As fast shipping increases emissions, delivery has become more polluting

This article was written by Aya Diab and was published in the Globe & Mail on December 29, 2025.

It feels simple: You shop, find something you want and click to buy. It shows up today, overnight or tomorrow. We’ve gotten used to that speed. But that convenience comes with a climate cost.

Multiple factors shape the environmental toll of a delivery. These include the distance from a fulfilment centre, whether the shipment rides in a half-empty truck, how many trips a driver makes in the same area and the type of transportation used to move the package.

When customers choose faster shipping and earlier delivery dates, the system shifts from optimized routing to whatever gets the package out fastest, and that means higher emissions, said Sreedevi Rajagopalan, a research scientist at MIT’s Center for Transportation and Logistics. For example, trucks may leave warehouses before they’re full and drivers might loop the same neighbourhood multiple times a day, she said.

“For the same demand, fast shipping definitely increases emissions 10 to 12 per cent,” she said.

To meet tight delivery windows, retailers may rely on air freight, which produces far more emissions than other options such as trains, making it the most carbon-intensive.

“Given that companies want to be competitive in terms of speed, it comes at the cost of your efficiency,” Ms. Sreedevi said. “Vans are half full, and you make multiple rounds, multiple trips to the same location … your fuel consumption goes up, and you’re not able to consolidate.”

One way companies such as Amazon.com Inc. try to minimize that is by placing their supply chain closer to customers to reduce mileage and improve speed for the customer. Their goal is to make the journey fast and effective, but reduce its emissions at the same time.

“By really leveraging our supply chain efficiencies that we have at scale, we’re able to both offer better speed and sustainability outcomes at the same time,” said Chris Atkins, director of Worldwide Operations Sustainability at Amazon.

Getting items to customers’ doors from a fulfilment centre – referred to as the “last mile” or “last kilometre” of shipping – is one of the hardest stages to make less polluting, Ms. Sreedevi said.

Emissions rise even more when customers place multiple small orders throughout the week.

“If I place an order this morning and then I place an order this evening and choose fast shipping, the company might have already processed my morning order and wouldn’t wait for my evening order to consolidate,” she said.

And sending more half-full trucks out on the road means more trips overall. “Imagine you’re not only sending a half-full truck, you’re also bringing back that truck empty. … Emissions are going to go up,” Ms. Sreedevi said.

Consumers can lower emissions if they’re willing to wait even a tiny bit, and they’ll save money at the same time, said Christopher Faires, assistant professor of logistics and supply chain management at Georgia Southern University.

Delaying delivery by one to two days can result in a 36-per-cent reduction in carbon-dioxide emissions, and three to four days pushes that reduction to 56 per cent, so opting for standard or delayed shipping instead of next-day or two-day shipping helps, according to Ms. Sreedevi.

Amazon’s Mr. Atkins said changes to their network are cutting emissions linked to fast delivery. The company has expanded the use of electric delivery vans and shifted more packages to rail and to delivering by foot or bicycle in dense cities.

“Aviation is very carbon-intensive relative to ground shipping,” said Mr. Atkins. “One of the other things that Amazon and other logistics companies are looking at doing is: How do we mode-shift to less carbon intensive forms of transportation?”

Amazon says providing shipping options that encourage customers to consolidate orders have also helped. Data for the first nine months of 2025 shows that when customers chose a single delivery day for all items, it reduced more than 300 million delivery stops and avoided 100,000 tons of carbon emissions, according to Mr. Atkins. People are more likely to delay or consolidate orders once they understand the environmental impact of fast shipping, according to Ms. Sreedevi, who coauthored a 2024 study of delivery customers in Mexico.

“A significant number of consumers decided to wait for longer delivery or delayed their shipping when we showed them the environmental impact information in the form of trees,” said Ms. Sreedevi. “So it’s important that they are educated.”

While fast shipping isn’t likely to go away, experts say its climate impacts can be meaningfully reduced through small behaviour shifts, both from shoppers and companies. Bundling orders, skipping the overnight option and choosing a single weekly delivery can all make a difference.

How to win the race for data centres

This editorial was written and published by the Globe & Mail on November 15, 2025.

Technology companies are throwing around extraordinary numbers as they race to build data centres.

OpenAI and Meta alone have promised to sink US$1-trillion into these facilities. Other companies are pledging to spend hundreds of billions more.

Although the bulk of the spending will happen in the United States, many Canadian politicians are hoping to get a piece of the action. Alberta is dreaming of $100-billion in data centre investment and Quebec wants to put out the welcome mat for these projects. They should be cautious.

