*5.1. Heterogenous Effect of TCZ Policy*

There is large heterogeneity contained by the average TCZ policy effect on green technological innovations. We further conduct our analysis to examine how environmental regulation impact differs for the R&D base and foreign investment utilized. R&D base is measured by cumulative amounts of total patents granted over the past five years, and foreign investment utilized is measured by FDI of the city in that year. We put the two new interaction terms (TCZ × post × R&D and TCZ × post × FDI) into Equation (1), respectively. Table 5 column (1) reports the result of the heterogeneous effect on the R&D base. The coefficient of TCZ × post × R&D is significantly positive, implying that the better the R&D base cities have, the larger the number of green patents they can apply for. Compared to the city with a relatively weaker R&D base, the city with a strong R&D base usually puts more emphasis on innovation activities and accumulates more experience in developing green technology innovation, indicating that it has more ability and recourses to conduct the development of green patents when the environmental regulation regime is enacted. As the results show in column (2), the coefficient of TCZ × post × FDI is positive and statistically significant at 1% level with the value of 0.167. The cities in two control zones with more FDI have better green innovation performance. Foreign firms usually have to face more strict environmental regulations in their home country, resulting in larger amounts of green technologies in the firms. Those target cities are likely to get more technology spillover from multinationals by FDI after the TCZ policy.

**Table 5.** Heterogenous effect of TCZ policy.


Note: For each regression, the log volume of green patent applications is used as an outcome variable. Controls include total population at the end of the year (Pop), annual gross regional product (GDP), investment in fixed assets (Fixedinvest), foreign investment utilized (FDI), the logarithm of number of students in higher education institutions (Students), the logarithm of number of teachers in higher education institutions (Teachers), the logarithm of the proportion of employment in the secondary industry (Second), the logarithm of employment at the end of the year (Employment), and the logarithm of the number of new contracts signed in the current year (Contracts). Standard errors in parentheses are clustered at the city–year level. \*\* *p* < 0.05, \*\*\* *p* < 0.01.

Therefore, the effect of environmental regulation on green innovation is significantly affected by the city's R&D base and FDI. For example, Beijing and Shanghai, as the pilot cities of TCZ policy, both have a high number of accumulated patents and a high level of FDI, and they are also the two cities with the largest number of green innovation patents in China.

#### *5.2. Mechanism*

We further examine the channels for cities in two control zones to increase green patents for environmental regulations. Theoretically, human capital is a crucial factor for technology innovation, indicating that the higher quality workforce a city has, the larger the number of green patents the city has. Environmental regulation has a positive impact on pollution reduction and urban quality, which attracts more talents to come to target cities. Thus, human capital is a significant mechanism in TCZ effect.

Table 6 reports the estimated results of the city and year–fixed effects models using the log number of patent inventors (Inventors), the log number of green patent inventors (Ginventors), and the percentage of population with college and higher education (Unipop) as the dependent variables according to Equation (1). We include control variables and control city–fixed effects and year–fixed effects. The coefficients are found to be positive and statistically significant at the 1% level. Column (1) and (2) lists the results of environmental regulation policy impact on patent inventors and green patent inventors. The estimates show that the number of patent inventors increases by 52.3% and the number of green patent inventors increases by 78.4% in treated cities compared to untreated cities by TCZ policy. As the result is shown in column 4, the TCZ policy increases the percentage of the population with high education by 0.9%.

**Table 6.** Mechanism of human capital.


Note: The dependent variable in each regression is the log number of patent inventors (Inventors), the log number of green patent inventors (Ginventors), and the percentage of the population with college and higher education (Unipop). Controls include total population at the end of the year (Pop), annual gross regional product (GDP), investment in fixed assets (Fixedinvest), foreign investment utilized (FDI), the logarithm of number of students in higher education institutions (Students), the logarithm of number of teachers in higher education institutions (Teachers), the logarithm of the proportion of employment in the secondary industry (Second), the logarithm of employment at the end of the year (Employment), and the logarithm of the number of new contracts signed in the current year (Contracts). Standard errors in parentheses are clustered at the city–year level. \*\*\* *p* < 0.01.
