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Does the SO2 emissions trading scheme encourage green total factor productivity? An empirical assessment on China’s cities

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Abstract

As a market-based environmental regulation tool, emissions trading scheme for SO2 (SO2 ETS) has been carried out in China for decades, so impacts of SO2 ETS have become a vital issue to the society. Based on the SO2 ETS of China in 2007, this paper attempts to test and verify impacts of the scheme on environment and economy, especially on green total factor productivity (TFP). We firstly combine biennial weight-modified non-radial direction distance function and Luenberger productivity indicator to measure and decompose the green TFP of 280 cities in China over the period 2003 to 2016, and apply a difference-in-differences method (DID) with fixed effect models to investigate whether SO2 ETS achieves a win–win scenario of “emission reducing” and “efficiency increasing.” The results show that the scheme significantly may decrease SO2 emissions and SO2 intensity by 12.3% and 11.0%, respectively, in ETS regions while no obvious impact on GDP. In terms of green TFP, we find SO2 ETS inhibits the growth of green TFP, and the negative impact mainly caused by the deterioration in efficiency change. Therefore, we hold that SO2 ETS is effective for improving environment, but it is still difficult to achieve the promotion of green TFP simultaneously.

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Notes

  1. The reasons of choosing SO2 as pollution are, i) due to the availability and continuity of data at the city level, and ii) the 2007 ETS we evaluated mainly focus on the emissions of SO2.

  2. Although China launched the SO2 emission trading policy in 2002, in the first few years, the policy was still in its infancy, the system was still not perfect, the activity was low and transaction scales were small until 2007, when 11 pilot areas were selected that have successively issued relevant policy documents. The scale, scope, and activity of transactions in recent years have expanded due to improvements in the trading system.

  3. Data derive from trading centers in each ETS region.

  4. We use the Luenberger BPLI, because i) it is considered to be a more robust indicator of productivity, and ii) its additivity is more compatible with the two-phase directional distance function. We also measured the ML productivity index to examine robustness of the results.

    \( {\displaystyle \begin{array}{l} BMLPI=\frac{N\overrightarrow{D^B}\left({x}^t,{y}^t,{b}^t;-{x}^t,{y}^t,-{b}^t\right)}{1+N\overrightarrow{D^B}\left({x}^{t+1},{y}^{t+1},{b}^{t+1},-{x}^{t+1},{y}^{t+1},-{b}^{t+1}\right)}=\left[\frac{1+N\overrightarrow{D^t}\left({x}^t,{y}^t,{b}^t;-{x}^t,{y}^t,-{b}^t\right)}{1+N\overrightarrow{D^{t+1}}\left({x}^{t+1},{y}^{t+1},{b}^{t+1};-{x}^{t+1},{y}^{t+1},-{b}^{t+1}\right)}\right]\\ {}\kern3em \times \left[\frac{1+N\overrightarrow{D^B}\left({x}^t,{y}^t,{b}^t;-{x}^t,{y}^t,-{b}^t\right)}{1+N\overrightarrow{D^t}\left({x}^t,{y}^t,{b}^t;-{x}^t,{y}^t,-{b}^t\right)}\times \frac{1+N\overrightarrow{D^{t+1}}\left({x}^{t+1},{y}^{t+1},{b}^{t+1};-{x}^{t+1},{y}^{t+1},-{b}^{t+1}\right)}{1+N\overrightarrow{D^B}\left({x}^{t+1},{y}^{t+1},{b}^{t+1};-{x}^{t+1},{y}^{t+1}-{b}^{t+1}\right)}\right]= EC\times TC\end{array}} \)

  5. Due to the lack of SO2 emissions data at the city level, we estimate total emissions for robustness tests. Sources of total SO2 emissions may be roughly divided into two: industrial and domestic. Therefore, we use domestic SO2 emissions at the provincial level to estimate domestic SO2 emissions at city level with the ratio of provincial economic output as the weight, and add it into the industrial emissions as the total emissions.

  6. Internationally, SO2 emissions per unit of GDP is commonly used to measure SO2 intensity.

  7. Errors, such that the sum of EC and TC in the table may not equal BLPI, are likely, due to averaging and rounding of data values in the finishing process.

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Funding

This paper was supported by the National Key R&D Program of China (2018YFC0213600), Philosophy and Social Sciences Research of Ministry of Education of China (17JZD013) and the National Natural Science Foundation of China (71822402,91746112,71473105).

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Authors and Affiliations

  1. College of Economics, Jinan University, Guangzhou, 510632, China

    Bingqing Hou, Bing Wang & Ning Zhang

  2. Research Center for Environmental Economics, School of Economics and Management, South China Normal University, Guangzhou, 510631, China

    Minzhe Du

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  1. Bingqing Hou
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  2. Bing Wang
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Correspondence to Ning Zhang.

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Hou, B., Wang, B., Du, M. et al. Does the SO2 emissions trading scheme encourage green total factor productivity? An empirical assessment on China’s cities. Environ Sci Pollut Res 27, 6375–6388 (2020). https://doi.org/10.1007/s11356-019-07273-6

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