Dynamic total factor carbon emissions performance changes in the Chinese transportation industry

Zhang, Ning; Xiao, Wei

doi:10.1016/j.apenergy.2015.01.072

Cited by 195 publications

(73 citation statements)

References 44 publications

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“…Moreover, Xinjiang and Inner Mongolia have been committed to the development of industries with high economic output and low carbon emissions in recent years, such as wind power technology. This is consistent with the findings of Zhang et al [28].…”

Section: Nlcpil and Its Decompositionssupporting

confidence: 83%

“…Heilongjiang, Ningxia and Shanghai appeared 2 times, and Hainan and Hunan each appeared only 1 time. The result is consistent with that of Zhang et al [28] and implies that the eastern region should be referenced as a benchmark for the low carbon utilization of industrial land. Moreover, provinces that are not listed in Table 6 should benchmark the innovating provinces and strive to improve their clean production technologies by learning from the innovators.…”

Section: Nlcpil and Its Decompositionssupporting

confidence: 82%

“…Many recent studies have noted that the results based on the Luenberger index are more robust than those based on the Malmquist index [26], and Chang et al [27] have made a further improvement by applying a non-radial Luenberger index. This approach was widely applied in later studies to model environmentally sensitive productivity [28].…”

Section: Introductionmentioning

confidence: 99%

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Measuring the Total-Factor Carbon Emission Performance of Industrial Land Use in China Based on the Global Directional Distance Function and Non-Radial Luenberger Productivity Index

et al. 2016

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Industry is a major contributor to carbon emissions in China, and industrial land is an important input to industrial production. Therefore, a detailed analysis of the carbon emission performance of industrial land use is necessary for making reasonable carbon reduction policies that promote the sustainable use of industrial land. This paper aims to analyze the dynamic changes in the total-factor carbon emission performance of industrial land use (TCPIL) in China by applying a global directional distance function (DDF) and non-radial Luenberger productivity index. The empirical results show that the eastern region enjoys better TCPIL than the central and western regions, but the regional gaps in TCPIL are narrowing. The growth in NLCPILs (non-radial Luenberger carbon emission performance of industrial land use) in the eastern and central regions is mainly driven by technological progress, whereas efficiency improvements contribute more to the growth of NLCPIL in the western region. The provinces in the eastern region have the most innovative and environmentally-friendly production technologies. The results of the analysis of the influencing factors show implications for improving the NLCPIL, including more investment in industrial research and development (R&D), the implementation of carbon emission reduction policies, reduction in the use of fossil energy, especially coal, in the process of industrial production, actively learning about foreign advanced technology, properly solving the problem of surplus labor in industry and the expansion of industrial development.Keywords: industrial land; total-factor carbon emission performance of industrial land use (TCPIL); non-radial Luenberger productivity index; non-radial Luenberger carbon emission performance of industrial land use (NLCPIL); directional distance function (DDF); China

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Section: Nlcpil and Its Decompositionssupporting

confidence: 83%

Section: Nlcpil and Its Decompositionssupporting

confidence: 82%

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Measuring the Total-Factor Carbon Emission Performance of Industrial Land Use in China Based on the Global Directional Distance Function and Non-Radial Luenberger Productivity Index

et al. 2016

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“…Coordinating economic growth alongside improvement in environmental quality may be daunting, but it is certainly not impossible [23]. We must pay close attention to the shifting patterns among energy economics and management norms and practices, and to governance institutions in China to keep pace with the world and its challenges [24].…”

Section: Discussionmentioning

confidence: 99%

Toward a Sustainable Low-Carbon China: A Review of the Special Issue of “Energy Economics and Management”

Zhang

Wang

2016

Sustainability

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Abstract:Severe environmental quality deterioration, along with predatory exploitation of energy resources, are generally associated with economic growth, especially in China. Against this background, the 6th Annual Conference of Energy Economics and Management provides a platform for examining outperforming governance factors and mechanisms of energy economics and policy. Thanks to Sustainability for providing this special issue. This editorial highlights the contents and methodologies of the special issue for this conference, presenting diverse issues in energy economics and management. We also suggest guidelines for future study in energy economics and management.

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“…Then, all energy types are converted into the standard measure of heat energy in China, known as standard coal. Carbon emissions are calculated according to IPCC Carbon Emissions Calculation Guide using the following formula [17,18].…”

Section: Explained Variablementioning

confidence: 99%

Analysis of the Threshold Effect of Financial Development on China’s Carbon Intensity

et al. 2016

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Using panel data on 30 provinces in China from 2005 to 2012, this paper conducts an empirical test on the threshold effect of the relationship between financial development and carbon emission intensity from the perspectives of financial scale and financial efficiency. The results show that at a low level of per capita GDP, the expansion of the financial scale and the enhancement of financial efficiency will increase carbon intensity. When the per capita GDP is greater than the threshold value (RMB 37,410), the expansion of the financial scale will also increase carbon intensity, but the potency of this effect will be weaker. At the same time, the improvement of financial efficiency will help reduce carbon intensity. Most provinces with per capita GDP greater than the threshold value (RMB 37,410) are located in the eastern coastal areas of China, whereas most provinces with per capita GDP less than the threshold value are located in the central and western areas of China. Both raising the level of openness and improving the industrial structure will have significantly positive effects on carbon intensity.

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Dynamic total factor carbon emissions performance changes in the Chinese transportation industry

Cited by 195 publications

References 44 publications

Measuring the Total-Factor Carbon Emission Performance of Industrial Land Use in China Based on the Global Directional Distance Function and Non-Radial Luenberger Productivity Index

Measuring the Total-Factor Carbon Emission Performance of Industrial Land Use in China Based on the Global Directional Distance Function and Non-Radial Luenberger Productivity Index

Toward a Sustainable Low-Carbon China: A Review of the Special Issue of “Energy Economics and Management”

Analysis of the Threshold Effect of Financial Development on China’s Carbon Intensity

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