“We all share a planet. I think that’s true for AI,” says Sundar Pichai, Google’s CEO. For sustainability, artificial intelligence is a double-edged sword — both a powerful tool for environmental conservation and a contributor to our carbon footprint. Understanding this complex relationship is the only way to make informed decisions in our increasingly AI-driven world.
Let's examine how AI is reshaping sustainability efforts across industries and define how AI and sustainability can exist in the same sphere, with good and bad consequences.
AI for sustainability: Two sides of the coin
AI is a powerful sustainability tool with both positive and negative environmental impacts. On one side, its energy and resource consumption accelerates climate change. On the other hand, AI solutions help us predict and eliminate the consequences of environmental degradation, optimize resource usage, and develop innovative technologies to combat climate change. Let’s explore these issues in more detail.
How is climate change accelerated by AI?
Let’s face it: AI requires a lot of natural resources. First off, it needs water to cool data centers. Training large language models like GPT-3 consumes millions of liters of fresh water. And running GPT-3 inference for 10-50 queries uses about 500 milliliters of water. All this usage accumulates into a massive amount of a very needed resource, and the numbers are hazardous.
By 2027, AI systems will consume 4.2-6.6 billion cubic meters of water annually, nearly half of the UK's yearly water usage. Moreover, training AI for a year uses 126,000 liters of water, with AI's computing needs doubling yearly! That’s a massive, ever-growing thirst.
Energy consumption is another concern with climate change and AI. AI servers are extremely power-hungry – a single server consumes energy equivalent to an entire house. Interestingly, water consumption is closely linked to energy consumption. In the US, data centers indirectly increase water consumption due to electricity generation, with an average of 43.8 liters of water withdrawn per kWh.
But is it all painted black for artificial intelligence and sustainability? No, if AI technology is used right.
AI as an ally for environmental sustainability
Despite these challenges, when AI is used for social good, it offers significant potential for addressing environmental issues. Despite the industry specifics, AI can greatly improve data traceability — the cornerstone of sustainability reporting, which can differentiate you as a green brand and reduce your carbon footprint. It improves decision-making, measures sustainability efforts, creates more responsible supply chains, and helps tackle climate change.
Today, 46% of executives view AI as important for advancing their reporting and performance efforts. A 2022 study showed that companies using advanced digital tech for emissions measurement are twice as likely to meet their reduction goals. But is it worth the effort you put into implementing AI? Let’s sort it out.
How can AI help sustainability and profits?
Together with the social positivity of the environmental startups using AI comes the increase in efficiency and returns. Any proof? Business leaders implementing artificial intelligence and sustainability efforts also make 43% more profit than other companies and believe their sustainability efforts contribute to their success. But how exactly can AI be used?
Energy efficiency
AI-driven solutions reduce energy consumption and improve overall efficiency. These advancements not only cut costs but also eliminated greenhouse emissions. How is this done?
Smart building management. AI optimizes HVAC systems, lighting, and appliance usage, decreasing energy consumption by up to 30%.
Industrial process optimization. AI analyzes manufacturing processes to identify energy-saving opportunities, potentially reducing energy use by 20-30%.
Grid management. AI predicts energy demand and supply, enabling better integration of renewable energy sources and reducing grid losses. For example, Google used DeepMind AI to minimize energy usage for cooling data centers by 40%.
Carbon footprint calculation
AI-powered tools improve individuals' and organizations' carbon footprint tracking and analysis. These innovations provide unprecedented insights into carbon impact, enabling more effective reduction strategies. How does AI achieve this?
Personal carbon tracking. AI apps analyze user behavior and purchases to estimate individual carbon footprints and suggest personalized reduction strategies.
Corporate emissions monitoring. AI systems integrate data from various sources to give businesses accurate, real-time carbon emission calculations.
Supply chain analysis. AI models assess the carbon impact of entire supply chains, identifying hotspots for emissions reduction and optimizing logistics.
