CAUSALITY

Revealing causal links in complex systems

MIT engineers’ algorithm may have wide impact, from forecasting climate to projecting population growth to designing efficient aircraft

Massachusetts Institute of Technology

Getting to the heart of causality is central to understanding the world around us. What causes one variable — be it a biological species, a voting region, a company stock, or a local climate — to shift from one state to another can inform how we might shape that variable in the future. 

But tracing an effect to its root cause can quickly become intractable in real-world systems, where many variables can converge, confound, and cloud over any causal links. 

Now, a team of MIT engineers hopes to provide some clarity in the pursuit of causality. They developed a method that can be applied to a wide range of situations to identify those variables that likely influence other variables in a complex system. 

The method, in the form of an algorithm, takes in data that have been collected over time, such as the changing populations of different species in a marine environment. From those data, the method measures the interactions between every variable in a system and estimates the degree to which a change in one variable (say, the number of sardines in a region over time) can predict the state of another (such as the population of anchovy in the same region). 

The engineers then generate a “causality map” that links variables that likely have some sort of cause-and-effect relationship. The algorithm determines the specific nature of that relationship, such as whether two variables are synergistic — meaning one variable only influences another if it is paired with a second variable — or redundant, such that a change in one variable can have exactly the same, and therefore redundant, effect as another variable. 

The new algorithm can also make an estimate of “causal leakage,” or the degree to which a system’s behavior cannot be explained through the variables that are available; some unknown influence must be at play, and therefore, more variables must be considered. 

“The significance of our method lies in its versatility across disciplines,” says Álvaro Martínez-Sánchez, a graduate student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “It can be applied to better understand the evolution of species in an ecosystem, the communication of neurons in the brain, and the interplay of climatological variables between regions, to name a few examples.” 

For their part, the engineers plan to use the algorithm to help solve problems in aerospace, such as identifying features in aircraft design that can reduce a plane’s fuel consumption. 

“We hope by embedding causality into models, it will help us better understand the relationship between design variables of an aircraft and how it relates to efficiency,” says Adrián Lozano-Durán, an associate professor in AeroAstro.

The engineers, along with MIT postdoc Gonzalo Arranz, have published their results in a study appearing in Nature Communications. 

Seeing connections

In recent years, a number of computational methods have been developed to take in data about complex systems and identify causal links between variables in the system, based on certain mathematical descriptions that should represent causality.

“Different methods use different mathematical definitions to determine causality,” Lozano-Durán notes. “There are many possible definitions that all sound ok, but they may fail under some conditions.”

In particular, he says that existing methods are not designed to tell the difference between certain types of causality. Namely, they don’t distinguish between a “unique” causality, in which one variable has a unique effect on another, apart from every other variable, from a “synergistic” or a “redundant” link. An example of a synergistic causality would be if one variable (say, the action of drug A) had no effect on another variable (a person’s blood pressure), unless the first variable was paired with a second (drug B). 

An example of redundant causality would be if one variable (a student’s work habits) affect another variable (their chance of getting good grades), but that effect has the same impact as another variable (the amount of sleep the student gets). 

“Other methods rely on the intensity of the variables to measure causality,” adds Arranz. “Therefore, they may miss links between variables whose intensity is not strong yet they are important.”

Messaging rates

In their new approach, the engineers took a page from information theory — the science of how messages are communicated through a network, based on a theory formulated by the late MIT professor emeritus Claude Shannon. The team developed an algorithm to evaluate any complex system of variables as a messaging network.

“We treat the system as a network, and variables transfer information to each other in a way that can be measured,” Lozano-Durán explains. “If one variable is sending messages to another, that implies it must have some influence. That’s the idea of using information propagation to measure causality.”

The new algorithm evaluates multiple variables simultaneously, rather than taking on one pair of variables at a time, as other methods do. The algorithm defines information as the likelihood that a change in one variable will also see a change in another. This likelihood — and therefore, the information that is exchanged between variables — can get stronger or weaker as the algorithm evaluates more data of the system over time. 

In the end, the method generates a map of causality that shows which variables in the network are strongly linked. From the rate and pattern of these links, the researchers can then distinguish which variables have a unique, synergistic, or redundant relationship. By this same approach, the algorithm can also estimate the amount of “causality leak” in the system, meaning the degree to which a system’s behavior cannot be predicted based on the information available. 

