Characteristics and evolution of hierarchical fishery policies in China – A textual analysis based on 5311 policies from 2003 to 2022
In: Marine policy, Band 155, S. 105699
ISSN: 0308-597X
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In: Marine policy, Band 155, S. 105699
ISSN: 0308-597X
In: Environmental science and pollution research: ESPR, Band 29, Heft 25, S. 38083-38096
ISSN: 1614-7499
In: Marine policy, Band 133, S. 104707
ISSN: 0308-597X
In: FRL-D-23-03183
SSRN
In: FINANA-D-24-01727
SSRN
In: IJDRR-D-22-00869
SSRN
In: Studies in educational evaluation, Band 83, S. 101390
ISSN: 0191-491X
In: Structural change and economic dynamics, Band 70, S. 607-618
ISSN: 1873-6017
In: Marine policy, Band 155, S. 105705
ISSN: 0308-597X
In: Marine policy, Band 137, S. 104973
ISSN: 0308-597X
In: Environmental science and pollution research: ESPR, Band 29, Heft 5, S. 6538-6551
ISSN: 1614-7499
In: Marine policy, Band 123, S. 104293
ISSN: 0308-597X
In: Han , M , Ding , L , Zhao , X & Kang , W 2019 , ' Forecasting carbon prices in the Shenzhen market, China : The role of mixed-frequency factors ' , Energy , vol. 171 , pp. 69-76 . https://doi.org/10.1016/j.energy.2019.01.009 ; ISSN:0360-5442
In this study, the hybrid of combination-mixed data sampling regression model and back propagation neural network (combination-MIDAS-BP) is proposed to perform real-time forecasting of weekly carbon prices in China's Shenzhen carbon market. In addition to daily energy, economy and weather conditions, environmental factor is introduced into predictive indicators. The empirical results show that the carbon price is more sensitive to coal, temperature and AQI (air quality index) than to other factors. It is also shown that the forecast accuracy of the proposed model is approximately 30% and 40% higher than that of combination-MIDAS models and benchmark models, respectively. Given these forecast results, China's government and enterprises can effectively manage nonlinear, nonstationary, and irregular carbon prices, providing a better investing and managing tool from behavioural economics. (C) 2019 Elsevier Ltd. All rights reserved.
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In: BITE-D-22-01759
SSRN
In: Environmental science and pollution research: ESPR, Band 25, Heft 3, S. 2899-2910
ISSN: 1614-7499