIBLE]. 260 00:11:18,820 --> 00:11:21,569 PROFESSOR: Anybody know why they chose five years? 261 00:11:21,569 --> 00:11:22,360 AUDIENCE: Warranty. 262 00:11:22,360 --> 00:11:23,945 PROFESSOR: That's what the warranty is. 263 00:11:23,945 --> 00:11:24,890 AUDIENCE: But how many people-- 264 00:11:24,890 --> 00:11:26,765 PROFESSOR: Throw it out if it's working fine. 265 00:11:31,130 --> 00:11:35,030 I'm going to Juan, this goes back 266 00:11:35,030 --> 00:11:41,440 to your first point about what they chose. 267 00:11:41,440 --> 00:11:45,780 The configuration of the supply chain was important, right. 268 00:11:45,780 --> 00:11:48,780 And this goes back to, if we're taking a static snapshot 269 00:11:48,780 --> 00:11:51,340 of something that all the different permutations and all 270 00:11:51,340 --> 00:11:53,690 the different combinations might be difficult to do, 271 00:11:53,690 --> 00:11:55,160 so you have to start making some judgments 272 00:11:55,160 --> 00:11:56,930 about the configuration of the supply chain. 273 00:11:56,930 --> 00:11:59,096 And so they did that, in this case, by assuming they 274 00:11:59,096 --> 00:12:00,000 all came from China. 275 00:12:00,000 --> 00:12:02,720 Except for the towels, which came from the US, 276 00:12:02,720 --> 00:12:07,120 because that's sort of the industry standard. 277 00:12:07,120 --> 00:12:10,730 That's the second one, these are related to each other. 278 00:12:10,730 --> 00:12:12,400 As you start digging into it, you 279 00:12:12,400 --> 00:12:14,090 start running into all these areas 280 00:12:14,090 --> 00:12:16,250 that you could do things differently, 281 00:12:16,250 --> 00:12:18,640 and there's sort of all these different variables that 282 00:12:18,640 --> 00:12:19,190 go into it. 283 00:12:19,190 --> 00:12:21,470 At some point, you have to start making 284 00:12:21,470 --> 00:12:23,770 some assumptions and some limitations 285 00:12:23,770 --> 00:12:26,210 to try to do better on it. 286 00:12:26,210 --> 00:12:29,190 Did any of you think any of these-- I mean, 287 00:12:29,190 --> 00:12:30,740 they did some sensitivity analysis, 288 00:12:30,740 --> 00:12:33,250 but which ones do you think would significantly 289 00:12:33,250 --> 00:12:34,580 have affected the outcome? 290 00:12:34,580 --> 00:12:35,669 Or were there any? 291 00:12:38,423 --> 00:12:41,460 AUDIENCE: [INAUDIBLE] that usage data, too, 292 00:12:41,460 --> 00:12:45,950 was just a huge factor, because if you looked at the outputs. 293 00:12:45,950 --> 00:12:47,390 The overall ranking of the product 294 00:12:47,390 --> 00:12:51,000 was so largely driven by what was said, 295 00:12:51,000 --> 00:12:53,660 was estimated to be your usage. 296 00:12:53,660 --> 00:13:00,950 That was one of the least effectively tracked, or easily 297 00:13:00,950 --> 00:13:02,160 quantifiable metrics. 298 00:13:02,160 --> 00:13:04,938 So to me, that was just sort of [INAUDIBLE] 299 00:13:04,938 --> 00:13:06,790 AUDIENCE: Going along those lines, 300 00:13:06,790 --> 00:13:08,830 I think that it was the number of papers, 301 00:13:08,830 --> 00:13:15,382 or the mass of the papers that is used for one usage. 302 00:13:15,382 --> 00:13:21,146 So that had a significant impact on how big 303 00:13:21,146 --> 00:13:26,635 of a GWP for the paper towels have 304 00:13:26,635 --> 00:13:29,140 compared to all the others. 305 00:13:29,140 --> 00:13:30,610 So that was a big assumption, too. 306 00:13:30,610 --> 00:13:35,012 They made some big assumptions on the hand dryers, 307 00:13:35,012 --> 00:13:36,503 but also on the paper towels. 308 00:13:36,503 --> 00:13:38,070 PROFESSOR: And in some sense, this 309 00:13:38,070 --> 00:13:42,290 gets back to-- Different bathrooms 310 00:13:42,290 --> 00:13:43,940 have different types of paper towels, 311 00:13:43,940 --> 00:13:46,895 and so identifying one paper towel 312 00:13:46,895 --> 00:13:49,020 that you're going to choose to be representative of 313 00:13:49,020 --> 00:13:51,775 can be quite difficult to do. 314 00:13:51,775 --> 00:13:53,777 AUDIENCE: I [? think also ?] throwing out 315 00:13:53,777 --> 00:13:57,940 the electric grid assumptions [INAUDIBLE] in use, 316 00:13:57,940 --> 00:14:00,906 and they had some pretty big impacts on the manufacturing 317 00:14:00,906 --> 00:14:01,879 for the paper towels. 318 00:14:01,879 --> 00:14:04,170 So the paper towels, if they weren't made in the United 319 00:14:04,170 --> 00:14:07,470 States and they weren't made on the average, 320 00:14:07,470 --> 00:14:08,870 that could have a huge impact. 321 00:14:08,870 --> 00:14:13,330 Likewise the usage, depending on where it was being used, 322 00:14:13,330 --> 00:14:17,066 [INAUDIBLE] would have a big impact on the electric dryers. 323 00:14:17,066 --> 00:14:19,160 PROFESSOR: So if you were Xlerator 324 00:14:19,160 --> 00:14:21,410 which came in probably as the most similar, 325 00:14:21,410 --> 00:14:24,070 in terms of products that it would be competing with. 326 00:14:24,070 --> 00:14:27,180 I don't know if you've used the Xlerator dryer before, but just 327 00:14:27,180 --> 00:14:29,742 really powerful heated air coming out. 328 00:14:29,742 --> 00:14:30,950 A little bit different setup. 329 00:14:30,950 --> 00:14:34,300 But it generally, and the results 330 00:14:34,300 --> 00:14:37,100 showed us the second best after the Dyson one, but probably 331 00:14:37,100 --> 00:14:38,720 their direct competitor. 332 00:14:38,720 --> 00:14:41,010 So what would your response be if you were Xlerator 333 00:14:41,010 --> 00:14:44,262 and you saw Dyson sort of promoting this study? 334 00:14:44,262 --> 00:14:46,720 AUDIENCE: [? I think I'd say ?] Xlerator was made in the US 335 00:14:46,720 --> 00:14:50,483 and Dyson was made in Malaysia, so you could point out that 336 00:14:50,483 --> 00:14:53,864 Dyson doesn't have to be-- Dyson using the [INAUDIBLE] 337 00:14:53,864 --> 00:14:58,211 [INAUDIBLE] the US electric grid for [? manual ?] [INAUDIBLE] 338 00:14:58,211 --> 00:15:03,041 and also have to ship the products across the ocean. 339 00:15:03,041 --> 00:15:05,040 PROFESSOR: That's a big part of the marketing, 340 00:15:05,040 --> 00:15:06,470 they need to have a made in the US 341 00:15:06,470 --> 00:15:08,830 certified, whatever that means, on their webpage. 342 00:15:12,134 --> 00:15:14,930 AUDIENCE: Also for Xlerator, the measured versus 343 00:15:14,930 --> 00:15:17,310 reported drying times were really different. 344 00:15:17,310 --> 00:15:18,980 For Dyson they were exactly the same, 345 00:15:18,980 --> 00:15:21,085 it was almost double for Xlerator. 346 00:15:21,085 --> 00:15:24,174 So they may want to get control of how much time they think 347 00:15:24,174 --> 00:15:26,925 people are spending and maybe make their machines 348 00:15:26,925 --> 00:15:28,091 a little bit more efficient. 349 00:15:28,091 --> 00:15:30,840 Because if they were to go to 12 seconds, which is lower, 350 00:15:30,840 --> 00:15:33,670 the difference between Dyson and Xlerator wasn't that big. 351 00:15:33,670 --> 00:15:35,695 But when you went with the 20-- I 352 00:15:35,695 --> 00:15:37,420 think it was 20 seconds-- that's where 353 00:15:37,420 --> 00:15:38,711 the difference was more severe. 354 00:15:38,711 --> 00:15:41,546 PROFESSOR: So where did that measured drying time come from? 355 00:15:44,790 --> 00:15:47,150 AUDIENCE: Because they looked for the measure, 356 00:15:47,150 --> 00:15:52,130 they were following the NSF standards of 0.1 gram of water 357 00:15:52,130 --> 00:15:54,810 on the hands when you dry it on a towel. 358 00:15:54,810 --> 00:15:58,090 But in [INAUDIBLE] I mean, it's really perceptions. 359 00:15:58,090 --> 00:16:01,130 The user doesn't actually measure 0.1 grams of water 360 00:16:01,130 --> 00:16:03,440 on my hand, you'd better stop using the dryer. 361 00:16:03,440 --> 00:16:07,170 It's really a perception issue. 362 00:16:07,170 --> 00:16:09,420 PROFESSOR: We had that NSF protocol, all right. 363 00:16:09,420 --> 00:16:11,325 This is not National Science Foundation, 364 00:16:11,325 --> 00:16:14,559 it's National Standards or International Standards 365 00:16:14,559 --> 00:16:15,850 Institute, something like that. 366 00:16:15,850 --> 00:16:19,270 They have standards for a lot of things, and in this case, 367 00:16:19,270 --> 00:16:21,620 it's how do you define dry hands. 368 00:16:21,620 --> 00:16:26,950 So it's whatever it was, 0.01 grams of moisture per whatever 369 00:16:26,950 --> 00:16:28,280 is how they defined it. 370 00:16:28,280 --> 00:16:31,774 So the idea would be that if you were trying to objectively make 371 00:16:31,774 --> 00:16:33,940 some claim about how long it takes to dry the hands, 372 00:16:33,940 --> 00:16:35,231 that might be one way to do it. 373 00:16:35,231 --> 00:16:37,596 Who actually did the testing for that, though? 374 00:16:37,596 --> 00:16:40,387 AUDIENCE: It was some study at University of Florida. 375 00:16:40,387 --> 00:16:42,720 I don't really know how they came up with those numbers. 376 00:16:42,720 --> 00:16:44,870 PROFESSOR: Yes. 377 00:16:44,870 --> 00:16:48,720 I think, actually, Dyson reported it. 378 00:16:48,720 --> 00:16:51,080 So it was coming from a document that Dyson 379 00:16:51,080 --> 00:16:54,590 had claiming that somebody had done this test 380 00:16:54,590 --> 00:16:55,440 and come up with it. 381 00:16:57,867 --> 00:16:59,700 So you see, we're starting to get this paper 382 00:16:59,700 --> 00:17:03,300 trail going back and figuring out that so much of the study 383 00:17:03,300 --> 00:17:08,470 was dependent on this value, and that the person reporting 384 00:17:08,470 --> 00:17:10,950 these values is essentially Dyson, 385 00:17:10,950 --> 00:17:13,089 who's making the claim that it came from. 386 00:17:13,089 --> 00:17:17,384 Even though we have sort of a standard that makes sense 387 00:17:17,384 --> 00:17:19,550 in terms of, OK, we're going to try to find some way 388 00:17:19,550 --> 00:17:21,040 to do this objectively. 389 00:17:21,040 --> 00:17:26,230 It still would definitely leave some room open for Xlerator 390 00:17:26,230 --> 00:17:28,174 to probably take some issues with it. 391 00:17:28,174 --> 00:17:30,720 AUDIENCE: If Xlerator was really concerned about the results 392 00:17:30,720 --> 00:17:33,129 of the study, too-- and something I've never noticed 393 00:17:33,129 --> 00:17:34,670 using one of the Airblades, they said 394 00:17:34,670 --> 00:17:37,970 that it has a digital motor, so it has no spin down time, 395 00:17:37,970 --> 00:17:43,480 so they didn't credit the Dyson with any 1.5 seconds spin down. 396 00:17:43,480 --> 00:17:47,670 Whereas the Xlerator was doing it out 3 over 750 watts, 397 00:17:47,670 --> 00:17:54,970 and assume 750 watts, which adds up over the 350,000 uses. 398 00:17:54,970 --> 00:17:58,820 So I guess if they wanted to borrow that idea 399 00:17:58,820 --> 00:18:01,060 and get rid of some energy consumption, 400 00:18:01,060 --> 00:18:03,024 they could do that. 401 00:18:03,024 --> 00:18:04,940 PROFESSOR: So if you were Xlerator you'd seen, 402 00:18:04,940 --> 00:18:07,900 you read this study, and wanted to respond to that, 403 00:18:07,900 --> 00:18:09,540 what might you go do differently? 404 00:18:12,676 --> 00:18:14,800 AUDIENCE: One, you might commission your own study. 405 00:18:14,800 --> 00:18:16,970 Since this was commissioned by Dyson. 406 00:18:16,970 --> 00:18:20,180 That's a pretty big starting point. 407 00:18:20,180 --> 00:18:23,220 Dyson commissioned-- not to say that the researchers at MIT 408 00:18:23,220 --> 00:18:25,850 weren't as objective as possible-- but as we noted, 409 00:18:25,850 --> 00:18:28,565 there was a good deal of data that the study was 410 00:18:28,565 --> 00:18:29,940 dependent on that came from Dyson 411 00:18:29,940 --> 00:18:32,112 and Dyson then commissioned the study with it. 412 00:18:32,112 --> 00:18:34,570 So if I'm Xlerator, I'd probably like to have a say in that 413 00:18:34,570 --> 00:18:34,910 as well. 414 00:18:34,910 --> 00:18:36,470 PROFESSOR: Right, so that was one of the other issues 415 00:18:36,470 --> 00:18:37,400 with the data access, right. 416 00:18:37,400 --> 00:18:38,926 Because Dyson was behind this, they 417 00:18:38,926 --> 00:18:40,550 supplied the bill of materials and they 418 00:18:40,550 --> 00:18:43,785 supplied all of the data about the factory and things 419 00:18:43,785 --> 00:18:45,535 like that, whereas all the other products, 420 00:18:45,535 --> 00:18:47,826 they had to make use of publicly available information. 421 00:18:47,826 --> 00:18:50,300 So they didn't have access to that inside information. 422 00:18:50,300 --> 00:18:54,250 So if you were Xlerator and saw this, and disagreed with it, 423 00:18:54,250 --> 00:18:56,604 you could commission your own study. 424 00:18:56,604 --> 00:18:58,020 You could now make use of the fact 425 00:18:58,020 --> 00:19:00,350 that you know about your supply chain, 426 00:19:00,350 --> 00:19:03,347 and you could start actually going and doing the details. 427 00:19:03,347 --> 00:19:05,430 You could go through the fact that you're actually 428 00:19:05,430 --> 00:19:06,846 made in the US, and you might want 429 00:19:06,846 --> 00:19:08,950 to redo the study under that assumption. 430 00:19:08,950 --> 00:19:12,574 That you're going look at it with your specific supply 431 00:19:12,574 --> 00:19:13,990 chain, no longer considering, say, 432 00:19:13,990 --> 00:19:17,160 a generic's supply chain from China. 433 00:19:17,160 --> 00:19:19,290 So what would you do to get around this? 434 00:19:22,280 --> 00:19:24,740 Because that is still sort of the crux of it, 435 00:19:24,740 --> 00:19:28,260 was that you both claim similar drying times, 436 00:19:28,260 --> 00:19:30,471 but they're saying that in practice, yours 437 00:19:30,471 --> 00:19:31,220 is actually worse. 438 00:19:34,636 --> 00:19:37,570 AUDIENCE: I don't know, it just depends on [INAUDIBLE] 439 00:19:37,570 --> 00:19:39,960 here's what they did. 440 00:19:39,960 --> 00:19:44,210 Putting cameras or individuals in bathrooms, 441 00:19:44,210 --> 00:19:48,030 but potentially building in some sort of chip that 442 00:19:48,030 --> 00:19:51,330 enables you to see what actual times are for drying, 443 00:19:51,330 --> 00:19:55,366 and then that could work in your favor. 444 00:19:55,366 --> 00:19:58,230 PROFESSOR: I might go out and actually redo this study 445 00:19:58,230 --> 00:20:01,190 that Dyson was reporting. 446 00:20:01,190 --> 00:20:05,490 If I use this NSF study, to go in and at least verify 447 00:20:05,490 --> 00:20:08,380 that it really does take my machine 448 00:20:08,380 --> 00:20:11,890 30 seconds versus their 12 seconds or something like that. 449 00:20:11,890 --> 00:20:16,090 So at MIT, if you wanted to do study of figuring out 450 00:20:16,090 --> 00:20:17,510 how long people actually dried it, 451 00:20:17,510 --> 00:20:21,430 you'd have to go through the human subject ethics 452 00:20:21,430 --> 00:20:22,410 board and all of that. 453 00:20:22,410 --> 00:20:23,826 So you might have trouble actually 454 00:20:23,826 --> 00:20:26,580 getting a study through where you actually went out and tried 455 00:20:26,580 --> 00:20:27,430 to observe it. 456 00:20:27,430 --> 00:20:29,140 But you could at least try to reduce 457 00:20:29,140 --> 00:20:32,050 some of that uncertainty that comes in the measurement, 458 00:20:32,050 --> 00:20:37,119 since it's unfavorable to you and not really much hard 459 00:20:37,119 --> 00:20:38,410 information out there about it. 460 00:20:38,410 --> 00:20:40,800 So you might want to commission, say, an outside source 461 00:20:40,800 --> 00:20:42,580 to come in and repeat that testing, 462 00:20:42,580 --> 00:20:44,830 and see if they really got it. 463 00:20:44,830 --> 00:20:46,840 In terms of what they're actually doing-- so 464 00:20:46,840 --> 00:20:49,970 they actually have commissioned a study to respond, 465 00:20:49,970 --> 00:20:52,740 to follow up on this. 466 00:20:52,740 --> 00:20:54,410 Because nobody's ever going to be 467 00:20:54,410 --> 00:20:55,930 happy with all the assumptions that 468 00:20:55,930 --> 00:20:58,790 go on in one of these, especially when they turn out 469 00:20:58,790 --> 00:21:02,400 to be-- if you look at two of these big ones 470 00:21:02,400 --> 00:21:06,060 that are certainly unfavorable to Xlerator, probably. 