Club of Amsterdam Journal, May 2024, Issue 264

Journals Archive
Journals – Main Topics
Climate Change Success Stories
The Future Now Shows

Club of Amsterdam Search
Submit your article



Lead Article

Generating Democracy: AI and the coming revolution in political communications
by Alice Dawson and James Ball

Article 01

AI In Banking and Financial Services
Article 1 of 4: Who’s Smarter?
by Rohit Talwar, CEO, Fast Future

The Future Now Show

with Marie-Hélène Caillol & Katie Schultz

Article 02

OpenAI-backed "AGI ROBOT" shocked the entire Industry
by Matthew Berman

News about the Future

> Forge Battery
> EPFL Tech4Dev

Article 03

Futurists predict a point where humans and machines become one.
But will we see it coming?

by John Kendall Hawkins and Sandy Boucher, University of New England

Recommended Book

Progressive Capitalism: How to Make Tech Work for All of Us
by Ro Khanna

Article 04

Four ways AI could help us respond to climate change
– despite how much energy it uses

by Lakshmi Babu Saheer, Anglia Ruskin University

Climate Change Success Story

Artificial Intelligence & Climate Change

How is Artificial Intelligence beneficial for climate change?
ChatGPT 3.5

Energy Efficiency
Renewable Energy Integration
Climate Modeling and Prediction
Natural Resource Management
Precision Agriculture

Climate Adaptation and Resilience
Carbon Capture and Sequestration
Behavioral Change and Awareness

Futurist Portrait

David L. Shrier

Agriculture, AI, Artificial Intelligence, Atmosphere, Banking, Bioacoustics,
Capitalism, Carbon Capture, Carbon Storage, ChatGPT, Climate Change,
Collective Intelligence, DEMOCRACY, Denmark, EDUCATION, EU, Europe,
Forest, Natural Language Processing, North Sea, Real-Future, Renewable
Energy, Resilience, SDGs, Singularity, Solar Energy, Sustainable Development
Goals, UN, Weather


Felix B Bopp
Producer, The Future Now Show
Founder & Publisher, Club of Amsterdam

Website statistics for
April 2024:





Rohit Talwar: “A great futurist really helps people get insights into the emerging future”

Marie-Hélène Caillol: "With internet and globalisation, we are de facto embarked on the same boat from Paris to Beijing, Johannesburg and Rio. We see today that we can't trust the system invented 70 years ago to produce peace any longer. It is high time to create new tools empowering stakeholders of the planet (citizens), stakeholders of the future (young leaders) ... "

David Shrier: "What is the metaverse? Quite simply, it’s a digital platform to help people collaborate, work and play in new ways, in an immersive 3D environment."

Lead Article:

Generating Democracy: AI and the coming revolution in political communications
by Alice Dawson and James Ball
Published by Demos January 2024


Alice Dawson


James Ball



We would first like to thank Cavendish for their generous support and enthusiasm for this

Thank you to the people who took part in our focus groups for candidly sharing their views
and perspectives on the use of AI in communications with us. All contributions were invaluable
in shaping our thinking for this project.

Thank you also to our workshop participants and the experts we interviewed from across
politics, the media, the third sector and the communications industry for their valuable
insights. Our expert interviewees included:

• Dr Keegan McBride, Oxford Internet Institute
• Adeela Warley, CharityComms
• Andrew Bruce Smith, Escherman and the Chartered Institute of Public Relations
• Dr Mike Katell, The Alan Turing Institute
• Professor Anne Gregory, University of Huddersfield
• Owen Meredith, News Media Association
• Joe Twyman, Deltapoll
• Ed Humpherson, Office for Statistics Regulation

At Demos, we would like to thank our current and former colleagues for their support.
In particular, thanks to Ellen Judson for her extensive planning and management of this
project. Thanks also to Lucy Bush, Polly Curtis, Sophia Knight, Billy Huband-Thompson and
Chloe Burke.
Any errors remain the authors’ responsibility.

Executive Summary

Artificial Intelligence (AI) has been shaping the way we communicate for many years now: from data-driven microtargeting of campaign messages to social media algorithms amplifying certain kinds of content. However, the advent of new forms of generative AI has changed the game with tools like ChatGPT and DALL-E enabling anyone to create convincing synthetic content.

AI’s integration in communications offers immense benefits such as streamlining content creation processes and enhancing engagement. Yet, these advantages must be tempered by a careful consideration of the risks. Irresponsible use of AI can propagate false information, erode trust, and exacerbate societal divides, posing grave implications for democracy. Fundamentally, AI-generated content can deepen existing difficulties in distinguishing between what’s real and what’s not, impacting public trust in the information they consume and even their engagement with political and democratic processes.

This year is one of the biggest years for democracy in recent decades: there are elections in India, Mexico, the EU parliament, and presidential elections in the US, Venezuela, and Taiwan. The UK general election will come no later than 28th January 2025. This offers political parties and communication experts new opportunities to create and personalise content at an unparalleled scale and pace.

Our research reveals a political communications industry poised to adopt new generative AI technologies at scale in 2024. However, there is a lack of guidelines and know-how to establish best practice on the use of AI in communications. The UK has established itself as an international leader in the field, convening the AI Safety Summit at Bletchley Park in November 2023 and launching its new AI Safety Institute. But despite showing this international leadership, there is nothing in place in the short term to mitigate the risks we face in the coming year regarding political communications.

In this paper, we explore the current trends in how AI is being used in communications, how this is changing communications and the benefits and risks associated with this to communications producers, the public, the information ecosystem and wider society and democracy. Particularly we are concerned with communications in politics and government, the third sector, the media and journalism, and the communications industry.

Based on our extensive research with experts and members of the public, we make a series of recommendations for politicians, regulators, generative AI companies and communications professionals to all play their part in safeguarding trust in our democratic information environments at this critical juncture in both the technology and our electoral cycles. A table of these recommendations can be found in Section 3 of this report. We also set out a framework to guide responsible use of AI in communications, based on five key principles of AI:

1. Transparency
2. Accountability
3. Fairness and inclusivity
4. Privacy
5. Reliability

Download the report

Demos is an independent, educational charity, registered in England and Wales.




Article 01

AI In Banking and Financial Services
Article 1 of 4: Who’s Smarter?

by Rohit Talwar, CEO, Fast Future


This is the first in a four part series exploring how Artificial Intelligence (AI) could impact financial services as its ever smarter capabilities enter the field over the course of the next few years.

In this first article, I start by examining the current state of play and consider how AI could outsmart the bankers. Subsequent articles will go on to look at how AI could change the rules of the game in banking, how established players can win in this environment, and how new entrants might fight back and disrupt. The third article will look at the opportunities that could emerge for incumbents and new entrants, the smart money opportunity, and the role of AI in enabling enhanced financial inclusion. The fourth and final article looks at the implications for regulators, central banks, and managing the ethical and societal challenges.

We are beginning to accept that the rise of AI is inevitable and inescapable. Everywhere we look, we see stories of how it is – or could soon be - transforming lives, activities, work, jobs, businesses, sectors, and nations. The areas receiving the most attention right now are banking and financial services. This interest is driven by a combination of the size of the institutions, their wealth, the volume of transactions, the number of users, and their centrality to life on the planet.