“Part of our method detects if there’s something missing,” Lozano-Durán says. “We don’t know what is missing, but we know we need to include more variables to explain what is happening.”

The team applied the algorithm to a number of benchmark cases that are typically used to test causal inference. These cases range from observations of predator-prey interactions over time, to measurements of air temperature and pressure in different geographic regions, and the co-evolution of multiple species in a marine environment. The algorithm successfully identified causal links in every case, compared with most methods that can only handle some cases.    

The method, which the team coined SURD, for Synergistic-Unique-Redundant Decomposition of causality, is available online for others to test on their own systems. 

“SURD has the potential to drive progress across multiple scientific and engineering fields, such as climate research, neuroscience, economics, epidemiology, social sciences, and fluid dynamics, among others areas,” Martínez-Sánchezsays.

This research was supported, in part, by the National Science Foundation. 

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Written by Jennifer Chu, MIT News

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Journal

Nature Communications

Article Title

“Decomposing causality into its synergistic, unique, and redundant components”

Using mathematics to better understand cause and effect

California Institute of Technology

 

image: 

This diagram depicts the decomposition of the various types of causality according to the SURD method: unique (pink), synergistic (orange), and redundant (dark grey) for two observed variables, to understand the future effect (Qj+). The hidden causality, or leak, is shown depicted in light grey, at the periphery.

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Credit: Martínez-Sánchez, et al., Nature Communications, 2024

Cause and effect. We understand this concept from an early age. Tug on a pull toy’s string, and the toy follows. Naturally, things get much more complicated as a system grows, as the number of variables increases, and as noise enters the picture. Eventually, it can become almost impossible to tell whether a variable is causing an effect or is simply correlated or associated with it.

 

Consider an example from climate science. Experts studying large atmospheric circulation patterns and their impacts on global weather would like to know how these systems might change with warming climates. Here, many variables come into play: ocean and air temperatures and pressures, ocean currents and depths, and even details of the earth’s rotation over time. But which variables cause which measured effects?

 

That is where information theory comes in as the framework to formulate causality. Adrián Lozano-Durán, an associate professor of aerospace at Caltech, and members of his group both at Caltech and MIT have developed a method that can be used to determine causality even in such complex systems.

 

The new mathematical tool can tease out the contributions that each variable in a system makes to a measured effect—both separately and, more importantly, in combination. The team describes its new method, called synergistic-unique-redundant decomposition of causality (SURD), in a paper published today, November 1, in the journal Nature Communications.

 

The new model can be used in any situation in which scientists are trying to determine the true cause or causes of a measured effect. That could be anything from what triggered the downturn of the stock market in 2008, to the contribution of various risk factors in heart failure, to which oceanic variables affect the population of certain fish species, to what mechanical properties are responsible for the failure of a material.

 

"Causal inference is very multidisciplinary and has the potential to drive progress across many fields," says Álvaro Martínez-Sánchez, a graduate student at MIT in Lozano-Durán’s group, who is lead author of the new paper.

 

For Lozano-Durán’s group, SURD will be most useful in designing aerospace systems. For instance, by identifying which variable is increasing an aircraft’s drag, the method could help engineers optimize the vehicle’s design.

 

"Previous methods will only tell you how much causality comes from one variable or another," explains Lozano-Durán. "What is unique about our method is its ability to capture the full picture of everything that is causing an effect."

 

The new method also avoids the incorrect identification of causalities. This is largely because it goes beyond merely quantifying the effect produced by each variable independently. In addition to what the authors refer to as "unique causality," the method incorporates two new categories of causality, namely redundant and synergistic causality.

 

Redundant causality occurs when more than one variable produces a measured effect, but not all the variables are needed to arrive at the same outcome. For example, a student can get a good grade in class because she is very smart or because she is a hard worker. Both could result in the good grade, but only one is necessary. The two variables are redundant.

 

Synergistic causality, on the other hand, involves multiple variables that must work together to produce an effect. Each variable on its own will not yield the same outcome. For instance, a patient takes medication A, but he does not recuperate from his illness. Similarly, when he takes medication B, he sees no improvement. But when he takes both medications, he fully recovers. Medications A and B are synergistic.

 

SURD mathematically breaks down the contributions of each variable in a system to its unique, redundant, and synergistic components of causality. The sum of all these contributions must satisfy a conservation-of-information equation that can then be used to figure out the existence of hidden causality, i.e., variables that could not be measured or that were thought not to be important. (If the hidden causality turns out to be too large, the researchers know they need to reconsider the variables they included in their analysis.)