Waste tracking and management
AI is transforming waste management, making it more efficient and environmentally friendly. These technologies are helping to reduce landfill usage, grow recycling rates, and minimize the environmental impact of waste. How is AI improving waste management?
Smart waste bins. AI-enabled bins sort waste automatically, improving recycling rates and reducing contamination in recycling streams.
Predictive maintenance. AI predicts when waste management equipment needs maintenance, reducing downtime.
Optimized collection routes. AI analyzes waste container fill levels and traffic patterns to create the most efficient collection routes and cut fuel consumption.
AI helps us track and predict climate-related changes on a global scale. These technologies provide scientists and policymakers with invaluable insights into the state of our planet. How does AI contribute to climate monitoring?
Satellite image analysis. AI processes satellite imagery to track deforestation, ice melt, and other climate-related changes over time with unprecedented accuracy.
Ocean health monitoring. AI-powered autonomous vehicles collect and analyze data on ocean temperatures, acidity, and marine ecosystem health, providing comprehensive insights into ocean dynamics.
Air quality prediction. AI models predict air quality based on weather patterns, traffic, and industrial activity, enabling public health measures and pollution control strategies.
Renewable energy optimization
AI is vital for maximizing the efficiency and reliability of renewable energy sources. By optimizing renewable energy systems, AI helps increase clean energy production and accelerate the shift away from fossil fuels. How does AI optimize renewable energy?
Wind farm efficiency. AI predicts wind patterns and optimizes turbine positioning and operation, increasing energy output by up to 20%.
Solar panel performance. AI analyzes weather data and panel conditions to maximize energy capture and predict maintenance, improving overall system efficiency by 10-15%.
Energy storage optimization. AI manages energy storage systems, balancing supply and demand to ensure optimal use of renewable energy and grid stability.
The challenge of implementing AI lies in balancing the environmental costs with its potential benefits. COAX offers artificial intelligence services development that can contribute to your sustainability efforts. We provide strategic consulting and help you find shortcuts to achieving this balance by creating and integrating AI solutions.
Now that we have discovered how AI can serve social good and showcased how an experienced tech company can improve its sustainability, let’s examine real-world applications and case studies that show the power of this technology.
AI for social good: Real-world examples
In 2024, artificial intelligence is no longer a science fiction magic pill for the planet and nature. It's getting real as more companies implement technologies to track, reduce, and optimize resource allocation and achieve other impactful ecology improvements. And the coolest part — they are wording and showing great results! Let’s look at the best examples of green tech in action.
AI for recycling and waste management: CarbonBright
AI holds immense potential to revolutionize waste management systems. The principles of AI applied in other sustainability domains can be adapted to improve waste sorting and recycling processes. Some applications in this sector include:
Implementing computer vision systems to enhance sorting accuracy
Optimizing collection routes through data analysis
Improving recycling plant operations with predictive maintenance
Developing AI-powered apps or chatbots for consumer education
These AI tools can significantly reduce waste and increase recycling rates, contributing to a more circular economy. Let’s see how a climate change startup uses AI to track and reduce the waste of everyday goods.
CarbonBright, a London-based climate tech startup, serves as an example of how AI for social good can address the complex challenge of product lifecycle analysis. Their platform uses AI to provide instant assessments of consumer goods' environmental footprint from production to recycling.
Here are some features of CarbonBright's solution:
Automated and accurate measurement of product impact throughout the supply chain
Data science-driven gap-filling for comprehensive analysis
Third-party verification of methodology
Recommendations for emissions reduction and material substitution
This innovative approach allows brands, suppliers, and retailers to identify emissions hotspots and transition to more sustainable products efficiently.
From monitoring climate change to optimizing renewable energy systems, AI is changing how we approach sustainability. By processing data from satellites, ground sensors, and other sources, AI algorithms can detect patterns and trends humans can’t determine manually.
The European Space Agency's Climate Change Initiative (CCI) shows the power of AI in climate monitoring. This innovative project utilizes AI to analyze data from over 700 satellites, significantly improving climate change prediction and the detection of key climate variables.
