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Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China
11 references·16 citing·42 second hop
Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China (2016)Prediction and analysis of the three majo…Factors affecting carbon dioxide (CO2) emissions in China's transport sector: a dynamic nonparametric additive regression model (2015)Factors affecting carbon diox…Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach (2015)Carbon dioxide emissions redu…The Environmental Kuznets Curve (EKC) theory—Part A: Concept, causes and the CO2 emissions case (2013)The Environmental Kuznets Cur…Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA (2011)Evaluation of potential reduc…Analyzing impact factors of CO2 emissions using the STIRPAT model (2006)Analyzing impact factors of C…2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006)2006 IPCC Guidelines for Nati…2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006)2006 IPCC Guidelines for Nati…Cointegration, causality and export-led growth in Mexico, 1895–1992 (1996)Cointegration, causality and …Statistical analysis of cointegration vectors (1988)Statistical analysis of coint…Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Co-Integration and Error Corr…Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Investigating Causal Relation…China's future energy vision: Multi-scenario simulation based on energy consumption structure under dual carbon targets (2024)China's future energy vision:…Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector (2023)Forecast energy demand, CO2 e…A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning (2023)A Review of Macroscopic Carbo…Probing CO2 emission in Chengdu based on STRIPAT model and Tapio decoupling (2022)Probing CO2 emission in Cheng…How Can China Achieve the 2030 Carbon Peak Goal—A Crossover Analysis Based on Low-Carbon Economics and Deep Learning (2022)How Can China Achieve the 203…An interpretable forecasting framework for energy consumption and CO2 emissions (2022)An interpretable forecasting …An LSTM-STRIPAT model analysis of China’s 2030 CO2 emissions peak (2020)An LSTM-STRIPAT model analysi…A novel approach to predict CO2 emission in the agriculture sector of Iran based on Inclusive Multiple Model (2020)A novel approach to predict C…A choice behavior experiment with circular business models using machine learning and simulation modeling (2020)A choice behavior experiment …Socioeconomic driving forces and scenario simulation of CO2 emissions for a fast-developing region in China (2019)Socioeconomic driving forces …Can China achieve its 2030 carbon emissions commitment? 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this work references cited by second hop

References

  • 1987Co-Integration and Error Correction: Representation, Estimation, and Testing31,983 cited
  • 1969Investigating Causal Relations by Econometric Models and Cross-spectral Methods22,971 cited
  • 1988Statistical analysis of cointegration vectors17,038 cited
  • 20062006 IPCC Guidelines for National Greenhouse Gas Inventories6,590 cited
  • 20062006 IPCC Guidelines for National Greenhouse Gas Inventories1,982 cited
  • 2013The Environmental Kuznets Curve (EKC) theory—Part A: Concept, causes and the CO2 emissions case725 cited
  • 2006Analyzing impact factors of CO2 emissions using the STIRPAT model646 cited
  • 2015Factors affecting carbon dioxide (CO2) emissions in China's transport sector: a dynamic nonparametric additive regression model233 cited
  • 2011Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA227 cited
  • 2015Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach165 cited
  • 1996Cointegration, causality and export-led growth in Mexico, 1895–1992121 cited

Cited by

  • 2018Research on the peak of CO2 emissions in the developing world: Current progress and future prospect155 cited
  • 2019Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network155 cited

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  • 2017Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization143 cited
  • 2023Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector117 cited
  • 2018Forecasting CO2 emissions in Hebei, China, through moth-flame optimization based on the random forest and extreme learning machine109 cited
  • 2022How Can China Achieve the 2030 Carbon Peak Goal—A Crossover Analysis Based on Low-Carbon Economics and Deep Learning100 cited
  • 2019Socioeconomic driving forces and scenario simulation of CO2 emissions for a fast-developing region in China96 cited
  • 2020A novel approach to predict CO2 emission in the agriculture sector of Iran based on Inclusive Multiple Model95 cited
  • 2022Probing CO2 emission in Chengdu based on STRIPAT model and Tapio decoupling86 cited
  • 2024China's future energy vision: Multi-scenario simulation based on energy consumption structure under dual carbon targets85 cited
  • 2022An interpretable forecasting framework for energy consumption and CO2 emissions80 cited
  • 2018Decoupling, decomposition and forecasting analysis of China's fossil energy consumption from industrial output77 cited
  • 2023A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning75 cited
  • 2018An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms68 cited
  • 2020An LSTM-STRIPAT model analysis of China’s 2030 CO2 emissions peak63 cited
  • 2020A choice behavior experiment with circular business models using machine learning and simulation modeling60 cited