 

To test the new method, Lozano-Durán’s team used SURD to analyze 16 validation cases—scenarios with known solutions that would normally pose significant challenges for researchers trying to determine causality.

 

"Our method will consistently give you a meaningful answer across all these cases," says Gonzalo Arranz, a postdoctoral researcher in the Graduate Aerospace Laboratories at Caltech, who is also an author of the paper. "Other methods mix causalities that should not be mixed, and sometimes they get confused. They get a false positive identifying a causality that doesn’t exist, for example."

 

In the paper, the team used SURD to study the creation of turbulence as air flows around a wall. In this case, air flows more slowly at lower altitudes, close to the wall, and more quickly at higher altitudes. Previously, some theories of what is happening in this scenario have suggested that the higher-altitude flow influences what is happening close to the wall and not the other way around. Other theories have suggested just the opposite—that the air flow near the wall affects what is happening at higher altitudes.

 

"We analyzed the two signals with SURD to understand in which way the interactions were happening," says Lozano-Durán. "As it turns out, causality comes from the velocity that is far away. In addition, there is some synergy where the signals interact to create another type of causality. This decomposition, or breaking into pieces of causality, is what is unique for our method."

 

More details about the research can be found in the paper, "Decomposing causality into its synergistic and redundant components" by Martínez-Sánchez, Gonzalo Arranz, and Lozano-Durán.

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The researchers used the SURD method to determine the causality of velocity motions in a turbulent boundary layer around a wall. This schematic shows the outer and inner-layer velocity motions and their interactions as well as two locations where velocity measurements were taken, one close to the wall and another higher up.

Caption

The data show the velocity signals captured at the two locations. SURD analyzed the data and broke up the causal contributions into redundant (dark grey), unique (pink), and synergistic (orange) causalities. This showed the researchers that at each location, it was the flow that was most distant that had the largest impact on the experienced flow, but there was also a synergistic causality that contributed.

3D rendering showing a Newton’s Cradle apparatus in action.

Credit

Please give attribution to 'ccPixs.com' (and point the link to www.ccPixs.com).

Journal

Nature Communications

DOI

10.1038/s41467-024-53373-4 

Subject of Research

Not applicable

Article Title

Decomposing causality into its synergistic, unique, and redundant components

Article Publication Date

1-Nov-2024

 

Policy realities on the ground: ForestPaths observes the effects of current legislation on Europe’s forests

To support its objective of advancing forest management practices in Europe with a focus on climate and biodiversity, the EU-funded ForestPaths project has published a new feature examining the current landscape of policies in the sector.

Pensoft Publishers

 

image: 

First page of ForestPaths Feature #3: How do current climate change and biodiversity policies affect European forests and wood use?

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Credit: Pensoft Publishers

At a time when both environmental challenges and the measures addressing them are growing in scope and complexity, navigating the landscape of existing legislative initiatives is of key significance. Researchers involved in the ForestPaths project have come forward with a policy brief on how forests and wood use in Europe are affected by policies targeting climate change and biodiversity. 

Co-funded under the European Commission’s Horizon Europe scheme, the project’s critical aim is to devise a balanced policy approach to forestry management that incorporates both climate mitigation and biodiversity. This effort is underpinned by a critical investigation into the governance of forest areas in Europe, which the aforementioned publication is an active contribution to. 

Authored by the Oeko-Institute, this brief, within the context of the data reviewed by the authors, presents a substantial number of the investigated policies, which relate to climate change mitigation and forests. While boasting fewer measures tied to them, climate adaptation and biodiversity are also supported by several official acts at the international, EU and state levels. Notably, most of these policies are expected to have a positive contribution across the board and were found to be mostly synergetic or neutral and not opposing any of the three aspects.

As a next step, 17 core green initiatives have been singled out for their particularly prominent relation to the conditions of forests across the continent. Each of them is assigned to one of four critical climate mitigation aims – protection, management, restoration and wood use. This classification ultimately showcases a great variety in contemporary forestry policy-making with a particular emphasis on conservation, active management, afforestation and wood processing activities.

The policy brief’s findings were presented in the third publication in the ForestPaths Features series, which aims to present the initiative’s research outputs in an accessible manner.