Hence the excitement in the sector over the potential to grow revenues, cut costs, improve security, enhance customer service, speed up new developments, innovate around products, develop new pricing models, tackle fraud, and improve regulatory compliance. Looking a little further ahead we see even more potential in all of these fields and in the creation of entirely new asset classes and sectors, in the emergence of smart savings and intelligent money, and in the potential for radically different approaches to personalisation, privacy, security, and governance.

In the next few years we can expect AI to evolve rapidly to go beyond its currently narrow but deep task specific capabilities. Current examples of such narrow applications include generative applications like ChatGPT and Bard, a range of robotic process automation (RPA) tools, and highly specialised solutions in areas such as compliance, fraud, and know your customer (KYC) verification. These are excellent in their assigned tasks, but they wouldn’t be very good at each other’s or in other domains such as drug development, autonomously driving a vehicle, or making decisions about a bank’s strategy. However, in the coming years we could see that change with the emergence of artificial general intelligence (AGI) that is as capable as humans across all our cognitive capabilities.

The Current State of Play
I n financial services, we are already seeing widespread and accelerating adoption of AI for consumer facing activities. These include enhancing service and engagement through personalized banking 24/7 virtual assistants that provide financial advice and support, help manage accounts, track spending, and make payments (e.g. Bank of America, HSBC). Other common applications include streamlining the mortgage lending process (Wells Fargo).

Others such as Vanguard are using AI wealth management and robo-advisors to support human financial advisors in giving tailored investment advice based on individual financial goals and risk tolerance. In investment banking, the use of AI-enabled big data analytics is being used in algorithmic trading to make investment decisions and manage risk (BlackRock). Others are using AI to assess market sentiment through analysis of real-time news and social media content (Bloomberg).

In order to enhance personalization and engagement, improve retention, and optimize marketing efforts to maximise customer lifetime value, machine learning models are frequently used to predict investment behaviour and product take up (Capital One).

In the ‘back office’, analysis of large historical transactional datasets is helping to improve efficiency, accuracy, and consistency in applications such as behaviour prediction and fraud detection and prevention (Barclays, American Express). Other common uses include interpretation of loan agreements and contract creation (JP Morgan), risk assessment, and credit management (Goldman Sachs). For small business loans, some are going further, by analysing non-traditional data sources to assessing creditworthiness. The technology is also in common use for regulatory compliance processes such as anti-money laundering (Standard Chartered), KYC, and AI-enabled biometric recognition and digital identity verification (BBVA).

A number of players are also adopting RPA technology to automate repeatable routine applications such as treasury and cash management (Citibank). In insurance, AI and chatbots are accelerating and streamlining underwriting and claims processing (Lemonade).In the crypto assets space, AI is being used in conjunction with blockchain technology to enhance security and fraud detection.

Can AI Outsmart the Bankers?
As confidence with AI increases, growing use can be expected of AI in areas where bankers simply don’t have the mental bandwidth or resources available. For example, by combining historic data with complex, non-linear risk factors, and publicly available market sentiment information such as social media posts, AI will be able to undertake valuable analytical and predictive tasks at scale and uncover insights that might not be readily apparent to humans.

Potential applications include identifying subtle hidden customer behavioral patterns - such as predicting financial distress or identifying unmet customer needs. This could lead to better risk management, tightening of controls, and more accurate pricing and development of micro-personalized and flexible products and services. Such analysis could also highlight fraud detection nuances – identifying anomalies and patterns indicative of complex and sophisticated fraud or money laundering schemes untraceable by humans.

Internally, AI will help highlight optimization opportunities – capturing, aggregating, and analysing exponentially increasing volumes of operational and staff behaviour data across the organisation. This will help surface inefficiencies and optimization opportunities in processes like loan approval, customer service, and compliance.

At the strategy level, deep analysis of highly interconnected financial markets and geopolitical developments could reveal complex correlations and network effects that could impact systemic risk and financial stability. Combining such data with insights on technology advances, and on hyperlocal developments, could help spot emerging risks such as cybersecurity threats. This analysis could also surface opportunities such as emerging financial tools and assets before they become mainstream knowledge. Economic and financial forecasts could become increasingly accurate using ever more advanced and complex and adaptive predictive AI algorithms. These would draw on the above insights, coupled with increasingly comprehensive global economic data to identify leading indicators and patterns that are not widely recognized.

Rohit Talwar
CEO, Fast Future

Fast Future is a foresight advisory firm focused on helping clients harness the trends, shifts, and ideas shaping the future. He has a particular focus on the implications and applications of rapidly evolving technologies such as AI.







The Future Now Show

with Marie-Hélène Caillol & Katie Schultz


Marie-Hélène emphasizes the importance of developing future literacy and integrating it into the education system. She also discusses the potential of AI in predicting the future and the need for individuals to understand their own brains better to improve decision-making. She suggested that organizations can become more adaptable by embracing collective intelligence. She is excited to explore the implications of a post-ChatGPT-society and plans to conduct further research in this area. - AI summary by Zoom








Marie-Hélène Caillol
Political and future scientist, analyst of the "global systemic transition", MENA expert, teacher and adviser
Paris, France

- Laboratoire Européen d’Anticipation Politique
Connecting Europe to its citizens, the world and the future

Co-founder and publishing consultant
The GEAB is your navigation system in a world in transition! Geopolitics, governance, economy, society, finance, currencies, new technologies, energy, ...

Conceptrice et animatrice
Pôle Excellence Real-Futur
Comprendre l'avenir en allant le chercher là où il est présent



Schultz (Miss Metaverse™)
Futurist and Content Creator
Bangkok, Thailand & Cary, North Carolina, USA

Felix B Bopp
Producer, The Future Now Show

Founder & Publisher, Club of Amsterdam

The Future Now Show

You can find The Future Now Show also atLinkedIn: The Future Now Show Group
YouTube: The Future Now Show Channel


Article 02

OpenAI-backed "AGI ROBOT" shocked the entire Industry
by Matthew Berman

Humanoid robots making huge advancements was one of my predictions for 2024, and it looks to be coming true. 1x just released new footage of their NEO robot, powered entirely by a single neural network end-to-end.







News about the Future

> Forge Battery
> EPFL Tech4Dev

Forge Battery

Forge Nano to Launch U.S Battery Manufacturing Business; North Carolina Gigafactory Planned for 2026

Forge Nano, Inc., a leading materials science company that enables peak performance products through atomic-level surface engineering, today announced plans to launch a lithium-ion battery manufacturing business called Forge Battery. The newly formed company intends to produce best-in-class high-energy and high-power lithium-ion battery cells for defense, aerospace and specialty electric vehicle markets at a newly constructed Gigafactory in Raleigh, North Carolina. Forge Battery plans to utilize a U.S-focused battery material supply chain to further bolster the domestic battery ecosystem and reduce reliance on foreign suppliers.

The company and outside investors plan for an initial investment of more than $165M in Morrisville, North Carolina to build the gigawatt hour-scale battery manufacturing facility. The Forge Battery facility expects to produce both 21700 and 18650 lithium-ion cells utilizing materials coated with Forge Nano’s Atomic Armor surface technology.

Forge Nano’s Atomic Armor will allow Forge Battery to produce first generation high-energy cells with an expected energy density of 300 Wh/kg with improved safety and extended lifetime that meet or beat performance of incumbent lithium-ion technologies. Because Atomic Armor can enhance the performance of existing and next-generation battery technologies, Forge Battery expects its cell technology to outperform current and future cell chemistries. Atomic Armor is also being used to improve production efficiency and drive down cell costs by removing electrolyte additives and reducing electrolyte consumption. The facility expects to begin production in 2026.

