Category Archives: Peer Reviewed Article Review

bibliometrix

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Bibliometrix is an R package for literature review and synthesis of past research on a topic. It now has a Shiny graphical interface.

bibliometrix: An R-tool for comprehensive science mapping analysis

The use of bibliometrics is gradually extending to all disciplines. It is particularly suitable for science mapping at a time when the emphasis on empirical contributions is producing voluminous, fragmented, and controversial research streams. Science mapping is complex and unwieldly because it is multi-step and frequently requires numerous and diverse software tools, which are not all necessarily freeware. Although automated workflows that integrate these software tools into an organized data flow are emerging, in this paper we propose a unique open-source tool, designed by the authors, called bibliometrix, for performing comprehensive science mapping analysis. bibliometrix supports a recommended workflow to perform bibliometric analyses. As it is programmed in R, the proposed tool is flexible and can be rapidly upgraded and integrated with other statistical R-packages. It is therefore useful in a constantly changing science such as bibliometrics.

Has Trump pulled off the equivalent of a politically impossible global carbon tax?

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Well, certainly not on purpose! He almost certainly thinks he is advancing the agenda of nominally US-based multinational oil companies. But by limiting the supply of oil and gas world wide, he has at least temporarily brought about the peak oil scenario that seemed to be fashionable a decade or two ago, and then mostly forgotten as it looked like new fossil fuel discoveries and exploitation technologies, along with non-fossil-fuel technologies, might outstrip any hard limit in the (economically viable) geologic supply.

But now we get to find out what an actual hard limit on supply looks like. It won’t be permanent – we can speculate months to years. But electrification technology was already a snowball rolling downhill in Asia, and this will just accelerate the takeover of electric vehicles, even if effective propaganda is hiding this from the U.S. public. Governments like Thailand’s are making rational policy choices such as incentivizing trade-in of internal combustion engines for electric. The economic incentive to do this is there, and has been slowed down until now only by infrastructure lock-in and path dependence. Even if this disruption is measured in months or years, the technology will continue to progress even in that time, and rational governments will realize this shut-down situation can happen again in the future and that they can mitigate the risk. So thank you to the one-man wrecking ball who has made all this short-term pain (i.e., horrible suffering and death for many, many human beings which I don’t mean to make light of) and long-term gain possible! (Now, you could say, and I admit, that with electrification coal will be substituted for oil and gas in the near to medium term, and this is not a win for the environment. I actually don’t know if it is a net win or loss, when you consider the greater efficiency of electrification over mobile internal combustion engines. But the incentives still favor renewables longer-term, and the incentives get stronger and stronger as renewables continue to get cheaper while coal as far as I know does not.)

A coalition of the willing implementing an international carbon tax is still a theoretical possibility. Here is one article on what that could look like. It is hard for me to imagine politically, but let’s say a group of large non-oil-producing economies, led for example by China, India, and the Asian Development Bank, decided they were going to do this and impose equivalent border-adjustment taxes (legal under the WTO I think not that this seems to matter any more) on all trading partners not doing it.

An international plan for sustainable development

International cooperation on climate and taxation remains inadequate to deliver decarbonisation, reduce poverty, and finance sustainable development at the required scale. We propose a Sustainable Union among willing countries, combining carbon pricing, new taxes on wealth, polluting fuels, financial transactions, and corporate income, with international revenue-sharing and conditional cooperation mechanisms. Most revenues would remain with participating governments for domestic spending, while a defined share would be pooled internationally. Specifically, participating countries would contribute 1% of gross national income (GNI) to a common pool redistributed in proportion to population, generating net transfers from richer to poorer countries. Meanwhile, the remainder of the revenue would increase domestic fiscal space by on average 2.2% of GNI. Although politically ambitious, such a framework might be credible, as governments are already advancing related forms of voluntary cooperation, and survey evidence indicates that it would be supported by majorities worldwide.

methylsiloxanes

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All we have to do is look, and we find more “forever chemicals” all throughout the environment and our bodies. This just adds to my suspicion that classes of chemicals like PFAS and microplastics (and before that lead, DDT, etc.) might not be the most pervasive or dangerous chemicals out there, but merely the ones we have put under the magnifying glass so far. Similarly, we put the Covid-19 virus under many microscopes for many years and found all kinds of things it does to our bodies and brains. But if we put similar scrutiny on other microorganisms, who knows what we might find?

