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The paper aims to know about energy condition’s impacts on inflation comprehensively.

This paper constructs China’s energy condition index (ECI) by bringing in three variables (China’s energy price, consumption and production) based on the financial condition index.

The result of empirical analysis shows that the index can predict China’s inflation well.

China’s ECI can predict China’s inflation well.

The purpose of this paper is to explore the effect of oil price shocks on the US Consumer Price Index over the monthly period from 1876:01 to 2014:04.

The author uses the Bai and Perron (2003) structural break test to split the data sample into sub-periods delimited by the computed break dates. Afterwards, the author uses the quantile treatment effects over the full sample and then, by including sub-periods dummies to accommodate the selected structural breaks that drive the relationship between inflation and oil price growth.

The findings include a decreased transmission effect of oil price changes on inflation in recent years; a varied elasticity of inflation to the growth rate of oil prices across the distribution; and, finally, evidence of asymmetry in the relationship between the growth rate of oil prices and inflation, with a higher transmission mechanism for decreasing rather than increasing oil prices.

Policymakers should remain alert to monitoring potential inflation increases and should take precautionary measures to anchor inflation expectations, because inflation reacts differently to positive and negative oil price shocks. Moreover, authorities should consider the asymmetric reaction of inflation to oil price shocks to adopt an appropriate monetary policy strategy to achieve the price stability target.

The paper used a quantile regression model with structural breaks, which has not yet been used in the literature.

The purpose of this paper is to explore how countries can make a more efficient and effective cooperation strategy, considering their competitive strengths and weaknesses.

This paper is an exploratory study in examining the efficient way of national cooperation from the competitiveness perspective. By applying the double diamond-based nine-factor model and the framework for the life cycle of national competitiveness, this study proposes the importance of cooperation strategy, considering the current competitiveness status. A case study of two economies of South Korea (hereafter referred to as Korea) and Dubai reveals a potentially substantial cooperative relationship.

Although Korea and Dubai are geographically and culturally distant, they share complementary resources to enhance their overall competitiveness. In addition, their past experiences of growth can effectively deal with their current challenges and help their economies move to more advanced stage.

The methodology used in this paper can provide a useful guideline for policy makers to examine the current development status of their economies, find an appropriate cooperation partner and decide the priority of cooperating areas.

Although most existing studies explain national competitiveness from a narrow perspective, this paper provides a more comprehensive analysis using the extended model of Porter’s single diamond model. In addition, this paper conducts an intensive case study of Dubai and Korea for possible cooperation.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early warning research is an urgent task to develop the new energy industry in China. The purpose of this paper is to build an indicator system of exports injury early warning of the new energy industry in China and corresponding quantitative early warning models.

In consideration of the actual condition of the new energy industry in China, this paper establishes an indicator system according to four aspects: export price, export quantity, impact on domestic industry and impact on macro economy. Based on the actual data of new energy industry and its five sub-industries (solar, wind, nuclear power, smart grid and biomass) in China from 2003 to 2013, GM (1,1) model is used to predict early warning index values for 2014-2018. Then, the principal component analysis (PCA) is used to obtain the comprehensive early warning index values for 2003-2018. The 3-sigma principle is used to divide the early warning intervals according to the comprehensive early warning index values for 2003-2018 and their standard deviation. Finally, this paper determines alarm degrees for 2003-2018.

Overall export condition of the new energy industry in China is a process from cold to normal in 2003-2013, and the forecast result shows that it will be normal from 2014 to 2018. The export condition of the solar energy industry experienced a warming process, tended to be normal, and the forecast result shows that it will also be normal in 2014-2018. The biomass and other new energy industries and nuclear power industry show a similar development process. Export condition of the wind energy industry is relatively unstable, and it will be partially hot in 2014-2018, according to the forecast result. As for the smart grid industry, the overall export condition of it is normal, but it is also unstable, in few years it will be partially hot or partially cold. The forecast result shows that in 2014-2018, it will maintain the normal state. In general, there is a rapid progress in the export competitiveness of the new energy industry in China in the recent decade.

Export injury early warning research of the new energy industry can help new energy companies to take appropriate measures to reduce trade losses in advance. It can also help the relevant government departments to adjust industrial policies and optimize the new energy industry structure.

This paper constructs an index system that can measure the alarm degrees of the new energy industry. By combining the GM (1,1) model and the PCA method, the problem of warning condition detection under small sample data sets is solved.