Below is a transcription of the webinar, A Primer on Using Analysis To Guide Plastic Circularity, which was held on Oct. 16, 2024, by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) and Advanced Materials & Manufacturing Technologies Office (AMMTO) Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment (BOTTLE™️) consortium.
[Begin Webinar]
Erik Ringle, National Renewable Energy Laboratory
Well, hello, everyone, and welcome to today's webinar, A Primer on Using Analysis to Guide Plastic Circularity presented by the U.S. Department of Energy's BOTTLE consortium. My name is Erik Ringle. And before we get started, I'm going to cover a couple of housekeeping items, so you know how to participate in the event today.
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All right. And without further ado, I'd like to turn it over to BOTTLE CEO Dr. Gregg Beckham to introduce our speaker. Take it away, Gregg.
Gregg Beckham, BOTTLE Consortium
Thanks, Erik. And thank you all for being here with us today. It's a pleasure to introduce Dr. Taylor Uekert. She is a chemist by training. She did her Ph.D. in chemistry at the University of Cambridge, which she received in 2021, where she worked on photocatalysis to turn plastics and other mixed waste streams into hydrogen fuel.
Taylor is currently the Co-Lead of the BOTTLE Consortium's Analysis Task, and she's a research scientist here in the Strategic Energy Analysis Center at the National Renewable Energy Laboratory. Taylor does a lot of different—leads a lot of different research projects focused on understanding the environmental, economic, and social impacts of new circular economy strategies. And really working on things like chemical circularity, decarbonization, environmental justice, and life cycle assessment. And she's going to talk to us today about the analysis work in the BOTTLE consortium. So, Taylor, please, over to you.
Taylor Uekert, BOTTLE Consortium
Thank you so much. So hi, everyone. Really excited to be here today and to be sharing some common analysis methods that we can use to understand the implications of plastic circularity technologies, and also to give some examples of how we can use these analysis techniques in the BOTTLE consortium.
I'm hoping there's going to be a little bit of something for everyone, whether you are also an analyst, whether you do kind of hands-on lab-based research, or whether you have more of a decision-making role in companies or in communities.
To get everyone on the same page, I'm going to go over some basic vocabulary and analysis methods so that we can all have the same fundamental level of understanding before I dive into those examples.
But before I do that, you might be wondering, what is the BOTTLE consortium? Well, this stands for Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment. Always a mouthful every single time I say it. Just remember BOTTLE. That's all you need to know.
But we are really focused on developing new processes to recycle the plastics that we have today, as well as to make new plastics that are more circular by design. And in order to take these processes from really small scale, lab proof-of-concept all the way to more up-scalable technologies, we work very collaboratively across different research disciplines, as well as across national labs, universities, and industry.
So, you can see here we have nine core members of the BOTTLE consortium, and all of this work is funded by the U.S. Department of Energy's Bioenergy Technologies Office, or BETO, as well as the Advanced Materials and Manufacturing Technologies Office, or AMMTO.
So, BOTTLE's approach to plastic circularity is really focused on three key research areas—deconstruction, building blocks, and redesign. Essentially what this means is that we recycle existing polymers into their constituent chemicals and use those either on their own or in combination with biomass feedstocks to recreate the plastics that we use today, or to create new, more easily recyclable plastics that could be used in the future.
And as we're developing these technologies, we're doing so in collaboration with analysis, as well as with different characterization and modeling techniques. But before I dive into the analysis piece, I wanted us all to take a little bit of a step back and take a moment to consider why we should be thinking about plastics in the first place.
So, plastics today are characterized by a linear trajectory, where we extract fossil fuels, we convert those into polymer products that we then use often for a short period of time, and then we throw them away. Much of those plastics end up in landfill or incineration, and a portion also leaks into our environment.
Plastics certainly have their benefits. They're lightweight, they're strong, they're flexible. And that means they have all sorts of really important applications. They can help us lightweight vehicles to make them more energy efficient. They can also help keep our food fresh so that we don't have as much food waste, and that we can transport it more easily. But the way that we use them today does have significant environmental implications.
