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If Europe fails to invest 302 billion euros in climate-relevant infrastructure over the next two years, it will not reach its target of net-zero greenhouse gas emissions by 2050, researchers report.

Both the European Union and Switzerland have set themselves the goal of becoming climate neutral by 2050 and of reducing their greenhouse gas emissions to net zero.

To achieve this target, major investments in power generation from renewable energies, electricity grids, storage capacities, and other climate-relevant infrastructure are required.

However, it has been unclear until now just how much investment will be necessary over the next 15 years and which areas are the most important.

A new meta-study published in Nature Climate Change fills this gap in knowledge.

“Investment in green infrastructure needs to urgently increase by 87 billion euros per year compared with the levels of recent years. This is an increase of more than one third,” says first author Lena Klaaßen, a doctoral student at ETH Zurich’s Climate Finance and Policy Group.

Investments in solar and wind

Although the money is readily available—given the size of the European equity and bond markets—the main challenge is to put the necessary political policies in place quickly enough to ensure that capital flows into the right projects.

The researchers built on 56 relevant technology and investment studies from academia, industry, and the public sector. They focused on the countries of the EU, but also took into account data on the UK, Norway, and Switzerland. The overall European trends are therefore also relevant for Switzerland.

The most dramatic increase in the need for investment is in power generation from renewable energies.

“In order to drive forward the decarbonization of all areas of life, around 75 billion euros needs to be invested in solar and wind power plants in the coming years. This is 24 billion euros more per year than in the recent past,” says professor Bjarne Steffen.

The situation is similar in the expansion of distribution networks and the railway. In these areas, too, 40 to 60% additional financial flows are required compared to the 2016–2020 period in order to expand electrification and to shift

The war in Ukraine is reinforcing these trends further, the researchers say.

“In order to import as little gas as possible from Russia, Europe would have to invest around 10 billion euros more per year in solar energy and wind power. In comparison, significantly less investment—around 1.5 billion euros per year—is needed in additional natural gas infrastructure such as LNG terminals,” Steffen says.

Carbon storage investments

According to the study, fossil fuels such as coal, oil, and gas are likely to tie up less capital in future in Europe. The investment required in conventional power plants in particular is set to fall by 70% within the space of a few years.

What can policymakers do to expedite the increase in capital made available for the expansion of green infrastructure?

“Political measures should be tailored to funding in those sectors where there is the greatest need for investment,” explains Klaaßen.

This is not a matter of course today, she says. For example, existing regulations in the EU focus on identifying sustainable securities, despite the fact that important climate-relevant infrastructure is not at all financed via the equity markets.

The expansion of renewable energies, in contrast, is often made possible by private investors such as pension funds and banks. The public sector should minimize their risk through revenue guarantees and by making approval procedures as quick and predictable as possible. Public investment in new technologies such as CO2 storage can also help to encourage private investors to venture into these areas.

Source: Christoph Elhardt for ETH Zurich

More than 90% of vehicle-owning households in the United States would see a reduction in the percentage of income they spend on transportation energy if they switched to electric vehicles, research finds.

And more than 90% of households that replace gas-powered vehicles with EVs would also reduce the amount of climate-warming greenhouse gases they generate, the findings show.

However, more than half of the lowest-income US households (an estimated 8.3 million households) would continue to experience high transportation energy burdens, defined in this study as spending more than 4% of household income on filling the tank or charging up.

“Our results confirm the potential for widespread benefits from EV adoption,” says corresponding author of the study Joshua Newell, an urban geographer at the University of Michigan Center for Sustainable Systems, part of the School for Environment and Sustainability.

“However, EV ownership in the US has thus far been dominated by households with higher incomes and education levels, leaving the most vulnerable populations behind. Policy interventions are needed to increase EV accessibility so that all Americans can benefit from the EV transition.”

The new study appears in the journal Environmental Research Letters. It is the first study to consider the spatial variation of both EV energy costs and greenhouse gas emissions across the country.

It’s also the first study to examine EV energy costs through the lens of distributive justice by calculating the EV energy burden (percentage of income spent on EV charging) for the entire United States. Distributive justice concerns the fair distribution of benefits and burdens.