A new study shows that a specific type of silicone, the so-called methylsiloxanes, is widely present in the atmosphere across diverse environments. Also, concentrations appear to be much higher than expected. According to the researchers, this raises concerns about their potential—yet poorly understood—effects on human health and the climate. Methylsiloxanes are commonly used in industry, transportation, cosmetics, and household products. The study was supervised by Utrecht University and the University of Groningen, and the results are published in Atmospheric Chemistry and Physics.

I’m probably a broken record on this, but chemistry really does make our modern lives safer and more convenient overall. Without water disinfection, food preservatives, antibiotics, vaccines, dental anesthesia, etc., our lives would be nasty, brutish, and short indeed, as they were before we had those things. We don’t want to give up the benefits of useful chemicals, but we also should always be searching for non-toxic alternatives that give us the same benefits.

instructions for the AI scientist

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Here’s an example of how AI science could work. If you ask machine learning to review a data set from a system and predict the future behavior of the system, it can often do a good job, but the mathematical approach it is using to do that is mostly opaque. It is also divorced from any sense of the system structure, its interaction with the physical universe, and the physical laws governing it. But you can ask the computer to constrain itself within those known physical laws, and then it may be able to provide you insights on the structure and physical processes inside the system. So at that point, you and/or the computer should be able to form scientific hypotheses and test them against the data. This example from Water Resources Research is about soil moisture.

Coupling Intuitive Physics Into Deep Learning for Soil Moisture Flow Processes Learning

Soil water flow processes in the unsaturated zone support ecosystems and regulate water, energy, and biogeochemical cycles. Recently, deep learning (DL) approaches have significantly advanced soil moisture (SM) prediction tasks yet still challenging to interpret. It’s difficult to peer into the internal reasoning procedures of algorithms, let alone associate them with specific physical processes. Thus, DL alone is unlikely to satisfy soil hydrological modeling needs and cannot advance process understanding. Here, we present DPL-S (deep process learning for SM dynamics) approach, which couples intuitive physics into deep learning architecture as structural guidance to facilitate comprehensive surrogate modeling of soil water flow. DPL-S discretizes the SM state evolution into multiple sub-process effects (e.g., gravity, matric potential) at the intuitive physics level and abstracts them into format-specific and learnable tensors. By cascading state-action matrices in a differentiable end-to-end framework and enforcing penalties for physical inconsistencies, DPL-S enables a profound understanding of physical functions and scenes of soil water flow. Comprehensive numerical experiments including layered soil conditions and tests with in situ observations, demonstrate that it achieves reliable SM profile reconstruction with predictive performance comparable to the state-of-the-art DL model on supervised items. The internal inference of DPL-S is fully transparent and the tensor representations achieve strict physical realism under limited water content supervision, thus enabling continuous predictions like physical models during the testing period. The model’s flexibility, generalization, noise resistance, and large-sample diverse data synergies are also evaluated. This work represents a solid step toward learning hydrophysical processes from large data sets.

the future of scientific and engineering modeling

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I’ve been using AI to assist me with coding (R, in my case) since shortly after ChatGPT came out. In engineering, we tend to run off-the-shelf models that were of course written in some kind of code. Sometimes these are open access but often they are proprietary. The brutality of market discipline pretty much requires specialized off-the-shelf solutions in industry because customers are not going to be willing to pay for custom coding. The proprietary ones are even often preferred for legal/liability reasons. Anyway, the future of modeling appears to be humans providing a detailed specification to an AI agent which then follows it to do the coding, debug the coding, run the model, process and present the results. The humans have to be able to detect whether the results are BS, of course, at this point in history. One can imagine using a different agent or a more specialized agent to assist the humans in the bullshit-detection stage, that agent getting more independent over time, and so in a cycle. I wonder if we will be using agents to set up, run, and post-process the specialized models, or if things will trend toward just letting the agents write more fundamental code over time. Or maybe the specification will be what future scientists, engineers, and business people focus their efforts on, with translating that into 0s and 1s being basically a commodity done on the fly by AIs. This makes sense to me – the most crystal clear function of AI so far, in my view, is making it easier and easier for humans to communicate with computers in more abstract language, logic, and mathematical symbols.

