Sustainable Code Generation Using Large Language Models: A Systematic Literature Review

This project is a Systematic Literature Review (SLR), not a software product or tool. Its defensibility is minimal (score 2) as it represents a meta-analysis of existing work rather than a proprietary algorithm or dataset. While the topic is highly relevant—transitioning the 'Green AI' focus from model training to the lifecycle impact of generated code—the project itself has no technical moat. In the academic and professional AI space, SLRs are common and have a short shelf life (displacement horizon: 6 months) because the field moves faster than the publication cycle. Quantitative signals (0 stars, 5 forks) indicate it is likely a new submission being shared within a specific research group or for peer review. Frontier labs like OpenAI or Google are unlikely to compete with a literature review; instead, they may eventually incorporate its findings into their model alignment or 'system prompt' strategies to encourage more efficient code generation. The primary risk is academic obsolescence rather than market competition.