How Does AI Help Fight Against Climate Change?

By contributing to sustainable practices, AI helps slow down climate change and reduces its environmental impact.

Nov 22, 2025 Article Team 1 AI

Introduction

Artificial Intelligence, more commonly known as AI, has emerged as a transformative technology that can be applied to every aspect of modern life. From healthcare and business to climate and environmental sustainability and helping the public conveniences through resources like Apple Intelligence, ChatGPT, and Gemini, AI has the potential to revolutionize our modern society.

Specifically, AI enables machines to mimic human cognitive functions, allowing them to analyze data, make decisions, and provide insights at unprecedented scales and speeds. This makes it quite a handy tool because not only is time cut down, but tedious tasks infested with human error can now be automated, increasing accuracy.

With growing climate change problems such as the destruction of wildlife and radically inconsistent weather patterns, concerns to mitigate its effects have grown. One key contributor to climate change is the rapid growth in global waste production. As the impact of climate change has worsened over the years, the imminent urgency to develop practices that prevent and reduce the consequences of climate change. While one may wonder how AI would benefit the fight against climate change, its cognitive functions have shown the potential to contribute to sustainable practices, reducing emissions, managing resources efficiently, and addressing global environmental challenges. AI’s integration into climate change initiatives achieves greater sustainability by slowing down climate change and reducing its environmental impact. Some of its major contributions include enhanced waste management, wildlife conservation, and weather modeling.

Waste Management

Waste generation is a significant contributor to aggravating climate change. By emitting greenhouse gases that harm the ozone layer and pollute the environment, waste generation fosters devastating consequences for life, including humans. Despite this, waste disposal has become a daily routine. From the food we waste to the plastic products we consume, waste generation has become a daily habit. However, how often do we consider the large amount of waste we generate?

According to the United Nations Environment Programme, global municipal waste produced in 2023 reached 2.1 billion tons. This number will only increase as the world population continues to grow. By 2050, the World Bank predicts this estimate will rise by 70%. While traditional waste management exists, their approaches have failed due to inefficient manual processes and contamination issues. For example, the NY Times reported that around 25% of recyclable materials collected are contaminated due to improper disposal practices.

However, artificial intelligence presents an opportunity to improve waste management. Through its ability to analyze data and make predictions, a waste management system with integrated AI will be more efficient and optimized to support an eco-friendly circular economy by keeping materials in use longer. Specifically, AI is improving this process by accelerating accurate collection and recovery using Smart Bins. This advancement promotes a more sustainable and less wasteful future. So what are Smart Bins?

Smart Bins are waste containers that use AI to reduce impact through improved collection and recovery techniques. Specifically, the ML model utilizes sensors and cameras to obtain data on the waste disposed of. Using collected data, these bins employ various protocols.

The primary goal of these containers is to sort waste. In this process, materials are scanned by cameras and sensors—such as gas, temperature, humidity, odor, sound, and infrared detectors— to accumulate information on their composition, size, chemical properties, etc. The data is then relayed to servers for a trained ML model to identify a material's contamination levels and recyclability based on its properties and categorize it. Once this information is broadcast back to the bin, waste is organized into appropriate containers using robotics. Utilizing this approach has facilitated waste to be more accurately organized, with an increase of 72.8% to 99.95% accuracy in predicting contamination levels and recyclability(Spring Nature Link). Compared to manual sorting through labor, this is a remarkable improvement and makes this process significantly efficient. According to The Strait Times, a pilot program in Singapore has reduced contamination rates from the national average of 40% to 5%. These improvements create a sustainable supply of resources that can be reused, fostering a circular economy where wastage and greenhouse gas emissions from trash are reduced, resulting in fewer pollutants being plunged into the environment.