Neural networks help map ocean colour
This initiative aims to lead the European Union to carbon neutrality by 2050. By leveraging machine learning algorithms, CCI can process and interpret complex climate data at an unprecedented scale, providing researchers and policymakers with crucial insights to inform climate action strategies.
AI and sustainability-led farming: Pendulum and Agritech
AI improves sustainability in agriculture, enabling more sustainable farming practices that optimize resource use while increasing yields. AI-powered solutions are helping farmers make data-driven decisions that benefit their operations and the environment. Let’s see how climate startups are doing it.
Pendulum helps organizations achieve more with fewer resources, particularly in supply chain management and precision agriculture. As a leading climate tech startup, Pendulum has developed the world's first AI-powered supply chain API that predicts demand, optimizes supply, and geolocates assets, continuously improving itself.
Pendulum refines a large language model (LLM) to convert unstructured data into formats that agricultural machinery can utilize, optimizing the use of pesticides, water, and other resources. Their clients saw a 92% reduction in excess inventory, directly contributing to reduced waste.
COAX, a company specializing in advanced solutions in varied sectors, partnered with a greenhouse business to develop a comprehensive agritech solution. This connected farming system integrated IoT devices for real-time data collection on crop stages, environmental conditions, and resource consumption, and a custom web application analyses data and allows remote management.
AI capabilities and precision have enhanced data processing from varied and scattered sources. The results of this AI-driven approach were significant:
14% increase in revenue
15% increase in crop yield
12% reduction in operational costs
25% reduction in resource waste
These results demonstrate the potential of AI-driven agritech solutions to enhance sustainability in farming operations while also improving profitability.
Boosting energy sustainability with AI: BrainBox and KoBold Metals
AI is crucial for optimizing renewable energy systems and improving energy efficiency across various sectors. From smart grids to building management, AI algorithms assist in reducing energy consumption and carbon emissions. Let’s see in detail how green companies use this technology to improve sustainability in the energy sector.
BrainBox AI showcases the potential of AI in enhancing energy efficiency in commercial buildings. Their autonomous AI solution integrates with existing HVAC systems, optimizing operations in real time. The results are impressive:
Up to 25% reduction in HVAC energy expenses
Up to 40% reduction in HVAC-associated greenhouse gas emissions
Moreover, BrainBox AI employs generative AI, powered by Amazon Bedrock, to streamline the incorporation of new buildings into their system. This innovation has reduced setup time by over 90%, allowing for faster deployment and broader impact.
KoBold Metals demonstrates how AI can support sustainable resource extraction for clean energy technologies. This innovative company uses AI to discover new lithium, cobalt, copper, and nickel sources – essential components for battery production. Their goal is to support the transition to electric vehicles, with ambitious targets of 60% of new light cars and trucks being electric by 2030, and 100% by 2050.
KoBold has developed two proprietary AI models:
TerraShedSM: An extensive database compiling over a century of global data on the earth's crust.
Machine Prospector: A suite of predictive models analyzing the data collected by TerraShedSM.
These AI-driven tools enable more efficient and environmentally friendly resource discovery, supporting the shift towards sustainable transportation.
Machine Prospector
AI's role in smart testing and greening the tech industry
The need for sustainable software development practices becomes increasingly crucial. AI is making the software development process more environmentally friendly and efficient. AI-powered tools are transforming various aspects of software development:
Smart testing and automated bug detection.
AI algorithms can execute test cases faster and more comprehensively than traditional manual testing, reducing time to market and saving energy and resources. This AI-enhanced testing automation improves efficiency, test coverage, accuracy, and reliability.
AI-powered design tools.
AI-powered tools are used to enhance sustainable UX design by analyzing user behavior patterns and suggesting energy-efficient design alternatives.
Energy consumption analysis and optimization tools.
AI can identify areas of high energy consumption in software applications and suggest ways to reduce it. It is often used as part of software modernization to help businesses meet current market needs and productivity demands.