471 00:21:06,060 --> 00:21:07,850 When you look at them it makes sense 472 00:21:07,850 --> 00:21:11,254 that maybe you would want to come back in and revisit this 473 00:21:11,254 --> 00:21:12,920 to try to make sure that the assumptions 474 00:21:12,920 --> 00:21:15,600 that your competitor is making that are unfavorable 475 00:21:15,600 --> 00:21:18,160 to you actually are grounded in reality, 476 00:21:18,160 --> 00:21:20,070 or don't necessarily make a difference 477 00:21:20,070 --> 00:21:21,440 in the final outcome. 478 00:21:21,440 --> 00:21:23,695 But you probably don't want them releasing 479 00:21:23,695 --> 00:21:26,450 a study that says this is your product 480 00:21:26,450 --> 00:21:27,910 and it's worse for the environment, 481 00:21:27,910 --> 00:21:29,500 it takes longer to dry, et cetera, 482 00:21:29,500 --> 00:21:32,390 without at least being prepared with some sort of follow up 483 00:21:32,390 --> 00:21:36,470 to dispute that, other than to just ignore it 484 00:21:36,470 --> 00:21:38,394 or claim that there's problems with it. 485 00:21:38,394 --> 00:21:39,810 You'd like to be able to come back 486 00:21:39,810 --> 00:21:43,657 with some sort of response that's grounded in some data 487 00:21:43,657 --> 00:21:44,740 or have some facts for it. 488 00:21:47,290 --> 00:21:50,670 We're going to walk through this a little bit more in depth 489 00:21:50,670 --> 00:21:53,680 in terms of looking at it within the scope of the methodology 490 00:21:53,680 --> 00:21:54,830 of life cycle assessment. 491 00:21:54,830 --> 00:22:00,724 But to start off with, just what is life cycle assessment. 492 00:22:00,724 --> 00:22:02,140 And that's a good question to ask, 493 00:22:02,140 --> 00:22:04,050 because it took some time for the industry 494 00:22:04,050 --> 00:22:05,890 itself to figure out what exactly it was. 495 00:22:05,890 --> 00:22:08,330 But there are a set of ISO standards 496 00:22:08,330 --> 00:22:11,810 that I think were produced in the 1997, the first version. 497 00:22:11,810 --> 00:22:12,860 Essentially it says this. 498 00:22:12,860 --> 00:22:15,410 LCA is a technique for assessing environmental aspects 499 00:22:15,410 --> 00:22:18,490 and potential impacts with a product by first, 500 00:22:18,490 --> 00:22:20,940 compiling an inventory of the inputs and outputs. 501 00:22:20,940 --> 00:22:24,770 So tracking, what's coming in and out of the environment. 502 00:22:24,770 --> 00:22:27,204 Second, evaluating the potential impacts associated 503 00:22:27,204 --> 00:22:28,370 with the inputs and outputs. 504 00:22:28,370 --> 00:22:30,280 So figuring out, now I know what I'm 505 00:22:30,280 --> 00:22:33,410 emitting to the air, what I'm taking out of the environment, 506 00:22:33,410 --> 00:22:35,490 actually figuring out what those impacts are. 507 00:22:35,490 --> 00:22:37,180 And one of the key points here is 508 00:22:37,180 --> 00:22:40,230 that it specifically mentions the potential environmental 509 00:22:40,230 --> 00:22:40,730 impacts. 510 00:22:40,730 --> 00:22:42,060 Because some of these studies are 511 00:22:42,060 --> 00:22:43,110 going to be forward looking. 512 00:22:43,110 --> 00:22:44,484 Trying to figure out what happens 513 00:22:44,484 --> 00:22:47,120 when we make a change as opposed to some of these studies that 514 00:22:47,120 --> 00:22:49,036 are looking backwards and trying to figure out 515 00:22:49,036 --> 00:22:50,740 what the actual impact is. 516 00:22:50,740 --> 00:22:52,837 So that's a big one, is that a lot of times, we 517 00:22:52,837 --> 00:22:55,420 don't necessarily know what the exact environmental impact is. 518 00:22:55,420 --> 00:22:58,169 We're trying to forecast ahead of time what it's going to be. 519 00:22:58,169 --> 00:22:59,710 And finally, interpreting the results 520 00:22:59,710 --> 00:23:03,229 in the inventory analysis and impact assessment in relation 521 00:23:03,229 --> 00:23:04,520 to the objectives of the study. 522 00:23:04,520 --> 00:23:09,630 So the big thing about this is that it's sort of fluid, 523 00:23:09,630 --> 00:23:10,824 in terms of what an LCA is. 524 00:23:10,824 --> 00:23:13,240 Because you start off with creating some kind of objective 525 00:23:13,240 --> 00:23:15,781 of what you want to do with the study, and all of the results 526 00:23:15,781 --> 00:23:17,930 that you generate are going to be interpreted back 527 00:23:17,930 --> 00:23:20,604 in terms of that scope and that goal that you started with. 528 00:23:20,604 --> 00:23:22,270 Some of the important things to remember 529 00:23:22,270 --> 00:23:24,277 is that it takes a life cycle view 530 00:23:24,277 --> 00:23:26,110 of the product, so raw materials all the way 531 00:23:26,110 --> 00:23:27,200 through to end of life. 532 00:23:27,200 --> 00:23:30,180 So to cradle to grave is typically the way 533 00:23:30,180 --> 00:23:33,140 that would be talked about. 534 00:23:33,140 --> 00:23:35,770 And while it specifically mentions products, 535 00:23:35,770 --> 00:23:38,460 it can be mentioned for services as well. 536 00:23:38,460 --> 00:23:41,180 So we can think of the hand drying example. 537 00:23:41,180 --> 00:23:42,950 It's not necessarily comparing products, 538 00:23:42,950 --> 00:23:45,725 it's more comparing a service, that of drying hands. 539 00:23:48,700 --> 00:23:52,250 LCA generally credited to Coca Cola 540 00:23:52,250 --> 00:23:56,760 as being the first person to develop this back in 1969, when 541 00:23:56,760 --> 00:23:58,310 they commissioned a study that would 542 00:23:58,310 --> 00:24:01,250 look at the environmental impact of using cans or glass bottles. 543 00:24:01,250 --> 00:24:03,960 So this is when they were first thinking about going to cans, 544 00:24:03,960 --> 00:24:05,918 and they were trying to understand not just all 545 00:24:05,918 --> 00:24:07,510 the economic and supply chain impact, 546 00:24:07,510 --> 00:24:10,300 but they wanted to consider the environmental impacts as well. 547 00:24:10,300 --> 00:24:13,327 They commissioned a study and actually hired some researchers 548 00:24:13,327 --> 00:24:14,910 to do it, and essentially they came up 549 00:24:14,910 --> 00:24:18,770 with what would eventually become life cycle assessment, 550 00:24:18,770 --> 00:24:21,210 in terms of quantifying that environmental impact 551 00:24:21,210 --> 00:24:24,750 in some manner by looking at the inputs and outputs. 552 00:24:24,750 --> 00:24:26,622 So that was in 1969. 553 00:24:26,622 --> 00:24:28,330 It started to gain attention in the '70s. 554 00:24:28,330 --> 00:24:31,300 There were other efforts going on in this area, 555 00:24:31,300 --> 00:24:33,830 especially in Europe with the energy crisis. 556 00:24:33,830 --> 00:24:36,040 There was a lot more concern about things 557 00:24:36,040 --> 00:24:38,500 like how much energy products were using, 558 00:24:38,500 --> 00:24:41,240 services, about what it was going to mean. 559 00:24:41,240 --> 00:24:44,660 As that started to fade away-- the energy 560 00:24:44,660 --> 00:24:48,150 crisis-- in the '80s, it became a little bit more 561 00:24:48,150 --> 00:24:50,350 known for companies making environmental claims 562 00:24:50,350 --> 00:24:52,840 about their products, so trying to use it in advertising 563 00:24:52,840 --> 00:24:54,150 and things like that. 564 00:24:54,150 --> 00:24:56,751 It gave rise to what they referred to as hired gun 565 00:24:56,751 --> 00:24:57,250 studies. 566 00:24:57,250 --> 00:25:00,220 Which is basically, as we saw, there's a lot of assumptions 567 00:25:00,220 --> 00:25:04,930 and a lot of questionable assumptions, 568 00:25:04,930 --> 00:25:07,044 at times, that go into doing one of these studies. 569 00:25:07,044 --> 00:25:08,710 And what they were finding is that there 570 00:25:08,710 --> 00:25:10,710 are companies out there that you would hire them 571 00:25:10,710 --> 00:25:12,940 and they would construct a study in such a way 572 00:25:12,940 --> 00:25:15,510 that it said basically whatever you wanted, reaching 573 00:25:15,510 --> 00:25:18,620 the point that 11 state attorney generals issued guidelines 574 00:25:18,620 --> 00:25:21,310 saying that you couldn't use life cycle assessment to make 575 00:25:21,310 --> 00:25:24,470 a claim or a comparative claim about the environmental impact 576 00:25:24,470 --> 00:25:26,440 of your product in an advertisement, 577 00:25:26,440 --> 00:25:28,510 until LCA became standardized. 578 00:25:28,510 --> 00:25:31,270 Until a uniform methodology was developed. 579 00:25:31,270 --> 00:25:34,020 So that sort of kicked it into gear 580 00:25:34,020 --> 00:25:37,260 of getting different companies, different organizations 581 00:25:37,260 --> 00:25:40,580 together to try to iron out what exactly a life cycle 582 00:25:40,580 --> 00:25:42,100 assessment was going to be. 583 00:25:42,100 --> 00:25:45,210 SETAC was probably the leading group, 584 00:25:45,210 --> 00:25:47,770 and then it's something like the something 585 00:25:47,770 --> 00:25:50,220 environmental toxicity and chemistry, 586 00:25:50,220 --> 00:25:53,057 society for environmental toxicity in chemistry, I think. 587 00:25:53,057 --> 00:25:53,890 Something like that. 588 00:25:53,890 --> 00:25:58,310 So it's generally chemists, people 589 00:25:58,310 --> 00:26:00,740 that study ecotoxicology, things like that, 590 00:26:00,740 --> 00:26:03,600 getting together and creating working groups, generally 591 00:26:03,600 --> 00:26:05,820 of academics and practitioners, trying to come up 592 00:26:05,820 --> 00:26:09,150 with a uniform methodology that can be used for life cycle 593 00:26:09,150 --> 00:26:10,140 assessment. 594 00:26:10,140 --> 00:26:14,710 And eventually this developed into some standards from ISO. 595 00:26:14,710 --> 00:26:17,830 14040 is the main one, but there are 596 00:26:17,830 --> 00:26:19,490 about four or five different offshoots 597 00:26:19,490 --> 00:26:22,010 from that that cover different aspects of it, 598 00:26:22,010 --> 00:26:26,530 from the guidelines for how you do it, to how you would use it, 599 00:26:26,530 --> 00:26:28,990 to making claims, or how you would use it 600 00:26:28,990 --> 00:26:31,260 for putting labels on products. 601 00:26:31,260 --> 00:26:35,530 So it's sort of grown quite a bit from those beginnings. 602 00:26:35,530 --> 00:26:38,770 So essentially, the methodology that they developed 603 00:26:38,770 --> 00:26:40,600 is going to consist of four stages. 604 00:26:40,600 --> 00:26:43,650 The first of which is the goal and scope definition. 605 00:26:43,650 --> 00:26:45,640 Which is comprised of a couple of things, 606 00:26:45,640 --> 00:26:49,130 namely deciding what it is you're analyzing, 607 00:26:49,130 --> 00:26:53,980 looking at what the scope of the system is under consideration, 608 00:26:53,980 --> 00:26:58,150 identifying what falls within your system boundary. 609 00:26:58,150 --> 00:27:00,960 The second step is referred to as the inventory analysis. 610 00:27:00,960 --> 00:27:03,550 This is where you're identifying and quantifying 611 00:27:03,550 --> 00:27:05,734 all the things that come in, raw materials 612 00:27:05,734 --> 00:27:07,400 that are coming in from the environment, 613 00:27:07,400 --> 00:27:09,691 all the emissions that are going back out, whether it's 614 00:27:09,691 --> 00:27:12,610 to air or water or land. 615 00:27:12,610 --> 00:27:16,700 Other products that you might be using, all of the inputs. 616 00:27:16,700 --> 00:27:18,920 And essentially creating an inventory of those, 617 00:27:18,920 --> 00:27:21,380 identifying how much there are. 618 00:27:21,380 --> 00:27:23,450 The third step is the impact analysis. 619 00:27:23,450 --> 00:27:26,110 This is where we go from the inventory, 620 00:27:26,110 --> 00:27:28,440 from identifying everything that's going in, 621 00:27:28,440 --> 00:27:32,060 to figuring out what exactly the environmental impact from that 622 00:27:32,060 --> 00:27:35,200 is going to be, and what methodology is used to do that. 623 00:27:35,200 --> 00:27:37,490 And then finally, as we talked about, 624 00:27:37,490 --> 00:27:39,040 interpretation is the fourth step. 625 00:27:39,040 --> 00:27:41,490 So we want to go back and, after we've 626 00:27:41,490 --> 00:27:44,160 set the goal of the study-- identified the scope 627 00:27:44,160 --> 00:27:47,180 and gone through and quantified the environmental impact-- how 628 00:27:47,180 --> 00:27:49,100 do we take these results and relate them back 629 00:27:49,100 --> 00:27:52,150 all the things we set out from the beginning that 630 00:27:52,150 --> 00:27:52,900 were our goals. 631 00:27:56,500 --> 00:27:58,840 So if we start off with the goal and scope definition, 632 00:27:58,840 --> 00:28:03,669 right, the first thing is to decide the product 633 00:28:03,669 --> 00:28:06,210 you're going to be studying and what the purpose of the study 634 00:28:06,210 --> 00:28:06,760 is. 635 00:28:06,760 --> 00:28:09,740 And that includes three points. 636 00:28:09,740 --> 00:28:11,990 What is the intended application of the study, so what 637 00:28:11,990 --> 00:28:13,290 are you trying to do with it. 638 00:28:13,290 --> 00:28:15,520 What's your reason for carrying it out. 639 00:28:15,520 --> 00:28:18,410 And third, for whom are the results going to be used. 640 00:28:18,410 --> 00:28:23,586 So if we look at the study that we looked at today, 641 00:28:23,586 --> 00:28:25,960 you'll notice when the study is laid out, as you read it, 642 00:28:25,960 --> 00:28:28,293 it was actually laid out in sections that followed along 643 00:28:28,293 --> 00:28:31,290 from our four step. 644 00:28:31,290 --> 00:28:33,060 Let's talk about the product to be studied 645 00:28:33,060 --> 00:28:34,268 and the purpose of the study. 646 00:28:34,268 --> 00:28:37,319 What did they claim were the reasons for doing this? 647 00:28:37,319 --> 00:28:39,610 First off, what was the product that was being studied? 648 00:28:42,490 --> 00:28:44,785 AUDIENCE: A dry set of hands. 649 00:28:44,785 --> 00:28:47,890 PROFESSOR: So it's a Dyson Airblade 650 00:28:47,890 --> 00:28:51,480 and it's not a paper towel. 651 00:28:51,480 --> 00:28:58,580 What they set up was a per pair of dried hands. 652 00:29:04,170 --> 00:29:07,400 So this is what we would refer to as the functional unit. 653 00:29:11,899 --> 00:29:13,690 This is actually what we're going to study, 654 00:29:13,690 --> 00:29:15,670 and everything that's done throughout the study 655 00:29:15,670 --> 00:29:16,780 is in terms of this. 656 00:29:16,780 --> 00:29:18,910 So this is going to be our unit of analysis, 657 00:29:18,910 --> 00:29:20,740 it's going to be how we're going to compare 658 00:29:20,740 --> 00:29:22,031 all of these different options. 