“Lithium-ion cells utilizing Forge Nano’s Atomic Armor technology have exceeded our customer’s expectations, and in turn, increased demand for the technology that outstrips the current manufacturing capabilities,” says Paul Lichty, CEO of Forge Nano. “We are extremely excited to launch Forge Battery in the Battery Belt, where we intend to produce batteries for the world’s most demanding applications, while showcasing the power of Forge Nano’s Atomic Armor coating equipment in a large-scale manufacturing environment.”

Forge Battery’s North Carolina facility will be facilitated, in part, by a Job Development Investment Grant (JDIG) approved by the state’s Economic Investment Committee. The JDIG agreement authorizes the potential reimbursement of up to $1,525,500, spread over 12 years. Forge Battery also expects to receive an additional $6,500,000 from the state of North Carolina based on its current investment projections, which include sales tax exemptions on planned capital investment.

“North Carolina’s growing leadership in clean energy can be seen everywhere you look, and Forge Battery’s decision continues our momentum,” said North Carolina Governor Roy Cooper. “New jobs, new investment, and new opportunities for our people are coming fast as we embrace this vital new sector of the global economy.”

Forge Battery’s facility is expected to create hundreds of high-paying jobs for Wake County residents. Although wages will vary depending on the position, the payroll impact for the project is expected to be more than $16 million each year. Through the JDIG agreement, new tax revenue generated through Forge Battery’s operation will go toward helping economically challenged communities throughout the state.


EPFL Tech4Dev

Accelerating innovative and beneficiary-centered technological solutions for positive impact

EPFL Tech4Dev aims to implement innovative technological solutions that can scale and reach a growing number of beneficiaries in the global South. The initiative is one among a few that explores novel ways for universities to contribute to the economic, social, and environmental development in the global South, and advances these current approaches with its dedicated technological entrepreneurship angle.

Tech4Dev will focus on providing experience-based education, promoting impactful entrepreneurial solutions, as well as innovative cross-sector research projects – all focused on relevant sustainability challenges sourced from Tech4Dev’s NGO Council.


Article 03

Futurists predict a point where humans and machines become one.
But will we see it coming?

by John Kendall Hawkins and Sandy Boucher, University of New England

John Kendall Hawkins

Sandy Boucher



John Kendall Hawkins
and Sandy Boucher,
University of New England

Most people are familiar with the deluge of artificial intelligence (AI) apps that seem designed to make us more efficient and creative. We've got apps that take text prompts and generate art, and the controversial ChatGPT, which raises serious questions about originality, misinformation and plagiarism.

Despite these concerns, AI is becoming ever more pervasive and intrusive. It's the latest technology that will irreversibly change our lives.

The internet and smartphones were other examples. But unlike those technologies, many philosophers and scientists think AI could one day reach (or even go beyond) human-style "thinking". This possibility, coupled with our increasing dependence on AI, is at the root of a concept in futurism called "technological singularity".

This term has been around for a while, having been popularised by the US science fiction writer Vernor Vinge a few decades ago.

Today, the "singularity" refers to a hypothetical point in time at which the development of artificial general intelligence (AGI) - that is, AI with human-level abilities - becomes so advanced that it will irreversibly change human civilisation.

It would mark the dawn of our inseparability from machines. From that moment on, we won't be able to live without them without ceasing to function as human beings. But if the singularity comes, will we even notice it?

Brain implants as the first stage

To understand why this isn't the stuff of fairy tales, we need only look as far as recent developments in brain-computer interfaces (BCIs). BCIs are a natural beginning to the singularity in the eyes of many futurists, because they meld mind and machine in a way no other technology so far can.

Elon Musk's company Neuralink is seeking permission from the US Food and Drug Administration to begin human trials for its BCI technology. This would involve implanting neural connectors into volunteers' brains so they can communicate instructions by thinking them.

Neuralink hopes to help paraplegic people walk and blind people see again. But beyond these goals are other ambitions.

Musk has long said he believes brain implants will allow telepathic communication, and lead to the co-evolution of humans and machines. He argues that unless we use such technology to augment our intellects, we risk being wiped out by super-intelligent AI.

Musk is understandably not everyone's go-to for tech expertise. But he's not alone in predicting a massive growth in AI's capabilities. Surveys show AI researchers overwhelmingly agree AI will achieve human-level "thinking" within this century. What they don't agree on is whether this implies consciousness or not, or whether this necessarily means AI will do us harm once it reaches this level.

Another BCI technology company, Synchron, has created a minimally invasive implant that allowed a patient with amyotrophic lateral sclerosis (ALS) to send emails and browse the internet using his thoughts.

A patient demonstrates the capabilities of Synchron's interface.

Synchron chief executive Tom Oxley believes brain implants could ultimately go beyond prosthetic rehabilitation and completely transform how humans communicate. Speaking to a TED audience, he said they may one day allow users to "throw" their emotions so others can feel what they're feeling, and "the full potential of the brain would then be unlocked".

Early achievements in BCIs could arguably be considered the first stages of a tumbling towards the postulated singularity, in which human and machine become one. This need not imply machines will become "sentient" or control us. But the integration itself, and our ensuing dependency on it, could change us irrevocably.

It's also worth mentioning that the start-up funding for Synchron partly came from DARPA, the research and development arm of the US Department of Defense that helped gift the world the internet. It's probably wise to be concerned about where DARPA places its investment monies.

Would AGI be friend or foe?

According to Ray Kurzweil, a futurist and former Google innovations engineer, humans with AI-augmented minds could be thrown onto the autobahn of evolution - hurtling forward without speed limits.

In his 2012 book How to Create a Mind, Kurzweil theorises the neocortex - the part of the brain thought to be responsible for "higher functions" such as sensory perception, emotion and cognition - is a hierarchical system of pattern recognisers which, if emulated in a machine, could lead to artificial super-intelligence.

He predicts the singularity will be with us by 2045, and thinks it might bring about a world of super-intelligent humans, perhaps even the Nietzschean "Uebermensch": someone who surpasses all worldly constraints to realise their full potential.

But not everyone sees AGI as a good thing. The late, great theoretical physicist Stephen Hawking warned super-intelligent AI could result in the apocalypse. In 2014, Hawking told the BBC

the development of full artificial intelligence could spell the end of the human race. (...) It would take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.

Hawking was, however, an advocate for BCIs.

Connected in a hive mind

Another idea that relates to the singularity is that of the AI-enabled "hive mind". Merriam-Webster defines a hive mind as

the collective mental activity expressed in the complex, coordinated behaviour of a colony of social insects (such as bees or ants) regarded as comparable to a single mind controlling the behaviour of an individual organism.

A theory has been developed by neuroscientist Giulio Tononi around this phenomenon, called Integrated Information Theory (IIT). It suggests we are all heading toward a merger of all minds and all data.

Philosopher Philip Goff does a good job of explaining the implications of Tononi's concept in his book Galileo's Error:

IIT predicts that if the growth of internet-based connectivity ever resulted in the amount of integrated information in society surpassing the amount of integrated information in a human brain, then not only would society become conscious but human brains would be 'absorbed' into that higher form of consciousness. Brains would cease to be conscious in their own right and would instead become mere cogs in the mega-conscious entity that is the society including its internet-based connectivity.