These centres use huge amounts of power and water.

They provide few local jobs once they are built. And if rising fears of an AI bubble prove accurate, communities could be left with white elephant facilities.

This is not to say that data centres should be blacklisted or banned. It would be hypocritical to do so considering how much of Canadians’ life is online. But governments should also not dangle incentives such as tax breaks to attract them.

Canada and Ontario’s recent woes with subsidizing electric-vehicle battery production is an example to avoid.

In that case, the governments eagerly offered billions to companies, in the hopes of creating a local EV economy that has proved elusive. One glimmer of a silver lining is that, in some cases, the proposal failed before serious money began to flow. But the whole saga suggests governments too eager to hitch their wagons to the hot new idea.

It’s good that some Canadian provinces are starting to think more critically about how to handle an influx of data centres, even if their approaches raise questions.

In Ontario, the government is seeking input on a proposal that would prioritize electricity requests based on how well the use would serve the province’s economic interests. In a worrying indicator that the province is buying into tech hype, though, the idea comes dressed up with some highly speculative language about how data centres “could anchor new high-tech ecosystems” in northern and rural communities.

British Columbia, meanwhile, announced last month that priority for power would go to natural resource and manufacturing projects. Data centres, AI and other users would be further down the list when applying for electricity supply.

This attempt at preserving local industries could result in rewarding lower-productivity businesses at the expense of more productive ones.

It’s understandable to want to prevent a situation in which new data centres put enormous additional demand on the power supply, pushing up costs for everyone. But if the companies are willing to pay the true marginal cost of providing additional power then there is no reason to refuse them. Long term, building out the electrical grid to maximize economic development is a good thing.

In the same way, provinces and municipalities should look more closely at how much large-scale users are paying for water. Too often, the heaviest consumers pay the least per litre. That can’t be allowed to continue, especially if new demand forces costly expansion of water infrastructure.

There are also questions about the federal approach. Fortunately, Ottawa’s promise last year to provide up to $15billion for loans and equity stakes in data centre projects was missing from this month’s budget. It did pledge, however, to change the Canada Infrastructure Bank’s mandate to allow it to invest in these facilities. The government is also developing a new AI strategy, and is considering whether there should be new incentives.

As well, the budget promised nearly $1-billion towards data sovereignty. This issue is on a lot of minds, understandably, given the drumbeat of threats from United States President Donald Trump. But the term itself is not easily defined and Ottawa risks being captured by tech prophets spinning an alluring web.

The history of industrial policy is littered with embarrassing examples of government largesse that didn’t pan out.

Hydroponic cucumber cultivation in Newfoundland springs to mind, as does the ill-fated Bricklin sports car.

Governments shouldn’t get starry eyed about data centres. Instead, make sure the appropriate regulations are in place and then let the market take over.

The Sunday Editorial: The airport of the future has arrived – just not in Canada. Landing tomorrow morning at theglobeandmail.com

AI is fast­track­ing cli­mate research, from weather fore­casts to sardines

This infrared satellite image from the National Oceanic and Atmospheric Administration shows Hurricane Helene in the Gulf of Mexico last September. When it comes to tracking such events, Microsoft's Aurora is said to be more accurate and cheaper than what has come before.

This article was written by Laura Millan and Yinka Ibukun, and was published in the Toronto Star on August 24, 2025.

Arti­fi­cial intel­li­gence is giv­ing some cli­mate research projects a much ­needed boost at a time of worsen­ing extreme weather and fund­ing cuts that threaten sci­ence in the U.S. and else­where.

While gen­er­at­ive AI faces cri­ti­cism due to the large amounts of power required to train and run soph­ist­ic­ated mod­els, it also holds the prom­ise of advan­cing sci­ence.

“It’s a gigantic step for­ward,” says Ángel Borja, a bio­lo­gist at AZTI mar­ine research centre in north­ern Spain. “It will allow us to pro­cess data and get res­ults much faster, so people that make decisions can act faster, too.”

Research­ers are teach­ing exist­ing AI mod­els and cre­at­ing new ones to per­form routine tasks that would require sev­eral people to work for weeks or even months. Data gathered in sci­entific exped­i­tions from the bot­tom of the oceans to the farthest corners of Ant­arc­tica can now be cata­logued in a mat­ter of hours.

