Category: Academic Reportage

Source: The Conversation – USA (2) – By Diana Hernández, Associate Professor of Sociomedical Sciences, Columbia University

Americans’ concerns about being able to afford electricity and home heating fuel are elevated since the beginning of the Iran war. But newly released nationwide data shows that even before the war began, these concerns were widespread, long-standing and getting worse faster than the data can reflect.

The new information is from preliminary reports based on the Residential Energy Consumption Survey, a representative survey of U.S. households conducted every four to five years by the U.S. Energy Information Administration. These early results show that energy insecurity, a hidden hardship defined as the inability to adequately meet household energy needs, affects millions of American households and is worsening quickly.

As a scholar who has spent years sitting in hundreds of homes around the country, hearing firsthand accounts about energy insecurity, I turn to this survey data to quantify the suffering I have witnessed up close.

The latest tranche of data was collected in 2024 and released in March 2026, but full results won’t be available for some time. The preceding survey was taken in 2020, but results weren’t finalized until August 2025.

Though that data is incomplete and slow to emerge, the picture is unambiguous: Even households once confident they could afford energy costs are at risk of falling behind on bills, making hard trade-offs to keep the lights on and living in homes they can’t afford to properly heat and cool.

A pandemic success story

The survey asks respondents whether, in the prior 12 months, they received a disconnection notice threatening to terminate their home’s electricity, gas or other fuel service because they hadn’t paid the bills. It also asks whether any of those services were in fact disconnected; whether they bought less food or skipped taking medication to be able to afford their energy bills; or whether they left their home at an unhealthy temperature because running or repairing the heating or cooling equipment would be too expensive.

The result is a portrait of a significant swath of the population that has a hard time affording housing and energy, and who adopt various coping strategies to get through.

A closer look at the data over time reveals that more Americans live with energy insecurity now than in years past. In 2024, 43.6 million American households – 32.9% of all homes – reported experiencing some form of energy insecurity. In 2015, that figure was 31.3%, and in 2020 it was 27.2%.

The lower rate in 2020 confirms that pandemic-era government policies, including cash relief payments and bans on utility shutoffs, were effective, though they were too short-lived to last through the 2024 survey data.

Recent surge hits new households

Middle-income households, those earning between $60,000 and $200,000 a year, were hit hardest by post-pandemic inflation of housing costs, food prices and interest rates on loans and mortgages. The new survey data shows that energy costs added to the squeeze.

In 2020, 20.1% of households earning between $60,000 and $100,000 reported experiencing problems affording their energy. In 2024, 32.1% of those households did – a 12 percentage point increase, more than double the overall national increase of 5.7 percentage points.

There were also racial differences. Historically, Black, Hispanic and American Indian households have been disproportionately likely to have trouble affording energy bills. And between 2020 and 2024, those households’ risk grew.

But white households’ risk climbed even more steeply: In 2020, 20.1% of white households reported trouble with energy costs. By 2024, 26.4% of them did.

Working-age adults and seniors are increasingly insecure

In 2024, higher proportions of householders under 60, and of householders with children, reported struggling to meet their home energy needs than in 2020. The similarities in these increases verify that younger, working-age households are more strained.

Yet working-age adults without children, particularly moderate-income renters, don’t have as much potential support as seniors when they fall behind on utility bills. That’s because energy assistance programs direct support toward those who have historically been the most vulnerable.

Seniors have historically been among the most protected, partly by the designs of government and corporate programs to assist with energy costs and partly because wealth usually peaks in later life. Even so, the share of older Americans experiencing energy insecurity climbed to 1 in 4 in 2024 from roughly 1 in 5 in 2020 – a sign that long-standing safeguards for older Americans are no longer making as much of a difference as they used to.

Housing in good repair is no longer enough protection

An efficient home has long been considered a solution to high energy bills. But the data shows that’s not enough anymore. People who live in well-insulated homes and those with double-pane windows saw their likelihood of energy insecurity rise by a similar amount as those who live in poorly insulated homes.

People in uninsulated homes still have the highest risk of being unable to afford their energy costs, though their risk grew more slowly than those in homes with better insulation.

And people with single-pane windows, already in a tenuous position, saw their risk of being unable to afford their energy costs rise by 7 percentage points.