It's worth noting there's little evidence such a thing could ever come to fruition. But the theory raises important ideas about not only the rapid acceleration of technology (not to mention how quantum computing might propel this) - but about the nature of consciousness itself.

Hypothetically, if a hive mind were to emerge, one could imagine it would mark the end of individuality and the institutions that rely on it, including democracy.

The final frontier is between our ears

Recently OpenAI (the company that developed ChatGPT) released a blog post reaffirming its commitment to achieving AGI. Others will doubtless follow.

Our lives are becoming algorithmically driven in ways we often can't discern, and therefore can't avoid. Many features of a technological singularity promise amazing enhancements to our lives, but it's a worry these AIs are the products of private industry.

They are virtually unregulated, and largely at the whims of impulsive "technopreneurs" with more money than most of us combined. Regardless of whether we consider them crazy, naive, or visionaries, we have a right to know their plans (and be able to rebut them).

If the past few decades are anything to go by, where new technologies are concerned, all of us will be affected. The Conversation

John Kendall Hawkins, Philosopher and Sandy Boucher, Lecturer in the Philosophy of Science, University of New England

This article is republished from The Conversation under a Creative Commons license.



Recommended Book

Progressive Capitalism: How to Make Tech Work for All of Us
by Ro Khanna


Congressman Ro Khanna offers a revolutionary, “progressive” (James J. Heckman, Nobel Prize winner and professor of economics at the University of Chicago) roadmap to facing America’s digital divide, offering greater economic prosperity to all. In Khanna’s vision, “just as people can move to technology, technology can move to people” (from the foreword by Amartya Sen, Nobel Laureate in Economics) where “Khanna envisions redistributing opportunities from coastal cities to rural middle-America…An exciting vision, brilliantly rendered.” (Arlie Russell Hochschild, author of Strangers in Their Own Land).

Unequal access to technology and the revenue it creates is one of the most pressing issues in the United States. An economic gulf exists between those who have struck gold in the tech industry and those left behind by the digital revolution; a geographic divide between those in the coastal tech industry and those in the heartland whose jobs have been automated; and existing inequalities in the technological access - students without computers, rural workers with spotty WiFi, and many workers without the luxury to work remotely.

Congressman Ro Khanna’s Progressive Capitalism tackles these challenges head-on and imagines how the digital economy can create opportunities for people across the country without uprooting them. Anchored by an approach Khanna calls “progressive capitalism,” he shows how democratizing access to tech can strengthen every sector of economy and culture. By expanding technological jobs nationwide through public and private partnerships, we can close the wealth gap in America and begin to repair the fractured, distrusting relationships that have plagued our country for fall too long.

Inspired by his own story born into an immigrant family, Khanna understands how economic opportunity can change the course of a person’s life. Moving deftly between storytelling, policy, and some of the country’s greatest thinkers in political philosophy and economics, Khanna presents a vision we can’t afford to ignore. Progressive Capitalism is a “practical and aspirational” (Kimberlé Crenshaw, professor of law at UCLA and Columbia University) roadmap to how we can seek dignity for every American in an era in which technology shapes every aspect of our lives.


Ro Khanna

is an American politician and lawyer serving as the U.S. representative from California's 17th congressional district since 2017. A member of the Democratic Party, he defeated eight-term incumbent Democratic Representative Mike Honda in the general election on November 8, 2016, after first running for the same seat in 2014. Khanna also served as the deputy assistant secretary in the United States Department of Commerce under President Barack Obama from August 8, 2009, to August 2011.

Khanna identifies as a progressive capitalist, and has called for a "new economic patriotism" as a governing philosophy. He states that he only accepts campaign donations from individuals and is one of only six members of the U.S. House of Representatives, and ten members of Congress, who state that they do not take campaign contributions from political action committees (PACs) or corporations.- Wikipedia

Article 04

Four ways AI could help us respond to climate change
– despite how much energy it uses

by Lakshmi Babu Saheer, Anglia Ruskin University


Lakshmi Babu Saheer




Advanced AI systems are coming under increasing critcism for how much energy they use. But it's important to remember that AI could also contribute in various ways to our response to climate change.

Climate change can be broken down into several smaller problems that must be addressed as part of an overarching strategy for adapting to and mitigating it. These include identifying sources of emissions, enhancing the production and use of renewable energy and predicting calamities like floods and fires.

My own research looks at how AI can be harnessed for predicting greenhouse gas emissions from cities and farms or to understand changes in vegetation, biodiversity and terrain from satellite images.

Here are four different areas where AI has already managed to master some of the smaller tasks necessary for a wider confrontation with the climate crisis.

1. Electricity

AI could help reduce energy-related emissions by more accurately forecasting energy supply and demand.

AI can learn patterns in how and when people use energy. It can also accurately forecast how much energy will be generated from sources like wind and solar depending on the weather and so help to maximise the use of clean energy.

For example, by estimating the amount of solar power generated from panels (based on sunlight duration or weather conditions), AI can help plan the timing of laundry or charging of electric vehicles to help consumers make the most of this renewable energy. On a grander scale, it could help grid operators pre-empt and mitigate gaps in supply.

A woman hanging out white clothes on a laundry line.
AI can help make the most of renewable energy when it is abundant. Kazoka/Shutterstock

Researchers in Iran used AI to predict the energy consumption of a research centre by taking account of its occupancy, structure, materials and local weather conditions. The system also used algorithms to optimise the building's energy use by proposing appropriate insulation measures and heating controls and how much lighting and power was necessary based on the number of people present, ultimately reducing it by 35%.

2. Transport

Transport accounts for roughly one-fifth of global CO2 emissions. AI models can encourage green travel options by suggesting the most efficient routes for drivers, with fewer hills, less traffic and constant speeds, and so minimise emissions.

An AI-based system suggested routes for electric vehicles in the city of Gothenburg, Sweden. The system used features like vehicle speed and the location of charging points to find optimal routes that minimised energy use.

3. Agriculture

Studies have shown that better farming practices can reduce emissions. AI can ensure that space and fertilisers (which contribute to climate change) are used sparingly.

By predicting how much of a crop people will buy in a particular market, AI can help producers and distributors minimise waste. A 2017 study conducted by Stanford University in the US even showed that advanced AI models can predict county-level soybean yields.

This was possible using images from satellites to analyse and track the growth of crops. Researchers compared multiple models to accurately predict crop yields and the best performing one could predict a crop's yield based on images of growing plants and other features, including the climate.

Knowing a crop's probable yield weeks in advance can help governments and agencies plan alternative means of procuring food in advance of a bad harvest.

Wicker baskets filled with purple acai fruit.
Accurate yield predictions can better prepare countries for food shortages. JBK/Shutterstock

4. Disaster management

The prediction and management of disasters is a field where AI has made major contributions. AI models have studied images from drones to predict flood damage in the Indus basin in Pakistan.

The system is also useful for detecting the onset of a flood, helping with real-time rescue operation planning. The system could be used by government authorities to plan prompt relief measures.

These potential uses don't erase the problem of AI's energy consumption, however, To ensure AI can be a force for good in the fight against climate change, something will still have to be done about this.

Lakshmi Babu Saheer, Director of Computing Informatics and Applications Research Group, Anglia Ruskin University

This article is republished from The Conversation under a Creative Commons license.


