Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier

Liu, Bingquan; Shi, Junxue; Wang, H.; Su, Xuelin; Zhou, P.

doi:10.1016/j.apenergy.2019.113986

Cited by 48 publications

(20 citation statements)

References 55 publications

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“…At the same time, in the western region, we found that a reduction in the effects of energy technological changes on emission intensity was more accentuated than that of the eastern region, which is consistent with what has been reported previously (Dong et al, 2016;Liu et al, 2019). Further, this phenomenon may be due to the energy technological progress in the western region being focused more on high-emission energy thus conserving more high-emission energy use, which has been confirmed by previous studies (Chen et al, 2010;Dong et al, 2016).…”

Section: Effects Of Technological Advance On Coal and Non-coal Emission Intensitysupporting

confidence: 92%

“…First, although the traditional PDA approach helps estimate the Malmquist index, which reflects technological changes, it can only obtain relative technological progress rates based on a contemporaneous benchmark technology set and fails to analyze the time-series technological changes based on an intertemporal benchmark technology set (Li et al, 2016). Second, considering that interregional technology differences may cause changes in carbon emissions (Du et al, 2014(Du et al, , 2017Zhang et al, 2015;2016a;Zha et al, 2019;Liu et al, 2019;Chen et al, 2020a), especially…”

Section: Environmental Production Technology Based On Meta-frontiermentioning

confidence: 99%

“…Regarding input variables, we considered capital stock (100 million yuan), human capital stock (10,000 people per year), and energy consumption (10,000 tons of standard coal equivalent, TCE). The perpetual inventory method was used to calculate fixed capital stock, as described in previous studies (Liu et al, 2019;Chen et al, 2020a;2020b), after which it was converted to its real value in 1978 to eliminate the impact of prices and inflation. The "education years law" method was used to estimate human capital, as described by many previous studies (Yang et al, 2017b;Chen et al, 2020a).…”

Section: Datamentioning

confidence: 99%

“…Hence, on the one hand, given that the promotion of renewable and sustainable energy was mainly concentrated in the east (Gu et al, 2019;Chen et al, 2020a), we speculate that the decreasing ratio of high-emission energy use in the east may have been mainly due to the first factor. On the other hand, since ''the optimized development of the energy and chemical industry'' was regarded as a significant development goal of the western region, the western region paid more attention to the development of high-emission energy technology and had a greater reduction of emission intensity than either the eastern or central regions (Chen et al, 2010;Dong et al, 2016;Liu et al, 2019). Therefore, we speculate that the decreasing ratio of high-emission energy use in the west may have been mainly due to the second factor.…”

Section: Impacts Of Regional Technological Progress On Emission Intensitymentioning

confidence: 99%

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A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach

Gao

Hou

et al. 2020

Energy Policy

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Although energy technological progress has been regarded as an important driver for reducing carbon emissions, the existence of carbon rebound effect prevents a portion of the potential carbon reductions to be realized. Compared with the energy rebound effect, research on the carbon rebound effect is scarce because it is always equated with the energy rebound effect. However, the carbon rebound effect is more complex. Given that the traditional method for carbon rebound effect assessment only reflects energy rebound effects, our study proposed an improved production-theoretical decomposition analysis (PDA)-Meta-frontier Malmquist index (MMI)-based method and explored carbon rebound effects in China from 2006 -2015. Our results show that (1) the eastern and western regions faced fewer carbon rebound effect risks compared with those of the central region due to decreasing emission intensity associated with energy technological progress; (2) the reductions in emission intensity in the eastern region relied both on coal and non-coal technology, whereas the western region only relied on coal technology; and (3) the non-coal technology in the eastern region was at the meta-frontier, whereas the non-coal technology of other regions exhibited catch-up effects.

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Section: Effects Of Technological Advance On Coal and Non-coal Emission Intensitysupporting

confidence: 92%

Section: Environmental Production Technology Based On Meta-frontiermentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Impacts Of Regional Technological Progress On Emission Intensitymentioning

confidence: 99%

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A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach

Gao

Hou

et al. 2020

Energy Policy

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“…Changes in CO 2 levels are known to be affected by population growth, economic activity, technological advances, and the carbon cycle itself (Liu et al 2019). Although natural ecosystems are resilient and slowly adapt to environmental change, however, the industrial revolution based on the extensive consumption of fossil fuels has caused irreversible environmental damage (Wood and Roelich 2019).…”

Section: Introductionmentioning

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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Does Port Smartization Policy Affect Port Carbon Emission Efficiency? -Validation Based on PSM-DID

Zhang,

Wang,

Yang

2024

Lecture Notes in Business Information Processing

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Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier

Cited by 48 publications

References 55 publications

A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach

A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach

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

Does Port Smartization Policy Affect Port Carbon Emission Efficiency? -Validation Based on PSM-DID

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