<sup>5</sup> Different kinds of environmental regulation policies

In order to reflect the heterogeneity of different environmental regulation policies, we considered two pilot environmental policies as representatives of market-oriented and command-and-control environmental regulations and adopted the difference-in-differences (DID) model to study this problem.

We use carbon emission trading pilot cities as a proxy for market-oriented environmental regulation. This is a Chinese policy launched in 2011, which is similar to the European Union Emissions Trading System (EU-ETS) and aims to achieve optimal economic output with minimal environmental costs. We took the pilot city as the experimental group and the non-pilot city as the control group and designed a DID model as model (9), in which the POL variable was a dummy variable. When the city was included in the list of pilot cities, it was set as 1; otherwise, it was set as 0, and the remaining variables remained unchanged. We focused on the regression coefficient and significance of POL. The regression results are reported in Table 10. Column (1) is listed as the two-way fixed effect regression results without control variables, and Columns (2)–(4) are listed as the regression results of the year fixed effect and city fixed effect gradually added after the addition of control variables. It can be seen that the coefficient of POL has been significantly negative at the level of 1%, which is consistent with the baseline regression results of this paper. It shows that market-oriented environmental regulation can also harm the improvement of urban innovation levels.

$$Innovation\_{\rm it} = \mathfrak{a} + \beta\_1 POL\_{\rm it} + \sum\_{j} \beta\_j \, control\_{\rm it} + \mu\_i + \gamma\_t + \varepsilon\_{\rm it} \tag{9}$$


**Table 10.** DID regression results of market-oriented environmental regulation.

Note: \*\*\*, \*\* and \* represent the significance at the 1%, 5% and 10% levels, respectively. The *t*-values are in parentheses.

Secondly, we adopt the Air pollution Prevention and Control Action Plan policy as the representative of command-controlled environmental regulation. The Air Pollution Prevention and Control Action Plan is a policy initiated by China in 2013. It is issued by the State Council on the air pollution prevention and control Action Plan, which makes mandatory requirements for the reduction of the concentration of inhalable particulate matter in different regions of the country. Therefore, it can be used as a representative of command-controlled environmental regulation policy. We took the pilot city as the experimental group and the non-pilot city as the control group and designed a DID model as model (10), in which the variable of AIR was a dummy variable. When the city was included in the list of pilot cities, it was set as 1; otherwise, it was set as 0, and the remaining variables remained unchanged. We focused on the regression coefficient and significance of AIR. The regression results are reported in Table 11. Column (1) is listed as the twoway fixed effect regression result without control variables, Columns (2)–(4) are listed as the regression results of the year fixed effect and city fixed effect gradually added after the addition of control variables. It can be found that the coefficient of AIR is always significantly negative at the level of 1%, which is consistent with the benchmark regression result of this paper. It shows that command-and-control environmental regulation will also damage the improvement of urban innovation levels.

$$Innovation\_{it} = \alpha + \beta\_1 AIR\_{it} + \sum\_{j} \beta\_j \cdot control\_{it} + \mu\_i + \gamma\_t + \varepsilon\_{it} \tag{10}$$


**Table 11.** DID regression results of command-controlled environmental regulation.


**Table 11.** *Cont.*

Note: \*\*\*, \*\* and \* represent the significance at the 1%, 5% and 10% levels, respectively. The *t*-values are in parentheses.

#### *4.3. Analysis of the Impact Mechanisms*

Regarding the previous baseline regression findings, we concluded that the environmental regulation intensity was strongly negatively related to the innovation capacity of cities during the sample period. We believe that there are two potential mechanisms: government subsidy and enterprise operation. Firstly, with the increase of environmental regulations, the decline in business efficiency of enterprises in the short term will bring a significant decline in local fiscal revenue, and the local government may be forced to reduce the subsidy support for enterprises' innovation, which will bring a restraining effect on urban innovation ability. Secondly, since most Chinese manufacturing enterprises are still in the transition stage from extensive production to efficient production, blindly strengthening environmental regulation intensity is easy to lead to the decline of local manufacturing output and obstacles to technology research, thus restricting the improvement of urban innovation ability.

