Assessment of the Total Factor Productivity Change in the English and Welsh Water Industry: a Färe-Primont Productivity Index Approach

Molinos-Senante, María; Maziotis, Alexandros; Sala-Garrido, Ramón

doi:10.1007/s11269-016-1346-2

Cited by 33 publications

(17 citation statements)

References 38 publications

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“…For the detailed study of TFP change, MPI or MLPI can be decomposed into technology change and efficiency indexes (Baležentis, 2015;Molinos-Senante, Maziotis, & Sala-Garrido, 2017). Furthermore, Hicks (1963), Harrod (1948), and Solow (1969) respectively define the bias of technological progress according to the causes of technological change following the relative marginal product of each factor of production as follows: Capital-oriented bias, labororiented bias, and neutral technical progress.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Technological Bias and Productivity Growth: A Case Study of China’s Three Urban Agglomerations

Wei

et al. 2020

Technological and Economic Development of Economy

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The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s environmental problems. This study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP) index of the Chinese three urban agglomerations from 2005 to 2014, and the reasons for its changes are also analyzed. Furthermore, the biases of technological progress from two perspectives of inputs and outputs (including the undersirable output, measured by CO2 emissions) are estimated. Main results are: (i) During the sample period, the TFP of the three urban agglomerations continues to increase, and the main driving force is technological change. (ii) From the perspective of inputs, the Beijing-Tianjin-Hebei prefers to use electricity, whereas the Pearl River Delta and the Yangtze River Delta urban agglomerations tend to use capital and save labor. (iii) From the perspective of outputs, the technological progress of the three major urban agglomerations is significantly biased toward GDP with a slight difference among the three urban agglomerations, which means its technological progress is conducive to reduce CO2 intensity, symbolizing low carbon development. From this point of view, their economic growth shows a low-carbon trend.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling Technological Bias and Productivity Growth: A Case Study of China’s Three Urban Agglomerations

Wei

et al. 2020

Technological and Economic Development of Economy

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“…Water use efficiency can be viewed as a kind of overall technical efficiency [10], which can be decomposed into pure technical and scale efficiencies [11]. The pure technical efficiency reflects the management ability to organize the inputs in the production process, whereas the scale efficiency reflects the management ability to choose the optimum size of resources, i.e., choosing a proper production scale to attain the expected output [12].…”

Section: Introductionmentioning

confidence: 99%

Direct or Spillover Effect: The Impact of Pure Technical and Scale Efficiencies of Water Use on Water Scarcity in China

Kai-sheng

2019

IJERPH

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The spatial relationship between water use efficiency and water scarcity has been widely discussed, but little attention has been paid to the impact of the pure technical and scale efficiencies of water use on water scarcity. Using input-oriented data envelopment analysis (DEA) and panel spatial Durbin models (SDM), the direct and spillover effects of different water use efficiencies on water scarcity from 2007 to 2016 in China were examined at the regional scale. The results show that the water use pure technical efficiency had significantly negative direct effects on water scarcity; however, the water use scale efficiency did not have a similar effect. The improvement in water use pure technical efficiency in one region could aggravate the water scarcity in neighboring regions through spatial spillover effects, but the same effect was not observed between the water use scale efficiency and water scarcity. Finally, we propose solutions to improve the water use efficiency to reduce the water scarcity.

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“…The total factor productivity, as an important concept by which to measure economic efficiency, is an important tool for use in analyzing the sources of economic growth [6,7]. In particular, it is an important basis upon which the government can formulate policies for long-term sustainable development [8].…”

Section: Introductionmentioning

confidence: 99%

Calculation of the contribution rate of China’s hydraulic science and technology based on a feedforward neural network

Chen

et al. 2019

PLoS ONE

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Quantitative analysis of the contribution rate of China’s hydraulic science and technology and analysis of the underlying reasons behind changes provide an important foundation upon which the government can formulate water policies. This paper abandons the assumption of a scale economy and separates the changes of benefits brought about by the scale from scientific and technological progress, thus changing the C-D production function from linear to nonlinear. Based on a feedforward neural network, it calculates the coefficient of the output elasticity, the economic contribution rate of China’s hydraulic science and technology and the scale economies for each year using relevant data from 1981 to 2016. The results show that (1) the average contribution rate of capital investment from 1981 to 2016 was 47.3%, and the average contribution rate of labor from 1981 to 2016 was 9.1%. It is not obvious that the significant increase in the labor force has contributed to the growth of China’s water conservancy industry. (2) The average contribution rate of scale economies in 1981–2016 was 26.7%, and the contribution rate of scale economies is negatively correlated with the capital contribution rate. (3) The average contribution rate of China’s hydraulic science and technology was 43.6% from 1981 to 2016, and the average contribution rate of the total factor productivity after removing scale economies from 1981 to 2016 was 16.9%. During the period of the 6th Five-Year Plan(1981~1985), the contribution rate of water conservancy science and technology was relatively high. Since that time, it has remained at 40%. In recent years, as water conservancy reforms in key areas have made positive progress, scientific and technological progress has increased the growth of water conservancy benefits annually.

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Assessment of the Total Factor Productivity Change in the English and Welsh Water Industry: a Färe-Primont Productivity Index Approach

Cited by 33 publications

References 38 publications

Modelling Technological Bias and Productivity Growth: A Case Study of China’s Three Urban Agglomerations

Modelling Technological Bias and Productivity Growth: A Case Study of China’s Three Urban Agglomerations

Direct or Spillover Effect: The Impact of Pure Technical and Scale Efficiencies of Water Use on Water Scarcity in China

Calculation of the contribution rate of China’s hydraulic science and technology based on a feedforward neural network

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