Ming Xie, University of Maryland, Baltimore County

Imagine a world in which a hurricane devastates the Gulf Coast, and the U.S. has no federal agency prepared to quickly send supplies, financial aid and temporary housing assistance.

Could the states manage this catastrophic event on their own?

Normally, the Federal Emergency Management Agency, known as FEMA, is prepared to marshal supplies within hours of a disaster and begin distributing financial aid to residents who need help.

However, with President Donald Trump questioning FEMA’s future and suggesting states take over recovery instead, and climate change causing more frequent and severe disasters, it’s worth asking how prepared states are to face these growing challenges without help.

What FEMA does

FEMA was created in 1979 with the job of coordinating national responses to disasters, but the federal government has played important roles in disaster relief since the 1800s.

During a disaster, FEMA’s assistance can begin only after a state requests an emergency declaration and the U.S. president approves it. The request has to show that the disaster is so severe that the state can’t handle the response on its own.

FEMA’s role is to support state and local governments by coordinating federal agencies and providing financial aid and recovery assistance that states would otherwise struggle to supply on their own. FEMA doesn’t “take over,” as a misinformation campaign launched during Hurricane Helene claimed. Instead, it pools federal resources to allow states to recover faster from expensive disasters.

During a disaster, FEMA:

• Coordinates federal resources. For example, during Hurricane Ian in 2022, FEMA coordinated with the U.S. Coast Guard, the Department of Defense and search-and-rescue teams to conduct rescue operations, organized utility crews to begin restoring power and also delivered water and millions of meals.

• Provides financial assistance. FEMA distributes billions of dollars in disaster relief funds to help individuals, businesses and local governments recover. As of Feb. 3, 2025, FEMA aid from 2024 storms included US$1.04 billion related to Hurricane Milton, $416.1 million for Hurricane Helene and $112.6 million for Hurricane Debby.

• Provides logistical support. FEMA coordinates with state and local governments, nonprofits such as the American Red Cross and federal agencies to supply cots, blankets and hygiene supplies for emergency shelters. It also works with state and local partners to distribute critical supplies such as food, water and medical aid.

The agency also manages the National Flood Insurance Program, offers disaster preparedness training and helps states develop response plans to improve their overall responses systems.

What FEMA aid looks like in a disaster

When wildfires swept through Maui, Hawaii, in August 2023, FEMA provided emergency grants to cover immediate needs such as food, clothing and essential supplies for survivors.

The agency arranged hotel rooms, rental assistance and financial aid for residents who lost homes or belongings. Its Direct Housing Program has spent $295 million to lease homes for more than 1,200 households. This comprehensive support helped thousands of people begin rebuilding their lives after losing almost everything.

FEMA also helped fund construction of a temporary school to ensure that students whose schools burned could continue their classes. Hawaii, with its relatively small population and limited emergency funds, would have struggled to mount a comparable response on its own.

Larger states often need help, too. When a 2021 winter storm overwhelmed Texas’ power grid and water infrastructure, FEMA coordinated the delivery of essential supplies, including water, fuel, generators and blankets, following the disaster declaration on Feb. 19, 2021. Within days, it awarded more than $2.8 million in grants to help people with temporary housing and home repairs.

Which states would suffer most without FEMA?

Without FEMA or other federal support, states would have to manage the disaster response and recovery on their own.

States prone to frequent disasters, such as Louisiana and Florida, would face expensive recurring challenges that would likely exacerbate recovery delays and reduce their overall resilience.

Smaller, more rural and less wealthy states that lack the financial resources and logistical capabilities to respond effectively would be disproportionately affected.

“States don’t have that capability built to handle a disaster every single year,” Lynn Budd, director of the Wyoming Office of Homeland Security, told Stateline in an interview. Access to FEMA avoids the need for expensive disaster response infrastructure in each state.

States might be able to arrange regional cooperation. But state-led responses and regional models have limitations. The National Guard could assist with supply distribution, but it isn’t designed to provide fast financial aid, housing or long-term recovery options, and the supplies and the recovery effort still come at a cost.

