The convergence of AI and sustainability offers a compelling solution to current climate change issues and corporate sustainability programs. Businesses are increasingly using AI to expedite their sustainability initiatives, particularly at a time when there is growing pressure from stakeholders for greater transparency. The alignment of AI and sustainability is not just a strategic move, but a critical response to meet the diverse demands of today’s stakeholders.
While AI has conventionally been associated with use of robotics in hazardous areas, such as manufacturing assembly lines, in addition to neural networks for analytical models and forecasting, now, with rapid advances in new technologies, a new breed of AI software models and technologies has emerged that builds sensitive and predictable models from examples, data and experience, rather than following pre-programmed, deterministic rules. The adoption of AI models provides the ability to treat large amounts of unstructured and structured data for better decision-making and to address sustainability issues such as climate, education, and health.
AI is anticipated to permeate every industry just as sustainability has done in the last decade, and solve some big problems plaguing sustainability stakeholders, such as efficient aggregation of disparate data sources, standardizing reporting methodologies, and uncovering insights that would be impossible with traditional approaches.
Sustainability Use Cases for AI
A number of sustainability use cases are emerging for AI, with more innovation occurring all the time. A few of the applications and industries that are already being transformed and enhanced by AI capabilities are outlined below.
Deforestation monitoring: Satellite imagery can detect illegal deforestation in real time, and now AI models using image/video annotation can be used in conjunction with this monitoring to identify patterns of forest loss. This approach will enable conservation organizations to take more timely action to intervene or mitigate deforestation.
Energy management: Energy use and consumption can be monitored through the use of AI models, which in turn can provide optimized usage settings to result in reduced greenhouse gas (GHG) emissions.
Financial inclusion: AI can help fintech companies provide affordable financial services to unbanked and excluded individuals by performing alternative credit checks.
Corporate governance: AI can assist in analyzing corporate governance data to assess organizations’ sustainability performance and identify possible enhancements and efficiencies.
Climate change monitoring: AI models can assist in providing accurate predictions to assist policy and decision-makers in implementing more effective strategies to mitigate the impacts of climate change.
Health and wellbeing: AI can help healthcare providers improve access to quality health care for underprivileged communities, such as with the use of delivery drones.
Public sector efficiency: AI can assist in streamlining public sector processes and ultimately improve service delivery.
Employment nondiscrimination: AI can assist companies in analyzing hiring and promotion data and correcting for any potential biases and ensure a more inclusive, fair, and objective workforce.
Regulatory compliance: AI can assist in monitoring large amounts of regulatory data to identify potential breaches in a timely manner. This will allow organizations to take proactive measures instead of being reactive.
Biodiversity: When paired with satellite imagery, AI algorithms can assist in identifying changes in land use, vegetation, forest cover, and the effect of natural disasters. Further, AI can improve waste management through better sorting across the entire waste management lifecycle. AI can forecast stream flow and examine water quality. It can assist in predicting droughts, as well as soil and subsurface water conditions. Airflow models can collate data from sensors and satellites and assist scientists in creating and integrating climate models. AI-enhanced purifiers can continually record air quality data and modify their filtering performance as needed. In addition, it can be used to better qualify localized emissions from satellite remote-sensing data.
Transportation: Computer vision techniques can aid decision-making in traffic management, public transportation, and urban mobility.
Agriculture: Farmers can use drones and satellite imagery to assess soil quality and crop productivity. This can increase efficiency, productivity, and yield. AI can also be used to monitor illegal fishing. Carbon-tracker is an open-source tool written in Python for tracking and predicting the energy consumption and carbon emissions of training deep learning models.
Governance Applications for AI
Employing natural language models, companies can efficiently scan regulatory sources, producing consolidated and relevant summaries for senior management review. By facilitating first drafts of policy documents, AI solutions can shorten the upfront time requirements, setting the stage for subsequent human refinement, and thereby reducing costs and enhancing procedural efficiency. Furthermore, automating tasks with AI allows compliance officers to focus on strategic matters, demonstrating AI’s role in identifying potential fraud or errors, akin to its function in safeguarding data against cyber threats.
Data governance entails using a set of metrics, standards, policies, and processes to ensure companies use customer data correctly and responsibly. In data governance, AI can be used for various purposes. Businesses can train AI-based solutions to help detect anomalies such as breaches in data centers as well as cyberattacks by identifying patterns of cyber threats, ensuring their customer data is protected 24/7. AI is also useful in secure data transmission by monitoring data traffic, leveraging advanced encryption methods and anomaly pattern recognition techniques to safeguard against interception by cybercriminals.
Presenting accurate and concise information is paramount for a board of directors’ effective decision-making. However, the process of preparing reports for the board and making sure all the information is correct and up to date can be a time-consuming task. AI technologies can be introduced to streamline this process, linking directly to databases to generate real-time, accurate board reports. Further, AI’s potential extends to personalizing reporting dashboards per board member, emphasizing distinct key areas of focus, thereby enhancing efficiency and responsiveness in governance analytics.
The Impact of AI on Sustainability Metrics
Artificial intelligence technologies can predict trends and risks based on past data patterns, allowing investors to gain insight into future risks and inform long-term decisions. News and social media are monitored by AI in real time, providing immediate insights into emerging sustainability risks and opportunities that traditional analysis methods might miss. Combining blockchain tech with AI could change sustainability data management profoundly. By utilizing this approach, we would have a secure, transparent, immutable record of all relevant metrics and claims. That combination makes it much harder for analysts to critically throw out false info without being detected. In turn, this approach makes reporting much easier to do accurately and reliably.
Large AI models such as deep learning (DL) and generative AI generally consume a significant amount of energy and generate a large volume of carbon emissions, since the process of training and operating large AI models requires vast amounts of energy. This results in increased air pollution, water usage, and carbon emissions that can accelerate climate change.
The process of training a single deep learning natural language processing (NLP) model can lead to approximately 600,000 pounds of CO2 emissions, similar to the amount produced by five cars over their entire useful life. Google’s AlphaGo Zero generated 96 tons of CO2 over 40 days of research training, which amounts to 1,000 hours of air travel or a carbon footprint of 23 American homes. Blackwell AI chips from Nvidia, the leading AI semiconductor company, contain 208 billion transistors and will cost somewhere between $30,000 and $40,000, revolutionizing the world of AI computing in its speed, capability, and impact.
AI has already made its entry in consumer homes through self-driving cars, smart homes that use lidar, radar, sonar, and other sensors to operate voice, facial, and vision activated appliances, smart factories, and consumer homes have been upgraded using smart meters, digital twins, and utility hubs offering smart grid integration. With this proliferation of applications and use cases, the global sustainability landscape will most certainly change dramatically with greater adoption of AI.
