Environmental Regulation, Smart Meter Adoption, and Carbon Emission: An Interpretable Machine Learning Approach
DGO '23: Proceedings of the 24th Annual International Conference on Digital Government Research
Information as a governance instrument has received increasing attention from e-government research on sustainable development. The implementation of advanced digital technology, such as smart meters, along with environmental regulations, plays an important role in curbing carbon emissions and creating a more sustainable future. In this paper, by combining decision tree and linear spline regression methods, we find a positive connection between smart meter adoption and reduced carbon emissions, and a negative relationship between state environmental regulatory stringency and carbon emissions. Our findings further indicate the impact of smart meter adoption on carbon emissions varies over different smart meter adoptions rate. The impact is stronger when the adoption rate reaches a certain threshold, and it becomes weaker when market saturation happens. These findings have important implications for the development and execution of environmental regulations and public policies for the adoption of smart meters in the United States.
ACM International Conference Proceeding Series
carbon emission; environmental regulation, machine learning models, Sustainable Development Goals, technology adoption
Gao, Yue; Zhao, Chunjie; and Zhang, Jing, "Environmental Regulation, Smart Meter Adoption, and Carbon Emission: An Interpretable Machine Learning Approach" (2023). School of Management. 203.