659 00:29:24,884 --> 00:29:26,800 And what did they say the intended application 660 00:29:26,800 --> 00:29:27,750 of the study was? 661 00:29:30,612 --> 00:29:33,487 What were they trying to do? 662 00:29:33,487 --> 00:29:38,756 AUDIENCE: [INAUDIBLE] dry hands [INAUDIBLE] 663 00:29:41,472 --> 00:29:43,180 PROFESSOR: So they actually had two uses. 664 00:29:47,140 --> 00:29:49,628 What was the target audience for this? 665 00:29:49,628 --> 00:29:50,836 AUDIENCE: [? The engineer. ?] 666 00:29:50,836 --> 00:29:51,590 PROFESSOR: Right. 667 00:29:56,400 --> 00:30:00,730 So if we look at in terms of this, 668 00:30:00,730 --> 00:30:03,235 one group of people that the results were for 669 00:30:03,235 --> 00:30:04,460 were the Dyson engineers. 670 00:30:04,460 --> 00:30:06,827 They wanted to go through and understand 671 00:30:06,827 --> 00:30:08,660 where were the impact-- what was the reason, 672 00:30:08,660 --> 00:30:11,230 it was to identify the impacts associated with the Airblade 673 00:30:11,230 --> 00:30:13,550 so that the engineers could take a look at that 674 00:30:13,550 --> 00:30:17,000 and start figuring out ways to reduce it. 675 00:30:17,000 --> 00:30:19,064 What was the other purpose? 676 00:30:19,064 --> 00:30:24,848 AUDIENCE: [INAUDIBLE] environmental impacts. 677 00:30:24,848 --> 00:30:26,240 PROFESSOR: Related to that. 678 00:30:26,240 --> 00:30:31,040 So if all I wanted to do was identify for my engineers 679 00:30:31,040 --> 00:30:33,560 where the impacts of the Airblade is, 680 00:30:33,560 --> 00:30:36,430 would I need to consider paper towels and the Xlerator 681 00:30:36,430 --> 00:30:37,971 and things like that? 682 00:30:37,971 --> 00:30:38,470 No. 683 00:30:38,470 --> 00:30:46,650 So that's our second reason, was comparative. 684 00:30:46,650 --> 00:30:48,910 They wanted to go through and say 685 00:30:48,910 --> 00:30:52,320 here's the environmental impact associated 686 00:30:52,320 --> 00:30:54,910 with a dry pair of hands when you use the Airblade, 687 00:30:54,910 --> 00:30:56,510 and here it is when you associated it 688 00:30:56,510 --> 00:30:59,290 with one of the other products under consideration. 689 00:30:59,290 --> 00:31:03,650 So they actually had the Airblade. 690 00:31:06,870 --> 00:31:08,040 What else did they study? 691 00:31:08,040 --> 00:31:10,884 The Xlerator. 692 00:31:10,884 --> 00:31:12,848 AUDIENCE: Recycled paper towels. 693 00:31:12,848 --> 00:31:16,850 PROFESSOR: Yeah, so they had two versions of the paper towel. 694 00:31:16,850 --> 00:31:20,060 And they had two versions of the Airblade, too. 695 00:31:20,060 --> 00:31:21,628 The aluminum and the plastic. 696 00:31:21,628 --> 00:31:22,711 AUDIENCE: Standard, right? 697 00:31:22,711 --> 00:31:23,820 PROFESSOR: Yeah, standard. 698 00:31:27,940 --> 00:31:29,125 Cotton Roll towels. 699 00:31:36,780 --> 00:31:40,290 So this gets back to our choice of a functional 700 00:31:40,290 --> 00:31:42,360 unit was the dry pair of hands, so this gives us 701 00:31:42,360 --> 00:31:45,020 the basis that we're going to do these comparisons on. 702 00:31:45,020 --> 00:31:47,970 But in terms of the goal and scope, that's important, 703 00:31:47,970 --> 00:31:51,327 because if I just wanted to use something internally, then 704 00:31:51,327 --> 00:31:52,660 I would never go to the trouble. 705 00:31:52,660 --> 00:31:55,900 There's no need to go to all of the trouble to do all of this. 706 00:31:55,900 --> 00:31:57,720 The big thing that drove a lot of this 707 00:31:57,720 --> 00:31:59,636 is the idea that it's going to be comparative. 708 00:31:59,636 --> 00:32:02,150 That they actually want to make a comparison between what 709 00:32:02,150 --> 00:32:04,400 happens when you use the Airblade between when you use 710 00:32:04,400 --> 00:32:07,340 some of these other systems. 711 00:32:07,340 --> 00:32:11,040 And it's going to come back because once they did this, 712 00:32:11,040 --> 00:32:13,540 then they had to start making a lot of assumptions about how 713 00:32:13,540 --> 00:32:15,831 these other systems work, since they didn't necessarily 714 00:32:15,831 --> 00:32:16,940 have the data to that. 715 00:32:16,940 --> 00:32:19,120 And then because you're doing that comparison, 716 00:32:19,120 --> 00:32:21,240 you sort of-- part of it is they're 717 00:32:21,240 --> 00:32:23,170 going to make this available externally. 718 00:32:23,170 --> 00:32:24,830 They've said that part of it might be 719 00:32:24,830 --> 00:32:26,310 to use this for making claims. 720 00:32:26,310 --> 00:32:27,893 And so once you've decided that you're 721 00:32:27,893 --> 00:32:31,430 going to start using it for public claims, 722 00:32:31,430 --> 00:32:33,780 you definitely want to be a little bit more rigorous 723 00:32:33,780 --> 00:32:35,430 in your application and your approach. 724 00:32:35,430 --> 00:32:38,500 Because as soon as you publish this and start making claims 725 00:32:38,500 --> 00:32:40,280 that X and all of the other companies 726 00:32:40,280 --> 00:32:42,030 are going to look at this and think, well, 727 00:32:42,030 --> 00:32:43,660 why didn't you do this, why this. 728 00:32:43,660 --> 00:32:46,120 So this sort of drives a lot of what 729 00:32:46,120 --> 00:32:47,610 goes on in the next few steps. 730 00:32:47,610 --> 00:32:50,890 That by doing this, now we've expanded the scope of our study 731 00:32:50,890 --> 00:32:53,010 from just looking at my supply chain, which 732 00:32:53,010 --> 00:32:54,785 I have pretty good visibility to, 733 00:32:54,785 --> 00:32:57,160 to starting to look at a lot of different other products. 734 00:32:57,160 --> 00:33:00,490 And this brings a lot of extra complexity to it, 735 00:33:00,490 --> 00:33:02,420 because I have a number of different studies. 736 00:33:02,420 --> 00:33:04,820 And if each of these has different configurations 737 00:33:04,820 --> 00:33:07,130 or different assumptions that I make, 738 00:33:07,130 --> 00:33:09,980 the complexity starts really blowing up. 739 00:33:13,420 --> 00:33:14,336 System boundary. 740 00:33:14,336 --> 00:33:16,210 What did they choose for the system boundary? 741 00:33:19,102 --> 00:33:22,970 AUDIENCE: They chose not to include things like maintenance 742 00:33:22,970 --> 00:33:27,178 or transportation [INAUDIBLE] So they 743 00:33:27,178 --> 00:33:32,030 had to make a lot of assumptions [? around ?] the [INAUDIBLE] 744 00:33:32,030 --> 00:33:35,220 PROFESSOR: So they start off by saying this is a life cycle 745 00:33:35,220 --> 00:33:35,800 assessment. 746 00:33:35,800 --> 00:33:39,830 So we're going to do cradle to grave. 747 00:33:39,830 --> 00:33:45,020 And you can think about that, that idea that you're 748 00:33:45,020 --> 00:33:47,020 doing cradle to grave creates a lot of problems, 749 00:33:47,020 --> 00:33:51,680 because they mentioned there's how many different screws 750 00:33:51,680 --> 00:33:53,164 in the Airblade? 751 00:33:53,164 --> 00:33:54,830 And you think that now you need to trace 752 00:33:54,830 --> 00:33:57,610 each of these little parts all the way up through their supply 753 00:33:57,610 --> 00:33:58,310 chain. 754 00:33:58,310 --> 00:34:00,690 So they started making some definitions 755 00:34:00,690 --> 00:34:04,450 about where they were going to sort of consider things outside 756 00:34:04,450 --> 00:34:06,020 of the system boundary. 757 00:34:06,020 --> 00:34:08,909 So there are a couple of issues that go into that. 758 00:34:08,909 --> 00:34:12,380 So one was cut off rules. 759 00:34:12,380 --> 00:34:14,308 So what did they use as a cut off rule? 760 00:34:18,700 --> 00:34:22,116 AUDIENCE: 1%, if it's less than 1% in effect 761 00:34:22,116 --> 00:34:23,590 then they didn't include it. 762 00:34:23,590 --> 00:34:26,174 PROFESSOR: Right, and so this creates-- 763 00:34:26,174 --> 00:34:27,090 what's the issue here. 764 00:34:27,090 --> 00:34:29,639 If it had less than 1% of the total impact, 765 00:34:29,639 --> 00:34:32,850 they would exclude it. 766 00:34:32,850 --> 00:34:35,962 What's the issue with using this as a rule? 767 00:34:35,962 --> 00:34:37,924 AUDIENCE: Number of [INAUDIBLE]? 768 00:34:37,924 --> 00:34:41,232 PROFESSOR: That's part of it. 769 00:34:41,232 --> 00:34:43,190 How do you know when something only contributes 770 00:34:43,190 --> 00:34:46,590 1% of the total impact? 771 00:34:46,590 --> 00:34:49,639 It's like a chicken and the egg problem. 772 00:34:49,639 --> 00:34:52,280 If you've quantified to identify that it's less than 1% 773 00:34:52,280 --> 00:34:54,760 of the impact, why wouldn't you include it. 774 00:34:54,760 --> 00:35:00,650 What they have actually done is for all those little things, 775 00:35:00,650 --> 00:35:03,310 they've done some basically back of the envelope 776 00:35:03,310 --> 00:35:05,842 or some other type of high level estimate 777 00:35:05,842 --> 00:35:07,300 to try to figure out what they are, 778 00:35:07,300 --> 00:35:08,925 without going to the trouble of digging 779 00:35:08,925 --> 00:35:12,524 down deep into the level of it to try to identify what it was. 780 00:35:12,524 --> 00:35:14,190 They mentioned some of the other, right? 781 00:35:14,190 --> 00:35:15,916 They were using a lot of information 782 00:35:15,916 --> 00:35:17,290 from other previous studies, they 783 00:35:17,290 --> 00:35:18,665 mentioned some of the other rules 784 00:35:18,665 --> 00:35:20,760 that some of the other studies had used. 785 00:35:20,760 --> 00:35:22,925 Remember what some of those were? 786 00:35:22,925 --> 00:35:24,705 AUDIENCE: Also on that, didn't they 787 00:35:24,705 --> 00:35:30,630 limit it to no more than like 5% of the total product 788 00:35:30,630 --> 00:35:33,156 on this 1% assumption so they couldn't 789 00:35:33,156 --> 00:35:35,032 say every screw didn't matter. 790 00:35:35,032 --> 00:35:35,740 PROFESSOR: Right. 791 00:35:35,740 --> 00:35:42,530 So we do any individual process that was less than 1%, 792 00:35:42,530 --> 00:35:46,049 and in total, everything that I excluded had to be less than 5% 793 00:35:46,049 --> 00:35:46,590 of the total. 794 00:35:49,410 --> 00:35:51,630 These are pretty common rules. 795 00:35:51,630 --> 00:35:54,960 So you can think about this is if you had a bunch of things 796 00:35:54,960 --> 00:35:57,970 that were less than 1%, but then you estimated the total impact, 797 00:35:57,970 --> 00:35:59,800 they were only covering 90%. 798 00:35:59,800 --> 00:36:02,270 So you would just start adding more and more of those 799 00:36:02,270 --> 00:36:05,930 back into the system boundary until you hit that 95% percent 800 00:36:05,930 --> 00:36:07,030 point. 801 00:36:07,030 --> 00:36:09,620 Some of the other studies used a slightly different rule 802 00:36:09,620 --> 00:36:12,130 than the impact. 803 00:36:12,130 --> 00:36:19,090 So some of them did this based on physical quantities. 804 00:36:19,090 --> 00:36:21,620 So anything that was less than 1% of the mass, 805 00:36:21,620 --> 00:36:23,720 I think one of the studies, was excluded. 806 00:36:27,300 --> 00:36:29,510 This tends to be easier, because you don't actually 807 00:36:29,510 --> 00:36:32,030 have to go to the trouble of estimating what the impact is. 808 00:36:32,030 --> 00:36:33,530 You can start saying, well, I'm just 809 00:36:33,530 --> 00:36:36,660 going to exclude all of the tiny little things that go. 810 00:36:36,660 --> 00:36:38,494 But you're making an assumption that those 811 00:36:38,494 --> 00:36:39,660 don't contribute big, right. 812 00:36:39,660 --> 00:36:43,410 So if it's got-- I don't know, some uranium in it 813 00:36:43,410 --> 00:36:45,240 or something else that's radioactive, 814 00:36:45,240 --> 00:36:49,460 that may have a very high impact in terms of certain categories 815 00:36:49,460 --> 00:36:51,770 even if they don't contribute much the overall weight. 816 00:36:51,770 --> 00:36:54,290 So generally, this would be preferred. 817 00:36:54,290 --> 00:36:57,730 We would like to make a ballpark estimate of what the impact is 818 00:36:57,730 --> 00:37:01,730 and then go through and exclude based on that 819 00:37:01,730 --> 00:37:03,420 rather than basically just using one 820 00:37:03,420 --> 00:37:04,900 of these physical quantities. 821 00:37:07,780 --> 00:37:10,130 There were some other things that they cut out 822 00:37:10,130 --> 00:37:13,210 from the system boundary. 823 00:37:13,210 --> 00:37:16,260 AUDIENCE: Something about excluding raw materials 824 00:37:16,260 --> 00:37:18,670 [INAUDIBLE] 825 00:37:18,670 --> 00:37:21,310 PROFESSOR: So I think they included that, 826 00:37:21,310 --> 00:37:23,212 or they should have, at least. 827 00:37:23,212 --> 00:37:25,930 I think it was mentioned earlier. 828 00:37:25,930 --> 00:37:28,141 We had capital goods, were not included. 829 00:37:34,620 --> 00:37:38,130 This is also a fairly common exclusion. 830 00:37:38,130 --> 00:37:45,450 Things that have a multiple year lifetime, not to include those. 831 00:37:45,450 --> 00:37:46,890 You could include in the same way 832 00:37:46,890 --> 00:37:48,070 that, when they made the decision 833 00:37:48,070 --> 00:37:50,350 to do a dry pair of hands, they had to start making assumptions 834 00:37:50,350 --> 00:37:52,400 about the lifetime of the product, how many uses 835 00:37:52,400 --> 00:37:53,700 it was going to get made. 836 00:37:53,700 --> 00:37:56,080 And then they allocated a portion of that 837 00:37:56,080 --> 00:37:56,965 to the life cycle. 838 00:37:56,965 --> 00:38:00,240 They included things like the bins and the liners 839 00:38:00,240 --> 00:38:02,900 they were using for paper towels and things like that. 840 00:38:02,900 --> 00:38:04,440 You could include capital goods. 841 00:38:04,440 --> 00:38:06,023 You'd have to start making assumptions 842 00:38:06,023 --> 00:38:11,125 about how many years it existed, how many products it read. 843 00:38:11,125 --> 00:38:13,250 If you were to include the trucks or the ships that 844 00:38:13,250 --> 00:38:15,422 move the goods, how many trips a year 845 00:38:15,422 --> 00:38:17,380 does it make, how many containers does it move. 846 00:38:17,380 --> 00:38:20,020 You could start doing that. 847 00:38:20,020 --> 00:38:22,030 They tend to be pretty low. 848 00:38:22,030 --> 00:38:25,316 If you're including the transportation 849 00:38:25,316 --> 00:38:26,940 piece of the ocean, so you're capturing 850 00:38:26,940 --> 00:38:28,880 all of the fuel that's being burned, 851 00:38:28,880 --> 00:38:30,440 but you're just not capturing what 852 00:38:30,440 --> 00:38:31,610 goes into building the ship. 853 00:38:31,610 --> 00:38:34,500 So that's another one that's a pretty common one. 854 00:38:34,500 --> 00:38:37,030 So they mentioned in the study that some 855 00:38:37,030 --> 00:38:39,450 of the studies they used had excluded the capital goods, 856 00:38:39,450 --> 00:38:41,720 and so they chose to exclude capital goods in this as 857 00:38:41,720 --> 00:38:44,250 well in order to maintain that consistency. 858 00:38:44,250 --> 00:38:46,580 So in this case, they might have otherwise 859 00:38:46,580 --> 00:38:47,860 chosen to include those. 860 00:38:47,860 --> 00:38:49,760 But because they were relying on past data, 861 00:38:49,760 --> 00:38:53,336 they went ahead and included them anyway. 862 00:38:53,336 --> 00:38:54,710 What about with the paper towels? 863 00:38:54,710 --> 00:38:56,960 This was specifically one of the issues that they had. 864 00:39:00,410 --> 00:39:02,785 AUDIENCE: Whether or not to include 865 00:39:02,785 --> 00:39:05,160 the environmental impact of previous recycling. 866 00:39:05,160 --> 00:39:07,108 PROFESSOR: Right, so this is another big one. 867 00:39:09,930 --> 00:39:12,720 Specifically, what was it about this type of recycling 868 00:39:12,720 --> 00:39:15,350 that was difficult? 