Next Article in Journal
Transport Demand Management Policy Integration in Chinese Cities: A Proposed Analysis of Its Effects
Next Article in Special Issue
Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions
Previous Article in Journal
Optimal Design of a High Efficiency LLC Resonant Converter with a Narrow Frequency Range for Voltage Regulation
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Energies 2018, 11(5), 1125; https://doi.org/10.3390/en11051125

The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model

1
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2
Postdoctoral Research Station, Dongbei University of Finance and Economics, Dalian 116025, China
3
Editorial Department, Dongbei University of Finance and Economics, Dalian 116025, China
*
Author to whom correspondence should be addressed.
Received: 30 March 2018 / Revised: 25 April 2018 / Accepted: 1 May 2018 / Published: 2 May 2018
(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)
  |  
PDF [2247 KB, uploaded 2 May 2018]
  |  

Abstract

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures. View Full-Text
Keywords: household consumption; total carbon emissions; CLA Model; influence factor; STIRPAT model household consumption; total carbon emissions; CLA Model; influence factor; STIRPAT model
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, Y.; Yang, G.; Dong, Y.; Cheng, Y.; Shang, P. The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model. Energies 2018, 11, 1125.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top