Enterprise Green Innovation Mechanism under the “Target-Resource-Network” System—An Empirical Study Based on Data of Listed Companies in China’s Construction Industry
Abstract
:1. Introduction
2. Literature and Theoretical Hypotheses
2.1. Government and Enterprise Target Driving and Enterprise Green Innovation
2.2. Resource Input and Enterprise Green Innovation
2.3. Interaction of Network Relationships and Target-Driven and Resource Input
2.4. The “Target-Resource-Network” System
3. Methods and Indicators
3.1. Sample Selection and Data Sources
3.2. Variable Definition and Indicator Construction
- (1)
- Dependent Variable: green innovation. In this paper, we select the green patent applications of listed construction companies to measure the green innovation level of enterprises, which is expressed by Green. Compared with authorized data, green patent application data have the advantage of timeliness and stability, and are less affected by external factors such as the testing process and political intervention [34], which is more accurate in reflecting the green innovation level of enterprises. This paper selects the green invention and green new utility patent application data because the WIPO green patent list does not include the appearance design patent [9]. All kinds of patent application data are obtained from the CNRDS platform, and given that the number of patent applications of some enterprises in some years is 0— refer to Li Xuesong et al. (2022)—the dependent variable is constructed by adding 1 and taking the logarithm to the corresponding data [35].
- (2)
- Independent variable: target-level indicators. The target proposed in this paper refers to the expectation of the internal and external environment of enterprises to achieve green development. For the treatment of this abstract element of the target level, this paper decomposes it into the government’s environmental targets and the enterprises’ environmental targets from the macro and micro perspectives, which are expressed by Tar1 and Tar2, respectively. According to Zhang G et al. (2022) [36], a machine learning algorithm was applied to evaluate the policy intensity. Compared with the pollutant emission data or policy quantity, the data results obtained by this method can effectively reduce the endogenous problems and represent the actual intensity of China’s environmental policies. The government’s environmental targets data for 2010–2019 were derived from the policy strength scores of the learning-based model provided by Zhang G et al. (2022). The data for 2020 and 2021 were also processed with the same methods provided by the scholars, and will not be repeated here. Finally, the data were standardized and used as the government’s environmental target index.
- (3)
- Mediation variable: resource-level indicators. The resources proposed in this paper refer to the investment of enterprises themselves and the investment of the government in green innovation and environmental protection governance, so the resource level includes government subsidies and enterprise research and development resources, which are expressed in Res1 and Res2, respectively. Regarding the measurement of indicators in the resource level, since it is difficult to separate green R&D personnel from traditional R&D personnel, and statistics for green innovation personnel and green R&D funding are not yet available in the current statistics [6], the two indicators of the resource level are constructed by adding one to the logarithm of both enterprise R&D investment and government subsidies, and the data are obtained from the CNRDS platform.
- (4)
- Adjustment variables: network-level indicators. The network proposed in this paper refers to the density of joint innovation between enterprises and other enterprises or universities and the degree of association between enterprises and the government. Therefore, the network level includes political networks and the joint networks of enterprise innovation, expressed in Net1 and Net2, respectively. The density of the joint networks of enterprise innovation is measured by logarithm of the number of enterprise joint patent applications add 1, with the data obtained from China National Intellectual Property Administration. The networks of business–government associations sets the ranking variables in terms of the nature of the enterprise and the degree of political association of the executives (the value for section-level cadres is 1, the value for division-level cadres is 2, the value for department-level cadres is 3, the value for ministerial-level cadres is 4, and the value for non-political affiliation is 0; the value for state-owned enterprises is 1, and the value for non-state-owned enterprises is 0. For example, if the senior executive of an enterprise is a ministerial-level cadre and the enterprise is a state-owned enterprise, the value is 5). The employment information of enterprise executives mainly comes from CSMAR, and the missing values are manually filled through websites such as Oriental Fortune and other websites.
