Institution
Anhui University of Finance and Economics
Education•Bengbu, China•
About: Anhui University of Finance and Economics is a education organization based out in Bengbu, China. It is known for research contribution in the topics: China & Hopf bifurcation. The organization has 933 authors who have published 1070 publications receiving 11500 citations. The organization is also known as: AUFE.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: In this article, a spectral characteristic analysis method used in the maturity detection of watermelon, which was based on the visible and near-infrared spectroscopy (Vis/NIR) technology.
21 citations
••
TL;DR: In this paper, the authors proposed a bootstrap two-step decomposition approach by combining logarithmic mean divisia index with production-theoretical decomposition analysis and bootstrap technique rectifying the efficiency bias, further decomposing the drivers of EC, energy intensity (EI) and economic scale (SC), into twelve interaction determinants.
21 citations
••
TL;DR: In this paper, a class of non-local equations involving the fractional p-Laplacian, where the non-linear term is assumed to have critical exponential growth, is considered.
Abstract: This paper deals with a class of non-local equations involving the fractional p-Laplacian, where the non-linear term is assumed to have critical exponential growth. More specifically, by applying variational methods together with a suitable Trudinger-Moser inequality for fractional Sobolev space, we obtain the existence of at least two positive weak solutions.
21 citations
••
TL;DR: In this article, the authors used Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data, net primary productivity of vegetation (NPP), and average slope data to fit the population distribution from the three dimensions of economic growth, ecological environment, and topographic factors, respectively.
21 citations
••
TL;DR: Wang et al. as discussed by the authors investigated the determinants for decoupling economic growth from CO2 emissions in China from 2011 to 2016, and they developed a novel decomposition approach by combining the TDEI with production-theoretical decomposition analysis.
Abstract: Understanding how CO2 emissions and economic growth can be decoupled and what drives this relationship is key to achieving long-term sustainable development. Current methods to decompose emissions and growth usually follow an approach known as the index decomposition method, which essentially decomposes changes in the Tapio decoupling elastic index (TDEI), a commonly used index describing the decoupling relationship, into different factors. However, in this method, it is difficult to separate technical efficiency from behavioral effects. To address this problem, we developed a novel decomposition approach by combining the TDEI with production-theoretical decomposition analysis. We then investigated the determinants for decoupling economic growth from CO2 emissions in China from 2011 to 2016. The results showed that (1) the overall decoupling states changed for different consecutive years in this period; (2) the decoupling states between economic growth and potential carbon factor, potential energy intensity, and energy usage technological change were negative factors while the decoupling states between economic growth and per capita GDP, population scale, CO2 emission technological change, technical efficiencies of energy usage, and CO2 emission were positive factors for the overall decoupling state; and (3) the differences in decoupling states were associated with the driving factors for changes in CO2 emissions. The variations in the decoupling states may partially be attributed to industrial structure, the efficiency of energy usage in provinces, and the “new normal” period in which the economic growth slows down. We advise fostering of diversified environment-friendly consumption hotspots.
21 citations
Authors
Showing all 949 results
Name | H-index | Papers | Citations |
---|---|---|---|
Xiaoping Liu | 59 | 268 | 10535 |
Malin Song | 42 | 190 | 5961 |
Jose Luis Menaldi | 22 | 86 | 1804 |
Ming-Hsiang Chen | 22 | 95 | 2766 |
Jung Wan Lee | 20 | 89 | 1850 |
Xueli Chen | 19 | 128 | 1273 |
Umer Shahzad | 18 | 46 | 979 |
Tony Fang | 18 | 63 | 1008 |
Yan Zhang | 16 | 96 | 1742 |
Zhiyang Shen | 12 | 31 | 345 |
Zeya Wang | 12 | 29 | 870 |
Kai Wang | 11 | 30 | 401 |
Zizhen Zhang | 9 | 38 | 240 |
Lianbiao Cui | 9 | 12 | 630 |
Kefei You | 9 | 29 | 299 |