Does Environmental Regulation Promote Industrial Green Technology Progress? Empirical Evidence from China with a Heterogeneity Analysis
Abstract
:1. Introduction
2. Literature Review and Research Hypothesis
2.1. Literature Review
2.2. Research Hypothesis
3. Model and Data
3.1. Empirical Model
3.1.1. Benchmark Model (i.e., Situation (a) of Research Framework)
3.1.2. Interaction Effect Model (i.e., Situation (b) of Research Framework)
3.1.3. Threshold Effect Model (i.e., Situation (c) of Research Framework)
3.2. Variables and Data
3.2.1. Explained Variable
3.2.2. Main Explanatory Variables
3.2.3. Control Variables
3.2.4. Data Descriptive Statistics
4. Results and Discussion
4.1. Baseline Regression
4.2. Interaction and Threshold Effect Regression
4.2.1. Interaction Regression
4.2.2. Threshold Regression
4.3. Further Analysis: Regional Comparison
4.3.1. Interactive Regression
4.3.2. Threshold Regression in the Central and Western Regions
5. Conclusions and Policy Implications
5.1. Conclusions
5.2. Enlightenment
- (1)
- Optimize the design of environmental regulation tools. Clarify the supervision responsibilities of relevant environmental laws and regulations, strengthen the targeted supervision of local government environmental policy implementation, and ensure that command-based environmental regulation can truly force enterprises to carry out green technology activities. Timely and proactively increase the intensity of environmental regulation, especially the autonomous and market-based regulation tools, and public supervision and market incentives should be combined to promote enterprises to consciously increase the R&D investment in clean technology, so as to further improve the level of industrial green technology. Evaluate the implementation effect of specific regulation tools, and dynamically adjust the environmental regulation system combined with its policy effectiveness. For example, China’s pollution discharge fee has changed to environmental protection taxes since 2018. Regional economic, industrial and technological innovation differences should be considered comprehensively, to formulate environmental regulation policy system in line with local high-quality development according to local conditions.
- (2)
- Strengthen the coordination of various environmental regulations and mine their complementary effects, such as raising the public’s awareness of environmental protection, regulating the public’s participation in ecological construction from the perspective of legislation, ensuring environmental evaluation information is released timely from the institutional perspective, ensuring the effectiveness of the public in the process of supervision and implementation, and reverse the current negative substitution effect between command-based and autonomous regulation tools into a positive synergistic effect. At the same time, increased public awareness of environmental protection can be coordinated with market-based environmental regulations, thus encouraging enterprises to engage in green technology research and development. In addition, when top managers become more aware of environmental protection, they will give more consideration to environmental benefits and improve the level of green technology while considering economic benefits in the decision-making process of enterprises.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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First-Level Indicator | Second-Level Indicator | Measurement and Notation |
