1. Introduction
In recent years, global economic development began facing severe challenges, so economies are seeking new drivers for economic growth. Innovation is considered an important means to break through the bottlenecks and shape new advantages in economic development. In the meantime, economic development resulted in serious environmental pollution problems all over the world. To balance environmental protection and sustainable economic development, the ability to innovate green technologies is seen as a potential solution. Green technology innovation refers to an economic behavior that emphasizes environmental performance improvement and can effectively balance economic development and ecological protection issues. Hence, with the increase in the challenges of resources and the environment, it is essential to promote the development of a green economy by promoting green technologies [
1,
2,
3]. The sustainable development can thus be achieved through the innovation and progress of green technology [
4]. Green technologies raised public attention in both academics and industry.
In the 2000s, Porter and Van Der Linde [
5] proposed that environmental regulation can reduce the pollution caused by enterprises and incentivize enterprises to innovate to make up for the cost of pollution. Since then, a number of studies explored the relationship between environmental regulation and innovation from the perspectives of different industries, regions, and countries [
6]. The Porter hypothesis is examined in developed economies [
7,
8], but it remains unclear whether it applies to emerging economies. Lanjouw and Mody [
9] argued that environmental regulation in emerging economies cannot enhance domestic investment in pollution control technologies or green patents. Instead, it may increase the probability of importing green technology from advanced economies and strengthen foreign patents. It is, therefore, still worthwhile to investigate whether the environmental regulation has a significant impact on green innovation and provide a rigorous theoretical analysis and causal identification framework to test the impacts.
As an emerging economy, China’s rapid economic development brought numerous forms of material national wealth. However, it brought a series of environmental problems, such as resource shortage, environmental pollution, and ecological deterioration, which became a public concern. To address these issues, the Chinese government took several environmental regulatory measures. However, we still have little knowledge about whether these regulatory measures have a positive effect on regional technological innovation, especially green innovation capacity. To fill in the gap, this study aims to examine and provide a robust estimation on the impacts of environmental regulation on green technology innovation within the Chinese context. Specifically, we focus on the “Two Control Zones” (TCZ) policy carried out in 1998, which was the first regulation policy for air pollution in China.
To control acid rain and sulfur dioxide pollution effectively, the Chinese government approved and implemented the “Two Control Zones” (TCZ) policy in 1998. The two control zones include the acid rain control zone and the sulfur dioxide control zone. In particular, the acid rain control zone is the region where the average pH value of rainfall is less than or equal to 4.5; the sulfur dioxide control zone is the area where the average sulfur dioxide concentration exceeds the national secondary standard of the past three years. The total area of the two control zones accounts for 11.4% of the total area of the national territory, the total population of the two control zones accounts for about 39% of the country’s population and the GDP accounts for 67%, indicating a wide coverage of the impacts of the “two control zones” policy. The cities on the list of the two control zones are subject to strict environmental regulation, including restrictions on high–energy consumption, use of heavy–polluting energy sources, and sulfur dioxide emissions.
In our study, we are interested in whether and how the TCZ policy affects green technology innovation. To provide a robust estimation, we apply our analysis to a city–level panel data set from 1997 to 2016 in China, and examine the effects of environmental regulation policies on green innovation, considering the implementation of TCZ policy as a quasi–natural experiment. We apply a DID model, which is considered as the most effective model for policy evaluation. We find that the TCZ policy effectively increases the number of green patents of cities in the two control zones. In particular, the TCZ policy has a significantly positive effect on the quantity and structure of human capital, including the number of inventors of patents and green patents, the ratio of incumbents and newcomers, and the percentage of population with higher education levels. The effects are heterogeneous, that is, the TCZ policy has a greater impact on the number of green patents of cities in the two control zones where there are more R&D bases and more foreign investment.
To our knowledge, our study makes three main contributions to the literature: (1) This study is among the first to investigate the effects of TCZ policy on green technological progress from the perspective of environmental regulation–influenced regions; (2) we apply a DID technique to address the potential endogenous issue arising from omitted variables. It provides reliable and robust empirical evidence for analyzing the impacts of the environmental policy of TCZ on green technological progress; (3) we examine the mechanism of environmental regulation policy impacting the green technology innovation from different perspectives of human capital. It provides a new perspective to explain how environmental regulation policy affects green technology innovation. In addition, the heterogeneous effects of environmental regulation on green technological innovation are examined in terms of R&D base and foreign investment, thus revealing the comprehensive impacts of environmental regulation on green technological innovation.
The remainder of the paper is organized as follows:
Section 2 presents a literature review and theoretical analysis,
Section 3 presents the data and empirical design,
Section 4 reports the empirical results, followed by the discussion in
Section 5, and
Section 6 concludes.
3. Data and Empirical Design
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.
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:
where i represents cities, t represents year; G
i,t represents green technological innovations, which are measured as the log of the number of green patents applied by city i at year t; Tcz
i is 1 if city i is in two control zones and equals 0 if city i is not; Post
t equals 1 for all years after 1998 (TCZ policy period) and otherwise equals 0. Tcz
i × Post
t is the interaction between the Tcz
i and Post
t, 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
is positive, which means the TCZ policy will increase the number of green patents effectively. X
it 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.
is a vector of city dummies and
is a vector of year dummies to control city–fixed effects and year–fixed effects, respectively.
6. Conclusions
To cope with air pollution, the “Two Control Zones (TCZ)” policy was issued and enacted by China’s government in 1998. As the first air pollution regulation in China, the impact of the TCZ policy influences the development of following environmental regulations.
The results in our study use a difference in difference model that explores the effect of environmental regulation on green technology innovations and the role of human capital in it. We find evidence consistent with the hypothesis that the TCZ policy significantly increases the number of green patents of cities in two control zones. The result is also robust through the method of PSM–DID and changing the dependent variable. Most importantly, our study also points out the crucial role human capital plays in the mechanism. TCZ policy, as the signal of regulating air pollution and improving urban quality, has a positive effect on the quantity and structure of human capital, leading to providing a talent pool for green technology innovation to reduce pollution.
To exploit the heterogeneity covered under the average treatment effect, the finding shows that TCZ policy is different in the R&D basis and foreign investment utilized. TCZ policy tends to improve more amounts of green patents in the cities with a stronger R&D base or with more FDI. The R&D base provides innovative talents for green technology innovation and FDI provides technology spillover and R&D funding for green technology innovation. The cities with those two characteristics have more ability to undertake the development and application for green patents to cope with TCZ policy.
The findings of this paper provide new insight into the Porter hypothesis, offering some valuable policy recommendations for developing economies. In the context of globalization, developing countries, as a link in the downstream production chain, are highly susceptible to becoming pollution havens. Policymakers of emerging economies draw environmental regulations to control pollution, while promoting the development of green technology innovation by attracting more high–quality human capital. In addition, based on our study, the government should pay more attention to strengthening its R&D base and attracting more FDI, as both of these conditions will enhance the positive impact of environmental regulation on green innovation performance.
There are also some limitations. First, the research sample of this paper is the city–level data in China. We can only control for regional and year effects. In the future, when the green patent data at the corporate level becomes available, we can study it from the perspective of micro firms. Second, green technological innovation can also be subdivided in terms of production processes, such as green process innovation and green product innovation. However, the data refinement is limited, and this paper only uses the number of green patent applications granted to measure the overall green technology innovation of cities. With the increasing availability of data, a comparative analysis of the variability in the impact of environmental regulations on the green production processes of cities will also be worthy of further research.