Efficiency evaluation of government investment for air pollution control in city clusters: A case from the Beijing–Tianjin–Hebei areas in China

Abstract

Air pollution poses a significant threat to human health, particularly in urban areas with high levels of industrial activities. In China, the government plays a crucial role in managing air quality through the Air Pollution Prevention and Control Action Plan. The government provides direct financial support and guides the investment direction of social funds to improve air quality. While government investment has led to improvements in air quality across China, concerns remain regarding the efficiency of such large-scale investments. To address this concern, we conducted a study using a three-stage data envelopment analysis (DEA)-Malmquist model to assess the efficiency of government investment in improving air quality in China. Our analysis revealed regional disparities and annual dynamic changes. Specifically, we focused on the Beijing–Tianjin–Hebei areas as a case study, as the investment primarily targeted industrial activities in urban areas with the goal of improving living conditions for urban residents. The results demonstrate significant differences in investment efficiency between regions. Beijing exhibits relatively high investment efficiency, while cities in Hebei Province require improvement. We identified scale inefficiency, which refers to the ratio of air pollutant reduction to financial investment, as the main factor contributing to regional disparities. However, we found that increasing the total investment scale can help mitigate this effect. Furthermore, our study observed positive but fluctuating annual changes in investment efficiency within this city cluster from 2014 to 2018. Investment-combined technical efficiency, which represents the investment strategy, is the main obstacle to improving yearly investment efficiency. Therefore, in addition to promoting investment strategies at the individual city level, it is crucial to enhance coordination and cooperation among cities to improve the investment efficiency of the entire city cluster. Evaluating the efficiency of government investment and understanding its influencing factors can guide future investment measures and directions. This knowledge can also support policymaking for other projects involving substantial investments.

Graphical abstract

Keywords

investment efficiency / government investment / air pollution control / three-stage DEA-Malmquist model

References

[1] Akbar, M W Peng, Y L Maqbool, A Zia, Z Saeed, M (2021). The nexus of sectoral-based CO₂ emissions and fiscal policy instruments in the light of Belt and Road Initiative. Environmental Science and Pollution Research International, 28( 25): 32493–32507

[2] Banker, R D Charnes, A Cooper, W W (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30( 9): 1078–1092

[3] Charnes, A Cooper, W W Rhodes, E (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2( 6): 429–444

[4] Chen, W Zhang, L Ma, T Liu, Q (2014). Research on three-stage DEA model. Systems Engineering, 32( 9): 144–149

[5] Chen, Y Li, C Li, X Zhang, X Tan, Q (2022). Efficiency of water pollution control based on a three-stage SBM-DEA model. Water, 14( 9): 1453

[6] Cheng, L Wu, J Song, L Xu, Y Wang, Z Chen, J (2020). Study on financial fund efficiency of air pollution prevention based on DEA. Friends of Accounting, ( 16): 59–66

[7] CooperW WSeiford L MZhuJ (2011). Handbook on Data Envelopment Analysis. New York, NY: Springer

[8] Cui, T Ye, Z Wang, Z Zhou, J He, C Hong, S Yang, L Niu, X Wu, Q (2022). Inequalities in PM_2.5 and SO₂ exposure health risks in terms of emissions in China, 2013–2017. Atmosphere, 13( 9): 1422

[9] DaraioCSimar L (2007). Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications. New York, NY: Springer

[10] Dong, G H (2019). Ambient air pollution in China. Respirology, 24( 7): 626–627

[11] FäreRGrosskopf SLindgrenBRoosP (1992). Productivity changes in Swedish pharmacies 1980–1989: A non-parametric Malmquist approach. In: Gulledge T R, Knox Lovell C A, eds. International Applications of Productivity and Efficiency Analysis: A Special Issue of the Journal of Productivity Analysis. Dordrecht: Springer, 81–97

[12] FäreRGrosskopf SLindgrenBRoosP (1994). Productivity developments in Swedish hospitals: A Malmquist output index approach. In: Charnes A, Cooper W W, Lewin A Y, Seiford L M, eds. Data Envelopment Analysis: Theory, Methodology, and Applications. Dordrecht: Springer, 253–272

[13] Feng, X Li, Q Zhu, Y J Wang, J J Liang, H M Xu, R F (2014). Formation and dominant factors of haze pollution over Beijing and its peripheral areas in winter. Atmospheric Pollution Research, 5( 3): 528–538

[14] Florea, N M Meghisan-Toma, G M Puiu, S Meghisan, F Doran, M D Niculescu, M (2021). Fiscal and budgetary policy efforts towards climate change mitigation in Romania. Sustainability, 13( 5): 2802

[15] Fried, H O Lovell, C Schmidt, S S Yaisawarng, S (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis, 17( 1/2): 157–174

[16] Fried, H O Schmidt, S S Yaisawarng, S (1999). Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis, 12( 3): 249–267

[17] Gramkow, C Anger-Kraavi, A (2018). Could fiscal policies induce green innovation in developing countries? The case of Brazilian manufacturing sectors. Climate Policy, 18( 2): 246–257

[18] Guo, S Tong, M Zhang, H (2018). Analysis on investment efficiency of environmental governance and its influencing factors in China. Statistics & Decisions, 34( 8): 113–117

