Long-abolished discriminatory lending practices in the US are still having an impact on the inequality of climate risks facing urban populations today, according to a new study.
The research, published in Nature Cities, looks at historically “redlined” neighbourhoods – those deemed highly risky for lenders, broadly due to the race and economic profile of those in the area – and compares them to neighbourhoods that were seen as less risky.
The scientists find that, across more than 200 US cities, once-redlined neighbourhoods are at higher risk of heat exposure and flooding.
Even homes just tens of metres apart have different climate risks, they find, with those located on the redlined side of a boundary at higher risk than those living on the other side of the boundary.
The lead author of the study tells Carbon Brief that the work underscores the historical legacy of planning decisions made in the last century, adding that she hopes that current policymakers can better consider the “impact of different planning policies and the unintended consequences”.
One researcher who was not involved in the study tells Carbon Brief that the work makes several significant contributions, but cautions that the authors were “pretty bold” in some of their conclusions.
‘Risky’ investments
“Redlining” refers to a discriminatory historical practice in the US, whereby neighbourhoods were graded as too “risky” for investment based on race, income levels and housing quality. These grades were used as justification for the denial of long-term mortgages and exacerbated existing racial segregation.
One of the most recognisable remnants of redlining is the set of maps produced by the Home Owners’ Loan Corporation (HOLC), established in 1933 as part of US president Franklin D Roosevelt’s “New Deal”. The HOLC refinanced foreclosed mortgages at lower interest rates with the intention of preserving and expanding homeownership.
The HOLC created maps of “riskiness” of investment in an attempt to guarantee that the loans would be paid back and that the burden on the taxpayer would be minimal.
The maps created by the HOLC classified neighbourhoods based on a four-point risk scale, with A-grades – the “best”, or least-risky, investments – outlined in green and D-grades – the most risky, termed “hazardous” – outlined in red, giving rise to the term.
B-graded neighbourhoods, outlined in blue, were termed “still desirable”, while C-graded ones, in yellow, were “declining”.
The HOLC created maps for more than 200 cities across at least 40 states. Other federal agencies and private companies later made their own “risk-assessment” maps, further cementing the practice into policy.
Although redlining was formally outlawed in 1968 by the US Fair Housing Act, the inequalities created and exacerbated by the practice persist in many places to this day, says Dr Arianna Salazar-Miranda, an urban planner and data scientist at Yale University.
Salazar-Miranda, who is the lead author of the new study, tells Carbon Brief:
“There are many social and economic dimensions for which we should be worried about this long-standing legacy of redlining.”
For example, previous research has shown that redlined neighbourhoods have lower rates of homeownership, lower credit scores and lower home values. There are also associations between historically redlined neighbourhoods and prevalence of cancer and asthma, air pollution and proximity to hazardous waste, among other dimensions of health inequality and environmental racism.
Prof Shannon Van Zandt, an urban planner at Texas A&M University, who was a reviewer of the new paper, but not involved in the study itself, tells Carbon Brief:
“Segregation is still so relevant in the experiences of families of colour and, in particular, Black or African American households, because of the very indelible lines that we literally drew [on the map].”
Climate risk
Using maps from 202 cities across the US, Salazar-Miranda and her colleagues examine the risk of heat extremes and flooding for homes in differently graded neighbourhoods. These factors, each graded on a 1-10 scale from least to most hazardous, were developed by the climate research and technology firm First Street.
The heat risk factor combines temperature and humidity to determine a “feels-like” temperature, averaged across the month of July for each location.
The flood risk factor uses flooding factors, such as rainfall and high tide levels, as well as variables that affect water runoff, including elevation and ground permeability. It also incorporates existing community flood defences. The risk is defined by both depth and likelihood of flooding.
Both the heat and flood risk scores also factor in projections of future climate change, including higher temperatures and sea level rise.
The researchers focused specifically on homes within 100 metres of a boundary between two different grades. Salazar-Miranda tells Carbon Brief:
“We’re trying to narrow down on a subset of properties that are very comparable, where they have the same underlying conditions and the only thing that changed is whether they’re on one side of the border or the other.”
The maps below show the digitised redlining map of Baltimore (left), with the colours indicating the different grades and the bold lines depicting boundaries between different grades.
On the right, a zoomed-in portion of the map shows the 100-metre buffer zones drawn around each boundary. Locations of houses are coloured according to which side of the border they fall on – grey for the lower-graded side and black for the higher-graded side.
