Effectiveness-based innovation or efficiency-based innovation? Trade-off and antecedents under the goal of ecological total-factor energy efficiency in China

Abstract

Pursuing innovation effect or efficiency is an important trade-off that Chinese local governments need to face in the process of developing economy and protecting the environment. From the perspective of the policy portfolio, we employ the industrial panel data of 30 provinces in China during 2000–2015 to analyze the impacts of effectiveness-based innovation and efficiency-based innovation on ecological total-factor energy efficiency (ETFEE), and further analyze the effects of command-and-control, market-based and voluntary environmental regulations on innovation. The findings reveal that (1) both effectiveness-based innovation and efficiency-based innovation have significant promoting effects on ETFEE. (2) Three types of environmental regulations have significantly inhibitory effects on effectiveness-based innovation and efficiency-based innovation. (3) The interaction term of command-and-control and market-based regulations plays a significant role in promoting effectiveness-based innovation and efficiency-based innovation, whereas the interaction term of market-based and voluntary regulations merely promotes efficiency-based innovation. The interaction term of three types of regulation only has a synergetic and positive effect on the efficiency-based innovation. Finally, this paper gives specific policy recommendations.

Appendix

In this study, suppose that there were n DMUs with m input indicators, s₁ desirable output indicators, and s₂ undesirable output indicators, the inputs, desirable outputs, and undesirable outputs were respectively expressed as x ∈ R^m, ${y}^g\in {R}^{S_1}$, and ${y}^b\in {R}^{S_2}$. The input vector was X = (x_ij) ∈ R^m × n. The desirable output vector was ${Y}^g=\left({y}_{rj}\right)\in {R}^{s_1\times n}$. The undesirable output vector was ${Y}^b=\left({y}_{rj}\right)\in {R}^{s_2\times n}$. The SBM model with undesirable outputs was expressed as follows.

$$ {\displaystyle \begin{array}{l}\min {\rho}^{\ast }=\frac{1-\frac{1}{m}\sum \limits_{i=1}^m\left({s}_i^{-}/{x}_{i0}\right)}{1+\frac{1}{s_1+{s}_2}\left(\sum \limits_{r=1}^{s_1}\left({s}_r^g/{y}_{r0}^g\right)+\sum \limits_{r=1}^{s_2}\left({s}_r^b/{y}_{r0}^b\right)\right)}\\ {}s.t.\Big\{\begin{array}{l}{x}_0= X\lambda +{s}^{-}\\ {}{y}_0^g={Y}^g\lambda -{s}^g\\ {}{y}_0^b={Y}^b\lambda +{s}^b\\ {}\lambda, {s}^{-},{s}^g,{s}^b\ge 0\end{array}\end{array}} $$

(3)

where s⁻, s^g, and s^b refer to slack variables of input, desirable output, and undesirable output, respectively. λis weight vector. When ρ^∗ = 1, namely, s⁻ = 0, s^g = 0, s^b = 0, the DMU is effective.

The super efficiency SBM model was expressed as follows.

$$ {\displaystyle \begin{array}{l}\min {\delta}^{\ast }=\frac{\frac{1}{m}\sum \limits_{i=1}^m\left(\overline{x}/{x}_{i0}\right)}{\frac{1}{s}\sum \limits_{r=1}^s\left({\overline{y}}_r/{y}_{r0}^g\right)}\\ {}s.t.\Big\{\begin{array}{l}\overline{x}\ge X\lambda \\ {}\overline{y}\le Y\lambda \\ {}\overline{x}\ge {x}_0,\overline{y}\le {y}_0\\ {}\lambda \ge 0,\overline{y}\ge 0\end{array}\end{array}} $$

(4)

According to the models (3) and (4), the super efficiency SBM model with undesirable outputs was expressed as follows (Yang et al. 2018).

$$ {\displaystyle \begin{array}{l}\min {\alpha}^{\ast }=\frac{\frac{1}{m}\sum \limits_{i=1}^m\left(\overline{x}/{x}_{io}\right)}{1+\frac{1}{s_1+{s}_2}\left(\sum \limits_{r=1}^{s_1}\left({s}_r^g/{y}_{r0}^g\right)+\sum \limits_{r=1}^{s_2}\left({s}_r^b/{y}_{r0}^b\right)\right)}\\ {}s.t.\Big\{\begin{array}{l}{\overline{x}}_0\ge X\lambda \\ {}\overline{y^g}\le {Y}^g\lambda \\ {}\overline{y^b}\ge {Y}^b\lambda \\ {}\overline{x}\ge {x}_o,\overline{y^g}\le {y}_0^g,\overline{y^b}\le {y}_0^b,\lambda >0\end{array}\end{array}} $$

(5)