GGDP Index Establishment Based on ARIMA-LSTM Prediction Model
DOI:
https://doi.org/10.54097/hset.v69i.11838Keywords:
SEEA Central Framework, Systematic clustering, Entropy TOPSIS model, Ridge Regression, ARIMA-LSTM model.Abstract
More and more studies have shown that GGDP may be more representative of the national economic health level and this paper establishes a GGDP calculation system based on the Environmental Economic Accounting System Central Framework. Then, with GGDP as the clustering index, the countries are divided into five categories. The Entropy TOPSIS model is established to quantify the impact of global climate. Then, based on the Ridge regression model, the functional relationship between GGDP and climate assessment values under different clusters is studied. Finally, two steps are taken to analyze the potential advantages and disadvantages of GGDP. In the case of using GDP, a prediction model based on ARIMA-LSTM is established to analyze global climate and economic development trends. In the case of using GGDP, to quantify the change of GGDP, international factor α and domestic factor β are introduced to characterize the impact of global efforts and domestic efforts on GGDP.
Downloads
References
Gavard C, Kirat D. Flexibility in the market for international carbon credits and price dynamics difference with European allowances [J]. Energy Economics, 2018(76): 504-518.
Wang Xue, Shi Xiaoqing et al. EUA and CER phase II price drivers: Unveiling the reasons for the existence of the EUA-CER spread [J]. Energy Policy, 2011, 39(3): 1056-1069.
Hu Biao, Fu Yeteng et al. Causes of the EU ETS price drop, Recession, CDM, renewable policies or a bit of everything? —New evidence [J]. Energy Policy, 2014(73): 676-685.
Shen Lulu, Liang Jiale, Zhou Wen. Energy prediction algorithm based on ARIMA-LSTM [J]. Radio Communication Technology,2023,49(01):150-156.
Xing Qi, Zhu Daolin, Wang Jian. Based on the natural resources of SEEA assets, international comparison of accounting index [J]. Journal of environmental management in China, 2022, 14 (01): 68-77.
Chen Yongzhi, Lin Yingxing, Cheng Hang. Does public evaluation really reflect the level of regional green development? Analysis based on α level set fuzzy TOPSIS method [J]. Journal of Fuzhou University (Philosophy and Social Sciences Edition), 201,35(03):46-52.
Niu Xuena, Shi Zhanhong, Zhou Shengwei et al. Grassland ecological security assessment in eastern Qilian Mountains of Gansu Province based on TOPSIS method of combinatorial weighting: A case study of Tianzhu Tibetan Autonomous County, Gansu Province [J]. Gansu Science and Technology,20,36(22):23-27.
Yang Kail. Datong green GDP calculation based on SEEA-2012 [C]//. China environmental science society proceedings of science and technology annual meeting (a). In 2022, 2022:114-119.
LIU Xiang. Research on Prediction and Early Warning Technology of Pipeline Stress Based on ARIMA-LSTM [J]. Petroleum Engineering Construction,202,48(05):38-43.
Song Geifeng, Liu Yanzhong, Zhu Xiaonan, Chen Yong, Zhang Zuo. Dynamic change of ecological efficiency in Wuhan based on emergy analysis [J]. Ecological Economy,2019,35(07):103-109.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
