A study on economic measures for CO2 reduction in Japan

Yamaji, Katsuhiko; Matsuhashi, Ryuji; Nagata, Yutaka; Kaya, Yoichi

doi:10.1016/0301-4215(93)90134-2

Cited by 65 publications

(30 citation statements)

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“…Mielnik and Goldemberg (1999) first evaluated carbon emission performance in developing countries based on the carbon index, a single-factor indicator that reflects the carbon emissions per unit of energy consumption. Yamaji et al (1993) defined the ratio of total CO 2 emissions to GDP as CO 2 productivity to study the carbon emission level in Japan. Other single-factor indexes have been used by scholars to evaluate carbon emission performance, including CO 2 emission intensity (Sun, 2005), per-capita emissions (Stretesky and Lynch, 2009), cumulative emissions per capita from industrialization (Zhang et al, 2008), and energy intensity (Ang, 1999).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

et al. 2020

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Climate change resulting from CO 2 emissions has become an important global environmental issue in recent years. Improving carbon emission performance is one way to reduce carbon emissions. Although carbon emission performance has been discussed at the national and industrial levels, city-level studies are lacking due to the limited availability of statistics on energy consumption. In this study, based on city-level remote sensing data on carbon emissions in China from 1992-2013, we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance. The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance. The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations. However, the overall level of carbon emission performance remains low, indicating great potential for improvements in energy conservation and emission reduction. The spatial pattern of urban carbon emission performance in China can be described as "high in the south and low in the north," and significant differences in carbon emission performance were found between cities. The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable, resulting in a "club convergence" phenomenon. Furthermore, neighborhood backgrounds play an important role in the transfer between carbon emission performance types. Based on the prediction of long-term trends in carbon emission performance, carbon emission performance is expected to improve gradually over time. Therefore, China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals. Meanwhile, neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth, energy conservation, and emission reductions to realize low-carbon construction and sustainable development.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

et al. 2020

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“…With climate and environmental problems becoming ever more serious in the world, scholars at home and abroad have been doing more and more research into carbon emission efficiency (CEE). Yamaji et al (1993) proposed evaluating carbon productivity by using single-element indicators to estimate CEE. Since then, some scholars have used various single-element evaluation indicators to measure the CEE; these include the ratio of gross national product (GNP) to total carbon emissions, unit energy of carbon emissions and unit gross domestic product (GDP) of energy consumption (Long et al 2016;Pretis and Roser 2017;Ferreira et al 2018;Vujović et al 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Research on carbon emission efficiency in the Chinese construction industry based on a three-stage DEA-Tobit model

Zhang

Cheng

2021

Environ Sci Pollut Res

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The traditional data envelopment analysis (DEA) model usually ignores the influence of external environmental factors and random interference. This can easily lead to deviations in efficiency estimates. In order to solve this problem, a three-stage DEA model was used to better reflect the carbon emission efficiency of Chinese construction industry (CEECI) (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) from the perspective of non-management factors. The internal influencing factors of CEECI are analyzed by the Tobit model, which provides a more accurate basis for formulating policies. It is found that the CEECI is significantly affected by the GDP, the level of industrialization, the degree of opening-up, technological innovation and energy structure. After excluding environmental factors and random interference, the average CEECI increased by 16%. The resulting calculations were noteworthy in three aspects. First, there are significant regional differences in the CEECI. Both the multipolarization phenomenon of CEECI and regional differences also reduced gradually over time. Second, the CEECI can be decomposed into pure carbon emission efficiency (PCEE) and scale efficiency (SE), which is mainly caused by SE. Excluding external environmental factors and random interference will have a specific impact on the CEECI. All the 30 provinces are divided into four categories to analyze the reasons and solutions of the differences in the CEECI in provinces. Third, many factors had inhibitory effects on the CEECI, PCEE and SE; these included energy structure optimization, labor force number, total power of construct ion equipment and construction intensity in the construction industry. Nevertheless, the development level of the construction industry did have a significant positive effect.

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“…CEE originated from the concept of carbon productivity, which was first raised by Yamaji et al in 1993. CEE refers to the amount of carbon dioxide required to produce each unit of economic output [ 10 ]. Up to now, the studies on CEE falls into three categories: (i) the calculation of CEE, (ii) the factors which affect CEE, and (iii) the analysis of regional differentiation of CEE.…”

Section: Literature Reviewmentioning

confidence: 99%

Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry

Zhang

Liao

2021

IJERPH

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Among China’s five major industries, the logistics industry is the only one in which carbon emission intensity is continuing to increase, so it is of great importance in developing a low-carbon economy for China. Thus, some scholars have learned about carbon emission efficiency (CEE) in logistic industry recently; however, few of them have considered the inner structure, regional differentiation, or dynamic items of CEE. To fill this gap, we first calculate the dynamic carbon emission efficiency of China’s logistics industry (CEELI) (2001–2017) using the three-stage DEA-Malmquist model, and then using the Dagum Gini coefficient method, the Kernel Density Estimation (KDE), and the panel vector auto-regression (PVAR) model to analyze regional differential decomposition and their formation mechanism. The results indicate that the dynamic CEELI is ‘inefficient’ overall; it shows a decreasing trend, and the decline of dynamic efficiency mainly comes from technical backwardness rather than efficiency decline. Moreover, the domestic differences are gradually narrowing; the Gini inequality between regions and the density of trans-variation between regions are the main reasons for the gap between different regions and different periods.

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A study on economic measures for CO2 reduction in Japan

Cited by 65 publications

References 1 publication

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Research on carbon emission efficiency in the Chinese construction industry based on a three-stage DEA-Tobit model

Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry

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