EVs currently account for about 1% of the cars, SUVs, and pickups on American roads. If all those vehicles were replaced with new EVs, the transportation energy burdens and associated greenhouse gas emissions would vary widely from place to place, according to the new study.

Reductions in both transportation energy burden and GHG emissions would be especially pronounced on the West Coast and in parts of the Northeast, due largely to cleaner energy grids and lower electricity prices.

Households in some locations could reduce their annual transportation-energy costs by $600 or more, and cut their annual carbon footprint by more than 4.1 metric tons of carbon dioxide equivalents, by buying a new EV.

But lower-income households in other parts of the country wouldn’t fare as well, Newell says.

Very high EV transportation energy burdens, ranging from 10% to 64%, would persist for the lowest-income households and would be concentrated in the Midwest and in the two states with the highest electricity prices: Hawaii and Alaska.

Of US households, 8% (an estimated 9.6 million) would see low savings in both transportation energy burden and greenhouse gas emissions by choosing an EV. “Both low” households are scattered across the country, with about half of them in Midwest states.

Factors that contribute to those low EV savings include cold winter temperatures that affect battery performance, electrical grids that rely largely on fossil fuels, or electricity prices that are higher relative to gasoline prices.

According to the study, the lowest-income households would continue to experience the highest transportation energy burdens. Essentially all households with incomes of less than 30% of the local median would experience moderate or high EV energy burdens.

“We identified disparities that will require targeted policies to promote energy justice in lower-income communities—including the subsidizing of charging infrastructure—as well as strategies to reduce electricity costs and increase the availability of low-carbon transportation modes such as public transit, bicycling, and car sharing,” says lead author Jesse Vega-Perkins, who did the work for a master’s thesis at the University of Michigan School for Environment and Sustainability.

“Our analysis indicates that future grid decarbonization, current and future fuel prices, and charging accessibility will impact the extent to which EV benefits will be realized, including lowering transportation energy burdens for low-income households,” says senior author Greg Keoleian, director of the university’s Center for Sustainable Systems.

The study used a geospatial model to evaluate three factors associated with the EV transition: transportation energy burden, fuel costs (meaning the cost of gasoline or the electricity needed to charge an EV), and greenhouse gas emissions.

The analysis does not include vehicle purchase cost. Total cost of ownership of EVs is the focus of a current study by the Center for Sustainable Systems.

The researchers calculated transportation energy burdens and lifetime greenhouse gas emissions of new battery-electric and internal-combustion vehicles at the census tract level. Then they compared the energy burdens of the new vehicles to the energy burdens of the current on-road vehicle stock. Finally, they compared the spatial variation and extent of energy burdens and greenhouse gas emissions for EVs and internal-combustion vehicles across the US.

Transportation accounts for the largest portion of the greenhouse gases emitted in the United States, with direct emissions from passenger vehicles and light-duty trucks comprising roughly 16% of US emissions. Electrification is seen as the primary pathway to reducing those emissions.

The study had support from the university’s School for Environment and Sustainability.

Source: University of Michigan

Researchers are using machine learning to map where sharks face the most risk from longline fishing.

The ocean can be a dangerous place, even for a shark. Despite sitting at the top of the food chain, these predators are now reeling from destructive human activities like overfishing, pollution, and climate change.

The new research used data from regional fisheries management organizations and machine learning algorithms. The findings in Frontiers in Marine Science highlight key regions where sharks can be protected with minimal impact on tuna fisheries.

Sharks take the bait

Offshore longline fisheries take an especially heavy toll on ocean life. The non-selective technique has the highest rate of shark bycatch.

“Longline fishing gear is exactly what it sounds like: a long line with lots of hooks attached to it that are baited. And they can be left in the water, waiting for fish to bite, for a very long time,” explains co-lead author Darcy Bradley, who heads the Ocean & Fisheries Program at the Environmental Markets Lab (emLab) at the University of California, Santa Barbara.

These baited hooks catch predators like tuna, but many nearby sharks will also converge on the bait.

Rather than simply report how many sharks were caught and where, the authors aimed to assess the relative risk sharks faced across different areas of the ocean.

“One of the main questions was ‘Where is the risk for catching sharks the highest, and does that overlap with fishing effort?’” says co-lead author Echelle Burns, a project scientist at emLab.