869 00:39:15,350 --> 00:39:17,454 Remember what the term they used for it was? 870 00:39:20,660 --> 00:39:23,030 Open loop. 871 00:39:23,030 --> 00:39:26,110 So this is the problem with things like paper, 872 00:39:26,110 --> 00:39:29,335 is that something that is a paper towel, if you were 873 00:39:29,335 --> 00:39:31,900 to collect it and recycle it, usually 874 00:39:31,900 --> 00:39:35,170 the strength of the paper you get from the recycled fiber 875 00:39:35,170 --> 00:39:37,610 is not as strong as it was when it started. 876 00:39:37,610 --> 00:39:40,460 So if it started off as a paper towel when you recycled it, 877 00:39:40,460 --> 00:39:43,120 it's usually no longer strong enough to be a paper towel. 878 00:39:43,120 --> 00:39:45,510 So it might get made into toilet paper, or newsprint, 879 00:39:45,510 --> 00:39:48,040 or whatever else is on down the line. 880 00:39:48,040 --> 00:39:53,680 You get into this situation where you have the product, 881 00:39:53,680 --> 00:39:55,760 and you recycle it, and then it's 882 00:39:55,760 --> 00:39:57,930 going to go get turned into some other product that 883 00:39:57,930 --> 00:40:00,540 has its own life cycle, and it's going through. 884 00:40:00,540 --> 00:40:04,146 As opposed to a closed loop recycling, 885 00:40:04,146 --> 00:40:06,020 that product would just get turned right back 886 00:40:06,020 --> 00:40:07,410 into an input. 887 00:40:07,410 --> 00:40:10,920 And if I were to do that, then I can draw my system boundary 888 00:40:10,920 --> 00:40:13,640 around this, and I don't have to worry about it, 889 00:40:13,640 --> 00:40:15,890 because it will get included in this system boundary. 890 00:40:15,890 --> 00:40:17,330 But when I'm doing this, I start getting 891 00:40:17,330 --> 00:40:19,200 into multiple different types of products. 892 00:40:22,460 --> 00:40:25,950 This is a problem because the input-- 893 00:40:25,950 --> 00:40:29,080 when it mentioned they had the two types of paper towels. 894 00:40:29,080 --> 00:40:30,650 Some were made from virgin fibers 895 00:40:30,650 --> 00:40:32,920 and some were made from recycled fibers. 896 00:40:32,920 --> 00:40:34,580 If it's made from a recycled fiber, 897 00:40:34,580 --> 00:40:38,620 it had its whole life cycle before it became a paper towel. 898 00:40:38,620 --> 00:40:43,510 When it was maybe copy paper, or something similar to that. 899 00:40:46,660 --> 00:40:48,436 That usually means you're going to have 900 00:40:48,436 --> 00:40:50,560 to make some decision about where to draw the line, 901 00:40:50,560 --> 00:40:52,114 in terms of how far we go chasing 902 00:40:52,114 --> 00:40:54,780 all of these other products that it might have been turned into. 903 00:41:01,510 --> 00:41:07,100 So, again, the goal-- this comes directly 904 00:41:07,100 --> 00:41:09,110 from what they wrote in the report. 905 00:41:09,110 --> 00:41:12,410 Compare the environmental impact of hand drying systems. 906 00:41:12,410 --> 00:41:16,190 Evaluate how the impact under different manufacturing and use 907 00:41:16,190 --> 00:41:16,700 scenarios. 908 00:41:16,700 --> 00:41:19,270 So this is part of it, is that they, rather than consider it 909 00:41:19,270 --> 00:41:21,490 within the context of just one scenario, 910 00:41:21,490 --> 00:41:23,920 they want to try a couple of those scenarios. 911 00:41:23,920 --> 00:41:25,420 This related back to the assumptions 912 00:41:25,420 --> 00:41:27,450 that they had to make. 913 00:41:27,450 --> 00:41:29,430 Two, identify impact drivers and ways 914 00:41:29,430 --> 00:41:32,580 to target those with the idea to inform product decisions. 915 00:41:36,130 --> 00:41:37,860 We mentioned this comparative assertion, 916 00:41:37,860 --> 00:41:39,210 so this is sort of the important one, 917 00:41:39,210 --> 00:41:40,834 because that lets us know this is going 918 00:41:40,834 --> 00:41:42,170 to go to an external audience. 919 00:41:42,170 --> 00:41:44,540 This brings its own set of requirements. 920 00:41:44,540 --> 00:41:46,510 So if you're going to use something 921 00:41:46,510 --> 00:41:48,440 for comparative assertions, the ISO standards 922 00:41:48,440 --> 00:41:52,170 generally require you to have a third party evaluation of it. 923 00:41:52,170 --> 00:41:54,830 So if you look through in one of the appendices, 924 00:41:54,830 --> 00:41:58,760 they mentioned the results of their third party, 925 00:41:58,760 --> 00:42:00,450 essentially, review of it. 926 00:42:00,450 --> 00:42:03,720 And so they had a panel of three experts 927 00:42:03,720 --> 00:42:06,110 go through and review the initial draft of the life cycle 928 00:42:06,110 --> 00:42:07,770 assessment study, and then they had to go back, 929 00:42:07,770 --> 00:42:09,350 and usually you get critical-- it's 930 00:42:09,350 --> 00:42:10,780 like submitting something for peer 931 00:42:10,780 --> 00:42:13,950 review at an academic journal or something similar, 932 00:42:13,950 --> 00:42:15,360 where you would get back comments 933 00:42:15,360 --> 00:42:16,620 about what you might want to change 934 00:42:16,620 --> 00:42:17,870 and what you might want to do. 935 00:42:17,870 --> 00:42:21,030 So once you start making some of these selections upfront 936 00:42:21,030 --> 00:42:23,070 about the goal of your study, that's 937 00:42:23,070 --> 00:42:24,650 going to drive extra requirements 938 00:42:24,650 --> 00:42:27,120 and make some complications for what 939 00:42:27,120 --> 00:42:29,040 you need to do down the road. 940 00:42:29,040 --> 00:42:34,330 Finally, they mentioned internal and external audiences. 941 00:42:34,330 --> 00:42:37,520 With the goal, the big thing to keep track of 942 00:42:37,520 --> 00:42:40,400 is, there's really two types of life cycle assessment studies 943 00:42:40,400 --> 00:42:41,930 and they go by different names. 944 00:42:41,930 --> 00:42:45,610 Attributional, first consequential is one of them. 945 00:42:45,610 --> 00:42:47,680 One I've heard is accounting based, 946 00:42:47,680 --> 00:42:49,180 that would be the attributional. 947 00:42:49,180 --> 00:42:51,770 And this would be change oriented. 948 00:42:51,770 --> 00:42:55,170 Really, these are little diagrams that somebody created 949 00:42:55,170 --> 00:42:56,900 that are useful to think of it. 950 00:42:56,900 --> 00:42:59,150 The attributional one, which is what they've 951 00:42:59,150 --> 00:43:03,200 done in this paper, is essentially taking a system 952 00:43:03,200 --> 00:43:05,910 and trying to figure out what portion of that system's 953 00:43:05,910 --> 00:43:09,510 environmental impact is related to whatever you're looking at. 954 00:43:09,510 --> 00:43:11,760 So it's taking a static snapshot of something 955 00:43:11,760 --> 00:43:15,070 and trying to attribute the various environmental impact 956 00:43:15,070 --> 00:43:17,010 to your product. 957 00:43:17,010 --> 00:43:18,900 The consequential, the change oriented, 958 00:43:18,900 --> 00:43:20,427 is going to take a system and it's 959 00:43:20,427 --> 00:43:22,760 going to try to model what happens when you make changes 960 00:43:22,760 --> 00:43:23,390 to it. 961 00:43:23,390 --> 00:43:25,770 So the big thing of that is that it's not static. 962 00:43:25,770 --> 00:43:27,130 The picture's going to change. 963 00:43:27,130 --> 00:43:29,510 You might reduce impact in certain areas, 964 00:43:29,510 --> 00:43:31,450 but it might increase it in other areas. 965 00:43:31,450 --> 00:43:36,220 And so this attributional style is generally 966 00:43:36,220 --> 00:43:37,840 things that are backwards looking, 967 00:43:37,840 --> 00:43:41,442 whether it be estimating the environmental impact of one 968 00:43:41,442 --> 00:43:42,150 of your products. 969 00:43:42,150 --> 00:43:45,290 If you wanted to, say, do a carbon label, 970 00:43:45,290 --> 00:43:48,600 or to meet some certain standard to get an eco label. 971 00:43:48,600 --> 00:43:52,130 To estimate the environmental impacts of different systems 972 00:43:52,130 --> 00:43:54,320 as they are now, is the big thing. 973 00:43:54,320 --> 00:43:56,070 The consequential is going to look at what 974 00:43:56,070 --> 00:43:57,440 happens when we make a change. 975 00:43:57,440 --> 00:44:01,410 So usually what this means is that could involve 976 00:44:01,410 --> 00:44:03,310 very big changes in the system. 977 00:44:03,310 --> 00:44:05,340 That if you make certain decisions, 978 00:44:05,340 --> 00:44:06,560 they're going to impact. 979 00:44:06,560 --> 00:44:08,440 That things might look this way now, 980 00:44:08,440 --> 00:44:11,614 if I look at just the system as it is, but say my demand is 981 00:44:11,614 --> 00:44:13,280 starting to increase and I start to have 982 00:44:13,280 --> 00:44:15,907 to start making different changes, that could 983 00:44:15,907 --> 00:44:16,990 shift the way it would be. 984 00:44:16,990 --> 00:44:19,340 So one good example of this would be looking 985 00:44:19,340 --> 00:44:22,080 at, say, the impact of ethanol. 986 00:44:22,080 --> 00:44:25,740 If you look at US policy on ethanol. 987 00:44:25,740 --> 00:44:27,770 If you just look at a static snapshot 988 00:44:27,770 --> 00:44:31,670 of where we're at right now, or the way it exists right now. 989 00:44:31,670 --> 00:44:35,180 You'd look at the life cycle for, say, corn ethanol, 990 00:44:35,180 --> 00:44:36,940 and you measure its environmental impact. 991 00:44:36,940 --> 00:44:38,770 And then you compare that to, say, 992 00:44:38,770 --> 00:44:41,156 what it takes to produce gasoline or diesel fuel, 993 00:44:41,156 --> 00:44:43,030 and you come up with an environmental impact. 994 00:44:43,030 --> 00:44:44,960 And so that generally would tell you 995 00:44:44,960 --> 00:44:47,650 the ethanol is slightly better on, say, a greenhouse gas 996 00:44:47,650 --> 00:44:49,560 perspective. 997 00:44:49,560 --> 00:44:51,570 But when you start looking at what 998 00:44:51,570 --> 00:44:53,300 happens when you start making big shifts, 999 00:44:53,300 --> 00:44:56,480 as we start converting crop land from other uses 1000 00:44:56,480 --> 00:44:58,680 in order to turn it into corn, in order to turn that 1001 00:44:58,680 --> 00:45:01,180 into ethanol, you start getting a cascade of change 1002 00:45:01,180 --> 00:45:02,560 as they go on. 1003 00:45:02,560 --> 00:45:06,160 So in the case of the US, we've switched a lot of farmland 1004 00:45:06,160 --> 00:45:09,080 over to corn, and so that has left 1005 00:45:09,080 --> 00:45:11,600 a gap in wheat and soybeans and other crops that used 1006 00:45:11,600 --> 00:45:13,380 to grow on land that were corn. 1007 00:45:13,380 --> 00:45:15,970 So now we don't have as much to export to other countries. 1008 00:45:15,970 --> 00:45:18,370 So those countries have to convert some of their land 1009 00:45:18,370 --> 00:45:23,180 to uses to grow, to make up for our wheat shortage. 1010 00:45:23,180 --> 00:45:26,430 And so then they may end up clearing rain forest 1011 00:45:26,430 --> 00:45:29,190 or some other sort of bad impact, 1012 00:45:29,190 --> 00:45:32,500 in order to turn that into land to grow these crops on. 1013 00:45:32,500 --> 00:45:35,710 So once you start looking at this cascade of system effects, 1014 00:45:35,710 --> 00:45:38,540 people think that ethanol is no longer preferable. 1015 00:45:38,540 --> 00:45:42,089 That once you include the indirect effects, it's better. 1016 00:45:42,089 --> 00:45:43,630 So in that case, that's what happened 1017 00:45:43,630 --> 00:45:44,921 when you start looking forward. 1018 00:45:44,921 --> 00:45:47,752 Looking at what happens when I start to make a change to this. 1019 00:45:47,752 --> 00:45:49,460 And so if you look at the characteristics 1020 00:45:49,460 --> 00:45:51,265 of these different types of systems, 1021 00:45:51,265 --> 00:45:52,640 they're going to have differences 1022 00:45:52,640 --> 00:45:53,380 in what you're going to do. 1023 00:45:53,380 --> 00:45:55,440 Part of that is the choice of data, right. 1024 00:45:55,440 --> 00:45:57,690 These we tend to based on average data, 1025 00:45:57,690 --> 00:46:00,940 because we look at the average electricity grid in the US. 1026 00:46:00,940 --> 00:46:03,430 You're usually not making, and we're not looking at, 1027 00:46:03,430 --> 00:46:06,010 something that creates enough change in the system 1028 00:46:06,010 --> 00:46:08,410 to really worry about whether it's impacting things 1029 00:46:08,410 --> 00:46:09,330 at the margins. 1030 00:46:09,330 --> 00:46:12,520 But once you start looking at energy policy or some 1031 00:46:12,520 --> 00:46:15,120 of these big areas, if you start looking at, 1032 00:46:15,120 --> 00:46:18,410 what if we were to shift a significant portion of steel 1033 00:46:18,410 --> 00:46:20,730 production to aluminum production, which might be much 1034 00:46:20,730 --> 00:46:23,050 more electricity intensive. 1035 00:46:23,050 --> 00:46:25,260 Then we start needing more power plants. 1036 00:46:25,260 --> 00:46:27,370 Maybe those new power plants come online, 1037 00:46:27,370 --> 00:46:30,490 are gas powered as opposed to coal fired, 1038 00:46:30,490 --> 00:46:33,450 and so the actual electricity and the actual emissions impact 1039 00:46:33,450 --> 00:46:35,940 is less than if you looked at the average of the grid. 1040 00:46:35,940 --> 00:46:38,829 So these are the ideas between what type of study 1041 00:46:38,829 --> 00:46:39,620 are you looking at. 1042 00:46:39,620 --> 00:46:41,328 It's going to drive some of these choices 1043 00:46:41,328 --> 00:46:44,984 and where you get the data, what the system boundary is. 1044 00:46:44,984 --> 00:46:47,150 One of the nice things about doing a change oriented 1045 00:46:47,150 --> 00:46:49,350 LCA is that if I have things that didn't change-- 1046 00:46:49,350 --> 00:46:51,930 I'm comparing two systems, a before and after, 1047 00:46:51,930 --> 00:46:54,320 and I have pieces of it that didn't change at all, 1048 00:46:54,320 --> 00:46:55,480 I can ignore those pieces. 1049 00:46:55,480 --> 00:46:56,980 Because they're not going to affect. 1050 00:46:56,980 --> 00:46:58,896 I'm just looking at the changes that happened, 1051 00:46:58,896 --> 00:47:02,440 so I can sort of exclude all of those from the system boundary. 1052 00:47:02,440 --> 00:47:04,310 Whereas with this type of assessment, 1053 00:47:04,310 --> 00:47:07,070 where I have to trace everything all the way up and back, 1054 00:47:07,070 --> 00:47:09,490 I have to make sure that it's fully complete to make sure 1055 00:47:09,490 --> 00:47:12,900 that I'm really getting that share of the emissions. 1056 00:47:12,900 --> 00:47:14,900 AUDIENCE: You mentioned the power grids. 1057 00:47:14,900 --> 00:47:16,550 Is there, to your knowledge, any effort 1058 00:47:16,550 --> 00:47:19,390 to track specifically which type of power 1059 00:47:19,390 --> 00:47:21,830 is being used at the point in question? 1060 00:47:21,830 --> 00:47:23,830 For example, if something's made in Pennsylvania 1061 00:47:23,830 --> 00:47:26,080 it's probably coal, but if it's made in Idaho, 1062 00:47:26,080 --> 00:47:28,442 it's almost exclusively hydraulic. 1063 00:47:28,442 --> 00:47:29,650 Anyone ever account for that? 1064 00:47:29,650 --> 00:47:30,316 PROFESSOR: Yeah. 1065 00:47:30,316 --> 00:47:32,660 So you can definitely get at that level. 1066 00:47:32,660 --> 00:47:35,050 Even in things like the greenhouse gas protocol 1067 00:47:35,050 --> 00:47:37,982 when we were talking about corporate level ones. 1068 00:47:37,982 --> 00:47:39,690 If you go and look at the tools that they 1069 00:47:39,690 --> 00:47:41,850 have for measuring the impact of electricity, 1070 00:47:41,850 --> 00:47:44,110 you can get regional specific factors. 