On the driving forces of green total factor productivity growth in China: evidence from biased technological progress analysis

Abstract

The traditional model of factor-driven economic growth can no longer be maintained due to an increasing demand to save energy and reduce emissions and a rapid rise in the cost of related factors. The concept of green total factor productivity (GTFP) has emerged as an essential component of high-quality economic growth; consequently, how to boost GTFP through the development of technical advancement has become an important question in recent years. An improved CES production function of labor, capital, and energy is created using the theoretical framework for biased technological growth as its foundation. Subsequently, the mechanisms resulting from the synergy of factor structure, factor efficiency and biased technological progress (BTP) on GTFP are theoretically deduced and empirically analyzed. The conclusion shows that the types of technical advancement are mainly net labor-enhancing and net energy-enhancing technical advancement in China from 2001 to 2017. Due to the biased effect of technical advancement, the relative comparison of the three elements is determined predominantly by excessive capital investment and less by labor and energy. Factor-enhancing technical advancement led to the growth of GTFP, while the synergistic effect of BTP, factor structure and factor efficiency hindered the growth of GTFP.

Keywords:

• biased technological progress (BTP)
• green total factor productivity (GTFP)
• decomposition
• factor structure
• factor efficiency

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The economic growth mode of “high input, high consumption, high pollution and low benefit” is largely driven by high input and consumption of energy and resources, or even at the expense of the environment, which is contrary to high-quality development and shows obvious extensive characteristics (Cao et al. 2020).

Additional information

Funding

The authors acknowledge financial support from the key project of Beijing Municipal Education Commission Social Science Planning Fund (SM202010017003), the Science and Technology Program of Zhejiang Province of China (2022C35060), the Technology Innovation Program of Beijing Institute of Technology (2022CX01013), and the Special Fund for the Joint Development Program of the Beijing Municipal Commission of Education. The authors are also very grateful to three anonymous reviewers and Editor Dr. Neil Powe for their insightful comments that helped us sufficiently improve the quality of this paper. The usual disclaimer applies.