Thus, we then clarified the specific mechanism of environmental regulations in affecting urban innovation capacity by constructing the corresponding mediating effect model with two mediating variables: regional fiscal revenue and regional manufacturing output. Drawing on the practice of Xu and Liu [18], the mediating effect was verified by investigating the regression coefficients of a recursive simultaneous equation using a stepwise approach. Taking regional revenue (REV) as a mediating variable, the following test model was constructed.

$$Innovation\_{\rm if} = \alpha + \beta\_1ERS\_{\rm if} + \sum\_{j} \beta\_j \cdot control\_{\rm it} + \mu\_i + \gamma\_t + \varepsilon\_{\rm it} \tag{11}$$

$$REV\_{it} = \mathfrak{a}\_2 + \delta\_1 ERS\_{it} + \sum\_j \delta\_j \operatorname{control}\_{it} + \mu\_i + \gamma\_t + \varepsilon\_{it} \tag{12}$$

$$Innovation\_{\rm it} = \mu\_3 + \omega\_1 ERS\_{\rm it} + \omega\_2 REV\_{\rm it} + \sum\_{j} \mathcal{OL}\_j \, control\_{\rm it} + \mu\_i + \gamma\_t + \varepsilon\_{\rm it} \tag{13}$$

We tested, in turn, the coefficients *β*<sup>1</sup> of the stepwise model (11), *δ*<sup>1</sup> of model (12) and *ω*<sup>2</sup> of model (13). If all three coefficients were significant, the mediating effect of REV was significant. This indicated that environmental regulation would influence the explained variable urban innovation capacity through the mediating variable REV, with a mediating effect size of *δ*<sup>1</sup> × *ω*2. The regression results of regional fiscal revenue are presented in Columns (1)–(4) of Table 12. The mediating effect of regional fiscal revenue was *δ*<sup>1</sup> × *ω*2, which was significantly negative. This indicated that environmental regulation negatively influenced urban innovation capacity through the mediating effect of regional fiscal revenue. Specifically, Column (2) in Table 12 proved the negative relationship between environmental regulation and regional fiscal revenue. Concurrently, Column (3) of Table 12 showed that an increase in fiscal revenue could enhance the city's innovation capacity. Ultimately, enhanced environmental regulation will bring reduced regional fiscal revenue, which in turn will lead to a decrease in local government support for enterprise innovation policies and, ultimately, a decrease in the urban innovation capacity. These findings are consistent with previous theoretical explanations.

**Table 12.** Mechanisms by which environmental regulation affects the innovation capacity of Chinese cities.


Note: \*\*\* and \*\* represent the significance at the 1% and 5% levels, respectively. The *t*-values are in parentheses.

Similarly, regional manufacturing output was also adopted as the mediating variable investigating how environmental regulation contributes to the innovation capacity of cities. We found that the mediating effect of regional manufacturing output was also significant and had a negative coefficient. This indicated that environmental regulations markedly dampened urban innovation capacity through the mediating effect of manufacturing output. Specifically, as can be deduced from Column (5) of Table 12, environmental regulation is significantly negatively related to regional manufacturing output, while Column (6) of Table 12 shows that manufacturing output is positively related to urban innovation capacity. In summary, increased environmental regulation leads to a certain degree of decline in regional manufacturing output, which may constrain firms' R&D and innovation

behaviour, and ultimately lead to a decline in regional urban innovation capacity, validating the relevant explanations presented in the previous section.