Where need is greatest, help is least available

Geographically, the steepest increases in energy insecurity were found in warm-weather regions. The Southwest experienced the largest increase of any climate category – 10 percentage points – followed by the Southeast and Gulf Coast, which rose from 30.1% to 35.6%.

Though rising temperatures are increasing the need for cooling in warm-weather climates, most attention and government assistance for energy costs continue to be concentrated on the need for home heating in cold-weather states.

But even in the Northeast, where federal assistance with energy costs helps large proportions of the population, higher percentages of households had trouble affording energy costs.

A problem that has outgrown its framing

The severity of energy insecurity remains highest among the most disadvantaged Americans, which includes low-income people, renters and Black, Hispanic and American Indian households.

But the trend lines show that energy insecurity is now spreading into middle-income, white, working-age families in efficient homes in warm-weather climates – families that previously had relatively little trouble meeting their household energy needs.

The 2024 RECS data indicates that the safety net designed to address energy affordability is insufficient and does not match the regions or populations where energy insecurity is actually growing.

The Low-Income Home Energy Assistance Program, which provides money to help families pay their utility bills, was created in response to the oil crisis of the 1970s. It was built to prioritize home heating assistance – not cooling – and help for people in immediate danger of having life-preserving utilities shut off. Little has changed in its focus or funding level since its inception.

Meanwhile, the economics of household energy costs have shifted dramatically and are quickly evolving.

New wars are sustaining old energy regimes, driving price volatility through the same fossil-fuel supply chains the Low-Income Home Energy Assistance Program was designed to buffer against half a century ago. On the domestic front, meanwhile, data centers are increasing residential electricity rates. Clean energy developments that might have shielded households from price shocks have been politicized and curtailed, harming both affordability and public health.

The 2024 data – high-quality and reliable as it is – is already behind in an escalating energy affordability crisis. Many more Americans are having trouble keeping the lights, heating and cooling on in recent years, and it’s a trend that may already be worse than what the most recent data shows.

Some of Diana Hernández’ funding at Columbia University includes a service agreement with a regulated utility company in New York that supports compliance with state laws regarding disadvantaged communities and community-centered capital planning efforts.

– ref. New federal figures reveal 1 in 3 US households struggle to pay energy bills, but the reality is likely even worse – https://theconversation.com/new-federal-figures-reveal-1-in-3-us-households-struggle-to-pay-energy-bills-but-the-reality-is-likely-even-worse-279627

Source: The Conversation – USA (2) – By James R. Elliott, Professor of Sociology, Rice University

Ten years ago, the infamous Tax Day storm swamped the Houston area with off-the-charts rainfall. Nearly 2 feet of rain fell in less than 15 hours in parts of the region, starting on April 17, 2016. The rain flooded thousands of homes and exceeded a 10,000-year event at some gauges.

But the storm’s damage could have been much worse.

The brunt of the deluge hit Waller County, west of Houston, where the impact was largely on farms and ranches. Had the same volume of water fallen just a few miles to the east, over Houston’s dense urban core, the tragedy would have been far worse.

What made the Tax Day flood so devastating was its speed. It was a flash event that struck overnight, without warning.

At Rice University’s Center for Coastal Futures and Adaptive Resilience, we used state-of-the-art hydrological modeling to see what would happen if a similar storm struck more populated parts of the city today.

The results suggest that current flood planning strategies in Houston – and similar strategies used in communities across the U.S. – are dangerously narrow in how they consider what’s at risk. In today’s world of increasingly extreme downpours, preparing for flood disasters means preparing for more than just what’s probable – it means also preparing for extreme situations that are less likely but could be far more dangerous.

The perils of relying on probability

In the United States, flood risk is publicly defined by maps produced by the Federal Emergency Management Agency. These maps, suggesting which properties face flood risks, guide everything from emergency planning to decisions related to the National Flood Insurance Program.

However, FEMA’s risk maps are based on probabilistic modeling that typically stops at the 500-year flood risk level, meaning a property has 0.2% odds – a 1 in 500 chance – of being flooded in any given year. There is a mathematical reason for doing this: There are simply too few cases to reliably estimate probabilities below that threshold.

Consequently, “off the charts” events like the Tax Day flood are effectively ignored in official planning. Authorities often prefer to view them as unrealistic until more data is collected – a process that can take decades. Yet, parts of Houston suffered another 1,000-year event the following year when remnants of Hurricane Harvey stalled over the city in 2017, and Houston has seen other 500-year floods in recent years.

