Estimating the Material Footprint at the National Level from 1993 to 2022 Based on Multi-Feature CNN-BiLSTM

被引:0
|
作者
Miao, Lizhi [1 ,2 ]
Wang, Yannan [3 ]
Wu, Kaiwen [1 ]
Huang, Lei [4 ]
Kwan, Mei-Po [5 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing 210023, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Smart Hlth Big Data Anal & Locat Serv Engn Res Ctr, Nanjing 210023, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Sch Commun & Informat Engn, Nanjing 210023, Peoples R China
[4] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
[5] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Dept Geog & Resource Management, Shatin, Hong Kong 999077, Peoples R China
关键词
SDG; grey relational analysis; CNN-BiLSTM; material footprint; RAW-MATERIAL CONSUMPTION; MATERIAL FLOWS; ECONOMIC-GROWTH; EUROPEAN-UNION; RESOURCE USE;
D O I
10.3390/ijgi14020086
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Global environmental issues are becoming increasingly serious. As a comprehensive indicator of environmental pressure, the material footprint reflects changing pressures amidst sustainable resource utilization. In this research, we conducted a time series prediction of material footprint using the Multi-Feature CNN-BiLSTM model and analyzed the material footprints of 77 countries or regions as well as four types of influencing factors from 1993 to 2022. The research results showed that: (1) The CNN-BiLSTM model (R2 = 0.861, Adjusted R2 = 0.860, NRMSE = 0.063) demonstrates excellent predictive performance. (2) From 2013 to 2022, the Chinese mainland reported the highest total material footprint, whereas Iceland had the least. Qatar had the highest per capita material footprint, and Pakistan had the lowest. Among the top 50% of countries or regions by average annual per capita material footprint during this period, 12 economies are G20 members, including all G7 nations except Italy. (3) The research results showed that among the top 20 economies, 18 economies are members of the G20, while Argentina and South Africa ranked 24th and 31st, respectively. The accurate spatiotemporal prediction of future material footprints can delineate the trajectory of human activities on the environment, enhance environmental management strategies, and advance sustainable development initiatives.
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页数:22
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