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Yiguo Hu

BIDIRECTIONAL ANALYSIS MODEL OF GREEN INVESTMENT AND CARBON EMISSION BASED ON LSTM NEURAL NETWORK

ABSTRACT
Clarifying how the green investment alleviates carbon emissions paves the way for achieving carbon peak and carbon neutralization at a faster pace. In order to propose an effective evaluation model and analyze the interaction between green investment and total carbon emissions, we first and foremost collected data from 30 provinces in China from 2007 to 2019. Secondly, we introduced long short-term memory (LSTM) neural network model, with the amount of government investment in pollution control and environmental infrastructure construction as the model input variables. We also select the total amount of carbon emissions as the model output variables to obtain a neural network model with multiple inputs and a sin-gle output, which can effectively analyze the potential relationship between green investment data and the total amount of carbon emissions data. Then, the OLS model is introduced to test the relationship obtained by LSTM neural network model and analyze its robustness. As a result, the experiment indicates that the LSTM network conceived by us has reliable robustness and fitting performance, with green investment positively affecting total carbon emissions. Meanwhile, we give corresponding policy recommendations according to the model results.
KEYWORDS
LSTM Neural Network Model, OLS Model, Green Investment, Total Carbon Emissions
PAPER SUBMITTED: 2022-12-01
PAPER REVISED: 2023-01-05
PAPER ACCEPTED: 2023-01-31
PUBLISHED ONLINE: 2023-02-11
DOI REFERENCE: https://doi.org/10.2298/TSCI221201033H
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 2, PAGES [1405 - 1415]
REFERENCES
  1. Juberias, G., et al., A New ARIMA Model for Hourly Load Forecasting, Proceedings, IEEE Transmission and Distribution Conference, New Orleans, La., USA, 1999, pp. 314-319
  2. Amjady, N., Short-Term Hourly Load Forecasting Using Time-Series Modeling with Peak Load Estimation Capability, IEEE Transactions on Power Systems, 16 (2001), 4, pp. 798-805
  3. Li, Y. Y., et al., Long-Term System Load Forecasting Based on Data-Driven Linear Clustering Method, Journal of Modern Power Systems and Clean Energy, 6 (2018), May, pp. 306-316
  4. Christiaanse, W., Short-Term Load Forecasting Using General Exponential Smoothing, IEEE Transactions on Power Apparatus and Systems, Pas-90 (1971), 2, pp. 900-911
  5. Tao, H., et al., A Naive Multiple Linear Regression Benchmark for Short Term Load Forecasting, Proceedings, IEEE Power & Energy Society General Meeting, Detroit, Mich., USA, 2011
  6. Chen, B. J., et al., Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001, IEEE Transactions on Power Systems: A Publication of the Power Engineering Society, 19 (2004), 4, pp. 1821-1830
  7. Mayur Barman, N. B., et al., A Regional Hybrid GOA-SVM Model Based on Similar Day Approach for Short-Term Load Forecasting in Assam, India, Energy, 145 (2018), Feb., pp. 710-720
  8. Kuo, P.-H., et al., A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting, Energy, 11 (2018), 213
  9. Wang, Y., et al., Probabilistic Individual Load Forecasting Using Pinball Loss Guided LSTM, Applied Energy, 235 (2019), Feb., pp. 10-20
  10. He, Y. Y., et al., Electricity Consumption Probability Density Forecasting Method Based on LASSO-Quantile Regression Neural Network, Applied Energy, 233-234 (2019), Jan., pp. 565-575
  11. Zhang, W. J., et al., An Improved Quantile Regression Neural Network for Probabilistic Load Forecasting, IEEE Trans. Smart Grid, 10 (2019), 4, pp. 4425-4434
  12. Li, X., Et Al., Forecasting China's Co2 Emissions For Energy Consumption Based On cointegration approach, Discret Dyn. and Nat. Soc., 2018 (2018), ID4235076
  13. Martin, P. R., Moser, D. V., Managers' Green Investment Disclosures and Investors' Reaction, Account. Econ., 61 (2016), 1, pp. 239-254
  14. Campiglio, E., Beyond Carbon Pricing: The Role of Banking and Monetary Policy in Financing the Transition to a Low-Carbon Economy, Ecol. Econ., 121 (2016), Jan., pp. 220-230
  15. Roc Liao, X., Shi, X., Public Appeal, Environmental Regulation and Green Investment: Evidence from China, Energy Pol., 119 (2018), Aug., pp. 554-562
  16. Li, Z. Z., et al., Determinants of Carbon Emission in China: How Good is Green Investment? Sustain Product Consumpt, 27 (2021), July, pp. 392-401
  17. Shen, Y. J., et al., Does Green Investment, Financial Development, and Natural Resources Rent Limit Carbon Emissions? A Provincial Panel Analysis of China, Science of the Total Environment, 755 (2021), 142538
  18. Wang, L., et al., How China is Fostering Sustainable Growth: The Interplay of Green Investment and Production-Based Emission, Environmental Science and Pollution Research International, 27 (2020), 31, pp. 39607-39618
  19. Li, P. Z., et al., On the Dynamic Relationship Between Green Finance and Agricultural Carbon Emission, Journal of Shanxi Radio & TV University, 25 (2020), 02, pp. 101-106
  20. Zhang, M. L., Study of the Spatial Effect of Green Investment in the Process of Marketization for High-Quality Economic Development－Empirical Analysis Based on Space Dubin Model, Journal of Guizhou University of Finance and Economics, 70 (2020), 04, pp. 89-100
  21. Yu, T. K., Huang, J. D., Research on the Impact of Green Finance on Carbon Emissions, Journal of Contemporary Financial Research, 35 (2022), 1, pp. 57-70
  22. Xu, H. L., Deng, Y. P., Does the Foreign Direct Investment Lead to Environmental Pollution in China? Spatial Measurement Research Based on Inter-Provincial Panel Data in China, Journal of Management World, 2 (2012), pp. 36-49
  23. Wang, Q., et al., Mechanism of Energy Efficiency Response to Industrial Restructuring and Energy Consumption Structure Change, Acta Geographica Sinica, 66 (2011), 6, pp. 741-749
  24. Guan, W. H., et al., Study on the Change of Energy Consumption Structure in China, Journal of Natural Resources, 21 (2003), 03, pp. 401-407
  25. Sachs, J. D., et al., Why is Green Finance Important? ADB Papers, Mandaluyong, Philippines, 2019
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Bidirectional analysis model of green investment and carbon emission based on LSTM neural network

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