The impact of environmental protection tax on green total factor productivity: China’s exceptional approach

Abstract

To empirically assess green taxation's role in environmental preservation, we leverage China's Environmental Protection Tax (EPT) law implementation as a quasi-natural experiment to examine its influence on Green Total Factor Productivity (GTFP). Our empirical results suggest that GTFP improved more considerably in localities that raised their EPT minimum taxable threshold. EPT affects GTFP through three primary mechanisms. First, it enhances the efficiency of solid waste utilization, contributing to environmental governance. Next, the law stimulates local advancements in green innovation. Finally, it elevates social awareness and commitment towards environmental protection. In addition, we discover that when two adjoining localities jointly raise their EPT taxable threshold, the effect on their GTFP is magnified due to spatial spillovers.

Appendix A

Chung et al. (1997) is the first to measure GTFP by constructing the Malmquist- Luenberger Index (MLPI), which however does not lead to analytic solutions. To address the issue Oh (2010) proposes the Global Malmquist-Luenberger Index (Global-MLPI), which is defined as follows:

$${GTFP}^{t,t+1}\left({x}^{t},{y}^{t},{b}^{t},{x}^{t+1},{y}^{t+1},{b}^{t+1}\right)=\frac{1+\vec{D}^{G}({x}^{t},{y}^{t},{b}^{t})}{1+\vec{D}^{G}({x}^{t+1},{y}^{t+1},{b}^{t+1})}$$

(8)

where $x$ represents inputs, $y$ represents desired outputs, and $b$ indicates undesired outputs (e.g. pollution). $\vec{D}^{G}\left(x,y,b\right)=max\left\{\beta |(y+\beta y,b-\beta b)\in {P}^{G}(x)\right\}$. ${P}^{G}(x)$ is global production possibilities set. GTFP can be further decomposed into two parts: efficiency change (EC) and technology change (TC), as in Eq. 3:

$$GTFP^{{t,t + 1}} \left( {x^{t} ,y^{t} ,b^{t} ,x^{{t + 1}} ,y^{{t + 1}} ,b^{{t + 1}} } \right) = \frac{{1 + \vec{D}^{G} \left( {x^{t} ,y^{t} ,b^{t} } \right)}}{{1 + \vec{D}^{G} \left( {x^{{t + 1}} ,y^{{t + 1}} ,b^{{t + 1}} } \right)}} = \frac{{1 + \vec{D}^{G} \left( {x^{t} ,y^{t} ,b^{t} } \right)}}{{1 + \vec{D}^{G} \left( {x^{{t + 1}} ,y^{{t + 1}} ,b^{{t + 1}} } \right)}} \times \left[ {\frac{{1 + \vec{D}^{G} \left( {x^{t} ,y^{t} ,b^{t} } \right)}}{{1 + \vec{D}^{G} \left( {x^{{t + 1}} ,y^{{t + 1}} ,b^{{t + 1}} } \right)}} \times \frac{{1 + \vec{D}^{G} \left( {x^{t} ,y^{t} ,b^{t} } \right)}}{{1 + \vec{D}^{G} \left( {x^{{t + 1}} ,y^{{t + 1}} ,b^{{t + 1}} } \right)}}} \right]^{{0.5}} = \frac{{TE^{{t + 1}} }}{{TE^{t} }} \times \left[ {\frac{{BPG_{{t + 1}}^{{t,t + 1}} }}{{BPG_{t}^{{t,t + 1}} }}} \right]^{{0.5}} = EC^{{t,t + 1}} \times BPC^{{t,t + 1}}$$

(9)

where ${EC}^{t,t+1}$ denotes the efficiency change and ${BPC}^{t,t+1}$ denotes the technology change.

For calculating GTFP in Eq. 1, we select the following city-level indicators: (1) For input indicators, we use the number of employed people to measure labor input; capital stock to measure capital input, and electricity consumption to measure resource utilization. (2) We use real GDP as the desirable output indicator, adjusted for inflation using 2011 as the base year. (3) We include industrial smoke emissions, wastewater emissions, sulfur dioxide emissions, and PM2.5 as undesirable output indicators.