Wealthier states might be better equipped to manage on their own, but poorer states would likely struggle. States with less funding and infrastructure would be left relying on nonprofits and community-based efforts. But these organizations are not capable of providing the scope of services FEMA can.

Any federal funding would also be slow if Congress had to approve aid after each disaster, rather than having FEMA already prepared to respond. States would be at the mercy of congressional infighting.

In the absence of a federal response and coordinating role, recovery would be uneven, with wealthier areas recovering faster and poorer areas likely seeing more prolonged hardships.

What does this mean?

Coordinating disaster response is complex, the paperwork for federal assistance can be frustrating, and the agency does draw criticism. However, it also fills an important role.

As the frequency of natural disasters continues to rise due to climate change, ask yourself: How prepared is your state for a disaster, and could it get by without federal aid?

Ming Xie, Assistant Professor of Emergency Management and Public Health, University of Maryland, Baltimore County

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Lu Wang, University of Michigan

AI has made it easier than ever to find information: Ask ChatGPT almost anything, and the system swiftly delivers an answer. But the large language models that power popular tools like OpenAI’s ChatGPT or Anthropic’s Claude were not designed to be accurate or factual. They regularly “hallucinate” and offer up falsehoods as if they were hard facts.

Yet people are relying more and more on AI to answer their questions. Half of all people in the U.S. between the ages of 14 and 22 now use AI to get information, according to a 2024 Harvard study. An analysis by The Washington Post found that more than 17% of prompts on ChatGPT are requests for information.

One way researchers are attempting to improve the information AI systems give is to have the systems indicate how confident they are in the accuracy of their answers. I’m a computer scientist who studies natural language processing and machine learning. My lab at the University of Michigan has developed a new way of deriving confidence scores that improves the accuracy of AI chatbot answers. But confidence scores can only do so much.

Popular and problematic

Leading technology companies are increasingly integrating AI into search engines. Google now offers AI Overviews that appear as text summaries above the usual list of links in any search result. Other upstart search engines, such as Perplexity, are challenging traditional search engines with their own AI-generated summaries.

The convenience of these summaries has made these tools very popular. Why scour the contents of multiple websites when AI can provide the most pertinent information in a few seconds?

AI tools seem to offer a smoother, more expedient avenue to getting information. But they can also lead people astray or even expose them to harmful falsehoods. My lab has found that even the most accurate AI models hallucinate in 25% of claims. This hallucination rate is concerning because other research suggests AI can influence what people think.

Language models hallucinate because they learn and operate on statistical patterns drawn from a massive amount of text data, much of which comes from the internet. This means that they are not necessarily grounded in real-world facts. They also lack other human competencies, like common sense and the ability to distinguish between serious expressions and sarcastic ones.

All this was on display last spring, when a user asked Google’s AI Overviews tool to suggest a way to keep cheese from sliding off a pizza. The tool promptly recommended mixing the cheese with glue. It then came to light that someone had once posted this obviously tongue-in-cheek recommendation on Reddit. Like most large language models, Google’s model had likely been trained with information scraped from myriad internet sources, including Reddit. It then mistakenly interpreted this user’s joke as a genuine suggestion.

While most users wouldn’t take the glue recommendation seriously, some hallucinated information can cause real harm. AI search engines and chatbots have repeatedly been caught citing debunked, racist pseudoscience as fact. Last year, Perplexity AI stated that a police officer in California was guilty of a crime that he did not commit.

Showing confidence

Building AI systems that prioritize veracity is challenging, but not impossible. One way AI developers are approaching this problem is to design models that communicate their confidence in their answers. This typically comes in the form of a confidence score – a number indicating how likely it is that a model is providing accurate information. But estimating a model’s confidence in the content it provides is also a complicated task.