1071 00:47:44,110 --> 00:47:47,490 One thing to remember with electricity, it's on the grid. 1072 00:47:47,490 --> 00:47:50,770 So even though you may locally be supplied 1073 00:47:50,770 --> 00:47:52,950 by a nuclear or coal plant, you're 1074 00:47:52,950 --> 00:47:54,490 part of, usually, a regional grid. 1075 00:47:54,490 --> 00:47:56,740 So they tend to break it out into inter-regional ones. 1076 00:47:56,740 --> 00:47:59,330 But you can get further down emissions factors 1077 00:47:59,330 --> 00:48:01,060 and start looking. 1078 00:48:01,060 --> 00:48:04,340 We did a study with Chiquita looking at bananas. 1079 00:48:04,340 --> 00:48:06,480 And so for that, we went-- part of that 1080 00:48:06,480 --> 00:48:09,120 is that they're grown in Central and South America. 1081 00:48:09,120 --> 00:48:12,230 So you can go out and look at the different electricity 1082 00:48:12,230 --> 00:48:13,900 factors at a country level there, 1083 00:48:13,900 --> 00:48:17,720 and they can make quite a bit of difference. 1084 00:48:17,720 --> 00:48:19,030 There are, definitely. 1085 00:48:19,030 --> 00:48:21,510 One, a lot of people of people already do that. 1086 00:48:21,510 --> 00:48:24,820 Two is that you tend to be able to model that 1087 00:48:24,820 --> 00:48:27,660 by looking at the percentage mix of coal 1088 00:48:27,660 --> 00:48:31,160 versus nuclear versus gas that might 1089 00:48:31,160 --> 00:48:33,200 be used in any specific area. 1090 00:48:36,949 --> 00:48:38,365 It's a really tricky question when 1091 00:48:38,365 --> 00:48:39,810 you start thinking about what happens 1092 00:48:39,810 --> 00:48:41,685 as I start to change things, because then you 1093 00:48:41,685 --> 00:48:45,240 get into where's the power going to, 1094 00:48:45,240 --> 00:48:48,465 which plants come online as demand starts to surge, 1095 00:48:48,465 --> 00:48:49,340 and things like that. 1096 00:48:49,340 --> 00:48:51,850 But that's definitely an area that people look at. 1097 00:48:51,850 --> 00:48:56,700 Usually the big question is whether-- if you looked at, 1098 00:48:56,700 --> 00:48:59,100 what if I did some small change where, 1099 00:48:59,100 --> 00:49:01,560 say, I moved my corporate offices from one location 1100 00:49:01,560 --> 00:49:02,940 to another. 1101 00:49:02,940 --> 00:49:05,767 You probably wouldn't want to use the marginal data 1102 00:49:05,767 --> 00:49:08,100 on electricity if you move from one region to the other, 1103 00:49:08,100 --> 00:49:09,683 because you're not really large enough 1104 00:49:09,683 --> 00:49:11,824 to have an effect on what's going on with the grid 1105 00:49:11,824 --> 00:49:12,740 at that sort of level. 1106 00:49:12,740 --> 00:49:15,610 So maybe it's better off to stick with averages and things 1107 00:49:15,610 --> 00:49:16,720 like that. 1108 00:49:16,720 --> 00:49:19,050 So there's some grey area in there in determining when 1109 00:49:19,050 --> 00:49:21,000 it's right to use which factor. 1110 00:49:27,220 --> 00:49:29,580 We've covered this, the functional unit 1111 00:49:29,580 --> 00:49:31,650 is a pair of dried hands. 1112 00:49:31,650 --> 00:49:37,540 Once you've made that, it makes sense because as we saw, 1113 00:49:37,540 --> 00:49:39,980 it's in the use where the impact comes from. 1114 00:49:39,980 --> 00:49:42,560 So the question becomes, you don't really 1115 00:49:42,560 --> 00:49:45,680 want to compare an Airblade versus an Xlerator. 1116 00:49:45,680 --> 00:49:47,824 You want to compare what happens over the use. 1117 00:49:47,824 --> 00:49:49,490 And definitely, when you start including 1118 00:49:49,490 --> 00:49:51,740 things that are not a one to one correspondence-- 1119 00:49:51,740 --> 00:49:53,930 comparing paper towels versus an Airblade, 1120 00:49:53,930 --> 00:49:59,650 you want to put them on a unit of analysis that makes sense. 1121 00:49:59,650 --> 00:50:01,300 But once you start doing that, you 1122 00:50:01,300 --> 00:50:02,740 start getting other-- right. 1123 00:50:02,740 --> 00:50:07,040 So that's one of the things you decide up front, and then that 1124 00:50:07,040 --> 00:50:09,240 creates a cascade of decisions about what you're 1125 00:50:09,240 --> 00:50:11,230 going to have to do later on. 1126 00:50:11,230 --> 00:50:14,440 Amongst that, making assumptions about the length. 1127 00:50:14,440 --> 00:50:17,810 How many hands get dried, the lifespan of it. 1128 00:50:17,810 --> 00:50:21,520 All the ancillary and secondary products to go along with it. 1129 00:50:21,520 --> 00:50:25,650 So it's the kind of thing you want to decide upfront, 1130 00:50:25,650 --> 00:50:29,185 because it creates consequences for what you do down the line. 1131 00:50:29,185 --> 00:50:31,310 And that's why it's usually one of the first things 1132 00:50:31,310 --> 00:50:34,260 that you'll do in the study. 1133 00:50:34,260 --> 00:50:37,460 Scope, we had seven different systems. 1134 00:50:37,460 --> 00:50:38,590 Cradle to grave. 1135 00:50:38,590 --> 00:50:40,680 They mention this within there, that they 1136 00:50:40,680 --> 00:50:43,040 are including packaging. 1137 00:50:43,040 --> 00:50:45,530 So the cardboard that they come in, or the plastic 1138 00:50:45,530 --> 00:50:48,320 wrap when it's there, and the bins and liners and dispensers. 1139 00:50:48,320 --> 00:50:50,780 So you want to be clear when you're 1140 00:50:50,780 --> 00:50:53,120 setting the scope about what things you're including 1141 00:50:53,120 --> 00:50:53,866 and what not. 1142 00:50:53,866 --> 00:50:55,490 So they include the packaging, but they 1143 00:50:55,490 --> 00:50:58,070 don't include the pallets that they get shipped on, 1144 00:50:58,070 --> 00:50:59,150 things like that. 1145 00:50:59,150 --> 00:51:01,967 Capital goods, et cetera. 1146 00:51:01,967 --> 00:51:03,175 And with the system boundary. 1147 00:51:06,060 --> 00:51:08,920 We covered some of the allocation, what it means. 1148 00:51:08,920 --> 00:51:11,660 So you had the end of life we talked about. 1149 00:51:11,660 --> 00:51:13,660 Co-products, if you have a manufacturing plant 1150 00:51:13,660 --> 00:51:15,285 or some other facility that's producing 1151 00:51:15,285 --> 00:51:17,890 a number of different goods, you usually 1152 00:51:17,890 --> 00:51:20,390 are going to run into that same allocation problem 1153 00:51:20,390 --> 00:51:22,720 where you're going to have to figure out 1154 00:51:22,720 --> 00:51:25,840 some way of apportioning the impacts of the production 1155 00:51:25,840 --> 00:51:29,190 process to the different products that come out. 1156 00:51:29,190 --> 00:51:32,490 And again, there's various rules for that. 1157 00:51:32,490 --> 00:51:42,440 Usually they're going to be simple things like mass, 1158 00:51:42,440 --> 00:51:46,480 energy, or economic impact. 1159 00:51:55,160 --> 00:51:57,160 One example in the textbook I have 1160 00:51:57,160 --> 00:51:59,490 is a chicken processing plant. 1161 00:51:59,490 --> 00:52:00,990 You're taking in chickens and you're 1162 00:52:00,990 --> 00:52:03,990 producing breasts, thighs, wings, 1163 00:52:03,990 --> 00:52:05,590 all of these different products. 1164 00:52:05,590 --> 00:52:07,940 You can divide up the impact of what 1165 00:52:07,940 --> 00:52:11,370 it takes to produce a chicken, to raise it, 1166 00:52:11,370 --> 00:52:13,072 to give it feed, all of that. 1167 00:52:13,072 --> 00:52:15,280 But at the end, you have all of these different types 1168 00:52:15,280 --> 00:52:17,636 of products that all come from that same input, 1169 00:52:17,636 --> 00:52:19,010 and then figure out how to divide 1170 00:52:19,010 --> 00:52:21,450 those impacts amongst the different products that 1171 00:52:21,450 --> 00:52:22,340 are coming out. 1172 00:52:22,340 --> 00:52:24,700 So mass or energy would be one way to do it. 1173 00:52:24,700 --> 00:52:26,440 I just weigh the different products 1174 00:52:26,440 --> 00:52:28,880 and I'll assign them each share of that burden 1175 00:52:28,880 --> 00:52:30,700 based on their weight. 1176 00:52:30,700 --> 00:52:34,180 But again, this may work well for some things, 1177 00:52:34,180 --> 00:52:34,972 but not for others. 1178 00:52:34,972 --> 00:52:37,388 Especially when you have some products that are coming out 1179 00:52:37,388 --> 00:52:38,460 that are very high value. 1180 00:52:38,460 --> 00:52:42,000 Since those tend to drive the decisions that you make. 1181 00:52:42,000 --> 00:52:44,640 So maybe you want to do it on the economic basis instead, 1182 00:52:44,640 --> 00:52:48,880 which is to say, I'm going to portion my impacts based 1183 00:52:48,880 --> 00:52:52,020 on the share of the value that each of these items generate. 1184 00:52:52,020 --> 00:52:54,190 And these tend to come back down to accounting. 1185 00:52:54,190 --> 00:52:58,310 So in the same way that our corporate carbon footprint 1186 00:52:58,310 --> 00:53:01,160 last week followed our financial accounting rules. 1187 00:53:01,160 --> 00:53:03,620 This is much more in the vein of managerial accounting, 1188 00:53:03,620 --> 00:53:06,300 where I'm trying to make certain decisions that reflect what's 1189 00:53:06,300 --> 00:53:08,820 actually going on as opposed to just necessarily following 1190 00:53:08,820 --> 00:53:10,950 some rules. 1191 00:53:10,950 --> 00:53:13,780 Cut off rules we covered. 1192 00:53:13,780 --> 00:53:15,570 One thing with the end of life-- so this 1193 00:53:15,570 --> 00:53:19,600 was back in the appendix of the document. 1194 00:53:19,600 --> 00:53:22,120 But this is essentially illustrating 1195 00:53:22,120 --> 00:53:23,990 the four different cut off rules that they 1196 00:53:23,990 --> 00:53:28,560 could apply for the waste management of the paper towels. 1197 00:53:28,560 --> 00:53:32,750 So the base scenario used, essentially, this cut off rule. 1198 00:53:32,750 --> 00:53:35,790 So what they're saying is that you have the first product that 1199 00:53:35,790 --> 00:53:41,390 takes the virgin material in, you produce it, you use it, 1200 00:53:41,390 --> 00:53:43,370 then it goes back to re-pulping. 1201 00:53:43,370 --> 00:53:46,630 So it's essentially recycled and turned into a second product. 1202 00:53:46,630 --> 00:53:48,560 Again, has its own life cycle. 1203 00:53:48,560 --> 00:53:50,520 You might repulp it a third time, 1204 00:53:50,520 --> 00:53:52,200 and again, it has its own life cycle. 1205 00:53:52,200 --> 00:53:55,050 And then maybe it's reached the end of its useful life 1206 00:53:55,050 --> 00:53:57,260 and it's going to go to waste management. 1207 00:53:57,260 --> 00:54:00,600 So all of these are essentially the same supply chain, 1208 00:54:00,600 --> 00:54:03,610 but we can divide up the impacts between the three 1209 00:54:03,610 --> 00:54:06,830 different product life cycles based 1210 00:54:06,830 --> 00:54:10,450 on how we divide up who gets credit for which 1211 00:54:10,450 --> 00:54:11,550 of these burdens. 1212 00:54:11,550 --> 00:54:13,730 I think they used the cut off rule 1213 00:54:13,730 --> 00:54:18,990 in this one, which is the first product took credit or took 1214 00:54:18,990 --> 00:54:20,770 the impact for all of the production 1215 00:54:20,770 --> 00:54:24,080 of the virgin material and all of the use. 1216 00:54:24,080 --> 00:54:27,090 But then once it moved off into the recycling system, 1217 00:54:27,090 --> 00:54:30,290 all of that impact was associated with the products 1218 00:54:30,290 --> 00:54:31,660 that went on down the line. 1219 00:54:31,660 --> 00:54:34,970 The second product took care of all that repulping, 1220 00:54:34,970 --> 00:54:36,580 the burden that went on there. 1221 00:54:36,580 --> 00:54:38,790 And the third product took that second set 1222 00:54:38,790 --> 00:54:42,070 of repulping and the final waste management disposal. 1223 00:54:42,070 --> 00:54:45,670 So if you think of this as comes from the original forestry 1224 00:54:45,670 --> 00:54:47,560 and paper production, and this is 1225 00:54:47,560 --> 00:54:49,690 the final when it gets thrown away in the landfill. 1226 00:54:49,690 --> 00:54:54,720 It's deciding who got those various impacts. 1227 00:54:54,720 --> 00:54:56,900 And you can see that they've drawn, depending 1228 00:54:56,900 --> 00:54:59,030 on where you want to draw the impact, 1229 00:54:59,030 --> 00:55:01,850 there's a lot of different rules you can apply. 1230 00:55:01,850 --> 00:55:04,874 And it's mainly a judgment call on which one 1231 00:55:04,874 --> 00:55:05,790 you think makes sense. 1232 00:55:05,790 --> 00:55:07,580 Since it's not entirely clear that one 1233 00:55:07,580 --> 00:55:10,140 is better than the other, these decisions are, in essence, 1234 00:55:10,140 --> 00:55:11,348 always going to be arbitrary. 1235 00:55:13,736 --> 00:55:16,630 OK. 1236 00:55:16,630 --> 00:55:19,270 So that was the first step, the goal and scope. 1237 00:55:19,270 --> 00:55:23,500 The second step is the inventory analysis. 1238 00:55:23,500 --> 00:55:26,730 So this is when you have to go out and identify and quantify 1239 00:55:26,730 --> 00:55:30,970 all of those impacts from the production system. 1240 00:55:30,970 --> 00:55:36,790 So in this case, it's a pretty straightforward process, 1241 00:55:36,790 --> 00:55:38,580 but not easy. 1242 00:55:38,580 --> 00:55:40,180 So simple, but not easy. 1243 00:55:40,180 --> 00:55:43,740 So the first is you build a model of the system. 1244 00:55:43,740 --> 00:55:46,820 This is sort of their stylized version of what 1245 00:55:46,820 --> 00:55:48,190 the model of the system is. 1246 00:55:48,190 --> 00:55:52,050 If you look at a more detailed life cycle assessment 1247 00:55:52,050 --> 00:55:55,480 study that's really getting down into the production process, 1248 00:55:55,480 --> 00:55:58,980 you might see a list of, say, hundreds of different processes 1249 00:55:58,980 --> 00:56:01,010 drawn out in a flow diagram. 1250 00:56:01,010 --> 00:56:03,000 A lot of the reason for this is that it's 1251 00:56:03,000 --> 00:56:05,980 coming from people from a science background that 1252 00:56:05,980 --> 00:56:06,770 have done this. 1253 00:56:06,770 --> 00:56:09,630 So it tends to follow a mass or energy flow, 1254 00:56:09,630 --> 00:56:12,250 where every time something comes in, 1255 00:56:12,250 --> 00:56:15,060 you have to trace it and figure out where it exits the system 1256 00:56:15,060 --> 00:56:16,900 or where it's going into, with the idea 1257 00:56:16,900 --> 00:56:19,233 being that nothing should be getting lost along the way. 1258 00:56:19,233 --> 00:56:22,320 If I have 100 kilograms of material coming in as input 1259 00:56:22,320 --> 00:56:25,320 and 86 kilograms of it go into the production system, 1260 00:56:25,320 --> 00:56:26,960 then that other 14 kilograms better 1261 00:56:26,960 --> 00:56:28,410 get traced where it's going. 1262 00:56:28,410 --> 00:56:30,840 Whether it's getting turned off into waste or burned 1263 00:56:30,840 --> 00:56:32,610 or whatever happens to it. 1264 00:56:32,610 --> 00:56:34,550 So that's generally the first part. 1265 00:56:34,550 --> 00:56:37,240 And this building a model of the system 1266 00:56:37,240 --> 00:56:39,620 is something that goes on by looking 1267 00:56:39,620 --> 00:56:42,440 at who are my suppliers, what are my inputs, 1268 00:56:42,440 --> 00:56:45,340 where do I get different things from, where's 1269 00:56:45,340 --> 00:56:49,360 energy coming into the system, electricity, all of that. 1270 00:56:49,360 --> 00:56:51,070 Where are things being transported 1271 00:56:51,070 --> 00:56:55,960 between, where are my different facilities located, interviews, 1272 00:56:55,960 --> 00:56:59,100 studying the bill of materials, looking at your procurement, 1273 00:56:59,100 --> 00:57:00,680 analyzing where your customers are. 1274 00:57:00,680 --> 00:57:03,940 This tends to be going through the records of trying 1275 00:57:03,940 --> 00:57:05,780 to figure out and map out the supply chain. 