Green Total Factor Productivity Growth: Policy-Guided or Market-Driven?
Abstract
:1. Introduction
2. Research Design
2.1. Hypothesis
2.2. Model
2.3. Variables
3. Results
3.1. The PSTR Test
3.2. Results Analysis
4. Additional Analysis
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Pradhan, P.; Costa, L.; Rybski, D.; Lucht, W.; Kropp, J.P. A Systematic Study of Sustainable Development Goal (SDG) Interactions. Earths Future 2017, 5, 1169–1179. [Google Scholar] [CrossRef] [Green Version]
- Brooks, T.M.; Akcakaya, H.R.; Burgess, N.D.; Butchart, S.H.M.; Hilton-Taylor, C.; Hoffmann, M.; Juffe-Bignoli, D.; Kingston, N.; MacSharry, B.; Parr, M.; et al. Analysing biodiversity and conservation knowledge products to support regional environmental assessments. Sci. Data 2016, 3, 160007. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, T.H.; Li, X.; Liao, G.K. Business cycles and energy intensity. Evidence from emerging economies. Borsa Istanb. Rev. 2022, 22, 560–570. [Google Scholar] [CrossRef]
- Li, Z.H.; Huang, Z.M.; Failler, P. Dynamic Correlation between Crude Oil Price and Investor Sentiment in China: Heterogeneous and Asymmetric Effect. Energies 2022, 15, 687. [Google Scholar] [CrossRef]
- Liu, C.Y.; Cui, L.H.; Li, C.X. Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China. Sustainability 2022, 14, 7274. [Google Scholar] [CrossRef]
- Ren, S.Y.; Hao, Y.; Wu, H.T. The role of outward foreign direct investment (OFDI) on green total factor energy efficiency: Does institutional quality matters? Evidence from China. Resour. Policy 2022, 76, 102587. [Google Scholar] [CrossRef]
- Wu, H.T.; Hao, Y.; Ren, S.Y. How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China. Energy Econ. 2020, 91, 104880. [Google Scholar] [CrossRef]
- Wu, H.T.; Hao, Y.; Ren, S.Y.; Yang, X.D.; Xie, G. Does internet development improve green total factor energy efficiency? Evidence from China. Energy Policy 2021, 153, 112247. [Google Scholar] [CrossRef]
- Yang, X.; Wang, W.; Wu, H.; Wang, J.; Ran, Q.; Ren, S. The impact of the new energy demonstration city policy on the green total factor productivity of resource-based cities: Empirical evidence from a quasi-natural experiment in China. J. Environ. Plan. Manag. 2021. [Google Scholar] [CrossRef]
- Pittman, R.W. Multilateral productivity comparisons with undesirable outputs. Econ. J. 1983, 93, 883–891. [Google Scholar] [CrossRef]
- Chung, Y.H.; Färe, R.; Grosskopf, S. Productivity and undesirable outputs: A directional distance function approach. J. Environ. Manag. 1997, 51, 229–240. [Google Scholar] [CrossRef] [Green Version]
- Färe, R.; Grosskopf, S.; Pasurka, C.A., Jr. Accounting for air pollution emissions in measures of state manufacturing productivity growth. J. Reg. Sci. 2001, 41, 381–409. [Google Scholar] [CrossRef]
- Fukuyama, H.; Weber, W.L. A directional slacks-based measure of technical inefficiency. Socio-Econ. Plan. Sci. 2009, 43, 274–287. [Google Scholar] [CrossRef]
- Oh, D.-H. A global Malmquist-Luenberger productivity index. J. Product. Anal. 2010, 34, 183–197. [Google Scholar] [CrossRef]
- Zhang, K.; Yi, Y.; Zhang, W.J.L.i.S.; Sciences, R. Environmental total factor productivity and regional disparity in China. Lett. Spat. Resour. Sci. 2014, 7, 9–21. [Google Scholar] [CrossRef]