Climate Change Success Story

Artificial Intelligence & Climate Change




How is Artificial Intelligence beneficial for climate change?
by ChatGPT 3.5:

Artificial intelligence (AI) offers numerous benefits for addressing climate change and facilitating sustainability efforts:

Energy Efficiency
AI can optimize energy consumption in various sectors such as transportation, manufacturing, and buildings. Machine learning algorithms can analyze energy usage patterns, identify inefficiencies, and recommend strategies for reducing energy consumption, thereby lowering greenhouse gas emissions.

Renewable Energy Integration
AI enables better integration and management of renewable energy sources such as solar and wind power. AI algorithms can forecast renewable energy generation, optimize energy storage systems, and dynamically adjust energy production and consumption to match supply and demand, enhancing the reliability and efficiency of renewable energy systems.

Climate Modeling and Prediction
AI techniques, including machine learning and neural networks, are employed in climate modeling to improve the accuracy and resolution of climate simulations. These models help scientists understand complex climate systems, predict future climate scenarios, and assess the potential impacts of climate change on various regions and ecosystems.

Natural Resource Management
AI technologies support sustainable natural resource management by monitoring and analyzing environmental data. For example, AI-powered remote sensing and satellite imagery can track deforestation, monitor biodiversity, and assess changes in land use, facilitating conservation efforts and sustainable land management practices.

Precision Agriculture
AI-driven precision agriculture techniques optimize farming practices by analyzing data on soil quality, weather conditions, crop health, and pest infestations. By providing real-time insights and recommendations, AI helps farmers improve crop yields, reduce resource inputs (such as water and fertilizers), and minimize environmental impacts.

Climate Adaptation and Resilience
AI can enhance climate resilience by analyzing climate risks and developing adaptive strategies for vulnerable communities and infrastructure. AI-powered risk assessment tools can identify areas prone to climate-related hazards such as floods, storms, and wildfires, enabling proactive planning and disaster preparedness measures.

Carbon Capture and Sequestration
AI plays a role in advancing carbon capture and sequestration technologies by optimizing the design and operation of carbon capture systems, predicting carbon dioxide storage capacity in geological formations, and developing novel materials for capturing and storing carbon emissions from industrial processes.

Behavioral Change and Awareness
AI-driven applications and platforms can raise awareness about climate change, promote sustainable behaviors, and facilitate citizen engagement in environmental conservation efforts. Personalized recommendations, interactive tools, and data visualization techniques help individuals and communities understand their environmental footprint and take actions to reduce it.

Overall, AI offers innovative solutions and tools for mitigating climate change, fostering sustainability, and building a more resilient and environmentally friendly future. By harnessing the power of AI technologies, we can address the complex challenges posed by climate change and work towards a more sustainable and equitable world.


Energy Efficiency

Building Energy Efficiency: AI climate tech lowering carbon footprints
by BrainBox AI

Imagine a building that's not just responsive to its internal sensors but also aware of its external environment. It considers weather conditions, energy sources, costs, and environmental impact in real-time. How is this possible? Through autonomous optimization technology that takes HVAC system control to a whole new level!

We dive deep into this cutting-edge solution created by BrainBox AI, going beyond temperature settings to optimize individual HVAC components, damper positions, fan speeds, and more. This granular approach revolutionizes building efficiency, making the building adapt to its environment minute by minute.

This technology makes hundreds or thousands of unique optimization decisions daily, tailored to each building's specific location and external factors. It's like waking up your building and allowing it to make intelligent decisions. You can now make your building more efficient and reduce its carbon footprint without the hassle of installing new sensors or investing in expensive equipment.

Join us on this journey toward a more sustainable and efficient future! Don't miss out on the building revolution powered by AI.


Schneider Electric CEO on how AI & other tech can accelerate the energy transition


Peter Herweck, CEO of Schneider Electric, discusses artificial intelligence, the energy transition and green hydrogen with CNBC.

ABB and Verdigris Technologies have developed machine-learning algorithms to predict unplanned peaks in power consumption – and identify strategies to prevent them

ABB is deploying artificial intelligence (AI) to help commercial and industrial buildings revolutionize their energy management and tackle rising electricity peak tariffs. The company has added two new AI-powered applications to the ABB AbilityTM Electrical Distribution Control System (EDCS): Energy Forecasting and Intelligent Alerts.

ABB has developed the AI functions in partnership with Silicon Valley AI specialist Verdigris Technologies as part of the company’s Open Innovation program. The Energy Forecasting app will enable users to reduce their electricity bills by reducing peak demand charges. The Intelligent Alerts app uses machine learning algorithms to help customers better manage their assets, identifying underlying issues before they become problems.

Andrea Temporiti, Digital Leader for ABB’s Electrification business, said: “Our use of AI to help customers make better energy management decisions demonstrates ABB’s commitment to innovation in our products and quality in our services. With the new Energy Forecasting and Smart Alerts apps, AI drills down into the facility’s power data to pinpoint actionable opportunities for productivity improvements and energy cost savings.”

ABB’s Open Innovation program engages incubators, accelerators, innovation centers and start-ups in the co-development and design of innovative new digital solutions and business models. The company is building an ecosystem of innovation partners to work on digital energy management services for applications that range from smart buildings to e-mobility. The collaborations help start-ups to develop services that can be marketed directly to ABB customers via its Digital Marketplace; the innovation strategy also enables ABB customers to benefit from cutting-edge digital technologies much sooner.

ABB AbilityTM Energy Forecasting uses AI to give facility managers accurate power consumption predictions. Energy Forecasting enables them to take timely action to reduce unplanned consumption spikes by re-scheduling or switching off non-critical loads – and taking full advantage of Time of Use (TOU) tariffs.

The Energy Forecasting AI uses neural network methods to identify and learn patterns in a circuit or a building’s energy consumption, while also factoring in weather data. Using weather forecasts and historical data, Energy Forecasting is then able to predict power consumption (kW) for the next 24 hours, updating its forecast every 15 minutes with best-in-class accuracy.

“This innovative digital service makes it easy to take the necessary corrective actions to minimize any peak demand charges,” said Temporiti. “The precision of the forecasting reduces hedging positions, narrows variability and produces meaningful energy cost savings for commercial and industrial buildings.”

ABB AbilityTM Intelligent Alerts uses machine learning to help customers better manage critical assets. Intelligent Alerts learns how various factors affect the building and key assets so that it can minimize the distraction of false alerts and information overload, allowing facility teams to focus their time more productively. Intelligent Alerts also identifies the relevant circuits and makes potential recommendations to ensure any response can be swift and decisive.

Thomas Chung, Head of Product Strategy at Verdigris said: “Verdigris AI is 10 times more effective than traditional energy management methods. Our partnership with ABB enables our AI capabilities to reach a significantly larger ecosystem of ABB users. These energy and asset management tools will cut through the noise to deliver actionable insights, identify real energy savings and make resource allocation more effective.”


ABB (ABBN: SIX Swiss Ex) is a technology leader that is driving the digital transformation of industries. With a history of innovation spanning more than 130 years, ABB has four customer-??focused, globally leading businesses: Electrification, Industrial Automation, Motion, and Robotics & Discrete Automation, supported by the ABB Ability™ digital platform. ABB’s Power Grids business will be divested to Hitachi in 2020. ABB operates in more than 100 countries with about 147,000 employees.

