Silicon Forests: How AI Is Regreening the Corporate Landscape

Lukas Vartiak () and Subhankar Das ()
Additional contact information
Lukas Vartiak: Comenius University in Bratislava
Subhankar Das: Duy Tan University

A chapter in Generative AI for a Net-Zero Economy, 2025, pp 19-36 from Springer

Abstract: Abstract Artificial intelligence is transforming corporate sustainability with new and innovative solutions to reduce environmental impact while improving profitability. The research implemented for this study is focused on analyzing how the latest technologies, such as AI-driven supply chain optimization strategies, energy management systems, and sustainable product design, reduce the environmental impacts in the industries. Case studies of Fortune 500 companies like Walmart and startups like Climatiq show how AI can reduce emissions (saving 25 million gallons of diesel annually) and support circular economies (as with biodegradable materials). However, there are challenges in AI’s energy consumption, algorithmic bias, and inequitable access for SMEs, among other things. The analysis highlights ethical governance frameworks—such as explainable AI and third-party audits—that can be employed to reduce risks such as privacy violations and greenwashing. It also underscores the importance of collaborative ecosystems that align policy assistance, open-source tools, and inclusive innovation to close the “sustainability divide.” Balancing technology promise with accountability, AI can grow a “Silicon Forest” of symbiotic economic growth and ecological resilience.

Keywords: Artificial intelligence; Corporate sustainability; Supply chain management; Ethical stewardship; Circular economy; Sustainable innovation (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_2

Ordering information: This item can be ordered from
http://www.springer.com/9789819680153

DOI: 10.1007/978-981-96-8015-3_2

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 ().