Geographical and climatic features, such as elevation and amount of rainfall, did not vary significantly across the boundaries because the researchers were only looking at homes close to a grade boundary.
They find that, aggregated across all cities, D-graded neighbourhoods have a flood risk factor that is 0.245 points higher than A-graded ones – more than three times higher than the additional risk of a C-graded neighbourhood.
The heat risk effect is smaller, but still significant, with D-graded neighbourhoods scoring 0.033 points higher than A-graded neighbourhoods.
The chart below shows the flood and heat exposure risks for neighbourhoods graded B, C and D, relative to the average risk for A-graded neighbourhoods. While both risk factors increase as the grade decreases, the effect is much more pronounced for flood risk.
They also find that flood risk factor increases by 0.1 points, or about 5.5% on average, for homes that are on the lower-graded side of a border, as compared to homes on the higher-graded sides. For the heat risk factor, this figure is 0.011 points.
Although the absolute change in the heat risk factor is relatively small, Salazar-Miranda tells Carbon Brief that these “very small changes…can really harm your health”. She adds:
“It really depends on your pre-determinants of health – how healthy you are, how well you eat, whether you have diabetes or an underlying health condition. And we know that these are particularly worse in disadvantaged communities.”
Doing the analysis on a parcel-scale – namely, house-by-house – is one of the most significant contributions of the new work, says Prof Vivek Shandas, a professor of geography focusing on urban climate at Portland State University in Oregon, who was not involved in the new research. However, Shandas adds:
“There’s a lot that happens across 200 or 100 metres in a city…If we’re doing parcel-scale assessments, we need to get parcel-scale understanding of movement of water and the way that heat is distributed.”
‘Environmental capital’
The researchers then investigate a potential mechanism for how historical redlining could still be impacting vulnerability to current and future climate risks.
They propose that lower-graded neighbourhoods had less investment in what they call “environmental capital”, such as trees, public parks and drainage systems.
This, they say, could be due to a combination of factors: lower property values in the neighbourhoods reduces the communities’ tax income that could be invested in such projects; places with high levels of income inequality tend to have lower community engagement; and low homeownership rates can lead to reduced community investment in public goods, such as parks.
As a proxy for environmental capital investment, the authors look at four measurable factors of environmental quality: tree canopy, street-level vegetation, ground-surface perviousness and home foundation height. Tree cover and street-level vegetation can both mitigate heat risk by providing shade and inducing a cooling effect. More pervious ground surfaces allow more drainage, while higher foundations can decrease an individual home’s risk of flooding.
They find that for each measure of environmental quality, lower-graded neighbourhoods score progressively worse than higher-graded ones, as seen in the chart below.
Houses in D-graded neighbourhoods are, on average, nearly 5.7 percentage points less pervious and have 3.4 percentage points less tree cover than those in A-graded areas.
Similarly, homes on the lower-graded side of a border have lower perviousness and foundations closer to the ground level than homes on the higher-graded side, by 1.9 and 2 percentage points, respectively. Tree canopy and street-level vegetation differ between the two by 1.03 and 1.2 percentage points.
Shandas tells Carbon Brief that introducing the idea of capital into this type of analysis is “really interesting”, but the claims the authors make about their proposed mechanism are “pretty bold”. He adds:
“Each city is so unique…We can find an association, but getting a mechanism has to be [on] a case-by-case basis.”
Van Zandt adds that the redlined maps are a “good proxy”, but not necessarily the driver of inequity. The important part, she says, is “that we identified neighbourhoods that banks should not invest in – and that those patterns persist to today”.
Lived experience
Given the disparities identified in the work, Salazar-Miranda says she hopes that policymakers can incorporate this type of information into funding and other policy decisions. As an added benefit, she says, many of the investments in environmental capital – such as additional green spaces – can improve mental and physical well-being. She adds:
“One of the conversations that would be interesting, from a policy point of view, is how do we bring the types of resources to these communities that can be helpful in mitigating these environmental risks, but also from a social point of view.”
While the findings themselves are not surprising, “it’s great to have systematic assessments” and scientific evidence to back up people’s firsthand knowledge, Shandas says. He tells Carbon Brief:
“Historically disinvested parts of cities tend to be at the frontline of extreme climate events – including flooding and heat. I know the communities that live in the cities that I [have worked with] regularly have brought this up for many, many years.
