In a recent post I made a very basic representation of the evil circle of grants and papers. This inspired me to draw a bigger diagram to show how postmodern research works. I say postmodern1 because, as I seem to understand from older colleagues, it has not always been like this and things were quite different say, 20 years ago. Or maybe the structure of things was quite similar but the attitude was way more relaxed and the pace way lower.
It is my pleasure to introduce a brand new diagram representation of the postmodern research system a.k.a. the research spaghetti monster.
Let’s break this down.
First we take a closer look at our acquaintaince, the evil circle of grants and papers. It’s pretty simple. It’s a vicious circle where it’s difficult to enter, but once you are inside, then it can really bring you in an upward spyral of outstanding achievements. Well, until you keep feeding it anyway. So let’s start somewhere, maybe with the grant money.
Grant money is needed to run a project, as the critical mass of a dedicated project is usually needed to make some breakthroughs, and get some great results publishable in a top journal. Papers in top journals are needed for a strong CV, that needs to be stronger and stronger to get (more, and bigger) grant money. When the project is small and maybe not-so-prestigious, the results are good but not-really-amazing, the papers published in second- or third-choice-journals, the CV just-fine… then the loop weakens, and this is not good. If you break the circle you end up with no money, and no papers. This is what is commonly known as the publish or perish situation. Which in postmodern research terms I would rather redefine as project (funded) or perish situation.
Note two important aspects here. One is the closed loop on grant money. Yes, grants bring you more grants, for various reasons. This is also referred to as the Matthew effect, which in research basically means those who are successfull in getting grant money will get even more of these money. And it’s a real thing.
The other aspect is the element of chance, that here I sympatically call luck. I placed it right in the middle because it plays a central role. This is also what people who take themselves very seriously usually call “success”, and indeed, sometimes the two things are indistinguishable. Why is luck such a central element here? Because you can be lucky and apply to a call where all the others have sent weak proposals, or find a favourably biased reviewer, or catch a flight and get to sit next to a person who runs an investment fund. And viceversa of course when you are unlucky - which is basically the business as usual case. Everybody needs (quite some) luck in this system, whether they believe it or not.
…give us courage to change what can be changed… Talking about beliefs, here a little parallel with the serenity prayer to introduce the second block of the postmodern research system, the part where one can play an active role and is not at the mercy of others and chance. Let’s call this the homo faber fortunae suae subsystem, which includes ideas generation and skill development.
Ideas, ah, I wish I had many of those. But ideas don’t grow on trees, and some are just not good. Especially new ideas are needed to get grant money. Because research projects always need a strong element of novelty. Unfortunately, there is so much postmodern research today that it’s very likely somebody else just had the same good idea as you. But as long as they are not in your scientific domain or even discipline, you might get away with this. Related to this, there is the risk of scooping, i.e. others taking your ideas. So one needs to keep the new ideas close, but also share them enough to get them validated and improved, etc. It’s complicated. Sometimes, people just recycle old ideas and give them a bit of polishing or rebranding - with some luck these can become a new trend (somebody just said circular economy???). New ideas can directly lead to top papers, because with some luck one might not necessarily need much grant money for publishing, and therefore shortcut the circuit.
We need to make a distinction between soft and hard skills. It’s important. Both are needed for postmodern research. One can be good at data analysis or coding, but if one can’t deliver a pitch to a funder or write a proposal, it will be very, very difficult to get the grant money… So hard skills is what gets the scientific breakthroughts and soft skills is what gets…all the rest. Managing dissemination and communication that give exposure, writing exciting top papers, drafting convincing proposals for grant money, and making good connections that build a network. Examples of soft skills include being good at: languages, writing basically anything in different styles, talking (and small-talking), communicating clearly and to different audiences, explaining complex research in simple terms, negotiating, making friends, making and keeping the right connections, understanding social dynamics, politics, and bureucracy and even some legal stuff, avoiding and managing conflicts, making budgets, etc. etc. etc. the list is huge.
The common point here is that one can develop both ideas and skills, train and get better, while one can’t change…
That’s just something one must deal with. And here we come to the fourth block, the outer world subsystem, that usally needs to be maximized (a weird term, that coincidentally sounds like something that a person with my first name would do).