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ForestPaths (Co-designing Holistic Forest-based Policy Pathways for Climate Change Mitigation) receives funding from the European Union’s Horizon Europe Research and Innovation Programme (ID No 101056755), as well as from the United Kingdom Research and Innovation Council (UKRI). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.

 

Education, occupation, and wealth affect the risk of cognitive impairment

Peer-reviewed | Observational study | People

University College London

Socioeconomic factors such as education, occupation, and wealth influence the likelihood of developing cognitive impairment or dementia in later life and whether a person is likely to recover, finds a new study led by UCL researchers.

The research, published in Scientific Reports, followed 8,442 adults aged 50 and above in England over 10 years from 2008/09 to 2018/19, to examine how socioeconomic factors at the start of the study were associated with changes in cognitive status.

The researchers tracked how these people moved between various states: healthy, mild cognitive impairment, and dementia. They also considered the possibility of reversals, where individuals improved from a mild cognitive impairment state to a healthy one.

Information on socioeconomic factors was collected through a self-completion questionnaire.  Cognitive impairment was determined using a mix of sources, including participants’ reports of a doctor’s diagnosis, cognitive test results, and their own reports of symptoms and complaints, providing a full picture of each participant’s cognitive health. In addition to these aspects, the study also accounted for demographic factors, such as age, sex, and marital status.

By estimating the time spent in each cognitive state and the likelihood of transitions to neurocognitive disorders such as cognitive impairment and dementia, the researchers were able to gain a comprehensive understanding of how socioeconomic factors impact the progression of a person’s disorder, as well as the duration spent within each cognitive state over time.

The team found that people from more socioeconomically advantaged backgrounds – particularly those with post-secondary school education (such as university or college), managerial or professional-level occupations, and those in the wealthiest third of the population – were less likely to move from a healthy cognitive state to mild cognitive impairment, or from mild cognitive impairment to dementia compared to those with primary education (no higher than secondary school), working in manual or routine occupations, and in the most socioeconomically disadvantaged third of the population.  

For instance, having a post-secondary education level was linked with a 43% lower chance of moving from a healthy cognitive state to mild cognitive impairment.

Meanwhile, being in the wealthiest third of the population was linked with a 26% lower chance of shifting from mild cognitive impairment to dementia.

Notably, these advantaged individuals were also more likely to recover from mild cognitive impairment and return to a healthy cognitive state, with wealthy individuals being 56% more likely and those with post-secondary education level or working in manual occupation being 81% more likely to improve, compared to socioeconomically disadvantaged individuals.

Senior author Dr Dorina Cadar (UCL Department of Behavioural Science and Health and Brighton and Sussex Medical School) said: “Our study highlights the critical role of wealth, education, and occupation not only in reducing the risk of transitioning from mild cognitive impairment to dementia but also in increasing the likelihood of reversing cognitive impairment to a healthy cognitive state, which is promising.

“This potential recovery is critical for enhancing the quality of life in later years and reducing the long-term burden of cognitive impairment on healthcare systems, families, and society as a whole. 

“Our findings highlight the potential protective power of financial stability and access to resources in promoting brain health and cognitive resilience, underscoring the importance of social policies that support mental and cognitive wellbeing across all income levels.”

The study cannot provide exact explanations of why certain socioeconomic factors impact cognitive health. However, the authors believe there may be several reasons behind their findings.

Lead author and PhD candidate Aswathikutty Gireesh (UCL Epidemiology & Health Care) said: “It is possible that education and intellectually demanding jobs provide more mental stimulation and help to build a stronger brain reserve to help protect individuals against cognitive impairment and dementia.

“Additionally, people with higher education, more intellectually demanding jobs, and wealth have better access to healthcare and health-promoting resources such as a nutritious diet, exercise, and preventive care – all of which can support cognitive health. These resources could also offer opportunities to stabilise or improve cognitive function, especially when cognitive impairment is detected early.”

The researchers hope that their findings will open new avenues for further exploration into how socioeconomic factors, particularly wealth, might protect against early progression to cognitive impairment.

The study was funded by the National Institute on Aging, National Institute for Health and Care Research (NIHR), and UKRI (Economic and Social Research Council and the Biotechnology and Biological Sciences Research Council).

