AI systems can potentially be used to expedite the transition to renewable energy and sustainable economic and commercial systems. One of the ways in which it can do this is by optimizing the use of power grids, diverse energy sources, shipping and transport routes, and the distribution of goods.
AI can be used to evaluate many layers of factors such as weather, traffic package or cargo volume and the energy available in the grid to maximize ideal routes and work methods to minimize carbon footprints.
The Current Climate:
◇ The highest greenhouse gas levels in 3 million years.
◇ Biodiversity has decreased by around 60% since 1970
◇ Deforestation of the amazon will cause an 8% drop in annual world-wide rainfall by 2050
◇ The chemistry of the oceans is changing more rapidly than it has for the past 300 million years, killing of coral reefs and fish populations, and created ‘dead zones’ in which life cannot survive in 10% of the oceans.
◇ Due to overpopulation and water pollution the world may have 40% less freshwater than needed to support the global population by 2030.
◇ 91% of the world's population lives under air quality conditions which do not meet the WHOs air quality standards.
The Transition from Demand Led Consumption to Supply Led Consumption:
Our current power grids are based on a demand led consumption model meaning they provide whatever amount of energy consumers are asking for, whenever they require it. It is difficult and unsustainable to store energy, this means that our energy grids need to change so that the amount of energy used and generated are commensurate.
This would mean using AI to identify which times it is best to use the grid for which purposes such as cooling homes and charging electric vehicles, and when it is most optimal to use which energy sources.
◇ ‘Zonal pricing’ of energy meaning pricing energy use in certain areas at certain times of the day week or year based on availability in order to encourage a more strategic use of energy.
Real-Time Data Analysis:
AI could be used to manage these prices on a daily and hourly basis – the level of data and analysis required to do this can only be executed with the assistance of AI.
◇ AI can be used to create efficient and strategic green energy networks, which can manage energy demand in real time, to optimize the use of the energy grid and enable energy trading in order to ensure that energy is not wasted and is priced in such a way to modulate/equivocate supply and demand.
AI will also be used to strategically integrate renewable systems with existing, non-renewable energy infrastructure.
◇ AI will be essential to managing power grids with multiple types of power sources, which will be the case with the many energy grids transitioning from traditional to renewable energy sources – the generation and use of energy must be constantly monitored and balanced to safely run a power grid and this will become more complex as we transition.
◇ AI can also be used to manage energy use on an individual building and construction level to determine the ideal heating, cooling and lighting systems and usage.
Transatlantic Green Marketplace:
The U.S. and the European Union have collectively resolved to create a transatlantic green marketplace with assistance of AI technology.
AI will be used for the following purposes, in addition to others: extreme weather and climate forecasting, emergency response management, health and medicine improvements, energy grid optimization, and agriculture optimization.
◇ AI optimization of energy systems has the potential to reduce greenhouse gas emissions by 1.5-4% by 2030
◇ AI deployment in energy optimization will need to be carefully guided and overseen to ensure that it does not exhaust resources because AI itself requires massive, high-power data centers to operate, and there most be constant modulation to ensure that it does not strain the power grid, and to ensure that the AI itself does not strain the grid for its own operations
◇ AI can be used to optimize food production and water usage, and can optimize the transport of goods.
AI and Green Jobs:
◇ The application of AI to the Green Transition is predicted to create 18-38 million jobs by 2030, which would constitute a very small percentage, 0.5-1%, of jobs globally. Most of these job gains are expected to be in East Asia, or 16-25 million jobs.
◇ Due to AI ‘optimization’ some sectors will see significant job losses, such as: transport, 15-21% of jobs will be lost, energy will see 4-6% of jobs lost to AI, water 5-8%, and agriculture will see 1.7-2.6% of jobs lost.
◇ There should be a 1.6% increase in managerial occupations concurrent with these shifts, many workers will require government assistance so aid with job displacement.
◇ AI will disrupt more jobs than it creates in the Green industry, and those created will not be equal to those that did exist.