Both breakthroughs and top papers can give a lot of exposure, if one has the skills to disseminate and communicate the research to wider audiences and to key people and fora. Having good exposure allows to enlarge the network. This because, when your research gets on the media, suddenly many people will contact you on LinkedIn to be your friends (it’s called connections…), propose collaborations (that might implicitly require you work for free), or just try to get a bit of your shine. And if you additionally have luck they migh even want to give you money.
But network beats exposure any day, if you ask me. Those who have the right connections, will get more grant money. Let’s not be naive. And one needs skills to build a network, as I wrote above. This can be facilitated by having grant money. For example, in my case joining collaborative research projects has always been a great opportunity to meet many new clever, skilled, interesting and well-connected people.
For lack of a better name, the institutional subsystem is the last block and it’s about time because this post is way too long. Two elements. The infrastructure is key to get grant money and gives a lot of competitive advantage. This is the organisation behind you, university or research centre, the people good at supporting with fundraising, networking, understanding the calls, making budgets, supporting with project management and communication, as well as its physical infrastructure including labs, computing power, software, technicians. And having good colleagues and non-toxic, reasonably competitive (“in a healthy way”) resarch environment that can give freedom to develop new ideas or just sign a support letter when needed, and share knowledge and help training skills.
What about politics what is this doing here? Well but this is the alfa and omega of the whole story. If the government, or the European Commmission, decides that now it’s time to boost the research on Artificial Intelligence, then that’s THE priority and driving force for the entire circus. That’s the highway for funneling the grant money, and everybody will run there. If they decide that there will be a funding round on the theme “green transition” then be sure that many will take some old proposals out of the drawer and spin them (somebody would say greenwash…) to fit in. And what about the huge companies driving the research agenda with their private research funds? Another example of how politics drives the system. And anybody who has worked in a big organisation knows that there is a lot of internal politics on distributing resources (read: money) and deciding who goes ahead in the career ladder and when. So I don’t need to say much more here I think.
There is one thing to mention though, which makes this entire system quite insane: the speed. Everything here happens on steroids, at high pace. But this is something for another post.
Welcome to the postmodern research system.
Originally I had used the term modern. One week after posting this, however, a colleague of mine gave an interview in Forskerforum (in danish) describing in a very critical way the problems with research today (universities as consultancy houses insanely focused on funding aquisition) compared to research a couple of decades ago (more free research time). He uses the term postmodern in the interview, and perhaps it is more appropriate. ↩
I probably won’t be publishing in the very top journals any soon.
So within a Horizon Europe-funded project we have new results, they look very promising, and of course we would try to publish them in the highest possible ranking journal. However, the Horizon Europe open access mandate requires immediate OA availability of final full-text, no embargo.
So today I was checking our options. In the top IF journal in our category: Nature Sustainability and Energy & Environmental Science we would have to pay between 10000 euro and 2800 euro for this. Well, thank you very much.
Yes, I could have put 20k euro in the project budget and I would be well covered. But honestly: why? Let aside for a sec the ethical considerations (funneling public money directly into the pockets of private publishers to generate a pdf file for me), we can pragmatically budget them for much needed travels and salary.
Consider I work in one of the EU countries with probably best publishing deals overall. I guess I should conclude that EU projects and top-ranking journals are not very compatible.
Anyway, plenty of other publishing options, but this is not free from consequences unfortunately, given how research evaluations work.
Here is the evil circle of grants and papers. You need articles in top journals to show a good CV, which in many places including EU funding is what evaluators look at. Good CVs get you grant money, and you need a lot of those to run a big project that makes breakthroughs, and delivers results that can potentially be published in top journals.
You don’t need to be an expert in system dynamics to understand this is a reinforcing loop. When you have the grant money and the results, but you can’t publish them in the top journals, you maybe don’t break the circle direclty but at least you weaken the force of the loop. In the long run, it has consequences on your chances of getting new grant money, until the circle finally breaks and you are in trouble (no money, and no papers).
I am perhaps giving the impression that when you have very good results these will be automatically published in a top journal and that top papers will automatically get you some grants.
But, of course, reality is not so simple. A lot of other factors are at play here - including a generous dose of chance - that complicate the process and make it much less logic and linear, but this is material for another post.
