Nature Climate Change, Published online: 10 March 2025; doi:10.1038/s41558-025-02286-1

Groundwater recharge in a warming world

Nature Climate Change, Published online: 10 March 2025; doi:10.1038/s41558-025-02284-3

Southern African flux variability

Nature Climate Change, Published online: 10 March 2025; doi:10.1038/s41558-025-02287-0

Germination timing shifts communities

Nature Climate Change, Published online: 10 March 2025; doi:10.1038/s41558-025-02298-x

The IPCC is in its seventh assessment cycle, and international collaboration, which established this organization, is still needed to ensure successful deliverables.

Nature Climate Change, Published online: 10 March 2025; doi:10.1038/s41558-025-02280-7

Energy-economic models are increasingly being used to inform climate mitigation policies. This Comment describes three situations where models misinform policymakers and calls for more iterative, policy-orientated modelling exercises that maximize learning in the pursuit of long-term emissions reductions goals.

The Justice Department is demanding documents from nonprofits that were awarded the money through the Inflation Reduction Act.

Squid is a loyalty card platform in Ireland.

Blog moderation policy.

The EFF has created an open-source hardware tool to detect IMSI catchers: fake cell phone towers that are used for mass surveillance of an area.

It runs on a $20 mobile hotspot.

I’m old enough to remember when age verification bills were pitched as a way to ‘save the kids from porn’ and shield them from other vague dangers lurking in the digital world (like…“the transgender”). We have long cautioned about the dangers of these laws, and pointed out why they are likely to fail. While they may be well-intentioned, the growing proliferation of age verification schemes poses serious risks to all of our digital freedoms.

Fast forward a few years, and these laws have morphed into something else entirely—unfortunately, something we expected. What started as a misguided attempt to protect minors from "explicit" content online has spiraled into a tangled mess of privacy-invasive surveillance schemes affecting skincare products, dating apps, and even diet pills, threatening everyone’s right to privacy.

Age Verification Laws: A Backdoor to Surveillance

Age verification laws do far more than ‘protect children online’—they require the  creation of a system that collects vast amounts of personal information from everyone. Instead of making the internet safer for children, these laws force all users—regardless of age—to verify their identity just to access basic content or products. This isn't a mistake; it's a deliberate strategy. As one sponsor of age verification bills in Alabama admitted, "I knew the tough nut to crack that social media would be, so I said, ‘Take first one bite at it through pornography, and the next session, once that got passed, then go and work on the social media issue.’” In other words, they recognized that targeting porn would be an easier way to introduce these age verification systems, knowing it would be more emotionally charged and easier to pass. This is just the beginning of a broader surveillance system disguised as a safety measure.

This alarming trend is already clear, with the growing creep of age verification bills filed in the first month of the 2025-2026 state legislative session. Consider these three bills: 

1. Skincare: AB-728 in California
   Age verification just hit the skincare aisle! California’s AB-728 mandates age verification for anyone purchasing skin care products or cosmetics that contain certain chemicals like Vitamin A or alpha hydroxy acids. On the surface, this may seem harmless—who doesn't want to ensure that minors are safe from harmful chemicals? But the real issue lies in the invasive surveillance it mandates. A person simply trying to buy face cream could be forced to submit sensitive personal data through “an age verification system,” creating a system of constant tracking and data collection for a product that should be innocuous.
2. Dating Apps: A3323 in New York
   Match made in heaven? Not without your government-issued ID. New York’s A3323 bill mandates that online dating services verify users’ age, identity, and location before allowing access to their platforms. The bill's sweeping requirements introduce serious privacy concerns for all users. By forcing users to provide sensitive personal information—such as government-issued IDs and location data—the bill creates significant risks that this data could be misused, sold, or exposed through data breaches. 
3. Dieting products: SB 5622 in Washington State
   Shed your privacy before you shed those pounds! Washington State’s SB 5622 takes aim at diet pills and dietary supplements by restricting their sale to anyone under 18. While the bill’s intention is to protect young people from potentially harmful dieting products, it misses the mark by overlooking the massive privacy risks associated with the age verification process for everyone else. To enforce this restriction, the bill requires intrusive personal data collection for purchasing diet pills in person or online, opening the door for sensitive information to be exploited.

