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California's electric grid, with its massive solar production and booming battery installations, is already on the cutting edge of the US' energy transition. And it's likely to stay there, as the state will require that all passenger vehicles be electric by 2035. Obviously, that will require a grid that's able to send a lot more electrons down its wiring and a likely shift in the time of day that demand peaks.

Is the grid ready? And if not, how much will it cost to get it there? Two researchers at the University of California, Davis—Yanning Li and Alan Jenn—have determined that nearly two-thirds of its feeder lines don't have the capacity that will likely be needed for car charging. Updating to handle the rising demand might set its utilities back as much as 40 percent of the existing grid's capital cost.

The lithium state

Li and Jenn aren't the first to look at how well existing grids can handle growing electric vehicle sales; other research has found various ways that different grids fall short. But they have access to uniquely detailed data relevant to California's ability to distribute electricity (they do not concern themselves with generation). They have information on every substation, feeder line, and transformer that delivers electrons to customers of the state's three largest utilities, which collectively cover nearly 90 percent of the state's population. In total, they know the capacity that can be delivered through over 1,600 substations and 5,000 feeders.

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Almost from the start, arguments about mitigating climate change have included an element of cost-benefit analysis: Would it cost more to move the world off fossil fuels than it would to simply try to adapt to a changing world? A strong consensus has built that the answer to the question is a clear no, capped off by a Nobel in Economics given to one of the people whose work was key to building that consensus.

While most academics may have considered the argument put to rest, it has enjoyed an extended life in the political sphere. Large unknowns remain about both the costs and benefits, which depend in part on the remaining uncertainties in climate science and in part on the assumptions baked into economic models.

In Wednesday's edition of Nature, a small team of researchers analyzed how local economies have responded to the last 40 years of warming and projected those effects forward to 2050. They find that we're already committed to warming that will see the growth of the global economy undercut by 20 percent. That places the cost of even a limited period of climate change at roughly six times the estimated price of putting the world on a path to limit the warming to 2° C.

Enlarge / NREL has taken some of the hassle out of getting permits for projects like these. (credit: owngarden )

Can government agencies develop software to help cut bureaucratic red tape through automation? The answer is “yes,” according to the promising results achieved by the National Renewable Energy Laboratory (NREL), which has saved thousands of hours of labor for local governments by creating a tool called SolarAPP+ (Solar Automated Permit Processing Plus) for residential solar permits.

“We estimate that automatic SolarAPP+ permitting saved around 9,900 hours of… staff time in 2022,” NREL staff wrote in the report, “ SolarAPP+ Performance Review (2022 Data) . “Based on median timelines, a typical SolarAPP+ project is permitted and inspected 13 business days sooner than traditional projects… SolarAPP+ has eliminated over 134,000 days in permitting-related delays.”

SolarAPP+ automates over 100 compliance checks in the permitting process that are usually the responsibility of city, county, or town employees, according to Jeff Cook, SolarAPP+ program lead at NREL and first author of the report. It can be more accurate, thorough, and efficient than a time-pressured local government employee would be.

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Humanity is on the cusp of radical changes in how we produce and consume energy, according to a new evaluation by the International Energy Agency. And that leaves us in a place where small changes can produce huge differences in the energy economy by the end of the decade—even a slight drop in China's economic growth, for example, could cut coal use by an amount similar to what Europe currently consumes.

Amidst the flux, governments are struggling to set policies that either meet our needs or reflect the changing reality. By 2030, the IEA expects that we'll have the capacity to manufacture more than double the solar panels needed to meet current policy goals. And those goals will leave us falling well short of keeping warming below 2° C.

In flux

The IEA's analysis focuses on two different scenarios. One of them, which it terms STEPS, limits the analysis to the policies that governments have already committed to. Those are sufficient to have energy-driven emissions peak in the middle of this decade—meaning within the next few years. But they stay above net zero for long enough to commit us to 2.4° C warming, a level that climate scientists indicate will lead to severe consequences.

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As the utility of AI systems has grown dramatically, so has their energy demand. Training new systems is extremely energy intensive, as it generally requires massive data sets and lots of processor time. Executing a trained system tends to be much less involved—smartphones can easily manage it in some cases. But, because you execute them so many times, that energy use also tends to add up.

Fortunately, there are lots of ideas on how to bring the latter energy use back down. IBM and Intel have experimented with processors designed to mimic the behavior of actual neurons. IBM has also tested executing neural network calculations in phase change memory to avoid making repeated trips to RAM.