]]>I spend some time on LinkedIn after Twitter turned chaotic evil. Usually the vibe is the “super positively enthusiastic” type. Amazing unicorns everywhere. But, some people complain! And they complain about academia. Specifically after careful and systematic review I identified three types of complaints.
This is just a classic and revolves around three pieces of information.
Did you know that… 1) We (academics) get research paid by public money and then… 2) We do free editorial and review work for private publishing companies… 3) who then ask us to pay to get access to our own work - and make a ton of money in the process?
(Bonus: we also give them all the publishing rights)
Ok, now that you know this inconvenient truth please start your own open access journal, retract all your Elsevier/Springer/Wiley papers, and join an international union of researchers fighting for better publishing conditions globally.
Second theme is variations of these headlines:
(bonus, just recently: AI is doing the reviews!…AI is even writing the papers!…Wait, AI is BOTH writing and revieweing the papers?!? There is no real knowledge being generated in this world anymore, universitis are doomed, TOTAL SYSTEMIC COLLAPSE!)
Yes paper production has exploded in the last 10-15 years. Yes objectively, the system is probably under pressure. Again, it’s all volunteeer work for the evil publishers (previous point). Yes AI is there too… Now please accept all nine review invitations you received in the last three days. And start writing less and better quality papers - thank you. Use ink and pen to be sure you keep AI out of this.
Something roughly along the lines of: I so much love science it was my childhood dream to do research but when I finally got a univ. job I was suffering so much the environment was toxic I was working so hard my proposals were rejected and my contracts were temporary…now I am in the PRIVATE SECTOR everything is sooo good I got gorgeous colleagues, company car with free gas, I do almost nothing and take long vacations and familiy time I make REAL IMPACT and feel fully realized in life almsot close to nirvana but most important I earn so much more money you are all a bunch of losers.
Ok. But if it’s sooo good on the other side why are you still complaining? Live your new life happily or find a good therapist.
In the comments sections of one of these complaining rants I found a little pearl: papers are overrated.
Wait. Yes, they are. Scientific articles are the main currecy of academia. It’s our product. I wrote a paper. I got my paper cited. I got it rejected. How many papers you got? We collaborate and then write a paper. I’ll need so many papers to get this grant/position/prize.
People talk about papers all the time. But outside academia this currency has much less value (zero value?). So there is an insane focus on writing and publishing papers that is…kind of misplaced? There are many other ways nowadays to make impact with research, that are not writing papers. There are many other important skills to gain in research that are not writing papers.
Papers are overrated. This one really made me think. I should probably make a LinkedIn post about it.
]]>Yesterday I attended a webinar by the prospective LCA network on uncertainty in prospective LCA. Well I also organized that together with Stefano Cucurachi and Heath Logan from the network steering group. Reinout Heijungs (VU) and Pierre Jouannais (INRAE) were presenters.
Super nice. Minutes will be in the network archives and when I find them I might post the link here.
We didn’t talk much about prospective scenarios though. I mean those done using tools like Premise 1. Simplifying a lot: Premise takes projections from integrated assessment models and uses those to make “future” versions of the ecoinvent database. These can be used to assess the impact of a technology at a future point in time.
Me and others have been thinking about the uncertainty in this case.
What is uncertainty when we have these scenarios?
Based on this question found on LinkedIn
[…] there are so many SSP/RCP scenario combinations and varying IAM approaches (e.g., REMIND, IMAGE, TIAM-UCL) and choosing what scenario to choose is very confusing.
I am posting my answer here for documentation and later use, and reminder to myself.
In principle, the many scenarios represent the full-spectrum of the uncertainty about the future.
So, again in principle, one should run the simulation across ALL scenarios, because nobody knows which scenario will become reality (maybe none of them, or something in-between them).
If there was a probability associated with each scenario, one could random sample across scenarios to get a likelihood of results. And then investigate whether the LCA conclusions hold across the simulated scenarios, and which conditions lead to the best environmental performance. And then one could make decisions and take actions that ensure to make these conditions become true.
Because, in the end, what you need to do is to support a decision, right? So how is the scenario exercise functional to making decisions? Or, in other words, WHY are you actually running the scenarios?
