Biased technical change and its influencing factors of iron and steel industry: Evidence from provincial panel data in China

The impact of technical change on economic growth is closely related to the structure of input factors, and guiding technical change towards energy-saving can achieve the dual goals of economic growth and energy conservation. Based on the provincial panel data of 2006-2016, this paper first uses the slacks-based measure model and the bootstrap data envelopment analysis (bootstrap-DEA) to calculate the biased technical change (BTC) index and its factor bias of China's iron and steel industry (ISI). Next, the influencing factors of BTC are examined using panel regression analysis. The results corrected by bootstrap-DEA show that the level of ISI's BTC in China is 1.0110, with a contribution rate of 20.75% to green total factor productivity. Moreover, China's ISI experienced a labor-saving, energy-saving, and capital-using technical change during 2006-2016. In two-factor comparisons, technical change biased towards saving labor in labor-capital and labor-energy, and towards saving energy in energy-capital. The results of regression analysis show that improving energy efficiency, promoting urbanization, expanding production scale and optimizing industrial structure effectively promote BTC and improving energy efficiency is the primary pathway; however, excessive capital deepening has a significant negative correlation with BTC. Based on these results, this paper proposes policy recommendations for ISI's green development. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study analyzed the biased technical change in China's iron and steel industry using provincial panel data from 2006 to 2016. The results showed that the industry experienced labor-saving, energy-saving, and capital-using technical changes, with improving energy efficiency being the primary pathway to promote biased technical change.