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Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network
16 references·2 citing·46 second hop
Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network (2022)Carbon emissions index decomposition and …The impacts of globalization, financial development, government expenditures, and institutional quality on CO2 emissions in the presence of environmental Kuznets curve (2020)The impacts of globalization,…Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100 (2020)Provincial and gridded popula…A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions (2020)A machine learning approach o…CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions (2018)CO2 emissions, economic and p…China CO2 emission accounts 1997–2015 (2018)China CO2 emission accounts 1…Relationships among carbon emissions, economic growth, energy consumption and population growth: Testing Environmental Kuznets Curve hypothesis for Brazil, China, India and Indonesia (2016)Relationships among carbon em…How urbanization affects CO 2 emissions in Malaysia? The application of STIRPAT model (2016)How urbanization affects CO 2…Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang (2016)Examining the driving factors…Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI (2014)Factors that influence carbon…A global review of energy consumption, CO 2 emissions and policy in the residential sector (with an overview of the top ten CO 2 emitting countries) (2014)A global review of energy con…Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses (2013)Impact of population, age str…Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China (2013)Examining the impact factors …Data mining with big data (2013)Data mining with big dataAn empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China (2012)An empirical research on the …Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm (2010)Forecasting stock indices usi…Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts (2010)Age-structure, urbanization, …Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering (2023)Industrial carbon emission ef…A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning (2023)A Review of Macroscopic Carbo…STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts (2003)The impact of population pressure on global carbon dioxide emissions, 1975–1996: evidence from pooled cross-country data (2003)Analyzing impact factors of CO2 emissions using the STIRPAT model (2006)How does urbanization affect carbon dioxide emissions? 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this work references cited by second hop

References

  • 2013Data mining with big data2,411 cited
  • 2014A global review of energy consumption, CO 2 emissions and policy in the residential sector (with an overview of the top ten CO 2 emitting countries)1,950 cited
  • 2018China CO2 emission accounts 1997–20151,423 cited
  • 2016Relationships among carbon emissions, economic growth, energy consumption and population growth: Testing Environmental Kuznets Curve hypothesis for Brazil, China, India and Indonesia737 cited
  • 2018CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions695 cited
  • 2016How urbanization affects CO 2 emissions in Malaysia? The application of STIRPAT model670 cited
  • 2010Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts560 cited
  • 2013Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China548 cited
  • 2014Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI544 cited
  • 2020Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100537 cited
  • 2020The impacts of globalization, financial development, government expenditures, and institutional quality on CO2 emissions in the presence of environmental Kuznets curve528 cited
  • 2012An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China416 cited

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  • 2010Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm400 cited
  • 2013Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses393 cited
  • 2020A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions380 cited
  • 2016Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang364 cited
  • Cited by

    • 2023Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering76 cited
    • 2023A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning75 cited