In addition, the ability of AI to adapt to new data and make accurate predictions is crucial in improving the efficiency of waste management municipalities utilizing Smart Bins. ML algorithms such as artificial neural networks multilayer perceptrons, support vector regression algorithms, linear regression algorithms, decision tree algorithms, and genetic algorithms can create better predictive models that utilize categorical variables generated by Smart Bins. Because of these models' increased accuracy by 85%(Science Direct), AI's analysis trends in the quantity of waste disposal and type of waste for specific geographical areas aid municipalities and disposal organizations in better allocating resources and planning strategies. For example, organizations can use historic waste trends of communities to determine the placement and quantity of Smart Bins based on different circumstances. Real-time monitoring helps notify municipalities of anomalies, such as accidents or damage to the bin that may hinder waste disposal. Additionally, a combination of waste generation, traffic conditions, and weather patterns analyzed by AI can optimize waste transportation routes and create dynamic schedules, reducing transportation distance by 36.8% and increasing cost savings by 13.35%(Springer Nature Link). Moreover, this helps reduce the carbon footprint of transportation from gasoline consumption and carbon emissions. Overall, integrating AI enables better optimization of plans and can improve the efficiency of waste management operations by up to 15%(Science Direct). This helps establish a long term continuous circular economy where minor disruptions in processes nor their negative environmental impact can disturb the benefits reaped from this system.

Therefore, these sorting and waste generation protocols can be crucial tools in reducing greenhouse emissions and poisoning the environment through increased recycling rates, reduction in contamination, decreased operational costs, and improved efficiency of waste collection operations. With the North America Smart Bin market expected to grow from $8.46 billion in 2022 to $16.3 billion by 2032, AI's potential impact seems to provide a promising eco-friendly future(Spherical Insights).

Wildlife Preservation

While AI has a significant impact in reducing the causes of climate change like waste generation, it also proves to be effective in reducing its impact. Wildlife conservation continues to become an increasingly pressing issue: The Living Planet Report marked a 73% decline in the average size of wildlife populations since 1970. The 2023 analysis Biodiversity in Focus: United States Edition found that 34% of plants and 40% of animals are at risk of extinction, and 41% of ecosystems are at risk of collapse in the United States. A significant contributor to this destruction is climate change. For example, climate change leads to habitat loss, extreme weather, and rising temperatures on land, water, and air. In light of the growing decline in the world's wildlife, conservation efforts have sprung up including rescuing endangered species, restoring ecosystems, and raising public awareness of habitat destruction.

Along with improving waste management and energy efficiency, artificial intelligence accelerates the ongoing wildlife conservation efforts, emerging as a promising tool to improve data processing and acquisition drastically. AI is often trained to recognize individual animals using camera trap footage as input. For example, Wild Me's AI employs face-recognition technology to track individual whale sharks based on their unique spot patterns. In doing so, machine learning eliminates the need to tag sharks physically, mitigating the pain and distress that tagging inflicts on the animals. Not only has Wild Me been able to track over 15,500 whale sharks successfully, but the project has inspired similar projects that track other endangered species. One such project adapts Wild Me's AI foundation to monitor brook trout populations. Similarly, the AI is trained to recognize individuals based on their spot patterns. The project also created the app “Trout Spotter” allowing fishermen to upload pictures of caught fish, crowdsourcing the AI's database (depicted in the photo below). AI has proved to be effective in perpetuating wildlife conservation efforts.

A properly handled fish uploaded to Trout Spotter https://www.tu.org/troutspotter/

However, the integration of AI into such efforts has brought up ethical concerns. AI entrepreneur and The Future Society founder Nicolas Miailhe emphasizes the need to monitor AI tools. Failure to do so could expose endangered species to poachers or cause misinterpretation of crucial data. On the contrary, many view AI's potential to replace invasive tagging and improve data collection as a worthwhile benefit. In a world where ecosystems are torn down and extinction continues to rise, AI inspires a new kind of hope in concerned environmentalists.

Weather Forecasting and Modeling

In addition to engendering wildlife, climate change also poses significant risks to us, humans. August 29th, 2005. American history's most devastating tropical cyclone breaks land over New Orleans, Louisiana. Over the next 24 hours, Hurricane Katrina would go on to cause over 125 billion dollars in damage, displace one million people, and unfortunately bring about a death toll of 1400. A tragedy such as Hurricane Katrina goes down in history for its impact on the country and its people, these phenomena, while unavoidable, aren't entirely unpredictable.

