Unlocking the carbon emission efficiency improvement path of technological innovation: a perspective on industrial restructuring and R&D element flows

Abstract

Technological innovation is regarded as an important means to improve carbon efficiency. However, there is no consensus on this view. Meanwhile, few studies have considered how technological innovation affects carbon efficiency. To this end, this study investigates the influencing mechanism and effects of technological innovation on carbon emission efficiency from the perspectives of industrial restructuring and R&D element flow. It establishes the influencing and mechanism model and then deeply studies the impact and paths of technological innovation on carbon emission efficiency, using panel data from 30 provinces in 1999–2020. Results show that (1) technological innovation improves carbon emission efficiency. (2) Regional differences in the impact effects of technological innovation are evident, with a greater contribution to carbon emission efficiency in eastern region. (3) Innovation improves carbon efficiency through two paths: advanced industrial structure and industrial structure rationalization. (4) The moderating effect demonstrates that the technological innovation’s influence is gradually enhanced with the interregional mobility of R&D personnel and capital. Hence, decision-makers should correctly guide the orderly flow of R&D factors and further improve the carbon emission reduction effect by increasing innovation support and helping optimize the industrial structure.

Appendix. The theoretical derivation

This paper draws on the research ideas of Tang and Li (2021) to construct a production model of endogenous innovation capability. Assuming that technological innovation is jointly determined by human capital, R&D capital and production technology input, and the output model of endogenous innovation capability can be obtained as follows:

$$Y(X,T)=A{e}^{\alpha t}{X}^{\beta }{T}^{\lambda }(h,r,i)$$

(1)

where Y is total output; A represents the production condition (exogenous constant value); α is exogenous technological progress; X denotes factor input, which is composed of capital (K) and labor (L); β is factor elasticity; T(h,r,i) represents endogenous technological innovation; and λ is the elasticity of innovation output.

Improving innovation capability needs to increase transformation of innovation intensity and innovation input. Assuming that $T=\phi \times I$ is satisfied between technological innovation and innovation intensity, where ϕ denotes the coefficient of transformation of innovation input into actual innovation capability and I represents innovation intensity. Therefore, the total output is as follows:

$$Y(X,T)=A{e}^{\alpha t}{X}^{\beta }{(\phi \times I)}^{\lambda }$$

(2)

It is further assumed that in the production process, the actual output is composed of high value-added products produced by advanced industrial sectors and low value-added products produced by ordinary industrial sectors, and the actual output is Y₁ and Y₀, respectively. In general, advanced industry sectors tend to have higher innovation intensity to produce high value-added products, while that of ordinary industry sectors is lower than average. Therefore, we assume that the conversion coefficient and output elasticity of innovation input are the largest in the advanced industry sector and the smallest in the general industry sector, with ϕ and λ satisfying $0<{\phi }_{0}<\phi <{\phi }_{1}<1$ and $0<{\lambda }_{0}<\lambda <{\lambda }_{1}<1$.

The ultimate purpose of industrial upgrading or optimization is to increase the added value of existing economic products. Accordingly, the ratio of the output of advanced industrial sectors to the total output is used to measure the industrial upgrading:

$$IU={Y}_{1}/Y=\frac{{A}_{1}}{A}{e}^{\alpha t}{\left(\frac{{X}_{1}}{X}\right)}^{\beta }\frac{{\phi }_{1}^{{\lambda }_{1}}}{{\phi }^{\lambda }}{I}^{{\lambda }_{1}-\lambda }$$

(3)

where X₁ and X₀ represent the factor input of advanced and ordinary industrial sectors, respectively, the total factor input X = X₁ + X₀, and the corresponding factor prices of the two sectors are p₁ and p₀, respectively. In actual production, advanced industrial sectors are often willing to pay higher prices to gain competitive advantages, that is, p₁ > p₀. In addition, when the factor market achieves long-run equilibrium, p₁ X₁ = p₀ X₀. Therefore, industrial optimization can be further summarized as follows:

$$IU={Y}_{1}/Y=\frac{{A}_{1}}{A}{e}^{\alpha t}{\left(\frac{{p}_{1}}{{p}_{1}+{p}_{0}}\right)}^{\beta }\frac{{\phi }_{1}^{{\lambda }_{1}}}{{\phi }^{\lambda }}{I}^{{\lambda }_{1}-\lambda }$$

(4)

Taking the partial derivative of Eq. (4), we get Eq. (5):

$$\frac{\partial IU}{\partial I}=\frac{{A}_{1}}{A}{e}^{\alpha t}{\left(\frac{{p}_{1}}{{p}_{1}+{p}_{0}}\right)}^{\beta }\frac{{\phi }_{1}^{{\lambda }_{1}}}{{\phi }^{\lambda }}\left({\lambda }_{1}-\lambda \right){I}^{{\lambda }_{1}-\lambda -1}>0$$

(5)

Therefore, technological innovation can significantly promote advanced industrial structure.