To be sure, the use of gen­er­at­ive AI has its dangers, some sci­ent­ists warn. Machine learn­ing tech­niques are tools that should never replace human think­ing, writ­ing and ana­lysis, said Jonathan Foley, the exec­ut­ive dir­ector of Project Draw­down. The organ­iz­a­tion, which uses sci­entific data to provide guid­ance on cli­mate solu­tions, has lim­ited the use of AI assist­ants to simple tasks like check­ing gram­mar, format­ting doc­u­ments and scrap­ing inform­a­tion from dis­persed sources.

“By defin­i­tion, gen­er­at­ive AI relies on pla­gi­ar­ism (albeit in a stat­ist­ical sense) and often fab­ric­ates inform­a­tion, cita­tions, data, and cre­at­ive con­tent,” Foley said.

But that hasn’t stopped other research­ers for­ging ahead. Here are three examples.

Research­ers now `reach faster con­clu­sions’

Borja remem­bers gath­er­ing data manu­ally and filling data­bases with hun­dreds of meas­ure­ments when he star­ted doing sci­entific work 45 years ago. The rise of com­puters and the inter­net helped speed up some of that work. But AI’s growth over the past three or four years has felt like something com­pletely dif­fer­ent and more trans­form­at­ive, he said.

“My younger col­leagues worry that AI will steal their jobs, that it will make us unne­ces­sary,” he said. “It’s the other way around: We’re advan­cing so much because AI is doing routine work that takes us so many hours, and we’ll be able to focus on inter­pret­ing that data.”

AI is set to tur­bocharge what the centre can offer poli­cy­makers, Borja said, allow­ing them to make more­informed decisions. Sci­ent­ists at AZTI work closely with poli­cy­makers to, among other things, estab­lish fish­ing quotas and set up mar­ine pro­tec­ted areas.

ATZI research­ers have star­ted feed­ing mil­lions of data points gathered over three dec­ades into an AI model. The data includes everything from water qual­ity to the pres­ence of dif­fer­ent types of fish and plank­ton. The model then pro­duces research notes that tell sci­ent­ists what inform­a­tion is in the data­base and how it’s struc­tured, allow­ing research­ers to more eas­ily decide which data sets to use for their invest­ig­a­tions.

The centre is also using videos and pic­tures from research exped­i­tions to train another model to recog­nize dif­fer­ent types of fish and mar­ine life. The task cur­rently requires sci­ent­ists to watch hun­dreds of hours of under­wa­ter video foot­age, and manu­ally record which spe­cies appear, where, how often and how abund­ant they are.

“It will allow us to reach faster con­clu­sions on the state of the mar­ine envir­on­ment in cer­tain places,” Borja said. “I expect within the next five years, we’ll see an explo­sion of AI applic­a­tions in sci­entific fields and in ways I can’t even ima­gine right now.”

Weather fore­cast­ing more accur­ate

Some AI­powered mod­els are already out­per­form­ing con­ven­tional fore­cast­ing sys­tems. Microsoft’s Aurora has been trained on over a mil­lion hours of diverse geo­phys­ical data. In 91per cent of the tar­gets estab­lished by its cre­at­ors, it is more accur­ate than the tra­di­tional model from the European Centre for Medium­Range Weather Fore­casts and Google Deep­Mind’s AI model Graph­Cast.

It’s able to pre­dict, among other things, air qual­ity, waves and trop­ical cyc­lone paths, accord­ing to a research paper authored by Microsoft Research employ­ees in the May edi­tion of Nature. Aurora can per­form these tasks at a frac­tion of the com­pu­ta­tional cost com­pared to tra­di­tional mod­els, they said.

“AI mod­els such as Aurora can enable cli­mate sci­ent­ists to explore hun­dreds of times more scen­arios than they can today, help­ing to unlock new insights at scale,” said a Microsoft Research spokes­per­son.

AI mod­els are some­what of a black box com­pared to their tra­di­tional weather coun­ter­parts, which wor­ries some fore­casters. But highqual­ity weather inform­a­tion is the first step to set­ting warn­ing sys­tems that give people time to find shel­ter when extreme events hit.

Cit­izen sci­ent­ists get an AI assist

The com­bin­a­tion of humans and AI can provide the best res­ults for sci­entific research, accord­ing to a paper pub­lished last year in Cit­izen Sci­ence: The­ory and Prac­tice. Lead author Nir­wan Sharma, a com­puter sci­ent­ist at The Open Uni­versity in the U.K., star­ted using nat­ural lan­guage gen­er­a­tion — an early name for what’s now known as gen­er­at­ive AI — in 2010 for a cit­izen sci­ence project.

