Category Archives: Peer Reviewed Article Review

pedestrian level of service

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

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

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.

habitat area and fragmentation

I gave a talk this week on a niche topic involving plant selection for stormwater management features like rain gardens in cities. I had just one slide on habitat connectivity and fragmentation as an interesting area for further research. That one slide generated a lot of interest. And it is an interesting topic. First of all, it has been looked at quite a bit in the design of nature reserves, but not so much in urban areas or areas with a mix of wild, agricultural, and urban land uses. And second, there is always the debate about whether focusing too much on connectivity metrics can detract from just preserving enough total habitat. The article below is an entry relevant to this question, relevant in the context of forests. In urban areas, in my view, this question gets flipped on its head. Fragmentation and disconnection is the de facto state, and can only be reversed on the margins. So the interesting question to me is what policy choices can make it the least bad. There is also the possibility that better policy choices in urban areas can reduce friction of (animal and plant) movement between wild landscapes, and even whether they can serve as relatively biologically functional islands in depleted agricultural landscapes.

Why controlling for habitat amount is critical for resolving the fragmentation debate

The need for a consensus on the effects of fragmentation per se is increasingly recognized (Miller-Rushing et al., 2019; Riva, Koper, et al., 2024; Valente et al., 2023) because deforestation continues and small forest patches are particularly vulnerable to destruction (Riva et al., 2022). If fragmentation per se reduces biodiversity, then policies should prioritize protection and restoration of large patches. If not, then policies should include all forest, irrespective of patch sizes (Riva & Fahrig, 2023). This would allow effective biodiversity conservation, even in human-dominated regions where no large patches remain, by protecting and restoring sufficient forest over a network of many small patches (Arroyo-Rodríguez et al., 2020).

what’s new with learning curves?

At least since reading some early Singularity-adjacent publications by Vernor Vinge, Ray Kurzweil, and Bill Joy, I’ve been interested in learning curves. (And for the record, the topic and these authors were were not considered politically “right wing” or even political at all at the time.) Progress, at least in certain technologies, tends to be exponential over time. This clearly applies to computer technology, where there are short product cycles, the needed infrastructure is more or less in place and/or can adapt as the technology is scaled/commercialized, and legal and institutional barriers to change are relatively low. Technologies with “recipes”, like chemicals, drugs, seeds and other agricultural technologies, might be other examples. For these we have the patent system to actually try to slow down scaling and commercialization to the pace of innovation. With energy technology, learning curves seem to play out over much longer periods of time because while available technology changes rapidly, our system tends to be locked into long-lived infrastructure that can only change slowly. So new energy technology rolls out slowly as it is scaled up and commercialized over decades. There are also entities with enormous political and propaganda power that fight tooth and nail to keep us locked into obsolete technologies and infrastructure that fit into their historical (and profitable) business models. Now when you get to other technologies, like transportation and housing, public policy, legal and institutional barriers are dominant and tend to retard or even prevent progress. Rollout is so hard that while there are pockets of innovation, many don’t see the light of day or don’t spread from the local/pilot scale, even if they are successful at this scale. These also vary by location and jurisdiction, so that progress is very uneven geographically. These are my thoughts anyway. As for what’s new, here’s a journal article from Advances in Applied Energy.

Variability of technology learning rates

Climate and energy policy analysts and researchers often forecast the cost of low-carbon energy technologies using Wright’s model of technological innovation. The learning rate, i.e., the percentage cost reduction per doubling of cumulative production, is assumed constant in this model. Here, we analyze the relationship between cost and scale of production for 87 technologies in the Performance Curve Database spanning multiple sectors. We find that stepwise changes in learning rates provide a better fit for 58 of these technologies and produce forecasts with equal or significantly lower errors compared to constant learning rates for 36 and 30 technologies, respectively. While costs generally decrease with increasing production, past learning rates are not good predictors of future learning rates. We show that these results affect technological change projections in the short and long term, focusing on three key mitigation technologies: solar photovoltaics, wind power, and lithium-ion batteries. We suggest that investment in early-stage technologies nearing cost-competitiveness, combined with techno-economic analysis and decision-making under uncertainty methods, can help mitigate the impact of uncertainty in projections of future technology cost.