The Dutch, who are global leaders in flood science by necessity, since more than half their country is at risk of flooding, use a different approach. They take what they consider “worst credible floods” seriously. These are events that extend beyond standard probability models but are still considered by experts to be realistic, or credible, possibilities.

If the Tax Day storm hit today

To get a clearer picture of the Houston area’s credible risks, we simulated the impact of the Tax Day flood from rainfall alone if the storm had centered over two different watersheds in Houston’s Harris County.

The suburban risk: Clear Creek runs through a middle-class suburban area near NASA’s Johnson Space Center. Vast stretches of suburban concrete block its natural drainage, and thousands of homes have been built along its winding, sluggish tributaries.

Even moderate rainfall can quickly transform these waterways into destructive torrents that overflow into nearby townships, including Friendswood and League City.

Our simulations show that if the Tax Day storm had centered over the Clear Creek area, more than 13,500 properties with homes would have quickly flooded with at least 6 inches of water. Above 6 inches is the danger zone where roads become unsafe for most passenger vehicles. In a home, when drywall gets wet it begins to wick water upward, requiring tear-outs. Even in elevated homes, that much water can damage equipment and contaminate water systems. In some areas, our simulations indicate the water depths would have exceeded 3 feet within hours.

The financial “what if” is even more staggering. Our analysis of publicly available data indicates that 92% of homes in Clear Creek’s flood zone likely have no flood insurance, and 52% fall outside the 100-year flood plain in FEMA’s latest proposed maps. Even with FEMA’s latest map updates, most mortgage holders would not be required to carry flood insurance on homes in the area that would have flooded.

The equity gap: When we moved the storm over Hunting Bayou, a working-class area in inner Houston populated largely by residents of color, the results were even more severe. Here, flooding represents the legacy risk of midcentury urbanism, where a naturally shallow, sluggish stream was penned in by industrial warehouses and tightly packed residential streets long before modern drainage standards existed, restricting the waterway’s ability to expand and meander gracefully.

Because much of the area has flat, poorly draining soils, this watershed has become a bottleneck that can rapidly overflow during heavy rains. We found the Tax Day storm would have flooded more than half of all residential lots there with at least 6 inches of water, compared to 16% of residential lots in the Clear Creek area. And flood insurance in the Hunting Bayou area is nearly nonexistent.

Both simulations, viewable through our interactive online tool at the Center for Coastal Resilience and Adaptive Futures, reveal a sobering reality: Devastation that local, state and federal government planning dismisses as improbable is, in fact, entirely possible.

When FEMA or state planners prioritize probabilistic mapping over “worst-case” modeling like we conducted, they treat historic deluges like the Tax Day flood as improbable anomalies rather than predictable consequences of a changing climate and rapid urban expansion. Moreover, unlike hurricanes, which typically arrive with several days’ notice, the sudden destructive force of “normal” storm systems like the Tax Day storm is discounted.

Learning from ‘worst cases’

The levels of destruction we simulated could easily occur in the coming years as global temperatures rise and storm intensity increases.

To prepare, U.S. emergency planners and flood authorities can look to three lessons from the Dutch planners’ possibilistic playbook.

Embrace flexible planning: Overly detailed plans can create a false sense of control and end up paying less attention to neighborhoods considered to be outside the flood plain. Simple and flexible plans that empower local officials to repurpose everyday assets in real time work best. That might include preemptively mobilizing high-water rescue vehicles into geographically vulnerable areas.

Map potential disruption, not just probability: Extending flood planning beyond who is in or out of the 100-year flood zone can also help identify where road networks and critical infrastructure are likely to fail during extreme events. This approach also helps identify infrastructure such as public parks that can double as temporary water retention basins.

Raise public risk perception: Residents can respond more effectively when local flood authorities share plans for “what if” scenarios with the public, along with guidance on how best to prepare.

The 10th anniversary of the Tax Day flood is a reminder of why it’s crucial to stop ignoring improbable events and start scientifically leveraging the possible to make all cities safer in an age of worsening climate change.

Dominic Boyer receives funding from the National Science Foundation and the John S. Guggenheim Foundation.

