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Canada oil producers grapple with Trudeau’s demand for faster emissions cuts

Source: Reuters

WINNIPEG, Manitoba, Oct 22 (Reuters) – Canada’s oil producers face new pressure from Prime Minister Justin Trudeau to reduce emissions in just three years, a sudden acceleration of their plans that at least one major company said looks unrealistic.

Suncor Energy (SU.TO), the second-largest Canadian crude producer, says it remains focused on cutting emissions by 2030, not 2025 as the Canadian government will require.

“Honestly, 2025 is going to be tough,” Martha Hall Findlay, Suncor’s Chief Sustainability Officer, told Reuters. “That’s not a number we’ve used, it’s a number the feds have used.”

Trudeau’s advanced timetable for cuts to the oil sector’s total emissions by 2025, announced last month, comes as the oil sector has focused on longer-term targets, and on reducing emissions on a per-barrel basis.

“That is light speed for an oil sands company. That’s tomorrow,” said Kevin Birn, chief analyst of Canadian oil markets at consultancy IHS Markit, of Trudeau’s demand. “They’re a very hard ship to turn because they have so much emissions.”

Previously, Ottawa had a target of cutting national emissions by at least 40% by 2030, but it did not single out the oil sector. Canada’s crude industry generates some of the highest emissions per barrel worldwide.

Suncor is the only big producer that has laid out a plan – in May – to cut total emissions by 2030, depending heavily on carbon capture, greener power sources and energy efficiency.

But Trudeau’s 2025 demand came as a surprise.

“We had obviously been having conversations with the feds long before the budget came out last spring, long before the (election) campaign,” Hall Findlay said. “None of those discussions have mentioned 2025. At Suncor, we’re laser-focused on 2030.”

Canadian Natural Resources Ltd (CNQ.TO) and Cenovus Energy (CVE.TO), have been planning for months to unveil their emissions targets by year-end.

Cenovus intends to cut emissions on an absolute and per-barrel basis, said spokesman Reg Curren, but he would not say if cuts would occur by 2025.

Canadian Natural is working on “mid-term” targets connected to the Pathways carbon capture project with its peers, said spokesperson Julie Woo. She would not say if they would address Trudeau’s 2025 requirement.

Governments and business would need to spend C$60 billion annually to cut Canada’s emissions by 75% in the next 30 years, RBC Economics said.

Canadian producers are expected to report big quarterly profits in coming weeks as oil and gas prices have soared. The companies have prioritized repaying debt and returning cash to investors, but Trudeau wants producers to spend some profits on curbing emissions.

He plans to unveil his new cabinet on Tuesday, just ahead of the United Nations’ Climate Change Conference in Glasgow, Scotland.

Ottawa wants to ensure there are ambitious emission reductions from the oil and gas sector, making a meaningful contribution to Canada’s climate goals, said Joanna Sivasankaran, spokesperson for the Canadian environment department.

Trudeau’s 2025 goal is “ambitious for sure” and it would be more realistic to expect the sector to cut emissions sharply by a decade later, said Steve MacDonald, CEO of Emissions Reduction Alberta, an arms-length corporation funded by the provincial government.

‘EASIER THAN ANYONE THINKS’

Some small conventional oil producers are already showing deep emissions cuts are possible, however, using methods that big producers Canadian Natural and Cenovus could widely apply. Both companies produce crude in the oil sands and by conventional methods.

Yangarra Resources (YGR.TO), which produces 10,000 barrels of oil equivalent per day, says it will cut total emissions by 47%, or 50,000 tonnes of carbon dioxide equivalent, by the end of 2022. Its plans involve powering 80 pumpjacks with electricity from the Alberta grid, instead of burning natural gas, and replacing older instruments that emit high amounts of methane.

“Cutting carbon in the oil patch is going to be a whole lot easier than anyone thinks,” said Yangarra CEO Jim Evaskevich. “All of the changes we are implementing make incredible economic sense.”

The moves are likely to generate substantial credits next year that Yangarra can sell to bigger emitters, although the monetary value has not yet been determined, Evaskevich said.