- (5)
- Control variables. The practice of drawing on relevant literature mainly includes the following control variables: enterprise age (FirmAge), which is the logarithm of the current year minus the year of incorporation plus one; cash flow levels (Cashflow), which is the net cash flow from operating activities divided by total assets; independent director ratio (Indep), which is the number of independent directors divided by the number of directors; whether loss (Loss), if the net profit of the year is less than 0 take 1, otherwise take 0; and equity concentration (Top1), which is the ratio of the number of shares held by the largest shareholder to the total number of shares. The data are obtained from the Cathay Security Database (CSMAR). In addition, this article also controls the year dummy variable and the province dummy variable.
3.3. Model Building
4. Empirical Analysis
4.1. Descriptive Statistics
4.2. Target-Level Elements and Enterprise Green Innovation
4.3. Robustness Test
4.3.1. Replace the Measurement Model
4.3.2. Replacement of Variable Measurement Method
4.3.3. Endogenous Testing
5. Further Analysis
5.1. The Mediating Effect of Resource-Level Elements in the Relationship between Target-Level Elements and Enterprise Green Innovation
5.2. Moderating Effect of Network-Level Elements in the Relationship between Target-Level Elements and Enterprise Green Innovation
5.3. Analysis of the Coupling Mechanism
5.4. Analysis of Heterogeneity
5.4.1. Considering Enterprise’s Own Characteristics
5.4.2. Considering the External Innovation Environment of Enterprises
5.4.3. Considering the Policy Context of Green Innovation
6. Conclusions and Implications
6.1. Conclusions
6.2. Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Definition | Mean | Standard Deviation | Min | Max |
---|---|---|---|---|---|
Green | Enterprise green innovation | 1.7232 | 1.6416 | 0.0000 | 7.0596 |
Tar1 | Government’s environmental targets | 0.0000 | 1.0000 | −1.8906 | 1.3408 |
Tar2 | Enterprises’ environmental targets | 0.0000 | 1.0000 | −1.0777 | 5.2578 |
Res1 | Government subsidized resources | 11.0667 | 7.2663 | 0.0000 | 20.7876 |
Res2 | Corporate R&D resources | 15.5977 | 7.0201 | 0.0000 | 24.4103 |
Net1 | Political networks | 1.2585 | 1.4379 | 0.0000 | 5.0000 |
Net2 | Joint networks of enterprise innovation | 0.3542 | 0.8594 | 0.0000 | 5.2204 |
FirmAge | Enterprise age | 2.8758 | 0.4414 | 1.0986 | 3.6376 |
Cashflow | Cash flow levels | -0.0079 | 0.3929 | −11.0562 | 0.4300 |
Indep | Independent Director Ratio | 0.3883 | 0.0759 | 0.1667 | 0.8000 |
Loss | Whether loss | 0.1151 | 0.3194 | 0.0000 | 1.000 |
Top1 | Equity concentration | 0.3735 | 0.1556 | 0.0442 | 0.7859 |
Green | ||||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Tar1 | 0.2602 *** | 0.6494 *** | 0.4861 *** | |
