An Estimation and Factor Decomposition Analysis of Energy-related Carbon Emissions in Beijing

Zhang, J.Y.; Zhang, Yan; Zhang, Yang; Li, S.S.

doi:10.1016/j.proenv.2012.01.152

Cited by 6 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inglesi-Lotz and Pouris (2012) used the LMDI to look at the factors affecting the trends in energy efficiency in South Africa from 1993 to 2006 and particularly focused on the impact of structural changes and consumption efficiency of the country's energy intensity. Recently, Zhang et al (2015) considered five factors (the economic activity effect, the energy intensity effect, the fossil energy structure effect, the renewable energy structure effect and the emission-factor effect) by applying the LMDI method to analyze the contribution of the factors which influence energy-related CO 2 emissions in South Africa over the period 1993-2011. The results revealed that the energy intensity effect played the dominant role in decreasing of CO 2 emission while the economic activity was a critical factor in the growth of energy-related CO 2 emission in SA.…”

Section: Introductionmentioning

confidence: 99%

Decomposition Analysis of CO2 Emissions from Coal - Sourced Electricity Production in South Africa

Beidari

Lin

Lewis

2017

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

In 2013, the electricity sector was the largest source of South Africa's CO 2 emissions, accounting for about 60% of its total. South Africa (SA) is one of the highest CO 2 emitters on a per capita basis when compared to many developed and developing countries. For a better understanding of the driving forces leading the electricity-related CO 2 emission per person, this paper applies the Log Mean Divisia Index (LMDI) to analyze the influence of the factors which ruled electricity generation-related CO 2 emission in SA over the period 1990-2013. We focused on coal which is the dominant fuel used in SA for electricity generation. The results show that the electricity generation intensity effect plays the dominant role in decreasing CO 2 emissions. However, the effect of economic activity is the major determinant that contributes to increasing CO 2 emissions. In order to reduce its greenhouse gas emissions levels, meet the agreement of the COP21 agreement, and fight against CO 2 emissions per-capita associated with electricity generation, it is recommended that SA's government should improve the efficiency of its existing electricity power generation plants and expand more of its renewable energy sources (nuclear included).

show abstract

Section: Introductionmentioning

confidence: 99%

Decomposition Analysis of CO2 Emissions from Coal - Sourced Electricity Production in South Africa

Beidari

Lin

Lewis

2017

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…One study concluded that population, GDP per capita, industrial structure, energy consumption intensity, and energy consumption structure were the important impact factors of CO 2 emissions through the STIRPAT model [31]. Another article showed that urbanization increased energy consumption and CO 2 emissions in China [32]. Yu et al [33] used the ridge regression method and the extended STIRPAT model to analyze CO 2 emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Probing Energy-Related CO<sub>2</sub> Emissions in the Beijing-Tianjin-Hebei Region Based on Ridge Regression Considering Population Factors

Wen¹,

Zhang²

2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

The main driving force for huge energy consumption is population growth and economic development, and many studies have analyzed the factors that influence carbon dioxide emissions. But the influencing factors mainly refer to the economic and social fields. Few studies have looked at population factors, and the extended STIRPAT model and ridge regression method are used to pay attention to the impact of population factors on carbon dioxide emissions in the Beijing-Tianjin-Hebei region. The conclusions drawn are as follows: 1) For Beijing, the urbanization level, population density, per capita disposable income, education level, GDP and energy intensity have a positive impact on CO 2 emissions. However, age structure, family size and industrial structure play negative roles. The improvement of urbanization level has a distinctive positive influence on CO 2 emissions. 2) For Tianjin, most impact factors have a positive effect on CO 2 emissions, except family size and energy intensity. The decrease of family size is the first contributor to CO 2 emissions growth. 3) For Hebei, the urbanization level, population density, per capita disposable income, age structure, GDP and industrial structure, have a positive influence on CO 2 emissions. Education level, family size and energy intensity have a negative impact on CO 2 emissions, and population density is the most important factor.

show abstract

“…Recent studies of accounting carbon emissions have focused on two levels, country and city [4]. A large number of literatures study influencing factors of CO2 emissions in macroscopic level, but relevant researches in Chinese city level is less [3].This paper selects Beijing city as the research area.…”

Section: Introductionmentioning

confidence: 99%

Research on Environmental Tax to Affect the Economic Development and GHG Mitigation in Beijing, China

Zou

Song

Mizunoya

et al. 2014

AMM

View full text Add to dashboard Cite

As the most industrialized and urbanized region, Beijing plays as a demonstration role to show the impact of environmental policies on the economy development and GHG mitigation. In this paper, we constructed a dynamic input-output model introducing the different levels of environmental taxes. It not only can choose the optimal tax level, explore the relationships between economy and environment, but also can analyze the future trends of the economy and GHG intensity from 2010 to 2025. The objective function is the maximized GRP, subject to GHG emissions constraint and a series of subjective functions. The simulation results illustrated that with the GHG emissions constraint as 1.5 times of the 2010 level, carbon tax as 50 CNY/t CO2-e is effective to promote the economic development and GHG emissions mitigation. Annual growth rate of GRP can be up to 6.1%. The economic growth rate increases 0.3% compared with the condition when not introducing the policies. In 2025, the GHG intensity will be 43.5 t CO2-e/million CNY, 38.8% reduced compared with the 2010 level. This research proves that the proposed environmental tax is effective to promote the economic development and GHG mitigation.

show abstract

An Estimation and Factor Decomposition Analysis of Energy-related Carbon Emissions in Beijing

Cited by 6 publications

References 15 publications

Decomposition Analysis of CO2 Emissions from Coal - Sourced Electricity Production in South Africa

Decomposition Analysis of CO2 Emissions from Coal - Sourced Electricity Production in South Africa

Probing Energy-Related CO<sub>2</sub> Emissions in the Beijing-Tianjin-Hebei Region Based on Ridge Regression Considering Population Factors

Research on Environmental Tax to Affect the Economic Development and GHG Mitigation in Beijing, China

Contact Info

Product

Resources

About