Plastic production alone accounts for about 2% of total United States' greenhouse gas emissions. And as we dispose of them, we're generating about 40 million metric tons of landfilled or incinerated waste in the United States alone.
But there's hope. It's not always just a grim picture when it comes to plastics. A circular economy where we're able to reuse and recycle these materials rather than throwing them away could help reduce these environmental impacts, reduce the amount we're having to produce from virgin materials, and reduce the amount of waste that we're generating.
And as we design these circular economy innovations, it's really crucial that these technologies are beneficial from an economic, environmental, and social standpoint; that they mitigate harms rather than causing more.
I think oftentimes, we think of the words circularity and sustainability as synonymous, but that's not actually always the case. One could be true while the other is not. And so we want to make sure that there is this clear link between circularity and sustainability. And the way we measure whether that is the case is through analysis.
So, say, for example, we want to make sure that a new plastic recycling technology emits fewer greenhouse gas emissions, it generates less waste than producing that plastic from fossil fuels. We can measure that with the analysis techniques that I'm going to discuss today. And the sooner we do that measurement, the easier it is to make changes rather than having to wait until we've already implemented some of these technologies at the deployment stage and it becomes much more challenging to make improvements.
And this is really why the BOTTLE consortium is so focused on the concept of analysis-guided R&D of using analysis hand-in-hand with research and development to optimize the environmental and economic outcomes of a technology from the very beginning.
Let's talk a little bit about what these different analysis methods are and what they can tell us. So, there are a number of different tools that we can use as analysts. This is just a couple of examples. Some of the more common ones you might see. It's certainly not comprehensive. But if we start from the top, material flow analysis allows us to identify how much of different plastics are available and in what form. And in doing so, that can help us guide and pinpoint which plastics we want to focus on improving.
We can then focus more on those specific plastic recycling or redesign technologies through process modeling, which gives us the information needed to conduct techno-economic analysis or TEA, which gives us the economic impacts of a technology, as well as life cycle assessment or LCA, which talks about the environmental impacts, and then environmental justice or EJ analysis, which explores more the social effects of a technology. And all of these different techniques are often used in combination with one another so that we have a more holistic perspective of the overall implications of a technology.
So, when we're first starting out with analysis project, we need to figure out, what is our goal? What is our scope? So, what technology are we evaluating? What is our plastic feedstock? What are you comparing it to? Baselining these technologies is really important. So you want to compare it to what you're going to be replacing.
Say you're comparing, for example, a new recycling technology for polyethylene to producing that polyethylene from virgin fossil fuels. And the reason that's so important is so that we can make sure that we are mitigating harms, that we're not causing more, as I mentioned in that previous slide. We also need to figure out what our system boundary is. So that's essentially all of the steps of our supply chain that we are going to include in our analysis. Some of the common terms you might hear for this are cradle-to-gate. So if you're talking about cradle-to-gate for conventional plastic production, that would be everything up to before the product makes it to us, to the consumers. So fossil fuel extraction, conversion into chemicals, conversion into polymers, and then manufacturing of your product. You might also see cradle-to-grave. So that's doing the full supply chain all the way to end of life. So you've made your plastic product, you've used it, and then you have disposed of it to say something like landfill.
These sorts of definitions are also used for a recycling process. So you can have cradle-to-gate for recycled plastic starting from collection of your feedstock through to the final usable product of recycling. For recycling, cradle-to-grave often is retermed cradle-to-cradle, because in theory, you're bringing everything back to a usable form rather than disposing of it to a landfill or an incinerator. And I do just want to emphasize that these system boundaries often play a really crucial role in determining what the final results of your analysis are. So if you have a different system boundary, then a different report in the literature, you might have different results as well.
So it's important to make sure you're consistent internally, that you have a consistent system boundary for both the processes that you're comparing, but also just to remember that your system boundary might not be the same for all other literature reports.