People were encour­aged to send Sharma and his co­research­ers pic­tures of bumble­bees as they walked in the woods or worked in their gar­dens. AI would then identify which of the 22 U.K. bee spe­cies they had spot­ted, and research­ers would verify the AI’s work. Finally, cit­izen sci­ent­ists would get an auto­mated email thank­ing them for the con­tri­bu­tion and reveal­ing the type of bee they pho­to­graphed.

The model, a part­ner­ship between Aber­deen Uni­versity and the Bumble­bee Con­ser­va­tion Trust, cor­rectly iden­ti­fied spe­cies about half the time, a rate on par with untrained human users. Ini­tially, about 10 people were required to identify the type of bee cor­rectly. As the model learned more, it helped cut down on the num­ber of people required to as little as three.

Using the large amount of pic­tures gathered over the years, research­ers trained the model to identify the plants that bees were pho­to­graphed on, allow­ing it to provide plant­ing recom­mend­a­tions depend­ing on the types of bees cit­izen sci­ent­ists wanted to attract.

“Much of the know­ledge about how to identify spe­cies is bottled up in sci­entific journ­als or places which are really dif­fi­cult to access for people,” Sharma said. “AI is another piece to improve our learn­ing — it’s a means to have a dia­logue with that know­ledge.”

Pho­tos of Brit­ish bees in the wild, such as this red mason hard at work, have been used to train arti­fi­cial intel­li­gence which uses the same images to make plant­ing recom­mend­a­tions.

A real fire photo or just `AI slop’?

B.C. Wild­fire Ser­vice slams online users for fake images of actual blazes it’s fight­ing

This article was written by Alex Boyd and was published in the Toronto Star on August 10, 2025.

In late July, a truck caught fire near Peach­land, B.C., ignit­ing a blaze that raced up a dry hill­side faster than fire­fight­ers could fol­low, even­tu­ally prompt­ing the clos­ures of two high­ways and evac­u­ations of 400 homes.

Mid­way through the sum­mer fire sea­son, pho­tos of what was dubbed the Drought Hill fire quickly popped up online, show­ing plumes of dull grey smoke rising from the dry scrub­land of the Okanagan Val­ley. But one image, pos­ted on the Face­book page of a self­described “digital cre­ator” the next day, claimed to show the “OUT OF CONTROL” fire in tech­nicolor. The image is so vivid it defies belief — the fluor­es­cent orange flames throw­ing up plumes of coal­black smoke as tiny heli­copters and water bombers whiz over­head.

The image is lush, bright — and fake.

In a rare move, the B.C. Wild­fire Ser­vice spe­cific­ally called out AIgen­er­ated images last week — cit­ing this image and another pur­port­ing to be of a fire near Bear Creek — for spur­ring fear and anxi­ety by spread­ing incor­rect inform­a­tion about the loc­a­tion and beha­viour of the very real fires it is bat­tling. “When mis­in­form­a­tion is spread online, it can start to take root,” says pro­vin­cial inform­a­tion officer Sarah Budd.

“Obvi­ously it’s frus­trat­ing for our staff. We work really hard to quickly and effi­ciently get accur­ate inform­a­tion out.”

Dra­matic pic­tures have a real psy­cho­lo­gical effect on people, says Ali Asgary, a York Uni­versity pro­fessor of dis­aster and emer­gency man­age­ment. The con­cern with overly dram­at­iz­ing or sen­sa­tion­al­iz­ing images is that they might spark panic or unne­ces­sar­ily over­whelm people with inde­cision, he says.

He stresses that gen­er­at­ive AI isn’t inher­ently bad — it’s already show­ing ser­i­ous poten­tial for help­ing those who over­see dis­aster response sift through the flood of data that gushes in dur­ing emer­gency situ­ations, and could even be used for spread­ing edu­ca­tional images or graph­ics that have been veri­fied by pro­fes­sion­als, he says.

But he also warns that there’s a real danger in let­ting just any­one pump out inform­a­tion of dubi­ous ori­gin. For one thing, veri­fy­ing bad inform­a­tion wastes resources that are already stretched thin in an emer­gency situ­ation.

Then there’s the down­stream effect of sow­ing doubt. “Over time, if a lot of this is hap­pen­ing, it cre­ates con­fu­sion for people,” he says. “They don’t really know which sources to fol­low, which sources to trust.

“People start los­ing trust in their agen­cies and people who are sup­posed to provide inform­a­tion to them.”

The image was first pos­ted on an account with the name “Joe­mar Sombero,” a “digital cre­ator” with 43,000 fol­low­ers, on July 31, when the fire had been burn­ing for more than a day. Along­side the dra­matic AI­gen­er­ated photo was an AI­gen­er­ated cap­tion that described the fire as “fast­mov­ing” and “human­caused” and stressed the “massive aer­ial attack under­way” to fight it.

