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The overabundance of carbon emissions is widely considered as a serious world problem. This paper focuses on analyzing the influence of economic factors on carbon emissions. Based on the traditional STIRPAT model, in terms of the "pollution haven hypothesis" and "pollution halo hypothesis", this paper employs the dynamic panel data model to explore the impact of economic elements such as economic growth, population, foreign direct investment and others on carbon emissions. Based on our research, China's urban carbon emissions do not follow the inverted U-shaped hypothesis of the traditional EKC curve theory and presents an inverted N-type. Moreover, current foreign direct investment increases the carbon emissions of Chinese cities due to the "implicit trade carbon". However, during the lagging period of one phase, it significantly reduced urban carbon emissions. In addition, the lag of one period of carbon emissions statistically led to carbon emissions at the current stage. According to the empirical analysis results, this paper proposes some reasonable improvements for carbon dioxide emission reduction, which have certain reference values for other developing countries facing similar carbon emission reduction challenges.

Improving energy efficiency and reducing environmental pollution emissions are two important ways to alleviate energy problems. Despite the progress in energy efficiency, the growth in energy demand still exceeds the efficiency improvements. This study adopts nonparametric methods to estimate the total factor energy efficiency (TFEE) of 105 resource-based cities covering the period 2010-2016 in China and analyzes the spatiotemporal characteristics of changes in energy efficiency. Furthermore, panel quantile regression is applied to analyze the multiple impacts of economic level, industrial structure, resource endowment, energy price, government intervention and degree of openness on energy efficiency. The main findings are as follows. (1) Each determinant has a different influence on TFEE at different levels; among them, the influence of the fuel and energy price index show an inverted U-shaped distribution as the quantile increases, and that of the GDP per capita shows a stronger heterogeneity than those of other factors. (2) Resource-based cities with lower efficiency are more sensitive to government intervention than are cities with higher efficiency. (3) A city's openness has a negative effect on TFEE, which partly supports the pollution haven hypothesis: the more foreign investment a resource-based city receives, the lower its energy and technology efficiency. Finally, some practical suggestions for the sustainable development of resource-based cities are discussed.

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An AIE-active probe of Cur-N-BF2 is developed for light-up detection of Aβ fibrils and plaques, inhibition of Aβ fibrillation, disassembly of preformed Aβ fibrils, and protection of neuronal cells.

Citation: KE, Y. ... et al, 2013. A new experimental study of influence of fabric permeability, clothing sizes, openings and wind on regional ventilation rates. Fibers and Polymers, 14 (11), pp. 1906 -1911 Additional Information:• Abstract:In this study, a local ventilation rates (VR) measuring system based on stead-state method was developed. This system can measure the local VR of the right arm, the left arm, the chest and the back locations of the upper body garment simultaneously. The whole clothing VR can also be computed. To study the influence of fabric permeability, clothing sizes, hem opening, and wind on local VR of the right arm, the chest and the back of the working garments, 9 jackets with different sizes and fabric permeability (permeable, semi-permeable and impermeable) were made. The results showed that the local VR for each garment location were significantly different.The chest had the largest local VR. Clothing ventilation rates were not liner with garment sizes. Closing garment bottom decreased more air exchange for chest and back comparatively. Wind increased both local and whole VR significantly. But the impacts were different according to different locations.