The problem with background scenarios is that there is very little agency because a single technology developer will have little influence on how the electricity mix will look like in 2050…
Sacchi, R., Terlouw, T., Siala, K., Dirnaichner, A., Bauer, C., Cox, B., Mutel, C., Daioglou, V., & Luderer, G. (2022). PRospective EnvironMental Impact asSEment (premise): A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models. Renewable and Sustainable Energy Reviews, 160, 112311. https://doi.org/10.1016/j.rser.2022.112311 ↩
There have been some interesting posts in the Pre mailing list about how to deal with confidentiality when performing LCA studies, expecially in comparative context.
Everybody who does LCA knows this issue. Companies and organisations of all sizes and types are usually reluctant to1 - or direclty against - sharing publicly the inventory data about their processes and activities. For example the amount of energy or chemicals needed to produce a unit of product. The reason that I hear the most is maintaining competitive advantage. They don’t want competitors to know what they do and how.
OK, fair enough.
But. Are these concerns reasonable or are producers magnifying the problem? Indeed publicly available inventory data on production activities can be a great help for LCA researchers! And for the general public to keep producers accountable for their claims. But do competitors really care about life cycle inventories? How many in the general public will actually take the time to check them in detail? How much valuable and practical is this rather aggregated information for reproducibility? And for process understanding2? And, in the worse case, for scooping company secrets?
The problem for me starts when organisations concerned with confidentiality are very eager to put their LCA results out in the open. Because I feel that LCA nowadays is unfortunately 10% a tool for process improvement and decision support, and 90% a tool for marketing of decision already been taken.
In cases of comparative assertions, ISO standards require that a panel reviews the LCA and there the common solution is to make the data available for the review panel, often under non-disclosure agreement, and to exclude the confidential data from the final published LCA report. However, for Environmental Product Declarations and other LCA-based reports on product environmental performance, not necessarily comparative, there is AFAIK no requirement to disclose any of the underlying data.
This is why I find the confidentiality issue a very controversial one. I do understand that producers might have legitimate confidentiality concerns, and a justified need to maintain competitive advantage and to keep details on their process hidden from competitors. But at the same time, if a producer wants to show to the world how “low-impact” they are at producing something, and to compare their product with other products (either directly or indirectly by letting consumers do it), then it seems to me rather absurd that they are not willing to share data about said production process.
When the data are not shared, they are basically saying: I am good, trust me.
But I don’t want to trust.
So either you want to keep confidentiality, or you want to go public with green claims. You can’t have your cake and eat it too… can you? The solution with LCA reviewers having full access to the model and data under non-disclosure agreement is a good starting point. But still a lot of trust is at work here, and it leaves out many cases. A much more virtuous approach should be to share at least the minimum amount of data needed to reproduce the results of the model, and ideally more…
The idea that we as a society should allow anybody to publish green claims without providing supporting evidence sounds, in all honesty, quote bogus to me.
For an overview of privacy-related as well as other challenges to data sharing see: Supply-chain data sharing for scope 3 emissions ↩
Anybody who has tried to reproduce LCA results starting from published data knows what I am talking about - this is a painful process. ↩
I get to say these things over and over so I thought about writing them here for further use.
You received a review of your paper. You feel the need to burn the world down in anger1 but your only viable action is to make a point-by-point response. What to do? Two things.
(First of all. Answer all comments - needless to say but, well….)
For each comment, keep the response short and copy-paste the new/revised text under that. Let the new/revised text speak for itself.
Don’t argue, don’t discuss with reviewers, this is not a chat, instead let the new/revised text answer their comment.
This is the “Show, don’t tell” approach.
Example:
Reviewer comment: The sources for the data mentioned in line 215 are not cited, this is a shortcoming.
Good response: Sources now added (Sect 2.1): “Data on icecream and pizza consumption were retrieved from Rossi et al. 2022 and from Nielsen et al. 2023 respectively.”
Bad response: We disagree this is a shortcoming because the data sources are explained in previous paragraphs and we think this level of detail is sufficient. As explained previously in the paper we took data about eating icecream from the paper of Rossi et al. 2022 and the data about eating pizza are from the report of Nielsen et al. 2023 and these are the best sources we could find and we also think they are the most reliable. Therefore we believe the lack of citation in this section can not be considered a shortcoming. …it is bad because: yawning long, no section mentioned, no change mentioned, a lot of irrelevant arguing - really, who cares about your rants?