Verdigris Technologies is a leading AI company based in historic NASA research park in Silicon Valley, California with offices in the US and Taiwan. They are a privately held company backed by prominent venture capitalists and have developed several award-winning products for commercial and industrial energy management.

What is Verdigris?
by Verdigris


ABB Ability™ Efficiency AI
by ABB Electrification

ABB Ability™ Efficiency AI, powered by BBAI, uses artificial intelligence (AI) to proactively optimize the HVAC system in buildings in order to achieve remarkable cost savings, reduce carbon emissions and increase tenant comfort.



Climate Change Success Story

Renewable Energy Integration

AI-Powered Solution to Optimize Renewable Energy Production

by Omdena


A leading energy company was facing the challenge of integrating renewable energy sources into its grid. The company was struggling to predict renewable energy generation and demand and to develop optimal dispatch plans for its renewable energy assets. This was leading to energy shortages and blackouts.

The energy company partnered with Omdena to develop an AI-powered solution to optimize renewable energy production. Omdena’s team of data scientists and machine learning engineers developed a variety of machine learning models to predict renewable energy generation and demand. The machine learning models were also trained to identify and mitigate potential grid constraints.

The AI-powered solution has been very successful. The solution has helped the energy company to increase renewable energy generation by 10% and reduce carbon emissions by 5%. The solution has also improved grid reliability by reducing the number of energy shortages and blackouts.

The AI-powered solution has provided a number of benefits to the energy company, including:

  • Increased renewable energy generation
  • Reduced carbon emissions
  • Improved grid reliability
  • Reduced costs

The development and implementation of the AI-powered solution has been a success for the energy company. The solution has helped the company to achieve its renewable energy goals and improve its bottom line.
Lessons Learned

There are a few key lessons that can be learned from this case study:

  • AI-powered solutions can be very effective in optimizing renewable energy production.

  • It is important to collect and prepare a large and diverse dataset of renewable energy generation data, weather data, and grid data in order to train accurate and effective machine learning models.

  • It is also important to evaluate the performance of the machine learning models on a held-out test set before deploying them to production.

  • By following these steps, energy companies can successfully develop and implement AI-powered solutions that will help them to increase renewable energy generation, reduce carbon emissions, and improve grid reliability.

Omdena’s Role
Omdena played a key role in the development and implementation of the AI-powered solution. Omdena’s team of data scientists and machine learning engineers provided the following services:

  • Data collection and preparation
  • Model development and training
  • Model evaluation
  • Model deployment

Omdena also provided the energy company with access to its AI platform, which made it possible to develop and deploy the AI-powered solution quickly and efficiently.

Overall, Omdena’s involvement in the project was essential to its success. Omdena’s expertise in AI and machine learning, as well as its AI platform, were critical to the development and implementation of the AI-powered solution.

What is Renewable Energy Integration?

Traditional power plants provide stable and consistent electricity, but one of the most troubling downsides is that they contribute substantially to greenhouse gas emissions. Renewable energy integration has emerged as a promising solution in the quest for sustainable energy, provided the sources can integrate into existing grid infrastructure. But what is renewable energy integration, and how does it work?

This guide closely examines renewable energy integration, explains how it works, and discusses its importance.

What is renewable energy integration?
Renewable energy integration is incorporating renewable energy sources into existing electric power grids. Renewable energy generates power from self-replenishing sources such as hydropower, geothermal, wind, and solar. These sources can provide enough energy for a green future if appropriately integrated. The sources of renewable power differ greatly from fossil fuels. Adding renewable sources to the existing power grid has many advantages and obstacles. Unfortunately, it is not as simple as connecting and transmitting electricity through the infrastructure.

Take wind turbines, for example. The wind turns propeller-like blades around a rotor. The rotor spins a generator and creates electricity. The electrical energy passes through a transformer that converts the voltage so the electricity system can use it. Wind energy is beneficial because it is a clean, renewable, and domestic energy source. However, wind power is inconsistent. Although wind farms are built in the windiest places possible, wind speeds still vary. As it stands now, wind power is best as a part of a larger renewable energy strategy.

Geothermal energy is another form of renewable energy. It uses heat from inside the Earth to warm buildings and generate electricity. Where geothermal energy is available, it is a powerful source of energy. However, the most significant disadvantage is that it is location-specific. Geothermal plants can only really exist where the energy is accessible. Thus, like wind power, geothermal energy is best as a part of a larger renewable energy strategy.

How does renewable energy integration work?
Let's discuss how renewable energy integration works to understand the advantages and challenges further. First of all, the objectives of renewable energy integration are to diversify the sources of energy generation to reduce reliance on fossil fuels and increase the power system's resilience. However, issues arise around consistency and availability. Whether the energy source is renewable or not, the electricity fed into the grid must always equal the amount taken out; otherwise, power outages may occur.

Renewable energy sources such as water and geothermal can generate energy consistently enough to meet baseload power needs, but these sources are unavailable or viable everywhere. Wind and solar are viable options in many places but vary significantly throughout the day and year.

Now, power grids depend upon a consistent stream of energy to generate, transmit, and distribute electricity from power plants to end consumers. On the other hand, renewable energy sources rely on grid optimization technologies such as advanced sensors, automation, and real-time data analytics to integrate successfully. Ultimately, renewable energy integration depends upon intelligent technologies to optimize energy distribution and elevate grid management efficiency.

Importance of Renewable Energy Integration
The International Energy Agency estimates that renewable sources must generate most global electricity to reach net-zero emissions by 2050. Renewable energy is the fastest-growing energy source in the United States, but more progress must be made to achieve 100% clean electricity by 2035. The strong drive for renewable energy integration is because:

  • It helps mitigate climate change by reducing greenhouse gas emissions and other air pollutants.
  • It is a self-replenishing source of energy that does not decrease in availability or require time to restore.
  • It supports distributed energy systems that lessen the power demand on the grid, extending the infrastructure's lifespan and reducing resource consumption.
  • It increases energy security by diversifying and localizing energy sources.
  • It lowers costs related to transportation and enhances local economies by increasing the number of ways communities can contribute energy to the power grid.

Renewable Energy Integration with Encora
By enabling the integration of cutting-edge technologies such as IoT sensors, machine learning, and data analytics, we help companies modernize infrastructure, develop smart grids, and harness the potential of renewables. Our solutions optimize operations and provide invaluable customer insights through digital transformation, leading to enhanced sustainability, cost reduction, and elevated customer experiences. We use intelligent technologies to optimize energy distribution and elevate grid management efficiency. Our renewable energy integration solutions seamlessly integrate renewable energy sources into existing systems, driving sustainable energy adoption.

Fast-growing tech companies partner with Encora to outsource product development and drive growth. We are deeply expert in the various disciplines, tools, and technologies that power the emerging economy, and this is one of the primary reasons that clients choose Encora over the many strategic alternatives that they have.

Climate Change Success Story

Climate Modeling and Prediction

Can AI help us predict extreme weather?
by VOX

We’ve learned how to predict weather over the past century by understanding the science that governs Earth’s atmosphere and harnessing enough computing power to generate global forecasts. But in just the past three years, AI models from companies like Google, Huawei, and Nvidia that use historical weather data have been releasing forecasts rivaling those created through traditional forecasting methods.

This video explains the promise and challenges of these new models built on artificial intelligence rather than numerical forecasting, particularly as it relates to the ability to foresee extreme weather.


Climate modeling with AI: Hype or Reality? & Deep learning and the dynamics of physical processes
AI for Good

Climate modeling with AI: Hype or Reality?

