Using Deep Learning to Fill Data Gaps in Environmental Footprint Accounting

Bu Zhao, Chenyang Shuai, Shen Qu, Ming Xu*

*此作品的通讯作者

    科研成果: 期刊稿件文章同行评审

    18 引用 (Scopus)

    摘要

    Environmental footprint accounting relies on economic input-output (IO) models. However, the compilation of IO models is costly and time-consuming, leading to the lack of timely detailed IO data. The RAS method is traditionally used to predict future IO tables but suffers from doubts for unreliable estimations. Here we develop a machine learning-augmented method to improve the accuracy of the prediction of IO tables using the US summary-level tables as a demonstration. The model is constructed by combining the RAS method with a deep neural network (DNN) model in which the RAS method provides a baseline prediction and the DNN model makes further improvements on the areas where RAS tended to have poor performance. Our results show that the DNN model can significantly improve the performance on those areas in IO tables for short-term prediction (one year) where RAS alone has poor performance, R2 improved from 0.6412 to 0.8726, and median APE decreased from 37.49% to 11.35%. For long-term prediction (5 years), the improvements are even more significant where the R2 is improved from 0.5271 to 0.7893 and median average percentage error is decreased from 51.12% to 18.26%. Our case study on evaluating the US carbon footprint accounts based on the estimated IO table also demonstrates the applicability of the model. Our method can help generate timely IO tables to provide fundamental data for a variety of environmental footprint analyses.

    源语言英语
    页(从-至)11897-11906
    页数10
    期刊Environmental Science and Technology
    56
    16
    DOI
    出版状态已出版 - 16 8月 2022

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