“You will know [the good response to the reviewer?] when you are calm, at peace. Passive. A Jedi uses the Force for knowledge and defense, never for attack.” — Yoda
Yes, a bit of cheap zen attitude helps a lot when writing your response.
Don’t argue with reviewers. This is not a discussion. Every time you argue you weaken your position. This is not a tennish match. There is no ball to throw on the other side to make a point.
In this sense it helps sometimes to think that you are writing to the editor, not to the reviewers (it helps to avoid arguing). You are writing to the editor to show what you have done, not to the reviewer to offer explainations or clarifications.
Sometimes I hear that people feel “attacked” by reviewers. Don’t let the reviewer get to your nerves. “Attacks” are irrelevant.
Was a specific revision required? Has the paper been revised accordingly? That’s all that counts.
Just write what revision has been done and where (show don’t tell, see above). As good old Dante Alighieri says:
“Non ragioniam di lor, ma guarda e passa”.
Of course, there are situations where one should forget everything I wrote above and go nuclear.
Personal offenses, implicit or suggested accusations of misconduct without evidence, denigrating, sexists or patronizing comments, etc.
Zero tolerance for this stuff. Write to the editor that you don’t want to deal with anything like that and offer no answer than that to the reviewer’s comment.
For a short period of my academic life, I used to first write a draft response document with very furious answers to the comments received, to let the anger out. Or the frustration, I guess. Then I would let the document “rest” for one day or two. Then read it again, cancel everything, and start over. This is a very stupid approach, don’t use it. ↩
I have been waiting to write about this article for SO long time: Green Tech for Green Growth? Insights from Nordic Environmental Innovation recently published together with Prof. Mikael Skou Andersen from Aarhus Univ., Dep. Environmental Sciences.
First, this is the result of the work done under the NOWAGG project that run between 2017-19. Yes, it took a long time to publish results, but better late than never.
In this study we started from a simple premise: achieving a “green growth” means relying a lot on technological solutions to decouple growth and emissions — but evidence shows that bringing new “green” technologies to the market is actually not so easy…🤔
(parenthesis: we used “green technology” to refer to a technology that is considered environmentally friendly based on its production process or supply chain. We chose it to match the “green growth” terminology and among various synonymous (“environmental technology,” “green tech” and “cleantech”, “environment-related technologies”))
So why the journey of many green technologies from their early conception to their large-scale adoption and upscaling remains a serious challenge? We wanted to find out.
There has been entire books on the topic of tech innovation and development, but we wanted an ampirical study on the nordics. Because the nordic countries are often taken as leaders in environmental innovation - so…it would be interesting to see what happens there.
We used a mixed-methods approach: quantiative (patent analysis) and qualitative (interviews). I was not used to any of those so…it wasn’t easy.
Why the patents? Because it’s a proxy for innovation, and it’s realtively good data. Very organized, very structured, realtively easy to retrieve (after you learn some SQL…)
So we got data for all the patents submitted in 2000–2017 in the five Nordic countries: Denmark🇩🇰, Finland🇫🇮, Iceland🇮🇸, Norway🇳🇴, and Sweden🇸🇪 and selected the “green” ones based on an existing classificaiton (Haščič & Migotto, 2015).
Below the result:
Here green patents classified according to technological domains (data from EPO).
— Massimo Pizzol 🇺🇦 (@m_outreach) November 8, 2022
- increasing steadily since 2000 (after 2015 the decrease is due to delays in reporting).
- but country-specific tech development pattern (strongholds or preferred areas) pic.twitter.com/TtwseAsjMy
Green patents classified according to technological domains (data from EPO) are
So far so good, we get the big picture: a lot of green tech developm,ent going on in all countries, with country specific differences in the type of techs developed. But we don’t know the details of this story.
So we went qualitative.
Conveniently, patent data include names of inventors… It’s a gigantic phoenbook…but without phone numbers 🙁 So we found the mails/phone numbers of about 200 inventors. Contacted them. Interviewed more than 40.
What did we ask? In simple terms, to tell us a story of their technology, what went well, what went wrong, and why. Then we made a qualitative content analysis to summarize findings.