1276 00:57:05,780 --> 00:57:10,680 So from that standpoint, it's pretty simple understanding 1277 00:57:10,680 --> 00:57:12,580 what's going on, not necessarily always 1278 00:57:12,580 --> 00:57:14,650 easy to do since this tends to be very data- 1279 00:57:14,650 --> 00:57:18,120 and labor-intensive to go through and figure this out. 1280 00:57:18,120 --> 00:57:19,830 The even worse part is that once you've 1281 00:57:19,830 --> 00:57:22,870 identified all these processes, is identifying the inputs 1282 00:57:22,870 --> 00:57:23,460 and outputs. 1283 00:57:23,460 --> 00:57:26,560 So in the inputs we have raw materials, energy, 1284 00:57:26,560 --> 00:57:30,440 and other products that are coming in, and on the output, 1285 00:57:30,440 --> 00:57:33,220 it's our emissions, to air, to water, and to waste. 1286 00:57:33,220 --> 00:57:35,720 So everything that comes into the system and everything that 1287 00:57:35,720 --> 00:57:36,220 goes out. 1288 00:57:36,220 --> 00:57:40,540 I'm essentially drawing a boundary 1289 00:57:40,540 --> 00:57:43,030 around my system which, inside it, may 1290 00:57:43,030 --> 00:57:45,280 have a number of different processes going on. 1291 00:57:50,490 --> 00:57:54,650 What I'm looking for is, once I've mapped out that process, 1292 00:57:54,650 --> 00:57:57,130 I need to go through and identify where 1293 00:57:57,130 --> 00:57:58,510 are all my inputs and outputs. 1294 00:57:58,510 --> 00:58:00,390 If this is my system boundary, these 1295 00:58:00,390 --> 00:58:04,030 should all be raw materials coming in, energy coming in, 1296 00:58:04,030 --> 00:58:07,000 and all of the emissions going out. 1297 00:58:07,000 --> 00:58:10,010 And so one way to do that is to go through step by step. 1298 00:58:10,010 --> 00:58:13,470 This is a production facility, start tracking. 1299 00:58:13,470 --> 00:58:16,710 What energy are they using, how many units did 1300 00:58:16,710 --> 00:58:18,080 they produce, things like that. 1301 00:58:18,080 --> 00:58:22,120 So this tends to be the time consuming part of it, 1302 00:58:22,120 --> 00:58:25,600 but pretty straightforward in terms of what you need to do. 1303 00:58:25,600 --> 00:58:30,000 So in terms of the study that we looked at, 1304 00:58:30,000 --> 00:58:31,990 how did they go about doing this? 1305 00:58:31,990 --> 00:58:33,710 They talk a little bit about it. 1306 00:58:33,710 --> 00:58:35,270 It's kind of vague in some areas, 1307 00:58:35,270 --> 00:58:38,120 but they talk about the main process that they use. 1308 00:58:43,040 --> 00:58:47,184 What do they start with for the Dyson system. 1309 00:58:47,184 --> 00:58:48,350 AUDIENCE: Bill of materials? 1310 00:58:48,350 --> 00:58:50,100 PROFESSOR: Yeah, so the bill of materials. 1311 00:58:52,712 --> 00:58:54,545 Are you familiar with the bill of materials? 1312 00:59:01,274 --> 00:59:04,780 Or the B-O-M, the BOM. 1313 00:59:04,780 --> 00:59:07,170 This is basically just a listing of all the parts 1314 00:59:07,170 --> 00:59:10,330 that are going into it. 1315 00:59:10,330 --> 00:59:13,490 If you were to look at a higher level, 1316 00:59:13,490 --> 00:59:19,601 you might look at it as the fan, the motor, the housing, 1317 00:59:19,601 --> 00:59:20,100 et cetera. 1318 00:59:20,100 --> 00:59:22,120 These are the different parts that go into it. 1319 00:59:22,120 --> 00:59:25,060 And these in themselves may have a number 1320 00:59:25,060 --> 00:59:27,070 of other different materials that go into them. 1321 00:59:27,070 --> 00:59:31,150 So if you look at it, it's sort of a diagram that 1322 00:59:31,150 --> 00:59:35,340 sort of spreads out, where you have one piece here, 1323 00:59:35,340 --> 00:59:37,420 and it might have a couple of inputs. 1324 00:59:37,420 --> 00:59:39,560 And those might, in turn, have their own inputs. 1325 00:59:39,560 --> 00:59:41,310 And essentially, it's going to expand out. 1326 00:59:41,310 --> 00:59:43,476 I'm going to have a big list of all of the materials 1327 00:59:43,476 --> 00:59:44,060 that I have. 1328 00:59:44,060 --> 00:59:46,890 So in this case, they went through and took the data 1329 00:59:46,890 --> 00:59:48,880 that Dyson gave them and said, we 1330 00:59:48,880 --> 00:59:52,140 have the bill of materials for all of this. 1331 00:59:52,140 --> 00:59:57,840 And the trick was to turn that into the life cycle inventory. 1332 00:59:57,840 --> 01:00:00,410 And so what they did is they took the bill of materials 1333 01:00:00,410 --> 01:00:01,940 data-- and they call it the bill of activities 1334 01:00:01,940 --> 01:00:04,148 because they also have data about the activities that 1335 01:00:04,148 --> 01:00:06,590 are going on, the energy use that's going on 1336 01:00:06,590 --> 01:00:09,690 in the production phase-- and they matched it up 1337 01:00:09,690 --> 01:00:11,680 with inventory data. 1338 01:00:11,680 --> 01:00:14,300 In this case, you can think of the work 1339 01:00:14,300 --> 01:00:17,060 that would go on to actually go and do one of these. 1340 01:00:17,060 --> 01:00:20,070 Especially because you think of-- they 1341 01:00:20,070 --> 01:00:25,500 mention in there something like the optic housing that 1342 01:00:25,500 --> 01:00:29,430 was on one of them that detects when the hand goes in 1343 01:00:29,430 --> 01:00:30,420 and when it comes out. 1344 01:00:30,420 --> 01:00:33,880 You think, that has a lens, it has a microprocessor, 1345 01:00:33,880 --> 01:00:35,590 it has whatever else goes into it. 1346 01:00:35,590 --> 01:00:38,410 And all of those products have their own complex supply 1347 01:00:38,410 --> 01:00:38,910 chains. 1348 01:00:38,910 --> 01:00:41,694 So the minute that we mention one of these, 1349 01:00:41,694 --> 01:00:43,610 if we're taking this cradle to grave approach, 1350 01:00:43,610 --> 01:00:45,010 then we need to start tracking each 1351 01:00:45,010 --> 01:00:47,385 of those products up all the way through their suppliers. 1352 01:00:47,385 --> 01:00:49,430 And so they're buying that assembly 1353 01:00:49,430 --> 01:00:52,960 from some supplier who in turn is buying 1354 01:00:52,960 --> 01:00:54,710 a processor from this company, and they're 1355 01:00:54,710 --> 01:00:56,560 buying a lens from this company, and they're 1356 01:00:56,560 --> 01:00:59,130 buying a plastic housing from this company. 1357 01:00:59,130 --> 01:01:01,650 And that company does some kind of injection molding, 1358 01:01:01,650 --> 01:01:03,404 and they buy raw plastic from here. 1359 01:01:03,404 --> 01:01:05,070 So you can imagine they're going to have 1360 01:01:05,070 --> 01:01:08,550 to start tracing all of that up, what a labor that would be 1361 01:01:08,550 --> 01:01:10,920 to try to do that for a complex product, 1362 01:01:10,920 --> 01:01:12,840 or even a relatively simple product. 1363 01:01:12,840 --> 01:01:15,120 So what they've done is they've gone out and found 1364 01:01:15,120 --> 01:01:18,020 this inventory data, which generally 1365 01:01:18,020 --> 01:01:19,650 comes from third party sources. 1366 01:01:19,650 --> 01:01:22,910 So if you look at how you would do one of these life cycle 1367 01:01:22,910 --> 01:01:27,270 inventories, usually there's a couple of big software vendors 1368 01:01:27,270 --> 01:01:29,800 that make tools that help you do this. 1369 01:01:29,800 --> 01:01:33,140 SimaPro, GaBi, are some of the big ones. 1370 01:01:33,140 --> 01:01:37,080 They, in turn, license databases from other companies. 1371 01:01:37,080 --> 01:01:39,380 ecoinvent was the one that was used in this study, 1372 01:01:39,380 --> 01:01:40,860 and they mention that in here. 1373 01:01:40,860 --> 01:01:44,450 So ecoinvent is a company that goes out and actually 1374 01:01:44,450 --> 01:01:45,750 tries to do that work. 1375 01:01:45,750 --> 01:01:48,260 They try to go out and look for common products 1376 01:01:48,260 --> 01:01:51,420 and go out and examine what types of inputs do they have, 1377 01:01:51,420 --> 01:01:54,030 what goes on at the factory, how much energy does 1378 01:01:54,030 --> 01:01:56,690 it take to make it, what are all the inputs 1379 01:01:56,690 --> 01:01:58,710 and outputs to the environment, and they sort of 1380 01:01:58,710 --> 01:01:59,740 start building up these. 1381 01:01:59,740 --> 01:02:02,130 So then they have a database that tells you things 1382 01:02:02,130 --> 01:02:05,219 like this type of steel has this sort of impact, here 1383 01:02:05,219 --> 01:02:07,010 are its inputs and outputs, different types 1384 01:02:07,010 --> 01:02:08,410 of plastic, et cetera. 1385 01:02:08,410 --> 01:02:10,260 And you go through and you match those up. 1386 01:02:10,260 --> 01:02:14,350 So now it says, I don't necessarily need to go through 1387 01:02:14,350 --> 01:02:16,980 and do all of this tracing of everything for me. 1388 01:02:16,980 --> 01:02:18,920 The software, the database that I'm using, 1389 01:02:18,920 --> 01:02:21,220 is going to have some of that in there. 1390 01:02:21,220 --> 01:02:24,585 It's enough for me to say that this particular pieces is, 1391 01:02:24,585 --> 01:02:29,560 say, 2.3 pounds of this type of material. 1392 01:02:29,560 --> 01:02:32,000 I'm going to go out and match that up with the database 1393 01:02:32,000 --> 01:02:33,950 to do all of that upstream tracking 1394 01:02:33,950 --> 01:02:36,754 and tracing of what's going on. 1395 01:02:36,754 --> 01:02:38,170 They talk about this in the study, 1396 01:02:38,170 --> 01:02:41,420 though, because this is going to run into some problems. 1397 01:02:41,420 --> 01:02:43,618 What are some of the issues that they had with this? 1398 01:02:49,080 --> 01:02:53,139 So there's a lot of different products that go into it. 1399 01:02:53,139 --> 01:02:55,180 What do you do when you run into a product that's 1400 01:02:55,180 --> 01:02:58,328 not in the database. 1401 01:02:58,328 --> 01:03:01,930 AUDIENCE: [INAUDIBLE] they didn't 1402 01:03:01,930 --> 01:03:05,275 have specific data on the different material 1403 01:03:05,275 --> 01:03:10,140 covers for the hand dryers, so they just averaged them. 1404 01:03:10,140 --> 01:03:10,960 PROFESSOR: Yeah. 1405 01:03:14,037 --> 01:03:16,120 In that case, that was a slightly different issue, 1406 01:03:16,120 --> 01:03:17,080 but related. 1407 01:03:17,080 --> 01:03:18,810 Because that one, they were relying 1408 01:03:18,810 --> 01:03:25,990 on-- they were taking the information from a third party 1409 01:03:25,990 --> 01:03:28,810 that had done the study already, and had just already averaged 1410 01:03:28,810 --> 01:03:32,520 the impact of the housing together. 1411 01:03:32,520 --> 01:03:34,350 Rather than not being able to find-- 1412 01:03:34,350 --> 01:03:36,830 I suppose it probably fits in there. 1413 01:03:36,830 --> 01:03:39,510 Nobody had done this study and said, 1414 01:03:39,510 --> 01:03:43,230 here is the impact for a Xlerator housing made 1415 01:03:43,230 --> 01:03:45,630 of plastic or made of aluminum. 1416 01:03:45,630 --> 01:03:48,290 So it is probably the same. 1417 01:03:48,290 --> 01:03:53,670 Some of the stuff, like there was no galvanized steel. 1418 01:03:53,670 --> 01:03:55,590 They said there was none of this. 1419 01:03:55,590 --> 01:03:59,740 There was no-- I forget which type. 1420 01:03:59,740 --> 01:04:03,120 There was some type of bleached paper that they didn't have. 1421 01:04:03,120 --> 01:04:06,320 And this was related to the recycled paper towels and some 1422 01:04:06,320 --> 01:04:07,180 of these issues. 1423 01:04:07,180 --> 01:04:10,960 So essentially, they had a list of maybe 20 different materials 1424 01:04:10,960 --> 01:04:15,620 or so that were listed in these bill of materials 1425 01:04:15,620 --> 01:04:17,140 or that were included in the system 1426 01:04:17,140 --> 01:04:18,677 that they didn't have data for. 1427 01:04:18,677 --> 01:04:20,510 So they had to go through and sort of create 1428 01:04:20,510 --> 01:04:22,330 their own version of some of these. 1429 01:04:22,330 --> 01:04:25,930 So they said well, one thing you can do is use proxy data. 1430 01:04:25,930 --> 01:04:28,500 So they said, we don't have this specific type 1431 01:04:28,500 --> 01:04:31,400 of bleached paper, so we'll just use as a substitute 1432 01:04:31,400 --> 01:04:33,320 this type of bleached paper. 1433 01:04:33,320 --> 01:04:36,060 Or they said, we don't have galvanized steel, 1434 01:04:36,060 --> 01:04:39,345 but we have regular, this type of steel, 1435 01:04:39,345 --> 01:04:41,470 and then we're going to add to that the impact that 1436 01:04:41,470 --> 01:04:46,330 would go with this process of applying the coating to it. 1437 01:04:46,330 --> 01:04:48,060 Even with this inventory data, you're 1438 01:04:48,060 --> 01:04:50,380 going to run into trouble finding all the products 1439 01:04:50,380 --> 01:04:52,230 that you need to do to do this. 1440 01:04:52,230 --> 01:04:55,927 And so it's generally not something-- 1441 01:04:55,927 --> 01:04:58,260 you're going to have to make some compromises as you go, 1442 01:04:58,260 --> 01:05:00,572 in terms of figuring out what you can include 1443 01:05:00,572 --> 01:05:02,530 and how to get around some of these limitations 1444 01:05:02,530 --> 01:05:05,690 that you might have. 1445 01:05:05,690 --> 01:05:11,410 Now, once they did that, they went through. 1446 01:05:11,410 --> 01:05:14,830 They had this chart in here which was showing, 1447 01:05:14,830 --> 01:05:16,104 essentially, what they had. 1448 01:05:16,104 --> 01:05:18,770 This is the assumption that they used to generate the inventory, 1449 01:05:18,770 --> 01:05:20,950 because part of it was that they had 1450 01:05:20,950 --> 01:05:23,690 to go through the manufacturing and identify 1451 01:05:23,690 --> 01:05:26,052 how much energy per unit. 1452 01:05:26,052 --> 01:05:27,760 And then they had to go through, and then 1453 01:05:27,760 --> 01:05:31,420 based on this, the life cycle, how many times would it draw. 1454 01:05:31,420 --> 01:05:34,830 They had to go through and capture how much material 1455 01:05:34,830 --> 01:05:37,816 was going into each of these. 1456 01:05:37,816 --> 01:05:39,440 One of the assumptions they had to make 1457 01:05:39,440 --> 01:05:41,160 was data about the scrappage rates. 1458 01:05:41,160 --> 01:05:43,970 Because part of this is that the goal is 1459 01:05:43,970 --> 01:05:45,910 to understand all of those inputs 1460 01:05:45,910 --> 01:05:49,110 that come into the system and the emissions, 1461 01:05:49,110 --> 01:05:50,770 and then what is my useful output. 1462 01:05:50,770 --> 01:05:54,200 So if I take in 100 kilograms of plastic, 1463 01:05:54,200 --> 01:05:57,360 but only 85 kilograms of plastic end up in the final unit, 1464 01:05:57,360 --> 01:05:59,110 because the other 15 goes to waste, 1465 01:05:59,110 --> 01:06:01,540 you have to make sure to allocate the emissions related 1466 01:06:01,540 --> 01:06:04,460 to that 15 kilograms of waste to those final products. 1467 01:06:04,460 --> 01:06:07,115 So it's not enough just to look at the end bill of materials. 