“The most significant part of this study is that it’s corroborating what the lived experiences of communities have been for quite some time.”
Van Zandt adds:
“It’s not a historical study. It’s a study of what’s happening today and what’s going to continue to happen in the future.”
The post Discriminatory ‘redlining’ increases climate risk in disadvantaged US neighbourhoods appeared first on Carbon Brief.
Discriminatory ‘redlining’ increases climate risk in disadvantaged US neighbourhoods
Hundreds of scientists in dozens of institutions are embarking on the next phase of the world’s largest coordinated climate-modelling effort.
Climate-modelling groups use supercomputers to run climate models that simulate the physics, chemistry and biology of the Earth’s atmosphere, land and oceans.
These models play a crucial role in helping scientists understand how the climate is responding as greenhouse gases build up in the atmosphere.
For four decades, the Coupled Model Intercomparison Project (CMIP) has guided the work of the climate-modelling community by providing a framework that allows for millions of results to be collected together and compared.
The resulting projections are used extensively in climate science and policy and underpin the landmark reports of the Intergovernmental Panel on Climate Change (IPCC).
Now, the seventh phase of CMIP – CMIP7 – is underway, with more than 30 climate-modelling centres expected to contribute more than five million gigabytes of data – so much that downloading it using a fast internet connection would take two and a half years.
Here, we look at what is new for CMIP7, including its model experiments, updated emissions scenarios and “assessment fast track” process.
What is CMIP?
Around the world, climate models are developed by different institutions and groups, known as modelling centres.
Each model is built differently and, therefore, produces slightly different results.
To better understand these differences, CMIP coordinates a common set of climate-model experiments.
These are simulations that use the same inputs and conditions, allowing scientists to compare the results and see where models agree or differ.
The figure below shows the countries that have either produced or published CMIP simulations.
During this time, scientists use new and improved models to run experiments from previous CMIP phases for consistency, as well as new experiments to investigate fresh scientific questions.
These simulations produce a trove of data, in the form of variables – such as temperature, rainfall, winds, sea ice extent and ocean currents. This information helps scientists study past, present and future climate change.
As scientific understanding and technical capabilities improve, models are refined. As a result, each CMIP phase incorporates higher spatial resolutions, larger ensembles, improved representations of key processes and more efficient model designs.
CMIP7 objectives
Each CMIP phase has an “experimental design” that outlines which climate-model experiments should be run and their technical specifications, including the time period the models should simulate.
The CMIP7 experimental design has several components.
As in CMIP6, for a modelling centre to contribute, they are asked to produce a suite of experiments that maintain continuity across past and future CMIP phases.
This suite of experiments is known as the “diagnostic, evaluation and characterisation of klima” (DECK) and is used to understand how their model “behaves” under simple, standard conditions. These experiments are designed and requested directly by CMIP’s scientific governing panel.
Alongside the DECK, CMIP also incorporates experiments developed by model intercomparison projects (MIPs) run by different research communities. For example, experiments exploring what the climate could look like under different levels of emissions or those that explore how sea ice might have changed between the last two ice-ages.
Currently, CMIP is working with 40 MIPs. These groups investigate specific scientific questions at their own pace, rather than on timelines prescribed by CMIP.
Running a large number of simulations can take modelling centres a long time. To speed up the process, CMIP7 has launched the “assessment fast track”.
This is a small subset of CMIP7 experiments, drawn from past and present community MIPs, identified through community consultation as being critical for scientific and policy assessments.
Data from the assessment fast track will be used in the reports that will together form the seventh assessment (AR7) of the IPCC.
It will also be used as an input by other groups that create climate information, including organisations involved in regional downscaling and modelling climate impacts and ice-sheet changes.
The figure below shows the different components of CMIP7. It shows how a subset of CMIP7 experiments will be delivered on an accelerated timeline, while the majority of experiments will be led by MIPs.
CMIP7 experiments
There are three categories of experiments set to take place in CMIP7:
- Historical experiments, which are designed to improve scientific understanding of past climates. Model runs exploring the recent historical period also allow scientists to evaluate the performance of models by checking how well they replicate real-world observations.
- Prediction and projection experiments, which allow scientists to analyse what different climates could look like under varying levels of greenhouse gas emissions, as well as near-term (10-year) prediction experiments.
