AI. Here & Now!
31 March 2023
Today, artificial intelligence is playing a major role in optimizing the mundane – it has been tuned to craft the perfect workplace email response while simultaneously churning out essays that make expertise sound easily attainable. The recent launch of generative AI tools led by Chat GPT has only expanded these conversations and turned the lens onto a global debate of ever-growing concern — climate change. Can such tools adequately capture the complexities of environmental science while delivering a textual narrative that is both compelling and authoritative? If recent developments are anything to go by, the answer is in the affirmative. AI algorithms may well be trained to navigate the sticky space of human expectations and desires, but that is only one part of what we see as being a relevant function of the technology.
As companies in the environmental arena become increasingly focused on creating persuasive arguments for their climate technologies, there is now a need to cultivate a technological culture that optimizes routine tasks such as the drafting of a project design and environmental impact assessments. This is likely to become a very significant component of projects working toward the objective of reducing carbon emissions by providing an effective and reliable method to create high-quality evaluations of a project’s emissions baselines while also possessing life cycle assessment capabilities. These same AI algorithms may also be trained to conduct their evaluations of project designs in ways that are compliant with existing carbon accreditation frameworks. This is especially important given the global and national interest in monetizing carbon sequestration activities for the generation of carbon credits.
At Zasti, we have already kicked off the process of streamlining our internal activities to better allocate time to carbon reduction requirements in need of human nuance. Our current work in this space focuses on the creation of custom algorithms that produces high quality documents in alignment with the requirements of our diverse roster of clients across the carbon reduction space. A unique aspect of the algorithm is its ability to address an issue that has long been a point of weakness for traditional AI algorithms, namely geospatial data. This has proved particularly beneficial to understanding how landscape level changes may be correlated with carbon sequestration activities by providing clients and carbon credit buyers with the capacity to see more and therefore do more.
We believe that by creating a mechanism that effectively addresses the routine aspects of project development, we free up valuable cognitive resources for the fulfillment of functions related to Monitoring, Reporting and Verification (MRV). Much like our earlier work, we have sought to integrate our efforts in the space of algorithm development with our commitment to the principle of transparency and immutability through smart contracts which offer an advanced mechanism for the authentication of emissions reductions. Given AI’s ability to learn and scale in new and unforeseen ways, it seems fitting to say that is only a matter of time before the practice of optimizing functions to better address climate change gains a wider audience. And we, at Zasti, couldn’t be happier!