The Problem with Age Verification: No Solution Is Safe

Let’s be clear: no method of age verification is both privacy-protective and entirely accurate. The methods also don’t fall on a neat spectrum of “more safe” to “less safe.” Instead, every form of age verification is better described as “dangerous in one way” or “dangerous in a different way.” These systems are inherently flawed, and none come without trade-offs. Additionally, they continue to burden adults who just want to browse the internet or buy everyday items without being subjected to mass data collection.

For example, when an age verification system requires users to submit government-issued identification or a scan of their face, it collects a staggering amount of sensitive, often immutable, biometric or other personal data—jeopardizing internet users’ privacy and security. Systems that rely on credit card information, phone numbers, or other third-party material  similarly amass troves of personal data. This data is just as susceptible to being misused as any other data, creating vulnerabilities for identity theft and data breaches. These issues are not just theoretical: age verification companies can be—and already have been—hacked. These are real, ongoing concerns for anyone who values their privacy. 

We must push back against age verification bills that create surveillance systems and undermine our civil liberties, and we must be clear-eyed about the dangers posed by these expanding age verification laws. While the intent to protect children makes sense, the unintended consequence is a massive erosion of privacy, security, and free expression online for everyone. Rather than focusing on restrictive age verification systems, lawmakers should explore better, less invasive ways to protect everyone online—methods that don’t place the entire burden of risk on individuals or threaten their fundamental rights. 

EFF will continue to advocate for digital privacy, security, and free expression. We urge legislators to prioritize solutions that uphold these essential values, ensuring that the internet remains a space for learning, connecting, and creating—without the constant threat of surveillance or censorship. Whether you’re buying a face cream, swiping on a dating app, or browsing for a bottle of diet pills, age verification laws undermine that vision, and we must do better.

The Biden-era initiatives are meant to help major polluters like Indonesia increase renewable energy generation.

The agency is making the same move its director assailed before taking office, with more than 1,200 households losing eligibility for Federal Emergency Management Agency-paid hotel rooms.

A ruling Thursday that blocks a Trump administration funding freeze cites “acute harm” from the loss of Federal Emergency Management Agency payments.

The reluctance from key Democrats comes as state lawmakers seek to spend more on climate-related projects.

Florida Crystals faces allegations that it misled consumers by falsely advertising its products as friendly to the climate and environment.

The European Commission proposed changes last week that would rein in the scope of two ESG laws. But companies with business in the EU would still comply.

While consumers won’t have to pay the price directly, the costs will likely be passed on by fuel suppliers.

The facility is expected to come online in the second half of 2025 with the capability of removing 1,000 tons of carbon dioxide per year.

Climate initiatives are also dealing with the slashing of aid and development budgets by rich European nations that are now diverting money to defense.

Nature Climate Change, Published online: 07 March 2025; doi:10.1038/s41558-025-02276-3

Rooftop photovoltaic systems are often seen as a niche solution for mitigation but could offer large-scale opportunities. Using multi-source geospatial data and artificial intelligence techniques, the authors map their potential for reducing global temperatures and analyse regional differences.

Imagine that a robot is helping you clean the dishes. You ask it to grab a soapy bowl out of the sink, but its gripper slightly misses the mark.

Using a new framework developed by MIT and NVIDIA researchers, you could correct that robot’s behavior with simple interactions. The method would allow you to point to the bowl or trace a trajectory to it on a screen, or simply give the robot’s arm a nudge in the right direction.

