Does the Water Resource Tax Reform Bring Positive Effects to Green Innovation and Productivity in High Water-Consuming Enterprises?

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Does the Water Resource Tax Reform Bring Positive Effects to Green Innovation and Productivity in High Water-Consuming Enterprises? 1 2 * Water 2024 , 16 (5), 725; https://doi.org/10.3390/w16050725 (registering DOI) Abstract : 1. Introduction 2. Literature Review 2.1. Water Resource Tax 2.2. Corporate Green Innovation 2.3. Total Factor Productivity of Enterprises 3. Theoretical Analysis and Research Hypothesis Hypothesis 1: Hypothesis 2: Hypothesis 3: 4. Research Design 4.1. Sample Selection 4.2. Variable Measurement it = Treated i × Time t . The term Treated i is a policy group dummy variable and has a value of 1 if the province where the firm is located has implemented the water tax reform, and 0 if it has not. The term Timet is a time dummy variable. Based on timing and sequential differences in the water resource tax reform, this variable has a value of 1 in the year of the reform pilot and later; otherwise, it is 0. Hebei Province was the first to start the water resource tax reform pilot on 1 July 2016. In 2017, the pilot scope of the reform was expanded to nine provinces: Beijing, Tianjin, Shanxi, Inner Mongolia, Shandong, Henan, Sichuan, Shaanxi, and Ningxia. 4.3. Model Setup 1 in Equation (2) is expected to be significantly positive, which indicates that the water tax reform will significantly promote corporate green innovation. 1 in Equation (3) is significantly positive, which indicates that enterprise green innovation will significantly enhance total factor productivity. 5. Empirical Results and Analysis 5.1. Descriptive Statistics and Pearson Correlation Analysis 5.2. Benchmark Regression Analysis 5.3. Robustness Tests 5.3.1. Fixed Effects Model 5.3.2. Substitution of Key Variables 5.3.3. PSM-DID Model 5.4. Intrinsic Mechanism of Action Test 5.5. Heterogeneity Analysis 6. Conclusions Author Contributions Funding Data Availability Statement Conflicts of Interest References Yao, P.; Li, J. 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Explanatory variable Water resource tax reform TT Whether to carry out pilot water resource tax reform, represented by the dummy variable TT, TT it = Treated i × Time t Control variable Company profitability ROA Net profit margin on total assets Company growth Growth Total assets growth rate Financial leverage Lev Asset–liability ratio Independent director governance Id The proportion of independent directors to the size of the board of directors Director board size Bs Total number of directors in the board of directors Governance of major shareholders Msg Shareholding ratio of the largest shareholder CEO duality Pt The value of the general manager concurrently serving as the chairman is 1; otherwise, the value is 0. Managerial ownership MS Proportion of shares held by company executives Executive compensation MC The total monetary compensation of company executives is calculated as the natural logarithm. Product market competition HHI HHI = represents the size of the i-th enterprise, and represents the total market size. Digital transformation DT Data calculation of text mining based on digital lexicon Financial subsidy FS (Government subsidies—returns of various taxes and fees received)/total assets Tax incentives TI Returns of various taxes and fees received/total assets Industry Industry Industry dummy variable Year Year Year dummy variable Variable Mean