scholarly journals Evaluation and Influencing Factors of Transportation Industry Energy Efficiency of Changjiang Economic Zone

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yi Xu ◽  
Xiaojuan Li
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Data Model ◽  
Evaluation System ◽  
Industrial Structure ◽  
Panel Data Model ◽  
Transportation Industry ◽  
Urgent Task ◽  
Economic Zone ◽  
Opening Up

Changjiang Economic Zone (CEZ) faces the urgent task to promote the energy conservation and emission reduction of the transportation industry. This study constructs an evaluation system for transportation industry energy efficiency (TIEE) and evaluates TIEEs of 11 CEZ provinces in 2000–2017, using the super-slack-based measure (Super-SBM) model containing undesired output. On this basis, the panel data model was adopted to explore the impactors of TIEE. The main results are as follows: CEZ provinces varied significantly in TIEE. In the sample period, Jiangsu, Jiangxi, Zhejiang, Sichuan, Shanghai, and Anhui achieved relatively satisfactory TIEEs; Hunan, Hubei, and Guizhou performed generally on TIEE, calling for some improvement; Chongqing and Yunnan did not perform well, leaving a huge room for improvement. Judging by TIEE trends in the lower reaches, middle reaches, and upper reaches, TIEE of the lower reaches exhibited a U-shaped trend (first decrease and then increase) and TIEEs of the middle reaches and upper reaches did not fluctuate significantly, except for a few years. There was a marked difference between the three regions in TIEE: TIEE in the lower reaches was much higher than that in the middle reaches and upper reaches. In addition, the panel data model demonstrates that TIEE is significantly promoted by economic growth and transportation structure, obviously suppressed by industrial structure, opening-up, and transportation infrastructure, and not clearly affected by government influence or environmental regulation.

The Spatial Influence Research of the Energy Efficiency in China — an Empirical Analysis Based on the Spatial Panel Data Model about the Provinces of China

2011 ◽  
Vol 361-363 ◽  
pp. 1071-1079 ◽  
Author(s):  
Wei Wei ◽  
Yi Hong Song ◽  
Zhi Hong Liu
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Data Model ◽  
Influential Factors ◽  
Panel Data Model ◽  
Energy Price ◽  
Spatial Panel ◽  
Spatial Panel Data Model ◽  
Spatial Panel Data ◽  
The Government

On the basis of the spatial panel data model, this article takes a empirical study on the energy efficiency. The results are as follows. (1) The energy efficiency has the obvious spatial dependence among the provinces of China, and the influential factors of the neighboring provinces will have impacts on the special province. (2) In various factors, the most intense are the energy price and the government intervention, which displays the inverse correlation relations with the energy efficiency. Therefore, more attention must be paid to the cooperation in provinces, the marketability reform of the energy price as well as government's unreasonable intervention in the process of the improvement of the energy efficiency.

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

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