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Making food go further. Submit your solution for the circular economy before 30 November: https://bit.ly/2GwAcuF
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Harnessing Artificial Intelligence to Decarbonize Industrial Sectors by Nina Gillespie | Hoffmann Centre for Sustainable Resource Economy

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Hoffmann Centre for Sustainable Resource Economy

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While AI applications to decarbonize heavy industries are promising, a focus on process optimization over its more radical use could limit its potential. However, establishing knowledge bases and policy frameworks to foster innovation could speed up the application of AI to disrupt business-as-usual operations and deliver decarbonization.

As global use of metals, fossil fuels, sand, limestone and gravel are forecast to more than double between 2011 and 2060, heavy industrial sectors are under increased scrutiny to take more significant steps towards reduced material consumption and decarbonization. Undoubtedly, the COVID-19 pandemic has hit production of cement, steel and chemicals processing: cement production was, globally, 4.4 per cent lower in the first quarter of 2020 than 2019. Yet such reductions have not significantly challenged estimates that these 'harder-to-abate' sectors account for almost 10 Gt or 30 per cent of total global carbon dioxide emissions annually, and may increase to 46 per cent of total global emissions by 2050.

In order to tackle this challenge, heavy industrial sectors are already adopting renewable energy and efficiency measures. Numerous steel manufacturers are pursuing hydrogen-powered production processes while the cement sector has started to turn to photovoltaics (PV) and concentrated solar power (CSP) to increase renewables into its energy mix. Aluminium company, Norsk Hydro, and cement producer, Cemex, among others, signed up to renewable-energy power purchase agreements. Other sector actors, like the Swedish steel producers making up the BRIT collaboration, are investing in renewably produced hydrogen for steel production plants and processes.

However, wide-scale adoption of renewable sources within the industrial sector has been somewhat limited - estimates suggest that they accounted for just 14 per cent of industry's energy use in 2015 increasing to just 14.5 per cent in 2018. The health crisis and looming recession may, however, have altered this trajectory and commitments to decarbonize industrial sectors through economic recovery packages could increase uptake.

The proportion of recycled aluminium scrap in total primary production.

Circular economy business models also have the potential to cut emissions from aluminium, plastics, steel and cement by 40 per cent globally and by 56 per cent in more developed economies by 2050. Despite this potential, rates of materials recycling remain stagnant - the Global Circularity Metric estimated 8.6 per cent global circularity in 2020. Aluminium production is a case in point: recycled aluminium scrap makes up around just 20 per cent of total primary production. Uptake remains low because adopting circular economy approaches requires a consistent supply of scrap provided through effective materials processing and redistribution across the board - from building demolitions to domestic waste.

Using AI to increase efficiency gains has already demonstrated significant potential that can be applied in industrial sectors. Notably, Google's DeepMind implemented a machine-learning system within a Google data centre to make significant gains in efficiency. Utilizing historic data collected by sensors, it successfully applied a machine-learning neural network to decrease the amount of energy used for cooling servers by up to 40 per cent in 2016.

The likes of Element AI have already implemented similar variance-analysis techniques to compare energy usage on high- and low-performing days within industrial settings. Such applications help to identify changes required and investments needed to reduce fuel consumption and emissions through industrial processes.

Companies are also making headway in applying AI to manage uncertainties and reduce inefficiencies within specific production practices. For instance, in manufacturing, the deployment of anomaly detection models can quickly identify defects that would otherwise lead to wasted products and rework, thereby reducing material consumption. In a similar vein, Yandex Data Factory's work with Russian company, MMK Steel, reduced ferroalloy consumption by an average of 5 per cent through the analysis of historical datasets.

Other innovations in AI applications could see the decarbonization of entire processes within production. Within the cement sector, a combination of machine vision and CSP have been deployed to optimize the position reflective mirrors to capture the most of the sun's rays. This has provided proof of concept that AI-enabled solar-powered furnaces can reach over 1,000 o C. Deployed for commercial use and scaled, solar furnaces could deliver emissions reductions from the carbon intensive calcination process.