Median Max Min SD Obs TFP_LP 10.9894 10.8418 14.7543 4.4336 1.2855 8949 GI 0.2082 0 6.6983 0 0.5843 8949 TT 0.1406 0 1 0 0.3476 8949 ROA 0.0410 0.0363 0.6271 -0.6449 0.0709 8949 Growth 0.1326 0.0780 19.0954 -0.8490 0.4286 8949 Lev 0.4453 0.4448 0.9970 0.0080 0.2014 8949 Id 0.3724 0.3333 0.8 0.1429 0.0555 8949 Msg 0.3661 0.3468 0.8999 0.0029 0.1559 8949 Pt 0.2206 0 1 0 0.4146 8949 Bs 8.9136 9 18 0 1.9163 8949 MS 0.0507 0.0001 0.7259 0 0.1216 8949 MC 14.7365 14.7958 18.5844 0 1.1287 8949 HHI 0.1248 0.1049 1 0.0144 0.1138 8949 DT 0.7037 0 5.0689 0 0.9758 8949 FS 0.0048 0.0023 0.4212 0 0.0166 8949 TI 0.0006 0 0.1132 0 0.0029 8949 Variable TFP_LP GI TT ROA Growth Lev Id TFP_LP 1 GI 0.3779 *** 1 TT 0.1292 *** 0.1051 *** 1 ROA 0.1375 *** −0.0017 0.0267 ** 1 Growth −0.0140 −0.0230 ** −0.0273 *** 0.1735 *** 1 Lev 0.3670 *** 0.1195 *** −0.0223 ** −0.3497 *** −0.0057 1 Id 0.0271 ** 0.0287 *** 0.0157 0.0080 0.0020 −0.0521 *** 1 Msg 0.3281 *** 0.1503 *** −0.0397 *** 0.1236 *** −0.0196 * 0.0588 *** 0.0551 *** Pt −0.1547 *** -0.0693 *** −0.0547 *** 0.0394 *** 0.0297 *** −0.1432 *** 0.1180 *** Bs 0.2549 *** 0.1430 *** −0.0026 0.0115 −0.0265 ** 0.2334 *** −0.6101 *** MS −0.1822 *** −0.0633 *** −0.0630 *** 0.1026 *** 0.0461 *** −0.2433 *** 0.1246 *** MC 0.3162 *** 0.1719 *** 0.1081 *** 0.1823 *** 0.0099 −0.0474 *** −0.0033 HHI −0.0911 *** −0.0116 −0.0868 *** −0.0891 *** −0.0326 ** 0.0177 0.0093 DT 0.0149 −0.0194 −0.0187 0.0144 −0.0061 0.0153 0.0114 FS −0.0365 ** −0.0599 *** 0.0175 0.0504 *** −0.0037 −0.0074 0.0189 TI 0.1645 *** 0.0590 *** 0.0335 ** −0.0261 * 0.0237 −0.0068 0.0956 *** Variable Msg Pt Bs MS MC HHI DT Msg 1 Pt 0.0518 *** 1 Bs 0.0806 *** −0.0755 *** 1 MS −0.3995 *** 0.0803 *** −0.1653 *** 1 MC 0.1003 *** −0.0647 *** 0.4409 *** −0.1910 *** 1 HHI 0.0408 *** −0.0296 *** 0.0534 *** 0.0688 *** 0.0514 *** 1 DT −0.0002 −0.0015 −0.0218 0.0446 *** −0.0390 *** 0.0148 1 FS 0.0458 *** 0.0380 *** −0.0441 *** 0.0432 *** 0.0369 ** −0.0513 *** −0.0015 TI 0.0331 ** 0.0629 *** −0.0717 *** 0.0654 *** 0.2166 *** 0.0732 *** −0.0077 Variable FS TI FS 1 TI 0.0358 ** 1 Variable Model (1) Model (2) Model (3) TT 0.1147 *** (4.49) 0.1029 *** (5.73) GI 0.3547 *** (24.29) ROA 3.4274 *** (27.16) −0.0214 (−0.24) 3.4413 *** (28.14) Growth −0.0775 *** (−4.15) −0.0189 (−1.44) −0.0716 *** (−3.96) Lev 1.6378 *** (35.40) 0.1726 *** (5.31) 1.5783 *** (35.14) Id 0.9539 *** (6.07) 0.6019 *** (5.45) 0.7413 *** (4.85) Msg 1.3241 *** (24.11) 0.4250 *** (11.02) 1.1692 *** (21.83) Pt −0.1292 *** (−6.06) −0.0361 ** (−2.41) −0.1197 *** (−5.80) Bs 0.0644 *** (12.99) 0.0365 *** (10.47) 0.0522 *** (10.80) MS −0.3707 *** (−4.95) −0.0710(−1.35) −0.3620 *** (−5.00) MC 0.2051 *** (26.57) 0.0594 *** (10.95) 0.1834 *** (24.36) HHI 0.1317 (0.83) 0.1510 (1.36) 0.0730 (0.48) DT 3.7172 (1.36) 2.1639 (1.13) 2.9031 (1.09) FS −3.6460 *** (−7.71) −0.4637 (−1.40) −3.4948 *** (−7.62) TI 0.1054 *** (11.16) 0.0069 (1.04) 0.1019 *** (11.14) Constant 2.8642 *** (19.23) −1.7303 *** (−16.54) 3.4882 *** (23.80) Year/industry Yes Yes Yes Adjust_R 2 0.4800 0.2191 0.5113 Obs 8949 8949 8949 Variable Model (1) Model (2) Model (3) TT 0.1147 *** (4.49) 0.1029 *** (5.73) GI 0.3547 *** (24.29) Control i,t Yes Yes Yes Constant 3.1830 *** (21.53) −1.6088 *** (−15.49) 3.7774 *** (26.05) Year/industry Yes Yes Yes Adjust_R 2 0.4639 0.2048 0.4966 Obs 8949 8949 8949 Variable Replacing the Explanatory Variables Replacing the Explained Variables Model (2) Model (3) Model (1) Model (3) TT 0.3387 *** (13.49) 0.0732 *** (3.83) GI′ 0.3135 *** (34.20) GI 0.1303 *** (10.57) Control i,t Yes