1468 01:06:07,115 --> 01:06:08,490 They had to make some assumptions 1469 01:06:08,490 --> 01:06:10,980 about what goes on the manufacturing process 1470 01:06:10,980 --> 01:06:12,314 and how that relates to it. 1471 01:06:15,240 --> 01:06:17,480 And so at the end, once they've gone through this, 1472 01:06:17,480 --> 01:06:19,880 they have enough to move on to the next stage. 1473 01:06:19,880 --> 01:06:23,210 I'm going to skip this piece here and talk 1474 01:06:23,210 --> 01:06:26,230 about our next stage, which is the impact analysis. 1475 01:06:26,230 --> 01:06:28,890 So this is where they go through and finally, now that they 1476 01:06:28,890 --> 01:06:31,090 have, for each of those different products systems, 1477 01:06:31,090 --> 01:06:32,290 they've made all of these assumptions, 1478 01:06:32,290 --> 01:06:33,876 they've put together the impact. 1479 01:06:33,876 --> 01:06:35,500 They're ready to go ahead and determine 1480 01:06:35,500 --> 01:06:40,180 what the environmental impact of each of those products was. 1481 01:06:40,180 --> 01:06:42,240 So there's essentially two steps that 1482 01:06:42,240 --> 01:06:45,420 go on in the impact assessment, referred to as classification 1483 01:06:45,420 --> 01:06:46,900 and characterization. 1484 01:06:46,900 --> 01:06:49,590 So classification, the first one, 1485 01:06:49,590 --> 01:06:53,630 essentially that inventory analysis you have 1486 01:06:53,630 --> 01:06:56,622 is going to list things like all of the outputs, all 1487 01:06:56,622 --> 01:06:57,580 of my emissions to air. 1488 01:06:57,580 --> 01:07:01,250 How much carbon dioxide, how much carbon monoxide, all 1489 01:07:01,250 --> 01:07:02,940 of these different types of gases. 1490 01:07:02,940 --> 01:07:05,580 But if I'm interested in the global warming potential, 1491 01:07:05,580 --> 01:07:07,450 I really only need to know which of these 1492 01:07:07,450 --> 01:07:08,700 are the greenhouse gases. 1493 01:07:08,700 --> 01:07:10,560 So I'm going to go through in the classification step, 1494 01:07:10,560 --> 01:07:12,400 and I'm going to look at all of these different outputs 1495 01:07:12,400 --> 01:07:14,289 that I have to the atmosphere, and I'm 1496 01:07:14,289 --> 01:07:16,580 going to gather together the ones that are contributing 1497 01:07:16,580 --> 01:07:17,870 to global warming. 1498 01:07:17,870 --> 01:07:19,530 That would be the first step. 1499 01:07:19,530 --> 01:07:21,182 That would be the classification step. 1500 01:07:21,182 --> 01:07:22,640 And you're going to need to do that 1501 01:07:22,640 --> 01:07:24,710 for each of the different environmental impact categories 1502 01:07:24,710 --> 01:07:25,543 that you might have. 1503 01:07:25,543 --> 01:07:27,759 So they'll go through and do that. 1504 01:07:27,759 --> 01:07:29,550 And the second one is the characterization. 1505 01:07:29,550 --> 01:07:34,750 So if we look at it in terms of the global warming potential, 1506 01:07:34,750 --> 01:07:37,180 after I've classified and gotten together my listing of, 1507 01:07:37,180 --> 01:07:39,550 OK, how much carbon dioxide, how much methane, how much 1508 01:07:39,550 --> 01:07:41,795 nitrous oxide, characterization is 1509 01:07:41,795 --> 01:07:43,670 when we apply those global warming potentials 1510 01:07:43,670 --> 01:07:46,440 and actually turn that into a unit of output. 1511 01:07:46,440 --> 01:07:48,110 Carbon dioxide equivalence. 1512 01:07:48,110 --> 01:07:51,750 And this applies to a wide range of different environmental 1513 01:07:51,750 --> 01:07:55,220 impacts that you might study. 1514 01:07:55,220 --> 01:07:57,470 What were some of the impacts that they 1515 01:07:57,470 --> 01:07:58,604 chose to study in this one? 1516 01:08:03,530 --> 01:08:06,141 They had a couple of basic ones and then some more complex 1517 01:08:06,141 --> 01:08:06,641 ones. 1518 01:08:09,623 --> 01:08:13,599 AUDIENCE: [INAUDIBLE] CO2 emissions. 1519 01:08:13,599 --> 01:08:16,290 And they didn't really understand how to get water. 1520 01:08:16,290 --> 01:08:18,366 But they said they included water, 1521 01:08:18,366 --> 01:08:20,626 and then they said they didn't, so I don't really 1522 01:08:20,626 --> 01:08:23,050 understand what went on there. 1523 01:08:23,050 --> 01:08:25,779 PROFESSOR: So they mentioned a couple things in there. 1524 01:08:31,762 --> 01:08:33,460 They had the global warming potential, 1525 01:08:33,460 --> 01:08:35,569 they have the carbon footprint, essentially. 1526 01:08:35,569 --> 01:08:40,220 They had water use. 1527 01:08:40,220 --> 01:08:42,620 AUDIENCE: Land occupation? 1528 01:08:42,620 --> 01:08:44,750 PROFESSOR: So we'll get to that in a second. 1529 01:08:47,939 --> 01:08:52,370 So they had, they called it CED. 1530 01:08:52,370 --> 01:08:54,420 Cumulative Energy-- yeah. 1531 01:08:57,220 --> 01:08:59,000 Then they had-- remember what the name 1532 01:08:59,000 --> 01:09:01,242 of the more complex one was? 1533 01:09:01,242 --> 01:09:02,950 AUDIENCE: [? Along the lines of ?] IMPACT 1534 01:09:02,950 --> 01:09:04,902 2002+. 1535 01:09:04,902 --> 01:09:08,399 PROFESSOR: They had something called IMPACT 2002+, 1536 01:09:08,399 --> 01:09:09,970 I think it was called. 1537 01:09:09,970 --> 01:09:11,977 So what they did, when they went through this, 1538 01:09:11,977 --> 01:09:13,810 they made a distinction about some of these. 1539 01:09:13,810 --> 01:09:17,290 So the global warming potential isn't environmental, 1540 01:09:17,290 --> 01:09:20,246 it's an impact category, because it basically 1541 01:09:20,246 --> 01:09:21,120 does these two steps. 1542 01:09:21,120 --> 01:09:23,160 I gather my greenhouse gases and then 1543 01:09:23,160 --> 01:09:25,550 I characterize the impact using their global warming 1544 01:09:25,550 --> 01:09:26,800 potentials. 1545 01:09:26,800 --> 01:09:28,979 The water use and cumulative energy demand 1546 01:09:28,979 --> 01:09:31,510 aren't really impact categories. 1547 01:09:31,510 --> 01:09:33,319 They're treated as such by a lot of people, 1548 01:09:33,319 --> 01:09:34,860 but they made the point in the study, 1549 01:09:34,860 --> 01:09:37,229 they're not technically impact areas. 1550 01:09:37,229 --> 01:09:39,020 And the reason for that is that there's 1551 01:09:39,020 --> 01:09:41,140 no actual characterization that goes on. 1552 01:09:41,140 --> 01:09:43,810 So for water use and cumulative energy demand, 1553 01:09:43,810 --> 01:09:46,600 they just went back through the inventory 1554 01:09:46,600 --> 01:09:49,950 and said during all these steps in the process, 1555 01:09:49,950 --> 01:09:53,240 I'll just sum up how much water was used at each stage. 1556 01:09:53,240 --> 01:09:55,610 At the end, I get a total amount of water use. 1557 01:09:55,610 --> 01:09:58,390 But there's no attempt to turn that into something 1558 01:09:58,390 --> 01:09:59,830 more complicated. 1559 01:09:59,830 --> 01:10:02,350 There's no attempt to rate. 1560 01:10:02,350 --> 01:10:07,540 You can think of if I'm using good, clean drinking water 1561 01:10:07,540 --> 01:10:09,809 or if I'm using salt water from the ocean. 1562 01:10:09,809 --> 01:10:11,350 You might think that those would have 1563 01:10:11,350 --> 01:10:14,170 different environmental impacts, because most people don't care 1564 01:10:14,170 --> 01:10:17,774 if I'm using up the salt water as opposed 1565 01:10:17,774 --> 01:10:18,940 to using up the clean water. 1566 01:10:18,940 --> 01:10:21,920 So you could think they're not actually really investigating 1567 01:10:21,920 --> 01:10:24,350 the impact of those, they're just calculating them 1568 01:10:24,350 --> 01:10:26,960 so you can see how much water and how much energy they use. 1569 01:10:26,960 --> 01:10:29,320 And specifically for the cumulative energy demand, 1570 01:10:29,320 --> 01:10:31,740 they mention that it's a proxy for a lot of other things. 1571 01:10:31,740 --> 01:10:33,950 The more energy intensive the production process 1572 01:10:33,950 --> 01:10:37,570 is, that's tied to a lot of other environmental impact 1573 01:10:37,570 --> 01:10:38,070 categories. 1574 01:10:38,070 --> 01:10:41,581 So they're sort of used as a proxy, but not really an impact 1575 01:10:41,581 --> 01:10:42,080 category. 1576 01:10:45,490 --> 01:10:47,230 So we had the single use indicators. 1577 01:10:49,857 --> 01:10:52,190 Cumulative energy demand on the side, and global warming 1578 01:10:52,190 --> 01:10:52,898 potential, right. 1579 01:10:52,898 --> 01:10:55,650 And we can go through, and they've calculated the carbon 1580 01:10:55,650 --> 01:10:58,650 footprint for each of these different products based 1581 01:10:58,650 --> 01:11:00,630 on that inventory analysis and broken it up 1582 01:11:00,630 --> 01:11:02,389 into the different stages of life. 1583 01:11:02,389 --> 01:11:04,680 And you can do the same thing for the cumulative energy 1584 01:11:04,680 --> 01:11:06,138 demand where, in this case, they're 1585 01:11:06,138 --> 01:11:09,710 measuring it in how many kilojoules of energy they use. 1586 01:11:09,710 --> 01:11:10,720 Those are pretty simple. 1587 01:11:10,720 --> 01:11:16,880 But the more complicated ones are this one like IMPACT 2002. 1588 01:11:16,880 --> 01:11:20,050 In this case, that's because it has 1589 01:11:20,050 --> 01:11:21,760 number of different factors going in, 1590 01:11:21,760 --> 01:11:24,120 and it's sort of divided up into a two stage approach. 1591 01:11:24,120 --> 01:11:26,890 Which is the first thing you have, 1592 01:11:26,890 --> 01:11:28,920 just like with global warming potential, 1593 01:11:28,920 --> 01:11:31,730 they started with the inventory results. 1594 01:11:31,730 --> 01:11:33,170 Then they went through and created 1595 01:11:33,170 --> 01:11:35,300 what they called a number of midpoint categories. 1596 01:11:35,300 --> 01:11:38,260 So if you look at what IMPACT 2002 is, 1597 01:11:38,260 --> 01:11:40,610 it's divided up into all of these different things that 1598 01:11:40,610 --> 01:11:44,270 are counting everything from carcinogens to ozone depletion 1599 01:11:44,270 --> 01:11:48,580 to how much mineral extraction you're doing, 1600 01:11:48,580 --> 01:11:49,880 aquatic acidification. 1601 01:11:49,880 --> 01:11:51,940 So a number of different environmental impacts. 1602 01:11:51,940 --> 01:11:55,120 This is getting at a lot more broader scope 1603 01:11:55,120 --> 01:11:57,240 than what we would get if we just looked at energy 1604 01:11:57,240 --> 01:11:59,070 or global warming potential. 1605 01:11:59,070 --> 01:12:00,870 It goes through sort of the same process 1606 01:12:00,870 --> 01:12:03,299 that you had before which is, now I'm 1607 01:12:03,299 --> 01:12:04,840 going to look at my inventory and I'm 1608 01:12:04,840 --> 01:12:07,400 going to classify all of my emissions that are carcinogens, 1609 01:12:07,400 --> 01:12:09,691 and I'm going to group together and figure out how much 1610 01:12:09,691 --> 01:12:10,950 I have of each different type. 1611 01:12:10,950 --> 01:12:12,658 They're going to go through this for each 1612 01:12:12,658 --> 01:12:15,960 of these different midpoint categories. 1613 01:12:15,960 --> 01:12:18,870 Then what they've done is that they've combined these, then, 1614 01:12:18,870 --> 01:12:20,320 into a second set. 1615 01:12:20,320 --> 01:12:22,960 So they figured out how many of the different types 1616 01:12:22,960 --> 01:12:24,814 of carcinogens, how many of these. 1617 01:12:24,814 --> 01:12:26,230 And then they relating these again 1618 01:12:26,230 --> 01:12:28,980 to overall indicators that capture a broad set. 1619 01:12:28,980 --> 01:12:31,610 All of these are things that impact human health. 1620 01:12:31,610 --> 01:12:33,210 So they're grouping these together 1621 01:12:33,210 --> 01:12:34,837 and one they're calling human health, 1622 01:12:34,837 --> 01:12:36,420 they're grouping all of these together 1623 01:12:36,420 --> 01:12:39,500 into ecosystem quality, climate change has its own, 1624 01:12:39,500 --> 01:12:41,850 and then they have resources. 1625 01:12:41,850 --> 01:12:45,350 So that in itself is a pretty complicated process, 1626 01:12:45,350 --> 01:12:49,370 because if you read about it or looked 1627 01:12:49,370 --> 01:12:51,990 into some of these charts, this is 1628 01:12:51,990 --> 01:12:57,250 what happened is that they-- and I'm not 1629 01:12:57,250 --> 01:12:58,520 even sure how you do this. 1630 01:12:58,520 --> 01:13:00,520 So the carcinogens are being measured 1631 01:13:00,520 --> 01:13:02,722 in-- I don't know what that is. 1632 01:13:02,722 --> 01:13:04,180 I need to brush up on my chemistry. 1633 01:13:04,180 --> 01:13:07,640 But it's some kind of grams of some chemical equivalent. 1634 01:13:07,640 --> 01:13:09,910 So they've taken all of the different outputs 1635 01:13:09,910 --> 01:13:12,220 that they have that are carcinogens, 1636 01:13:12,220 --> 01:13:14,690 and they've found a way to measure them in terms 1637 01:13:14,690 --> 01:13:17,445 of some equivalent units. 1638 01:13:17,445 --> 01:13:19,820 I assume this is producing the same amount of carcinogens 1639 01:13:19,820 --> 01:13:24,250 as 0.277 grams of whatever that compound is. 1640 01:13:24,250 --> 01:13:26,250 That's how they've gone through, and for each 1641 01:13:26,250 --> 01:13:28,427 of these categories, they've created 1642 01:13:28,427 --> 01:13:30,760 some measure that they're going to use, some equivalent. 1643 01:13:30,760 --> 01:13:34,870 So respiratory inorganics, we can 1644 01:13:34,870 --> 01:13:38,440 assume that these have the same impact as it would 1645 01:13:38,440 --> 01:13:41,310 if you produced this many grams of particulate matter. 1646 01:13:41,310 --> 01:13:43,730 2.5 millimeter particulate matter, right. 1647 01:13:43,730 --> 01:13:45,340 Or whatever that is. 1648 01:13:45,340 --> 01:13:48,890 They've gone through and, for each of these categories, 1649 01:13:48,890 --> 01:13:50,550 they've come up with a way to do this. 1650 01:13:50,550 --> 01:13:55,220 And so this is pretty complex and probably nobody on earth 1651 01:13:55,220 --> 01:13:56,930 is enough of an expert to be an expert 1652 01:13:56,930 --> 01:13:58,209 on each of these categories. 1653 01:13:58,209 --> 01:14:00,250 So what they've done is they've gathered together 1654 01:14:00,250 --> 01:14:02,050 experts from all of these different areas 1655 01:14:02,050 --> 01:14:05,210 to figure out how you would go through and judge 1656 01:14:05,210 --> 01:14:06,667 each of these. 1657 01:14:06,667 --> 01:14:09,000 And they actually go a step further from this, actually, 1658 01:14:09,000 --> 01:14:10,380 two steps further from this. 1659 01:14:10,380 --> 01:14:12,470 So you can see for each of these products, 1660 01:14:12,470 --> 01:14:15,152 we're going to get all of these different ratings. 1661 01:14:15,152 --> 01:14:17,110 And the question is, how do we put all of these 1662 01:14:17,110 --> 01:14:20,760 together in terms of an indicator. 1663 01:14:20,760 --> 01:14:24,330 Because I can look at what the value is for an Airblade 1664 01:14:24,330 --> 01:14:27,420 aluminum, and I compare that to Cotton Roll towels, and I see, 1665 01:14:27,420 --> 01:14:31,250 well, Cotton Roll towels have less carcinogens. 1666 01:14:31,250 --> 01:14:35,060 Does that make them better than the Dyson Airblade. 1667 01:14:35,060 --> 01:14:36,670 And the question is, that depends. 1668 01:14:36,670 --> 01:14:39,086 Because if you start looking at some of these other impact 1669 01:14:39,086 --> 01:14:42,325 categories, you find ones where, well, that actually contributes 1670 01:14:42,325 --> 01:14:45,290 a higher value, 290 to 118 in terms 1671 01:14:45,290 --> 01:14:47,470 of terrestrial ecotoxicity. 