AI could realize Saudi Vision 2030’s climate ambitions
Sustainability has been at the heart of Vision 2030 since its inception. Saudi Arabia is ushering in a new era as the Kingdom aims to reach net-zero by 2060. According to the UN-backed Science Based Target initiative, carbon emissions must reduce by 50 percent by the end of this decade to limit the increase in average global temperatures to 1.5 degrees Celsius, as the 2016 Paris Agreement specified. Boston Consulting Group recently found that artificial intelligence could help reduce greenhouse gas emissions of 5 to 10 percent of an organization’s carbon footprint or 2.6 gigatons and 5.3 gigatons of carbon dioxide emissions. The potential impact of applying AI to corporate sustainability could generate revenue and cost savings from $1.3 trillion to $2.6 trillion by 2030.
Some industries are predicted to become more disruptive than others, such as IT and technology, followed by logistics, transportation, biofarming and biotech. AI will be an effective tool for stakeholders — from businesses and governments to NGOs and investors — to take a more informed and datadriven approach while offering them opportunities to create meaningful change in this critical moment.
Moreover, data science capabilities can unlock environmental and social opportunities through various tools, such as descriptive modeling, which analyses historical data to understand patterns and driving factors, identifying situations such as a person has threats of long-term employment.
On the other hand, predictive modeling could use machine learning algorithms to predict future events accurately. For instance, satellite imagery and computer vision can predict deforestation months in advance. Similarly, optimization tools could use advanced algorithms to find best-fit solutions to complex problems. Therefore, AI can optimize steel production by turning furnace parameters to reduce emissions and costs. The use of natural language processing has opened a window of new opportunities. For example, using language models to process large quantities of unstructured text can interpret and elucidate hard-to-access environment, social and governance dimensions. Replicating a process digitally to test scenario effects is also gaining traction in AI. Such a system can simulate COVID-19 effects on social safety nets in vulnerable populations. Data analytics and AI can be leveraged for the most pressing climate challenges. However, these solutions must be userfriendly, valuable and effective to achieve widescale adoption. They should be designed, scaled, and marketed to make them readily available to practitioners.
It should offer enough value that its benefits can be immediately perceived, potentially changing how users think or behave. And it should provide clear information for the users. Promising solutions also need resources and networks to maximize AI’s potential in addressing climate change. They need access to capital investments, decision-makers and trained practitioners to be deployed at scale. While financial support can bridge the gap between academic research and at-scale deployment, connections to policymakers and corporate leaders can help boost awareness and adoption.
Furthermore, training and additional skilling can ensure that civil servants, private sector leaders and other stakeholders use and interpret AI solutions effectively in the most critical contexts. Finally, given its complexity and the risks of unethical behavior, whether intentional or unintentional, users of AI need to earn the confidence of climate leaders. Solutions must therefore use AI responsibly, employ granular and reliable underlying datasets and emphasize results that can be interpreted and understood.
Although there are many critical AI applications in the climate change arena, it is just one of many tools available to meet this global challenge. Like any technology, it has limitations and requires effective deployment to achieve the desired results. Further, solving the crisis requires not just technological innovation but decision-makers to act and make the necessary changes — supported partly by AI and other emerging technologies.