Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology
By Bangzhu Zhu, Yi-Ming Wei. 2013
411 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Environmental Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/7746d97dc45cd4f907f3d336e6fb4fe9344ad8f1
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In Economics and Econometrics
In Environmental Engineering
In Management Science and Operations Research
Citations in the corpus
- Climate policy modeling: An online SCI-E and SSCI based literature review (2015, background): “that the reduction level can be controlled directly while the carbon price is uncertain [102].”
- Tackling Climate Change with Machine Learning (2019, background): “econometricapproaches fordeveloping arbitrage strategies thattakeadvantage ofthecarbonriskfactorinfinancial markets [705]; and ML approaches for forecasting the price of carbon in emission exchanges59 [707, 708]. To date, the field of climate finance has been largely neglected within the larger scope o”
- Effects of carbon emission reduction policies on transportation mode selections with stochastic demand (2016, background): “Cap-and-trade policy, such as the European Union Emissions Trading Scheme, has been proven to be an important tool to address climate change, and becomes a major choice for investors to decentralize their investment risks (Zhu and Wei, 2013).”
- Potential gains from carbon emissions trading in China: A DEA based estimation on abatement cost savings (2016, methodology): “…emission at minimal cost, for example, the U.S. tradable permits program for SO2 started with the enactment of the Clean Air Act (Sueyoshi and Goto, 2013), and the EU Emissions Trading System (EU ETS) established as a tool for reducing greenhouse gas emissions cost-effectively (Zhu and Wei, 2013).”
- Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors (2019, methodology): “Zhu and Wei [19] combined an ARIMAmodel and least squares support vector machine (LSSVM) to forecast carbon prices and demonstrated greater accuracy than that of single ARIMA, LSSVM, and artificial neural network (ANN).”