“Sombero” then com­men­ted under­neath: “Stay safe every­one, and huge respect to the fire­fight­ers bat­tling this non­stop!” he wrote, adding a pray­ing hands emoji and the hashtag #Pray­forBC.

Many of the com­ments were furi­ous. “Why are you using AI pic­tures and AI write ups to sen­sa­tion­al­ize Cana­dian Wild­fires? Please stop. It’s not help­ful,” reads one of the first. “Sombero” did not respond to requests for an inter­view the Star sent to the Face­book and asso­ci­ated Ins­tagram accounts, but did hop back into the com­ments to explain their actions.

“The images and write­ups I share are AI­gen­er­ated for illus­trat­ive pur­poses only and are always tagged with a dis­claimer,” they wrote to the com­menter. (When the image was first pos­ted it did not include an AI dis­claimer, accord­ing to the post’s edit his­tory on Face­book; a day later, one was added.) “My goal is never to sen­sa­tion­al­ize but to raise aware­ness about the sever­ity of these events.”

In another com­ment, they say its neces­sary to have more eye­catch­ing images to help people “pay atten­tion in a sea of scrolling.”

In some ways, the account mir­rors the tra­ject­ory of pop­u­lar AI use. It came to life in the spring of 2023 and ini­tially pos­ted mostly nature shots that appear to be reg­u­lar pho­to­graphs, mostly of moun­tains and lakes in the Cana­dian Rock­ies and Cal­gary area.

The poster seems to dis­cover gen­er­at­ive AI around early 2024, and began exper­i­ment­ing with gen­eric Cana­diana con­tent — north­ern lights, moun­tain ranges, people wear­ing plaid — that dis­plays the slightly car­toon­ish look of earlier AI mod­els. A well­received post dur­ing this time period got maybe a few dozen likes or heart emo­jis.

But earlier this year, the account appeared to zero in on nat­ural dis­aster con­tent and begins post­ing increas­ingly real­istic AI­gen­er­ated images of forest fires and vol­ca­noes and in one case, a grizzly bear attack. The AI­gen­er­ated cap­tions got longer, more dra­matic, more emoji­stud­ded.

Fires are a major focus. There’s a AI image of the fires in La Loche, Sask., and fake images of people who evac­u­ated ahead of blazes in Cali­for­nia. (“The after­math … is heart­break­ing,” the cap­tion notes.)

But its earth­quakes that seem to be par­tic­u­larly enti­cing to social media eye­balls.

Last week, an AI­image and post about how “THE EARTH’S CRUST WON’T STAY QUIET” got more than 1,000 inter­ac­tions, as did one about “SHAKING” in South­ern Cali­for­nia. Then a big win. A single post about the num­ber of earth­quakes world­wide in the last month — “EARTH IS SHAKING HARD” — saw more than 6,000 people hit the react but­ton.

A short video pos­ted to the account fea­tures a cas­cade of hap­pylook­ing emo­jis with a cel­eb­rat­ory title: “I got over 6,500 reac­tions on one of my posts last week! Thanks every­one for your sup­port!”

Another term for the flow of fake imagery being churned out by the account here is AI slop, says Lauren Dwyer, an asso­ciate pro­fessor at Mount Royal Uni­versity who stud­ies emer­ging tech­no­lo­gies and how they influ­ence beha­viour.

“It’s just AI­gen­er­ated images for the sake of AI­gen­er­ated images,” she says.

Even the poster’s responses to com­menters have the fawn­ing tone of Chat­GPT, she points out. (“I hear your frus­tra­tion,” he assures one per­son upset about his AI use.) It’s pos­sible this is one per­son let­ting gen­er­at­ive AI run amok, she says, but it’s also pos­sible the account is run by bots. Either way, these accounts are often set up less to spread reli­able inform­a­tion than to gen­er­ate clicks.

Much like how snack foods are engin­eered to keep you eat­ing, AI is also trained to ensnare your atten­tion. “AI cre­ates really, really eye­c­atch­ing images,” Dwyer says.

“Get­ting that bal­ance of light­ing and drama, all of the aspects that go into incred­ible photo journ­al­ism? To be able to do that with a click, as opposed to hav­ing to find the right angle and nav­ig­ate wild­fire smoke, it just removes all the bar­ri­ers.”

The gold rush to get social media engage­ment is why AI­images of everything from nat­ural dis­asters to celebrit­ies behav­ing badly to weirdly unap­peal­ing recipes have flooded social media.