All right, so once you've figured out what you want to analyze, what steps you're including, it's time to start collecting data. Things like, what are the yield of your process? What are the reaction conditions? You might get this from literature, from patents, from experimental results. And in the BOTTLE consortium, we feed all of this data into a process model that we build in Aspen Plus, which is a chemical engineering software. And once we do that, we're able to get the material and energy flows of our technology. You might also hear these flows referred to as a life cycle inventory or foreground data.
That information is then going to feed into our techno-economic analysis. We basically couple them with known prices from databases to calculate the operational expenses or OPEX of your technology. You can also use that information to size the equipment that you're going to need for this process and get quotes for that equipment so that you can determine the capital expenses or CAPEX. Then you combine all of these OPEX and CAPEX into discounted cash flow analysis in Excel to calculate your minimum selling price or MSP, which is basically just the minimum amount you have to charge for your product in order to recoup all of those expenses.
Similar process for LCA, where we link those material and energy flows to known impacts using databases such as ecoinvent or Carbon Minds. These are called background data sources. Essentially, what this means is if your process is, say, using sodium hydroxide, the background data will tell you, here are all of the processes related to making that sodium hydroxide in the first place so that you can know what the impact of that material is. You're going to do all these linkages in some sort of LCA software. Some common ones are SimaPro, openLCA, or Brightway. And then you will choose an assessment methodology to get your actual environmental impacts. Some examples include TRACI. This is a U.S.-specific methodology or ReCiPe, which is more global in nature, but it does include more different metric categories. So it just depends on what you're trying to look at here, which method you choose.
And although in the context of LCA, we often talk about greenhouse gas emissions. It is important to know that LCA gives you a lot more than just greenhouse gas emissions. It gives you other environmental impacts, such as land use, water use, or human toxicity, which can tell you about the different facets of sustainability. And then lastly, for environmental justice, take those same material and energy flows and start to qualitatively explore some of the local community and worker safety impacts of these technologies.
Now, there's a lot of assumptions and data that is required for any type of analysis. And as we go through this, there are inevitably going to be data gaps. So imagine you're developing this brand new technology, and it's using a really specialized material. So, say, a complex catalyst or maybe a really specialized solvent. And when it comes to the process modeling in Aspen, there just might not be thermodynamic data available in the Aspen database. So in order to fill that data gap, we might have to do literature search on these materials, see if anything is available in the literature. We might have to do experimental validation where we go to our colleagues in the lab and say, hey, can you measure these thermodynamic properties? Or we might choose a proxy material, something similar in structure, in chemical structure, and use that as a proxy for this more complicated material.
We might face similar problems in TEA and LCA where we don't know the cost of this material. We don't know the environmental impacts because it's not in those background databases that I just mentioned. So, in this case, we actually might have to go back through that, that whole process I just showed you, to model that specific material so that we can understand more about the costs and environmental impacts. Or alternatively, we can make some proxy assumptions by choosing, say, the most expensive component of that catalyst, like a precious metal, and using that as our baseline cost and impacts, or using a similar chemical.
So once we've gone through this whole analysis process, we have a bunch of info on economic and environmental impacts. And we need to figure out how to interpret all of these results. Some common ways you might see this portrayed is comparison. So that's the figure on the left where we just show how different processes stack up against one another for different metrics. You might also see hotspot analysis, which allows you to determine which materials or energy components are the biggest contributors to each metric so you know what to focus on. Sensitivity analysis is another common one. It's often shown as a tornado plot, which is the far-right figure on this graph, on this slide. And that just means that we varied a bunch of different parameters in our system and figured out how much of an effect those have on our metric.
You might also see uncertainty analysis where people report standard deviations rather than a single point estimate to give you more of a sense for how reliable our results are, given that there are always assumptions. And then lastly, you might see multicriteria decision analysis or MCDA, which gives you—it basically allows you to normalize all of these metrics into a single score so that you can still make decisions even if there are trade-offs across these different metrics.
And of course, no analysis, no process is going to be perfect the first time. So it's always important to iterate, to go back to the lab, make improvements based on what we've seen in the analysis, and then adapt our model accordingly.