- Lei, M.; Yu, X.J. Local Fiscal Expenditure, Environmental Regulation and the Transition to a Low-Carbon Economy in China. Econ. Sci. 2013, 5, 47–61. [Google Scholar]
- Yuan, Y.; Xie, R.J. FDI, environmental regulation and green total factor productivity growth of China’s industry: An empirical study based on Luenberger index. J. Int. Trade 2015, 8, 84–93. [Google Scholar]
- Wang, J.; Sheng, P. Does Environmental Management Reduce Chinese Industrial Total-factor Productivity: A Study Based on Modified Directional Distance Function. Ind. Econ. Res. 2015, 5, 31–39. [Google Scholar]
- Wang, B.; Liu, G.J. Energy conservation and emission reduction and China’s green economic growth—Based on a total factor productivity perspective. China Ind. Econ. 2015, 5, 57–69. [Google Scholar]
- Yin, B.J. Environmental regulation and China’s green total factor productivities: Based on the perspective of vertical specialization. China Popul. Resour. Env. 2012, 22, 60–66. [Google Scholar]
- Wang, Y.; Shen, N. Environmental regulation and environmental productivity: The case of China. Renew. Sustain. Energy Rev. 2016, 62, 758–766. [Google Scholar] [CrossRef]
- Greening, L.A.; Bernow, S. Design of coordinated energy and environmental policies: Use of multi-criteria decision-making. Energy Policy 2004, 32, 721–735. [Google Scholar] [CrossRef]
- Emerson, K.; Nabatchi, T.; Balogh, S. An Integrative Framework for Collaborative Governance. J. Public Adm. Res. Theory 2012, 22, 1–29. [Google Scholar] [CrossRef] [Green Version]
- Zhou, P.; Poh, K.L.; Ang, B.W. A non-radial DEA approach to measuring environmental performance. Eur. J. Oper. Res. 2007, 178, 1–9. [Google Scholar] [CrossRef]
- Mageau, G.A.; Vallerand, R.J.; Charest, J.; Salvy, S.-J.; Lacaille, N.; Bouffard, T.; Koestner, R. On the Development of Harmonious and Obsessive Passion: The Role of Autonomy Support, Activity Specialization, and Identification with the Activity. J. Personal. 2009, 77, 601–646. [Google Scholar] [CrossRef]
- Kleijn, D.; Sutherland, W.J. How effective are European agri-environment schemes in conserving and promoting biodiversity? J. Appl. Ecol. 2003, 40, 947–969. [Google Scholar] [CrossRef]
- Huang, I.B.; Keisler, J.; Linkov, I. Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Sci. Total Environ. 2011, 409, 3578–3594. [Google Scholar] [CrossRef]
- Dinda, S. Environmental Kuznets Curve hypothesis: A survey. Ecol. Econ. 2004, 49, 431–455. [Google Scholar] [CrossRef] [Green Version]
- Sun, Y.; Liu, N.; Zhang, Y.J. Regional comparison of the relationship between China’s environmental regulation and total factor productivity. J. Dongbei Univ. Financ. Econ. 2018, 1, 33–40. [Google Scholar]
- Cai, N.; Wu, J.; Liu, S. On Environmental Regulation and the Green Industrial Total Factor Productivity—Empirical Analysis Based on the Data of 30 Provinces in China. J. Liaoning Univ. Philos. Soc. Sci. 2014, 42, 65–73. [Google Scholar]
- Gang, F.; Xiaolu, W.; Guangrong, M. The Contribution of Marketization to China’s Economic Growth. China Econ. 2012, 7, 4. [Google Scholar]
- Hainaut, D.; Shen, Y.; Zeng, Y. How do capital structure and economic regime affect fair prices of bank’s equity and liabilities? Ann. Oper. Res. 2018, 262, 519–545. [Google Scholar] [CrossRef]