Cenovus, which generates 18% of its production from conventional operations, has cut its methane emissions by nearly half from 2015 levels, a spokesperson said. Canadian Natural has cut methane emissions by 28% since 2016, Woo said.

“They’re big, large operations, and they can’t pivot quite as quickly,” MacDonald said. “But that doesn’t mean they aren’t moving forward in the same areas.”

Emissions reductions are difficult for oil sands operations because of the energy they require, while conventional methane emissions are easier to tackle, said Keith Stewart, senior energy strategist at Greenpeace Canada.

Oil sands producers are counting on expanded carbon capture and sequestration facilities to cut emissions. But the economics requiregovernment funding, said Greg McNab, a partner at the Baker McKenzie law firm. Using renewable power to run oil sands facilities may be the quickest way to curb emissions, he said.

Reporting by Rod Nickel in Winnipeg; Editing by David Gregorio

Our Standards: The Thomson Reuters Trust Principles.

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Business

A journey into the heart of sustainable practices

By Rosemary Thomas, Senior Technical Researcher, AI Labs, Version 1

Artificial Intelligence is a transformative force that is reshaping our daily lives. It serves as an instrument of change, driving innovation across various sectors by automating tasks, providing insightful data analysis, and enabling new forms of interaction. AI is fostering a new era of efficiency, productivity, and creativity.

More importantly though, through transparency, ethical AI practices, and healthy privacy safeguards, AI can help to strengthen our trust in technology and its role in our daily lives. It is a catalyst for changing how society perceives sustainability, helping us predict and work towards a more sustainable, ethical future.

Making a difference with AI for good

‘AI for good’ pertains to the use of AI technologies to help solve specific societal challenges and contribute towards making people’s lives better. It leverages the strength of AI to address issues like economic hardship, physical and mental wellbeing, academic achievement, and the preservation of nature.

For businesses, ‘AI for good’ can mean using AI to contribute towards environmental, social, and governance (ESG). Used correctly, AI can help to create sustainable strategies, powering solutions that present a greater advantage to society. It can also help with ESG reporting, which has become a highly time-consuming process involving data collection, the use of multiple frameworks, rapidly changing disclosure requirements, the integration of different models, reporting, and data analysis. By adding AI capabilities into this process, businesses can streamline their operations, increase data accuracy, and increase confidence in stakeholder engagement.

A recent example of an ‘AI for good’ application is the TNMOC Mate designed for The National Museum of Computing. The app offers a different experience, tailored to each guest meaning neurodiverse and non-English speaking individuals, as well as young children, can engage with the museum exhibits equally. This is a prime example of AI being used to bring societal advantage, helping people regardless of their background or abilities to enjoy the museum experience as intended, by using generative AI to present complex exhibit information in a way that is easily understandable.

Improving sustainability with green AI

Green AI is another aspect of ‘AI for good’. It relates to eco-friendly artificial intelligence algorithms, models or systems that use less computational power and emit lower carbon. It holds significant importance, given that a call for a thorough review of sustainability has arisen since Large Language Models (LLMs) have been criticised for their large carbon footprints and energy usage.

One way of implementing Green AI, is leveraging AI systems for efficient inventory and resource management. Machine Learning models can analyse the performance data of equipment and devices, then use this data to help extend the lifespan of resources and ensure their optimal utilisation. They can also schedule updates, hardware upgrades and maintenance proactively, avoiding potential downtime. Furthermore, these models can detect abnormalities in system operations early, allowing organisations to conduct timely maintenance. This can help them save time and money, as well as reducing wastage.

AI models also play a crucial role in computing and energy efficiency. They can analyse and optimise energy consumption patterns, leading to significant improvements in operational efficiency.

Additionally, while LLMs can contribute to carbon emissions, they can also serve as a powerful tool in battling climate change. LLMs can expedite research and innovation processes while maintaining a focus on sustainability. By generating creative and diverse solutions, they can help organisations stay at the forefront of their industries, while keeping sustainability at the core of their operations.