To answer this, Burns, Bradley, and coauthor Lennon Thomas (also at emLab) went hunting for data on longline fisheries. They sourced publicly available data from tuna regional fisheries management organizations. These intergovernmental institutions manage, collect data, and perform scientific assessments for tuna and tuna-like species.

The authors compiled data on shark catch from industrial longline fishing across all the world’s tuna fisheries into one comprehensive resource. This was quite a task. Each fisheries management organization operates differently, meaning their data isn’t always in the same format.

“Now anyone who’s interested in shark catches and other happenings in these global longline fisheries has access to that information,” Bradley says.

Even top predators face risks

The authors paired spatial shark catch data with environmental data like sea-surface temperature and factors correlated with food abundance. They also included economic data like ex vessel price—the price that fishers receive directly for their catch—for different shark species each year.

“Because you can’t catch a shark where it doesn’t live,” Bradley adds, “we used species distribution models to delineate where different sharks actually live in the ocean to inform our risk assessment.”

Still, there were a lot of unknowns. Not every fishing vessel has an impartial observer recording catch for the fisheries management organizations. And not every report is completely accurate.

So Burns, Bradley, and Thomas used a model to fill in the gaps, recognize trends and draw conclusions from this incomplete data. “The whole reason to use a model is because we have imperfect data,” Bradley says. “If we knew everything we wouldn’t need a model.”

This was a new approach to estimating the interactions between fisheries and marine species. Using machine learning enabled the team to extrapolate trends from their messy datasets. First, the model assessed whether a shark species was present in an area, and if so, how likely it was to be caught there. Then it looked at how many sharks of each species were caught in an area.

The authors prioritized predictive power in this study. “Our goal was to identify where sharks are at the highest risk of being caught by tuna longline fisheries,” Bradley explains. “For this study, we were not trying to understand the extent to which various factors influence this risk.”

The authors used a random forest model, which combines the outputs of many decision trees. Each decision tree considers a different variable, and its outcome is a vote for the final prediction.

“The basic idea is that a bunch of poor decision-makers, the trees, can share information to ultimately make a better decision: the forest,” Bradley says. While this method doesn’t provide a clear picture of how each factor influences the system, it is very good at making sense of messy and incomplete datasets. What emerged was a map of catch risk for shark populations across the globe.

Good for sharks, good for fishers

Tunas and sharks are both predators and target similar prey, so they’re often found together. But while they may share some traits, sharks and tunas are fundamentally different types of animals. Tunas grow quickly and produce many offspring, while sharks mature relatively late and reproduce slowly.

As a result, tuna can withstand much higher fishing pressures than sharks, and even a small impact on shark numbers can affect the population of a threatened species.

Fortunately, the scientists found that hotspots for longline-shark interactions didn’t correspond with preferred fishing grounds. “This suggests that we can design management strategies that can protect vulnerable and threatened shark species without having to close the most productive tuna fishing grounds,” Bradley says. The team found this particularly heartening, since it could encourage actions that help sharks while appealing to fishers as well.

Shark catch hotspots

The difference between hotspots and good fishing grounds could stem from those differences between tuna and sharks.

“For example, we noticed that some of the shark catch hotspots overlap with areas that play a critical role in a shark’s life cycle,” Thomas says. Take the ocean off the coast of Namibia, a well-known nursery habitat and juvenile feeding ground for blue sharks.

In fact, blue sharks dominated interactions with longline fishing fleets. This common and widespread species contributed over 78% of the total shark catch reported by tuna regional fisheries management organizations between 2012 and 2020.

As a result, the majority of the paper’s findings for sharks as a whole are driven by blue shark catch in particular. This was one reason the authors investigated hotspots for 12 species individually in their supplementary materials.

The team is working on a follow-up study estimating global shark mortality due to fishing as a whole—not just longline. They also plan to assess whether regulations have helped prevent shark catch. What’s more, this paper’s random forest model can provide insights on other species threatened by overfishing.

Better data will allow the team to improve their model, but it’s already providing useful lessons. For instance, we can design management strategies to protect vulnerable and threatened shark species without disrupting prime tuna fishing grounds.

“Making small adjustments to tuna fishing regulations to avoid shark catch hotspots could make a huge difference for shark populations in the future,” Burns says, “while also ensuring that the tuna fisheries remain successful.”

Source: UC Santa Barbara