- Process understanding experiments, which are designed to better understand specific processes and isolate cause-and-effect relationships. For example, a set of experiments might change the emissions of one greenhouse gas at a time to see how much each pollutant contributes to warming or cooling the climate.
Modelling centres typically produce and publish their data for the historical and projection experiments first.
CMIP expects the first datasets to be available by this summer, with broader publication recommended by the end of the year, in time to be assessed by IPCC AR7 authors.
Drafting of the reports of AR7 is currently underway. However, countries are yet to agree on the timeline for when they will be published. This presents a challenge for the climate-modelling community, given the difficulties of working with a moving deadline.
(For more on the ongoing standoff between countries around the timing of publication of the reports, read Carbon Brief’s explainer.)
New emissions scenarios
Scientists use emissions scenarios to simulate the future climate according to how global energy systems and land use might change over the next century.
Crucially, these scenarios – also known as “pathways” – are not forecasts or predictions of the future.
The group tasked with designing the scenarios for CMIP phases, as well as producing the “input files” for climate models, is the “scenario model intercomparison project”, or ScenarioMIP.
In a new paper, the group has set out the new set of scenarios for CMIP7:
- High (H): Emissions grow to as high as deemed plausibly possible, consistent with a rollback of current climate policies. This scenario will result in strong warming.
- High-to-low (HL): Emissions rise as in the high scenario at first, but are cut sharply in the second half of the century to reach net-zero by 2100.
- Medium (M): Emissions consistent with current policies, frozen as of 2025, leading to a moderate level of warming.
- Medium-to-low (ML): Emissions are slowly reduced, eventually reaching net-zero emissions by the end of the century.
- Low (L): Emissions consistent with likely keeping warming below 2C and not returning to 1.5C before the end of the century.
- Very low (VL): Emissions are cut to keep temperatures “as low as plausible”, according to the paper. This scenario limits warming to close to 1.5C by the end of the century, with limited overshoot beforehand.
- Low-to-negative (LN): Emissions fall slightly slower than in the VL scenario, with temperatures just rising above 1.5C. Emissions then rapidly drop to negative to bring warming back down.
The figures below show the emissions (left) and the estimated global temperature changes (right) under the seven new scenarios for CMIP7, from the low-to-negative emissions scenario (turquoise) to a high-emissions scenario (brown).
As a set, the ScenarioMIP scenarios “cover plausible outcomes ranging from a high level of climate change (in the case of policy failure) to low levels of climate change resulting from stringent policies”, the paper says.
Compared to the scenarios in CMIP6, the range in future emissions they cover is now narrower, the authors say:
“On the high-end of the range, the CMIP6 high emission levels (quantified by SSP5-8.5) have become implausible, based on trends in the costs of renewables, the emergence of climate policy and recent emission trends…At the low end, many CMIP6 emission trajectories have become inconsistent with observed trends during the 2020-30 period.”
Put simply, progress on climate policies and cheaper renewable technologies means that scenarios of very high emissions have now been ruled out.
However, this progress has not been sufficient to keep society on track for the Paris Agreement’s 1.5C goal. The paper notes that, “at this point of time, some overshoot of the 1.5C seems unavoidable”.
The change to the high end of the scenarios has sparked misleading commentary in the media and on social media – even from US president Donald Trump. A Carbon Brief factcheck unpacks the debate.
Also notable in the new scenarios is the “low-to-negative” pathway, which has the explicit feature of emissions becoming “net-negative”. In other words, through carbon dioxide removal (CDR) techniques, society reaches the point at which more carbon is being taken out of the atmosphere than is being added through greenhouse gas emissions.
Reaching net-negative emissions is fundamental to “overshoot scenarios”, where global warming passes a target and then is brought back down by large-scale CDR.
Overshoot scenarios allow scientists and policymakers to investigate the impacts of a delay to emissions reductions and better understand how the world might respond to passing a warming target. This includes the question of whether some impacts of climate change, such as ice sheet melt, are reversible.
CMIP has encouraged modelling centres to run simulations using the “high” and “very low” scenarios first to ensure downstream users of the data – including groups working on regional climate projections (CORDEX), climate impacts modelling (ISIMIP) and ice-sheet modelling (ISMIP) – have enough time to produce their data for IPCC reports.
These two scenarios were selected as they sit at opposite ends of the spectrum of climate outcomes. The high scenario will demonstrate how models behave under high emissions, while the very low scenario will demonstrate how models behave when emissions are rapidly reduced.