Measure more than carbon footprint in AI metrics

It is no doubt important to measure carbon emissions during the training of models. It can prove crucial when considering regional differences, as this plays a key role in promoting sustainability. But given the wide range of energy efficiency measurements across different AI algorithms, it is essential to include additional energy metrics along with traditional performance indicators. Choosing cloud providers that prioritise eco-friendliness is recommended, as well as strategically selecting the locations of data centres; the ultimate aim should be to foster the creation of AI solutions that are not only energy-efficient, but also environmentally friendly.

There is a call to standardise energy and carbon data reporting, which has been seen as a step towards encouraging social responsibility in the field of AI research and development. However, reporting cannot be done without accurate calculations, and carbon measurement is still in its early stages. When calculating the carbon footprint of a model, we should consider all variables equally, not just the final value of carbon. This is fundamental because, without this knowledge, we are ill-equipped to manage or improve it.

Fortunately, there are organisations working to solve this challenge. For example, The Green Software Foundation (GSF) is a non-profit organisation that aims to create a trusted ecosystem of people, standards, and best practices for developing green software and AI. The GSF have various tools and methods to help us measure and reduce the environmental impact such as the ‘Impact Framework’,‘Software Carbon Intensity’ (SCI) specification, and the Green Software Maturity Matrix[1].

Inclusion and diversity in the ethical use of AI

Safeguarding ethical use involves laying the groundwork for ethical standards, tackling biases in AI systems, prioritising transparency and explainability, and protecting against privacy concerns. The impact on human autonomy and responsibility gaps must also be contemplated, along with calculating the financial and environmental costs of training deep learning models.

There are implications arising from both responsible and irresponsible AI deployment, and it is important to illustrate examples of both sides in AI applications. In healthcare, for example, AI systems are used to assist medical professionals in transparent diagnosis and accountable treatment planning. This boosts patient care, promotes fair and informed decision-making, and contributes to better health outcomes.

In human resources, AI can be used for unbiased staffing processes. It moderates human biases, elevates inclusion and diversity, and promotes evenly balanced opportunities for all candidates.

Finally, in environmental monitoring, AI is used for the transparent monitoring and managing of eco-friendly dynamics, such as air and water quality, using sensors, transmitters, and data analytics. This helps to care for the environment, protect ecosystems and support the well-being of groups by addressing environmental hazards.

The non-ethical use of AI is more prevalent in surveillance systems, especially with facial recognition deployed in public spaces. This technology is used for mass surveillance, tracing individuals without their consent, and disregarding privacy rights, and in the US in particular this can be easily misused. AI tools can also be used in the creation of deepfakes to create dangerous misinformation.

Additionally, if the training data consists of historical biases, AI systems can spread and increase prejudice – resulting in unjust treatment which can excessively impact certain demographic communities. Finally, social engineering attacks using AI systems can be much more difficult to detect, and prompt injection attacks and LLM poisoning can intentionally cause harm and malice for a larger population.

Ethical, sustainable AI

As we collectively strive towards a sustainable future, AI is emerging as a key driving force. It is steering us towards solutions that are not only economically viable, but also environmentally sound and socially responsible.

Organisations should start to leverage sustainable AI, making sure that these technologies are having a positive impact of the ESG commitments, while ensuring they are created and used in a way that is ethical, fair, and transparent. In this journey, every algorithm we design, every model we train, and every AI-powered solution we deploy can take us one step closer to our goal of sustainability.


[1] https://medium.com/version-1/what-really-matters-for-green-calculations-a-practical-perspective-0bc0f5c7540c

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Could electric vehicles be the answer to energy flexibility?

Rolf Bienert, Managing and Technical Director, OpenADR Alliance

Last year, what was the Department for Business, Energy & Industrial Strategy and Ofgem published its Electric Vehicle Smart Charging Action plans to unlock the power of electric vehicle (EV) charging. Owners would have the opportunity to charge their vehicles while powering their homes with excess electricity stored in their car.

