Decarbonisation and Artificial Intelligence

Introduction

Decarbonisation is the process of reducing carbon emissions into the atmosphere. This concept exists as part of a broader effort to improve the state of the climate and reverse some of the damage that has been caused by pollution and greenhouse gas emission. Decarbonisation encompasses changes to a business or government’s  energy consumption, policies and economics that result in a reduction of its carbon footprint. 

Carbon emissions have continued to rise steadily over the past few decades. Despite the fall during the COVID-19 pandemic, the International Energy Agency reported that emissions climbed back up by around 5% in 2021. Globally, greenhouse gas emissions grew by over 50% between 1900 and 2019. It is worthy of note that carbon emissions differ across countries as well as industries. The energy sector, including transportation and electricity remains the biggest source of emissions worldwide. Other sectors producing large amounts of greenhouse gases are the agricultural and industrial sectors. Reports show that China, the United States and the European Union are the biggest contributors to emissions. The highest per capita emissions are found in Qatar and Australia, although they do not feature among the top emitting countries.

Currently, the world is facing the impacts of climate change. Global temperatures and sea levels are rising, resulting in natural disasters and loss of habitat for both wildlife and human populations. Beyond this, fluctuating temperatures are affecting food growth and supply and contributing to food insecurity. In addition, biologists are recording mutations and reemergence of various disease-causing organisms. These represent only a small fraction of the changes we can expect to see in the coming decades as a result of climate change. In order to curtail these events and ensure preservation of the necessary conditions to maintain life on the planet, research has suggested that emissions must be reduced by half by the year 2030 to limit global temperature rise. Attempts at global commitment to this effort has resulted in the search for sustainable solutions to the problem of rising carbon emissions.

Different paths have been considered on the path to decarbonisation by several businesses and governments. These include measures to switch to electrical energy or substitute fuels to cleaner forms that will reduce pollution. As expected with the emergence of a new digital age, technological solutions have also been proposed as part of the efforts to halt the climate crisis. It has been said that using AI can help to achieve at least 5-10% of our climate goals. As a whole these systems function broadly in 3 major aspects- monitoring, predicting and reducing emissions. The systems that are being developed in each of these areas provide multiple options and opportunities for reducing emissions.

Environmental Friendliness: Using data collected from oil and natural gas wells, scientists are developing AI models to optimize the process of drilling, fracking and processing oil and gas from natural reserves. In doing this, workers are able to select the methods least likely to release large amounts of gas into the atmosphere. AI monitoring systems are also being used to evaluate CO2 emissions resulting from specific projects. 

Energy Management: Control of electricity grids can AI will mean that systems are able to determine patterns in consumer behavior and alert systems during periods of increased collective use of energy. These periods can then be targeted during the process of reducing and managing consumption.

Weather Forecast: As renewable energy systems become central to energy production, weather forecasts will become increasingly important in predicting the supply of energy. AI systems can function to make these predictions regarding the impact of weather on increasing energy demand. Machine learning can be employed in learning weather patterns and using them to forecast energy needs. 

Upgrading Design Systems: Automated processes of constructing solar plants are replacing long and tedious engineering projects, speeding up the transition from traditional to renewable energy. In addition to these, AI can be used to optimize other options of energy and reconfigure the existing design methods. AI systems can develop production processes that are far more efficient than human models and minimize carbon waste.

Machinery Inspection: Sensors and cameras are becoming essential in detecting leaks and faults around gas plants and cutting down on accidental emissions of harmful gases into the atmosphere.

Regulatory Oversight: Companies must be made to account for their carbon footprint. AI systems can be used by international agencies to monitor businesses and governments and confirm that their reports match their efforts in reducing emissions.

Construction: The process of supply heating and cooling to buildings has been found to be among the leading contributors to energy usage. Most of this energy is wasted and not effectively used. AI systems can be used in building construction and wiring to propose the most efficient ways to supply energy to buildings.

Preventing Deforestation: Deforestation continues to contribute to the increased presence of carbon in the atmosphere. Computer Vision can be used to observe illegal activities and track down areas where intervention may be made.

Waste Avoidance: AI can be used to develop and identify novel materials that will be more efficient in producing energy while resulting in less waste than existing systems. 

Data Analytics: In tying all these efforts together, data sharing systems are necessary to effect these changes across various industries and agencies. This will also help to save on costs and provide more insight on the impact of industries

Despite these various ways in which AI can contribute to reducing our carbon footprint, it must be understood that AI has a carbon footprint of its own too. AI systems themselves require much energy to run. It is forecast that data centers may contribute as much as 10% of total electricity usage in only a few years. The question then becomes how to ensure that AI itself is used in an ethical manner that does not defeat its purpose in this regard. Combining these efforts with adequate regulation is also necessary to prevent cyber threats that may be employed to sabotage these efforts. In conclusion, while AI carries great promise in supplementing global efforts at mitigating the climate crisis, it must be properly applied and rigorously enforced to ensure success. 


References 

https://www.wri.org/insights/4-charts-explain-greenhouse-gas-emissions-countries-and-sectors

https://www.nature.com/articles/d42473-021-00508-6

https://www.weforum.org/agenda/2021/09/how-ai-is-transforming-decarbonising-and-cleaning-up-the-grid/

https://www.anthropocenemagazine.org/AI/

https://www.startup-energy-transition.com/unlocking-the-potential-of-ai-for-decarbonisation/

https://www.consultancy-me.com/news/4978/three-ways-how-ai-can-help-decarbonise-the-oil-gas-sector

https://www.gtlaw.com.au/knowledge/get-smart-using-artificial-intelligence-blockchain-decarbonise-energy-sector

https://www.cioandleader.com/article/2021/09/22/how-ai-and-ml-can-accelerate-decarbonization-chemical-industry

https://www.forbes.com/sites/markminevich/2021/10/08/11-examples-of-ai-climate-change-solutions-for-zero-carbon/

https://www.bcg.com/publications/2021/ai-to-reduce-carbon-emissions

https://ec.europa.eu/research-and-innovation/en/horizon-magazine/ai-can-help-us-fight-climate-change-it-has-energy-problem-too

https://www.technologyreview.com/2019/06/20/134864/ai-climate-change-machine-learning/

https://www.digitalinformationworld.com/2022/04/decarbonization-in-oil-gas-industry-and.html

https://accelerator.chathamhouse.org/article/harnessing-artificial-intelligence-to-decarbonize-industrial-sectors-2

https://www.opex-group.com/solutions/ai-for-emissions-reduction



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