---|---|---|
Inputs | Capital | Industrial fixed capital stock |
Labor | Number of industrial employees | |
Energy | Industrial terminal energy consumption | |
Outputs | Output value scale | Gross industrial output |
Pollution emissions | Industrial wastewater emissions | |
Industrial SO2 emissions | ||
Industrial solid waste emissions |
Type | Indicator | Calculation and Variable Notation |
---|---|---|
Command | Number of laws and regulations issued by local governments | Number of laws issued by local governments + Number of regulations issued by local governments |
Number of environmental administrative punishment cases per capital | Number of provincial environmental administrative penalty cases/Provincial total population | |
Market | Per capita pollution charges | The amount of provincial pollution fees paid into the treasury/ Provincial total populational |
Intensity of pollution control investment completion | The amount of investment completed in provincial industrial pollution control/ Provincial industrial added value | |
Autonomous | Number of petitions per capita | Number of provincial petitions (telephone, WeChat, etc.) 1/ Provincial total population |
Number of NPC and CPPCC Proposals | Number of provincial National People’s Congress proposals + Number of provincial CPPCC proposals |
Variables | Eastern | Central | Western | China | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | CV | Mean | SD | CV | Mean | SD | CV | Mean | SD | CV | |
Kc (%) | 1.89 | 1.40 | 0.74 | 1.02 | 0.37 | 0.37 | 0.88 | 0.57 | 0.65 | 1.29 | 1.04 | 0.81 |
Tr (%) | 32.39 | 21.37 | 0.66 | 5.86 | 2.57 | 0.44 | 6.56 | 4.70 | 0.72 | 15.84 | 18.32 | 1.16 |
FDI (%) | 4.45 | 2.48 | 0.56 | 2.07 | 1.03 | 0.50 | 1.07 | 0.86 | 0.80 | 2.58 | 2.23 | 0.87 |
Zl | 2.60 | 1.83 | 0.70 | 1.06 | 0.87 | 0.83 | 0.62 | 0.59 | 0.95 | 1.46 | 1.52 | 1.04 |
S (%) | 46.33 | 11.33 | 0.24 | 38.28 | 5.35 | 0.14 | 40.16 | 4.23 | 0.11 | 41.92 | 8.54 | 0.20 |
P | 1.74 | 0.60 | 0.35 | 1.72 | 0.51 | 0.30 | 1.63 | 0.53 | 0.32 | 1.69 | 0.55 | 0.33 |
Co (%) | 47.68 | 15.90 | 0.33 | 66.32 | 11.13 | 0.17 | 58.73 | 11.90 | 0.20 | 56.70 | 15.27 | 0.27 |
Er1 (order) | 2.47 | 2.89 | 1.17 | 3.11 | 4.72 | 1.52 | 2.10 | 4.72 | 2.24 | 2.51 | 4.15 | 1.66 |
Er2 (case/10,000 person) | 1.24 | 1.52 | 1.22 | 0.60 | 1.34 | 2.24 | 0.42 | 0.29 | 0.69 | 0.77 | 1.22 | 1.58 |
Er3 (RMB yuan/person) | 13.46 | 7.87 | 0.58 | 11.59 | 12.75 | 1.10 | 11.55 | 10.04 | 0.87 | 12.26 | 10.17 | 0.83 |
Er4 (%) | 0.35 | 0.24 | 0.69 | 0.39 | 0.29 | 0.75 | 0.59 | 0.47 | 0.79 | 0.45 | 0.37 | 0.82 |
Er5 (petition/person) | 9.83 | 7.68 | 0.78 | 3.80 | 2.45 | 0.64 | 5.90 | 6.59 | 1.12 | 6.78 | 6.72 | 0.99 |
Er6 (proposal) | 537.2 | 426.9 | 0.79 | 463.9 | 313.9 | 0.68 | 361.5 | 254.9 | 0.71 | 453.2 | 349.6 | 0.80 |