Face­book itself allows accounts with a large num­ber of fol­low­ers to make money through ads in videos and paid sub­scribers. It’s not clear if this is the page’s goal — it cur­rently appears to have two sub­scribers pay­ing $1.29 a month, one of whom is a Toronto Star reporter — but it provides a win­dow into the incent­ives at play.

“It’s a busi­ness,” Dwyer says. “Every­one who is doing it is tak­ing up space in a media land­scape and try­ing to find their niche.

“Like, it’s about try­ing to make money, and it doesn’t really mat­ter how that gets done.”

The B.C. Wild­fire Ser­vice took the rare move last week of warn­ing the pub­lic that an image pos­ted online claim­ing to be of the Drought Hill fire in the Okanagan Val­ley was not real, but had been gen­er­ated by arti­fi­cial intel­li­gence.

Canadian cleantech veteran aims to make AI a force for good on sustainability front

This article was written by Jeffrey Jones and was published in the Globe & Mail on July 14, 2025.

Long-time investor Nicholas Parker is credited with coining the term cleantech in the early 2000s as co-founder of the Cleantech Group, a research and advisory company.

A Canadian cleantech veteran is betting artificial intelligence will be a force for sustainability – and that there’s money to be made putting it to work.

Nicholas Parker, a long-time investor and adviser to companies and policy makers, has been busy in recent years evaluating how AI can boost energy efficiency, streamline industrial processes and reduce CO2 emissions across numerous industries.

Now, he and his team have launched a networking and financing ecosystem to bring together experts, entrepreneurs and investors from around the world to marshal some of the US$138-billion they say will be required to scale AI technologies for sustainability over the next five years.

“We’ve created a platform that will enable us to build and serve a global community. We had lots of experience and last year was very experimental – we just didn’t know if the concept would catch on, if it was rigorous enough, or if it would all end up being about data centres,” Mr. Parker said in an interview.

“It was so affirming that we went out and raised a little bit of cash from some AI entrepreneurs and sustainability types, and we’ve been building a team and a platform since then.”

The concept is counterintuitive, Mr. Parker concedes, because of the immense energy it takes to power AI data centres – its biggest hurdle and point of contention.

The hub, called the CleanAI Initiative, is aimed at countering the notion that the world must abandon the environmental and climate fight in favour of endless growth in computing power, he said.

On that front, AI and machine learning can be used to regulate itself, controlling the timing of power use and transmission, as well as tapping green energy when available.

Canadian companies such as ThinkLabs AI Inc. and BluWave-ai are active in that area.

Apart from energy, AI is proliferating in agriculture, materials, chemicals, transport, logistics, resources, and environmental technology. For example, using vast amounts of data, AI can pinpoint tracts of forest at risk of catching fire, allowing preventive action. That can both save carbon sinks and prevent the release of CO2 in wildfires.

According to CleanAI figures, investors have plowed US$30-billion into the nexus of AI and sustainability over the past six years, more than two-thirds of that on early-stage technology. The group estimates that such technologies have the potential to reduce global carbon emissions by 10 per cent by 2030.

Members in the CleanAI hub get access to other players in the field, as well as research, data and conferences. It held its inaugural event in Toronto last year, and has scheduled a follow-up for October.

“Where the rubber hits the road is deal sharing, co-investments, limited partners, go-tomarket partners who want to see stuff. It’s meant to be action-oriented, not just, ‘Get smart and turn up and collect business cards,’ ” Mr. Parker said.

“This has been encouraged by the big corporates, for example, who are saying, ‘Look, we’re dealing with privacy and security of data, and we’re dealing with making our work force feel safe with all this change. We’re not really diving into some of the things that you’re sharing with us. This would be hugely useful.’ ”

Mr. Parker is well known in Canadian sustainability and venture capital circles. He is credited with coining the term cleantech in the early 2000s as co-founder of the Cleantech Group LLC, a research and advisory company.

“They’re starting to aggregate an incredible set of data for a niche within this climate technology world that is becoming a lot bigger than a niche,” said Murray McCaig, co-founder and managing partner of ArcTern Ventures, a cleantech venture capital fund.

“Most companies now are either heavily leveraging it, or are themselves AI companies, and that would include our portfolio,” Mr. McCaig said. “It’s not like we set out to invest in a bunch of AI companies. It just so happens that these climate companies are leveraging AI to make the world a better place, and that’s naturally become a focus area for us.”

Energy demand for AI is expected to double in the next five years. Power for server farms, networking gear, cooling equipment and backup systems totalled 415terawatt hours in 2024, or about 1.5 per cent of global electricity demand, according to the International Energy Agency.

