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Application of Machine Learning Models in Analyze Carbon Emissions With COVID-19 in Guangdong

https://doi.org/10.2139/ssrn.4658722
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inferredcites →https://doi.org/10.1007/978-3-642-30223-7_87openalex-extracted
Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement
inferredcites →https://doi.org/10.1007/s10661-022-10088-7openalex-extracted
Application of grey model in influencing factors analysis and trend prediction of carbon emission in Shanxi Province
inferredcites →https://doi.org/10.1007/s42398-021-00166-wopenalex-extracted
Recent advances in satellite mapping of global air quality: evidences during COVID-19 pandemic
inferredcites →https://doi.org/10.1016/0304-3932(92)90016-uopenalex-extracted
Modeling long-run behavior with the fractional ARIMA model
inferredcites →https://doi.org/10.1016/j.apenergy.2020.115425openalex-extracted
Urban carbon emissions associated with electricity consumption in Beijing and the driving factors
inferredcites →https://doi.org/10.1016/j.bbi.2020.05.028openalex-extracted
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inferredcites →https://doi.org/10.1016/j.chaos.2021.110968openalex-extracted
Forecasting Chinese carbon emissions using a novel grey rolling prediction model
inferredcites →https://doi.org/10.1016/j.chieco.2012.02.004openalex-extracted
Economic development and carbon dioxide emissions in China: Provincial panel data analysis
inferredcites →https://doi.org/10.1016/j.cities.2018.10.011openalex-extracted
Deciphering the spatial structure of China's megacity region: A new bay area—The Guangdong-Hong Kong-Macao Greater Bay Area in the making
inferredcites →https://doi.org/10.1016/j.comnet.2021.108102openalex-extracted
A network traffic forecasting method based on SA optimized ARIMA–BP neural network
inferredcites →https://doi.org/10.1016/j.energy.2007.01.008openalex-extracted
A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies
inferredcites →https://doi.org/10.1016/j.enpol.2015.07.007openalex-extracted
LMDI decomposition approach: A guide for implementation
inferredcites →https://doi.org/10.1016/j.erss.2020.101685openalex-extracted
Covid-19 and the politics of sustainable energy transitions
inferredcites →https://doi.org/10.1016/j.fuel.2020.120048openalex-extracted
A machine learning model for predicting the minimum miscibility pressure of CO2 and crude oil system based on a support vector machine algorithm approach
inferredcites →https://doi.org/10.1016/j.jbusres.2013.03.021openalex-extracted
The role of innovation in driving the economy: Lessons from the global financial crisis
inferredcites →https://doi.org/10.1016/j.jclepro.2016.02.053openalex-extracted
Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China
inferredcites →https://doi.org/10.1016/j.jclepro.2017.03.189openalex-extracted
Provincial-level carbon emission drivers and emission reduction strategies in China: Combining multi-layer LMDI decomposition with hierarchical clustering
inferredcites →https://doi.org/10.1016/j.jclepro.2017.05.102openalex-extracted
A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression
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What drives the carbon emission in the Chinese cities?—A case of pilot low carbon city of Beijing
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How technological progress affects the carbon emission efficiency? Evidence from national panel quantile regression
inferredcites →https://doi.org/10.1016/j.jes.2018.03.026openalex-extracted
Factor decomposition of carbon emissions in Chinese megacities
inferredcites →https://doi.org/10.1016/j.physd.2019.132306openalex-extracted
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
inferredcites →https://doi.org/10.1016/j.rser.2012.10.029openalex-extracted
Evaluating the relationships among economic growth, energy consumption, air emissions and air environmental protection investment in China
inferredcites →https://doi.org/10.1016/j.rser.2014.09.021openalex-extracted
The decomposition of energy-related carbon emission and its decoupling with economic growth in China
inferredcites →https://doi.org/10.1016/j.rser.2016.09.006openalex-extracted
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inferredcites →https://doi.org/10.1016/j.scitotenv.2018.08.183openalex-extracted
Carbon emissions from energy consumption in China: Its measurement and driving factors
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Spaceborne detection of localized carbon dioxide sources
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Learning to Forget: Continual Prediction with LSTM
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The Global Financial Crisis: Causes and Consequences
inferredcites →https://doi.org/10.1162/neco_a_01199openalex-extracted
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
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The World Economy at COVID-19 Quarantine: Contemporary Review
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