Ze (ton/person) | 0.02 | 0.01 | 0.30 | 0.02 | 0.00 | 0.22 | 0.02 | 0.01 | 0.31 | 0.02 | 0.01 | 0.30 |
Gy | 1.04 | 0.10 | 0.10 | 1.11 | 0.24 | 0.22 | 1.08 | 0.20 | 0.19 | 1.07 | 0.18 | 0.17 |
Variables | T11 FE | T12 FE | T21 FE | T22 FE |
---|---|---|---|---|
0.0002 (0.12) | 0.0019 (0.79) | |||
−0.0001 (−0.84) | ||||
0.0056 (1.35) | 0.0035 (0.24) | |||
−0.0006 (−0.42) | ||||
CX R-squared LR test Hausman test Obs | Yes 0.6684 492.77 *** 23.66 *** 570 | Yes 0.6693 493.33 *** 19.70 *** 570 | Yes 0.6700 491.45 *** 24.74 *** 570 | Yes 0.6703 491.01 *** 26.03 *** 570 |
Variables | T31 RE | T32 RE | T41 FE | T42 FE |
---|---|---|---|---|
0.0016 ** (2.03) | 0.0043 ** (2.52) | |||
−0.0001 *** (−2.80) | ||||
−0.0267 (−1.56) | −0.0987 *** (−2.62) | |||
0.0437 *** (2.59) | ||||
CX R-squared LR test Hausman test Obs | Yes 0.6629 497.80 *** 10.71 570 | Yes 0.6673 500.41 *** 13.05 570 | Yes 0.6691 485.65 *** 20.69 *** 570 | Yes 0.6774 474.838 *** 20.11 *** 570 |
Variables | T51 FE | T52 FE | T61 FE | T62 FE |
---|---|---|---|---|
0.0018 * (1.69) | 0.0026 (0.99) | |||
−0.0001 (−0.33) | ||||
0.0001 * (1.74) | 0.0001 * (1.79) | |||
−0.0001 (−0.60) | ||||
CX R-squared LR test Hausman test Obs | Yes 0.6719 487.75 *** 22.18 *** 570 | Yes 0.6721 486.03 *** 23.19 *** 570 | Yes 0.6685 494.55 *** 47.68 *** 570 | Yes 0.6686 494.86 *** 30.21 *** 570 |
Variables | T1 FE | Variables | T2 RE |
---|---|---|---|
0.0016 (0.98) | 0.0042 (0.45) | ||
0.0076 *** (3.49) | −0.1064 ** (−2.07) | ||
−0.0001 *** (−2.63) | 0.0425 ** (2.23) | ||
0.0058 *** (3.08) | 0.0001 * (1.81) | ||
−0.0001 (−0.13) | 0.0242 (1.37) | ||
−0.0002 (−0.61) | −0.0001 *** (−2.80) | ||
−0.0003 ** (−2.57) | −0.0001 (−0.41) | ||
−0.0083 (−0.36) | 0.0022 (0.12) | ||
−0.0021 ** (−2.40) | −0.0028 *** (−3.56) | ||
0.0239 *** (5.59) | 0.0229 *** (5.51) | ||
0.0763 *** (8.01) | 0.0719 *** (8.48) | ||
−0.0079 *** (−5.82) | −0.0073 *** (−5.45) | ||
0.0292 ** (2.29) | 0.0529 *** (4.93) | ||
−0.0028 *** (−3.21) | −0.0016 ** (−1.99) | ||
R-squared | 0.6891 | R-squared | 0.6810 |
LR test | 479.67 *** | LR test | 465.65 *** |
Hausman test | 33.40 *** | Hausman test | 3.02 |
Obs | 570 | Obs | 570 |
Threshold Variable | Market-ER | Number of Thresholds | F-Statistic | p-Value | Threshold Estimators | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Single | 17.647 ** | 0.022 | 0.034 | 0.019 | 0.035 | ||
Double | 8.694 | 0.118 | |||||
Triple | 7.426 | 0.124 | |||||
Single | 12.477 ** | 0.032 | 0.014 | 0.013 | 0.020 | ||
Double | 4.680 | 0.170 | |||||
Triple | 3.943 | 0.176 |
Variables | T3 | Variables | T4 |
---|---|---|---|
0.0011 (0.65) | 0.0027 (0.30) | ||
0.0019 * (1.91) | −0.1494 *** (−2.62) | ||
−0.0136 *** (−3.30) | 0.0282 (1.06) | ||
0.0041 ** (2.29) | 0.0001 * (1.94) | ||
−0.0000 (−0.13) | 0.0161 (0.94) | ||
−0.0001 (−0.46) | −0.0001 ** (−2.09) | ||
−0.0002 * (−1.66) | −0.0001 (−1.15) | ||
−0.0152 (−0.67) | −0.0009 (−0.04) | ||
−0.0020 ** (−2.29) | −0.0021 ** (−2.41) | ||
0.0260 *** (6.16) | 0.0264 *** (6.31) | ||
0.0838 *** (8.99) | 0.0806 *** (8.67) | ||
−0.0077 *** (−5.73) | −0.0074 *** (−5.49) | ||