Because data centres must be up and running 100 per cent of the time, natural gas-fired power is touted as a key electricity source for the “hyperscalers” – companies such as Microsoft, Google and Amazon that are planning to construct the facilities. That raises concerns about the effects on climate. But it also fuels interest in technologies to improve environmental performance, said Tyler Hamilton, senior climate director at MaRS Discovery District, the Torontobased innovation support organization and accelerator. MaRS provides a venue for CleanAI’s conferences.

“There are different flavours of AI companies, some that are actually developing core platforms and others that are just using AI tools to enhance what they’re doing,” Mr. Hamilton said. “So trying to tease out those different opportunities adds a different layer of complexity for investors, but generally, yeah, there’s huge interest in the space.”

Last year’s conference attracted hyperscalers, startups, industrials and investors from 11 countries, Mr. Parker said. “It was a real microcosm of the ecosystem, and that’s coming on top of the data that we were seeing and the gaps that we were seeing between the AI world and the cleantech world.”

“There were very low levels of literacy, and connective tissue. If you’re doing a deal and you want a co-investor, who else is out there that’s interested in AI as it applies to agriculture? So that’s when we decided to go for it,” he said.

Alberta oils­ands com­pan­ies embrace robots, drones, AI

Pro­du­cers are increas­ingly turn­ing to new advances in auto­ma­tion

This article was written by Lauren Krugel and was published in the Toronto Star on May 13, 2025.

Haul trucks, shovels, pumps and pipes are com­mon sights at Imper­ial Oil’s vast oils­ands oper­a­tions in north­east­ern Alberta, but so too are robots and drones, with gen­er­at­ive arti­fi­cial intel­li­gence a newer addi­tion to the tech­no­lo­gical mix.

“We’ve been laser­focused on this digital jour­ney since 2018,” Cheryl Gomez­Smith, the senior exec­ut­ive in charge of Imper­ial’s pro­duc­tion, told a recent investor con­fer­ence.

Gomez­Smith said as of last year, Imper­ial’s bot­tom line has seen a $700­mil­lion boost from high­tech ini­ti­at­ives — and that’s on track to rise to $1.2 bil­lion by 2027. The com­pany has an in­house team ded­ic­ated to ramp­ing up new tech­no­lo­gies, and also draws on expert­ise from its U.S. major­ity owner Exxon­Mobil Corp.

For the past few years, Imper­ial has been using self­driv­ing haul trucks at its Kearl oils­ands mine. It has also enlis­ted Spot, a four­legged robot developed by Boston Dynam­ics that bears an eerie resemb­lance to a dog, for routine inspec­tions and main­ten­ance at Cold Lake, an oils­ands site in east­ern Alberta that uses steam wells to extract bitu­men.

“We estim­ate Spot can con­duct almost 70 per cent of some oper­ator rounds, allow­ing us to real­loc­ate oper­ator and main­ten­ance resources to higher value work,” Gomez­Smith said.

“We cur­rently have two Spots at site. We have two more inbound for deliv­ery at this quarter, so we’re well on our way to hav­ing a lit­ter.”

Imper­ial is build­ing on those advance­ments by expand­ing into gen­er­at­ive AI, Gomez­Smith said.

“This is where we’re chat­ting with our own data to allow oper­a­tions to gain real­time insights to drive bet­ter and faster decisions.”

At Cold Lake, remote piloted drones are help­ing save money on main­ten­ance and they’re on the brink of being AI­enabled. Sensors with AI cap­ab­il­it­ies are also help­ing auto­mate pump­jack speed at the site in order to boost effi­ciency.

Shan­non Wilson, who leads the energy divi­sion at IBM Canada, said the oil and gas industry has been using auto­ma­tion for a long time and it’s begin­ning to “take it to the next level” by incor­por­at­ing AI.

In addi­tion to bol­ster­ing auto­ma­tion already on site, Wilson said AI is being used to improve pro­ductiv­ity by quickly sift­ing through or com­pil­ing reams of inform­a­tion — tasks that would have oth­er­wise been time con­sum­ing for work­ers. It’s also been help­ful in mon­it­or­ing oper­a­tions and bet­ter plan­ning main­ten­ance activ­it­ies, redu­cing down­time.

Lar­ger com­pan­ies have the scale to invest in their own in­house tech­no­logy, while smal­ler ones are tak­ing advant­age of com­mer­cial offer­ings, Wilson said.