This blog in Construction Physics has a deeper dive across more industries, and discusses at least one large data set that is available for analysis. If you could accurately predict learning rates (and successful scaling/commercialization rates) for specific technologies based on known factors, then theoretically you could fine-tune policies and incentives to increase the rate of progress in the technologies you want. So this is an area of research that could boost all other areas of research and progress.

forecasting extinction risk

I agree with this article that it doesn’t make sense to start protecting species only after they become rare and threatened. Forecasting which ones will become rare and threatened in the future could make sense. Of course, serious efforts to protect, create, and connect habitats would make the most sense. The method I am familiar with, which is appeals to me most, is the geographically-based metapopulation method of Ilka Hansky. But there are some others mentioned here that are new to me, or at least unfamiliar names for concepts I might have come across.

Forecasting extinction risk for future-proof conservation decisions

Conservation prioritisation emphasises currently threatened species, but there are strong arguments for complementary, more proactive approaches based on forecasting future extinction risk for unthreatened species. Forecasting methods vary in the timescale of extinction risk estimation and include established methods such as Population Viability Analysis (PVA) and Early Warning Systems, and emerging ‘Over-the-Horizon’ (OTH) methods. We develop a framework that integrates extinction risk assessment across timescales and outlines tradeoffs between shorter- and longer-term extinction prevention goals. This framework facilitates use of extinction risk forecasting in decision-theoretic conservation prioritisation that explicitly considers alternative time horizons for extinction prevention. Considering extinction risk on extended timescales offers a future-proof approach to conservation planning that may prevent more extinctions than focusing exclusively on currently threatened species.

maybe global warming isn’t causing as much drying as we thought?

I certainly have had the impression that both agriculture and natural ecosystems are becoming more water scarce due to global warming, and that this is going to be a big problem at some point. We hear that the Amazon (River basin) may be tipping into an arid ecosystem, which has implications for the entire global climate and food supply, for example. But this article in Water Resources Research suggests there may be some feedback loops being overlooked. If I can try to summarize in a couple sentences, the concern is that higher temperatures cause greater evaporation from soil and transpiration (evaporation of soil moisture through the pathway of plant roots and leaves), and this will lead to drying both in agricultural and natural ecosystems. But plants have mechanisms to resist this loss of moisture, specifically by closing stomata which are the openings in leaves through which transpiration takes place. The mechanism can offset some but not all of the increased drying effect.

The CO2 Balancing Act: Why Global Warming and Greening Don’t Dry Earth as Much as We Thought

While air warming and vegetation greening are widely assumed to intensify terrestrial drying through enhanced evapotranspiration, rising atmospheric CO2 concentration ([CO2]) may counteract this effect by inducing stomatal closure and reducing water depletion. However, the complex interplay between these factors has obscured their net impact on global terrestrial drying. Here, we develop a model that physically and effectively quantifies the relationships among evapotranspiration, [CO2], and climate and vegetation changes, which can explicitly reflect how CO2-mediated stomatal regulation interacts with climate and vegetation changes to modulate evapotranspiration. We find that, globally, the drying effects of warming and greening are largely offset by CO2-induced reductions in surface conductance (69.4% ± 16.9%) and associated meteorological feedbacks. This compensatory mechanism is overlooked in traditional drying indicators, that is, the Palmer Drought Severity Index (PDSI) overestimates trends in drought-affected area (63.2% ± 10.1%), drought duration (58.7% ± 9.5%), and drought intensity (43.9% ± 7.7%) during 1982–2014 by ignoring CO2-vegetation-climate interactions, and similarly, potential evapotranspiration-based aridity index underestimates wetting trends by 66.1% ± 3.5%. Our results reveal a systematic bias in current global drying assessments, which exaggerate drying in aridifying regions while underestimating wetting trends elsewhere. These findings reinterpret the hydrological impacts of global change, demonstrating that [CO2] rise acts as a critical buffer against terrestrial drying. The study provides a mechanistic framework to reconcile observed greening with hydrological trends, offering transformative insights for ecohydrological modeling and water resource management in a high-[CO2] climate.

biodiversity decline

Out of many doom and gloom topics, biodiversity decline may be the gloomiest, or at least the gloomiest that the global political system and public by and large are not thinking about. With climate change, at least we all know something is going on even if we are bickering about it and not doing enough 50 years after we needed to start acting in a concerted way. Anyway, global insect decline is just beyond shocking. Here is just one article hot off the presses:

