Facilitating or inhibiting: coal-fired power generation and firm productivity in China

ABSTRACT

The study investigates the causal impact of opening coal-fired power plants on firm productivity, utilizing data on the distribution of these plants and firm-level microdata in China from 1998 to 2014. The research design relies on a difference-in-differences strategy to compare productivity differences between firms located near and far away from coal-fired power plants before and after their opening. The results show that the opening of coal-fired power plants reduced the total factor productivity of surrounding firms by 7.8% on average. By exploring its mechanism, we found that this negative effect is mainly due to serious air pollution caused by the opening of coal-fired power plants. For every 1% increase in sulphur dioxide emitted by a coal-fired power plant, the TFP of surrounding firms decreases by 0.42%. Further analysis shows that coal-fired power plants have a greater negative impact on total factor productivity of private firms, small firms, and regions with lower environmental regulation intensity. This study deepens the understanding of the negative externalities of energy infrastructure and resource utilization, and emphasizes the importance of energy transition.

KEYWORDS:

• Coal-fired power plants
• energy
• infrastructure
• productivity
• environment

JEL CLASSIFICATION:

• O13
• Q41
• Q53

Acknowledgements

The authors would like to thank the School of Economics, Shanghai University of Finance and Economics, for providing the datasets. We would like to thank Editage (www.editage.cn) for English language editing.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from Data Center, School of Economics, Shanghai University of Finance and Economics but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of School of Economics, Shanghai University of Finance and Economics.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tao Lin, Minhao Qi and Sijie Wei. The first draft of the manuscript was written by Tao Lin and Zhao Chen, and all authors commented on previous versions of the manuscript.

Consent to Publish

All authors have read and agreed to the paper.

Notes

¹ Also called The China’s Industrial Enterprise Database (CIED).

² The most ideal data is by 2023. However, the available data sources for financial information of Chinese firms are limited to 2014. The analysis based on the data during this period may weaken the effectiveness of current policy-making.

³ The calculation of TFP is relatively complicated, see Levinsohn and Petrin (2003) for a detailed procedure. We use total firm output as an output variable and the number of workers, capital stock and intermediate inputs as input variables.

⁴ https://www.bakerinstitute.org/chinas-energy-infrastructure.

⁵ In 2021, there were a total of 3090 coal-fired power generation units in China, merged into 1041 coal-fired power plants.

⁶ The DID method is a relatively mature analytical method for conducting policy research, and its principle is similar to that of a natural experiment. It views the implementation of a policy as a natural experiment and examines the net effect of the policy implementation on treatment individuals by comparing the differences between the treatment group affected by the policy and the control group not affected by the policy before and after the policy implementation. Compared to other methods of causal inference, such as instrumental variables (IV) and regression discontinuity design (RDD), the DID method has more easily met identifying assumptions, and it requires only that the treatment and control groups have ex ante parallel trends.

⁷ We also used criterion (2) to redefine large and small firms, and estimate the effects of the coal-fired power plants separately for small and large firms. We find that the results using criterion (2) are very similar to those using criterion (1), which validates that our analysis of heterogeneity in firm size is robust.

Additional information

Funding

This work was supported by the Major Projects of the National Social Science Foundation of China [23ZDA051]; the Graduate Innovation Fund of Shanghai University of Finance and Economics [kycx-2023-04].