Climate simulations remain one of the best tools to understand and predict global and regional climate change. Uncertainties in climate predictions originate partly from the poor or lacking representation of processes, such as ocean turbulence and clouds, that are not resolved in global climate models but impact the large-scale temperature, rainfall, sea level, etc. The representation of these unresolved processes has been a bottleneck in improving climate simulations and projections. The explosion of climate data and the power of machine learning (ML) algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty? This talk discusses the advantages and challenges of using machine learning for climate projections. The focus will be on recent work in which we leverage ML tools to learn representations of unresolved ocean processes – in particular, learning symbolic expression. Some of this work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions. Yet, many questions remain unanswered, making the next decade exciting and challenging for ML + climate modeling for robust and actionable climate projections. 

Deep learning and the dynamics of physical processes

Deep learning has been studied for a few years for the modeling of complex physical processes in industrial fields such as aeronautics or energy production and in scientific fields such as environment or health.  This area of research, although still emerging, is rapidly gaining momentum and developing as an interdisciplinary field. It raises new challenges for the interaction between machine learning and physics. This talk will focus on deep learning approaches for modeling dynamic physical systems and illustrate three main challenges: incorporating prior physical knowledge into learning models, generalizing learning models to multiple environments, and learning models operating continuously in space and time thus allowing flexible extrapolation at arbitrary spatiotemporal locations. This presentation will be illustrated by applications in different domains.

Laure Zanna, Courant Institute, New York University
Patrick Gallinari, Sorbonne University

Markus Reichstein, Max Planck Institute for Biogeochemistry

Join the Neural Network!
The AI for Good networking community platform powered by AI.
Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.


Climate Change Success Story

Natural Resource Management

FS Talks! The Future of AI in Natural Resource Management
by Purdue FNR

Dr. Jingjing Liang shares his research using artificial intelligence in forestry in this edition of the FS Talks Eastern Region seminar series for the U.S. Forest Service. The talk "The Future of AI in Natural Resource Management – the Self Learning Forest Growth Model” was given on April 4, 2023.

As a certified forester and seasoned quantitative ecologist, Liang has been working on the interface between artificial intelligence and its applications in forest sciences. His research connects machine learning and big data in the study of fundamental questions of biodiversity and ecosystem processes, as well as ecological and socioeconomic impacts of biological conservation.


How AI is Transforming Remote Sensing Applications
by Geospatial World

Artificial Intelligence today is transforming remote sensing by improving data processing accuracy and speed. Let’s look at how AI is used in different remote sensing applications.




Climate Change Success Story

Precision Agriculture

Applications of Remote Sensing in Precision Farming
by Geospatial World

What Is Precision Agriculture?
by PostHarvest Technologies

Precision Agriculture means using exact science & methods to produce more for less. It involves observing multiple changes in the environment and its effects on all the growing stages of produce over past experiments.


The European CAP Network

Optimising the flow of information about agriculture and rural policy within the EU is what the EU’s Common Agricultural Policy (CAP) Network is all about.

The Network is a forum through which National CAP Networks, organisations, administrations, researchers, entrepreneurs and practitioners can share knowledge and information (e.g. via peer-to-peer learning and good practices) about agriculture and rural policy.

The European Commission set up the EU CAP Network in line with the Regulation of the European Parliament and of the Council to support CAP strategic plans (CSPs).

The Network supports the design and implementation of CAP strategic plans, innovation and knowledge exchange, including EIP-AGRI, and evaluation and monitoring of the CAP.


Climate Change Success Story

Climate Adaptation and Resilience

AI for Climate Action: Technology Mechanism supports transformational climate solutions
by UN Climate Change

Artificial intelligence (AI) can make substantial contributions to climate-resilient and low-emissions development. UN Climate Change’s Initiative on Artificial Intelligence for Climate Action (#AI4ClimateAction) explores the role of AI as a powerful tool for advancing and scaling up transformative climate action in developing countries.


Explainer: How AI helps combat climate change
by UN News

Artificial intelligence (AI) is already making inroads worldwide in health, education and industry, but how can this cutting-edge technology help the world combat and mitigate the effects of climate change?

The recent launch of the UN-led AI Advisory Body advanced a growing global trend to harness machine learning to find solutions to common challenges. AI is upping the data crunching game and a growing number of governments, businesses and civil society partners are working together to reap its many benefits.

That includes speeding up and scaling efforts to realize such global ambitions as the 2030 Agenda and its 17 Sustainable Development Goals (SDGs), which serve as the world’s blueprint to make the planet greener, cleaner and fairer.

Using AI to Build Climate Resilience Where It's Needed Most
by Boston Consulting Group

87% of public and private sector leaders who oversee climate and #AI topics believe that AI is an asset in the fight against #ClimateChange.

But there are challenges to implementation at scale, like lack of funding, training, and access to decision-makers.

Learn more about how we can overcome these challenges and use AI to build climate resilience in the areas that need it most.


Climate Change Success Story

Carbon Capture and Sequestration

Project Greensand | Carbon Capture and Storage

A world first, Project Greensand, shows that Carbon Capture and Storage can work after CO2 from Belgium is successfully captured, transported and stored under the Danish North Sea.

By 2030, Project Greensand could safely store up to 8 million tonnes of CO2 a year, which is the equivalent of 40% of Denmark’s emission reduction target.


Project Greensand
is working towards enabling Denmark to use CO2 storage as part of the solution to the climate challenges.

The consortium consists of 23 Danish and international partners who contribute with expertise from transportation, storage and monitoring of CO2 in the subsoil. The consortium's members include everything from Danish and international companies, research institutes, universities, and start-ups.

We are already underway with a green transformation of Denmark. Solar cell panels are being installed, offshore wind farms are being built, and we are replacing petrol and diesel cars with electric cars. These are all steps in the right direction - but it is not enough. If we are to solve the climate challenges and achieve the objectives of the Paris Agreement, we must also capture CO2 from emissions and store it.

The United Nation’s Intergovernmental Panel on Climate Change (IPCC) points to CO2 capture and storage as an effective tool for rapidly reducing our CO2 emissions, and in Denmark we have decided that CO2 storage must be a key part in achieving our climate goals. Project Greensand is the most mature project for storage of CO2 in Denmark with the possibility of storing up to 1,5 million tonnes of CO2 per year in 2025/2026 and up to 8 million tonnes of CO2 per year in 2030. This corresponds to emissions from approximately 725.000 Danes a year - or more than 13% of Denmark's annual CO2 emissions.

Project Greensand is supported by the Danish state through the Energy Technology Development and Demonstration Program - EUDP.

The goal for Project Greensand is to help establish a value chain for transportation and geological CO2 storage offshore in Denmark in 2025/2026. The project is currently in the pilot phase, which is called phase 2. Here the project is developed and demonstrated. The first CO2 to be stored in the North Sea will be sailed from Antwerp in Belgium to the Nini platform in the North Sea. Here it is sent underground via the existing offshore platform and a dedicated well for the purpose. The final stop for the CO2 is in a sandstone reservoir 1800 meters below the seabed, where it is permanently stored.

Geological and production data on the Nini field have been collected for more than 20 years. This means that the consortium's leading partners INEOS and Wintershall Dea know the structures of the underground extremely well. All this data is important knowledge when the CO2 is to be sent underground - and subsequently monitored carefully to ensure efficient and secure storage.

