The shift to the sustainable global economy imposes the need to merge an artificial intelligence (AI) with the entrepreneurial ecosystem, particularly in the green technology (green tech) industry. The paper is developing a computational model that employs AI to help startups in environment-oriented businesses to maximize learning, resource distribution, adaptability in the marketplace, and cooperation among stakeholders. Based on system dynamics, agent-based modeling, and machine learning, the model simulates different growth paths of green startups in different conditions of policy, investment and innovation. To test the framework, multiregional research in three green tech centers Bangalore (India), Berlin (Germany) and San Jose (USA) was done using a combination of publicly available metrics of startups, environmental key performance indicators and innovation indices. Findings demonstrate that AI-based decision-support systems substantially enhance the scalability and sustainability of startups by enhancing an effective distribution of resources and recognising risks at an early stage. The novelty of the presented model is its feedback-loop structure connecting AI analytics, the investor behaviour, as well as the ecological targets as the three spheres to focus on, providing policymakers and incubators with a data-based blueprint towards resilient sustainable startup ecosystems. The article illustrates how an environment of a computationally intelligent ecosystem can reinvent innovation within the age of climate-tech, connecting the success of entrepreneurs to planetary health.
D. Srikanth (Mon,) studied this question.
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