Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis

Chen, Jingjing; Lin, Yangyang; Wang, Xiaojun; Mao, Bingjing; Peng, Lihong

doi:10.3390/en15145002

Cited by 13 publications

(3 citation statements)

References 53 publications

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“…To examine the causes of energy consumption and household socio-economic factors on CO 2 emissions, a wide range of studies have used various methodologies 40 – 43 . Verified the impacts of economic growth, population, and energy expenditure on household CO 2 emissions in the framework of the LMDI decomposition model.…”

Section: Methodsmentioning

confidence: 99%

Driving factors of energy related CO2 emissions at a regional level in the residential sector of Iran

Ata,

Pakrooh,

Pénzes

2023

Sci Rep

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Iran has increased its CO2 emissions significantly during the past few decades. The household sector in Iran contributes one of the largest sectors of CO2 emissions. Despite this significant contribution, the existing policies have predominantly concentrated on large-scale initiatives while overlooking the regional role in shaping and implementing these plans. Therefore, this study investigates the relationship between CO2 emissions and the efficient factors in three major groups including energy, climate, and household socio-economic factors. This study aims to address regional carbon emissions and develop CO2 reduction policies tailored to each region's specific circumstances. It focuses on planning strategies at the regional level to effectively tackle CO2 emissions. Household panel data of 28 provinces of Iran are employed by using both static and dynamic panel models for the years 2001 to 2019. Static estimation includes Fixed Effect (FE), Random Effect (RE) and pooled Partial least squares (PLS), Dynamic estimation includes difference Generalized Method of Moments (GMM) and system Generalized Method of Moments (GMM). The empirical result of the static method showed positive dependence of household CO2 emissions on Heating Degree Days (HDD), Cooling Degree Days (CDD), precipitation level, oil consumption, gas consumption, household income, size of household, and also building stocks. In more detail, educational rate, dummy variable (removal of energy subsidy), and oil price reveal the greatest negative impact on the emissions with elasticities of − 0.428, − 0.31, and − 0.15; It represents 1% increase causes − 0.428, − 0.31, − 0.15, decrease CO2 emissions, respectively. however, household size, gas consumption, and oil consumption show the most significant positive effects on CO2 emissions with 1 percent increase causes CO2 emissions increases by 0.1, 0.044, and 0.026, respectively. Regarding the impact of climate factors, a 1% increase in Heating Degree Days, Cooling Degree Days, and precipitation level causes CO2 emissions increase by 0.024%, 0.004%, and 0.011% respectively, due to an increase in fossil energy demand. Results of the dynamic method of the system Generalized Method of Moments are similar to the static estimation results, except for that household size and urbanization are not significant. Also, removing the energy subsidy for fossil fuels due to substantial subsidy in fossil fuels in Iran or implementing a re-pricing energy policy can be a beneficial way to control carbon emissions from households within the provinces of the country. However, it is important to consider that this shift could potentially transfer subsidies to investments in the private sector for renewable energies.

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Section: Methodsmentioning

confidence: 99%

Driving factors of energy related CO2 emissions at a regional level in the residential sector of Iran

Ata,

Pakrooh,

Pénzes

2023

Sci Rep

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“…A first motivation for including behavioural advice in national climate policies aimed at reducing GHG emissions consists in spreading the idea that the actors involved in the fight against climate change are not only large energy companies and productive realities in general, but that even an individual citizen has tangible responsibilities towards the planet. Considering that since 1990, the energy consumption in the residential sector of some developed countries has exceeded that in the industrial sector, highlights the role of households as an important source of carbon emissions (Chen, Lin, & Wang, 2022). Moreover, carbon emissions from household consumption practices are not only generated directly, for example through the transportation of people or the residential heating, but also indirectly, for example in the products or foods purchased: therefore, it is essential to raise awareness also of these indirect emissions (e.g.…”

Section: To Raise the Awareness Of Every Citizenmentioning

confidence: 99%

Carbon Neutral Lifestyles and NDCs: Advice and Policy Perspectives

Cavalli

Boeri

2023

SSRN Journal

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“…The results showed that there is strong spatial heterogeneity in the estimated elasticity coefficients. Chen et al [29] studied the effects of influencing factors on direct household carbon emissions and indirect carbon emissions of urban and rural households in Fujian province by using LMDI and SDA models. (3) In China, gray correlation analysis is frequently used to investigate the correlation degree between the influencing factors of carbon emissions and carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Study on Influencing Factors and Spatial Effects of Carbon Emissions Based on Logarithmic Mean Divisia Index Model: A Case Study of Hunan Province

et al. 2022

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China has committed to peaking carbon dioxide emissions by 2030 and has set a goal of working towards carbon neutrality by 2060. Hunan province is a vital undertaking place for national industrial transfer. It is of great significance for promoting energy conservation and emission reduction to investigate the influencing factors and spatial effects of carbon emissions in Hunan province. Firstly, based on the energy consumption data of Hunan province from 2005 to 2017, this paper uses the method recommended by the Intergovernmental Panel on Climate Change (IPCC) to measure the carbon emissions of Hunan province and its economic zones. Secondly, the five-factor Logarithmic Mean Divisia Index (LMDI) model is constructed to analyze the influence degree of population size, economic development, industrial structure, energy intensity, and energy structure on carbon emissions. Finally, the spatial differences of the influencing factors in the four economic zones of Hunan province are analyzed. The research shows that: (1) An overall carbon emission reduction has been achieved in Hunan province since 2011. (2) Changsha–Zhuzhou–Xiangtan Economic Zone is the key area to achieve carbon emission reduction, while there is still the phenomenon of emission increase in the other three economic zones. (3) For all economic zones, economic development contributes the most to the increase in carbon emissions, while energy intensity shows the strongest inhibitory effect. Other factors have various effects on the four economic zones.

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Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis

Cited by 13 publications

References 53 publications

Driving factors of energy related CO2 emissions at a regional level in the residential sector of Iran

Driving factors of energy related CO2 emissions at a regional level in the residential sector of Iran

Carbon Neutral Lifestyles and NDCs: Advice and Policy Perspectives

Study on Influencing Factors and Spatial Effects of Carbon Emissions Based on Logarithmic Mean Divisia Index Model: A Case Study of Hunan Province

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