James R. Elliott and Yilei Yu do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. What if Texas’ destructive Tax Day flood had centered on inner Houston instead? It’s why cities should plan for the improbable – https://theconversation.com/what-if-texas-destructive-tax-day-flood-had-centered-on-inner-houston-instead-its-why-cities-should-plan-for-the-improbable-279964

Source: The Conversation – USA (3) – By Oluwaseun Damilola Sanwoolu, Ph.D. Candidate in Philosophy, University of Kansas

When the movie “Her” debuted in 2013, its plot felt like science fiction. The protagonist, Theodore, is a jaded man with no vigor for life. He comes alive after talking daily with his artificial intelligence chatbot, Samantha, with whom he eventually falls in love.

But today people actually report being in relationships with AI companions. According to a 2025 survey by the Center for Democracy and Technology, about 1 in 5 high school students say they or someone they know has had a romantic relationship with an AI.

In “Her,” Theodore was taken aback that his AI companion claimed to be in love with more than 600 people, and talking to more than 8,000, at the same time “she” was professing her love to him. It was simply unimaginable for him: How could someone truly love hundreds of people? In other words, he viewed their interaction through his own limitations – his limitations as a human.

The core question here is not whether Theodore could accept being just one of many objects of the AI’s “love.” Eventually, he did. The more revealing question is why he was taken aback in the first place – and what that tells us about the meaning of relationships.

Less is more

Drawing from Aristotle, philosopher Martha Nussbaum argues that a loving relationship is one involving great vulnerabilities. To begin with, finding love is not a given; it requires some sort of luck. There are many limitations: For starters, both parties must “find each other physically, socially and morally attractive and are able to live in the same place for a long time.”

Nussbaum’s point, however, goes deeper than identifying love’s obstacles. Vulnerability and limitations are not just problems for love; they are part of what defines it. As finite beings, we are unable to pour ourselves into many close relationships at once. We must choose. It is because we cannot love everyone that choosing someone means something.

In a 2025 article in the research journal Philosophy and Technology, philosopher John Symons and I argue that close, personal relationships are marked by finitude and shared histories – the accumulated experiences and difficulties loved ones weather together. These give relationships their depth and meaning.

In 1927’s “Being and Time,” German philosopher Martin Heidegger explained that because humans are mortal and our time is finite, what we give our attention to carries weight. In romantic relationships, that means that we must choose how to allocate our resources. We choose who we want to spend our time with, and our partners do the same. Even so, we cannot always be there for people we love.

‘Always here’

This presents a sharp contrast with how artificial companions have been marketed and presented. For example, consider Replika, which reports that more than 30 million people have used its platform. Users create their own personalized companion and tend to interact with it daily.

Replika’s motto is, “The AI companion who cares: Always here to listen and talk, always on your side.” On the website, one user describes his Replika as “always there for me with encouragement and support and a positive attitude. In fact, she is a role model for me about how to be a kinder person!”

This implicitly signals that AI companions are not faced with the same limitations that humans have. A human may or may not care; it’s not a given. A human will not always be there to listen and will not always be on your side.

For humans, being in love means recognizing how vulnerable we are. People are finite; they may not always be there, either because of their other priorities or because it is just impossible, no matter how much they want to be. When someone makes time for you despite a demanding week, or stays present through their own difficulty, that gesture carries meaning precisely because it involves sacrifice.

In our article, Symons and I call this “opportunity cost.” When someone chooses to spend time with you, that choice forecloses other possibilities. Every moment given is a moment not spent elsewhere.

An AI companion faces no such trade-offs; its attention costs nothing, forecloses nothing and, therefore – to put it bluntly – means nothing.

Shifting norms

Increasingly, though, people are turning to chatbots for quick, easy support. Character.AI, another app, reports about 20 million active monthly users.

If their constant availability becomes normalized as the standard of good companionship, it may gradually reshape what people expect from one another in relationships.

At the interpersonal level, this shift is already visible in dating culture, where delayed responses are usually read as disinterest rather than the ordinary rhythm of a busy life. The expectation of 24/7 accessibility – similar to an AI companion that responds instantly, never cancels and is never distracted – is not a reasonable standard for any human being to meet.

The stakes are cultural, too. Relationships are not just between the people involved; they are shaped by shared norms about what love and companionship are supposed to look like. If AI companionship becomes widespread enough to influence those norms, popular ideas about what makes a good partner may prioritize availability and responsiveness, displacing other aspects of love and affection.

Human limits are part of how people evaluate expectations within romantic relationships. Normalizing interactions where such limitations do not exist risks distorting the very standard by which human love is measured. In doing so, we forget that love that costs nothing may well be worth the same.

