AI can be an effective weapon against climate change. But like all technologies, it has its downsides; technology companies must find an equilibrium between making AI affordable and managing its energy and carbon emission consumption throughout its lifecycle.
One such tool is PAWS, which assists wildlife organizations in anticipating when poachers will strike and providing patrol officers with actionable data.
Wildlife
People tend to fear artificial intelligence (AI), yet conservationists are turning more and more to technology-led solutions to tackle 21st-century drivers of species loss. AI can assist them by increasing speed, accuracy, and cost-efficiency during data collection and analysis processes.
Data-intensive tasks, like identifying wildlife species from images or analyzing audio recordings, require AI-powered image recognition technology for accurate results. Washington State University researchers utilize AI to quickly and effectively identify rare and endangered animal species using camera trap photos from thousands of camera traps as well as hours of field recordings, drastically decreasing manual work to collect this data.
AI can help conservationists in various ways. For instance, they can track animals in their natural environments to detect any declines in population numbers, monitor water levels, predict floods, and use this information for conservation strategies such as reforestation, protecting marine life, and mitigating climate change.
AI technology is also making an impactful statement against wildlife poaching. Poachers can use AI software to track and locate them, helping stop the illegal trade of elephant ivory or koala fur. Furthermore, this type of software provides rangers with information on where particular animals may be found for better patrol routes.
Although AI holds great promise for conservation efforts, there are a few challenges associated with its deployment that must be considered first. Energy consumption and emissions created when operating large AI models may become significant environmental concerns when deployed in sensitive and remote areas like forests, oceans, or other high-energy settings.
However, it is also vital to take into account the indirect environmental impacts associated with AI technology use, including how it might influence human behavior. Therefore, ethical considerations must be incorporated into any new technology development process, such as AI, to ensure algorithms do not become biased and accurately represent different groups.
Climate Change
AI can aid several global goals related to climate change, including SDG 1 for poverty relief, SDG 4 for education, SDG 6 for clean water and sanitation, and SDG 7 for affordable clean energy. Furthermore, it can accelerate carbon capture technologies while tracking greenhouse gas emissions to help reduce their overall levels.
Many AI solutions rely on high-performance computing systems that consume vast quantities of energy and generate considerable waste heat, both of which contribute significantly to climate change. New chips have become more powerful and efficient, while research into less energy-intensive models continues.
Technology can also be utilized to mitigate climate change’s effects by improving building designs to be more energy-efficient, decreasing deforestation rates, and optimizing renewable energy deployment. For instance, Deloitte AI Institute is working with WWF on satellite imagery-based deforestation forecasting tools in order to predict regions deemed most at risk from deforestation and provide protection measures accordingly.
Climate data analysis can also support efforts to manage climate change by providing insights on an enormous scale, for instance, by calculating the potential effects of an approaching weather pattern on infrastructure and populations. TPG’s Rise Fund invested $100 million in Climavision, a company using AI and data to predict current and future weather patterns with pinpoint precision.
AI can accelerate carbon sequestration by supporting site evaluation and methods for sequestering CO2. As well as supporting mitigation measures, such as removing existing emissions from the atmosphere through carbon capture and storage technologies and natural processes like mineral carbonation. To maximize its potential advantages, AI must be applied responsibly, with consideration given to its environmental impacts throughout its lifecycle. It should also strive to avoid biased and discriminatory results, be used only when needed, provide accurate, granular, transparent data sets that build trust among its stakeholders, and promote trust and confidence in its outcomes; all in order to fully realize AI’s environmental and societal benefits.
Water
AI can provide human efforts with some assistance in conserving land and water. For instance, AI can be used to optimize building water usage efficiency or monitor and predict shortages; additionally, it can monitor ecosystem health or identify species at risk of becoming endangered.
As technology becomes ever more indispensable in today’s society, it is essential that its environmental impacts remain minimal. This is particularly relevant to AI technology, whose development outpaces government efforts in terms of understanding its effects on individuals, communities, and the environment.
Many of the most promising applications of green AI involve environmental monitoring and conservation, yet we must remain mindful of how such technologies are developed. A study published by Kate Crawford and Vladan Joler shows how modern technologies have deep roots in exploitation, from mining metals for their construction to sweatshops for programmers working on them.
Artificial Intelligence can also play a crucial role in helping reduce energy consumption by optimizing renewable energy deployment and helping reduce waste through more effective recycling processes. Furthermore, AI can be utilized to monitor deforestation and mitigate climate change by tracking changes in glacier mass and methane emissions.
The CounterMEASURE project uses geospatial data and machine learning models to monitor plastic pollution sources in the Mekong River and measure how they enter bodies of water.
Overall, evidence reveals that green AI’s positive impacts outweigh its negative ones (21 targets; 80%). To ensure its minimal environmental impact as we develop and advance it further, regulatory oversight paired with insight is needed; ensuring policymakers possess all of the knowledge they need to formulate effective policies is also vital – without which legislation may be passed that may actually have more of a detrimental impact.
Energy
AI can assist with energy conservation in numerous ways. For instance, it can help design new materials with properties that reduce our reliance on fossil fuels and reduce environmental impacts during manufacturing, identify ideal locations for solar and wind farms as well as electric vehicle charging stations, and analyze data to create tailored mitigation strategies against climate change.
One potential drawback of AI is its consumption of resources such as water and raw materials in the creation and operation of its models, creating waste that must be recycled or disposed of responsibly. But AI can also be used to optimize resource efficiency and minimize waste; for instance, a company could implement an AI-enabled system that detects when devices stop functioning correctly before restarting them to save energy costs.
Companies can utilize AI to optimize production processes, ensure compliance with sustainability standards, develop mitigation strategies for fossil fuel emissions, and better understand the complex mechanisms that lead to CO2 emissions. AI systems may even help companies increase output without increasing costs.
AI can also help companies optimize the deployment of renewable energy by monitoring weather patterns and making predictions for when solar or wind will produce maximum energy output. AI systems also make waste reduction possible by streamlining supply chains, for instance, by providing information regarding the best time to purchase energy from renewable sources and thus optimizing the energy efficiency of their supply chains.
AI in energy also aids mining operations by using AI-enabled systems that analyze and map mineral deposits more accurately, shortening the time spent finding essential minerals used for battery production.
Thus, it is vital that we consider the environmental implications of AI and how to minimize them. For instance, regulations should prioritize sustainable creation when creating AI systems as well as collaboration among industries to decrease carbon emissions. We should also use renewable energy sources for training and operating AI algorithms.
