AI-Driven Scaling of Sustainable Startups: Business Models and Environmental Impact

With fast growth in sustainable startups at variance with profitability, the likelihood of high profitability contrasted with minimal environmental impact becomes a great challenge and opportunity. In this article, the role of AI in scaling through innovative business models to meet sustainability goals is investigated. The latest development in optimized operating efficiency, lesser resource consumption, and improved scalability has seen AI use go unexplored in business models designed particularly for environmental sustainability goals. This paper addresses how AI-enabled business model innovation can facilitate scaling up of sustainable start-ups in renewable energy, waste management, and circular economy practices. In addition, resource reallocation under such a scenario comes with a double dividend: it brings better economic growth alongside lower ecological damage, while reduction in AI's adverse environmental impact arises as a by-product. Case studies are expected to summarize the complexity in practice and limitations of AI for sustainable business growth. This study, therefore, provides an answer that contributes to understanding how startups may leverage AI to drive not only economic value but also create environmental value, thus providing the much-needed roadmap for future research in a rapidly changing field.

Authors

Publication Details

Journal
International Journal of Advanced Multidisciplinary Research and Studies
Published
2024-11-06
DOI
https://doi.org/10.62225/2583049x.2024.4.6.3391
Citations
2
Primary Topic
Entrepreneurship Studies and Influences
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Driven Scaling of Sustainable Startups: Business Models and Environmental Impact

Dinesh Kumar Jain, N Subramanyam, Yerru Bhavitha, Vadlamanu Pallavi et al.
2 citations
International Journal of Advanced Multidisciplinary Research and Studies
Entrepreneurship Studies and Influences
1.46
article

AI-Driven Scaling of Sustainable Startups: Business Models and Environmental Impact

Dinesh Kumar Jain, N Subramanyam, Yerru Bhavitha, Vadlamanu Pallavi, Goli Yeshwanth Seshu
article en
2 citations

Abstract

With fast growth in sustainable startups at variance with profitability, the likelihood of high profitability contrasted with minimal environmental impact becomes a great challenge and opportunity. In this article, the role of AI in scaling through innovative business models to meet sustainability goals is investigated. The latest development in optimized operating efficiency, lesser resource consumption, and improved scalability has seen AI use go unexplored in business models designed particularly for environmental sustainability goals. This paper addresses how AI-enabled business model innovation can facilitate scaling up of sustainable start-ups in renewable energy, waste management, and circular economy practices. In addition, resource reallocation under such a scenario comes with a double dividend: it brings better economic growth alongside lower ecological damage, while reduction in AI's adverse environmental impact arises as a by-product. Case studies are expected to summarize the complexity in practice and limitations of AI for sustainable business growth. This study, therefore, provides an answer that contributes to understanding how startups may leverage AI to drive not only economic value but also create environmental value, thus providing the much-needed roadmap for future research in a rapidly changing field.

International Journal of Advanced Multidisciplinary Research and StudiesVol. 4(6)
Openalex Percentile: Top 14%
Entrepreneurship Studies and Influences
1.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.