Combating Climate Crisis: The Challenges Of Carbon Neutrality With AI

Climate change is one of the biggest threats affecting the global environment. Since 1880, our planet’s temperature has increased by an average of 0.14° Fahrenheit (0.08° Celsius) per decade. This projection indicates that since 1981, global warming has doubled to 0.32° F (0.18° C) every decade. Also, did you know that the last year, i.e. 2022 was actually the sixth-warmest year based on NOAA’s temperature data? And the 10 warmest years have occurred since 2010.

The amount of carbon dioxide and other greenhouse gases we produce over the next few decades will determine the extent to which Earth will warm in the future.

While these numbers and facts are devastating, the breather is that brands and businesses across industries have already set the course to a greener future, i.e. a carbon-neutral future. They are leveraging artificial intelligence and machine learning tools to roll back their carbon footprints and overall environmental impact.

From the predictive analysis in logistics and monitoring deforestation to recommending sustainable alternatives and designing low-carbon materials, AI has been instrumental in combating climate change and attaining carbon neutrality. To be brutally honest, AI is not the miracle drug that can instantly turn things around, but it is a crucial part of the fight against climate change.

Recommended: A Green Future: 10 Ways to Achieve Carbon Neutrality with AI

Let’s take a look at the most basic and common ways in which AI is effectively (albeit slowly) controlling the effects of global warming.

AI’s Role in Carbon Neutrality

AI is assisting climatic leaders and businesses across the world in fighting global warming in more ways than one. Its ability to collect, analyze, and interpret vast, complicated data sets on emissions, and climate effects are enabling stakeholders to take a more structured, data-driven approach to establish a greener future.

The large data sets on emissions, the effects of climate change, and future estimates gathered by AI can be used to improve planning and decision-making, optimize workflows, support collaborative ecosystems, and promote climate-friendly behavior.

Better Electrical System

The electrical system is filled to the brim with data, but not much has been done with the information in the last few years. Enter AI and machine learning, now suppliers can keep a check on electricity generation, and demand through AI forecasting. This way, the suppliers can explore ways to integrate renewable resources more effectively into national grids and bring down the waste generated. This process is underway at Google’s UK research facility DeepMind, which is leveraging AI to predict wind farms’ energy output.

Agricultural Emissions and Deforestation Surveillance

If you thought only engines and power plants were primarily responsible for greenhouse gas emissions, here’s an eye-opener. Destruction of trees and other plant life that have accumulated carbon mainly due to the photosynthesis process also contributes to greenhouse gas emissions. Unsustainable agriculture and deforestation release carbon back into the atmosphere. With the help of satellite imagery and AI, we can get to know the location where all this is happening and protect it.

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Predictions of Extreme Weather

AI has an immense capacity to detect major weather events like a shift in ice sheet dynamics and cloud cover. Scientists, with the help of AI, can assist in predicting extreme natural conditions like droughts and hurricanes. These forecasts can enable governments to deal with the effects of natural calamities more efficiently.

Creating Materials with Low-Carbon Content

Steel and concrete production are the greatest producers of greenhouse gas emissions. In this case, machine learning can help to reduce the number of emissions by creating sustainable alternatives. AI enables scientists to simulate the characteristics and interactions of previously undiscovered chemical compounds, which aids in the discovery of novel materials.

Efficient Transportation

Did you know that a quarter of global energy-related CO2 emissions is associated with the transportation sector and road users are responsible for two-thirds of this amount? Machine learning can improve efficiency by:

• Decreasing the number of unnecessary trips.
• Improving vehicle performance.
• Switching to low-carbon solutions like rail.

Shared autonomous vehicles with a reduction of car usage could be a viable solution in the near future, although no concrete evidence supports this.

Reduction in Waste Energy

About another quarter of energy-related CO2 emissions on a global level come from the energy consumed in buildings. It’s proven that buildings are seldom updated with newer technologies and innovations.

While a single building’s energy consumption can be reduced by 20% with the use of a few smart sensors that monitor the temperature of the air, water, and electricity, large-scale projects are likely to have a greater impact.