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Journal

Scientific Reports

Method of Research

Observational study

Subject of Research

People

Article Title

Socioeconomic inequalities linked to the transitioning to neurocognitive disorders and mortality

Article Publication Date

1-Nov-2024

 

China University of Geosciences (Beijing) unveils clues to an enigmatic geological process

Using advanced computational models, researchers explain how the North China Craton, an ancient and stable geological structure, is falling apart

Peer-Reviewed Publication

Cactus Communications

 

video: 

For years, scientists have been trying to understand the mechanisms behind the destruction of Earth’s cratons, but results have remained elusive. Now, in a Nature Geoscience study, researchers employed four-dimensional mantle-flow models to study the mechanisms behind the deformation of NCC. Findings provide new insights into the dynamic interplay of several geological processes behind this deformation.

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Credit: Professor Shaofeng Liu from China University of Geosciences (Beijing)

Cratons are fascinating yet enigmatic geological formations. Known to be relatively stable portions of the Earth’s continental crust, cratons have remained largely unchanged for billions of years. Although cratons have survived many geological events, some are undergoing decratonization—a process characterized by their deformation and eventual destruction. For example, the North China Craton (NCC), an ancient continental crust block, is known to have begun extensive decratonization during the Mesozoic era, largely due to tectonic and geochemical modifications and destabilization of its base (or ‘keel’). However, explaining the mechanisms behind these complex geological transformations has proven difficult with existing techniques and current understanding.

In a recent study published in volume 17 of Nature Geoscience on September 6, 2024, a research team led by Professor Shaofeng Liu from China University of Geosciences (Beijing) successfully addressed this knowledge gap. As explained in their paper, the researchers developed a computational model supported by extensive geological, geophysical, and empirical geochemical data that explains the puzzling deformation of the NCC. Moreover, for a better understanding of the mechanism, an animated video illustrating the model’s dynamics and implications is also available.

Specifically, the developed model focuses on the subduction of the Izanagi plate beneath the Eurasian plate, where the NCC is located, as the reason underlying the observed decratonization. The researchers compared several possible subducted plate geometries using earthquake seismicity and basin stratigraphy evidences to narrow down potential reconstructions. Finally, using their geodynamic mantle-flow model, they simulated the full extent of the subduction process and validated the predictions empirically.

Their analysis explains the decratonization of the NCC in three phases. First, the Izanagi plate underwent initial subduction and slid beneath the Eurasian plate. However, instead of progressing downward, the Izanagi plate flattened and started moving parallel to the Eurasian plate, in a process called flat-slab subduction. Fluids from the subducted plate altered the NCC’s keel above, initiating its destruction. Moreover, squeezing forces caused other deformations, such as thrusting, craton thickening, and surface uplift.

Interestingly, there was then a rollback process, as a result of which the subducted plate steepened again and progressed deeper below the Eurasian plate, reaching the upper-lower mantle interface and undergoing horizontal subduction into the mantle transition zone. This rollback caused extensional deformation, resulting in thinning of the lithosphere and the formation of rift basins with surface topographic lowing on the craton. Additionally, a large region of upper mantle material, known as a ‘large mantle wedge,’ developed between the advancing slab and the craton, leading to convection that can induce intense metasomatism and partial melting along with heating and erosion at the base of the sub-craton, as well as magmatism.

Speaking to us about these findings. Prof. Liu says, “We successfully developed a new mantle-flow model incorporating flat-slab and rollback subduction, which aligns with surface geological evolution and the present-day mantle slab structure.” Adding further, he says, “Interestingly, our validated model can effectively describe the space–time dynamics and topographic response of mantle slab subduction over time.”

Given that cratons contain mineral and rare-earth element deposits with immense value for technological applications, understanding the life cycle of cratons is important from both an academic and a practical standpoint. Building on these insights, further inquiries into the geological history of our planet will hopefully lead us to a deeper understanding of geological processes like decratonization, revealing paths towards a more sustainable future.

 

Subduction and Rollback Led to the Deformation of the North China Craton, Reveals Study (IMAGE)

Cactus Communications

The subfigures above depict how the four-dimensional model predicts the subduction of the Izanagi plate, which occurred from 163 to 90 million years ago. The unique features of this geological process ultimately destabilized the craton’s keel, caused geochemical alterations and mixing with mantle materials, and produced a sequence of compressive and extensional deformations that altered the landscape.

