Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission

Optimal strategies for carbon reduction at dual levels in China based on a hybrid nonlinear grey-prediction and quota-allocation model

“…As effective tools, methods that can forecast carbon intensity were attractive to many researchers across the world. For example, Behrang et al (2011), andPao andTsai (2011) investigated CO 2 emission intensity with an integrated multilayer perception neural network and the Bees Algorithm method, and an autoregressive integrated moving average (ARIMA) method. Generally, these studies were individually conducted on carbon intensity forecasting, reduction target identification, carbon abatement cost analysis, and emission reduction scheme formulation.…”

Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei Province, China

“…With the propositions and prosperities of artificial intelligent algorithms, these AI algorithms have also begun to be applied gradually to the field of carbon dioxide emissions research. Behrang et al (2011) used the Bees Colony Algorithm (BCA) and the Artificial Neural Network (ANN) to forecast global carbon dioxide emissions. They utilized the BCA to determine the indicators and the ANN to make predictions, but did not overcome the shortcomings of the neural network, such as premature and excessive fitting.…”

Application of Computer for Analyzing World Co2 Emission

“…oil consumptionnatural gas consumption-coal consumption-primary energy consumption) should be forecasted in future time domain (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030). To achieve this, the designed scenarios for future projection of each input variable remained the same which were developed by Bhreng et al (2011a). The values of oil, natural gas, coal, and primary energy consumptions between 2011 and 2030 based on the designed scenario by Bhreng et al, (2011a) are shown in Table 3.…”