One common approach to making this estimate involves asking the model to repeatedly respond to a given query. If the model is reliable, it should generate similar answers to the same query. If it can’t answer consistently, the AI is likely lacking the information it needs to answer accurately. Over time, the results of these tests become the AI’s confidence scores for specific subject areas.

Other approaches evaluate AI accuracy by directly prompting and training models to state how confident they are in their answers. But this offers no real accountability. Allowing an AI to evaluate its own confidence leaves room for the system to give itself a passing grade and continue to offer false or harmful information.

My lab has designed algorithms that assign confidence scores by breaking down a large language model’s responses into individual claims that can be automatically cross-referenced with Wikipedia. We assess the semantic equivalence between the AI model’s output and the referenced Wikipedia entries for the assertions. Our approach allows the AI to quickly evaluate the accuracy of all its statements. Of course, relying on Wikipedia articles, which are usually but not always accurate, also has its limitations.

Publishing confidence scores along with a model’s answers could help people to think more critically about the veracity of information that these tools provide. A language model can also be trained to withhold information if it earns a confidence score that falls below a set threshold. My lab has also shown that confidence scores can be used to help AI models generate more accurate answers.

Limits of confidence

There’s still a long way to go to ensure truly accurate AI. Most of these approaches assume that the information needed to correctly evaluate an AI’s accuracy can be found on Wikipedia and other online databases.

But when accurate information is just not that easy to come by, confidence estimates can be misleading. To account for cases like these, Google has developed special mechanisms for evaluating AI-generated statements. My lab has similarly compiled a benchmarking dataset of prompts that commonly cause hallucinations.

But all these approaches verify basic facts – there are no automated methods for evaluating other facets of long-form content, such as cause-and-effect relationships or an AI’s ability to reason over text consisting of more than one sentence.

Developing tools that improve these elements of AI are key steps toward making the technology a source of trustworthy information – and avoid the harms that misinformation can cause.

Lu Wang, Associate Professor of Computer Science and Engineering, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Narcisa Pricope, Mississippi State University

Valentine’s Day often conjures images of chocolates and romance. But the crop behind this indulgence faces an existential threat.

Regions like northeastern Brazil, one of the world’s notable cocoa-producing areas, are grappling with increasing aridity – a slow, yet unrelenting drying of the land. Cocoa is made from the beans of the cacao tree, which thrives in humid climates. The crop is struggling in these drying regions, and so are the farmers who grow it.

This is not just Brazil’s story. Across West Africa, where 70% of the world’s cacao is grown, and in the Americas and Southeast Asia, shifting moisture levels threaten the delicate balance required for production. These regions, home to vibrant ecosystems and global breadbaskets that feed the world, are on the frontlines of aridity’s slow but relentless advance.

Over the past 30 years, more than three-quarters of the Earth’s landmass has become drier. A recent report I helped coordinate for the United Nations Convention to Combat Desertification found that drylands now cover 41% of global land, an area that expanded by nearly 1.7 million square miles (4.3 million square kilometers) over those three decades — about half the size of Australia.

This creeping dryness is not just a climate phenomenon. It’s a long-term transformation that may be irreversible and that carries devastating consequences for ecosystems, agriculture and livelihoods worldwide.

What causes aridity?

Aridity, while often thought of as purely a climate phenomenon, is the result of a complex interplay among human-driven factors. These include greenhouse gas emissions, land use practices and the degradation of critical natural resources, such as soil and biodiversity.

These interconnected forces have been accelerating the transformation of once-productive landscapes into increasingly arid regions, with consequences that ripple across ecosystems and economies.

Greenhouse gas emissions: A global catalyst

Human-induced climate change is the primary driver of rising aridity.

Greenhouse gas emissions, particularly from fossil fuel combustion and deforestation, increase global temperatures. Rising temperatures, in turn, cause moisture to evaporate at a faster rate. This heightened evaporation reduces soil and plant moisture, exacerbating water scarcity – even in regions with moderate rainfall.

Aridity began accelerating globally in the 1950s, and the world has seen a pronounced shift over the past three decades.

