*3.1. Data*

To evaluate the impact of TCZ policy on green innovation performance, the number of green patents of the city is used to measure the development of green technology innovation. Green patent data are from the Chinese invention patent database, and the identification of green patents is based on the International Patent Classification (IPC) system code of the "IPC Green Inventory" published by the World Intellectual Property Organization (WIPO) on 16 September 2010. We can merge the Chinese invention patent database with IPC code to identify whether the patent is green or not. Green inventory patents are those related to

non–fossil fuel–based methods of propulsion, such as electric or hydrogen cars and related technologies (e.g., batteries). After classifying whether each patent is a green patent, we build a year–city level green patent database based on the city and year information of the patent.

The cities in two control zones are identified by the state document named "The Official Reply of the State Council Concerning Acid Rain Control Areas and Sulfur Dioxide Pollution Control Areas". The document specifies 175 cities and regions in the two control zones, including 158 prefecture–level cities, 13 regions, and 4 municipalities directly under the central government.

The city–level data comes from China City Statistic Yearbook (CCSY) from 1990 to 2016. The control variables include total population, annual gross regional product, investment in fixed assets, foreign investment utilized, number of students in higher education institutions, number of teachers in higher education institutions, the proportion of employment in the secondary industry, employment at the end of the year, and number of new contracts signed in the current year.

Table 1 summarizes the statistic (observations, mean value, and stand deviation) of the main characteristics we used in this paper. The logarithm of the number of green patents each city applies for is 1.109 on average per year. The average annual total population is 5.652 ten thousand persons. The average annual gross regional output value is CNY 14.895 ten thousand. On average, the investment in fixed assets and foreign investment utilized in each city is CNY 13.809 ten thousand and USD 8.225 ten thousand per year, respectively. The logarithm of the number of students and teachers in higher education institutions is 8.87 and 6.537 per year on average, respectively. Approximately, the logarithm of employment and proportion of employment in the secondary industry is 3.746 and 3.503 on average in each city. The log number of new contracts each city signs is 3.746 on average per year.


**Table 1.** Summary statistic.

Notes: Number of green patent applications, number of students in higher education institutions, number of teachers in higher education institutions, the proportion of employment in the secondary industry, and employment number of new contracts signed are measured in logs.

#### *3.2. Empirical Design*

To estimate the efficacy of TCZ policy for green technological innovations, a difference– in–differences (DID) model is used. Compared to changes in cities that were never under environmental regulation, we focus on how the number of green patents of cities in two control zones changed when the TCZ policy was enacted.

The DID method is adept at catching pre–existing differences between treated cities and untreated cities, thus eliminating selection bias while controlling for confounding variables that are likely to impact both sets of cities. The estimated equation is as follows:

$$\mathbf{G}\_{\mathbf{i},\mathbf{t}} = \beta \mathbf{T} \mathbf{c} \mathbf{z}\_{\mathbf{i}} \times \mathbf{Post}\_{\mathbf{t}} + \eta \mathbf{X}\_{\mathbf{i},\mathbf{t}} + \gamma\_{\mathbf{i}} + \delta\_{\mathbf{t}} + \varepsilon\_{\mathbf{i},\mathbf{t}} \tag{1}$$

where i represents cities, t represents year; Gi,t represents green technological innovations, which are measured as the log of the number of green patents applied by city i at year t; Tczi is 1 if city i is in two control zones and equals 0 if city i is not; Postt equals 1 for all years after 1998 (TCZ policy period) and otherwise equals 0. Tczi × Postt is the interaction between the Tczi and Postt, which captures the average difference change in the number of green patents of treated cities compared to untreated cities. Therefore, we focus on the coefficient measuring the DID effect and we posit β is positive, which means the TCZ policy will increase the number of green patents effectively. Xit represents city–level control variables, including total population (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). The control variables represent the number of labors, development of economy and education, as well as degree of investment and openness. γ<sup>i</sup> is a vector of city dummies and δ<sup>t</sup> is a vector of year dummies to control city–fixed effects and year–fixed effects, respectively.
