When will China’s industrial carbon emissions peak? Evidence from machine learning

Abstract

The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP’s neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level.

Spatio-Temporal Characteristics of Industrial Carbon Emission Efficiency and Their Impacts from Digital Economy at Chinese Prefecture-Level Cities

“…In this study, we used balanced panel data from 270 Chinese cities from 2005 to 2020 as a research object: (1) to show the spatio-temporal evolution pattern of urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using the Global Moran's I statistic; and (3) to use the hierarchical regression model for panel data to explore the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The main conclusions are as follows: The regression coefficient of population size is positive, so the regression coefficient increases and then decreases with the increase in quartiles, with a positive effect on the enhancement of urban industrial carbon emission efficiency in the middle quartile and more obvious inhibitory effects in the low and high quartiles.…”

“…In this study, we used balanced panel data from 270 Chinese cities from 2005 to 2020 as a research object: (1) to show the spatio-temporal evolution pattern of urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using the Global Moran's I statistic; and (3) to use the hierarchical regression model for panel data to explore the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The main conclusions are as follows:…”

“…Carbon peaking and carbon neutrality are two important measures in the global response to climate change; together, these also constitute an important development goal put forward by China [1]. Industry, as an important pillar of economic development, is also one of the main sources of carbon emissions [2]. The Second China Digital Carbon Neutrality Summit Forum pointed out that industrial green and low-carbon transformation is essential for integrating development and achieving emission reductions and that energy transformation and digital twinning will act as a powerful stimulant to China's "dual carbon" goal of carbon peaking and carbon neutrality.…”

“…Answering the above question is of potentially great significance for advancing the digital power of China and for the Chinese achievement of carbon peaking and carbon neutrality. In this study, then, to make up for the shortcomings of previous research, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show the spatio-temporal evolution pattern of urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran's I statistics; and (3) to use the hierarchical regression model for panel data to explore the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities.…”

Prediction of China Automobile Market Evolution Based on Univariate and Multivariate Perspectives

“…The BP neural network, put forward by scholars such as Rinehart and MeClelland, is the most widely used artificial neural network [37], which has the characteristics of self-learning adaptation, parallel processing, strong learning ability and generalization [38], and generally consists of an input layer, hidden layer and output layer. Studies have shown that a three-layer BP neural network prediction model can approximate any nonlinear function [39,40].…”