- Chunxiang, A.; Shen, Y.; Zeng, Y. Dynamic asset-liability management problem in a continuous-time model with delay. Int. J. Control 2022, 95, 1315–1336. [Google Scholar] [CrossRef]
- Bi, J.; Cai, J.; Zeng, Y. Equilibrium reinsurance-investment strategies with partial information and common shock dependence. Ann. Oper. Res. 2021, 307, 1–24. [Google Scholar] [CrossRef]
- Zhao, M.; Liu, F.; Sun, W.; Tao, X. The relationship between environmental regulation and green total factor productivity in China: An empirical study based on the panel data of 177 cities. Int. J. Environ. Res. Public Health 2020, 17, 5287. [Google Scholar] [CrossRef] [PubMed]
- Porter, M.E.; Van der Linde, C. Toward a new conception of the environment-competitiveness relationship. J. Econ. Perspect. 1995, 9, 97–118. [Google Scholar] [CrossRef] [Green Version]
- Li, T.H.; Li, X.; Albitar, K. Threshold effects of financialization on enterprise R&D innovation: A comparison research on heterogeneity. Quant. Financ. Econ. 2021, 5, 496–515. [Google Scholar] [CrossRef]
- Zheng, Y.H.; Chen, S.L.; Wang, N. Does financial agglomeration enhance regional green economy development? Evidence from China. Green Financ. 2020, 2, 173–196. [Google Scholar] [CrossRef]
- Liu, C.Y.; Xin, L.; Li, J.Y. Environmental regulation and manufacturing carbon emissions in China: A new perspective on local government competition. Environ. Sci. Pollut. Res. 2022, 29, 36351–36375. [Google Scholar] [CrossRef]
- Shi, X.Y.; Xu, Y.Z. Evaluation of China’s pilot low-carbon city program: A perspective of industrial carbon emission efficiency. Atmos. Pollut. Res. 2022, 13, 101446. [Google Scholar] [CrossRef]
- Xin, L.; Sun, H.; Xia, X.C.; Wang, H.; Xiao, H.Y.; Yan, X.J. How does renewable energy technology innovation affect manufacturing carbon intensity in China? Environ. Sci. Pollut. Res. 2022. [Google Scholar] [CrossRef]
- Zhao, S.; Cao, Y.; Feng, C.; Guo, K.; Zhang, J. How do heterogeneous R&D investments affect China’s green productivity: Revisiting the Porter hypothesis. Sci. Total Environ. 2022, 825, 154090. [Google Scholar] [CrossRef]
- Scherer, F.M. Market structure and the employment of scientists and engineers. Am. Econ. Rev. 1967, 57, 524–531. [Google Scholar]
- Li, H.; He, F.; Deng, G. How does environmental regulation promote technological innovation and green development? New evidence from China. Pol. J. Environ. Stud. 2020, 29, 689. [Google Scholar] [CrossRef]
- González, A.; Teräsvirta, T.; van Dijk, D. Panel smooth transition regression model and an application to investment under credit constraints. Stockh. Sch. Econ. 2004, unpublished manuscript. [Google Scholar]
- Fouquau, J.; Hurlin, C.; Rabaud, I. The Feldstein-Horioka puzzle: A panel smooth transition regression approach. Econ. Model. 2008, 25, 284–299. [Google Scholar] [CrossRef]
- Hao, Y.; Huang, J.W.; Guo, Y.X.; Wu, H.T.; Ren, S.Y. Does the legacy of state planning put pressure on ecological efficiency? Evidence from China. Bus. Strategy Environ. 2022. [Google Scholar] [CrossRef]
- Zhang, J.X.; Liu, X.; Zhang, X.; Chang, Y.; Wang, C.B.; Zhang, L.X. Enhancing the green efficiency of fundamental sectors in China’s industrial system: A spatial-temporal analysis. J. Manag. Sci. Eng. 2021, 6, 393–412. [Google Scholar] [CrossRef]
