DYNAMIC EVOLUTION MECHANISM OF CHINA TOTAL FACTOR ENERGY EFFICIENCY: COINTEGRATION ANALYSIS USING THE PANEL DATA FROM 1991 TO 2009

This study estimates the effects of energy efficiency policy in the residential sector using panel data of 48 contiguous states starting from 1970 to 2017. To avoid any unobserved heterogeneity and facilitate efficiency in estimation, this study employs a Dynamic Panel Data model with a two-step Generalized Method of Moments technique. The results suggest that energy efficiency policy for the residential sector has saved about 8.6 percent in energy consumption, which is about 22 percent of the total stated saving, leaving an energy efficiency gap of 1.5771 quadrillion Btu. Consistent with previous estimations, this study finds that theoretical saving amounts overestimate energy efficiency output and overinflate the increase in potential energy efficiency by about 32 percent. Since energy efficiency policy has failed to achieve the stated amount of saving in the residential sector, households have no incentive to adopt the energy efficiency policy, which has created an unusual gap in energy efficiency.

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An Analysis of Energy Use Efficiency in China by Applying Stochastic Frontier Panel Data Models

Energies ◽

10.3390/en13081892 ◽

2020 ◽

Vol 13 (8) ◽

pp. 1892 ◽

Cited By ~ 1

Author(s):

Xiaoyan Zheng ◽

Almas Heshmati

Keyword(s):

Energy Efficiency ◽

Panel Data ◽

Energy Policy ◽

Energy Use ◽

Stochastic Frontier ◽

Control Variables ◽

Energy Use Efficiency ◽

Level Data ◽

Use Efficiency ◽

Province Level

This paper investigates energy use efficiency at the province level in China using the stochastic frontier panel data model approach. The stochastic frontier model is a parametric model which allows for the modeling of the relationship between energy use and its determinants using different control variables. The main control variables in this paper are energy policy and environmental and regulatory variables. This paper uses province level data from all provinces in China for the period 2010–2017. Three different models are estimated accounting for the panel nature of the data; province-specific heterogeneity and province-specific energy inefficiency effects are separated. The models differ because of their underlying assumptions, but they also complement each other. The paper also explains the degree of inefficiency in energy use by its possible determinants, including those related to the public energy policy and environmental regulations. This research supplements existing research from the perspective of energy policy and regional heterogeneity. The paper identifies potential areas for improving energy efficiency in the western and northeastern regions of China. Its findings provide new empirical evidence for estimating and evaluating China’s energy efficiency and a transition to cleaner energy sources and production.

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New Evidence on the Relationship Between Economic Freedom and Growth: A Panel Cointegration Analysis for The Case of OECD

Global Economy Journal ◽

10.1515/1524-5861.1796 ◽

2012 ◽

Vol 12 (3) ◽

pp. 1850263 ◽

Cited By ~ 3

Author(s):

Ekrem Erdem ◽

Can Tansel Tugcu

Keyword(s):

Economic Growth ◽

Panel Data ◽

Total Factor Productivity ◽

Economic Freedom ◽

Global Financial Crisis ◽

Oecd Countries ◽

Cointegration Analysis ◽

Factor Productivity ◽

The Impact ◽

The Relationship

The aim of this paper is to find a new answer to an old question “Is economic freedom good or not for economies?” which was refreshed after the Global Financial Crisis of 2008. For this purpose, the relationship between economic freedom and economic growth, and the relationship between economic freedom and total factor productivity in OECD countries were investigated by using panel data for the period of 1995-2009. Study employed the recently developed cointegration test by Westerlund (2007) and the estimation technique by Bai and Kao (2006) which account for cross-sectional dependence that is an important problem in the panel data studies. Although no significant relationship found between economic freedom and total factor productivity, cointegration analysis revealed that economic freedom matters for economic growth in OECD countries in the long-run, and estimation results showed that direction of the impact is negative.

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Measurement and evaluation of Chinese regional energy efficiency based on provincial panel data

Mathematical and Computer Modelling ◽

10.1016/j.mcm.2012.09.020 ◽

2013 ◽

Vol 58 (5-6) ◽

pp. 1000-1009 ◽

Cited By ~ 24

Author(s):

Gaofeng Zou ◽

Longmei Chen ◽

Wei Liu ◽

Xiaoxin Hong ◽

Guijun Zhang ◽

...

Keyword(s):

Energy Efficiency ◽

Panel Data ◽

Regional Energy ◽

Measurement And Evaluation

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Energy Efficiency of E.U. Member States: A Panel Data Analysis

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH ◽

10.24818/18423264/54.4.20.10 ◽

2020 ◽

Vol 54 (4/2020) ◽

pp. 153-171

Author(s):

DRACEA RALUCA MIHAELA ◽

IGNAT RALUCA ◽

TRICA CARMEN LENUTA ◽

TEODOR CRISTIAN ◽

CIOBANU LAURA ◽

...

Keyword(s):

Energy Efficiency ◽

Data Analysis ◽

Panel Data ◽

Panel Data Analysis ◽

Member States

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

Discrete Dynamics in Nature and Society ◽

10.1155/2021/1999385 ◽

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.

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