[19] Halkos, G Argyropoulou, G (2021). Pollution and health effects: A nonparametric approach. Computational Economics, 58( 3): 691–714

[20] Halkos, G E Paizanos, E A (2016). The effects of fiscal policy on CO₂ emissions: Evidence from the USA. Energy Policy, 88: 317–328

[21] HanSWeiQ (2002). The nonparametric DEA models for resource allocation. Systems Engineering: Theory & Practice, (7): 59–64, 70 (in Chinese)

[22] He, Y Zhu, Z Xie, H Zhang, X Sheng, M (2023). A case study in China of the influence mechanism of industrial park efficiency using DEA. Environment, Development and Sustainability, 25( 7): 7261–7280

[23] Hu, X R Sun, Y N Liu, J F Meng, J Wang, X J Yang, H Z Xu, J Y Yi, K Xiang, S L Li, Y Yun, X Ma, J M Tao, S (2019). The impact of environmental protection tax on sectoral and spatial distribution of air pollution emissions in China. Environmental Research Letters, 14( 5): 054013

[24] Jondrow, J Knox Lovell, C A Materov, I S Schmidt, P (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19( 2–3): 233–238

[25] Liu, H Wu, W Yao, P (2022). Assessing the financial efficiency of healthcare services and its influencing factors of financial development: Fresh evidences from three-stage DEA model based on Chinese provincial level data. Environmental Science and Pollution Research International, 29( 15): 21955–21967

[26] Liu, J Liang, B (2010). Evaluation results of Malmquist productivity index-new insight of technical change. Operations Research and Management Science, 19( 1): 170–175

[27] Liu, M Saari, R K Zhou, G Li, J Han, L Liu, X (2021). Recent trends in premature mortality and health disparities attributable to ambient PM_2.5 exposure in China: 2005–2017. Environmental Pollution, 279: 116882

[28] Liu, X Liu, J (2016). Measurement of low carbon economy efficiency with a three-stage data envelopment analysis: A comparison of the largest twenty CO₂ emitting countries. International Journal of Environmental Research and Public Health, 13( 11): 1116

[29] Liu, Y S Zhou, Y Wu, W X (2015). Assessing the impact of population, income and technology on energy consumption and industrial pollutant emissions in China. Applied Energy, 155: 904–917

[30] Luo, D (2012). A note on estimating managerial inefficiency of three-stage DEA model. Statistical Research, 29( 4): 104–107

[31] Mei, G Gan, J Zhu, S (2014). Study the management efficiency of Chinese commercial banks based on three-stage DEA and Malmquist index decomposition. Journal of Jiangxi Normal University (Philosophy and Social Sciences Edition), 47( 4): 39–48

[32] Song, M L Jia, G S Zhang, P W (2020). An evaluation of air transport sector operational efficiency in China based on a three-stage DEA analysis. Sustainability, 12( 10): 4220

[33] Sun, W Q Zhou, Y Lv, J X Wu, J Z (2019a). Assessment of multi-air emissions: Case of particulate matter (dust), SO₂, NO_x and CO₂ from iron and steel industry of China. Journal of Cleaner Production, 232: 350–358

[34] Sun, Y Jiang, N Cui, Y (2019b). Research on economic, social and environmental efficiency of environmental protection investment: Based on three-stage DEA model. Science and Technology Management Research, 39( 21): 219–226

[35] Wang, Y S Yao, L Wang, L L Liu, Z R Ji, D S Tang, G Q Zhang, J K Sun, Y Hu, B Xin, J Y (2014). Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Science China: Earth Sciences, 57( 1): 14–25

[36] Wen, L Zhang, Z Q (2020). Probing energy-related CO₂ emissions in the Beijing–Tianjin–Hebei region based on ridge regression considering population factors. Polish Journal of Environmental Studies, 29( 3): 2413–2427

[37] World Bank (1997). World Development Report 1997: The State in a Changing World. Washington, D.C.: World Bank Group

[38] World Health Organization (WHO) (2021). WHO Global Air Quality Guidelines

[39] XuQ C (2019). Evaluation of air pollution control efficiency in Hebei Province based on DEA method. In: Proceedings of the 2nd International Conference on Air Pollution and Environmental Engineering. Xi’an: IOPscience, 012107

[40] Ye, M Jin, Y Deng, F (2022). Municipal waste treatment efficiency in 29 OECD countries using three-stage Bootstrap-DEA model. Environment, Development and Sustainability, 24( 9): 11369–11391

[41] Yu, H Lin, X (2018). Environmental pollution control investment efficiency in Beijing–Tianjin–Hebei region based on EBM super efficiency model. Journal of Hebei University of Technology (Social Sciences Edition), 10( 1): 9–16

[42] Yu, Y Dai, C Wei, Y Ren, H Zhou, J (2022). Air pollution prevention and control action plan substantially reduced PM_2.5 concentration in China. Energy Economics, 113: 106206

[43] Zhang, Y Shen, L Shuai, C Bian, J Zhu, M Tan, Y Ye, G (2019). How is the environmental efficiency in the process of dramatic economic development in the Chinese cities?. Ecological Indicators, 98: 349–362

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