Generative Artificial Intelligence in Environmental Economics Education: A Review of Current Research, Limitations, and Future Directions
TL;DR
This paper recommends practices for students, teachers, and researchers for a systematic and ethical integration of GenAI in environmental economics courses and recommends three avenues for impactful research.
Abstract
Generative Artificial Intelligence (GenAI) is a computer-generated technology that allows data and information to be created through synthesis. It also allows for code generation and simulation of complex scenarios. By leveraging GenAI tools such as ChatGPT, Claude, and coding agents, researchers can develop new applications in the environmental economics sub-discipline that connect economic modelling with real-world data. Academic works and intellectuals have paid a lot of attention to environmental economics. Three avenues for impactful research are provided in the study. These include personalized tutoring for students learning to code, coding assistance for experts analysing climate data, and simulation of policy impacts. At the same time, actual deficiencies in the literature are identified. Few studies examine task-level performance evaluation; the economic reasoning is poorly structured with prompt design; and human verification is weak. Furthermore, there is little research that looks into the particular problems in environmental economics such as the non-market valuation, intergenerational equity and assessment of ecosystem services. This paper recommends practices for students, teachers, and researchers for a systematic and ethical integration of GenAI in environmental economics courses.