Green total factor productivity of extractive industries in China: An explanation from technology heterogeneity

Fang, Chuandi; Cheng, Jinhua; Zhu, Yiqun; Chen, Jia-Hao; Peng, Xinjie

doi:10.1016/j.resourpol.2020.101933

Cited by 57 publications

(31 citation statements)

References 38 publications

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“…For every 1% increase in FDI, the mining eco-efficiency will also increase by 0.209%. The research results are consistent with Fang et al [ 57 ]. The FDI generally has technical spillover channels by horizontal, forward, and backward, which affect the local enterprises.…”

Section: Resultssupporting

confidence: 93%

“…It promoted the construction of ecological civilization to an important strategic position, namely, the ecological efficiency and environmental efficiency of the mining industry in Guangxi. Significant improvement has been achieved, which shows that the paid development of mineral resources and the system of mineral resources compensation play an important role in improving the ecological efficiency of China’s mining industry [ 57 ].…”

Section: Resultsmentioning

confidence: 99%

“…Technological progress has had a profound impact on the environmental results of mining economic activities, and it is also affected by the degree of openness to the outside world [ 56 ]. Since the foreign direct investment (FDI) data of the extractive industries in the region cannot be obtained, this paper calculates the ratio of the regional extractive industry output value to the regional GDP and then multiplies it by the FDI of the Chinese extractive industry to replace [ 57 ].…”

Section: Methodology and Data Sourcementioning

confidence: 99%

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Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China

Zuo

et al. 2021

IJERPH

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The mining industry is one of the pillar industries of Guangxi’s economic and social development. The output value of mining and related industries accounts for 27% of the whole district’s total industrial output value. Therefore, the mining eco-efficiency measurement in Guangxi can be of great significance for the sustainable development of Guangxi’s mining industry. This study adopted Meta-US-SBM to measure the mining eco-efficiency in Guangxi from 2008 to 2018, including economic efficiency, resource efficiency, and environmental efficiency. It used the standard deviation ellipse model to simulate the migration trend of four efficiencies in Guangxi and used GeoDetector and Tobit models to explore the internal and external factors that affect the mining eco-efficiency. The four efficiencies in Guangxi show large temporal and spatial heterogeneity, and the internal and external factors that affect the mining eco-efficiency are different. The following conclusions can be drawn. (1) Environmental efficiency and mining eco-efficiency are improving, while economic efficiency and resource efficiency are deteriorating. Cities bordering other provinces have a significantly better mining eco-efficiency than non-bordering cities. (2) The development center in Guangxi has migrated to the Beibu Gulf Economic Zone. (3) Natural resources index and mining economic scale have a great impact on the mining eco-efficiency, and with the increase of the mining economic scale, the mining eco-efficiency showed a typical “U-shaped” curve. Finally, this study put forward corresponding policy recommendations to improve the mining eco-efficiency in Guangxi from four aspects: opening-up, technological progress, regional coordination, and government control.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Methodology and Data Sourcementioning

confidence: 99%

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Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China

Zuo

et al. 2021

IJERPH

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“…GTFP has attracted widespread attention, and many scholars have researched GTFP. Based on the meta-frontier theory and data envelopment analysis method, C Fang et al calculated the green total factor productivity of extractive industries in China from 2006 to 2017, and analyzed its influencing factors [13]. Based on the comprehensive control method (SCM) using microdata, Jiang Yufan et al studied the net impact of the establishment of China's (Shanghai) pilot free trade zone on Shanghai's green total factor productivity [14].…”

Section: Research On Gtfpmentioning

confidence: 99%

“…For positive indicators : X θij = X θij /X max (13) For negative indicators : X θij = X min /X θij (14) where X θij is the jth index value of province i in the year θ.…”

Section: Intelligencementioning

confidence: 99%

Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

Zhang

Zhong

2021

Sustainability

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The application of intelligent technology has an important impact on the green total factor productivity of China’s manufacturing industry. Based on the provincial panel data of China’s manufacturing industry from 2008 to 2017, this article uses the Malmquist–Luenburger (ML) model to measure the green total factor productivity of China’s manufacturing industry, and further constructs an empirical model to analyze the impact mechanism of intelligence on green total factor productivity. The results show that intelligence can increase the green total factor productivity of the manufacturing industry. At the same time, mechanism analysis shows that intelligence can affect manufacturing green total factor productivity by improving technical efficiency. However, the effect of intelligence on the technological progress of the manufacturing industry is not significant. In addition, the impact of intelligence has regional heterogeneity. It has significantly promoted the green total factor productivity in the eastern and central regions of China, while its role in the western region is not obvious. The research in this article confirms that intelligence has a significant positive impact on the green total factor productivity of the manufacturing industry, and can provide suggestion for the current further promotion of the deep integration of intelligence and the green development of the manufacturing industry to achieve the strategic goal of industrial upgrading.

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The impact of urbanisation on green total factor productivity: Exploring threshold and mediating effects of human capital

Wang,

Salman,

Zhang

2024

Health Planning & Management

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Green development is main direction of high‐quality economic development in China. Urbanisation and human capital (HC) are important factors affecting green development. This study examines effects of urbanisation and HC on green total factor productivity (GTFP) using least squares dummy variable model, based on provincial data in China from 2001 to 2019. This study then explored impact of urbanisation on GTFP through HC (educated HC, healthy HC, and HC structure), adopting mediating effect and threshold effect model. The results showed that urbanisation and three types of HC promote GTFP in China and coastal region. In inland region, urbanisation has insignificant impact on GTFP, while educated HC and HC structure improve it. The mediating analysis revealed that educated and healthy HC in China and coastal region enhance promotion effect of urbanisation on GTFP, whereas HC structure hinders it. In inland region, only healthy HC stimulates promotion effect of urbanisation on GTFP. Considering threshold effect of educated and healthy HC, promotion effect of urbanisation on GTFP is weaker than threshold value; but effect is stronger above threshold value. The promotion effect of urbanisation on GTFP is stronger when value of HC structure is below the threshold, while effect is weakened as value exceeds threshold value. Policy implications are documented to promote urbanisation in a differentiated manner, increase investment in HC, and strengthen coordination between urbanisation and HC.

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Green total factor productivity of extractive industries in China: An explanation from technology heterogeneity

Cited by 57 publications

References 38 publications

Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China

Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China

Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

The impact of urbanisation on green total factor productivity: Exploring threshold and mediating effects of human capital

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