And again:
And here the result, we were able to find a structure that fits basically all stories and described the "life cycle" or "journey" of a green tech from conception to market. 7 steps influenced by 4 factors: Organisation, Funding, Technology, Market. pic.twitter.com/rPfK0fUbKG
— Massimo Pizzol 🇺🇦 (@m_outreach) November 8, 2022
We were able to find a structure that fits basically all stories and described the “life cycle” or “journey” of a green tech from conception to market. Seven steps influenced by four factors: Organisation, Funding, Technology, Market.
Not surprisingly, this scheme fits previous theories about innovation and the different steps in technology development (there are several models in the literature about this). So what is the new stuff here?
Is that we have now a pretty good picture of what is happening in the Nordics when it comes to green tech development. And all the juicy details are in the paper and I can’t possibly summarize here. Some key points.
The process of environmental innovation is complex. One does not just…bring a green idea to market. This journey is complicated by several obstacles, technological, organisational, economic, poltical.
If you think technology is going to save us, you have to arm yourself with patience. Green tech development takes long time. The scales are huge. The money is scarce. The failure rate high.
☑️Funding: a lot of research money but when it comes to upscaling…the costs are orders of magnitude higher and —there is no money (valley of death issue for greeen tech).
☑️“catch 22” problem or “chicken-and-egg” 🐓🥚problem: investors want to see the green techn at work in real scale before investing, but funding is required precisely to proof that the techn can work at large scale (because pilot and industrial are hugely dfifferent)
☑️What is scale up for green tech? From building many large plants of relatively low-tech machinery operating on huge amounts of material (biomass valorisation) to mass-produice high-tech components with high degree of automation (fuel cells)
☑️Policy, it’s tricky. Sometimes policies that try to favour one type of green tech innovation prevent another. E.g. incentives for biofuels do not help developing high-value products from biomass.
☑️Investors: when it comes to green technology are risk-averse and keen to ensure stable market conditions in the long term, which is only rarely achieved. They want policy to make the market stable…and secure…
☑️(True story) To get the funding many responded one needs “the right guy” or “the guy who is good at getting money”. Forget about green tech as just engineers, this is a business issue just as much.
..and much more. Here a link to the paper1 . More details there. Enjoy
Yeah, I know, the paper is paywalled….Send a request here on Researchgate or write me and I will happily send a copy of it. ↩
Nordjyske is a Danish newspaper and prints every week an opinion piece via a “relay” where one author reccommends another, etc. I was invited and this below is a translation of what I wrote there, with links added. Here the original article if you read Danish.
I was chairing a session about climate change at the Havforskermøde, a scientific conference that was organized by Prof. Niels Madsen and others at our premises in Aalborg university 1.
It was a very mixed session, that is different from the more specialized ones (on LCA) that I usually attend, but it was a very touching one – I am going to explain a bit why.
There was a presentation where a researcher showed how much the sea level rise is already erasing coastal area in Denmark, and how much of coastal area will be lost, erased, and transformed in the next 100 years. Some popular Danish beaches will disappear completely, some unique Danish coastal ecosystems will also disappear. 100 years, it’s not a log time when you must make decisions about which coast areas to protect and preserve, and which to lose, and then build infrastructure like dams for this protection.
Another researcher showed clearly how huge, colossal amounts of water are warming up in areas of the arctic, at an alarming speed. What is particularly impressive is the size of these water masses, which is massive. Imagine how much heat you need to warm the entire sea between Iceland and Greenland.
I have also seen another researcher giving a more “exotic” presentation about sea turtles in Australia. Sea turtles are amphibians, and their sex at birth is determined by the temperature at which the eggs are conserved under the sand. When the sand where they nest gets warmer, because of warming climate, more female turtles are born. The researchers were fining imbalances so serious that some populations of turtles had more than 90% female individuals. There are hardly any males left in these turtle population. Researchers were able to prove that these imbalances had been going on for the last thirty years, and getting more and more pronounced, and to correlate very well these changes in the sex ratios with data about warming climate.