Weather prediction is a subset of the greater field of climatology; climatology refers to the study of the earth's climates through data collected over an extended period to decide on a region's average weather patterns. Climate prediction is the process of using large data sets of various weather-based readings and data from all around the world to predict the weather forecast in certain areas accurately. While still an evolving concept, weather prediction is a prominent piece of modern life being the technology used for the weather applications used on phones. Weather forecasting dictates most pieces of contemporary life, it has the power to influence how you dress for the day, to cancel multi-million dollar sports games, or even to save the lives of those in high-risk areas (like those afflicted by Hurricane Katrina). The process of weather forecasting involves the synthesis of large datasets known as GCMs, (General Circulation Models), through the information recorded by thousands of global systems. The information recorded includes points such as temperature, humidity, wind speed, and barometric pressure at ground levels. Once researchers get a hold of such data though, the real process begins; analysts will input the data into supercomputers known as NWPs,(Numerical Weather Predictors), where they will spit out predictions for categories like temperature, conditions, and wind speed. Unfortunately, this process is lengthy and can very often be incredibly inaccurate, especially at farther predictions; weather systems predicting over 7 days in advance are often less than 50% accurate.

It is at this point in that process that AI becomes a beneficial tool. AI would be used to more efficiently read and compute the massive amounts of data present in these data sets to provide a quicker, (upwards of 10-100x faster) and more, (upwards of 20%), accurate result. Specifically, AI could use processes like machine and deep learning to analyze sets more complexly and uncover underlying weather and geological patterns that could lead to long- and short-term issues. This idea has already been implemented in many notable scenarios, one being Google's DeepMind GraphCast. GraphCast's accuracy has been reported to be 90% better than the leading source of weather data right now, the European Centre for Medium-Range Weather Forecasts (ECMWF). Additionally, it can deliver a determination upon the next 10 days of weather within just a minute; this coupled with its notably higher accuracy in severe weather predictions makes GraphCast an important up-and-coming invention. Another type of system using AI is the Nowcasting system. Nowcasting is the process of using radar systems in order to provide instant and currently relevant weather data to improve navigation that may depend on sensitive changes in such, for example: planes and boats.

The graph above depicts the accuracy of AI over traditional methods in severe weather storm predictions.

The Impact such changes would have on society cannot be underestimated, the newfound reliability would at the very least provide a helpful increase in weather app accuracy for the general populace, and if applied correctly save millions of lives by predicting tropical storms weeks before they happen. In fact, AI has been generally proven to increase hurricane pathing accuracy by 20-25% and flood risk predictions have reached up to 85% accuracy.

Despite these success stories, there are still significant challenges in this emerging field. The accuracy of the predictions relies heavily on the accuracy of the data provided, shoddy data will lead to poor and slow results. Another is the need for high computational power, these AIs must run on power and rescue hungry supercomputers that similarly don't run cheap. Lastly in the field of weather forecasting, there is always a chance for a completely unprecedented scenario to arise; one in which no previous meteorological data would lead to such a conclusion logically. These instances have plagued the field of science since its inception and are an issue that requires care and hard work to mitigate the risk.

The future of this field is bright and those interested can look forward to the creation of hybrid weather predictors that combine both AI and traditional methods as well as further global collaboration with worldwide systems.

Conclusion

In conclusion, Artificial Intelligence is a game changer in facing some of the biggest environmental challenges we face today. By improving recycling efficiency through AI-powered sorting systems, this technology is already making impactful differences in reducing the impacts of climate change. Whether aiding wildlife conservation by tracking endangered species impacted by human-led crises such as climate change or refining weather models for better forecasting, AI offers essential tools for protection. While there are concerns about the usage of AI itself, ongoing advancements are addressing these issues, ensuring the benefits outweigh the drawbacks. As we continue to refine and apply this technology, its role in creating a more sustainable and environmentally conscious future becomes increasingly important and prevalent.

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