Technological factors and total factor productivity in China: Evidence based on a panel threshold model

Huang, Junbing; Cai, Xiaochen; Huang, Shuo; Tian, Sen; Lei, Hongyan

doi:10.1016/j.chieco.2018.12.001

Cited by 108 publications

(61 citation statements)

References 55 publications

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“…where λ represents a constant; α and β are the coefficients of technological progress and the relative price of energy, respectively; and ε t denotes the error term. With regard to an open economy, as suggested by an array of earlier research works, the technological progress is dependent not just on indigenous innovations but also on foreign innovations by means of openness [26][27][28][29][30]. Therefore, indigenous innovations as well as foreign innovations, in the shape of technology spillovers, are selected as the key factors impacting China's technological progress.…”

Section: Generalized Modelmentioning

confidence: 99%

The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China’s Industries

Chen

Huang

et al. 2019

Sustainability

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China’s industrial sectors have an approximate consumption amounting to 70% of the aggregate power of the entire country. Investigating the driving forces of the decline in the energy intensity is essential for accelerating China’s conversion into a low-carbon economy. Nowadays, there has been no agreement as yet when it comes to the impacts of China’s industrial sectors on energy intensity. The current research work studies the impacts of key driving forces, in particular foreign as well as indigenous innovations, on China’s industrial energy intensity in 34 industrial sectors between 2000 and 2010. Linear and nonlinear analysis methodologies are put to use. The linear empirical findings show that indigenous innovation primarily contributes to driving down the industrial energy intensity across the sampling duration. The foreign innovations, which take the shape of FDI as well as imports, are seen as benefiting the decline in industrial energy intensity; on the other hand, exports ramp up the industrial energy intensity. An additional investigation, on the basis of the panel threshold framework, indicates that the impact of foreign innovations by means of openness as well as industrial energy intensity has an association with the technological absorptive potential. The empirical evidence puts forward some pivotal inferences for policymakers with regard to China’s declining industrial energy intensity—for instance, exploitation of the maximum benefit associated with the technology spillovers; in addition, it is important to take into consideration the attributes and scenarios that impact industrial energy intensity.

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Section: Generalized Modelmentioning

confidence: 99%

The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China’s Industries

Chen

Huang

et al. 2019

Sustainability

Self Cite

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“…where I is the identity matrix. 6 Calculate the influence degree and extent of influence between elements. The influence degree among influencing factors is represented by element t ij (i, j = 1, 2, .…”

Section: Deviation Maximization Methodsmentioning

confidence: 99%

“…These problems are often the reflection of regional innovation ability and regional innovation performance. The root cause is that regional innovation factors cannot be effectively transformed into real productivity, thus affecting regional innovation performance and regional sustainable development [6]. Therefore, effectively adjusting the allocation of regional innovation resources plays an important role in accelerating the formation of a regional economic system mainly supported by innovation, and promoting the sustainable development of regional economy.…”

Section: Introductionmentioning

confidence: 99%

“…Regional innovation performance is of great significance to regional economic sustainable development and national overall economic development [7]. Heilongjiang province, an old industrial base in northeast China, ranked 22nd in China in 2017 with a GDP of 1.590268 billion yuan, up 6.4% year-on-year to 41,916 yuan, lower than the national average. In 2018, the GDP reached 1,636,162 billion yuan, ranking 23rd in China, with a year-on-year growth rate of 4.7%, lower than the national average and better than expected [8].…”

Section: Introductionmentioning

confidence: 99%

“…The number of government research institutes in Heilongjiang decreased a lot, while the number of enterprise research institutes increased a little. The total quantity presents the decreasing trend, and Heilongjiang province still has certain development potential [6,9,10].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Influencing Factors for Sustainable Development: Evaluation and Management of Regional Innovation Performance in Heilongjiang, China

Xia

Yin

2020

Sustainability

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Although the innovation factor endowment of Heilongjiang province is relatively rich, many innovation resources cannot effectively promote the sustainable development of innovation-driven performance. The combined drift degree evaluation method and grey-DEMATEL model were used to study regional innovation performance in Heilongjiang province and the key factors influencing it. The results of this study show that the overall trend of regional innovation performance is slowly rising. Compared with other regions, the level of science and technology output is in the forefront, and the level of economic output and social effect is at the end. The innovation performance level of enterprises and research institutions is at the end position, showing a trend of slow rise and then slow decline. The innovation performance level of universities is in the middle position, showing a trend of rising first, then falling and then rising. The degree of marketization, the financial environment, the intellectual property system, the transformation of scientific and technological achievements and the level of human capital are the core factors that affect the regional innovation performance. The dominant position and leading role of regional enterprises and the cooperation between industry, university and research institutes should be strengthened through government policies.

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Sources and Channels of International Knowledge Spillovers in ASEAN‐5: The Role of Institutional Quality

Doğan

Wong

2020

J of Intl Development

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Association of Southeast Asian Nations (ASEAN) is a dynamic and outward‐looking regional economy, which has made notable progress in expanding trade and investment. This paper examines whether knowledge spillovers are prevalent among ASEAN‐5, focusing on the issues of which channels and which sources are the potential drivers of total factor productivity. The findings reveal that the key spillover channels are exports and non‐capital imports coming from source countries such as the Organisation for Economic Co‐operation and Development (OECD) countries, the G7 countries. The institutional quality plays an instrumental role in increasing total factor productivity through foreign direct investment, especially when the spillovers originate from the OECD and the G7. © 2020 John Wiley & Sons, Ltd.

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Technological factors and total factor productivity in China: Evidence based on a panel threshold model

Cited by 108 publications

References 55 publications

The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China’s Industries

The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China’s Industries

Identification of Influencing Factors for Sustainable Development: Evaluation and Management of Regional Innovation Performance in Heilongjiang, China

Sources and Channels of International Knowledge Spillovers in ASEAN‐5: The Role of Institutional Quality

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