(0.0506) | (0.0708) | (0.0697) | ||
Tar2 | 0.5402 *** | 0.4226 *** | 0.4450 *** | |
(0.0651) | (0.0559) | (0.0548) | ||
L.Tar1 | 0.4757 *** | |||
(0.0760) | ||||
L.Tar2 | 0.3734 *** | |||
(0.0581) | ||||
FirmAge | −1.0399 *** | −1.1705 *** | −1.2044 *** | |
(0.1421) | (0.1513) | (0.1547) | ||
Cashflow | −0.0809 * | 0.1838 | 0.2252 | |
(0.0437) | (0.3896) | (0.3949) | ||
Indep | 3.6350 *** | 3.8370 *** | 3.8014 *** | |
(0.6875) | (0.6800) | (0.6993) | ||
Loss | −0.2931 ** | −0.3464 ** | −0.3629 ** | |
(0.1385) | (0.1414) | (0.1418) | ||
Top1 | 1.4445 *** | 1.4769 *** | 1.4786 *** | |
(0.3173) | (0.3387) | (0.3471) | ||
Constant | 1.7232 *** | 3.2747 *** | 3.7944 *** | 3.9864 *** |
(0.0511) | (0.5848) | (0.6210) | (0.6393) | |
Year fixed effect | YES | YES | YES | YES |
Province fixed effect | YES | YES | YES | YES |
N | 886 | 886 | 782 | 782 |
R2-adj | 0.1429 | 0.4805 | 0.4970 | 0.4792 |
Tobit Model | Replace the Explanatory Variable | Endogenous Problems | ||
---|---|---|---|---|
Green | Green1 | Green2 | Green | |
Tar1 | 0.6720 *** | 0.4803 *** | 0.5695 *** | |
(0.0718) | (0.0634) | (0.0623) | ||
Tar2 | 0.5880 *** | 0.3726 *** | 0.3436 *** | |
(0.0630) | (0.0509) | (0.0492) | ||
L.Tar1 | 0.4924 *** | |||
(0.0787) | ||||
L.Tar2 | 0.3734 *** | |||
(0.0581) | ||||
Constant | 1.3722 ** | 2.5933 *** | 2.6719 *** | 3.8681 *** |
(0.6888) | (0.5330) | (0.5258) | (0.6387) | |
Control variables | YES | YES | YES | YES |
Year fixed effect | No | YES | YES | YES |
Province fixed effect | No | YES | YES | YES |
N | 886 | 886 | 886 | 782 |
R2-adj | - | 0.4268 | 0.4896 | 0.4792 |
Res1 | Green | Res1 | Green | Res2 | Green | Res2 | Green | |
---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
Tar1 | −1.1652 *** | 0.7871 *** | 3.5588 *** | 0.4055 *** | ||||
(0.3785) | (0.0620) | (0.4058) | (0.0673) | |||||
Tar2 | 0.7282 *** | 0.3914 *** | 1.0112 *** | 0.3401 *** | ||||
(0.2329) | (0.0529) | (0.2153) | (0.0519) | |||||
Res1 | 0.0493 *** | 0.0428 *** | ||||||
(0.0071) | (0.0069) | |||||||
Res2 | 0.0911 *** | 0.0816 *** | ||||||
(0.0065) | (0.0066) | |||||||
Constant | 11.3412 *** | 3.5022 *** | 12.4504 *** | 1.5139 *** | 26.3997 *** | 1.6566 *** | 18.1533 *** | 0.5655 |
(2.9367) | (0.5413) | (2.8665) | (0.5208) | (2.5976) | (0.5607) | (2.6659) | (0.5265) | |
Control variables | YES | YES | YES | YES | YES | YES | YES | YES |
Year fixed effect | YES | YES | YES | YES | YES | YES | YES | YES |
Province fixed effect | YES | YES | YES | YES | YES | YES | YES | YES |
N | 886 | 886 | 886 | 886 | 886 | 886 | 886 | 886 |
R2-adj | 0.3246 | 0.4590 | 0.3319 | 0.5039 | 0.4040 | 0.5173 | 0.4201 | 0.5506 |
Sobel tests the p value | 0.0029 | 0.0041 | 0.0000 | 5.172 × 10−6 | ||||
Percentage of mediation effect/% | 7.8700% | 7.3785% | 44.4231% | 19.5270% |
Green | |||||
---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | |
Tar1 | 0.7382 *** | ||||
(0.0605) | |||||
Tar2 | 0.3304 *** | 0.3625 *** | 0.2610 *** | 0.3247 *** | |
(0.0689) | (0.0532) | (0.0668) | (0.0520) | ||
Res1 | 0.0382 *** | 0.0407 *** | 0.0325 *** | ||
(0.0074) | (0.0070) | (0.0072) | |||
Res2 | 0.0817 *** | 0.0719 *** | |||
(0.0066) | (0.0063) | ||||
Net1 | 0.0981 *** | 0.1098 *** | |||
(0.0300) | (0.0283) | ||||
Net2 | 0.2012 ** | 0.1650 * | −0.4838 | ||
(0.1010) | (0.0955) | (0.4948) | |||
Res1 ×Net1 | |||||
Res1 ×Net2 | 0.0175 *** | 0.0176*** | |||
(0.0063) | (0.0061) | ||||
Res2 × Net2 | 0.0386 * | ||||
(0.0232) | |||||
Tar2 ×Net1 | 0.0490 * | 0.0612 ** | |||
(0.0294) | (0.0273) | ||||
Constant | 3.9383 *** | 1.3779 *** | 2.0523 *** | 0.3770 | 1.0794 ** |
(0.5287) | (0.5281) | (0.5101) | (0.5321) | (0.5103) | |
Control variables | YES | YES | YES | YES | YES |
Year fixed effect | YES | YES | YES | YES | YES |
Province fixed effect | YES | YES | YES | YES | YES |
N | 886 | 886 | 886 | 886 | 886 |
R2-adj | 0.5045 | 0.5102 | 0.5428 | 0.5595 | 0.5765 |
Year | South Region | East Region | North-East Region | South-West Region | Central Region | North-West Region | North Region | Mean |
---|---|---|---|---|---|---|---|---|
2010 | 0.1771 (D−−−−) | 0.2897 (D−−−) | 0.1978 (D−−−−) | 0.0000 (D−−−−−) | 0.4911 (D−−) | 0.2652 (D−−−) | 0.3654 (D−−) | 0.2552 (D−−−) |
2011 | 0.2794 (D−−−) | 0.3904 (D−−) | 0.2811 (D−−−) | 0.2865 (D−−−) | 0.5877 (D) | 0.4697 (D−) | 0.4284 (D−) | 0.3890 (D−−) |
2012 | 0.2830 (D−−−) | 0.4735 (D−) | 0.3198 (D−−) | 0.3163 (D−−) | 0.6875 (D+) | 0.5227 (D) | 0.5174 (D) | 0.4457 (D−) |
2013 | 0.2844 (D−−−) | 0.3676 (D−−) | 0.2518 (D−−−) | 0.2326 (D−−−) | 0.6267 (D+) | 0.4039 (D−) | 0.4282 (D−) | 0.3707 (D−−) |
2014 | 0.3191 (D−−) | 0.4169 (D−) | 0.3263 (D−−) | 0.2896 (D−−−) | 0.6516 (D+) | 0.5147 (D) | 0.4831 (D−) | 0.4288 (D−) |
2015 | 0.2657 (D−−−) | 0.3794 (D−−) | 0.4330 (D−) | 0.3666 (D−−) | 0.6680 (D+) | 0.5538 (D) | 0.5610 (D) | 0.4611 (D−) |
2016 | 0.2666 (D−−−) | 0.3342 (D−−) | 0.3888 (D−−) | 0.3081 (D−−) | 0.2691 (D−−−) | 0.3736 (D−−) | 0.4988 (D−) | 0.3485 (D−−) |
2017 | 0.3302 (D−−) | 0.3793 (D--) | 0.3863 (D−−) | 0.3444 (D−−) | 0.3549 (D−−) | 0.3490 (D−−) | 0.5415 (D) | 0.3837 (D−−) |
2018 | 0.3412 (D−−) | 0.3975 (D−−) | 0.4615 (D−) | 0.3942 (D−−) | 0.4187 (D−) | 0.4853 (D−) | 0.6264 (D+) | 0.4464 (D−) |
2019 | 0.2776 (D−−−) | 0.3943 (D−−) | 0.3994 (D−−) | 0.3622 (D−−) | 0.4746 (D−) | 0.5925 (D) | 0.5786 (D) | 0.4399 (D−) |
2020 | 0.2819 (D−−−) | 0.4401 (D−) | 0.4560 (D−) | 0.5203 (D) | 0.3665 (D−−) | 0.5178 (D) | 0.5553 (D) | 0.4483 (D−) |
2021 | 0.2481 (D−−−) | 0.3618 (D−−) | 0.4367 (D−) | 0.4153 (D−) | 0.2098 (D−−−) | 0.6397 (D+) | 0.4538 (D−) | 0.3950 (D−−) |
Mean | 0.2841 (D−−−) | 0.3866 (D−−) | 0.3615 (D−−) | 0.3514 (D−−) | 0.4210 (D-) | 0.4873 (D−) | 0.5114 (D) | 0.4005 (D−) |