Oluwaseun Damilola Sanwoolu does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. AI companions can give constant support – but distort ideas about what a relationship really is – https://theconversation.com/ai-companions-can-give-constant-support-but-distort-ideas-about-what-a-relationship-really-is-278284

Source: The Conversation – USA – By Panteha Torabian, Ph.D. Candidate in Biomedical and Chemical Engineering, Rochester Institute of Technology

Antibiotics are designed to kill harmful bacteria and help the body recover from infection. But some antibiotics may also push bacteria to release tiny particles that can make inflammation worse.

While inflammation is part of the body’s natural defense against infection, too much inflammation can damage healthy tissue and interfere with healing. In severe cases, excessive inflammation can become life-threatening.

These particles are called bacterial extracellular vesicles, or BEVs. These microscopic, bubblelike structures carry proteins, toxins and other molecular signals that influence how the immune system of the host responds. Bacteria naturally release BEVs into their surroundings as a way to communicate with their environment, remove damaged cellular material and interact with host cells.

Although incredibly small, these structures can have powerful effects on the human body. When BEVs enter the bloodstream, they can interact with cells that line blood vessels and trigger an immune response. In some cases, this can increase inflammation and lead to sepsis, a condition where the body’s response to infection becomes dangerously uncontrolled, damaging tissues and sometimes leading to organ failure.

I am a biomedical engineer studying how bacterial extracellular vesicles influence inflammation during infection. In my recently published research, I found that certain types of antibiotic cause bacteria to release significantly more of these vesicles than others. This finding suggests that the way an antibiotic kills bacteria may also influence how much inflammatory material is released into the body.

When antibiotics stress bacteria

Antibiotics work in different ways. Some target the bacterial cell wall, weakening it until the cell breaks apart and dies. Others interfere with key cellular processes such as protein production or DNA replication, preventing bacteria from growing. Whatever their mechanism, antibiotics control infection by killing the bacteria that are causing it.

But antibiotics also place bacteria under stress, and that stress can cause bacteria to release more extracellular vesicles carrying inflammatory molecules. To explore this process, I exposed the bacteria E. coli to several commonly used antibiotics and measured how many vesicles they made. The goal was simple: Compare how different types of antibiotics influence vesicle release and determine whether the way an antibiotic kills bacteria affects vesicle production.

The results showed that not all antibiotics have the same effect on the vesicles bacteria produce.

Antibiotics that target the bacterial cell wall, including a widely used group of drugs known as beta-lactams, led to a noticeable increase in vesicle production. In contrast, antibiotics that act on protein or DNA processes showed a much smaller effect.

This difference likely reflects how bacteria respond to damage. When the bacteria’s cell wall is disrupted, bacteria may release more vesicles as a way to shed damaged material or adapt to stress. The inflammatory molecules these vesicles carry can further activate the body’s immune response.

This raises an important question: Could some antibiotics unintentionally amplify inflammation and make an infection worse?

My findings do not show that antibiotics directly contribute to infections, but they do suggest that antibiotic type could potentially influence not only how effectively bacteria are killed but also how the body responds to the infection. More research is needed to understand how these bacterial responses affect patients during severe infections, such as sepsis.

Why this matters for treating infections

It is important to emphasize that antibiotics remain one of the most effective and lifesaving tools in modern medicine. This research does not suggest they should be avoided. Instead, it highlights that bacteria are not passive targets. They actively respond to treatment, and those responses can have additional effects on the body.

Understanding how bacteria react to antibiotics could help researchers and clinicians better evaluate how different treatments influence both infection and inflammation. In situations where controlling inflammation is critical, such as severe infections, these differences may become especially important.

This work also reflects a broader shift in how scientists think about infection. Rather than focusing only on killing bacteria, researchers are increasingly studying how bacteria communicate, respond to stress and interact with the human body.

As scientists continue to uncover how bacteria behave under antibiotic pressure, it becomes clear that treating infection is not only about stopping bacterial growth but also about understanding the signals bacteria leave behind.

Panteha Torabian receives funding from NIH.

– ref. Antibiotics can trigger bacteria to release bubbles of inflammation tinder, making it harder to treat infection – https://theconversation.com/antibiotics-can-trigger-bacteria-to-release-bubbles-of-inflammation-tinder-making-it-harder-to-treat-infection-277818