- Liu, S.M.; Shen, X.Y.; Jiang, T.P.; Failler, P. Impacts of the financialization of manufacturing enterprises on total factor productivity: Empirical examination from China’s listed companies. Green Financ. 2021, 3, 59–89. [Google Scholar] [CrossRef]
- Chen, Y.; Ma, X.X.; Yan, P.; Wang, M.Y. Operating efficiency in Chinese universities: An extended two-stage network DEA approach. J. Manag. Sci. Eng. 2021, 6, 482–498. [Google Scholar] [CrossRef]
- Demirtas, Y.E.; Kececi, N.F. The efficiency of private pension companies using dynamic data envelopment analysis. Quant. Financ. Econ. 2020, 4, 204–219. [Google Scholar] [CrossRef]
- Kolia, D.L.; Papadopoulos, S. The levels of bank capital, risk and efficiency in the Eurozone and the U.S. in the aftermath of the financial crisis. Quant. Financ. Econ. 2020, 4, 66–90. [Google Scholar] [CrossRef]
- Zhu, N.; Zhu, C.J.; Emrouznejad, A. A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies. J. Manag. Sci. Eng. 2021, 6, 435–448. [Google Scholar] [CrossRef]
- Yao, Y.; Hu, D.; Yang, C.; Tan, Y. The impact and mechanism of fintech on green total factor productivity. Green Financ. 2021, 3, 198–221. [Google Scholar] [CrossRef]
- Yang, C.; Li, T.; Albitar, K. Does Energy Efficiency Affect Ambient PM2.5? The Moderating Role of Energy Investment. Front. Environ. Sci. 2021, 9, 210. [Google Scholar] [CrossRef]
- Su, Y.; Li, Z.; Yang, C. Spatial Interaction Spillover Effects between Digital Financial Technology and Urban Ecological Efficiency in China: An Empirical Study Based on Spatial Simultaneous Equations. Int. J. Environ. Res. Public Health 2021, 18, 8535. [Google Scholar] [CrossRef]
- Jia, S.; Qiu, Y.; Yang, C. Sustainable Development Goals, Financial Inclusion, and Grain Security Efficiency. Agronomy 2021, 11, 2542. [Google Scholar] [CrossRef]
- Li, Z.H.; Zou, F.Q.; Mo, B. Does mandatory CSR disclosure affect enterprise total factor productivity? Econ. Res.-Ekon. Istraz. 2021. [Google Scholar] [CrossRef]
- Li, Z.; Zou, F.; Tan, Y.; Zhu, J. Does Financial Excess Support Land Urbanization-An Empirical Study of Cities in China. Land 2021, 10, 635. [Google Scholar] [CrossRef]
- Zhong, K.; Fu, H.; Li, T. Can the Digital Economy Facilitate Carbon Emissions Decoupling? An Empirical Study Based on Provincial Data in China. Int. J. Environ. Res. Public Health 2022, 19, 6800. [Google Scholar] [CrossRef]
- Cao, J.H.; Law, S.H.; Samad, A.R.B.; Mohamad, W.N.B.W.; Wang, J.L.; Yang, X.D. Impact of financial development and technological innovation on the volatility of green growth-evidence from China. Environ. Sci. Pollut. Res. 2021, 28, 48053–48069. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.L.; Wang, W.L.; Ran, Q.Y.; Irfan, M.; Ren, S.Y.; Yang, X.D.; Wu, H.T.; Ahmad, M. Analysis of the mechanism of the impact of internet development on green economic growth: Evidence from 269 prefecture cities in China. Environ. Sci. Pollut. Res. 2022, 29, 9990–10004. [Google Scholar] [CrossRef] [PubMed]
- Jia, S.; Yang, C.; Wang, M.; Failler, P. Heterogeneous Impact of Land-Use on Climate Change: Study From a Spatial Perspective. Front. Environ. Sci. 2022, 10, 510. [Google Scholar] [CrossRef]