CMIP has recommended that modelling centres then run the “medium” and “high-to-low” scenarios. The remaining scenarios should then follow and no official recommendation has been made yet on their production order.
Other new features
In addition to the assessment fast track and new scenarios, CMIP7 has a number of other new developments.
Updated data for simulations
Climate models use input datasets to define the set of external drivers – or “forcings” – that have caused the global warming observed so far. These drivers include greenhouse gases, changes to incoming solar radiation and volcanic eruptions.
CMIP recommends modelling groups use the same input datasets, as this makes it easier to compare model results.
In CMIP7, the historical forcing datasets available for modelling groups to use have been improved to better represent real-world changes and extended closer to the present day. The historical simulations will be able to simulate the past climate from 1850 through to the end of 2021, whereas CMIP6 only simulated the past climate through to 2014.
CMIP is also planning to extend these historical datasets through to 2025 and maybe further throughout the course of CMIP7.
Emissions-driven simulations
CMIP7 introduces a new focus on CO2 emissions-driven simulations, providing a more realistic representation of how the climate responds to changes in emissions.
In older generations of climate models, atmospheric levels of CO2 and other greenhouse gas concentrations have been needed as an input to the model. These levels would be produced by running scenarios of CO2 emissions through separate carbon cycle models. The resulting climate-model runs were known as “concentration-driven simulations”.
However, many of the latest generation of models are now able to run in “emissions-driven mode”. This means that they receive CO2 emissions as an input and the model itself simulates the carbon cycle and the resulting levels of CO2 in the atmosphere.
This development is important, as climate policies are typically defined in terms of emissions, rather than overall atmospheric concentrations.
This new development in modelling will enable a more realistic representation of the carbon cycle and a better understanding of how it might change under different levels of warming.
Enhanced model documentation and evaluation
All CMIP7 models will be required to supply standardised model documentation that ensures consistency across model descriptions and makes it easier for end users to understand the data.
Additionally, CMIP scientists have developed a new open-access tool that dramatically speeds up the evaluation of climate models.
This “rapid evaluation framework” allows researchers to compare model outputs with real-world observations, providing immediate insight into model performance.
The post Guest post: How CMIP7 will shape the next wave of climate science appeared first on Carbon Brief.
Guest post: How CMIP7 will shape the next wave of climate science
Democrats are within reach of a majority on Georgia’s Public Service Commission, a little-known body that oversees Georgia Power and utility rates.
Georgia Public Service Commission elections historically received limited public attention and turnout. That changed last year, when voters, frustrated by rising electric bills, ousted two GOP members of the utility regulator, previously made up entirely of Republicans. This year, Democrats have a chance to flip control of the five-member commission.
Like many Yemeni farmers, Salem Abdallah first bought solar panels to power a well pump to irrigate his fruit and vegetable crops. Now, he has a new use for the surplus electricity they generate – a Chinese-made electric pickup truck.
“The roads between villages are rough and my farms aren’t all in one place, so the power and height give me a real advantage,” the 60-year-old told Climate Home News as he charged his plug-in hybrid Geely Riddara in Yemen’s capital of Sanaa, where nearly a dozen charging stations have sprung up in the last two years.
Prices for Abdallah’s Riddara model run from $25,000 to $40,000 – out of reach for all but a few in the impoverished country, where more than a decade of civil war has shattered the economy and made fuel supplies unaffordable for many.
The conflict has also taken a heavy toll on the national grid, which only 12% of Yemenis rely on for electricity, according to the World Bank.
Many homes and businesses have instead installed off-grid solar systems to confront frequent blackouts and patchy coverage in rural areas, and this improbable solar boom has caught the attention of Chinese electric vehicle (EV) brands.
Solar boom stirs Chinese interest
China’s BYD, Geely and Jetour have opened dealerships in Yemen in recent years, betting that enthusiastic solar uptake, coupled with high fuel prices and shortages, will lead to rapid growth in the nation’s small and incipient EV market, at least among those able to afford the initial outlay.
At the other end of the scale, electric two-wheelers are also starting to make inroads in Yemen among delivery services and salaried employees.
Mohammed Ali, 25, an accountant at an exchange office in Sanaa, said the $1,050 he spent on a Chinese-made electric motorcycle was “the best decision I ever made”.
“I charge my electric motorcycle at work and it saves me transportation expenses and time,” he said.