“There’s cre­ativ­ity hap­pen­ing in the mar­ket­place and they’re buy­ing the embed­ded solu­tions from some of their exist­ing ser­vice pro­viders.”

Wilson called AI a tool to “aug­ment” human intel­li­gence.

“Ulti­mately, humans are the decision­makers,” she said. “The more repeat­able a pro­cess is, the more AI can lend itself.”

Efforts buoyed

New tech­no­lo­gies aim to help ships in the fight to reduce emis­sions

This article was written by Todd Woody and was published in the Toronto Star on April 20, 2025.

Across the far reaches of the ocean, hun­dreds of yel­low, beach ball­ s­ized buoys called Spot­ters bob in the swell, silently meas­ur­ing sur­face tem­per­at­ure, wind speed, atmo­spheric pres­sure and wave height. The real­ time data they col­lect alerts cargo ship cap­tains of the best routes to cut their car­bon emis­sions.

At the water­front offices of San Fran­cisco star­tup Sofar Ocean, which makes the Spot­ters, a large wall screen dis­plays the loc­a­tions of the buoys and cli­ent ships as they criss­cross the globe. As one owned by Singa­pore­based Berge Bulk rounds the Cape of Good Hope, Sofar’s ser­vice noti­fies the cap­tain that adjust­ing the ves­sel’s tra­ject­ory to take advant­age of a nearby ocean cur­rent would save $13,000 (U.S.) in fuel costs and reduce the jour­ney’s car­bon emis­sions by 11 met­ric tons.

About a thou­sand cargo ships sub­scribe to the fore­cast­ing ser­vice, called Way­finder, which incor­por­ates data col­lec­ted by more than 500 Spot­ters spread over the open ocean.

“Way­finder has proven to be very accur­ate in fore­cast­ing and route optim­iz­a­tion,” says James Mar­shall, Berge Bulk’s chief exec­ut­ive officer.

Route optim­iz­a­tion is one of the tech­no­lo­gies ship­ping com­pan­ies are embra­cing as reg­u­lat­ory pres­sures grow to reduce green­house gas emis­sions in an industry that trans­ports more than 80 per cent of the inter­na­tional trade in goods and gen­er­ates three per cent of global emis­sions. With the con­ver­sion of ship­ping fleets to low­car­bon fuels likely years if not dec­ades away, ship own­ers are also installing high­tech sails on cargo car­ri­ers and test­ing on­board car­bon cap­ture sys­tems.

Moves to limit ships’ green­house gas emis­sions are spur­ring the adop­tion of such tech­no­lo­gies, accord­ing to experts. The Inter­na­tional Mari­time Organ­iz­a­tion, the ship­ping industry’s global reg­u­lator, last week approved draft rules man­dat­ing reduc­tions in ves­sel emis­sions. The European Union in Janu­ary began impos­ing a sur­charge on ships that don’t meet its emis­sions stand­ards.

“The ocean is a large place and you need lots of data to make a bet­ter weather fore­cast,” says Tim Janssen, Sofar’s chief exec­ut­ive officer.

To that end, Sofar engin­eered the Spot­ter to be eas­ily deployed. Weigh­ing about 17 pounds, the buoy can be tossed off the back of a boat or dropped from a plane, activ­at­ing when it hits the water. The com­pany has stra­tegic­ally placed the Spot­ters along ship­ping lanes and in loc­a­tions where data on ocean con­di­tions is sparse.

While satel­lites and other plat­forms mon­itor the ocean from afar and provide data for weather fore­casts, the Spot­ters gen­er­ate real­time mari­time obser­va­tions that let ship cap­tains make small changes in their routes that can yield sig­ni­fic­ant sav­ings in fuel and emis­sions. For instance, a satel­lite can approx­im­ate the height of waves from orbit, but Spot­ters dir­ectly meas­ure a wave’s actual size as well as its dir­ec­tion and fre­quency, alert­ing cap­tains if one large enough to roll a ves­sel is headed their way.

Sofar owns the Spot­ters in the open ocean and there’s about 2,000 of the buoys mon­it­or­ing coastal waters. The com­pany sells a com­mer­cial ver­sion start­ing at $6,600. Com­pet­it­ors like LR OneOcean, owned by Lloyd’s Register, also offer route optim­iz­a­tion assist­ance.

Mar­shall estim­ates Way­finder cuts Berge Bulk’s fuel con­sump­tion three to five per cent across its 89ship fleet. Dorian LPG, which oper­ates 25 liquid pet­ro­leum gas tankers, says Way­finder has cur­tailed fuel con­sump­tion by nine per cent.