Category Archives: Peer Reviewed Article Review

the latest from Rockstrom et al.

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Johan Rockstrom and company have a new paper talking about possible trajectories for the climate through the year 3000. There is a fair amount here, but one thing I find interesting is that while they are not talking about the highest emissions scenarios anymore (SSP3 under the CMIP6 framework), they are looking at a range of equilibrium climate sensitivities (ECS), and this makes a big difference. ECS, if I remember correctly, is the amount of global warming that occurs from a doubling of atmospheric carbon dioxide concentrations. If this value is 4 degrees C rather than 3, our situation gets much worse, with a peak of around 5 degrees C above preindustrial temperatures rather than the 3.5 expected under the lower ECS value. They show the peak sometime between 2200 and 2300. If this sets off catastrophic feedback loops, the system could get to a point well outside human control, even if we found the capacity and geopolitical will to get our act together.

That might happen, for example, in the case of large-scale dying or burning of forests (Reyer et al., 2015), or when carbon release from thawing permafrost (Turetsky et al., 2020) or gas hydrates on the ocean floor (Minshull et al., 2016) increases beyond current expectations. If such “natural” carbon release exceeds a potentially modest threshold (compare residual emissions experiment, Figure 1), warming may continue even if human forcing from GHG emissions or further land-use changes are virtually stopped. These human efforts would then be thwarted by the Earth system itself.

An important point is we don’t know the correct value of ECS yet – that should become clear with more data collection and research in coming decades. But the risk is existential enough that a species and civilization able to act together in a rational manner would be managing it now.

remote work as the cause of youth unemployment

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This article says remote work, rather than AI, explains much of the recent slow hiring of new college graduates. It kind of makes sense to me – older managers came up in an environment where building relationships face-to-face was the foundation of productive teams. In my earlier career years, the unspoken expectation was that we spent more than 8 hours in the office, and a lot of that was “wasted” in a straight-up productivity sense, but not wasted in terms of building those relationships. There was also a fair amount of work-based socializing over lunch and after those 8+ hours in the actual office, and it was not unusual for significant quantities of alcohol to be involved. Mad Men may have been an caricature exaggerated for dramatic and comedic effect, but it gives some idea of what the work culture has lost. And some elements we have lost should not be missed. The work culture has just changed – even if younger workers are present face to face in the office, they are on screens and wearing headphones a lot of the time. If they are paid by the hour, they are not taking lunch breaks and they will stand up and walk away without saying goodbye at 5 pm. Without these younger workers around, I think older workers are also forgetting how to train and mentor younger workers effectively. I’m not saying all of this is bad – it represents a shift of priorities in our society. Maybe young people are using those hours outside of work to form relationships and find meaning that my generation tried to find at work. Their livers and odds of dying or killing someone else in a drunk driving accident are almost certainly better off. We may need to adapt to this rather than find “solutions”. Maybe AI can be the glue that holds together work culture in place of yesterday’s water cooler conversations and happy hours. Anyway, that’s my preamble – here is the article about remote work…

The Broken Ladder: AI, Remote Work, and Early-Career Hiring

Is generative AI replacing junior workers? A growing literature answers yes, citing large declines in early-career hiring concentrated in GenAI-exposed occupations. We argue that this verdict is premature because GenAI exposure is strongly correlated with another post-pandemic shock, working from home (WFH). Using two data sources spanning 243 million new hires and 407 million online job postings, collected across the US, UK, Canada, and Australia during 2017-2025, we estimate difference-in-difference designs at the occupation, region, and firm level. When estimated separately, a two-standard-deviation increase in GenAI and WFH exposure each predicts, by 2025, a fall of around 5pp in the junior-share of new hires and around 3pp in the share of job ads requiring limited experience. Estimated jointly, the WFH effect remains, while the GenAI coefficient attenuates sharply and is often statistically indistinguishable from zero. Alternative exposure measures, residualization designs, flexible non-parametric co-treatment controls, and replacing exposure-measures with actual WFH adoption as the treatment all support our finding that WFH is a robust predictor of the decline in early-career hiring.