Anyway, this example used something called Roo Code which included a couple versions of Claude Code along with some other agents, to run a fishery-related model. There is a peer reviewed article, but I also like this blog post and this example of a specification given to the agents.

MEMOP, MOP, and SMOP

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These are some more decision-making frameworks I hadn’t heard of, at least by name. I am hopeful that AI can help techniques like this make the transition from gown to town.

Developing Robust Management Pathways for Nutrient Pollution in Watersheds Under Climate Uncertainty

Nutrient management represents an enduring effort toward sustainability. However, long-term management planning faces notable challenges, mainly due to substantial investments required under uncertainty of forthcoming climate. To address these challenges, this paper proposes and tests a Multi-climate-scenario (MCS) Multi-epoch Multi-objective Planning (MEMOP) framework (in combination MCS-MEMOP). This framework divides the long-term planning horizon into multiple epochs, allowing nutrient mitigation measures (e.g., fertilization management, filter strip) to be initiated at any epoch, each with its own water quality and investment constraints. To tackle climate uncertainty, it incorporates principles of Robust Decision-Making. MCS-MEMOP generates solution pathways outlining the timeline and progression of management measures, tested here for a case of a small, agriculture-dominated watershed. Considering a single climate scenario, the MEMOP method was compared with Multi-objective Planning (MOP) and Stepwise MOP (SMOP) for a 25-year nutrient management horizon, using the SWAT model to evaluate the test case water quality effects of solution pathways. Results show that MEMOP’s multi-epoch approach generates a larger and more diverse set of solutions than MOP and SMOP, offering greater flexibility to select optimal trade-offs among objectives. Additionally, MEMOP solutions exhibit superior cost-effectiveness compared to MOP and SMOP solutions. Applied separately to different climate scenarios, the MEMOP results show that changed climate conditions may significantly alter the Pareto front. In contrast, MCS-MEMOP yields robust solutions that can consistently satisfy 72%∼89% of epoch-specific constraints under new climate conditions in the test case, with a cost increase of 12% that reflects the price of addressing climate uncertainty in this case.

pedestrian level of service

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I had actually never heard of pedestrian level of service, but it appears to be just applying traffic flow modeling principles to pedestrian flow. It is intuitively appealing to me because you can just add pedestrians, cyclists, or whatever mode you want to a transportation model and specify which links in the network are open to which types of “traffic”. Theoretically, you could try to optimize the total flow of people from where they live to the places they need to get to, and not just maximize the flow of motor vehicles. Surely someone must have looked at this. A valid criticism, of course, is that these models can be short-term focused, even looking just at a single weekday peak hour and certainly missing longer-term dynamics like how infrastructure capacity and land use policy affect trip generation over time. Another criticism is that this engineering approach completely misses the quality of life aspects of urban design.

Pedestrian infrastructure assessment: Walkability vs. pedestrian level of service

This paper explores two of the most explored indicators for evaluating pedestrian infrastructure: walkability and Pedestrian Level of Service (PLOS). Walkability, typically used by urban planners, emphasises the qualitative aspects of the built environment, such as safety, comfort, accessibility and aesthetics, whereas PLOS, primarily employed by transport engineers, quantifies the operational performance of infrastructure based on pedestrian flow, density and speed. A systematic PRISMA-based literature review was conducted, covering 60 PLOS studies and 55 walkability studies were analysed in terms of definitions, contributory factors, data collection methods and modeling techniques. Despite sharing the goal of promoting pedestrian-friendly environments, these frameworks differ fundamentally in scope, purpose and methodology and are often applied independently. The findings indicate that walkability indicators vary in how factors are measured and allocated across dimensions. Moreover, walkability is treated as a “static” factor, both conceptually and methodologically. Relatively limited research examines how walkability changes over time (e.g., day vs. night) or varies across population groups. Conversely, PLOS generally excludes socio-spatial dimensions, a choice consistent with its original purpose rather than a methodological limitation. Some approaches attempt to incorporate subjective factors, but usually in ways resembling traditional walkability metrics. This study highlights the need for greater standardization in definitions and assessment frameworks, while also identifying challenges that complicate their practical applicability. Their complementary use can significantly enhance the design, evaluation and planning of pedestrian infrastructure, supporting more livable, sustainable and inclusive cities.

