scholarly journals The Influence of Energy Price Distortion on Region Energy Efficiency in China’s Energy-Intensive Industries from the Perspectives of Urban Heterogeneity

2021 ◽  
Vol 14 (1) ◽  
pp. 88
Author(s):  
Xiaolei Wang ◽  
Hui Wang ◽  
Shuang Liang ◽  
Shichun Xu
Keyword(s):  
Energy Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Energy Price ◽  
Analysis Model ◽  
Frontier Analysis ◽  
Price Distortion ◽  
Industrial Sectors ◽  
The Impact ◽  
Energy Intensive Industries

As typical representatives of China’s industrial sectors, energy intensive industries are the focus of energy conservation. This study constructs a trans-log production function and stochastic frontier analysis model to analyze the impact of energy price distortion on total factor energy efficiency in energy intensive industries on the city level. The results reveal that the phenomenon of energy price distortion existed in all cities from 2003 to 2019, with an average degree of −0.175; and the total factor energy efficiency in China’s energy intensive sectors showed an upward trend, with an average efficiency of 0.729. Further deep analysis of affecting mechanisms concluded that the price distortion showed a significant restraining effect on improving energy efficiency, while the improvement of urban agglomeration systems had an opposite effect. In addition, energy consumption structure, foreign trade, and infrastructure construction are positively correlated with energy efficiency. Therefore, promoting the market-oriented reform of China’s energy market is of great significance to improve city energy efficiency and build a resource-conserving society.

scholarly journals Analysis on Technical Efficiency and Influencing Factors of Agricultural Production in China —— Based on the Stochastic Frontier Analysis model

2021 ◽  
Vol 235 ◽  
pp. 02005 ◽  
Author(s):  
Weiwei Huang ◽  
Liwenjun Xu ◽  
Yinru Guo
Keyword(s):  
Technical Efficiency ◽  
Influencing Factors ◽  
Production Technology ◽  
Agricultural Production ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Eastern Region ◽  
Analysis Model ◽  
Frontier Analysis ◽  
The Impact

This paper selects the panel data on 31 provinces (cities) in China, from 2007 to 2017, and uses Stochastic Frontier Analysis Model to measure the efficiency of technical efficiency and influencing factors of agricultural production in China. The research shows that from 2007 to 2017, China’s agricultural average production technology efficiency has steadily improved, but there is still much room for development. The agricultural production technology efficiency is greatly different in different part of China. The eastern region has the highest efficiency, and the central and western regions are lower than the national average. Labor input, fertilizer application, and diesel fuel are factors that directly affect the technical efficiency of agricultural output. Time trend and urbanization level indirectly affect the agricultural production technology efficiency, and the impact is positive.

scholarly journals International R&D Spillovers and Innovation Efficiency

2018 ◽  
Vol 10 (11) ◽  
pp. 3974 ◽  
Author(s):  
Jianping Liu ◽  
Kai Lu ◽  
Shixiong Cheng
Keyword(s):  
Capital Stock ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Sensitivity Analyses ◽  
Frontier Analysis ◽  
Country Level ◽  
Scientific Papers ◽  
Catch Up ◽  
The Right ◽  
The Impact

The objective of this study is to examine the impact of international research and development (R&D) spillovers on innovation efficiency of specific R&D outcomes, employing the country-level panel data for 44 countries in the 1996–2013 period. Fully considering the heterogeneity of different R&D outputs, scientific papers, PCT (Patent Cooperation Treaty) patents, US patents, and domestic patents are observed separately, which enriches the angles of measuring international R&D spillovers. By applying a stochastic frontier analysis to knowledge production function, we find that foreign R&D capital stock positively contributes to the innovation efficiency of scientific papers, but suppresses the productivity of domestic patents, whereas it does not really matter for PCT or US patents. These results are robust to control for a set of institutional factors and also in sensitivity analyses. Hence, dependence on international R&D spillovers seems neither to be the right way for emerging economies to catch up, nor to be a sustainable model for developing countries to fill the technical gap. Local R&D capital stock, instead, keeps an essential contributor to all four R&D outputs, so raising internal R&D expenditure is actually the key to improving innovation level and sustainable development ability.

Intellectual capital: a determinant of firms' operational efficiency

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kanishka Gupta ◽  
T.V. Raman
Keyword(s):  
Intellectual Capital ◽  
Stochastic Frontier Analysis ◽  
Stock Exchange ◽  
Stochastic Frontier ◽  
Value Added ◽  
Financial Sector ◽  
Operational Efficiency ◽  
Content Type ◽  
Frontier Analysis ◽  
The Impact

PurposeIntellectual capital (IC) has been recognized in improving the efficiency of businesses and gaining competitive edge in the developed world. The present study offers perspectives into the effect of IC on the efficiency of the Indian financial sector companies.Design/methodology/approachFor the purpose of evaluating efficiency, the research has used stochastic frontier analysis (SFA). All Indian financial sector companies listed in National Stock Exchange (NSE-500) for the timeframe of ten years (2008–2018) have been considered. The paper has employed modified Pulic's Value Added Intellectual Coefficient (VAICTM) as a proxy to measure IC. Correlation and panel data regression have been used in order to examine the relationship.FindingsThe results of the study indicate positive and significant relationship between IC and efficiency of the firm. The results also show that all the components of IC, that is, human capital, relational capital, process capital and capital employed have a significant impact on firms' efficiency. Additionally, it has been seen that sample companies do not invest in research and development leading to no innovation capital.Practical implicationsThe research will assist managers in managing and controlling the IC, investors in matters related to investment and financial experts in improving the company's IC and value creation.Originality/valueThe current research is one of the pioneering studies in the context of Indian financial sector that examines the impact of modified VAIC on operational efficiency calculated using SFA.