Known as vehicle to grid (V2G) or vehicle to everything (V2X), it is the communication between a vehicle and another entity. This could be the transfer of electricity stored in an EV to the home, the grid, or to other destinations. V2X requires bi-directional energy flow from the charger to the vehicle and bi- or unidirectional flow from the charger to the destination, depending on how it is being used.

While there are V2X pilots already out there, it’s considered an emerging technology. The Government is backing it with its V2X Innovation Programme with the aim of addressing barriers to enabling energy flexibility from EV charging. Phase 1 will support development of V2X bi-directional charging prototype hardware, software or business models, while phase 2 will support small scale V2X demonstrations.

The programme is part of the Flexibility Innovation Programme which looks to enable large-scale widespread electricity system flexibility through smart, flexible, secure, and accessible technologies – and will fund innovation across a range of key smart energy applications.

As part of the initiative, the Government will also fund Demand Side Response (DSR) projects activated through both the Innovation Programme and its Interoperable Demand Side Response Programme (IDSR) designed to support innovation and design of IDSR systems. DSR and energy flexibility is becoming increasingly important as demand for energy grows.

The EV potential

EVs offer a potential energy resource, especially at peak times when the electricity grid is under pressure. Designed to power cars weighing two tonnes or more, EV batteries are large, especially when compared to other potential energy resources.

While a typical solar system for the home is around 10kWh, electric car batteries range from 30kWh or more. A Jaguar i-Pace is 85kWh while the Tesla model S has a 100kWh battery, which offers a much larger resource. This means that a fully powered EV could support an average home for several days.

But to make this a reality the technology needs to be in place first to ensure there is a stable, reliable and secure supply of power. Most EV charging systems are already connected via apps and control platforms with pre-set systems, so easy to access and easy to use. But, owners will need to factor in possible additional hardware costs, including invertors for charging and discharging the power.

The vehicle owner must also have control over what they want to do. For example, how much of the charge from the car battery they want to make available to the grid and how much they want to leave in the vehicle.

The concept of bi-directional charging means that vehicles need to be designed with bi-directional power flow in mind and Electric Vehicle Supply Equipment will have to be upgraded as Electric Vehicle Power Exchange Equipment (EVPE).

Critical success factors

Open standards will be also critical to the success of this opportunity, and to ensure the charging infrastructure for V2X and V2G use cases is fit for purpose.

There are also lifecycle implications for the battery that need to be addressed as bi-directional charging can lead to degradation and shortening of battery life. Typically EVs are sold with an eight-year battery life, but this depends on the model, so drivers might be reluctant to add extra wear and tear, or pay for new batteries before time.

There is also the question of power quality. With more and more high-powered invertors pushing power into the grid, it could lead to questions about power quality that is not up to standard, and that may require periodic grid code adjustments.

But before this becomes reality, it has to be something that EV owners want. The industry is looking to educate users about the benefits and opportunities of V2X, but is it enough? We need a unified message, from automotive companies and OEMs, to government, and a concerted effort to promote new smart energy initiatives.

While plans are not yet agreed with regards to a ban on the sale on new petrol and diesel vehicles, figures from the IEA show that by 2035, one in four vehicles on the road will be electric. So, it’s time to raise awareness the opportunities of these programs.

With trials already happening in the UK, US, and other markets, I’m optimistic that it could become a disruptor market for this technology.

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Navigating the commercial vehicle sustainability conundrum

By David Wilson, Business Development Advisor, NEOL Copper Technologies Ltd.

As road transport companies implement their environmental, social, and governance (ESG) strategies to ensure they are contributing positively to the planet and society while also being run ethically and transparently, they are faced with a conundrum.

With increasing regulatory and social scrutiny on carbon emissions, the transportation industry which is the second largest (20%) contributor to carbon emissions worldwide, faces growing pressure to meet the near-term net-zero targets, requiring an immediate move to being more sustainable.

The industry has recently undergone significant changes that have impacted the cost of running a successful business. Factors such as high fuel costs, increased labour expenses, and maintenance costs, as well as excessive costs to renew the fleet, have all contributed to this. Additionally, businesses now need to consider how to incorporate the future of electric and autonomous vehicles.