And then another researcher reporting a series of first-hand witnesses of fishermen in Greenland who told the scientists how the sea is changing in front of their own eyes. In their lifetime, fish species that used to be there are not there anymore, or have moved north, and new species have arrived that are typical or warm waters and were never or rarely seen before. This makes fishing much more challenging and sometimes impossible and affects severely the livelihoods of the Greenlandic fishermen and their families.
And finally there were presentations by my close colleagues about how the fishery itself contributes with CO2 emissions to the changing climate. The harder it is to find fish, the more fuels consumed, the more carbon is emitted, the more climate changes, the more difficult it is to fish…it’s a vicious cycle.
One presentation after the other showed the dramatic impact that human-driven climate change is already having on our society and on the ecosystem.
like me and you behind these studies that every day are confronted with the harsh reality of how much our climate is changing and how devastating the effects are already being and how more devastating they will be.
I can imagine them sitting in their offices, in front of the computer, plotting the data for the first time, looking at these graphs and trends, the hard evidence. The inevitability. What do they do when they see these data? Do they cry? Bang their head on the desk? Or are they just used to it, indifferent as a self-defense mechanism? There is a sort of weird resignation in their eyes when you ask them…so what now? in the corridor after the session. They shrug their shoulders in a gesture of impotence…like they have lost something very precious, irreversibly.
It is usually at this point that any written piece about climate change ends with a message of hope. Because we don’t have to scare you too much dear reader. Because…doomism. We can still make it, you know…probably. But let’s be honest, this is less likely every day that passes. Climate change is already here, it’s not in 100 years as you are maybe imagining. The situation is serious, for real. This is no joke.
to inform yourself about how your actions contribute to climate change. Leave your car at home, walk, take the bike, take the bus or train. But please no, don’t take that Aalborg-Copenhagen plane. Save as much energy as you can. Insulate your house, electrify. Eat much less meat and diary. Don’t buy so much stuff that you don’t actually need, just because it’s cheap, but learn how products are made and where they come from.
And since yes, of course this is not just an individual responsibility, demand some systemic change. Demand a serious environmental commitment from your favorite political party, your municipality, your neighborhood, whatever they are, demand a green agenda. Vote.
There is a lot you can do, everything counts. Indifference, instead, will only bring more coast to disappear, more warm water in the arctic, more female turtles, more sense of we lost it all.
]]>
I have recorded several teaching videos about LCA during the COVID-19 pandemic due to the shift to online teaching and most importantly due to the gradual shift to a flipped classroom teaching approach - you find them all here.
Some disclaimers before watching:
Used in courses at Aalborg University and University of Southern Denmark during years 2020-2022. Videos show how to structure and conduct a LCA, and how to implement LCA on the software SimaPro with database ecoinvent (if you have a license).
2.2-MP-Product-system-flow-chart
3.1-MP-product-system-diagram-to-matrix
3.2-MP-understand-the-product-system-matrix
3.3-MP-multifunctional-activities-explained
3.4-MP-multifunctional-activities-matrix
4.1-MP-SimaPro-foreground-system
4.2-MP-SimaPro-background-system-described
4.3-MP-SimaPro-background-system-howtolink
4.4-MP-SimaPro-match-with-excel
Used for PhD course in Advanced LCA at Aalborg University during years 2021-2022. They cover topics ranging from the computational structure of LCA to in particular the use of the Brightway2 python-based LCA software, and uncertainty and sensitivity analysis. The notebooks are available on the github repository advanced-lca-notebooks.
Videos from other teachers are available too for the Consequential LCA module.
AdvLCA-M2.7-OAT-sensitivity-analysis
AdvLCA-M2.8-Global-sensitivity-analysis
Are very welcome. Please write me an email.
]]>Anyway, good to see and catch up with so many people live at #SETACCopenhagen. I won’t join the next one because I would have to fly there so see you all maybe in 2 years? Here a pic of the today (admittedly not very diverse 😓) session chair group as postcard. Cheers!👋👋👋 pic.twitter.com/rECDMKmufL
— Massimo Pizzol ☮🇺🇦 (@m_outreach) May 19, 2022Short report of the first conference I attended in person after the pandemic.
Warming up.