Long-term decline in montane insects under warming summers

Widespread declines in the abundance of insects portend ill-fated futures for their host ecosystems, all of which require their services to function. For many such reports, human activities have directly altered the land or water of these ecosystems, raising questions about how insects in less impacted environments are faring. I quantified the abundance of flying insects during 15 seasons spanning 2004–2024 on a relatively unscathed, subalpine meadow in Colorado, where weather data have been recorded for 38 years. I discovered that insect abundance declined an average of 6.6% annually, yielding a 72.4% decline over this 20-year period. According to model selection following information theoretic analysis of 59 combinations of weather-related factors, a seasonal increase in insect abundance changed to a seasonal decline as the previous summer’s temperatures increased. This resulted in a long-term decline associated with increasing summer temperatures, particularly daily lows, which have increased 0.8°C per decade. However, other factors, such as ecological succession and atmospheric elevation in nitrogen and carbon, are also plausible drivers. In a relatively pristine ecosystem, insects are declining precipitously, auguring poorly for this and other such ecosystems that depend on insects in food webs and for pollination, pest control, and nutrient cycling.

For a more general overview of the insect decline issue, I suggest this paper: Insect decline in the Anthropocene: Death by a thousand cuts.

There is some debate about which causes are more important than others, but like climate change, the causes are pretty much known (and one of them is climate change). Destruction of natural ecosystems to clear land for urban areas and agriculture is the biggest and most obvious. Massive use of pesticides and other agricultural chemicals. Heat and drought. The spread of invasive species.

The destruction of nature is just sad for everyone who values nature for its own sake. For those who don’t, it’s a little harder to come up with the elevator pitch for why this matters. Pollination has huge and obvious economic value, but maybe we can replace natural pollinators with domesticated bees for the most critical crops.

Beyond pollination, insects are the base of the food chain. Their disappearance is actually a symptom of loss of plant life, since many of them are herbivores and depend on plants. We should be able to help a little bit just by conserving or replanting some of the native trees and other plants we know they depend on in our urban areas and on farms. A guy I know wrote a paper about this.

Insects, in their function as herbivores, are also critical in transferring energy, biomass (i.e. carbon), and nutrients up the food chain to everything from birds to amphibians to fish. So their loss is a direct cause of the loss of a lot of these other animals. But in terms of the food supply, we can probably produce chickens and pigs and cows without them I suppose. So it’s a little hard to tell that “conservative” uncle at the Thanksgiving table that there is some imminent tipping point where the bugs dwindle to a certain level and then we all starve to death. (“Conservative” is in quotes because a true conservative would be interested in, well, conserving things not destroying them.)

unifying “Green Area Factor” and “No Net Loss of Biodiversity” measures

Here in the US, implementing these types of policies seems mostly like a political pipedream at the moment. I could imagine a really smart developer doing this as a marketing scheme, maybe. maybe. But this is a great article that gives us a window into some things that are being tried in Europe (although, I also hear voices in Europe speaking longingly of the perceived lack of regulation in the US). I don’t know – our regulations may be equally strong or stronger in some areas like hydrology and water quality (which is missing from the framework discussed here btw) wetland and floodplain protection, and endangered species (although these are under constant political threat). Ideally in my view, species would not have to get endangered first before we will do anything for them.

More than the sum of its parts – Integrating the use of green area factor tool and biodiversity offsetting for no net loss urban planning

As part of the actions to fight biodiversity loss, the European Union is working on a restoration regulation demanding the principle of no net loss (NNL) state of biodiversity of urban green space. Applying this principle in urban planning may raise conflicts between biodiversity conservation and ecosystem services provision. Furthermore, integrating the NNL of biodiversity principle into urban planning cannot be isolated from existing planning tools or processes. Here we present a novel approach where the green area factor tool and biodiversity offsetting are integrated to achieve NNL of biodiversity in urban planning, while maintaining the necessary ecosystem services and avoiding the negative, unintended tradeoffs that may occur if only one of these tools is used in the planning process. We provide a model which combines the two approaches to create a holistic method to understand and govern both biodiversity and ecosystem services of urban greenery. The model is intended to be used as part of urban planning processes.