The future of electric vehicles

ESG strategies such as investing in fuel-efficient, low-emission technologies and adopting alternative sustainable fuel sources are essential to reduce carbon emissions, air pollution, and preserve natural resources, while protecting the industry’s long-term viability.

In order to make the industry more sustainable electric trucks will need to play a significant role. The migration to electric trucks is also an option for the fleet manager but there is presently a narrow choice of vehicles, an associated high procurement or lease cost, and a lack of public charging infrastructure.

Most commercial vehicle OEMs (original equipment manufacturers) now offer a range of electric trucks that are specifically designed for zero-emission deliveries. However, the use of heavy-duty electric trucks for long-range transport is not feasible yet, mainly because the batteries and charging power are insufficient. The large-scale adoption of electric trucks is going to take time, and it may not be until 2035 – emphasizing that the electrification of the trucking industry is around 10 years behind passenger cars in terms of electrification.

Transitioning away from fossil fuel is a complex challenge for fleet managers. It will take time for a complete shift of the 600,000+ heavy good vehicles currently navigating the UK roads to electric power. To address the issue promptly and enhance the fuel efficiency and sustainability of the current fleet, proactive measures are imperative to optimise their performance and curtail emissions immediately.

Addressing the sustainability conundrum

The vast majority of today’s commercial vehicles on the road today are powered by internal combustion engines (ICE) that run on diesel fuel. Since the first introduction of European exhaust emission standards in 1993, more stringent guidelines have been released every four to five years to reduce and eliminate harmful pollutants such as carbon dioxide, nitrogen oxide, hydrocarbons, and particulate matter from new vehicles sold in the EU.

 To meet the latest Euro VI (2015) emission standard, trucks are now typically equipped with diesel particulate filters (DPF) to capture particulate matter and lubricant ash, and selective catalytic reduction (SCR) technology to convert harmful nitrogen oxides to nitrogen and water, and exhaust gas recirculation (EGR) technology to lower the combustion temperature, reduce nitrogen oxides, and improve engine efficiency.

Euro VI engines are advanced and highly sophisticated systems that offer dependable and efficient performance. Together with the correct low-SAPS (sulphated ash, phosphorous, and sulphur) and low viscosity e.g. SAE 5W-30 engine lubricant, the fleet manager will benefit from reduced fuel consumption and warranted protection of the engine and exhaust aftertreatment devices (ATD).

As engine hardware has advanced, so has the lubricant technology. However, even with the latest low-viscosity oils, levels of fuel saving at 1-1.5% (compared to higher-viscosity oils) have not reached its full potential. Moreover, the continued use of metal-containing detergents and ZDDP (zinc dithiophosphate) antiwear components risk negatively impacting the performance and efficiency of the DPF, as well as the precious metal catalysts & sensors in the SCR units. This can lead to unplanned service and replacement of one or more of the ATDs, causing costly downtime for fleet managers.

 Euro 7 emissions regulations will be implemented in a few years, and it will require ATDs to perform as new for 200,000 km or 10 years. Therefore, the lubricant industry is facing a new challenge of lowering the levels   in engine lubricants even further.

Reducing unexpected downtime with technical lubricants

The fleet manager has access to high-quality diesel engines and lubricant technology, but they are concerned about unplanned mechanical issues due to the wear and tear of components from extended use. Additionally, the blockage of DPFs (which creates backpressure and increases fuel consumption) and the possible failure of sensors may lead to faults being registered on the truck’s OBD (on-board diagnostics) computer systems, still causing great concern for managers as they strive for maximum productivity and profitability.

Whilst the use of fossil fuels will remain crucial to power heavy-duty diesel engines, we must wait for further advancements in electrification. However, we can improve the lubricants currently being used to make commercial vehicles more efficient, with lower emissions and greater fuel economy. By doing this, we can reduce unwanted unplanned downtime for repairs or component replacements.

It is easy to see the clear link between reducing wear to increase the longevity of your machine assets. Additionally, by reducing friction, we can improve fuel savings which helps to increase efficiency, all essential steps towards acting more sustainably and making changes for a better future.

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