Yes Yes Yes Constant −2.7356 *** (−17.19) 3.7379 *** (26.46) 0.9412 *** (7.77) 1.1697 *** (9.56) Year/industry Yes Yes Yes Yes Adjust_R 2 0.2442 0.5264 0.2499 0.2580 Obs 8949 8949 8949 8949 Column 1: TT → TFP_LP Weighted variable(s) Mean control Mean treated Diff. |t| Pr (|T| > |t|) TFP_OLS 8.275 8.458 0.182 5.63 0.0000 *** ROA 0.037 0.039 0.002 0.88 0.3773 Growth 0.145 0.150 0.005 0.32 0.7504 Lev 0.493 0.491 −0.002 0.33 0.7419 Id 0.364 0.364 0.000 0.13 0.8999 Msg 0.384 0.390 0.006 1.12 0.2642 Pt 0.117 0.112 −0.004 0.45 0.6532 Bs 9.415 9.429 0.013 0.20 0.8416 MS 0.023 0.024 0.001 0.38 0.7046 MC 14.402 14.427 0.025 0.56 0.5783 HHI 0.134 0.139 0.005 1.02 0.3087 TI 0.001 0.001 0.000 0.18 0.8593 FS 0.004 0.004 0.000 0.35 0.7250 DT 0.311 0.310 -0.001 0.06 0.9529 Column 2: TT → GI Weighted variable(s) Mean control Mean treated Diff. |t| Pr (|T| > |t|) GI 0.128 0.219 0.091 5.18 0.0000 *** ROA 0.037 0.039 0.002 0.88 0.3773 Growth 0.145 0.150 0.005 0.32 0.7504 Lev 0.493 0.491 −0.002 0.33 0.7419 Id 0.364 0.364 0.000 0.13 0.8999 Msg 0.384 0.390 0.006 1.12 0.2642 Pt 0.117 0.112 −0.004 0.45 0.6532 Bs 9.415 9.429 0.013 0.20 0.8416 MS 0.023 0.024 0.001 0.38 0.7046 MC 14.402 14.427 0.025 0.56 0.5783 HHI 0.134 0.139 0.005 1.02 0.3087 TI 0.001 0.001 0.000 0.18 0.8593 FS 0.004 0.004 0.000 0.35 0.7250 DT 0.311 0.310 −0.001 0.06 0.9529 Variable Path c (Model with dv Regressed on iv) Path a (Model with Mediator Regressed on iv) Paths b and c’ (Model with dv Regressed on Mediator and iv) TT 0.2412 *** (9.64) 0.1550 *** (9.02) 0.1847 *** (7.59) GI 0.3646 *** (24.44) Control i,t Yes Yes Yes Constant 2.4059 *** (17.11) −1.9716 *** (-20.42) 3.1247 *** (22.43) Year/industry Yes Yes Yes Adjust_R 2 0.3682 0.0903 0.4077 Obs 8949 8949 8949 Column 1: Sobel–Goodman Mediation Tests Est Std_err z P > |z| Sobel 0.057 0.007 8.463 0.000 Aroian 0.057 0.007 8.457 0.000 Goodman 0.057 0.007 8.469 0.000 Column 2: Indirect, Direct, and Total Effects Est Std_err z P > |z| a_coefficient 0.155 0.017 9.021 0.000 b_coefficient 0.365 0.015 24.441 0.000 Indirect_effect_aXb 0.057 0.007 8.463 0.000 Direct_effect_c’ 0.185 0.024 7.586 0.000 Total_effect_c 0.241 0.025 9.637 0.000 Proportion of total effect that is mediated: 0.234 Ratio of indirect to direct effect: 0.306 Ratio of total to direct effect: 1.306 Variable SOEs Non-SOEs Model (1) Model (2) Model (3) Model (1) Model (2) Model (3) TT 0.0484(1.17) 0.0615 * (1.88) 0.1149 *** (3.79) 0.0640 *** (3.73) GI 0.3704 *** (19.84) 0.1708 *** (6.63) Control i,t Yes Yes Yes Yes Yes Yes Constant 3.0283 *** (15.46) −2.0909 *** (−13.49) 3.8029 *** (19.88) 3.4272 *** (13.03) −0.6637 *** (−4.46) 3.5634 *** (13.57) Year/industry Yes Yes Yes Yes Yes Yes Adjust_R 2 0.5202 0.3210 0.5613 0.4537 0.0834 0.4571 Obs 4237 4237 4237 4712 4712 4712 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style
Xu, C.; Gao, Y.; Hua, W.; Feng, B.
Does the Water Resource Tax Reform Bring Positive Effects to Green Innovation and Productivity in High Water-Consuming Enterprises? Water 2024 , 16 , 725.
https://doi.org/10.3390/w16050725
AMA Style
Xu C, Gao Y, Hua W, Feng B.
Does the Water Resource Tax Reform Bring Positive Effects to Green Innovation and Productivity in High Water-Consuming Enterprises? Water . 2024; 16(5):725.
https://doi.org/10.3390/w16050725
Chicago/Turabian Style
Xu, Chaohui, Yingchao Gao, Wenwen Hua, and Bei Feng.
2024. “Does the Water Resource Tax Reform Bring Positive Effects to Green Innovation and Productivity in High Water-Consuming Enterprises?” Water 16, no. 5: 725.
https://doi.org/10.3390/w16050725