So a few caveats, some things to keep in mind. We really strive for consistency and transparency in all of our analysis. So making sure we are reporting what data we're using, what our system boundary is, what our key assumptions are. But that being said, analysis is not static. This is a changing discipline just like any sort of scientific research. And so we do periodically update our methods and our data to make sure that we are providing the most up-to-date, the most realistic results for everyone to understand.
This timeline is just showing a couple examples of changes we've made to some of our TEA and LCA data and assumptions. And what this means is that what we publish today might not be directly comparable to what we published three years ago, although we do usually try to note what the differences are in any supporting information of the things we publish.
And lastly, I want to emphasize that while I'm presenting the BOTTLE analysis approach, this is not going to be the approach for everyone. There are differences in needs and requirements of analysis. So always do take the time to understand the assumptions behind any published work that you are using.
And just to give you a kind of an example of why it's important to understand these assumptions, I wanted to talk about two studies that were published this year. Actually, I think it was last year. Yeah. In 2023. They were published in 2023. The life cycle assessments of plastic pyrolysis, which is using elevated temperatures in the absence of oxygen to convert mixed plastic waste into a combination of gas, liquid, and solid hydrocarbons. And these two LCAs, one showed that pyrolysis had higher impacts than making those products from fossil fuels, whereas the other showed that you had pretty similar impacts. Now, you might be wondering they're both LCAs, they're looking at the same technology, why are they different? And so if we go back through the assumptions, we can see that these LCAs use different data sources. They assumed slightly different feedstocks. They were targeting different pyrolysis products. And they applied credits in different ways.
And so all this means that if you strip many of these assumptions away, the core results are the same. So pyrolysis, naptha has about 2 times the impact of virgin fossil fuel-based naptha. But the way that they fit into the system boundary of the analysis and were communicated led to different results and interpretations. So this is why it's really important to check the assumptions of any work before deciding if it's applicable to your own research questions.
All right. Hopefully, we're all kind of on the same page about some key analysis, vocabulary, and methods. And I'm now going to go into a couple of analysis examples from the BOTTLE consortium. I am only going to give three examples today, but I want to emphasize that we're doing a lot more work across many different polymers, across many different recycling and redesign technologies. So if you are interested in any of these that I don't talk about today, you can check out the BOTTLE analysis website with this QR code. And just a reminder that we will be posting all of the slides later on. So don't feel like you have to fumble and get your phone out this very instant.
So let's dive in. The first example I'm going to talk about is around comparison. I just showed you that there's a whole bunch of different recycling options for many of the plastics that we use today. And when there are so many choices ranging from mechanical recycling, which is what we use now, to basically grind and melt down our plastic back into something usable, or some of these new chemical recycling technologies where we break the bonds in the plastic to generate constituent chemicals that we can reuse for polymers.
It can become overwhelming to make decisions when there are so many choices. And so analysis can help guide us towards the best choice given a certain priority. Comparison is relevant to pretty much everyone, whether you are a researcher trying to contextualize your work or if you're a decision-maker in a company or a community trying to figure out what to focus on. And so this example used consistent LCA and TEA to look at the environmental, economic, and technical performance of different mechanical and chemical recycling strategies for polyethylene, polypropylene, and polyethylene terephthalate.
And basically, what this allowed us to do was to quantify some of the trade-offs that had previously only been discussed qualitatively. Things like knowing that mechanical recycling has lower environmental impacts, but it's at the trade-off of having less tolerance towards contaminants in comparison to chemical recycling or even virgin polymer production. And I'm not expecting you to look through all of this info, but if we synthesize all of that into a decision tree, we can start to figure out which technology makes the most sense given certain technical constraints and priorities. So, say you have a really pure post-consumer plastic feedstock and you're interested in having the lowest cost recycling process, you might focus on mechanical recycling. Whereas if you have a more contaminated feedstock and you're really interested in having a high-quality product at the end of the day, you might focus on a chemical recycling technique such as methanolysis.