AI and an Ethical Green Transition:
To ensure that AI actually serves sustainability and environmentally optimized purposes it will be essential to train, design and operate AI through ethical and just frameworks to ensure that it does not simply serve economic growth frameworks, which is otherwise the de facto mode of all products and technology.
◇ AI and AI systems are inherently extractive from and detrimental to the environment, and requires huge, energy sucking data centers that are already straining grids.
◇ AI should be trained with climate and justice data concurrently to ensure that the systemic iniquities inherent in our infrastructure and geographic grids are not reproduced in energy transitions.
◇ The actual ethical and environmental value and effect of AI application depends very much on the data used both to train the AI and to evaluate it.
* Systems built to optimize economic growth, technological progress and efficiency often do not serve ethical purposes.
* AI systems are already have a negative polluting impact on the global south were many data centers are based.
Our current economic model is linear rather than circular, meaning that it extracts resources from nature, reconstitutes these into products, then discards them as waste. This is a highly corrosive system which exhausts natural resources, destroys the environment and maximizes production and profit at the expense of environmental conditions, among other things. An alternative to this economic model is the circular economic model.
* In the past twenty years material consumption has increased by 65% globally and this is absolutely untenable. Likewise, around 13% of food produced for human consumption goes to waste, while 17% of food intended for household consumption is also wasted.
This indicates defective management of our production, demand and consumption and presents a major challenge and opportunity for the application of AI in restructuring these systems.
The concept of the circular economy:
A circular economy is a particular economic model that prioritizes the efficient use, re-use, remanufacturing and maintenance of existing goods, materials and infrastructure in order to minimize the environmental impact of human consumption and trade.
There are many examples and scale of circular economics, one of which would be a school which grows its own produce and later returns the food waste to the growing process in the form of compost. In this example, waste is minimized and the condition of the land and environment is taken into consideration.
A circular economy entails repurposing products and materials creating systems and factories that deconstruct and reconstitute existing products and materials, remanufacturing and recycling materials, to eliminate waste from the production/consumption system
Developing a circular economy will require oversight and collaboration with AI
* The circular use of industrial materials, meaning production that better matches actual demand, resume and remanufacturing, refurbishment, and other circular economic strategies could reduce global emissions resulting from production by 40%.
Challenges to Transitioning to a Circular Economy:
This transition requires knowledge, time and financial and material resources to transition to a circular economic system and many companies would require government assistance and incentive to make this transition which would temporarily impair profit and increase expenses.
AI, Climate Disaster and Emergency Response:
◇ AI can be used to develop strategic emergency response plans and systems.
◇ AI’s predictive capacities can be used to adapt to crises before they happen by running and planning for probable scenarios.
◇ AI may be more adaptable in terms of ideation surrounding disaster response and can respond to a larger volume of real time information than can humans.
The Environmental Impact and Risks of AI Itself:
Ironically, AI systems such as ChaptGPT have a huge and highly detrimental carbon footprint because they require an enormous amount of computational power.
◇ The amount of carbon used to train an AI system depends on the number/volume of parameters used to inform the system but quickly becomes prodigious.
Carbon Aware Computing: This would be a way of designing AI and other tech systems in a way that is cognizant of the carbon footprint of the computing system required to run these applications.
◇ Carbon accounting and reporting, meaning monitoring the carbon footprint of AI systems and strategizing the timing, location and power sources used when training a system are essential
AI and Climate Misinformation:
There are major concerns that ChatGPT and other AI systems will be used to accelerate climate denial financed by fossil fuel companies and major corporations looking to serve profit agendas
◇ There is a acute risk of the perpetuation of climate misinformation if people use ChatGPT as an information source on climate change because it simply synthesizing existing information whether it is correct or not and corporations can feed self serving and skewed or incorrect data into the system to further enforce misconceptions about the climate
Continue Reading about the AI transition and the ways it will effect how we live, work, and think.