Some facts and figures on how birds die

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This article on a site called ZME Science has some facts and figures on how many birds are killed (in the U.S., I think) by wind turbines compared to other causes.

  • wind turbines: 140,000-680,000 birds per year; 0.3 – 0.4 birds per GW-hr of power production
  • power lines: 12-64 million birds per year [Daddy, how can the birds sit on the power lines without being electrocuted? Well, I guess the answer is that the ones you see on the power lines are the ones that didn’t get electrocuted.]
  • vehicle collisions: 89 million – 340 million birds per year
  • glass buildings: 1 billion birds per year [well, the article says “up to almost”. Rounding is fine with me, but they are oddly precise on some numbers and willing to round on others.]
  • cats: 1.3 billion – 4 billion birds per year [I happen to like cats just fine, but if you consider yourself a friend of animals in general it is really kind of immoral and hypocritical to have an unsupervised outdoor cat.]
  • fossil fuels: 5.2 birds per GW-hr of power produced “through habitat destruction, mercury poisoning, and acid rain” [presumably, the study referenced here followed some pretty different methods than the ones that looked at birds running into stuff and getting disemboweled by cat claws. But still, you can say this is an order of magnitude more than the wind turbines per unit of energy supplied, controlling for the fact that fossil fuels are supplying more energy in an absolute sense.]

There are some things you can do to make the wind turbines less bad for birds. But really, it’s the cats and the cars and just the general wanton destruction/displacement of nature by our civilization. Now I’ve depressed myself, as I often do.

Decision Making Under Deep Uncertainty

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I like the idea of applying decision theory to real-world problems. The ideal concept is that decision makers and stakeholders have a body of highly relevant objective information available to them at the time they are also applying judgment and values (and politics) to make those decisions. There is a huge academic endeavor, but the hard part in my work experience is pulling together relevant information in a timely manner. Today’s schedule and budget expectations just don’t allow complex analyses in many cases, so we default to relatively simplistic approaches to complex problems. Maybe AI can help apply these methods more quickly, alongside more traditional methods like benefit-cost analysis. I like the focus on robust decisions in this paper, and I like the collection of practical tools including some R packages.

A review of tools and resources to support Decision-Making Under Deep Uncertainty

Decision-making under Deep Uncertainty (DMDU) offers approaches to support robust, adaptive strategies for complex decision-making. However, practical uptake of DMDU remains limited, partly due to fragmented access to resources and a lack of an inventory of available tools. This study introduces a comprehensive catalogue of tools and resources. Through a structured survey and expert elicitation, we identify 28 resources and 16 tools that support DMDU research and practice and classify them using an established DMDU taxonomy. Our analysis reveals a focus on introductory guidance regarding theory and methods of DMDU application, with some bias toward water-related applications. Technical, method-specific resources on how to implement existing frameworks remain limited. Our results identify tools supporting all core DMDU components, though they highlight persistent scalability challenges. The resulting online catalogue provides a foundation for expanding the use of DMDU in practice and is intended as a living, community-driven platform.

more on how tipping points could unfold

I’ve suggested that the climate tipping point might only be called in retrospect, and that the year we pick might be 2025. Not because it can be pin-pointed that precisely, but because if we decide in retrospect that the 2020s were about when it happened, that will be a nice round number to pick.

Here is one scenario from OneEarth journal on what a cascade of tipping points could look like.

The risk of a hothouse Earth trajectory

Warming from greenhouse gas emissions accelerates Arctic sea ice and Greenland Ice Sheet loss, reducing albedo and adding meltwater that weakens the Atlantic Meridional Overturning Circulation (AMOC). A weakened AMOC shifts tropical rainfall patterns, increasing drought risk and potential dieback in the northern Amazon forest, further amplifying global warming through the feedback involving carbon loss. Note that once one tipping point is crossed, it will likely impact the timing and temperature thresholds for other tipping points.