Suchergebnisse

Data envelopment analysis (DEA) has been widely applied to evaluate the performance of banks, enterprises, governments, research institutions, hospitals, and other fields as a non-parametric estimation method for evaluating the relative effectiveness of research objects. However, the composition of its effective frontier surface is based on the input-output data of existing decision units, which makes it challenging to apply the method to predict the future performance level of other decision units. In this paper, the Slack Based Measure (SBM) model in DEA method is used to measure the relative efficiency values of decision units, and then, eleven machine learning models are used to train the absolute efficient frontier to be applied to the performance prediction of new decisions units. To further improve the prediction effect of the models, this paper proposes a training set under the DEA classification method, starting from the training-set sample selection and input feature indicators. In this paper, regression prediction of test set performance based on the training set under different classification combinations is performed, and the prediction effects of proportional relative indicators and absolute number indicators as machine-learning input features are explored. The robustness of the effective frontier surface under the integrated model is verified. An integrated models of DEA and machine learning with better prediction effects is proposed, taking China's regional carbon-dioxide emission (carbon emission) performance prediction as an example. The novelty of this work is mainly as follows: firstly, the integrated model can achieve performance prediction by constructing an effective frontier surface, and the empirical results show that this is a feasible methodological technique. Secondly, two schemes to improve the prediction effectiveness of integrated models are discussed in terms of training set partitioning and feature selection, and the effectiveness of the schemes is demonstrated by using ...

Tip-enhanced Raman spectroscopy (TERS) of a single molecule is commonly described by considering the change in the polarizability of the molecule with respect to a normal coordinate induced by homogeneous illumination. However, the local fields induced by nanoscale and atomic-scale features at the surface of metallic clusters and nanogaps show strong inhomogeneities in their spatial distribution, which induces breaking of Raman selection rules. In this context, the spatial extension of the molecular electronic states subjected to strongly varying local fields challenges the validity of the point–dipole approximation as an adequate description of TERS in such configurations. Here, we introduce a general treatment to simulate single-molecule TERS spectra and their energy-filtered vibrational fingerprints maps, in which the polarization properties of the single molecule and that of the optical enhancing nanoresonator can be calculated separately and then conveniently combined to obtain the total Raman cross section of the molecule under the strongly inhomogeneous field. We apply the general method to study tip-enhanced scanning Raman picoscopy of a 4,4′-bipyridine and biphenyl molecules in the proximity of a silver icosahedral cluster with a few atoms at the tip apex mimicking an enhancing picocavity. The polarization of the molecules is calculated within density functional theory (DFT), and the optical response of the tip is calculated within a classical atomistic discrete–dipole approximation. The Raman spectra are found to be extremely sensitive to the spatial distribution of the local fields and to the orientation of the molecule. Our calculations show that the spatial mapping of molecular vibrational fingerprints, as probed by a tip with atomic protrusions, is capable to reveal intramolecular features of a single molecule in real space and thus establish a robust basis for scanning Raman picoscopy. ; J. A. acknowledges projects IT1164-19 and KK-2019/00101 from the Government of the Basque Country and project PID2019-107432GB-I00 from the Spanish Ministry of Science and Innovation. Y. Z. acknowledges the National Natural Science Foundation of China (21804125), and Z. C. D. acknowledges the Chinese Academy of Science, the National Key R&D Program of China, and Anhui Initiative in Quantum Information Technologies. ; Peer reviewed

PurposeThe purpose of this paper is to explore the threshold effect of firm size on technological innovation using panel data from 2007 to 2012 for listed enterprises in China's manufacturing sector.Design/methodology/approachConsidering the aim of research question is to examine the nonlinear relationship, this paper utilizes the threshold regression proposed by Hansen's (2000).FindingsBased on a threshold regression model using panel data from 2007 to 2012 for listed enterprises in China's manufacturing sector, we find a series of new results. This nonlinear relationship is under the restrictions and impacts of various factors, such as industry characteristics and government subsidies. The results suggest that the threshold regression model well explains the complicated nonlinear relationship and transition process, and it can also shed light on management practice and policy.Originality/valueThere are categorical arguments regarding why firm size is not as effective as before in explaining the monotonic principle of industrial innovation, especially for establishing an effective industrial policy in a particular situation. One of the important reasons is that we have begun to adopt a new perspective from the nonlinear view on the relationship between firm size and industrial innovation. In this study, we have examined the threshold effect of firm size on industrial technological innovation, which is the most representative nonlinear relationship.

AbstractTo prevent local governments from transferring industrial land at low prices to attract investment, the central government of China enacted a land price control policy that stipulates a minimum price for land transfers. Using a sample of 235,803 land transactions in China from 2008 to 2015, we estimate the effects of this policy on local governments and companies. First, we find that local governments abide by the policy to some extent. However, owing to the constraints of local economic performance, there is a degree of motivation not to apply the policy, which is manifested in selective law enforcement. Second, for some companies, the policy pushes the land purchase cost above the minimum price, and the distribution of land costs across the sample shows a bunching effect in certain neighborhoods. Third, companies with rising land costs have a significant incentive to pass on their increased costs, which is manifested in a significant decline in their expenditure on employee wages, particularly in companies with poor liquidity ratios. However, there is no evidence that the increase in land costs has an impact on other company behaviors, such as investment or tax compliance.