Yet, when looking to deliver more significant optimization of industrial processes, AI is only as good as the data at its disposal. Existing data collected may be incomplete, lack granularity or fail to address gaps and inefficiencies in the system that could have the most significant impact on resource-use reduction. As such, data collection that is only driven for the purpose of economic savings or productivity gains can severely limit potential carbon reductions. AI's potential to deliver deep decarbonization requires heavy industries to establish stronger data readiness and to specifically refocus data collection strategies on to decarbonization.

While the uptake of disruptive technologies is expected to grow exponentially across the board, adoption within heavy industries is expected to be below average by 2022. Chemicals and materials processing have been demonstrative of this point continuing to lag behind market leaders, a trend that may be perpetuated by the COVID-19 pandemic: R&D spending declined by more than 10 per cent in the chemicals industry in Q1 of 2020 versus 2019.

Importantly, a lack of ambition to address decarbonization in its own right means heavy industries may fail to fully utilize the potential of AI to shift beyond domain-specific optimization towards supply-chain disruption. 'Light' decarbonization through process optimization has the appeal of causing minimal disruption to existing production practices, low implementation costs and immediate yield improvements to boot.

In contrast, while exact estimates vary, the cost of innovative solutions required for deep decarbonization could see up to a 20 per cent increase for the cost of steel and up to 115 per cent for cement and concrete. This could prove a significant barrier to the uptake of disruptions across the supply chain. Institutional inertia from powerful incumbents could, therefore, threaten the scaling up of AI beyond solutions that maintain business-as-usual operations.

AI's potential to deliver deep decarbonization requires heavy industries to establish stronger data readiness and to specifically refocus data collection strategies on to decarbonization.

AI could deliver more radical disruptions across industrial supply chains for greater decarbonization potential. AI has been applied in the realm of material science in order to simulate scenarios and predict properties of novel materials: AI systems are already up to 30 per cent faster than existing trial-and-error approaches.

Using AI to identify novel materials can also be harnessed to identify resource use and select materials that best meet sustainability criteria. For example, The European Space Agency funded a project to conduct research into developing new, more efficient alloys that also met certain sustainability principles. Utilizing AI algorithms to analyse vast datasets of existing materials, researchers set out to test new alloy formulations including their predicted impact on resource depletion, greenhouse gas emissions and acid rainfall - meaning partners could feasibly calculate the environmental impacts for the new alloy formulations.

In order to scale up AI's potential, efforts in materials science need to continue to be trained on knowing the footprint of a given material (e.g. carbon and water) as well as the footprint of its supply chain. This type of innovation could push the industrial sector to substitute existing materials and processes in a way that could unlock wide-scale decarbonization - dependant on the potential resource footprint of a specific material and its supply chain - and enable the adoption of this change at a rapid rate.

AI can also deliver more effective circular economy business models across the supply chain in order to slow down and narrow options for material consumption. Predictive maintenance, for example, can extend product life meaning that the material input to make new products is reduced.

Furthermore, AI can also be implemented to track materials in order to re-manufacture and re-enter them into the supply chain. Machine learning, in particular visual identification systems, are being adopted to improve rates of municipal recycling where intelligent sorting machines are able to better identify materials and could, in the future, identify material composition.

Data sharing could enable more effective resource allocation and redistribution across the supply chain. Increased transparency and traceability could ensure more effective management of industrial resource use, incorporating data inputs from across a product's lifecycle, for example, to optimize stock replenishment. Digital twins, whereby exact replicas of physical assets are digitally recreated in order to test different scenarios and conditions, can be used to predict the most promising modes of operation for decarbonization which, if interoperable, could be applied across supply chains.

Fragmented supply chains and data silos reduce the potential for integrative end-to-end applications of AI. There is a need to align the huge array of inputs and actors along the supply chain while also ensuring an ecosystem of trust that will enable open data sharing from diverse and competing companies that also recognizes data sovereignty and security threats, for example, through data-security frameworks.