scholarly journals Market Structure, International Competitiveness, and Price Formation of Hainan’s Fruit Exports

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hailing Fu ◽  
Chongli Huang ◽  
Zhuoqi Teng ◽  
Yuantao Fang
Keyword(s):  
Bargaining Power ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
International Competitiveness ◽  
Production Costs ◽  
Analysis Model ◽  
Frontier Analysis ◽  
Index Model ◽  
Gradual Loss ◽  
Constant Market Share

Through a constant market share (CMS) model and a price index model, current research aims to analyze the impetus of the growth of Hainan, China’s fruit exports and the adverse impact of rising production costs. This paper considered the changes in the international competitiveness of Hainan’s fruit exports and analyzed the reasons for these changes. Additionally, this manuscript analyzed the effects of Hainan’s price-bargaining power on fruit exports under the condition of asymmetric information by applying a two-tier stochastic frontier analysis model. The results show that the rising costs of labor led to the gradual loss of price advantage, and the contribution of competitiveness to the growth of Hainan’s fruit exports has rapidly declined. The results also indicate that the degree of information held by both importers and Hainan has an important influence on the final exporting price, whereas the importers hold more information and have stronger price-bargaining power than Hainan. Policy suggestions based on the results are proposed.

scholarly journals The Impact of Urbanization on Farmland Productivity: Implications for China’s Requisition–Compensation Balance of Farmland Policy

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 311 ◽  
Author(s):  
Zhongqi Deng ◽  
Qianyu Zhao ◽  
Helen X. H. Bao
Keyword(s):  
Labor Force ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Agricultural Labor ◽  
Frontier Analysis ◽  
Conservation Policies ◽  
Use Efficiency ◽  
Urbanization Rate ◽  
Impact Of Urbanization ◽  
The Impact

The rapid growth of China’s economy since the reform in 1978 should be largely attributed to urbanization. Nonetheless, in terms of farmland productivity, urbanization may lead to perverse incentives and thus threaten food security. On the one hand, the requisition–compensation balance of farmland (RCBF) policy could reduce farmland productivity because of a “superior occupation and inferior compensation”; on the other hand, urbanization promotes the transfer of the younger labor force and thus reduces the productivity of the agricultural labor force. To investigate the undesirable effects, based on some stylized facts, this study selects 29,415 county-level samples in a Chinese county from 2000–2014 to construct an empirical model. With a new stochastic frontier analysis method that eliminates the classical econometric issues of endogeneity and heterogeneity, the empirical results show that there is a U-shaped relationship between the farmland use efficiency (productivity) and urbanization rate, indicating that only when the urbanization rate is relatively low would urbanization decrease the farmland use efficiency; in contrast, when the urbanization rate is relatively high, technical progress would obviously be accompanied by urbanization, and thus, the undesirable effects are fully offset. Furthermore, the U-shaped relationship is robust after considering the endogeneity of the urbanization rate and total-factor farmland use efficiency. With these findings, recommendations to implement sustainable management and conservation policies regarding farmland resources are made.

scholarly journals Determining the efficiency of residential electricity consumption

2020 ◽  
Author(s):  
Mark A. Andor ◽  
David H. Bernstein ◽  
Stephan Sommer
Keyword(s):  
Energy Efficiency ◽  
Stochastic Frontier Analysis ◽  
Energy Savings ◽  
Stochastic Frontier ◽  
Electricity Consumption ◽  
Frontier Analysis ◽  
Residential Electricity ◽  
Main Determinants ◽  
Residential Electricity Consumption ◽  
Reduction Of Energy Consumption

AbstractIncreasing energy efficiency is a key global policy goal for climate protection. An important step toward an optimal reduction of energy consumption is the identification of energy saving potentials in different sectors and the best strategies for increasing efficiency. This paper analyzes these potentials in the household sector by estimating the degree of inefficiency in the use of electricity and its determinants. Using stochastic frontier analysis and disaggregated household data, we estimate an input requirement function and inefficiency on a sample of 2000 German households. Our results suggest that the mean inefficiency amounts to around 20%, indicating a notable potential for energy savings. Moreover, we find that household size and income are among the main determinants of individual inefficiency. This information can be used to increase the cost-efficiency of programs aimed to enhance energy efficiency.

scholarly journals Energy Efficiency of the Baltic Sea Countries: An Application of Stochastic Frontier Analysis

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 104 ◽  
Author(s):  
Wen-Ling Hsiao ◽  
Jin-Li Hu ◽  
Chan Hsiao ◽  
Ming-Chung Chang
Keyword(s):  
Energy Efficiency ◽  
Baltic Sea ◽  
Urban Population ◽  
Energy Use ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
The Baltic Sea ◽  
Renewable Energy Consumption ◽  
Frontier Analysis ◽  
The Baltic

Using the stochastic frontier analysis (SFA) model, this research measures total-factor energy efficiency (TFEE) and disaggregate input efficiency for 10 countries across the Baltic Sea from 2004 to 2014. Real capital, labor, energy use, and carbon dioxide (CO2) are input variables, real gross domestic product (GDP) is the output variable, and renewable energy consumption and urban population are the environmental variables. The results provide not only the TFEE scores, in which statistical noise is considered, but also the determinants of inefficiency, which show the following. (i) Norway, Sweden, Finland, and Latvia perform better with respect to energy efficiency than other countries in the Baltic Sea Region. (ii) Interestingly, the average energy use efficiency scores from 2004 to 2014 in the 10 Baltic countries exhibit a gradual upward trend except for 2009. (iii) For the inefficiency estimates, higher renewable energy consumption and urban population correspond to higher TFEE scores.

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