Optimal green technology investment and emission reduction in emissions generating companies under the support of green bond and subsidy

Income inequality and carbon efficiency: The mediating role of population aging in 139 countries.

An assessment of socioeconomic indicators and energy consumption by considering green financing

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves.The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

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Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

Is electricity consumption of Chinese counties decoupled from carbon emissions? A study based on Tapio decoupling index

Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency

Kaitlyn J. Bunker

Papers

Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency