Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China

Jinying Li,Jianfeng Shi,Jinchao Li

doi:10.3390/en9080615

Jinying Li, Jianfeng Shi + Show 1 more

Open Access

https://doi.org/10.3390/en9080615

Copy DOI

Journal: Energies	Publication Date: Aug 4, 2016
Citations: 32	License type: CC BY 4.0

Affiliation: North China Electric Power University

Abstract

Carbon emissions are the major cause of the global warming; therefore, the exploration of carbon emissions reduction potential is of great significance to reduce carbon emissions. This paper explores the potential of carbon intensity reduction in Beijing in 2020. Based on factors including economic growth, resident population growth, energy structure adjustment, industrial structure adjustment and technical progress, the paper sets 48 development scenarios during the years 2015–2020. Then, the back propagation (BP) neural network optimized by improved particle swarm optimization algorithm (IPSO) is used to calculate the carbon emissions and carbon intensity reduction potential under various scenarios for 2016 and 2020. Finally, the contribution of different factors to carbon intensity reduction is compared. The results indicate that Beijing could more than fulfill the 40%–45% reduction target for carbon intensity in 2020 in all of the scenarios. Furthermore, energy structure adjustment, industrial structure adjustment and technical progress can drive the decline in carbon intensity. However, the increase in the resident population hinders the decline in carbon intensity, and there is no clear relationship between economy and carbon intensity. On the basis of these findings, this paper puts forward relevant policy recommendations.

Highlights

With the accelerated melting of two polar glaciers and rising sea levels in recent decades, global warming has become a major concern of the international community
Aiming at the problems existing in the above methods, the back propagation (BP) neural network optimized by improved particle swarm optimization algorithm (IPSO) combined with scenario analysis method are proposed to forecast the carbon emissions and carbon intensity in Beijing in this paper
Through comparison of the three development scenarios, we found that if the economic growth rate increased by 0.1%, the carbon emissions in 2020 would increase by 0.24%

Summary

Introduction

With the accelerated melting of two polar glaciers and rising sea levels in recent decades, global warming has become a major concern of the international community. To reduce greenhouse gases emissions and curb further global climate warming has turned into the common challenge faced by human society in this century. As the harm caused by climate warming continues to emerge, as well as the deepening awareness of the international community, a binding “Paris Agreement” was adopted in 2015. Of all the greenhouse gases, CO2 emissions from human activity contribute most of the warming effect, and it has become the focus of emission reduction. As of 2013, China contributed 27.1% of the world’s total carbon emissions [1]. China is facing increasing international pressure in terms of carbon emissions

Methods

Discussion

Conclusion