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But even as the global energy shock caused by the Iran war spurs the shift to electric transport in some lower-income countries, buying an EV still remains an impossible dream for most of Yemen’s 40 million people, said Mustafa Nasr, head of the Yemen-based Centre for Economic Studies and Media.
“Most Yemenis can barely secure their basic needs,” Nasr said.
Shrinking incomes, rising prices
Yemen has been gripped by civil war since 2014, plunging it into one of the world’s worst humanitarian crises.
Gross domestic product (GDP) per capita is projected to fall to about $384 this year, according to estimates from the International Monetary Fund – less than a quarter of what it was when the war began.
At the same time, petrol and diesel for transport and to power generators have become increasingly out of reach. A litre of petrol in Sanaa costs the equivalent of $0.94 – close to what many Yemenis earn in a day.
Charging stations spring up
But for those able to buy them, EVs are proving a revolutionary solution to Yemen’s road transport woes. Sustained fuel price rises and solar adoption could push a gradual widening of the market, particularly if EV and battery prices continue to fall, Nasr said.
For large-scale farmers like Abdallah who already own solar installations generating between 60 and 80 kilowatts, built to run irrigation systems, charging an EV at night is a no-brainer.
EVs started appearing on the streets of Sanaa and the southern port city of Aden in late 2024, when the first charging point was installed by Al-Raebi Company, which holds the concession to build charging infrastructure in Sanaa and several other provinces and also sells electric Farizon trucks and Riddara pickups.
Al-Raebi’s sales manager, engineer Mundhar al-Farran, said the company has sold hundreds of electric vehicles this year to farmers, traders and institutions. Like Abdallah, many of them say EVs’ simpler construction reduces breakdowns, while the immediate torque of electric motors suits Yemen’s mountainous terrain, he said.
There are now 11 charging stations in Sanaa, and one each in Aden, Dhamar, Ibb and Hodeidah. On long inter-provincial routes there is one station per corridor, al-Farran said.
The price per kilowatt at a public charging station is 120 Yemeni rials ($0.22). According to economic expert Ali al-Tuwaiti, this translates to a per-kilometre cost of about 18 rials for an EV – two and a half times less than for a fuel-efficient petrol car.
“The absence of charging infrastructure was the biggest obstacle at the start,” al-Tuwaiti said. “Al-Raebi’s initiative was the first turning point in this sector.”
Al-Raebi is also working to bring fuel station operators into the transition, offering to cover half the cost of installing solar-powered charging equipment and financing the rest, al-Farran said.
Solar power backbone
Such efforts seek to leverage the country’s investments in solar generation. Over recent years, the country has imported solar systems totalling more than 1,000 megawatts of capacity, representing an estimated investment of about $250 million, al-Tuwaiti said.
That accounts for almost a quarter of Yemen’s current electricity needs of 4,500 megawatts, he added.
It has also given an unexpected boost to the climate-vulnerable country’s efforts to further shrink its tiny carbon emissions. Al-Tuwaiti estimates that solar generation now displaces the equivalent of 7,800 barrels of oil and more than 1.2 million litres of diesel per day.
Recent estimates show Yemen contributes only around 0.03%-0.06% of global emissions, with most energy-related emissions coming from transport and power generation.
China’s BYD starts with hybrids
Yemen’s nascent EV market comes amid faster-than-expected transport electrification in some emerging countries, where Chinese manufacturers are seeking to attract buyers with lower prices in markets seen as having unlocked potential.
China’s EV giant BYD mostly sales hybrid models at its dealership in Aden for now, but it also offers repayment plans for its popular battery electric Seagull car model, which retails for about $13,000.
The dealer also sells several other models that are available as plug-in hybrids, which tend to be popular in places with limited charging infrastructure and erratic power supplies.
One recent buyer, food trader Amin, 50, paid $50,000 for his new BYD model.
“It’s powerful, has four-wheel drive, and a better launch than modern conventional cars,” he told Climate Home News outside his home, adding that the air conditioning runs efficiently even when stationary – a serious consideration in Aden’s sometimes sweltering heat.
“It’s wonderful … it has all that I want in a car,” he said.
This story was published in collaboration with Egab.
The post Chinese EV brands woo Yemen’s wealthy elite as war prompts solar boom appeared first on Climate Home News.
Chinese EV brands woo Yemen’s wealthy elite as war prompts solar boom
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