This process is particularly stark in regions already prone to dryness, such as Africa’s Sahel region and the Mediterranean. In these areas, reduced precipitation – combined with increased evaporation – creates a feedback loop: Drier soils absorb less heat, leaving the atmosphere warmer and intensifying arid conditions.

Unsustainable land use practices: A hidden accelerator

Aridity is also affected by how people use and manage land.

Unsustainable agricultural practices, overgrazing and deforestation strip soils of their protective vegetation cover, leaving them vulnerable to erosion. Industrial farming techniques often prioritize short-term yields over long-term sustainability, depleting nutrients and organic matter essential for healthy soils.

For example, in cocoa-producing regions like northeastern Brazil, deforestation to make room for agriculture disrupts local water cycles and exposes soils to degradation. Without vegetation to anchor it, topsoil – critical for plant growth – washes away during rainfall or is blown away by winds, taking with it vital nutrients.

These changes create a vicious cycle: Degraded soils also hold less water and lead to more runoff, reducing the land’s ability to recover.

The soil-biodiversity connection

Soil, often overlooked in discussions of climate resilience, plays a critical role in mitigating aridity.

Healthy soils act as reservoirs, storing water and nutrients that plants depend on. They also support biodiversity below and above ground. A single teaspoon of soil contains billions of microorganisms that help cycle nutrients and maintain ecological balance.

However, as soils degrade under aridity and mismanagement, this biodiversity diminishes. Microbial communities, essential for nutrient cycling and plant health, decline. When soils become compacted and lose organic matter, the land’s ability to retain water diminishes, making it even more susceptible to drying out.

In short, the loss of soil health creates cascading effects that undermine ecosystems, agricultural productivity and food security.

Global hot spots: Looming food security crises

Cocoa is just one crop affected by the encroachment of rising aridity.

Other key agricultural zones, including the breadbaskets of the world, are also at risk. In the Mediterranean, Africa’s Sahel and parts of the U.S. West, aridity already undermines farming and biodiversity.

By 2100, up to 5 billion people could live in drylands – nearly double the current population in these areas, due to both population growth and expansion of drylands as the planet warms. This puts immense pressure on food systems. It can also accelerate migration as declining agricultural productivity, water scarcity and worsening living conditions force rural populations to move in search of opportunities.

Aridity’s ripple effects also extend far beyond agriculture. Ecosystems, already strained by deforestation and pollution, are stressed as water resources dwindle. Wildlife migrates or dies, and plant species adapted to moister conditions can’t survive. The Sahel’s delicate grasslands, for instance, are rapidly giving way to desert shrubs.

On a global scale, economic losses linked to aridification are staggering. In Africa, rising aridity contributed to a 12% drop in gross domestic product from 1990 to 2015. Sandstorms and dust storms, wildfires and water scarcity further burden governments, exacerbating poverty and health crises in the most affected regions.

The path forward

Aridity is not inevitable, nor are its effects completely irreversible. But coordinated global efforts are essential to curb its progression.

Countries can work together to restore degraded lands by protecting and restoring ecosystems, improving soil health and encouraging sustainable farming methods.

Communities can manage water more efficiently through rainwater harvesting and advanced irrigation systems that optimize water use. Governments can reduce the drivers of climate change by investing in renewable energy.

Continued international collaboration, including working with businesses, can help share technologies to make these actions more effective and available worldwide.

So, as you savor chocolate this Valentine’s Day, remember the fragile ecosystems behind it. The price of cocoa in early 2025 was near its all-time high, due in part to dry conditions in Africa. Without urgent action to address aridity, this scenario may become more common, and cocoa – and the sweet concoctions derived from it – may well become a rare luxury.

Collective action against aridity isn’t just about saving chocolate – it’s about preserving the planet’s capacity to sustain life.

Narcisa Pricope, Professor of Geography and Land Systems Science and Associate Vice President for Research, Mississippi State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.