目次 : 『千葉医学雑誌 オープン・アクセス・ペーパー』 92E巻4号 2016年8月

<p>(a) Clustering evolution of oil importers. (b) Evolution of cluster ratios.</p

Table1_How to promote the energy transition? —An analysis based on the size and technology effect in new energy industry.DOCX

This paper attempts to explore the dynamic relationship between new energy industry development and energy structure transformation in China. Based on the real option model and stochastic frontier analysis (SFA), the development scale and technical level of the new energy industry are measured at the provincial level. The eastern region is in the lead, but there has been a certain degree of technological efficiency retreat, especially in Liaoning. The new energy industry in the central region has developed rapidly due to the deepening of the industrialization process. With the aid of PVAR model, impulse response function and variance decomposition, the results show that there exists a bidirectional dynamic relationship between the new energy industry and energy structure. In other words, the development of the new energy industry and the energy transition can be mutually predicted. Specifically, technology effect has a positive continuous and dramatic influence on the transformation of energy structure. In turn, the energy transition first elicits a response to size effects, but has a long-term impact on technology effects. This implies that the new energy industry will usher in scale expansion at the early stage of energy transition. It is worth noting that scale expansion will not always accelerate the transition process. At that time, technology played a long-term and central role. Therefore, reasonable expansion of new energy industry scale and efforts to develop new energy technology are important measures to ensure the orderly energy transition.</p

Evolution of the number of countries with annual oil trading volume in excess of 1 million tons during 2001–2013.

<p>Evolution of the number of countries with annual oil trading volume in excess of 1 million tons during 2001–2013.</p

Evolution of absorption ratio <i>E</i>_<i>i</i>(<i>t</i>) for <i>i</i> = 1, 2, 3.

<p>Evolution of absorption ratio <i>E</i>_<i>i</i>(<i>t</i>) for <i>i</i> = 1, 2, 3.</p

Evolution of information entropy and standard information entropy of global oil trade network.

<p>The overall trend is increasing gradually and slowly. It means the global oil trade network tends to be homogeneous slightly.</p

Correlation matrices <i>C</i>(<i>t</i>).

<p>The ending time <i>t</i> of the windows are 2007.06, 2008.01, 2008.11, 2010.12 and 2013.05 respectively.</p

Market effect hidden in the largest eigenvalues.

Each symbol shows the evolution of the correlation coefficient kn(t) between Dn and D in each moving window. The vertical lines indicate the period-shift points.</p

Evolution of average correlation coefficient.

<p>The horizontal blue line represents the critical value at significance level 5% of the correlation coefficient at each time <i>t</i>.</p

Evolutions of global oil market fitness and GDP (the totality of the 38 oil-dependent countries GDP).

<p>Evolutions of global oil market fitness and GDP (the totality of the 38 oil-dependent countries GDP).</p

Evolution of the three largest eigenvalues <i>λ</i>_<i>i</i>(<i>i</i> = 1, 2, 3) of 〈<i>C</i>(<i>t</i>)〉.

<p>The horizontal red line shows the critical values <i>λ</i>_5% of eigenvalues at the significance level of 5% at each time <i>t</i>.</p