Disruptions can be difficult to predict and focus on process optimization can stifle step change towards industrial transformation. Existing risk-averse financial models can punish industry leaders while there is lack of robust policy frameworks to drive change. The landscape for facilitating deep decarbonization within the industrial sector needs to provide a framework in which heavy industries can better harness disruptive technologies and balance risk and reward for actors leading the way driving applications that goes beyond process optimization. While COVID-19 recovery packages have begun to make moves to drive industrial decarbonization, more coherent policies are required:

1. Knowledge sharing

Developing a database of frameworks and cases of best practice from across different sectors can draw attention to the 'win-win' potential in harnessing AI for industrial decarbonization. Building policies that deliver a stronger framework for the data economy, for example by establishing data trusts, could drive forward data sharing across and within industries and build trust.

2. Piloting technologies

Test beds and test centres are crucial to demonstrate to industry actors the opportunities brought about by implementing AI for sustainability. By piloting technologies, leaders in industry can demonstrate the value and viability of adopting these digital technologies.

3. Supporting SMEs and startups

Establishing a stronger framework for small and medium-sized enterprises and startups could also foster the development of innovative technologies that incumbents may be reluctant to adopt.

4. Aligning digital and green agendas

The digital and green policy transitions must be coherently and holistically aligned in order to demonstrate the opportunities but also the potential trade-offs required between the sectors. Placing them under a single policy framework will allow digital and green agendas to strengthen one another.

Radically reducing emissions from cement, steel and chemicals processing requires supporting political, economic and financial frameworks in order to deliver on AI's potential.

This drive for the innovative use of AI could also come from shifts in public sentiment as demand for action and transparency on environmental and sustainability issues increases. Consumers have the power to push companies to question their existing practices and reorganize their supply chain as well as to steer demand towards more sustainable resource use through a cascading process that requires actors along the supply chain to align themselves with consumer wishes.

Provided the enabling conditions are in place, the COVID-19 pandemic could increase the pace of AI adoption. Radical shifts online, both by companies and individuals, demonstrate the viability of transformations to business practices. At the same time economic disruption may push companies to increase digitalization, including AI, in order to remain economically viable in the face of future lockdowns. Here, aligning the digital and green agendas will be vital.

AI alone will not solve the problems of decarbonization - it must be aligned with other tools to drive the industrial sector to act. Radically reducing emissions from cement, steel and chemicals processing requires supporting political, economic and financial frameworks in order to deliver on AI's potential. Establishing an enabling environment for AI while also shaping narratives around industrial decarbonization will mean that AI can be better applied as a specific solution to help deliver decarbonization within heavy industry.

This summary is based on a workshop hosted by Hoffmann Centre for Sustainable Resource Economy with the generous support of DeepMind Ethics & Society.

References

1. ↑ IEA (2020),The Covid-19 Crisis and Clean Energy Progress, IEA, Paris.

2. ↑ Vattenfall (2019), 'SEK 200 million invested in pilot plant for storage of fossil-free hydrogen in Lulea', 3 October 2019.

3. ↑ IRENA (2018),Global Energy Transformation: A roadmap to 2050.

4. ↑ Energy Transitions Commission (2018),Mission Possible.

5. ↑ de Wit, M., Hoogzaard, J. and von Daniels, C. (2020),The Circularity Gap Report 2020, Circle Economy.

6. ↑ Element AI (2020),AI for Climate.

7. ↑ Element AI (2020),AI for Climate.

8. ↑ S&P Global Platts (2017), 'Metal Insight. Artificial Intelligence in steel: mills embrace Big Data, robotics', 20 July 2017.

9. ↑ Accenture (2018),Accenture Metals: Process Reimagined, Accenture, London; Accenture (2018), Accenture Chemicals: Process Reimagined, Accenture, London.

10. ↑ IEA (2020),Energy Technology Perspectives 2020, IEA, Paris.

11. ↑ World Economic Forum (2019),How can the EU hit net-zero emissions? World Economic Forum, 18 July 2019.

12. ↑ Material Economics (2019),Industrial Transformation 2050 - Pathways to Net-Zero Emissions from EU Heavy Industry, Material Economics, Stockholm.