- Aydin, C.; Esen, O.; Aydin, R. Analyzing the economic development-driven ecological deficit in the EU-15 countries: New evidence from PSTR approach. Environ. Sci. Pollut. Res. 2022, 29, 15188–15204. [Google Scholar] [CrossRef] [PubMed]
- Chang, Y.; Huang, Y.; Li, M.; Duan, Z. Threshold Effect in the Relationship between Environmental Regulations and Haze Pollution: Empirical Evidence from PSTR Estimation. Int. J. Environ. Res. Public Health 2021, 18, 2423. [Google Scholar] [CrossRef]
- Chen, X.; Chen, Y.E.; Chang, C.-P. The effects of environmental regulation and industrial structure on carbon dioxide emission: A non-linear investigation. Environ. Sci. Pollut. Res. 2019, 26, 30252–30267. [Google Scholar] [CrossRef]
- Wustenhagen, R.; Bilharz, M. Green energy market development in Germany: Effective public policy and emerging customer demand. Energy Policy 2006, 34, 1681–1696. [Google Scholar] [CrossRef] [Green Version]
- Zeng, S.X.; Meng, X.H.; Zeng, R.C.; Tam, C.M.; Tam, V.W.Y.; Jin, T. How environmental management driving forces affect environmental and economic performance of SMEs: A study in the Northern China district. J. Clean. Prod. 2011, 19, 1426–1437. [Google Scholar] [CrossRef]
- Du, K.; Cheng, Y.; Yao, X. Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. Energy Econ. 2021, 98, 105247. [Google Scholar] [CrossRef]
Variable | Observation | Mean | Standard Deviation | Q25 | Q75 | Source |
---|---|---|---|---|---|---|
3408 | 0.260 | 0.172 | 0.145 | 0.310 | Web search through Python | |
3408 | 0.301 | 0.127 | 0.211 | 0.374 | DEA calculation | |
3408 | 0.210 | 0.190 | 0.089 | 0.252 | Urban Statistical Yearbook of China | |
3408 | 0.330 | 0.248 | 0.143 | 0.442 | Urban Statistical Yearbook of China | |
3408 | 0.089 | 0.092 | 0.019 | 0.128 | Urban Statistical Yearbook of China | |
3408 | 0.654 | 0.145 | 0.571 | 0.768 | Urban Statistical Yearbook of China | |
3408 | 0.417 | 0.122 | 0.346 | 0.478 | Urban Statistical Yearbook of China | |
3408 | 0.309 | 0.142 | 0.216 | 0.375 | Urban Statistical Yearbook of China | |
3408 | 0.746 | 0.161 | 0.649 | 0.871 | Urban Statistical Yearbook of China | |
3408 | 0.517 | 0.286 | 0.269 | 0.768 | National School of Development |
Independent Variable | ||||||
---|---|---|---|---|---|---|
24.229 *** (0.000) | 3.721 *** (0.001) | 24.315 *** (0.000) | 56.423 *** (0.000) | 4.366 *** (0.000) | 56.895 *** (0.000) | |
20.313 *** (0.001) | 3.740 *** (0.002) | 20.374 *** (0.001) | 69.961 *** (0.000) | 6.527 *** (0.000) | 70.689 *** (0.000) |
Independent Variable | m = 1 | |||||
---|---|---|---|---|---|---|
ER | 3.513 (0.742) | 0.534 (0.783) | 3.515 (0.742) | - | - | - |
MAR | 7.737 (0.171) | 1.415 (0.216) | 7.746 (0.171) | - | - | - |
Independent Variable | ||||||
29.096 *** (0.004) | 2.225 *** (0.009) | 29.221 *** (0.004) | 9.326 (0.675) | 0.707 (0.746) | 9.338 (0.674) | |
17.572 (0.063) | 1.606 (0.098) | 17.617 (0.062) | - | - | - |
Independent Variable | ||||
---|---|---|---|---|
AIC | −3.995 | −3.996 | −4.042 | −4.044 |
BIC | −3.970 | −3.953 | −4.020 | −4.007 |
Whether the location parameter is within the range | No | Yes | No | Yes |
Model | PSTR Model | Fixed Effect Model | ||
---|---|---|---|---|
GTFP | GTFP | |||
(1) | (2) | |||
0.408 ** (2.28) | −0.173 * (−1.87) | 0.122 * (1.80) | 0.166 *** (10.02) | |
