Investigation for the Decomposition of Carbon Emissions in the USA with C-D Function and LMDI Methods

Jiang, Rui; Li, Rongrong; Wu, Qiuhong

doi:10.3390/su11020334

Cited by 19 publications

(9 citation statements)

References 59 publications

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“…Among various IDA methods, LMDI is the preferred one because it can perfectly handle zero value and conduct without residuals ( Ang, 2004 ; Ang and Choi, 1997 ). Scholars have done enormous researches on carbon emission decomposition analysis, which covers a wide area from province ( Wang and Jiang, 2020 ), country ( Jiang et al, 2019 ; Ma et al, 2019 ; Yasmeen et al, 2020 ), region ( Li et al, 2019 ; Li et al, 2020 ), to even globe ( Chang et al, 2019 ). Li and Qin (2019) examined challenges for China to peak carbon emission in 2030 from both historical and future perspective.…”

Section: Literature Reviewmentioning

confidence: 99%

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

Wang

2020

Science of The Total Environment

121

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Existing studies have shown that the COVID-19 pandemic caused a sharp drop in carbon emissions in 2020. A recent example of the impact of sudden extreme events on carbon emissions occurred in the 2008 global financial crisis, in which carbon emissions droped in 2009, but jumped in 2010. This study is aimed to discuss how to prevent the retaliatory growth of carbon emissions post COVID-19 through learning the lessons from analysis of short-term and long-term drivers of carbon emissions. This study explored the short-term (annual) effects (population scale. affluence level, carbon intensity, energy intensity) of changes in carbon emissions by decomposing carbon emissions in the world, different income groups and selected countries before and after the 2008 financial crisis using LMDI technique. In addition, this study explored the long-term effects (energy consumption per capita, energy structure, energy intensity, foreign direct investment, and trade openness) of changes in carbon emissions by decomposing carbon emission in the world and different income groups from 1990 to 2014 using VAR technique. The decomposition results of short-term drivers of carbon emission uncovered that the deterioration in energy efficiency (increase in energy intensity) was the main reason for the retaliatory rebound in carbon emissions post-2008 financial crisis, especially in high-income countries. The decomposition results of long-term drivers of carbon emission uncovered that trade openness contributed to reduce carbon emission in the world and the incomes groups in the long term, although trade openness led to increase in carbon emission in developing countries in the short term. To prevent retaliatory rebound of carbon emissions, what we should learn two lessons from the decomposition of carbon emission: improving energy efficiency, and expanding trade openness. Unfortunately, energy efficiency has been neglected in the economic recovery plans to respond to COVID-19 of various countries, especially developed countries, and worse, trade protectionism is on the rise, especially in developed countries. Therefore, we are pessimistic about preventing a retaliatory rebound in carbon emissions post-COVID-19 for now.

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Section: Literature Reviewmentioning

confidence: 99%

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

Wang

2020

Science of The Total Environment

121

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“…Popular methods, such as the IPAT equation (impact on environment equals the product of population, affluence, and technology) and Kaya identity (an extended form of the IPAT equation), have been widely applied for carbon emission assessments (Rafaj et al 2014;Liu and Wang 2017). In this regard, the LMDI decomposition method has also been extensively employed in combination with Kaya identity to decompose drivers of energy-based carbon emissions and has received the greatest attention, application, and acceptance (Ang and Liu 2001;Jeong and Kim 2013;Karmela et al 2016;Jiang et al 2019). The initial application of decomposition analysis began during the early 1970s and has become an important tool for studying factors affecting emissions from energy use (Ang 1995).…”

Section: Literature Reviewmentioning

confidence: 99%

“…When the coefficient value is maximum (the maximum value is 1), its impact on carbon emissions will be high. Similarly, when the coefficient value is small (the minimum value is 0), its impact is lowest (Jiang et al 2019). In addition, we also calculated the coefficient effect of each driving force using Eq.…”

Section: Lmdi For Critical Drivers Of Co 2 Emissions In Vietnammentioning

confidence: 99%

Decarbonization of Vietnam’s economy: decomposing the drivers for a low-carbon growth

Huong

Shah

Park

2020

Environ Sci Pollut Res

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Vietnam has witnessed a rapid increase in national-level CO 2 emissions due to rising urbanization, economic expansion, export growth, and industrial development. Moreover, to support the ambitious economic growth targets, reliance on and consumption of fossil fuels are increasing by each passing year. With this circumstance, this study aims to analyze the key drivers of CO 2 emissions in Vietnam from 1990 to 2016 using the Kaya identity and decomposition method. Following this approach, CO 2 emissions have been decomposed into five effect categories comprising population, affluence, energy intensity, fuel mix, and emission intensity. As per the results, CO 2 emissions in Vietnam were mainly driven by rising affluence (58.5%) and changing fuel mix (33.2%) which have resulted from improved living standards, rapid industrial development, and higher fossil fuel consumption. Moreover, population (13.8%) and emission intensity (3.1%) exhibited a relatively lower impact on CO 2 emissions during 1990-2016. However, energy intensity (− 8.7%) was the only negative driver which has resulted in the slowdown of carbon emissions in Vietnam. Based on the analysis of energy policy development, the share of renewable energy resources was still quite low in the national energy mix with higher reliance on traditional fossil fuels (mainly coal and petroleum). Therefore, to make a transition towards low-carbon economic growth, significant improvements in energy efficiency and emission intensity are necessary together with national energy mix restructuring for low-carbon economic growth.

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“…et al (2015), Gonzalez et al (2014), and Diakoulaki & Mandaraka (2007) use the decomposition approach to analyze the driving factors of CO 2 emissions in the European Union. Wang et al (2020), Jiang et al (2019), and Vinuya et al (2010) apply the decomposition approach to CO 2 emissions in the United States. Cheng et al (2022), Wang et al (2019), Ma et al (2018), Zhang et al (2013), andDonglan et al (2010) analyze China.…”

Section: Introductionmentioning

confidence: 99%

The Driving Forces of Energy-Related CO2 Emissions in the United States: A Decomposition Analysis

Sadler¹,

Bucher²,

Sehgal³

2022

EER

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This paper uses the logarithmic mean Divisia index (LMDI) approach to decomposition analysis to identify the factors that influence changes in carbon dioxide (CO2) emissions in the United States.  The LMDI approach decomposes CO2 emissions into specific determinants.  The data set includes the 50 states plus the District of Columbia from 1998 – 2018.  The five factors that influence the change in CO2 emissions include the emissions per unit of fossil fuel consumption, share of fossil fuels in total energy consumption, energy intensity, GDP per capita, and population.  The results indicate that, during the 20-year period, CO2 emissions declined in 36 states plus the District of Columbia.  The reduction in energy intensity served as the most important factor in the change of CO2 emissions, offsetting 63 percent of the effects of per capita GDP and population.  From the perspective of climate change, the importance of a change in energy intensity demonstrates the effectiveness of a decrease in primary energy consumption per unit of GDP.   

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Investigation for the Decomposition of Carbon Emissions in the USA with C-D Function and LMDI Methods

Cited by 19 publications

References 59 publications

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

Decarbonization of Vietnam’s economy: decomposing the drivers for a low-carbon growth

The Driving Forces of Energy-Related CO2 Emissions in the United States: A Decomposition Analysis

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