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.
A new technology may be biased towards saving energy, or reducing pollution emission or increasing economic output. It is necessary for the high-quality development of marine economy to promote environment-based technological progress. The purpose of this paper is to estimate the biased technological progress and its influencing factors of China's marine economy from 2002 to 2016. We used a DEA-Malmquist model to measure the biased technological progress. Then we further analyzed influencing factors of biased technological progress. Our research found that the TFP of the marine economy was basically growing, and this growth was mainly due to a positive impact of technological progress. In general, the technological progress of marine economy is gradually biased towards energy conservation and emissions reduction. Furthermore, technological progress in the Yangtze River Delta are more conducive to energy conservation. The marine economic production in the Pan-Pearl River Delta is more inclined to promote production growth, while tit is most concerned about environmental protection in the Bohai Rim region. In addition, factors such as environmental regulation, economic level, FDI and industrial scale have different impacts on biased technological progress of marine economy. The results show that the directive regulations have a greater impact on the input-biased technological progress, while the incentive regulations have a greater impact on the output-biased technological progress. Therefore, this study has important guiding significance for energy conservation and emission reduction of marine economy in China. And the green development path of China's marine economy can provide a reference for the development of European blue economy.
This paper investigates the dynamic relationship between steam coal price and its drivers sampling mixed frequencies to improve the prediction of weekly steam coal price. A novel hybrid method, combining the mixed data sampling (MIDAS) model with eXtreme Gradient Boosting (XGBoost) algorithm, is proposed to perform forecast of weekly steam coal prices by applying the latest mixed factors with high frequencies. The empirical evidences indicate that the daily natural gas prices, temperatures, and air quality index (AQI) have better predictive abilities for steam coal prices than the A-share index and crude oil prices. It's shown that the hybrid model has approximately 23.27% and 78.39% accuracy improvement over the combination-MIDAS and other benchmark models, respectively. The empirical results are helpful for the government to effectively capture the fluctuation and uncertainty of steam coal prices from the energy market and environmental conditions to make reasonable strategies in China.
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