Benefits of AI-powered software development and design
AI-enhanced software development contributes to sustainability efforts in several ways:
Shorter development cycles and faster time-to-market reduce overall resource consumption
Early defect identification and prevention minimize rework and energy waste
Support for iterative development and continuous delivery optimizes resource use
Improved software efficiency leads to reduced energy consumption during operation
Let’s explore how sustainable tech companies are using the power of AI and machine learning to enhance software development, testing, and energy efficiency of the solutions developed.
Climate tech startup for clean data and green coding: VIA
VIA (Via Science, Inc.) underlines how AI for good can be applied in sustainable software development. As one of the innovative green coding companies, VIA offers a solution that allows organizations to lower their carbon footprint while ensuring data privacy and security.
Key features include:
Uses zero-knowledge proofs to provide sustainability data
Enables organizations to track energy usage and achieve sustainability objectives without compromising privacy
Developed a decentralized software solution, initially for the U.S. Air Force, allowing authorized users to utilize generative AI models without data sharing
Their tool, SLAM AI, creates SQL queries and front-end code for local data retrieval and display.
This case study demonstrates how sustainable tech companies can leverage AI to address both environmental and privacy concerns simultaneously.
Our expertise in sustainable AI software development
At COAX, we specialize in developing sustainable software solutions that leverage AI to optimize performance and reduce environmental impact. As one of the environmentally friendly startups with 14 years of experience, our approach includes:
Product strategy aligned with sustainability goals
User experience design that promotes eco-friendly behaviors
Iterative product development using Agile methodologies
Market launch support to ensure product-market fit and business viability
By focusing on these aspects, COAX demonstrates how green companies can integrate AI into their sustainable product development practices to contribute to a more sustainable future.
AI for sustainability: Promoting environmental conservation
As we've explored throughout this article, AI for sustainability is not just a futuristic concept but a present reality transforming multiple sectors. From waste tracking and recycling to optimizing renewable energy systems, AI-powered tools are proving instrumental in addressing our most pressing environmental challenges.
Climate tech startups and established green companies are leveraging the power of AI to innovate and create more sustainable solutions. Whether it's through smart testing in software development, AI-powered design in resource extraction, or revolutionary approaches to carbon footprint calculation, artificial intelligence is consistently demonstrating its potential for environmental good.
By harnessing the power of AI for good, we can drive innovation in climate change mitigation, resource conservation, and environmental protection. The future of sustainability is here, and it's powered by AI.
FAQ
Which AI applications can be used to help with sustainability problems?
AI applications that can help with sustainability problems include predictive analytics for climate modeling, smart grid management for energy efficiency, AI-powered waste sorting and recycling systems, precision agriculture tools for optimizing resource use, AI-driven design for sustainable product development, carbon footprint calculation, and tracking tools.
How does AI contribute to energy efficiency?
AI contributes to energy efficiency by optimizing power distribution in smart grids, improving building energy management systems, enhancing the performance of renewable energy sources, and reducing energy consumption in data centers and industrial processes.
Can AI help in reducing carbon emissions?
Yes, AI can lower carbon emissions by optimizing transportation routes to reduce fuel consumption, improving energy efficiency in buildings and industrial processes, enhancing the performance of renewable energy systems, and providing accurate carbon footprint calculations for better decision-making.
What role does AI play in sustainable agriculture?
AI in sustainable agriculture optimizes irrigation and fertilizer use, predicts crop yields and identifies pest outbreaks, enables precision farming techniques, and helps in soil health management.
How is AI being used in waste management?
AI is used in waste management for automated waste sorting, optimizing waste collection routes, predicting waste generation patterns, and improving recycling processes.
How does AI contribute to green software development?
AI contributes to green software development by optimizing code, automating testing processes to reduce resource consumption, enhancing predictive maintenance to extend software lifespan, improving algorithm efficiency, and creating UX that encourages environmentally friendly behaviors and minimizes digital waste.
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