The rationalization of industrial structure pursues the coordination ability and correlation level between industries, which is mostly measured by the their index. Considering the relatively low output and employment share of the primary industry in China, and to simplify the analysis, we construct the deviation index ID of the industrial structure, which includes the advanced industry sector (the tertiary industry) and the ordinary industry sector (the secondary industry). A larger ID indicates a lower level of industrial structure rationalization:

$$ID=\frac{{Y}_{0}}{Y}{\text{ln}}\left(\frac{{Y}_{0}/Y}{{L}_{0}/L}\right)+\frac{{Y}_{1}}{Y}{\text{ln}}\left(\frac{{Y}_{1}/Y}{{L}_{1}/L}\right)$$

(6)

where L₁ and L₀ are the number of laborers in advanced and ordinary industry sectors, respectively. We further split ID into M and N, and take partial derivatives of Eqs. (7) and (8):

$$M=\frac{{Y}_{0}}{Y}{\text{ln}}\left(\frac{{Y}_{0}/Y}{{L}_{0}/L}\right)=\left(1-\frac{{Y}_{1}}{Y}\right)\left[{\text{ln}}\left(1-\frac{{Y}_{1}}{Y}\right)-{\text{ln}}\left(1-\frac{{L}_{1}}{L}\right)\right]$$

(7)

$$N=\frac{{Y}_{1}}{Y}{\text{ln}}\left(\frac{{Y}_{1}/Y}{{L}_{1}/L}\right)=\frac{{Y}_{1}}{Y}\left[{\text{ln}}\frac{{Y}_{1}}{Y}-{\text{ln}}\frac{{L}_{1}}{L}\right]$$

(8)

$$\frac{\partial M}{\partial I}=-\frac{{A}_{1}}{A}{e}^{\alpha t}{\left(\frac{{p}_{1}}{{p}_{1}+{p}_{0}}\right)}^{\beta }\frac{{\phi }_{1}^{{\lambda }_{1}}}{{\phi }^{\lambda }}\left({\lambda }_{1}-\lambda \right){I}^{{\lambda }_{1}-\lambda -1}\left[{\text{ln}}\left(1-\frac{{Y}_{1}}{Y}\right)+1-{\text{ln}}\left(1-\frac{{L}_{1}}{L}\right)\right]$$

(9)

$$\frac{\partial N}{\partial I}=\frac{{A}_{1}}{A}{e}^{\alpha t}{\left(\frac{{p}_{1}}{{p}_{1}+{p}_{0}}\right)}^{\beta }\frac{{\phi }_{1}^{{\lambda }_{1}}}{{\phi }^{\lambda }}\left({\lambda }_{1}-\lambda \right){I}^{{\lambda }_{1}-\lambda -1}\left[{\text{ln}}\frac{{Y}_{1}}{Y}+1-{\text{ln}}\frac{{L}_{1}}{L}\right]$$

(10)

Thus, we can obtain the partial derivative of industrial structure deviation degree on technological innovation:

$$\begin{array}{c}\frac{\partial ID}{\partial I}=\frac{{A}_{1}}{A}{e}^{\alpha t}{\left(\frac{{p}_{1}}{{p}_{1}+{p}_{0}}\right)}^{\beta }\frac{{\phi }_{1}^{{\lambda }_{1}}}{{\phi }^{\lambda }}\left({\lambda }_{1}-\lambda \right){I}^{{\lambda }_{1}-\lambda -1}\left[{\text{ln}}\frac{{Y}_{1}}{{Y}_{0}}-{\text{ln}}\frac{{L}_{1}}{{L}_{0}}\right]\\ =\frac{{A}_{1}}{A}{e}^{\alpha t}{\left(\frac{{p}_{1}}{{p}_{1}+{p}_{0}}\right)}^{\beta }\frac{{\phi }_{1}^{{\lambda }_{1}}}{{\phi }^{\lambda }}\left({\lambda }_{1}-\lambda \right){I}^{{\lambda }_{1}-\lambda -1}ln\frac{L{P}_{1}}{L{P}_{0}}\end{array}$$

(11)

where LP₁ and LP₀ denote the labor productivity of advanced and ordinary industrial sectors, respectively. Considering that the labor productivity of China’s secondary industry is higher than that of the tertiary industry (Zhang et al. 2019), $\partial ID/\partial I<0$. According to this, technological innovation can promote industrial structure rationalization.