The scale effect in China's power grid sector from the perspective of malmquist total factor productivity analysis

Xie, Bai-Chen; Ni, Kang-Kang; O’Neill, Eoghan; Li, Hongzhou

doi:10.1016/j.jup.2021.101187

Cited by 7 publications

(3 citation statements)

References 59 publications

(66 reference statements)

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“…Hence, the evidence on the impact of scale on firms' productivity appears to be inconclusive. Nonetheless, in the presence of increasing returns to scale, i.e., if there is an industryspecific optimal scale, then TFP increases with scale [39][40] and we expect a positive coefficient for s.…”

Section: Domestic Randd Expenditurementioning

confidence: 78%

Manufacturing Productivity and FDI Externalities: is Small Beautiful?

Santos

Castanho

Couto

2022

Wseas Transactions on Business and Economics

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The role of FDI as a vehicle for economic growth is debatable in practice. On the other hand, the size of the company and the technological groups can influence the occurrence and magnitude of FDI externalities. Thus, this article investigates the impact of firm size on the occurrence of foreign direct investment externalities in the Portuguese industry from 1995 to 2007, by technology groups, using panel data at the firm level. To this end, we estimate the TFP and regress it on a set of variables, including the foreign presence in the same sector, upstream and downstream. The results show that only (small and large) companies in scale-intensive industries; and small firms in science-based industries benefit from the positive externalities of FDI. This suggests that firm size can influence the occurrence of FDI externalities in the manufacturing sector, but only in some technology groups. Based on the results, investment policy recommendations are made.

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Section: Domestic Randd Expenditurementioning

confidence: 78%

Manufacturing Productivity and FDI Externalities: is Small Beautiful?

Santos

Castanho

Couto

2022

Wseas Transactions on Business and Economics

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“…Wei et al [7], Du and Mao [8], Peng et al [9], and Wei and Zhang [10] employed the parametric linear programming (PLP) approach. Zhao and Ma [11], Zhang et al [12], Bi et al [13], Wei et al [14], and Xie et al [15] used the data envelopment analysis (DEA) method, while Chen et al [16], Wang and Jiang [17], Qi and Choi [18], Xie et al [19], and Zhang et al [20] applied the stochastic frontier analysis (SFA) approach. However, these existing studies assumed that power plants are consistent with technical homogeneity.…”

Section: Introductionmentioning

confidence: 99%

Inequality in Fossil Fuel Power Plants in China: A Perspective of Efficiency and Abatement Cost

Choi

Zhao

et al. 2023

Sustainability

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Quantifying the shadow price (SP) of CO2 emissions is the key to achieving China’s “double carbon” targets. Considering technology heterogeneity, this study applies stochastic frontier analysis combined with meta-frontier technology to estimate the environmental technical efficiency (ETE) and SP of CO2 emissions for China’s fossil fuel power plants from 2005 to 2015. This approach overcomes the lack of statistical inference and consistency of traditional methods and improves the reliability of results. The main results are as follows: (a) the average ETE of China’s power plants is 0.9444, indicating that inefficient production accounts for 5.66%. The difference in efficiency between the central and local groups is significant. (b) The national average SP of CO2 is 266.8 US dollars per ton, which is much higher than the carbon price in the emission trading system. This result implies the need to design a carbon trading price mechanism. (c) The distribution of SP shows obvious corporation and geographical characteristics that are closely related to the level of regional economic development. Finally, the findings provide policy implications for the improvement of the efficiency and abatement of costs of power plants and the determination of carbon prices.

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“…(2) represent the non-neutral and non-monotonic technical changes. The parameters, viz., , , , , ,     are to be estimated.Heterogeneous factors are involved in the pre-truncation mean and variance of the inefficiency term it u .Based on the research by Zhang et al[10] and Xie et al[11], we consider the proportion of secondary industry (SEP), thermal power (THP), hydropower (HYD), and wind and photovoltaic power generation (WPP) as heterogeneous factors that influence the mean and variance of non-efficiency. The distribution is as follows:…”

mentioning

confidence: 99%

The impact of energy system transformation on the efficiency of China's power grid

Li,

2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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The scale effect in China's power grid sector from the perspective of malmquist total factor productivity analysis

Cited by 7 publications

References 59 publications

Manufacturing Productivity and FDI Externalities: is Small Beautiful?

Manufacturing Productivity and FDI Externalities: is Small Beautiful?

Inequality in Fossil Fuel Power Plants in China: A Perspective of Efficiency and Abatement Cost

The impact of energy system transformation on the efficiency of China's power grid

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