All right. So our next example is focused on optimizing a technology. This is probably most relevant to researchers, whether you're in a lab or a company trying to figure out how to improve your technology to its absolute maximum. And the example I'm going to give here today is around enzymatic recycling of polyethylene terephthalate or PET—the number one symbol on your plastic water bottles. How this works is you basically mix up a ground-up PET in a solution containing an enzyme at near-neutral pH, relatively low temperatures, and the enzyme breaks the bonds in your PET to release the monomers, ethylene glycol and terephthalic acid, which could then be separated out and repolymerized back into a virgin quality polymer.
Conventionally, in the enzymatic recycling space, there's been a lot of focus on residence time. So, can you make the enzyme work faster so it doesn't have to sit with your plastic bottle for quite as long? Well, we did the TEA and LCA on this and actually found that speed was one of the less impactful parameters. It was actually much more important to focus on maximizing yields, increase the amount of plastic you can have in your solution, and also focus on problematic process steps like the energy-intensive amorphization pretreatment, as well as the sodium hydroxide intensive terephthalic acid separation process.
So, with all of this in mind, we could go back to the lab. Well, we, it was not me, it was amazing colleagues in the lab. But we could go back to the lab, make changes to our process, and then redo our analysis, and we could actually see that making these improvements significantly reduced our greenhouse gas emissions, reduced our cost in comparison to the original enzymatic recycling technology configuration. So this really shows how analysis can help pinpoint the parameters that are actually going to make your technology more viable in the long run.
It can also go beyond LCA and TEA, although that's been our focus traditionally. We're now incorporating some of the social aspects of the circular economy into our analysis. For enzymatic recycling, making those experimental changes helped to reduce the toxicity risks to workers, as well as reduce the hazardous waste disposal burden on the communities in which these facilities would eventually be sited. Now EJ is a relatively new area to us. If you are also interested in learning more about this area, you can feel free to sign up for one of our trainings, which will hopefully be happening next year.
My last example for today is perhaps geared more towards the analysts in the room, and is focused on how we can incorporate circularity into our LCA and TEA. The example here is for a biobased alternative to polymethylmethacrylate or PMMA. PMMA is one of the top 10 most commonly used plastics in the U.S., but currently doesn't have much of a recycling system in place. So the key benefit of this replacement polymer is that it can be easily recycled back to its monomer and then repolymerized back into the plastic.
So the question is, how can we incorporate this added value functionality into our LCA and TEA? And the way we did this was by using the yields of the recycling process to estimate how many lifetimes you could get out of continuing to recycle this biobased acrylic. You then normalize your costs and your impacts across those number of lifetimes. And what this allows us to see is that if we reach a recycling rate of at least 50%, this biobased acrylic could be competitive with PMMA. Obviously, 50% is much higher than recycling rates today, which are somewhere between 1% to 15%. So you would definitely need sufficient collection systems. But this does help to give the researchers a target so that they know from their side of the technical perspective, they still need to be hitting at least a 50% number in their recycling process itself.
All right. So I hope that I've kind of convinced you that analysis can really help guide us towards a more sustainable, more circular future, especially for plastics. It can help us benchmark and optimize and hopefully streamline the future deployment of these technologies that we're developing.
If this has made you interested, sparked your interest, there's a lot of ways you can think about incorporating analysis into your own work. So make sure you're reading the literature critically, of course. Keep those assumptions in mind, as I mentioned. You can try conducting your own analysis. You could collaborate with experts like my amazing team. And from all of these different approaches, you can incorporate those learnings into your experimental process. Figure out, where should I actually be focusing? Are there metrics that I haven't considered before that maybe I should be? And overall, I just encourage all of you to think holistically. There are a lot of different sustainability, economic viability, social components that feed into making a technology actually feasible in the real world. And I encourage all of you to consider those through an analysis mindset.
And lastly, I am the one speaking to all of you today, but I'm certainly not the one who did most of this work. So I just want to highlight my fantastic analysis team at NREL, in the BOTTLE consortium, and all of the fantastic work that they do. And with that, thank you very much and looking forward to questions.
Gregg Beckham
Great. Thanks, Taylor. So just as a reminder for folks, as Erik mentioned earlier, if you wanted to ask any questions, please put those in the Q&A chat window. So Thanks a lot, Taylor. That was fantastic.
