Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Abstract

Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

Ensemble Framework for Multistep Carbon Emission Prediction: An Improved Bi-LSTM Model Based on the Bayesian Optimization Algorithm and Two-Stage Decomposition

“…Huang et al (2019) employed long short-term memory (LSTM) networks to forecast annual total carbon emissions (Huang et al, 2019). Jin et al (2021) used Elman neural networks (ENNs) to predict carbon emissions from direct consumption by residents for the upcoming year (Jin, 2021). Jena et al (2021) applied multilayer artificial neural network modeling (MLANN) to predict the annual total carbon emissions of various countries (Jena et al, 2021).…”

Application of carbon emission prediction based on a combined neural algorithm in the control of coastal environmental pollution in China

“…For the prediction of total CEs, most of the existing studies used the partial least squares regression method, the STIRPAT model, and the scenario analysis method to predict the peak of China's CE, and some scholars combined various methods to predict China's CEs (Yang et al, 2018;Li et al, 2021). Jin et al used the radial basis function (RBF) to predict the urban CE content in China from 2027 to 2032 (Jin, 2021). The experimental results showed that the number of samples will affect the prediction ability and network structure complexity of RBF, and the network is not suitable for large sample data prediction.…”

Advanced Neural Network Models in Forecasting Carbon Emissions

“…To offer a scientific foundation for CE management and planning, the trend and characteristics of changes in CE between 2027 and 2032 are studied and anticipated based on data on direct CEs of people of Chinese provinces from 1999 to 2019. The weighted factors included as input data were residential energy consumption types, CO2 emission coefficients, electrical CE coefficients, and populations (total, urban, and rural) [7].…”

“…To offer a scientific foundation for CE management and planning, the trend and characteristics of changes in CE between 2027 and 2032 are studied and anticipated based on data on direct CEs of people of Chinese provinces from 1999 to 2019. The weighted factors included as input data were residential energy consumption types, CO2 emission coefficients, electrical CE coefficients, and populations (total, urban, and rural)[7].Within this paper, three types of artificial neural networks were discussed (Backpropagation Neural Network (BPNN), Radial Basis Function (RBF), and Elman neural network). After considering prediction accuracy, the complexity of construction, robustness, and fault tolerance, the Elman neural network seems to be the best one (Figure5).…”