Spatial club convergence of regional energy efficiency in China

Pan, Xiongfeng; Liu, Qing; Peng, Xiaoxue

doi:10.1016/j.ecolind.2014.10.026

Cited by 100 publications

(32 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Markov chain (MC) will converge to a stable probability distribution by multiplying the Markov transition matrix several times, which can reflect the global characterization of the Markov chain, according to [45][46][47]. This property has been applied to many types of research, such as capturing the dynamics embodied in the data [45], calculating the regional energy efficiency [46], and optimization problems [47].…”

Section: Convergence Features Based On Markov Chainmentioning

confidence: 99%

Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

et al. 2020

View full text Add to dashboard Cite

Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams.

show abstract

Section: Convergence Features Based On Markov Chainmentioning

confidence: 99%

Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In order to overcome the limits of the approach of purely temporal club convergence, we believe that it is necessary to advance the concept of spatial club convergence, based on the phenomena of spatial correlation, spatial heterogeneity and clustering of regional economic growth, to fully describe the spatial concentration or regional clustering phenomenon associated frequently with regional economic growth. This paper defines spatial club convergence as follows: it is the economic growth (or decline) of a group of regions which are spatially adjacent and have similar initial conditions and structural characteristics that converge to the same steady state [28,29]. If there is spatial club convergence among regions, they will form a spatially convergence club.…”

Section: The Basic Idea Of the Hypothesis Of Spatial Club Convergencementioning

confidence: 99%

Spatial Club Convergence of Regional Economic Growth in Inland China

Cheng-lin

Liu

2017

Sustainability

View full text Add to dashboard Cite

Spatial club convergence is a group of regions which are adjacent to each other in space and have similar initial conditions and structural features while converging towards the same steady state in the economic development. Based on the Solow model, this paper builds a theoretical model to prove the mechanism of spatial club convergence. The spatial club convergence process is explored in inland China, using the case of the Zhongyuan urban agglomeration during the years of 1993-2009. This region has been experiencing a dramatic economic development and serves as an ideal test bed of the theory of spatial club convergence. The results show that in the two periods of 1993-1999, and 1993-2009, there was spatial club convergence in the 56 regions of Zhongyuan urban agglomeration of China. The respective convergence rates were 2.0% and 1.0%. Hence, both theoretical deduction and empirical studies verify the hypothesis of spatial club convergence.

show abstract

“…Also, the definition of spatial club convergence is not clearly defined. In Qin, Ye and Liu (2017), Papalia and Betarelli (2012) and Pan, Liu and Peng (2015) a spatial club convergence is defined as the economic growth of a group of regions that are spatially "adjacent" with the same initial conditions and structural characteristics to approach the same equilibrium state. The mechanism of the formation of the spatial convergence clubs has similar characteristics as the formation mechanism of the traditional convergence clubs, but it also has its own spatial aspects.…”

Section: Introductionmentioning

confidence: 99%

Does Spatial Heterogeneity Matter in the EU Regions’ Convergence Income Process?

Furková¹

2020

ekoncas

View full text Add to dashboard Cite

This paper explores the role spatial heterogeneity in the EU regions' convergence income process. For this purpose we tested income convergence hypothesis of the 245 NUTS 2 European Union regions during the period 2003-2014. We used spatial econometric approach which allowed an explicit modelling of both spatial effects: spatial heterogeneity and spatial autocorrelation. Our results showed an appropriate consideration of the role of spatial effects. First, we found that the rate of economic growth in the region is not only affected by the exogenous characteristics of the region, but also potentially by the rate of economic growth in neighbouring regions. Secondly, estimated spatial regime model identified two spatial regimes-convergence clubs. The regions under the first spatial convergence regime included economically "weaker" regions (mainly regions of post-socialist countries, regions of Portugal, Spain, Greece and southern regions of Italy) and these regions are converging separately from the rest of regions of the EU.

show abstract

Spatial club convergence of regional energy efficiency in China

Cited by 100 publications

References 3 publications

Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

Spatial Club Convergence of Regional Economic Growth in Inland China

Does Spatial Heterogeneity Matter in the EU Regions’ Convergence Income Process?

Contact Info

Product

Resources

About