2019
DOI: 10.1007/s11027-019-09901-5
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Carbon emission efficiency of thermal power in different regions of China and spatial correlations

Abstract: In China, the power industry contributes significantly to carbon emissions, reducing carbon emissions in this industry is conducive to China's adaptation and mitigation of climate change. Researches on green and low-carbon power have attracted increasing attention. In this paper, we analyze and compare the carbon emissions from thermal power sector in 30 Chinese provinces, divided into three main regions. Based on the panel data over the period 2002-2016, we use a slacks-based measurement (SBM) model to measur… Show more

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Cited by 15 publications
(5 citation statements)
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“…Both technology and institutions are important in reducing emissions from the transportation sector (Yang et al 2017;Zhu et al 2019;Shao et al 2019;Sun et al 2020). Academically, many relevant studies have been conducted both internationally and domestically.…”
Section: Introductionmentioning
confidence: 99%
“…Both technology and institutions are important in reducing emissions from the transportation sector (Yang et al 2017;Zhu et al 2019;Shao et al 2019;Sun et al 2020). Academically, many relevant studies have been conducted both internationally and domestically.…”
Section: Introductionmentioning
confidence: 99%
“…As shown in columns ( 1) and (2) of Table 5, both before and after the 2008 fi nancial crisis, NSI signifi cantly aff ected the carbon emissions embodied in exports. We refer to Conley and Molinari (2007) and used the spatial heteroscedasticity and autocorrelation consistent (spatial HAC) model to repeat the regression, noting that carbon emissions are spatially correlated (Zhu et al, 2020). As column (3) of Table 5 shows, the conclusion that NSI has a signifi cantly positive impact on carbon emissions embodied in exports is robust when spatial correlation is considered.…”
Section: Robustness Testsmentioning
confidence: 99%
“…SFA is a parametric method, which requires the determination of the production function form and has been applied to measure CEE by some scholars [12][13][14][15][16]. However, SFA cannot solve the problem of collinearity between variables and requires the establishment of a specific production function, which may have practical limitations [17,18]. Data envelopment analysis (DEA), first proposed by Charnes et al [19], is a non-parametric method and suitable for the comprehensive evaluation model with various inputs and outputs.…”
Section: Literature Reviewmentioning
confidence: 99%