Credit

Professor Shaofeng Liu from China University of Geosciences (Beijing) Image Source Link: https://www.nature.com/articles/s41561-024-01513-2/figures/4

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Reference

DOI: https://doi.org/10.1038/s41561-024-01513-2

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Journal

Nature Geoscience

DOI

10.1038/s41561-024-01513-2 

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

Craton deformation from flat-slab subduction and rollback

 

Fueling greener aviation with hydrogen

American Chemical Society

Despite ongoing efforts to curb CO2 emissions with electric and hybrid vehicles, other forms of transportation remain significant contributors of greenhouse gases. To address this issue, old technologies are being revamped to make them greener, such as the reintroduction of sailing vessels in shipping and new uses for hydrogen in aviation. Now, researchers reporting in ACS Sustainable Chemistry & Engineering have used computer modeling to study the feasibility and challenges of hydrogen-powered aviation.

“While there is a long way to go for hydrogen aviation to be realized at scale, we hope that our analysis of both onboard system design and enabling infrastructure will be used to prioritize development efforts,” says Dharik Mallapragada, one of the study’s coauthors.

The aviation industry’s energy-related CO2 emissions have grown faster than those of rail, road and shipping in recent decades, according to the International Energy Agency. To reduce the potential climate impacts of this growth, scientists are improving aircraft design and operation, and developing low-emission fuels such as hydrogen, which is used for direct combustion or to power electric fuel cells. Hydrogen’s appeal as a fuel source is that its use produces no CO2 and provides more energy per pound than jet fuel. To understand the potential impact of switching from traditional jet fuel to hydrogen fuel in aviation, Anna Cybulsky, Mallapragada and colleagues modeled its use in the electrification of regional and short-range turboprop aircraft.

The researchers calculated that the extra bulk of a hydrogen fuel tank and fuel cells retrofitted to an existing plane would need to be offset by weight reductions elsewhere, such as reducing the aircraft’s payload (cargo or passengers). This could mean that more flights would be needed to deliver the same payload. The team’s model suggested, however, that improvements in fuel cell power and the fuel system’s gravimetric index (the weight of the fuel in relation to the weight of the full fuel tank) could eliminate the need to reduce payload, thus eliminating the environmental impact of additional flights. At the same time, they noted that shifting to hydrogen-powered flight may reduce the aviation industry’s CO2 emissions by up to 90%.

A bigger challenge than switching aviation fuel types may be providing the infrastructure needed to generate and distribute hydrogen in a low-carbon and cost-effective manner. One low-carbon production method uses natural gas reforming (extracting hydrogen from methane gas) coupled with carbon capture, but it requires access to CO2 infrastructure and sequestration sites. Another green option is electrolysis, which splits water into hydrogen and oxygen, and could be done by using electricity from a nuclear plant or renewable resources. But this would add substantial demand to electrical grids. Cybulsky and colleagues noted that because grid electricity prices can be highly variable across a region, it may be more cost-effective to transport hydrogen from a low-cost production facility to end-users.

For these reasons, the researchers suggest that the rollout of hydrogen-based aviation might start at locations that have favorable conditions for hydrogen production, such as Hamburg, Germany, or Barcelona, Spain. The infrastructure required to support hydrogen use in aviation would also benefit decarbonization efforts in other industries, including road transportation and shipping, by making hydrogen fuel more available.

The authors acknowledge funding from the Massachusetts Institute of Technology Energy Initiative Low-Carbon Energy Centers for Energy Storage and Future Energy Systems Center.

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The American Chemical Society (ACS) is a nonprofit organization chartered by the U.S. Congress. ACS’ mission is to advance the broader chemistry enterprise and its practitioners for the benefit of Earth and all its people. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, e-books and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio. 

Registered journalists can subscribe to the ACS journalist news portal on EurekAlert! to access embargoed and public science press releases. For media inquiries, contact newsroom@acs.org. 

Note: ACS does not conduct research but publishes and publicizes peer-reviewed scientific studies. 

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Journal

ACS Sustainable Chemistry & Engineering

DOI

10.1021/acssuschemeng.4c02868 

Article Title

“Challenges of Decarbonizing Aviation via Hydrogen Propulsion: Technology Performance Targets and Energy System Trade-Offs”

 

Warmer homes for healthier hearts and brains: insulated housing reduces cardiovascular risk in Japan

Scientists report that upgrading the insulation in new homes is a cost-effective method for reducing the risk of cardiovascular diseases

Institute of Science Tokyo

 

image: 

When purchasing new houses, upgrading insulation and living in a warm home is cost-effective.