−1.114 *** (−5.08) | 1.023 *** (4.59) | 0.041 (0.93) | 0.038 (0.97) | |
0.610 (0.95) | −0.679 (−1.04) | 0.111 (1.08) | −0.062 * (−1.92) | |
−0.818 *** (−2.78) | 0.940 *** (3.13) | 0.092 * (1.95) | 0.786 *** (3.00) | |
−0.351 (−0.97) | 0.476 (1.29) | −0.251 *** (−3.63) | −0.043 * (−1.72) | |
1.448 *** (3.32) | −0.938 ** (−2.12) | 0.164 * (1.79) | 0.483 *** (23.86) | |
- | ||||
129.2577 | - | |||
0.416 | - | |||
61.317 | 0.3155 |
Model | PSTR Model | Fixed Effect Model | |
---|---|---|---|
GTFP | GTFP | ||
(1) | (2) | ||
1.332 *** (4.41) | 0.152 * (1.88) | 0.173 *** (8.24) | |
0.093 (1.01) | 0.030 (0.52) | 0.024 (0.60) | |
0.479 *** (2.69) | −0.371 *** (−3.04) | −0.038 (−1.18) | |
−0.354 *** (−2.73) | 0.441 *** (3.55) | 0.735 *** (2.79) | |
0.536 *** (3.94) | −0.031 (−0.32) | −0.036 (−1.40) | |
−0.055 (−0.33) | 0.183 * (1.76) | 0.478 *** (22.70) | |
- | |||
47.7281 | - | ||
0.5092 | - | ||
58.596 | 0.3085 |
Dependent Variable | ||||||
---|---|---|---|---|---|---|
0.408 ** (2.28) | −0.173 * (−1.87) | 0.122 * (1.80) | - | 0.018 (0.58) | 0.147 (1.53) | |
- | - | - | 0.065 *** (9.29) | 0.103 *** (4.19) | 0.492 *** (4.43) | |
−1.114 *** (−5.08) | 1.023 *** (4.59) | 0.041 (0.93) | 0.023 (1.43) | −0.105 *** (−6.31) | −0.357 *** (−5.30) | |
0.610 (0.95) | −0.679 (−1.04) | 0.111 (1.08) | 0.086 *** (6.38) | −0.073 *** (−2.60) | 0.114 (0.81) | |
−0.818 *** (−2.78) | 0.940 *** (3.13) | 0.092 * (1.95) | 0.814 *** (7.40) | 0.107 *** (5.00) | −0.322 *** (−4.39) | |
−0.351 (−0.97) | 0.476 (1.29) | −0.251 *** (−3.63) | 0.052 *** (4.88) | 0.071 *** (2.62) | −0.222 ** (−2.19) | |
1.448 *** (3.32) | −0.938 ** (−2.12) | 0.164 * (1.79) | 0.160 *** (18.83) | 0.491 *** (13.32) | 0.266 * (1.91) | |
- | ||||||
129.2577 | - | 46.5565 | ||||
0.416 | - | 0.5106 | ||||
61.317 | 0.2316 | 60.579 |
Dependent Variable | |||||
---|---|---|---|---|---|
1.332 *** (4.41) | 0.152 * (1.88) | - | 0.650 ** (2.01) | 0.067 (1.61) | |
- | - | 0.063 *** (9.02) | 0.210 *** (3.67) | 0.346 *** (4.58) | |
0.093 (1.01) | 0.030 (0.52) | 0.043 *** (2.63) | 0.114 * (1.71) | 0.076 (1.53) | |
0.479 *** (2.69) | −0.371 *** (−3.04) | 0.087 *** (6.33) | 0.378 ** (2.19) | −0.532 * (−1.74) | |
−0.354 *** (−2.73) | 0.441 *** (3.55) | 0.633 *** (4.44) | −0.343 *** (−2.67) | 0.135 ** (2.55) | |
0.536 *** (3.94) | −0.031 (−0.32) | 0.048 *** (4.48) | 0.123 *** (2.87) | 0.022 (0.42) | |
−0.055 (−0.33) | 0.183 * (1.76) | 0.172 *** (20.44) | 0.078 (0.95) | 0.142 * (1.13) | |
- | |||||
47.7281 | - | 1.1580 | |||
0.5092 | - | 0.4030 | |||
58.596 | 0.3718 | 58.008 |
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Wang, S.; Yang, C.; Li, Z. Green Total Factor Productivity Growth: Policy-Guided or Market-Driven? Int. J. Environ. Res. Public Health 2022, 19, 10471. https://doi.org/10.3390/ijerph191710471
Wang S, Yang C, Li Z. Green Total Factor Productivity Growth: Policy-Guided or Market-Driven? International Journal of Environmental Research and Public Health. 2022; 19(17):10471. https://doi.org/10.3390/ijerph191710471
Chicago/Turabian StyleWang, Shuai, Cunyi Yang, and Zhenghui Li. 2022. "Green Total Factor Productivity Growth: Policy-Guided or Market-Driven?" International Journal of Environmental Research and Public Health 19, no. 17: 10471. https://doi.org/10.3390/ijerph191710471