Climate Change Success Story

Behavioral Change and Awareness

Using AI to listen to all of Earth's Species
by Earth Species Project (ESP)

A presentation on decoding animal language by Aza Raskin, followed by a panel discussion hosted at the World Economic Forum in San Francisco on October 25, 2022 exploring how AI is expanding our ability to understand other species and the benefits and potential challenges that this new understanding will bring.

Featuring Kay Firth-Butterfield, Head of AI at the WEF; Aza Raskin, President and Cofounder, Earth Species Project; Dr. Karen Bakker, Professor at University of British Columbia and Fellow at Harvard Ratcliffe Institute; Dr. Ari Friedlaender, Professor at University of California, Santa Cruz


Earth Species Project (ESP)

We are thinkers, makers, AI research scientists, conservation technologists, learners, entrepreneurs and artists who care deeply about the interdependent future of our planet and its species.

Our work to decode communication of other species builds on the extraordinary advances we are seeing in AI being applied to human language, which we are extending to the non-human world. It also builds off many decades of bioacoustics and behavioral ecology research which has already uncovered complex communication systems in other species.

The machine learning models we are building are also supporting and deepening ongoing research into the behavior of other species and advancing conservation efforts on the ground today.

All our work is inspired by current challenges being faced in conservation biology. It is also all publicly accessible, helping to ensure researchers no longer need to reinvent the wheel for each project, and can focus precious resources on ensuring we have forward momentum as we seek to listen, to understand and, ultimately, to protect.

The Technology
The field of AI and natural language processing has seen exponential growth in the last 10 to 15 years.

In 2013 there was a significant breakthrough with a new machine learning technique that could learn a geometric representation of an entire language, like a galaxy where each star is a word and the distance and direction between stars encodes relational meaning.

Using Artificial Intelligence to Visualize the Impacts of Climate Change
January 2021, IEEE Computer Graphics and Applications

Alexandra Luccioni, Victor Schmidt, Vahe Vardanyan, Yoshua Bengio, Université de Montréal, Mila, Ubisoft, Montréal , Canada

Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment. One reason for this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract. To overcome these challenges, we propose to use cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer. It will allow a user to enter an address - be it their house, their school, or their workplace - and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires. This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.

Download full-text pdf

Behaviour Change - Behaviour Change and Public Engagement | Environment 101
by Civil Service Climate + Environment Network

How are people's behaviours contributing to environmental change? How can we make positive changes to societal habits? What effect can an individual even make to our climate's future?

CSEN Environment 101 explains and explores the biggest issues in environmental policy in short and accessible videos. This video details the types of damaging behaviour people engage in, the effect this has on the environment, and what behavioural changes will be needed to reach net zero.


Climate Change Success Story



Futurist Portrait

David L. Shrier





David Shrier is a futurist and innovation catalyst with expertise in artificial intelligence, data/analytics, fintech, digital identity, cybersecurity, and collaborative innovation. He leads venture studio Visionary Future LLC, helping Global 2000 companies create breakout growth as well as incubating new technology-enabled businesses.

David led a SPAC that trades on the NYSE under the ticker ADEX into its announced merger with Griid Infrastructure*. David is a Senior Advisor to Dandelion Science, an AI healthtech company that treats the brain as a data processing engine to address mental health issues; Kaleidoco, a revolutionary artificially intelligent Augmented Reality (AI/AR) company; and The Emissions Capture Company (ECCO), a machine learning-driven carbon utilization provider.

David holds an appointment as Professor of Practice, Imperial College Business School, Imperial College London, and is a Visiting Scholar with the GEAR Lab at the School of Engineering, Massachusetts Institute of Technology. He previously was an Associate Fellow with Saïd Business School, University of Oxford, and a Lecturer & Futurist at the Massachusetts Institute of Technology, where he also held positions as Managing Director, Connection Science and New Ventures Officer, MIT Sloan. David specializes in helping established organizations to build new revenue and new markets. Notable engagements include Kyriba, Dun & Bradstreet, Wolters Kluwer, Ernst & Young, The Massachusetts Institute of Technology, GE/NBC, The Walt Disney Company, AOL Verizon, and Starwood, as well as private equity funds. He has led a number of private equity and venture capital-backed companies as CEO, CFO or COO, in either interim or full-time capacities, and has cofounded four AI-enabled MIT spinout enterprises.

David sits on the European Parliament's advisory committee for artificial intelligence, having provided input into the EU AI Act. He served as a founding member of the FinTech Industry Committee for FINRA, the U.S. securities industry's self-regulatory body; advisor to the European Parliament on distributed ledger applications and policy; a senior advisor to Revenue & Customs (UK HMRC), and on the Fintech Trade & Investment Steering Board for the Department of International Trade. He also informally engages with OECD, Bank of England, FCA, SEC, US Treasury, and FDIC on innovation, cybersecurity, digital identity, blockchain, and AI. David is on the advisory board of WorldQuant University, a program offering a totally-free, accredited, online Master’s degree in financial engineering.

David has previously counseled the Commonwealth of Nations (the 53 countries formerly comprising the British Empire) on fintech and innovation policy, having served as principal author on their 2020 Commonwealth Fintech Toolkit; was a member of the European Commission-affiliated High Level Group on citizenship innovation; Copper Technologies (UK) Ltd, a markets infrastructure provider for institutional crypto trading, during its growth from Series A to Series C unicorn valuation; Millennium Advisors, a middle market credit liquidity provider, on founder succession, growth strategy, corporate culture, and technology trends; helped realign strategy for Mode Global Holdings plc (LSE: MODE), one of only three FCA-licensed crypto wallet providers, as a non-executive director; the Government of Dubai on blockchain and digital identity; the OECD, on blockchain policy and standards; and the European Commission on commercializing innovation across Europe.

David and MIT Professor Alex “Sandy” Pentland have published books including Global Fintech (MIT Press; winner of Outstanding Academic Title from the American Library Association's Choice Awards), as well as Trusted Data (MIT Press), New Solutions for Cybersecurity (MIT Press), Frontiers of Financial Technology, and Trust::Data. David’s monographs include Basic Blockchain (2020) and Augmenting Your Career: How to Win at Work in the Age of AI (2021), both from Little Brown.

His most recent book is Basic Metaverse. His next book, Basic AI: A Human Guide to Artificial Intelligence will be published in January 2024. Both Basic Metaverse and Basic AI are jointly published by Little Brown and Harvard Business Publishing.

David’s writing has appeared in,, and, and he also co-edits, together with Professor Pentland, the Connection Science imprint of MIT Press. He was named 2018 Global Fintech Person of the Year by Fintech Galaxy, a Refinitiv Top 100 Social Influencer in Financial Technology, and listed on One World Identity’s Top 100 People for both 2017 and 2019.

David Shrier was granted an Sc.B. from Brown University in Biology and Theatre, and worked professionally in theatre after college.


David L. Shrier - Visionary Future
by Hub Culture Studio Davos 2023

Hub Culture Executive Editor Edie Lush interviews David L. Shrier - Managing Director at Visionary Future about the Metaverse, and how it fits into the idea of collaboration in a fragmented world. He talks about the effects of isolation, particularly during the pandemic, and how the Metaverse helped people to escape, and what exactly the Metaverse means, and what its opportunities are.







Copyright © 2002-2024, Felix Bopp. All rights reserved.