first hints on CMIP7

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This is the first story or post I personally have read on the IPCC’s Coupled Intercomparison Project (CMIP) round 7. I’m sure people who do climate science for a living are all over it. But I am not exactly a layperson – I’m an engineer who should aware of and probably incorporating climate science into my daily work. And not to brag but I am an engineer who has done Ph.D.-level research on how to use climate science and climate modeling outputs in daily engineering work. So if this is only on the periphery of my awareness, it is nowhere in the awareness of the typical intelligent and educated engineer out there designing the infrastructure that is going to be in place for the next decades to century in our cities.

As climate folk will know, the community is currently embarking on a new round of climate model simulations to support analyses and projections for the next IPCC report (due in 2028/9). This new effort has been dubbed CMIP7, because it is the sixth iteration of the CMIP effort (IYKYK), that started in the late 1990s. For each of these iterations, a new set of projections has been formulated for the modeling groups to use and the ones for this round were just published (van Vuuren et al., 2026).

For those who don’t know, the scenarios mentioned here are the inputs to the global circulation models that actually give us projections of what the global climate might look like under various…er…scenarios. Here is the abstract of the reference mentioned above:

The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7)

Scenarios represent a critical tool in climate change analysis, enabling the exploration of future evolution of the climate system, climate impacts, and the human system (including mitigation and adaptation actions). This paper describes the scenario framework for ScenarioMIP as part of CMIP7. The design process, initiated in June 2023, has involved various rounds of interaction with the research community and user groups at large. The proposal covers a set of scenarios exploring high levels of climate change (to explore high-end climate risks), medium levels of climate change (anchored to current policy action), and low levels of climate change (aligned with current international agreements). These scenarios follow very different trajectories in terms of emissions, with some likely to experience peaks and subsequent declines in greenhouse gas concentrations. An important innovation is that most scenarios are intended to be run, if possible, in emission-driven mode, providing a better representation of the earth system uncertainty space. The proposal also includes plans for long-term extensions (up to 2500 AD) to study slow climate change-related processes, and (ir)reversibility. This proposal forms the basis for further implementation of the framework in terms of the derivation of climate forcing pathways for use by earth system models and additional variants for adaptation and mitigation studies.

rewilding the city?

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I think of “rewilding” as something that happens outside the city, but this literature review looks at how the concept could apply in an urban context.

Exploring rewilding as a potential design approach to improve urban biodiversity: A systematic review of the literature

Urban biodiversity continues to decline under rapid urbanization. Although biophilic design and green–blue infrastructure have strengthened nature integration into cities, planning and design practice often prioritizes human-centered service delivery and overlooks process-led recovery. Rewilding addresses this gap by foregrounding ecological autonomy, succession, and trophic complexity, yet its urban translation remains limited. This review therefore examined how rewilding could be operationalized as a design-oriented strategy in cities. An evidence-to-design roadmap was proposed to link rewilding logics, biodiversity aims, and urban planning and design, with a dual-level protocol adopted: 1) a macro-level scientometric overview of 1184 records (1990–2025) to locate urban rewilding studies within the broader literature; and 2) a micro-level PRISMA-guided synthesis of 103 spatially explicit studies coded by rewilding pathway (active/passive), targets, spatial and temporal scales, data, methods, and spatial variables. Macro mapping indicated that urban-related studies were late-emerging and weakly consolidated. Micro synthesis revealed pathway differences: passive studies primarily traced vegetation and landscape trajectories, while active studies employed suitability and connectivity modeling for intervention screening. Urban studies remained scarce (N = 9), with passive studies dominant and connectivity analyses particularly common, focusing on vegetation, birds, and planning-oriented targets. Building on these patterns, this study derived a four-level framework comprising design intent, strategic planning, tactical methodology, and operational application, translating rewilding into staged, scalable decisions aligned with urban planning and design workflows. This review positioned rewilding as a process-centered complement to conventional landscape design approaches, offering an actionable pathway for embedding biodiversity regeneration within urban systems.

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

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

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

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.