Unlike other methods for correcting robot behavior, this technique does not require users to collect new data and retrain the machine-learning model that powers the robot’s brain. It enables a robot to use intuitive, real-time human feedback to choose a feasible action sequence that gets as close as possible to satisfying the user’s intent.

When the researchers tested their framework, its success rate was 21 percent higher than an alternative method that did not leverage human interventions.

In the long run, this framework could enable a user to more easily guide a factory-trained robot to perform a wide variety of household tasks even though the robot has never seen their home or the objects in it.

“We can’t expect laypeople to perform data collection and fine-tune a neural network model. The consumer will expect the robot to work right out of the box, and if it doesn’t, they would want an intuitive mechanism to customize it. That is the challenge we tackled in this work,” says Felix Yanwei Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this method.

His co-authors include Lirui Wang PhD ’24 and Yilun Du PhD ’24; senior author Julie Shah, an MIT professor of aeronautics and astronautics and the director of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL); as well as Balakumar Sundaralingam, Xuning Yang, Yu-Wei Chao, Claudia Perez-D’Arpino PhD ’19, and Dieter Fox of NVIDIA. The research will be presented at the International Conference on Robots and Automation.

Mitigating misalignment

Recently, researchers have begun using pre-trained generative AI models to learn a “policy,” or a set of rules, that a robot follows to complete an action. Generative models can solve multiple complex tasks.

During training, the model only sees feasible robot motions, so it learns to generate valid trajectories for the robot to follow.

While these trajectories are valid, that doesn’t mean they always align with a user’s intent in the real world. The robot might have been trained to grab boxes off a shelf without knocking them over, but it could fail to reach the box on top of someone’s bookshelf if the shelf is oriented differently than those it saw in training.

To overcome these failures, engineers typically collect data demonstrating the new task and re-train the generative model, a costly and time-consuming process that requires machine-learning expertise.

Instead, the MIT researchers wanted to allow users to steer the robot’s behavior during deployment when it makes a mistake.

But if a human interacts with the robot to correct its behavior, that could inadvertently cause the generative model to choose an invalid action. It might reach the box the user wants, but knock books off the shelf in the process.

“We want to allow the user to interact with the robot without introducing those kinds of mistakes, so we get a behavior that is much more aligned with user intent during deployment, but that is also valid and feasible,” Wang says.

Their framework accomplishes this by providing the user with three intuitive ways to correct the robot’s behavior, each of which offers certain advantages.

First, the user can point to the object they want the robot to manipulate in an interface that shows its camera view. Second, they can trace a trajectory in that interface, allowing them to specify how they want the robot to reach the object. Third, they can physically move the robot’s arm in the direction they want it to follow.

“When you are mapping a 2D image of the environment to actions in a 3D space, some information is lost. Physically nudging the robot is the most direct way to specifying user intent without losing any of the information,” says Wang.

Sampling for success

To ensure these interactions don’t cause the robot to choose an invalid action, such as colliding with other objects, the researchers use a specific sampling procedure. This technique lets the model choose an action from the set of valid actions that most closely aligns with the user’s goal.

“Rather than just imposing the user’s will, we give the robot an idea of what the user intends but let the sampling procedure oscillate around its own set of learned behaviors,” Wang explains.

This sampling method enabled the researchers’ framework to outperform the other methods they compared it to during simulations and experiments with a real robot arm in a toy kitchen.

While their method might not always complete the task right away, it offers users the advantage of being able to immediately correct the robot if they see it doing something wrong, rather than waiting for it to finish and then giving it new instructions.

Moreover, after a user nudges the robot a few times until it picks up the correct bowl, it could log that corrective action and incorporate it into its behavior through future training. Then, the next day, the robot could pick up the correct bowl without needing a nudge.

“But the key to that continuous improvement is having a way for the user to interact with the robot, which is what we have shown here,” Wang says.

In the future, the researchers want to boost the speed of the sampling procedure while maintaining or improving its performance. They also want to experiment with robot policy generation in novel environments.