So, we have several questions that we can get started with. The first one is, do you have a recommendation for what maturity level a process should reach, for example, grams of product produced, grams of catalyst, TRL x, etc., before utilizing techno-economic analysis and life cycle assessment to inform decision making? And second question on that. Is it possible to start too early?
Taylor Uekert
I love this question so much. For me, I would always say you can do analysis any time. Any time, it will be useful. I would say start as early as you can. Obviously, it's not going to be as rigorous, and there will be much more uncertainty associated with your results—and I certainly wouldn't compare something working at the milligram scale to industrial production of polyethylene—but you could do an analysis of the process with the information that you have to figure out, hey, are there certain materials that I'm using that are actually really expensive or really environmentally impactful? Are there maybe downstream separations that I've only started to think about in the lab, but the energy use for that is going to be important? And so you can start to tweak your process at the earliest stages with analysis.
And then as you start going up and up in the technology readiness levels, that's when you could start comparing to some of the incumbent technologies, baselining, seeing if you're competitive, if you're already better and so on. So overall, I would say never too early. Always take analysis with a grain of salt, because there's a lot of assumptions, but it can still give you useful information for sure.
Gregg Beckham
Awesome. Thanks, Taylor. The next question we have is, does your analysis include additives such as plasticizers, antioxidants, and other additives that are used in the processing of polymers?
Taylor Uekert
Great question. In general, we focus on pure polymers. Part of this comes back to the data gaps that I mentioned. We often do not know exactly which additives are in a certain plastic in a certain product.
It is something we incorporate into some of the recycling technology process models. So we might, for example, assume that our incoming plastic waste stream has a 2% dyes or other additives that we just mass into a single lump and we say, these are extra things that we're going to have to deal with in our process, whether that's filtering them out or other removal methods. So, it's not—we don't include them on a super explicit basis, but it is something that we're thinking about for sure.
Gregg Beckham
Awesome. So, the next question. Can analysis be applied to new products for which databases are not easily available? And how do we approach that? If you could please give some short insights on that or examples.
Taylor Uekert
Yeah, absolutely. A lot of the things that we're analyzing, there is no data set available for them. So, say we're developing a new recycling technology for nylon and it's a chemical recycling process. And there's no entry in ecoinvent or U.S. Life Cycle Inventory that we can use. And that's why we have to go back to these fundamental process models with Aspen, although I will say you can also do process modeling in Excel if you don't have access to some of these tools, just using general stoichiometry, thermodynamics, basic chemical engineering understanding. And so you have to go back to these preliminary models to get the information you need to feed into an LCA or TEA. So you can do it for new processes. It just will take more work.
Gregg Beckham
Awesome. So, another question is, what are the best tools to use to try out analysis? The person asking the question is thinking about tools that are accessible to undergraduate level students who could use this in their courses, their design work, toxicology studies, etc.
Taylor Uekert
Ah, great question. I'm going to speak more from the LCA side of things. So for LCA, I'd recommend going for some sort of open access software. Things like the Materials Flows through Industry tool or MFI which came out of NREL. It can give you an LCA lite. So it'll give you greenhouse gas emissions and energy use of—you can play around with a bunch of different chemicals, materials, processes. So that's completely open access, it's free. You could sign up for an account now. If you want more detail, so say you want to think about water use, toxicity, etc., you're probably going to have to go for a full LCA software. Things like openLCA or Brightway are fully open access. The one thing you do need with those is the background data. And so you can either source open access background data from the U.S. Life Cycle Inventory Database from Federal LCA Commons, or you could purchase, say like an ecoinvent license. I know they have academic licenses that you could then use in those open access LCA tools. So those are probably a couple places that would be easiest to start.
Gregg Beckham
Awesome. So the next question is, is there enough data or information to compare costs of glycolysis versus methanolysis as practiced by current chemical recycling companies for PET? And do the cost structures vary greatly in different regions and geographies?
