Exploring the boundary conditions of AI-driven environmental value creation: evidence from China’s livestock sector

Abstract

The transition toward sustainable food systems demands effective interventions that can reduce the environmental footprint of livestock production without compromising its economic functions. Artificial intelligence (AI) offers a promising technological lever, yet evidence on its environmental effectiveness and the conditions under which its benefits materialize remains scarce. This study investigates whether and when AI adoption improves corporate environmental management performance, drawing on a panel data-set of Chinese A-share listed livestock firms from 2011 to 2024. We identify two critical boundary conditions-financing constraints and the separation of ownership and control rights-that shape the environmental outcomes of AI deployment. Our findings demonstrate that AI adoption significantly enhances environmental management performance, confirming its viability as a sustainability intervention in the livestock sector. However, this positive effect is substantially weakened by financing constraints, indicating that capital shortages impede firms’ ability to realize AI-driven environmental gains. Conversely, a higher degree of separation between ownership and control rights amplifies the positive impact of AI, suggesting that governance structures insulating managerial decisions from short-term ownership pressures facilitate long-term investments in digital and green technologies. These results highlight that the environmental value of AI is not automatic but conditional upon adequate financial resources and appropriate governance arrangements. Our study contributes empirical evidence on the economics of sustainable food system transitions, offering actionable policy implications for promoting digital-green synergies in resource-intensive agricultural sectors.