Now, IBM is back with yet another approach, one that's a bit of "none of the above." The company's new NorthPole processor has taken some of the ideas behind all of these approaches and merged them with a very stripped-down approach to running calculations to create a highly power-efficient chip that can efficiently execute inference-based neural networks. For things like image classification or audio transcription, the chip can be up to 35 times more efficient than relying on a GPU.

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On Tuesday, the US National Academies of Science released a report entitled "Accelerating Decarbonization in the United States." The report follows up on a 2021 analysis entitled, "Accelerating Decarbonization in the US Energy System." When the earlier report was prepared, the US didn't have a decarbonization policy, although the growth of natural gas and renewables was dropping the emissions involved in producing electricity. Within the following year, the US passed an infrastructure law, the CHIPS and Science Act, and the Inflation Reduction Act (IRA), all of which contained provisions intended to help cut the US's emissions in half by 2030. The Environmental Protection Agency has also formulated policies that should radically reduce the emissions of generating electricity.

In other words, shortly after the report's release, the US formulated a plan to accelerate decarbonization and a target of a 50 percent emissions reduction by 2030.

Rather than pat themselves on the back, however, the experts who prepared the original report recognized that the US's climate goals require it to reach net-zero emissions by mid-century, and that will require lots of policy changes beyond the ones already in place. The new report is largely a call for people to start thinking of what we need to implement to ensure emissions keep dropping after 2030.

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A decade ago, our present renewable energy situation was unimaginable. Most projections had wind and solar as niche players on the electric grid due to their relatively high cost. In the US, the reality is anything but. Combined, wind and solar have now passed coal; throw in hydro, and they've passed nuclear, too. In most areas of the country, they're now far and away the cheapest means to generate electricity; the same holds true for most locations around the world.

Despite the changed economics, most countries have fallen behind on their climate pledges, and fossil fuels aren't being pushed off the grid fast enough to get us back on track. While the entire globe is suffering the consequences of climate change, the factors that are keeping renewables from reaching their full potential vary from country to country. What should we be doing to get past these roadblocks?

Today, I'll be at the United Nations with the chance to get some answers to that question. The UN, as part of its General Assembly meeting, is hosting a series of events called Climate Week, which includes a Sustainable Development Goals Summit. Associated with that will be a series of talks and panels on relevant topics. I'll be hosting one called "Clean Energy Trends to Power the World" that will happen at 2:25 pm Eastern Time.

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Our atmosphere holds six times more water than you’ll find in all the rivers on Earth. The dew drops you see on grass and water droplets on a cold juice bottle are evidence of this natural reservoir of water. Despite its ubiquity, 2 billion people on Earth still don’t have access to clean drinking water .

A technique called atmospheric water harvesting (AWH) can allow us to extract some of this freshwater out of the air. But there are various challenges that have prevented us from implementing AWH on a large scale. In order to create an effective and continuous AWH system, scientists need to ensure two things. The first is that the water absorption from the air is fully reversible so that the water can be retrieved for use.

The second is efficient waste heat management . When an AWH system captures water from the air, the condensation of water releases heat. If this excess heat is not processed carefully, it can interfere with the entire process. However, it seems that we are now closer to a solution. Inspired by the structure of plant leaves, a team of researchers in China has created a core-shell structural cellulose nanofiber-based aerogel (called Core-Shell@CNF for short) that promises to overcome these challenges.

Enlarge / RoboMapper in action. (credit: Aram Amassian)

Earlier this year, two-layer solar cells broke records with 33 percent efficiency. The cells are made of a combination of silicon and a material called a perovskite. However, these tandem solar cells are still far from the theoretical limit of around 45 percent efficiency, and they degrade quickly under sun exposure, making their usefulness limited.

The process of improving tandem solar cells involves the search for the perfect materials to layer on top of each other, with each capturing some of the sunlight the other is missing. One potential material for this is perovskites, which are defined by their peculiar rhombus-in-a-cube crystal structure. This structure can be adopted by many chemicals in a variety of proportions. To make a good candidate for tandem solar cells, the combination of chemicals needs to have the right bandgap—the property responsible for absorbing the right part of the sun’s spectrum—be stable at normal temperatures, and, most challengingly, not degrade under illumination.

The number of possible perovskite materials is vast, and predicting the properties that a given chemical composition will have is very difficult. Trying all the possibilities out in the lab is prohibitively costly and time-consuming. To accelerate the search for the ideal perovskite, researchers at North Carolina State University decided to enlist the help of robots.