Taylor Uekert
So there is certainly enough data to analyze glycolysis and methanolysis. We've done that previously. I will say that was not for specific companies. So it was using average data from academic literature, as well as from patents. If you wanted to do something that was company-specific, you would likely have to work with that company to get the data on their exact process. Do they generate steam on site? Things along these lines that just don't come up if you're averaging from open access literature. So I'd say the information is certainly out there if you wanted to do it and collaborate with those companies.
In terms of how impacts vary on, say, a regional perspective, they absolutely will. In general, we tend to use average costs, average impacts across the U.S. That misses lot of the nuance of say, like local electricity grids. Is it natural gas powered or is it coming from a solar farm in California? Are you sourcing your chemicals, maybe you're based in Texas, where a lot of petrochemicals are sourced and maybe it's cheaper to get the chemicals there versus if you have to transport it further north? So these are the different granularities that will show up if you do a more region-specific analysis. That's something we're starting to look into a bit, although traditionally we've done more averages.
Gregg Beckham
So the next question is, in terms of environmental justice, which metrics should be emphasized more? And how should those metrics be evaluated?
Taylor Uekert
I would always hesitate to say that there is one winning environmental justice metric. I think the key is to choose a suite of metrics that capture the different aspects of environmental justice, ranging from job creation to the safety of those jobs, like the toxicity that I mentioned. Impacts on your local community in terms of resource use. So say, are you going to be pulling a bunch of water from local systems? Are you going to be generating a bunch of waste that needs to go to local landfills? I think there's a lot of different aspects that we need to bring together to make sure we're having a holistic approach to justice. I will say the ones we've picked are five really simple metrics related to supply chain. So is there child or forced labor in the materials you're using? What's the toxicity of the components you're using? What waste are you generating? Things along those lines. There's been other research at NREL called JUST-R, which includes a much more comprehensive suite of metrics. And while I know it can get overwhelming to think about all of these different things, I think a lot of times, once you start analyzing multiple metrics, you start to see trends. And you're like, actually, this component is a problem across all of these metrics. So that's just what I'm going to focus on. So I would encourage you not to get overwhelmed by the metrics, but instead to see it as an opportunity for gathering data that can then really tell you where to focus.
Gregg Beckham
Awesome. And then another question, Taylor, So the question is, I'm a Ph.D. student and I want to have a comprehensive training on TEA and LCA. How should I get started? And are there courses offered on this?
Taylor Uekert
For TEA and LCA, I would first check with your university. So check in, for example, the chemical engineering department, if you have one. They might be running an elective on this. A lot of chemical engineering departments do. If that is not an option, I know that on things like edX or other free platforms, there are some basic LCA and TEA courses that you can find online. I will say blatant self-promotion here, but I am putting together a 90-minute webinar on how to start doing LCA, especially for lab-scale experimental work. So that will be coming soon. And I'd love to chat more about that if anyone wants to reach out to me directly with questions. But yeah, I'd say order—order would be, look at your own university, look for online courses, and then perhaps conferences or other workshops could be an option as well.
Gregg Beckham
Excellent. And so, Taylor, part of—probably the last question we'll have time for today is, how do we reach out to collaborate on incorporating these tools into our work?
Taylor Uekert
Well, my email is on the slide. The BOTTLE website is also on this slide. And the BOTTLE website does have a Contact Us section. We do check that. We have had many collaborations start as a result of that form. So please feel free to reach out through any of those venues and always happy to discuss.
Gregg Beckham
Awesome. Well, Thank you so much, Taylor. That was fantastic. That looks like it's all the time that we have today. Really appreciate everyone joining us. And definitely, of course, a special thanks to Taylor for sharing a lot about what the BOTTLE consortium is doing in terms of our work and analysis-guided research and development.
Just as a reminder to everyone, the slides and a recording of this presentation will be posted to the webinars section of the U.S. Department of Energy Bioenergy Technologies Office website as soon as it's available. And with that, I hope everyone has a good rest of your day. And thank you all again for joining us. Thanks all. Take care. Bye bye now.
[End of Webinar]