Digital Photosynthesis: AI’s Blueprint for a Carbon-Neutral Economy
Smriti Tandon (),
Patita Paban Mohanty () and
Subhankar Das ()
Additional contact information
Smriti Tandon: Graphic Era Deemed to be University
Patita Paban Mohanty: S O A University
Subhankar Das: Duy Tan University
A chapter in Generative AI for a Net-Zero Economy, 2025, pp 145-159 from Springer
Abstract:
Abstract Transformative responses beyond traditional sustainability are imperative in the face of a rapidly accelerating climate crisis. We introduce the concept of digital photosynthesis, a design paradigm in which artificial intelligence (AI) emulates the optimal structure of a natural ecosystem to build a carbon-neutral economy. This framework seeks to build dynamic systems by integrating AI with urban planning (such as smart buildings and transportation systems), sustainable agriculture, and green transportation to improve resource use, reduce emissions, and increase resilience. This cross-sector model integrates technological innovation with ecological values, financial models, and social equity. AI-based smart grids, precise farming type applications, and autonomous networks of electric vehicles have shown datasets from such applications, indicating that AI can decrease urban emissions by up to 70% and reduce agricultural water consumption by approximately 30% during re-engineering logistics. However, ethical risks, including data privacy, algorithmic bias, and inequitable access, must be governed through active mechanisms. This report confirms that AI-enabled sustainability is both possible and critical and hinges on collaboration, policy, and continuous calibration globally. By leveraging AI’s computing power in the service of planetary boundaries, this blueprint reveals a viable path towards a regenerative future.
Keywords: Artificial intelligence; Carbon neutrality; Digital photosynthesis; Smart cities; Sustainable agriculture; Interdisciplinary framework (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-8015-3_9
Ordering information: This item can be ordered from
http://www.springer.com/9789819680153
DOI: 10.1007/978-981-96-8015-3_9
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().