Green | ||||||
---|---|---|---|---|---|---|
State-Owned Enterprises | Non-State-Owned Enterprises | Managers Are Older | Managers Are Younger | Women Are Predominant | Women Are Underrepresented | |
Tar1 | 0.4617 *** | 0.1204 | 0.2891 ** | 0.2347 * | 0.3453 ** | 0.2627 ** |
(0.0949) | (0.1404) | (0.1185) | (0.1226) | (0.1513) | (0.1048) | |
Tar2 | 0.1981 *** | 0.1466 ** | 0.2648 *** | 0.1035 * | 0.2775 *** | 0.0795 |
(0.0536) | (0.0724) | (0.0649) | (0.0605) | (0.0702) | (0.0574) | |
Constant | 2.5625 ** | −0.3953 | 1.5866 | −0.8335 | 0.0534 | 0.3891 |
(1.0789) | (1.6003) | (1.1240) | (1.4719) | (1.8230) | (1.1229) | |
Control variables | YES | YES | YES | YES | YES | YES |
Year fixed effect | YES | YES | YES | YES | YES | YES |
Province fixed effect | YES | YES | YES | YES | YES | YES |
N | 453 | 433 | 412 | 474 | 398 | 488 |
R2-adj | 0.3956 | 0.1892 | 0.2908 | 0.1790 | 0.1651 | 0.2895 |
Green | ||||
---|---|---|---|---|
Eastern Region | Midwest Region | Cities with High Innovation Resources | Cities with Low Innovation Resources | |
Tar1 | 0.3138 *** | 0.2991 | 0.2956 *** | 0.2318 |
(0.0875) | (0.1954) | (0.0824) | (0.2956) | |
Tar2 | 0.1365 *** | 0.4674 *** | 0.1522 *** | 0.1267 |
(0.0449) | (0.1262) | (0.0463) | (0.1042) | |
Constant | 1.2303 | 1.0212 | 1.4640 | −4.7255 |
(0.9434) | (2.4649) | (0.9011) | (3.8720) | |
Control variables | YES | YES | YES | YES |
Year fixed effect | YES | YES | YES | YES |
Province fixed effect | YES | YES | YES | YES |
N | 699 | 187 | 776 | 110 |
R2-adj | 0.3111 | 0.2801 | 0.2568 | 0.5772 |
Green | ||
---|---|---|
Phase 1 (2010–2015) | Phase 2 (2015–2021) | |
Tar1 | 0.2995 *** | 0.8691 *** |
(0.1092) | (0.2379) | |
Tar2 | 0.0358 | 0.2624 *** |
(0.0528) | (0.0641) | |
Constant | −1.0944 | 8.2010 *** |
(1.3731) | (1.9565) | |
Control variables | YES | YES |
Year fixed effect | YES | YES |
Province fixed effect | YES | YES |
N | 344 | 607 |
R2-adj | 0.0862 | 0.0295 |
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Chen, S.; Wang, J.; Zhang, Z. Enterprise Green Innovation Mechanism under the “Target-Resource-Network” System—An Empirical Study Based on Data of Listed Companies in China’s Construction Industry. Sustainability 2023, 15, 3687. https://doi.org/10.3390/su15043687
Chen S, Wang J, Zhang Z. Enterprise Green Innovation Mechanism under the “Target-Resource-Network” System—An Empirical Study Based on Data of Listed Companies in China’s Construction Industry. Sustainability. 2023; 15(4):3687. https://doi.org/10.3390/su15043687
Chicago/Turabian StyleChen, Songchuan, Jinhang Wang, and Zhiwei Zhang. 2023. "Enterprise Green Innovation Mechanism under the “Target-Resource-Network” System—An Empirical Study Based on Data of Listed Companies in China’s Construction Industry" Sustainability 15, no. 4: 3687. https://doi.org/10.3390/su15043687