First session: improving data quality. Two presentations on food databases increasing granularity, two on uncertainty analysis - it seems to me that we have now both the data to make some inventories more accurate and the tools to simulate the uncertainty on the rest
Here I presented a recent study where we test the impact of different uncertain factors in total uncertainty and we figure out…database is the largest source. Which is a problem. Credits go mainly to Giovanni Codotto (AAU) who did the work under my supervision and in collaboration with Laurent Vandepear (AN-OG).
Second session: bio based products. Integration of economics / biochemistry tools with LCA and finally, finally we are talking about constraints in biomass supply, and how “circular” solutions must account for that. Søren Løkke from the AAU side presenting some of the ideas behind our recent ALIGNED project.
Heads up and congrats to our very own PhD stud Maddalen Ayala who presented Plastisea project results on LCA of biolastfc from seaweed! She rocked it 🤘.
Finally, very tired right into chairing a discussion session together with our Agneta Ghose (AAU) and Tomas Ekvall (Chalmers) about striving for a common LCA methodology…hot topic indeed.
A survey across the audience showed that although they were undecided on whether we should go for a common LCA method, we will never make it anyway (not realistit an objective to achieve). Great comments from the audience overall. A quote about data interoperability for example:
“[adapting data from diff sources and software] is a nightmare for us and a source of huge pain and cost”
This says it all…
I actually missed it.
So I tried collecting ‘highlights’ from the participants to the LCA session.
The first two informants were not very informative: “Session was a bit weird” and “Yesterday? It is all a bit blurried” is all I could get 🤌
Apparently the session on LCA of biofuels was very interesting: critique of iLUC models, and strikes that people are still doing research on 1st gen biofuels? I thought we were past that?!?
Sorry for lousy coverage. I asked around but there was nothing major to report. Skip to day 3 report below.
Go with the flow.
Session in the morning about impact assessment. I am a bit rusty on LCIA tbh but I get the impression this stuff is still as much of an open research area as 10y ago when I started. There is so much to do to increase coverage of impact pathways, increase robustness, reduce uncertainty, etc.
Anyway, some presentations about methodologies and frameworks for human and ecosystem damage, as well as methods for specific sources (fisheries, wind turbines).
The boundary with risk assessment is getting very blurried IMO.
A couple of presentations didn’t show results, this was…surprising?
Afternoon session about methodological harmonization. Great session. Thomas Schaubroeck (LIST) criticizing ISO for inconsistency, Tomas Ekvall (Chalmers) reporting on the inconsistency of how people interpret system expansion
I made a comment here that came out all wrong but the essence is: before writing guidelines about system expansion or substitution…do the math! Words can be misleading or confusing but matrices and equations shuold be quite univocal.
Finally, networking session on prospective LCA: huge attendance and interest. We are going to build a small knowledge-sharing network on the topic, stay tuned.
Survival mode.
Only two sessions on this day about prospective LCA of emerging tech, that I was so kindly invited to co-chair by Nils Thonemann (DTU) and Alexis Laurent (DTU). Without any bias I can tell this was the absolutely BEST session in the conf.
Presentations & posters covered several emerging technologies some of which were even completely new to me. And a variety of modeling approaches
My impression, the modus operandi is generally:
This was done to various levels of sophistication in the presentations, some covering foreground modeling only, some bagroung only, some both. Interesting to see the combinations and interactions between the two layers (a presenter nicely represented this as an iceberg, foreground = tip, background = bottom)
A presentation about a validation study showed that upscaling methods can not be fully trusted! Do not work for specific tech e.g. bio based. Difficult to generalize but attention (and better methods and validation) needed!
And again congrats to our very own PhD stud Pierre Jouannais who presented some of the Aquahealth project results on modelling microalgae cultivation prospectively and stochastically - very advanced modelling effort.
In general, impressive computational and data intensive approaches! Coding skills for using - and good practices for sharing - prospective databases increasingly needed…
I speculate, looking at the funding, that this wave of prospective LCA studies is partly due to the EU Commission and Horizon programme. Many EU projects are initiated in green/emerging tech development and there is always a LCA WP…so LCA research followed.
Also, if I can be critical, we need to start thinking a bit critically about these large prospective simulations: where does the science ends and the speculation begins? How can we validate any of this? What is the meaning of uncertainty in a scenario-world? Etc.
Exhausted after the conf but nice to be there again. That’s all from my side about SETAC-Copenhagen.
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