Miners face funding squeeze as green investing surges

Environmental activists from ‘Extinction Rebellion’ stage a demonstration outside the venue hosting the Southern African Coal Conference in Cape Town. (Reuters)
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Updated 02 February 2020

Miners face funding squeeze as green investing surges

  • Global investors shift away from heavy industry in favor of cleaner sectors

CAPE TOWN: As global investors shift away from heavy industry in favor of cleaner sectors, mining companies are losing billions in financing, raising the cost of capital and jeopardizing projects. 

Making the mining industry more sustainable by running mines on renewable energy, for example, will be a key focus at the annual Investing in African Mining Indaba conference in Cape Town this week, as companies hunt for new sources of capital including private equity, debt, offtake finance and royalty finance. 

Environmental, social and governance (ESG) concerns have driven money into specialized ESG funds which often exclude mining stocks among other “dirty” assets. 

“You talk to anyone at the moment, they say there’s no money,” said Boris Kamstra, executive director of Alphamin Resources, which manages the Bisie tin project in Democratic Republic of Congo.  The capital squeeze that started about two years ago has worsened recently, said Julian Treger, CEO of Anglo Pacific Group, a mining royalty and streaming company. 

The average cost of capital for early-stage mining projects rose by two percentage points over the past two years, he estimates. 

“Even for companies that have good projects it’s very difficult for them to raise any money in these markets,” said Caroline Donally, managing director at private equity firm Denham Capital, in Houston. 

“Previous investors who would provide equity appear to have withdrawn. A number of specialist funds have shut up shop, and generalists aren’t investing in commodities anymore,” said Donally, who will be attending Mining Indaba, the world’s biggest mining investment conference, which takes place from Monday to Thursday. 

Cryptocurrencies are among alternative assets that are luring retail investors away from miners. 

Mining-specific private equity funds raised $0.3 billion in 2019, a fifth of the amount raised in 2009, and just barely more than the $0.2 billion raised in 2014 during a global commodity crash, data from Preqin shows. 

Coal miners — especially those extracting thermal coal, burnt to produce electricity — are bearing the brunt of the sustainable investing trend. Norway’s sovereign wealth fund divested from all fossil fuel last year, and the world’s biggest asset manager Blackrock said on Jan. 14 it would sell active holdings in companies generating more than 25 percent of revenues from thermal coal. 

“If you’re a small coal explorer, I don’t think you stand much of a chance of raising any money at all,” said Fred White, associate director at Medea Capital Partners in London.  “There’s still a huge market and huge demand (for coal), but it’s not getting financed by Western banks,” he added. 

Local trading houses and lenders are stepping in instead. Thermal coal accounts for nearly 40 percent of the world’s electricity generation and more than 40 percent of energy-related carbon dioxide emissions, according to the International Energy Agency. 

In Africa, coal-to-power projects could previously rely on support from development finance institutions. 

But even they are withdrawing under pressure. In November, the African Development Bank (AfDB) decided against funding a Kenya coal project that was halted by a local environmental tribunal in June. 

The continent’s biggest coal producer, South Africa, is also seeing funding dry up. 

South Africa’s Nedbank has stopped funding coal-related projects, while FirstRand cut greenfield thermal coal projects to less than 0.5 percent of its lending.


Man vs. machine in bid to beat virus

Updated 20 February 2020

Man vs. machine in bid to beat virus

  • Human and artificial intelligence are racing ahead to detect and control outbreaks of infectious disease

BOSTON: Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?

In a narrow sense, yes. But what the humans lacked in sheer speed, they more than made up in finesse.

Early warnings of disease outbreaks can help people and governments to save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

What’s more, the mere mortals produced a similar alert only a half-hour behind the AI systems.

For now, AI-powered disease-alert systems can still resemble car alarms — easily triggered and sometimes ignored. A network of medical experts and sleuths must still do the hard work of sifting through rumors to piece together the fuller picture. It is difficult to say what future AI systems, powered by ever larger datasets on outbreaks, may be able to accomplish.

The first public alert outside China about the novel coronavirus came on Dec. 30 from the automated HealthMap system at Boston Children’s Hospital. At 11:12 p.m. local time, HealthMap sent an alert about unidentified pneumonia cases in the Chinese city of Wuhan. The system, which scans online news and social media reports, ranked the alert’s seriousness as only 3 out of 5. It took days for HealthMap researchers to recognize its importance.

Four hours before the HealthMap notice, New York epidemiologist Marjorie Pollack had already started working on her own public alert, spurred by a growing sense of dread after reading a personal email she received that evening.

“This is being passed around the internet here,” wrote her contact, who linked to a post on the Chinese social media forum Pincong. The post discussed a Wuhan health agency notice and read in part: “Unexplained pneumonia???”

Pollack, deputy editor of the volunteer-led Program for Monitoring Emerging Diseases, known as ProMed, quickly mobilized a team to look into it. ProMed’s more detailed report went out about 30 minutes after the terse HealthMap alert.

Early warning systems that scansocial media, online news articles and government reports for signs of infectious disease outbreaks help inform global agencies such as the World Health Organization — giving international experts a head start when local bureaucratic hurdles and language barriers might otherwise get in the way.

Some systems, including ProMed, rely on human expertise. Others are partly or completely automated.

“These tools can help hold feet to the fire for government agencies,” said John Brownstein, who runs the HealthMap system as chief innovation officer at Boston Children’s Hospital. “It forces people to be more open.”

The last 48 hours of 2019 were a critical time for understanding the new virus and its significance. Earlier on Dec. 30, Wuhan Central Hospital doctor Li Wenliang warned his former classmates about the virus in a social media group — a move that led local authorities to summon him for questioning several hours later.

Li, who died Feb. 7 after contracting the virus, told The New York Times that it would have been better if officials had disclosed information about the epidemic earlier. “There should be more openness and transparency,” he said.

ProMed reports are often incorporated into other outbreak warning systems. including those run by the World Health Organization, the Canadian government and the Toronto startup BlueDot. WHO also pools data from HealthMap and other sources.

Computer systems that scan online reports for information about disease outbreaks rely on natural language processing, the same branch of artificial intelligence that helps answer questions posed to a search engine or digital voice assistant.

But the algorithms can only be as effective as the data they are scouring, said Nita Madhav, CEO of San Francisco-based disease monitoring firm Metabiota, which first
notified its clients about the outbreak in early January.

Madhav said that inconsistency in how different agencies report medical data can stymie algorithms. The text-scanning programs extract keywords from online text, but may fumble when organizations variously report new virus cases, cumulative virus cases, or new cases in a given time interval. The potential for confusion means there is almost always still a person involved in reviewing the data.

“There’s still a bit of human in the loop,” Madhav said.

Andrew Beam, a Harvard University epidemiologist, said that scanning online reports for key words can help reveal trends, but the accuracy depends on the quality of the data. He also notes that these techniques are not so novel.

“There is an art to intelligently scraping web sites,” Beam said. “But it’s also Google’s core technology since the 1990s.”

Google itself started its own Flu Trends service to detect outbreaks in 2008 by looking for patterns in search queries about flu symptoms. Experts criticized it for overestimating flu prevalence. Google shut down the website in 2015 and handed its technology to nonprofit organizations such as HealthMap to use Google data to build their own models.

Google is now working with Brownstein’s team on a similar web-based approach for tracking the geographical spread of the tick-borne Lyme disease.

Scientists are also using big data to model possible routes of early disease transmission.