0.0425 *** (3.64) | 0.0541 *** (4.59) | ||
−0.0029 *** (−3.27) | −0.0030 *** (−3.39) | ||
R-squared | 0.7188 | R-squared | 0.7016 |
Obs | 570 | Obs | 570 |
Variables | E1 | C1 | W1 | Variables | E2 | C2 | W2 |
---|---|---|---|---|---|---|---|
FE | FE | FE | FE | FE | RE | ||
−0.0020 (−0.49) | −0.0019 (−0.41) | −0.0001 (−0.06) | −0.0027 (−0.34) | −0.0170 (−0.36) | 0.0794 (1.13) | ||
0.0063 *** (3.45) | 0.0069 (1.29) | −0.0123 *** (−3.29) | 0.0196 (−0.53) | −0.3930 ** (−2.36) | 0.0741 (1.50) | ||
0.0004 *** (5.57) | 0.1983 ** (2.29) | ||||||
0.0038 ** (2.33) | 0.0160 * (1.75) | −0.0004 (−0.12) | 0.0001 *** (2.86) | 0.0001 * (1.73) | −0.0001 (−0.51) | ||
0.0001 (0.23) | −0.0018 (−1.53) | 0.0003 (0.78) | −0.0213 (−1.37) | 0.0308 (0.96) | −0.0843 (−1.11) | ||
−0.0001 (−0.59) | 0.0010 (1.24) | 0.0007 (1.39) | 0.0001 (1.40) | 0.0001 (0.15) | 0.0001 (0.21) | ||
−0.0003 *** (−3.24) | −0.0005 *** (−3.72) | −0.0002 (−0.88) | 0.0001 (1.00) | −0.0001 * (−1.77) | −0.0001 (−0.49) | ||
CX | Yes | Yes | Yes | CX | Yes | Yes | Yes |
R-squared | 0.6629 | 0.8249 | 0.7578 | R-squared | 0.6488 | 0.8424 | 0.7032 |
LR test | 96.60 *** | 60.18 *** | 53.63 *** | LR test | 92.34 *** | 63.97 *** | 75.42 *** |
Hausman test | 52.32 *** | 1358.21 *** | 32.61 *** | Hausman test | 46.87 *** | 76.80 *** | 3.20 |
Obs | 209 | 152 | 209 | Obs | 209 | 152 | 209 |
Region | Threshold Variable | Market-ER | Number of Thresholds | F-Statistic | p-Value | Threshold Estimates | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|---|
Single | 46.431 *** | 0.000 | 0.020 | 0.019 | 0.021 | |||
Western | Double | 5.064 | 0.110 | |||||
Triple | 2.655 | 0.202 | ||||||
Single | 9.799 | 0.120 | 0.021 | 0.020 | 0.023 | |||
Central | Double | 18.418 *** | 0.002 | 0.026 | 0.015 | 0.028 | ||
Triple | 9.607 | 0.234 |
Variables | C3 | Variables | W3 |
---|---|---|---|
−0.0471 (−1.21) | 0.0013 (0.66) | ||
0.1318 * (1.80) | −0.0032 (−1.34) | ||
−0.0451 (−0.80) | 0.0057 *** (2.91) | ||
0.1414 ** (2.33) | |||
0.0002 *** (2.75) | −0.0064 (−0.84) | ||
0.0274 (1.11) | −0.0001 (−0.15) | ||
0.0001 (1.15) | 0.0010 (1.10) | ||
−0.0004 *** (−2.64) | 0.0003 (1.34) | ||
CX | Yes | CX | Yes |
R-squared | 0.8813 | R-squared | 0.7730 |
Obs | 152 | Obs | 209 |
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Ji, Y.; Xue, J.; Zhong, K. Does Environmental Regulation Promote Industrial Green Technology Progress? Empirical Evidence from China with a Heterogeneity Analysis. Int. J. Environ. Res. Public Health 2022, 19, 484. https://doi.org/10.3390/ijerph19010484
Ji Y, Xue J, Zhong K. Does Environmental Regulation Promote Industrial Green Technology Progress? Empirical Evidence from China with a Heterogeneity Analysis. International Journal of Environmental Research and Public Health. 2022; 19(1):484. https://doi.org/10.3390/ijerph19010484
Chicago/Turabian StyleJi, Yanli, Jie Xue, and Kaiyang Zhong. 2022. "Does Environmental Regulation Promote Industrial Green Technology Progress? Empirical Evidence from China with a Heterogeneity Analysis" International Journal of Environmental Research and Public Health 19, no. 1: 484. https://doi.org/10.3390/ijerph19010484