13. ↑ Ellen MacArthur Foundation (2019),Artificial Intelligence and the Circular Economy - AI as a Tool to Accelerate the Transition, Ellen MacArthur Foundation.

14. ↑ CORDIS (2017),Accelerated Metallurgy - the accelerated discovery of alloy formulations using combinatorial principles.

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Harnessing Artificial Intelligence to Decarbonize Industrial Sectors by Nina Gillespie

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While AI applications to decarbonize heavy industries are promising, a focus on process optimization over its more radical use could limit its potential. However, establishing knowledge bases and policy frameworks to foster innovation could speed up the application of AI to disrupt business-as-usual operations and deliver decarbonization.

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Do elections influence market volatility?

Over 90 years of data shows that volatility jumps 30% in the five months leading up to an election. But while elections have historically stoked uncertainty in the market, in reality, the scale of their impact plays a relatively minor role.

This Markets in a Minute chart from New York Life Investments shows volatility trends surrounding elections over the last century, and how investors can best position themselves amid market turbulence.

Making Sense of Market Volatility

Volatility is when a security has sharp price movements in either direction. The market's volatility is measured by the CBOE Volatility Index (VIX), also known as the 'fear gauge' for the market. The higher the VIX reading, the higher the volatility.

The five-year average VIX value is 15.8, with an an all-time low of 9.1 in November 2017, and reaching an all-time high of 82.7 in March 2020. Specifically, in the five months ahead of U.S. elections, the VIX tends to fall between 14 and 18.
Source: Eureka Report
After the dust settles from elections, market volatility reduces as investors gain more clarity on government direction.

In short, in the six months following an election, volatility tends to fall on a downward sloping trajectory.

Finding Opportunity Surrounding U.S. Elections

With volatility here to stay, investors can utilize a number of portfolio strategies prior to elections.

1. Stay the course: The easiest thing investors can do is nothing. Ignoring irrational market activity and staying invested will help you keep your investment goals on track.

2. Focus on value: Investors can focus on companies with sound balance sheets that return value back to shareholders, such as fixed-income investments or dividend-paying stocks. For instance, when concerns circled around increased taxes on investment income in 2012, no less than 1,100 companies issued a special dividend following the election.

3. Bargain hunt: Overvalued stocks, or sectors in the policy spotlight, can temporarily dip amid market fear. For example, in 2016 the health care sector saw new policies that investors feared would have damaging effects. Ultimately, these concerns were overdone, and the sector rallied after the election.

Focusing on solid company fundamentals can offer windows of opportunity to investors who look past the short-term volatility.

Long-Term Areas to Focus On

Investors can look to structural factors, such as the economic environment, that have a more powerful impact on financial markets.

Interest rates, low bond yields and policy measures, among others, have a greater influence on market performance. Rather than paying attention to short-term volatility, investors can also focus on policy changes that have a lasting impact on the economy:

1. Employment: Economic policies that help to promote workforce outcomes will have positive impacts on earnings growth, market performance, and investor portfolios.

2. Taxes: Tax policies reallocate capital. Corporate tax cuts, for instance, can buoy markets and investor optimism.

3. COVID-19 containment: The policies in place in response to COVID-19, such as the CARES Act, will have a marked impact on investor sentiment, company earnings, and ultimately economic resilience.

Looking past the election, and keeping an eye on policy shifts, could provide more insight into key forces shaping the future of the economy.

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EAE: Introduction to the big questions about energy

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EAE: Introduction to the big questions about energy

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The World Economic Forum is an independent international organization committed to improving the state of the world by engaging business, political, academic and other leaders of society to shape global, regional and industry agendas. Incorporated as a not-for-profit foundation in 1971, and headquartered in Geneva, Switzerland, the Forum is tied to no political, partisan or national interests.

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Just because he's probably wrong doesn't mean we ignore his reasoning (though the site is very old). Read it and judge for yourself.
https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)

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This (old) article provides a very conservative view, but is a good place to start to list the issues