Tools for every individual to reduce carbon emissions.

It is a common misunderstanding that individual people are incapable of taking serious steps on climate change. And, citizens are required to have an understanding of how they can contribute. Calculating a person’s carbon footprint and highlighting small adjustments they might implement to lessen it using machine learning could be helpful.

• Opting for public transport or carpooling while traveling to work.
• Reducing meat consumption.
• Consciously reducing the use of electricity like switching off the fans and lights when not in use.
• Abstaining from using plastic.

Challenges of Carbon Neutrality with AI

A survey conducted by Boston Consulting Group (BCG) revealed that 87% of business executives agree that AI may be an effective weapon in the battle against global warming. These leaders claim that 43% of their companies can see adopting AI to aid in achieving their environmental objectives. Yet, to achieve holistic results, AI and individuals, communities, and organizations need to work together and fight the challenges and roadblocks.

Recommended: Industries that are Benefiting from Carbon Neutrality Using AI

User-friendliness

Whether AI solutions are created for companies, governments, or consumers in general, user-friendliness is essential if they are to be adopted widely. They must be easily available, give the user with real benefits, and present precise details which will help the user make decisions.

For instance, in some places, measuring emissions and monitoring natural carbon sinks with the help of AI tools is a little difficult. The basic reason being the majority of currently available AI-related environmental alternatives is dispersed, challenging to reach, and underfunded. In fact, even when it comes to public- and private sectors, 77% of the leaders are of the opinion that the lack of AI solutions is a big issue, while 67% blame a lack of faith in AI-related data and analysis, and 78% claim that a lack of AI experience is a glaring barrier to their organization’s efforts to apply AI to address climate change.

The Quest for the Right Resources

To reap the maximum benefits of artificial intelligence and its promise to tackle environmental issues and global warming, one needs the right kind of resources and the appropriate network to support the initiative. To optimize AI’s efforts, we need financial protection through the correct resources, decision-makers, and skilled professionals. While financial support can help to bridge the gap between deploying AI and academic research, effective networking involving leaders and policymakers can establish awareness and encourage adoption. Discussing and sharing knowledge on ethical practices and potential applications can further ensure that technologies are ready for wider government and commercial adoption.

Enhancing Knowledge & Strengthening Skills

Capacity building is a very crucial part of leveraging AI for carbon neutrality. It can be defined as the process of enhancing the knowledge, talents, procedures, and assets that enable communities and groups to endure, adjust, and prosper in a world that is undergoing rapid change. Civil employees, business executives, and various other stakeholders can be properly trained and re-skilled to utilize and understand AI solutions in the most crucial situations. Additionally, given that many nations will be differentially impacted by environmental shifts, current AI studies require being more inclusive.

The Trust Factor

AI is dynamic, complex, and promising, yet, in the end, it often boils down to one common point – the risk of ethical behavior, be it intentional or unintentional. And so, it is of utmost importance that industry leaders gain the trust of environmental leaders and visionaries and properly leverage AI. They must ensure the use of detailed and trustworthy supporting datasets, and prioritize interpretable and understandable results.

Final Thoughts

Global temperatures are rising, droughts are getting worse, and storms are getting more intense, all signs that the changing climate has begun to have a significant influence on the environment, society, and economy. Every region of the world is being affected by significant changes in physical threats, be it sea level rise or wildfires. Needless to say, artificial intelligence algorithms and machine learning analytics can assist businesses, cities, and people in many different ways to combat the battle against global warming. Artificial intelligence-powered gadgets that lower emissions are still being developed and designed by businesses.

Despite the advancements in technologies backed by cutting-edge research, there are still many obstacles in the way of AI deployment to assist emerging solutions overcome them and reach their full potential at scale. Irrespective of their formal involvement in AI or ecological issues, all individuals, groups, and entities have an essential part to play. To sum it up, the sooner we reduce greenhouse gas emissions, the lesser risks and impacts we have to deal with in the coming years.

The support that AI solutions currently receive is far from enough; they require access to funding, knowledgeable decision-makers, and skilled practitioners.