1672 01:14:47,470 --> 01:14:49,800 So then you have to come back through and figure out 1673 01:14:49,800 --> 01:14:52,730 a way to balance all of these different impacts together. 1674 01:14:52,730 --> 01:14:55,320 And that's what that impact system does. 1675 01:14:55,320 --> 01:14:58,420 It essentially produces two things. 1676 01:14:58,420 --> 01:15:01,430 So first, they group all of those together. 1677 01:15:01,430 --> 01:15:04,020 They took all those human health impacts together 1678 01:15:04,020 --> 01:15:05,510 and created a single indicator. 1679 01:15:05,510 --> 01:15:08,750 So they're bringing together the carcinogens, 1680 01:15:08,750 --> 01:15:14,090 the inorganic respiration, all of these different types 1681 01:15:14,090 --> 01:15:17,510 of impacts that they said impacts human health, 1682 01:15:17,510 --> 01:15:20,300 and they put them all on a single unit of measure. 1683 01:15:20,300 --> 01:15:23,710 Which I believe is disability adjusted life years. 1684 01:15:23,710 --> 01:15:26,020 So if any of you are following health care, 1685 01:15:26,020 --> 01:15:28,850 that's how you measure when should we 1686 01:15:28,850 --> 01:15:31,500 pay for this type of health care or not. 1687 01:15:31,500 --> 01:15:33,100 This is one of the things that you do. 1688 01:15:33,100 --> 01:15:36,300 You try to judge what the consequences of it will be, 1689 01:15:36,300 --> 01:15:38,310 and then you have to make some trade offs about, 1690 01:15:38,310 --> 01:15:40,390 would it be better to be blind in one eye 1691 01:15:40,390 --> 01:15:42,270 or walk with a limp and all of these things. 1692 01:15:42,270 --> 01:15:44,510 So it's very complicated and requires 1693 01:15:44,510 --> 01:15:49,100 a lot of value judgments to get into some of these levels. 1694 01:15:49,100 --> 01:15:52,627 The nice thing about these categories like IMPACT 2002 1695 01:15:52,627 --> 01:15:54,210 is that they have what would hopefully 1696 01:15:54,210 --> 01:15:57,160 be a panel of experts going through and doing this in sort 1697 01:15:57,160 --> 01:15:58,960 of a peer reviewed fashion. 1698 01:15:58,960 --> 01:16:00,590 And they're coming up, and so now this 1699 01:16:00,590 --> 01:16:03,114 is giving me a human health comparison 1700 01:16:03,114 --> 01:16:04,280 of these different products. 1701 01:16:04,280 --> 01:16:08,640 So it's bringing together all of those different seven or eight 1702 01:16:08,640 --> 01:16:11,330 different indicators that impacted human health 1703 01:16:11,330 --> 01:16:13,250 and put them together on one scale. 1704 01:16:13,250 --> 01:16:15,990 And they do the same thing for ecosystem quality. 1705 01:16:15,990 --> 01:16:18,060 And in fact, you can take it a stage farther, 1706 01:16:18,060 --> 01:16:20,430 which is that you can put these all together in terms 1707 01:16:20,430 --> 01:16:22,060 of eco points. 1708 01:16:22,060 --> 01:16:25,490 So then that makes the balance between how much human health 1709 01:16:25,490 --> 01:16:28,880 would you be willing to trade off against how much ecosystem 1710 01:16:28,880 --> 01:16:30,600 quality. 1711 01:16:30,600 --> 01:16:33,070 So the important thing to remember about this 1712 01:16:33,070 --> 01:16:36,080 is that as you start-- so that's very nice when I have something 1713 01:16:36,080 --> 01:16:39,360 like IMPACT 2002 because, theoretically, I can compare 1714 01:16:39,360 --> 01:16:40,930 these systems and I can get a picture 1715 01:16:40,930 --> 01:16:42,500 of the overall environmental quality. 1716 01:16:42,500 --> 01:16:44,000 Not just looking at one single issue 1717 01:16:44,000 --> 01:16:45,910 like global warming potential, and not 1718 01:16:45,910 --> 01:16:49,200 having to look simultaneously at 27 different indicators 1719 01:16:49,200 --> 01:16:50,840 and decide how I want to trade it off. 1720 01:16:50,840 --> 01:16:52,550 I've hopefully got some experts out there 1721 01:16:52,550 --> 01:16:55,490 that have done that already, and I can sum everything up 1722 01:16:55,490 --> 01:16:58,912 in terms of, say, one unit of eco points. 1723 01:16:58,912 --> 01:17:00,370 But the important thing to remember 1724 01:17:00,370 --> 01:17:02,780 is that when you do that, you're making value judgments. 1725 01:17:02,780 --> 01:17:05,150 That's built into the system that scores that. 1726 01:17:05,150 --> 01:17:07,730 It's deciding how much you're willing to trade off 1727 01:17:07,730 --> 01:17:08,855 these different categories. 1728 01:17:14,000 --> 01:17:18,610 So finally, the last step we do is the interpretation phase. 1729 01:17:18,610 --> 01:17:22,070 So this was actually the long one, 1730 01:17:22,070 --> 01:17:24,220 when we looked at the study overall, 1731 01:17:24,220 --> 01:17:26,860 because they went through and trying 1732 01:17:26,860 --> 01:17:30,332 to interpret all those earlier results in terms of what 1733 01:17:30,332 --> 01:17:31,415 the goal in the study was. 1734 01:17:31,415 --> 01:17:35,380 And essentially to do that, they had to go through and do all 1735 01:17:35,380 --> 01:17:37,142 sorts of uncertainty analysis on that 1736 01:17:37,142 --> 01:17:39,100 to make sure all of those assumptions they made 1737 01:17:39,100 --> 01:17:42,144 along the way were still valid when they tried 1738 01:17:42,144 --> 01:17:43,310 to go back and interpret it. 1739 01:17:43,310 --> 01:17:46,250 If you wanted to come back and make a claim that the Dyson 1740 01:17:46,250 --> 01:17:48,170 Airblade was better than the others, 1741 01:17:48,170 --> 01:17:50,740 you better make sure that all of the limitations in the study 1742 01:17:50,740 --> 01:17:52,110 aren't going to impact that. 1743 01:17:52,110 --> 01:17:55,050 So they essentially did three types. 1744 01:17:55,050 --> 01:17:58,360 They did a sensitivity analysis, they did uncertainty analysis-- 1745 01:17:58,360 --> 01:17:59,990 which was really two different types, 1746 01:17:59,990 --> 01:18:01,970 one was a scenario uncertainty and the other 1747 01:18:01,970 --> 01:18:04,800 was a bill of activities analysis. 1748 01:18:04,800 --> 01:18:07,855 So in terms of what they did, the sensitivity analysis 1749 01:18:07,855 --> 01:18:08,980 was pretty straightforward. 1750 01:18:08,980 --> 01:18:11,690 They basically went through all of those assumptions they made, 1751 01:18:11,690 --> 01:18:13,140 and they had a lot of them. 1752 01:18:13,140 --> 01:18:16,200 There were probably 12 or 15 different major assumptions 1753 01:18:16,200 --> 01:18:17,180 that they made. 1754 01:18:17,180 --> 01:18:19,420 And they did a one factor at a time change 1755 01:18:19,420 --> 01:18:21,010 to test the impact of that. 1756 01:18:21,010 --> 01:18:30,719 So they went through and some of them, they tested. 1757 01:18:30,719 --> 01:18:32,510 When it goes to waste, there's a percentage 1758 01:18:32,510 --> 01:18:33,880 of that that gets incinerated. 1759 01:18:33,880 --> 01:18:36,940 So they tested how that changed, and different countries that 1760 01:18:36,940 --> 01:18:40,275 have different waste systems and different ways of taking 1761 01:18:40,275 --> 01:18:42,156 care of the waste will do that differently. 1762 01:18:42,156 --> 01:18:43,780 So they went back and tested the impact 1763 01:18:43,780 --> 01:18:46,300 of changing that percentage on the scenarios. 1764 01:18:46,300 --> 01:18:48,744 If we look at the scenarios, none of these 1765 01:18:48,744 --> 01:18:51,160 really get changed because they're not really sending much 1766 01:18:51,160 --> 01:18:52,730 to waste that's getting burned. 1767 01:18:52,730 --> 01:18:54,230 And these change a little bit. 1768 01:18:54,230 --> 01:18:56,780 But overall, looking at these different scenarios, 1769 01:18:56,780 --> 01:18:59,380 you get a very similar picture than you did before. 1770 01:18:59,380 --> 01:19:01,970 So it's not particularly sensitive to the assumption 1771 01:19:01,970 --> 01:19:03,780 that we have here. 1772 01:19:03,780 --> 01:19:07,760 The other one has to do with the intensity of the drying time 1773 01:19:07,760 --> 01:19:08,620 on the system. 1774 01:19:08,620 --> 01:19:14,280 And so in this case, you start to get a very different picture 1775 01:19:14,280 --> 01:19:16,060 between different scenarios, where 1776 01:19:16,060 --> 01:19:19,100 you have some of these that are getting definitely 1777 01:19:19,100 --> 01:19:21,090 higher impact and changing as we go through. 1778 01:19:21,090 --> 01:19:24,310 So the sensitivity analysis was pretty straightforward. 1779 01:19:24,310 --> 01:19:27,599 Go through one at a time, changing those factors 1780 01:19:27,599 --> 01:19:29,640 between some different values, seeing what impact 1781 01:19:29,640 --> 01:19:30,590 it has in the system. 1782 01:19:33,070 --> 01:19:34,570 The two things we said it identified 1783 01:19:34,570 --> 01:19:38,350 were the electricity and the drying time, 1784 01:19:38,350 --> 01:19:40,960 which drying time scenario you used. 1785 01:19:40,960 --> 01:19:43,540 So it identifies those two as sort of the major ones that 1786 01:19:43,540 --> 01:19:45,240 can affect the system. 1787 01:19:45,240 --> 01:19:47,706 But one of things it doesn't really capture 1788 01:19:47,706 --> 01:19:49,330 is that it does all this one at a time. 1789 01:19:49,330 --> 01:19:51,070 And especially those two are related, 1790 01:19:51,070 --> 01:19:54,290 so if I'm off on how long I dry, then I'm 1791 01:19:54,290 --> 01:19:57,120 also off on my assumption about the intensity 1792 01:19:57,120 --> 01:19:58,200 of the electricity. 1793 01:19:58,200 --> 01:20:00,130 Those two factors might compound together 1794 01:20:00,130 --> 01:20:02,020 to make my results even worse. 1795 01:20:02,020 --> 01:20:04,080 So this only captures what happens 1796 01:20:04,080 --> 01:20:05,830 when they did it one at a time. 1797 01:20:05,830 --> 01:20:08,420 So they went through and did a scenario uncertainty analysis. 1798 01:20:08,420 --> 01:20:10,900 And essentially, they had 20 different parameters, 1799 01:20:10,900 --> 01:20:13,450 and they had a range for each of them. 1800 01:20:13,450 --> 01:20:16,920 And they assumed the distribution, uniform 1801 01:20:16,920 --> 01:20:18,696 or some other type, and they went through 1802 01:20:18,696 --> 01:20:20,320 and did a Monte Carlo simulation, where 1803 01:20:20,320 --> 01:20:24,520 they did 20,000 different simulation runs where they 1804 01:20:24,520 --> 01:20:27,180 randomly picked a value from that distribution, 1805 01:20:27,180 --> 01:20:30,850 and calculated the impact on each of the different product 1806 01:20:30,850 --> 01:20:31,350 systems. 1807 01:20:31,350 --> 01:20:33,170 So this is global warming potential, 1808 01:20:33,170 --> 01:20:34,920 and you get some sort of distribution 1809 01:20:34,920 --> 01:20:38,750 about what the impact of the different products looked like. 1810 01:20:38,750 --> 01:20:41,485 You can see how it changes based on all 1811 01:20:41,485 --> 01:20:45,410 the different assumptions of what the possible range was. 1812 01:20:45,410 --> 01:20:50,560 One of the important things they did with this is you 1813 01:20:50,560 --> 01:20:51,910 get some overlap. 1814 01:20:51,910 --> 01:20:54,030 But if we look at that, the Airblade 1815 01:20:54,030 --> 01:20:57,632 is definitely the lowest in terms of where it's shifted. 1816 01:20:57,632 --> 01:20:59,090 But there's definitely some overlap 1817 01:20:59,090 --> 01:21:00,690 between some of these other systems, 1818 01:21:00,690 --> 01:21:02,065 and one of the things they did is 1819 01:21:02,065 --> 01:21:05,280 they went back and checked that just because there's overlap 1820 01:21:05,280 --> 01:21:09,147 doesn't mean that Xlerator was lower than this. 1821 01:21:09,147 --> 01:21:10,605 It just means in certain scenarios, 1822 01:21:10,605 --> 01:21:13,840 the Xlerator was lower than what the Airblade had 1823 01:21:13,840 --> 01:21:14,740 in certain scenarios. 1824 01:21:14,740 --> 01:21:17,670 So what they did is they did a check. 1825 01:21:17,670 --> 01:21:20,300 Because you can think that some of the assumptions 1826 01:21:20,300 --> 01:21:22,700 might affect only one product or the other. 1827 01:21:22,700 --> 01:21:26,540 But I may have underestimated the drying time on the Xlerator 1828 01:21:26,540 --> 01:21:29,390 and overestimated the drying time on my product, 1829 01:21:29,390 --> 01:21:31,450 and that may cause the products to shift in terms 1830 01:21:31,450 --> 01:21:32,990 of which was preferred. 1831 01:21:32,990 --> 01:21:36,260 Some of the others, like if the average electricity makes up 1832 01:21:36,260 --> 01:21:38,037 the grid, I wouldn't expect those 1833 01:21:38,037 --> 01:21:40,120 to be different between one product and the other. 1834 01:21:40,120 --> 01:21:42,140 They're both using the same electrical supply, 1835 01:21:42,140 --> 01:21:45,120 so my assumptions about the impact of the grid 1836 01:21:45,120 --> 01:21:46,610 should be the same between them. 1837 01:21:46,610 --> 01:21:48,210 So they made sure they did that check 1838 01:21:48,210 --> 01:21:51,110 by each time they ran one of these scenarios, they compared. 1839 01:21:51,110 --> 01:21:55,029 Did the Airblade have a better performance than, say, 1840 01:21:55,029 --> 01:21:56,320 the Xlerator, and went through. 1841 01:21:56,320 --> 01:21:59,380 And even though you get this overlapping like this, 1842 01:21:59,380 --> 01:22:01,920 they still found something like 98% of the scenarios, 1843 01:22:01,920 --> 01:22:05,820 the Airblade was better. 1844 01:22:05,820 --> 01:22:07,667 Finally, the last thing they did was looking 1845 01:22:07,667 --> 01:22:08,750 at the bill of activities. 1846 01:22:08,750 --> 01:22:12,666 So for every emissions factor, essentially, 1847 01:22:12,666 --> 01:22:14,540 and for every input to each of those systems, 1848 01:22:14,540 --> 01:22:16,710 they assigned an uncertainty level to it, 1849 01:22:16,710 --> 01:22:19,391 and assumed a distribution, and did another simulation 1850 01:22:19,391 --> 01:22:21,640 so they'd get a range of different products like this. 1851 01:22:21,640 --> 01:22:24,290 So these are just three different types of analysis 1852 01:22:24,290 --> 01:22:26,210 they went through to try to figure out 1853 01:22:26,210 --> 01:22:28,200 how much those assumptions and that uncertainty 1854 01:22:28,200 --> 01:22:30,150 impacted what the results were. 1855 01:22:30,150 --> 01:22:33,280 So the big thing, though, is that even after they 1856 01:22:33,280 --> 01:22:37,290 went through with that, the Dyson Airblade still 1857 01:22:37,290 --> 01:22:39,840 showed the lowest impact in general. 1858 01:22:39,840 --> 01:22:42,720 Under certain scenarios, it may be that the Xlerator performs 1859 01:22:42,720 --> 01:22:44,540 better, but under most scenarios, 1860 01:22:44,540 --> 01:22:47,110 they were pretty confident that the Dyson showed up better. 1861 01:22:50,060 --> 01:22:52,390 They broke it out by different types of scenarios 1862 01:22:52,390 --> 01:22:55,290 and said when one was going to be better than the other. 1863 01:22:55,290 --> 01:22:58,050 So in the conclusion they identified 1864 01:22:58,050 --> 01:23:00,790 what are the most sensitive parameters, what 1865 01:23:00,790 --> 01:23:04,360 was the key driver, the environmental impact, and what 1866 01:23:04,360 --> 01:23:06,950 was the general overall environmental performance 1867 01:23:06,950 --> 01:23:08,850 comparing the two, taking into account 1868 01:23:08,850 --> 01:23:10,820 all of that sensitivity. 1869 01:23:10,820 --> 01:23:12,370 OK.