It is natural and massive. We are talking about one of the most disturbing challenges of the world- Climate Change. Many conferences, seminars, and government meetings have been conducted across the world around climate change. While it is a good thing that there has been an increasing awareness across the planet, the reality is that we are still far from the ideal objective.
The need of the hour is to accelerate our ground-level efforts to gain tangible advantages in this regard. With its sophisticated capabilities, Artificial Intelligence can be beneficial in this context. As opposed to present-day computers, the AI has tremendous processing capacity, and most importantly, it can “learn and evolve” continuously- just like us- the human beings.
One of the most common benefits of AI is its advanced capabilities to offer sophisticated remote working solutions that can significantly reduce pollution and carbon footprints. Likewise, the ability to predict environmentally-friendly products, understand a commercial vehicle’s long-term climate effects and facilitate autonomous battery-oriented cars are some other significant benefits of AI. However, in this post, we will mention some of the more specific ways in which Artificial Intelligence plays a proactive role in reducing and even reversing climate change:
Improving our Knowledge about Climate Change
During this decade, a specific discipline was created for climate change called Climate Informatics, entailing disparate factors like reconstructing historical climatic conditions, extreme weather predictability, and its effect on socio-economic conditions. AI studies climate informatics for offering informed insights. These insights can help whether organizations determine the best course of action for saving the environment and community.
Climate change is determined by a large volume of diverse variables that keep on fluctuating, making it out of the scope of the human brain to forecast the changes or even understand them properly.
We still have to go a long way to refine this model further to ensure more credible insights. Presently, there are several key factors where the models disagree- for instance, the role of precipitations in future changes.
An app designed by MILA gives a futuristic peep into the picture of their immediate vicinity post the climate change impacts. This app works on a dynamic model where the users can upload images of present-day climatic situations (floods, bush fires, etc.) to foresee their future impact on the climate conditions.
Extracting More Specific Details
AI algorithms can logically extract meaningful insights out of a massive pile of data to tell us about the future impact of present-day measurements. It allows us to gain even figure-specific insights like the time it will take for the global temperature to rise to a specific number if we take certain measures. This insight allows us to design, optimize, and re-strategize our climate conservation measures.
These technologies are cleverly designed to facilitate maximum output with a reasonably lower amount of input, like the basic monthly temperature history of the world. Feeding a few decades of such historical data into an AI system – without any other dataset, can fetch climate forecasts for the upcoming decade(s). With accuracy above 95%, it can provide insights that can be used as solid parameters to work upon.
A growing number of countries are taking strict measures to conserve the climate. Here, AI can act as an ally to allay negative climate impact by studying satellite images to accurately identify and point places that don’t follow these climate regulations- like primary CO2 emitting sources.
AI Can Help in the Logical Drilling of Available Data
We have tremendous data sets that directly or indirectly influence climate change- like earth conditions, weather reports, etc. The challenge, however is to unlock the real potential of this data in practical circumstances. It requires tremendous efforts and “logical drilling” to understand this data and identify the mutual relationship. With its sophisticated computational and predictive capabilities, AI can help us extract the vital details out of this data and take the right actions at the right time. Taking the required measures in advance can empower us to take corrective steps or precautions to stall the negative impact.
Promotes Economic Use of Energy Expenditure
Another central area where AI can help is economizing the energy expenditure with the help of sophisticated monitoring and intelligent distribution for minimizing the loss. By stopping energy leakages, we can lower down the carbon footprint and determine accountability and design the right strategies to reverse it.
Creating an Entire Ecosystem to Stop the Negative Impact
Using intelligent and coherent methodologies, sophisticated ML algorithms can be formed to fine-tune present utility systems for minimizing their negative effects on climate. This helps in diverse ways like monitoring usage, increasing efficiency, and setting thresholds.
A financial think tank named Carbon Tracker uses an AI-based study of satellite data to track emissions by coal plants and logically persuade the industry about its low profitability profile in the long run. This initiative by CarbonTracker (and other similar initiatives) will help the UN stop new coal plants.
Increasing its scope of impact, the Carbon tracker now also takes satellite imagery of emissions from the plants that run on gas. The objective is to detect different significant causes of air pollution precisely. With the help of a sophisticated system, the think-tank will conduct a deep analysis of other gas-based plants at a massive scale- to the tune of up to 5 thousand plants at different locations. It plans to make things public and take reasonably bold measures like identifying and mentioning defaulters and determining an emission price. This massive and highly effective data bank will facilitate a comprehensive picture of things.
The datacenters of Google gobble up a tremendous amount of energy, amounting to as high as 3% of the global consumption. To effectively economize the energy consumption, Google has employed its sophisticated ML- DeepMind for automatically optimizing diverse settings to lower energy consumption while improving cooling.
Another innovative approach has been employed by Microsoft that has built underwater data centers. These data centers are powered by wave energy which means a tremendous reduction in the worldwide energy consumption levels. The need for the hours status initiatives should be replicated globally by other smaller businesses as well.
Making More Accurate Predictions
Predictions play a very important role in the affairs of climate change affairs. Making it more precise can improve the way we understand climate change which can empower us to make informed decisions and take the right actions at the right time.
We are sitting on a huge pile of data- temperatures, weather conditions, historical graphs, etc. The challenge, however, is how to use these diverse and different databases to make logical and most accurate predictions.
One study by eminent personalities in climate change found that ML and AI can help us tackle over 10 different aspects of this area. Some of them include:
- Energy consumption
- Accelerating climate education
- CO2 removal
- Solar engineering
- Tackling financial issues related to climate change
Research and Development for Commercial/utility Optimization
For producing energy-efficient technology/products with a high sustainability profile, it is important to go through a detail-oriented R&D process. It is not an easy task to consider many commercials, scientific, and even social factors. How well you understand these disparate factors and identify the equation they form together determines the outcome. Such a complex analysis is beyond the scope of the human brain.
ML packs the powerful combo of exceptional processing speed and human-like discretion to connect seemingly unknown factors for forming logical perceptions. Most importantly, ML can drill much deeper in its investigation into various facts and stats to arrive at logical conclusions. Last but not least, the ML can combine multiple qualities to make pretty reliable predictions extending to the distant future, up to several decades.
There are several areas where these qualities can be utilized for tangible benefits: developing energy-efficient batteries with exceptional durability that can store a vast amount of energy. Likewise, ML can also be a great tool to unlock the potential of promising zero/low-carbon substitutes for generating electricity like nuclear fusion reactors.
Promoting Purpose-specific Design to Save Energy
A wide array of factors is to be considered while designing an environment-friendly product. Right from actual effects to regulations, long-term impact, toxicity, raw material profile, and cost factors, several complex factors must be considered.
So it is beyond the capacity of a human brain to work simultaneously on all these factors and design a product/technology that is sustainable, business-friendly, and practically suitable.
ML can deeply study all these disparate factors and explore and compare different configurations/materials/possibilities with its tremendous processing power. Based on the same, it suggests the best combination ideally meeting maximum parameters. Airbus used this approach to design an airplane that emits significantly lower amounts of CO2 than its conventional counterpart. It also requires less raw material due to its smaller size, and it can be printed using 3D printing technology.
AI or Artificial Intelligence comes with advanced digital capabilities and extensive processing speed that can effectively combat the impacts of climate change by accelerating our efforts to find energy-efficient substitutes. It will help in lowering down the carbon footprint. AI can also help in improving our understanding of climate change to take better control of the situations. While the initial results are encouraging, we still have to cover a long distance, but due to its constant evolution and self-learning model, AI can also help reduce this gap.