To further examine the effect of mediating variables, we have added the interaction term between fiscal revenue and environmental regulations, as well as the interaction term between manufacturing output value and environmental regulations, to model Equation (8), which will be used as a supplement for the robustness test. After adding interaction items into model Equation (8) separately, the new regression results are shown in Table 13. It can be found that no matter the interaction term between fiscal revenue and environmental regulation, or the interaction term between industrial output and environmental regulation, are all significantly negative. This means that environmental regulation policies really have an impact on urban innovation ability through the two factors of regional fiscal revenue and industrial output value. Meanwhile, the higher the regional fiscal revenue and industrial output value, the more significant the inhibition effect of environmental regulation on regional innovation ability. For local governments in China, the negative effects brought by environmental regulation policies will temporarily outweigh the positive effects, which is also an issue that government departments need to consider further.

**Table 13.** Regression results of interaction terms between intermediate variable and environmental regulations.


Note: \*\*\*, \*\* and \* represent the significance at the 1%, 5% and 10% levels, respectively. The *t*-values are in parentheses.

However, although environmental regulation may inhibit innovation in the short term, there may exist a compensatory effect of innovation; with appropriate environmental regulation incentives, firms may also increase their R&D and innovation, boost energy efficiency and emission reduction and improve their innovation and competitiveness. This finding may also apply to the association between environmental regulation and urban innovation; if regions are able to provide attractive incentives during the environmental regulation process, they may drive business involvement in technological innovation, thus promoting the city's ability to innovate. Thus, this study further examined the moderating effect of regional economic development and environmental investment on the relationship between environmental regulation and urban innovation capacity by cross-multiplying regional gross national product per capita (GDPPER) and regional environmental investment in pollution control (ENIVES) with the environmental regulation intensity, respectively (Table 14). The coefficients of both the cross multiplier of environmental investment and that of GDP per capita were distinctly positive. Concurrently, the environmental regulation's coefficient remained apparently negative, but its magnitude was significantly reduced. This indicated that as the regional economy expanded, the inhibitory influence of environmental regulation regulating urban innovation capacity could be effectively mitigated by increasing the positive regulation incentive. Moreover, when the government increases the environmental investment in pollution control, it can somewhat reduce the detrimental influence of environmental regulation on innovation.


**Table 14.** Moderating effects of regional economic development and investment.

Note: \*\*\*, \*\* and \* represent the significance at the 1%, 5% and 10% levels, respectively. The *t*-values are in parentheses.

#### *4.4. Further Discussion*

As mentioned earlier, environmental regulation and innovation have a non-linear connection. At lower environmental regulation intensity, it is unnecessary to innovate for environmental protection due to the low expenditure on circumventing environmental regulation. However, the adoption of other methods to circumvent environmental regulation will crowd out investment in innovation to a certain extent. In such cases, environmental regulation can further reduce innovation, whereas as its intensity surpasses a certain level, it becomes

challenging to circumvent environmental regulation by other means. Thus, firms may be forced to get around the negative consequences of environmental regulation via innovation. Hence, there may exist a non-linear U-shape relationship between environmental regulation and urban innovation. The current study analysed this issue deeply.

To examine this issue, the squared environmental regulation was introduced in the model, which was designed as follows:

$$innovation\_{it} = \mathfrak{a} + \beta\_1 ERS\_{it} + \beta\_2 ERS\_{it}^2 + \sum\_{j} \beta\_j control\_{it} + \mu\_i + \gamma\_t + \varepsilon\_{it} \tag{14}$$

If non-linear characteristics existed, then the inflection point at which environmental regulation affects urban innovation was calculated as follows:

$$\text{Inflection Point} = -\frac{\beta\_1}{2\beta\_2} \tag{15}$$

Table 15 reports the regression results for model (14). In the full sample, the squared environmental regulation was remarkably positive, while the first-order term coefficient was obviously negative, indicating the validation of the non-linear characteristic of the U-shape. The calculated inflection points at approximately 0.7 could turn the impact of environmental regulation from negative to positive. Heterogeneity existed in the results for the above three regions. The second-order term coefficient for environmental regulation was insignificant in the eastern region. Conversely, the central and western regions exhibited a non-linear U-shaped characteristic, with an inflection point at approximately 0.6. We think there may be several reasons for this phenomenon. Firstly, the eastern region, as an economically developed region, has been attaching more and more importance to the development of a green economy in recent years, and the intensity of environmental regulations has been increasing year by year. However, local enterprises are generally still in the transition of technological innovation and cannot meet the requirements of environmental regulations in the short term. As a result, environmental regulation has led to the increase of pollution treatment costs and the deterioration of the business environment; innovation behaviour will be significantly inhibited accordingly. Secondly, due to the underdeveloped economy, the central and western regions often enjoy various industrial support policies and capital subsidies from different level governments. This can effectively reduce the problem of rising costs caused by increasing R&D investment. Due to a higher degree of marketisation and fewer government subsidies for production in the eastern region, based on cost-benefit analysis, local enterprises' operation strategies will be more cautious. Especially in the face of increased environmental regulation, enterprises are more inclined to maintain the stability of daily operation funds, thus reducing R&D investment.

**Table 15.** Non-linear relationship between environmental regulation and the innovation capacity of cities.



**Table 15.** *Cont.*

Note: \*\*\*, \*\* and \* represent the significance at the 1%, 5% and 10% levels, respectively. The *t*-values are in parentheses.

#### **5. Conclusions**

Porter's hypothesis has prompted scholars to address the micro effects of environmental regulations from a firm's perspective. The current research examined the micro effects and mechanisms affecting the innovation capacity of cities by constructing theoretical models and empirical tests. We discovered that environmental regulation substantially negatively correlated to China's urban innovation capacity during the sample period, and its increasing intensity could dampen China's urban innovation capacity. Concurrently, this inhibitory effect was mainly transmitted through two mediating variables: lower regional fiscal revenue and reduced manufacturing output. Increased regional economic development helps to bring positive incentives for environmental regulation, thus somewhat mitigating the inhibiting effect of increased environmental regulation on urban innovation capacity. Additionally, there may exist a non-linear U-shape relationship between environmental regulation and urban innovation; the sufficiently high intensity of environmental regulation will force firms to innovate and circumvent the drawbacks of environmental regulation.

Thus, the following issues should be considered during the policy development of environmental regulation in China. Firstly, as China is in the transition from extensive development to high-quality development, the negative effects of environmental regulation policies will temporarily outweigh the positive effects. Therefore, it is necessary to be alert to the negative spillover effects of environmental regulation on technological innovation. Secondly, local governments should be cautious about the adverse effects brought about by environmental regulation at the initial stage and implement reasonable emission policies to enable enterprises to survive the period of loss resulting from environmental regulation to help enhance innovation in companies and cities. Thirdly, local governments must rationally judge the characteristics of environmental regulation inflection points on their own circumstances and formulate corresponding environmental regulation policies. Fourthly, in more economically developed regions, local governments may consider supporting innovative firms with financial subsidies or tax concessions to enter the innovation dividend period of environmental regulation. Fifthly, for economically less-developed regions, due to the lack of sufficient innovative means, the intensity of environmental regulation by the local government should not be too high, which can avoid directly increasing the pressure on business operations. It will be helpful to mitigate the negative impact of environmental regulation on local economic development. Future studies may focus on a comparison between developing and developed regions in China to find the differences in innovation levels and remedies to boost innovation through environmental regulations in the deprived areas.

**Author Contributions:** Conceptualization, X.Z., M.J. and S.P.; methodology, X.Z.; software, S.P.; validation, X.Z. and M.J.; formal analysis, X.Z.; investigation, X.Z. and S.P.; resources, X.Z.; data curation, X.Z. and S.P.; writing—original draft preparation, X.Z. and S.P.; writing—review and editing, M.J.; visualization M.J.; project administration, X.Z.; funding access, X.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is partially sponsored by the Youth Innovative Project of Guangdong Universities (2021WQNCX022).

**Institutional Review Board Statement:** These studies did not involve human participants and were reviewed and approved by the ethics committee of the Guangdong University of Finance and Economics.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No datasets were generated or analyzed in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

