  The increasing use of information technology has led to a significant share
of energy consumption and carbon emissions from data centers. These
contributions are expected to rise with the growing demand for big data
analytics, increasing digitization, and the development of large artificial
intelligence (AI) models. The need to address the environmental impact of
software development has led to increased interest in green (sustainable)
coding and claims that the use of AI models can lead to energy efficiency
gains. Here, we provide an empirical study on green code and an overview of
green coding practices, as well as metrics used to quantify the sustainability
awareness of AI models. In this framework, we evaluate the sustainability of
auto-generated code. The auto-generate codes considered in this study are
produced by generative commercial AI language models, GitHub Copilot, OpenAI
ChatGPT-3, and Amazon CodeWhisperer. Within our methodology, in order to
quantify the sustainability awareness of these AI models, we propose a
definition of the code's "green capacity", based on certain sustainability
metrics. We compare the performance and green capacity of human-generated code
and code generated by the three AI language models in response to easy-to-hard
problem statements. Our findings shed light on the current capacity of AI
models to contribute to sustainable software development.