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Credit: Science Tokyo

During winter, cold indoor temperatures can increase blood pressure, leading to hypertension and an increased risk of cardiovascular diseases (CVDs). While insulation and heating can keep homes warm, they may be expensive to install and maintain. However, these costs may be offset by health benefits, such as lower medical bills and longer, healthier lives from insulating homes. Identifying the most cost-effective insulation strategy can help keep homes warm, prevent diseases, and save lives.

In this vein, a research team led by Assistant Professor Wataru Umishio from Institute of Science Tokyo (Science Tokyo) investigated the cost-effectiveness of living in well-insulated warm homes to prevent CVDs by comparing houses in Japan with different levels of thermal insulation and indoor temperature. The multidisciplinary team included researchers from Science Tokyo, Institute for Built Environment and Carbon Neutral for SDGs, Jichi Medical University School of Medicine, University of Occupational and Environmental Health, Tokyo Dental College Ichikawa General Hospital, The University of Kitakyushu, and Nara Medical University School of Medicine. Their study was published in Volume 2, Issue 2 of BMJ Public Health on September 24, 2024.

Explaining the motivation behind their study, Umishio says, “In Japan, a rapidly aging country, over 90% of homes had indoor temperatures below 18°C, which is the level recommended by the World Health Organization. Since older adults are more vulnerable to low temperatures and at a higher risk for CVDs, there is an urgent need to address housing and health issues here.”

The researchers collected data from a nationwide health survey supported by the Ministry of Land, Infrastructure, Transport and Tourism of Japan and developed an economic model to explore the relationship between indoor temperature, blood pressure, and CVDs. Using this model, they created five home scenarios. The base scenario represented a typical home in Japan with lower insulation and an indoor temperature of 15°C. Two scenarios (1-1 and 1-2) focused on upgrading insulation when purchasing new homes at the age of 40 years, with insulation grades ranging from moderate (Grade 4, 18°C) to high (Grade 6, 21°C). The other two scenarios (2-1 and 2-2) examined retrofitting older homes with similar insulation improvements at the age of 60 years. They then ran simulations with 100,000 virtual husband-and-wife pairs to analyze lifetime costs for insulation, heating, and medical treatments for hypertension and CVDs, and measured healthy life expectancy using quality-adjusted life years (QALYs).

For the scenarios of upgrading insulation, lifetime costs increased by JPY 0.26 million for Scenario 1-1 and JPY 0.84 million for Scenario 1-2 compared with the base scenario. This means that 74.1% and 57.9% of the insulation costs were recovered, primarily due to reduced medical expenses for CVDs.

Moreover, healthy life expectancy for couples increased by 0.31 QALYs in Scenario 1-1 and 0.48 QALYs in Scenario 1-2. As a result, the incremental cost-effectiveness ratio (ICER) for both scenarios was lower than Japan’s threshold of JPY 5 million per QALY, indicating that the health benefits gained justify the upgrading costs.

On the other hand, retrofitting insulation may not be as favorable. The probabilistic sensitivity analyses showed that Scenario 2-2 was cost-effective only for those willing to pay JPY 6.5 million or more. However, this amount exceeds Japan’s ICER threshold, making it less reasonable. Therefore, more affordable options, such as partial insulation retrofitting, should be considered. In this evaluation, the researchers focused solely on hypertension and CVDs. However, well-insulated and warm houses are known to help prevent respiratory diseases and nocturia. By including other health conditions in the scope of evaluation, retrofitting insulation may become a more reasonable option.

Umishio emphasizes, “These findings will help policymakers make informed decisions about health and housing improvements, and encourage the use of well-insulated houses, contributing to the Sustainable Development Goals 3, 10, 11, and 13, which focus on health, reduced inequalities, sustainable cities, and climate action.”

To conclude, this study can pave the way for a future where healthy housing is prioritized to prevent winter-related health risks.

 

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Institute of Science Tokyo (Science Tokyo) will be established on October 1, 2024, following the merger between Tokyo Medical and Dental University (TMDU) and Tokyo Institute of Technology (Tokyo Tech), with the mission of “Advancing science and human wellbeing to create value for and with society.”

https://www.isct.ac.jp/en

 

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Journal

BMJ Public Health

DOI

10.1136/bmjph-2024-001143 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Effect of living in well-insulated warm houses on hypertension and cardiovascular diseases based on a nationwide epidemiological survey in Japan: a modelling and cost-effectiveness analysis