Designing AI-based decision support systems for carbon stock estimation and planning: A design science research

Mitigating climate change presents significant challenges, particularly in effective carbon stock estimation and planning. Decision-making based on quantified carbon stock is essential for enabling carbon sequestration efforts in natural ecosystems, yet traditional field measurements are labor-intensive, geographically constrained, and difficult to be adopted at scale. Motivated by this practical challenge, we undertook a design science research (DSR) project to develop AI-based decision support systems that enhance the efficiency, accessibility, and scalability for carbon stock estimation and planning. Guided by technology affordance theory, our iterative design, development, demonstration, and evaluation process has led to the conceptualization of four affordance-based design principles that build on and contribute to the growing design knowledge for environmental sustainability. This research advances our understanding of the critical problem and solution domains of carbon stock estimation and planning. It also facilitates the emerging discourse on balancing AI and human intelligence in designing systems for complex decision-making challenges for sustainability. This study provides an exemplar of DSR for supporting Sustainable Development Goal 13 by demonstrating how to design systems that facilitate carbon sequestration and societal transition toward environmental sustainability.