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Hybrid framework combining grey system model with Gaussian process and STL for CO 2 emissions forecasting in developed countries
16 references·0 citing·48 second hop
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References

  • 1982Control problems of grey systems4,503 cited
  • 2012Grey system model with the fractional order accumulation622 cited
  • 2012Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model361 cited
  • 2021The impact of energy depletion and renewable energy on CO2 emissions in Thailand: Fresh evidence from the novel dynamic ARDL simulation313 cited
  • 2021Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms279 cited
  • 2020Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms265 cited
  • 2017A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression207 cited
  • 2019Forecasting of CO 2 emissions in Iran based on time series and regression analysis207 cited
  • 2022China's progress toward sustainable development in pursuit of carbon neutrality: Regional differences and dynamic evolution201 cited
  • 2020Fractional Hausdorff grey model and its properties190 cited
  • 2020Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO178 cited
  • 2020Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion162 cited
  • 2017Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization

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143 cited
  • 2020A novel fractional grey Riccati model for carbon emission prediction142 cited
  • 2020A novel hybrid model for forecasting crude oil price based on time series decomposition137 cited
  • 2019Influencing factors analysis and forecasting of residential energy-related CO2 emissions utilizing optimized support vector machine135 cited
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