Literature Review

There are two types of literature closely related to this paper; the first is the literature on IP protection. The mainstream view in academia is that IP protection can promote foreign direct investment, industrial structure optimization and upgrading, as well as green innovation capacity. Awokuse and Yin [20] verified that IP protection could promote FDI on a national level. Klein [21] considered the impact of factors such as imitation risk and transaction costs and concluded that the higher the IP in the host country, the more attractive it will be to foreign investment. The level of IP protection also largely affects the country’s industrial structure [15] and improves the optimization of industrial structure [16], which is especially crucial for capital and technology-intensive industries [17]. As an important institutional innovation of IP protection, the IPDC policy can improve the IP management system and enhance the development level of the urban IP service industry. Xu [22] found that the IPDC policy promotes industrial structural adjustment by accelerating human capital flow and encouraging innovative enterprise development. In terms of innovation, strengthening IP protection can stimulate the innovation vitality of enterprises by creating a favorable innovation environment [23-25]. Enhancing enterprises’ open innovation capability can improve green technology innovation [26]. Meanwhile, the strengthened IP protection gives inventors monopoly rights to protect the power of patent owners and incentivizes firms to expand the scale of innovation [27]. Some scholars have also argued that high-intensity IP protection hinders knowledge spillovers and technology diffusion and dampens innovation [28, 29]. For example, Cho et al. [30] found that IP protection raises the threshold for firms to imitate and innovate and that countries with weaker innovation capabilities, such as Vietnam and Indonesia, will see a decline in firms’ willingness to innovate.

Another category is the literature on the factors affecting environmental pollution. Since environmental pollution problems can cause serious harm and economic losses to society, they have now become the focus of extensive attention worldwide. A large amount of existing literature focuses on the analysis of various types of factors affecting environmental pollution. Shen and Lin [1] argued that technological innovation can improve energy saving and emission reduction and then promote low-carbon development. Liu [31] found that reducing environmental pollution mainly relies on the technological progress of labor production. The impact of technological progress on environmental pollution will, in turn, be affected by environmental regulation. Liu et al. [32] found a nonlinear dynamic environmental regulation threshold effect of technological progress and environmental pollution, but Guo et al. [33] found that environmental regulation strengthens the inhibitory effect. FDI inflows can similarly promote the application of green technology and reduce air pollution [34, 35]. In addition, industrial structure and fiscal decentralization are also important factors that influence environmental pollution. The irrationality of industrial structures can cause ecological pollution, and Hammond et al. [36] revealed that the manufacturing industry improved environmental quality through industrial structure upgrading. Scott [37] argued that optimizing industrial structure improves productivity and energy efficiency and reduces environmental pollution. Silva and Caplan [38] believed that fiscal decentralization reduces the intensity of environmental governance and is not conducive to environmental improvement. More scholars support the conclusion that fiscal decentralization can improve environmental quality [39-41].

After a comprehensive analysis of the existing literature, it was found that current research on the influence factors of IP protection and environmental pollution has been quite perfect. Existing literature focuses on IP protection’s impact on FDI, industrial structure upgrading, technological innovation, green innovation, environmental regulation, industrial structure, fiscal decentralization, and other factors on environmental pollution, answering the question of how much these factors will affect environmental pollution, but little research on how to manage environmental pollution to improve environmental quality. Although the important role of IP protection in global environmental pollution governance has been confirmed from international experience [13], the research focusing on China’s environmental pollution governance from the perspective of IP protection has not been sufficient. Based on the existing research, this paper brings IP protection and environmental pollution into a unified framework, analyzes the impact of IP protection on China's environmental improvement, and discusses in depth the mechanism of the impact, which is of great practical significance.

Theoretical Analysis

Policy Background

The development of the IPDC policy is a key measure taken by China to strengthen IP protection and promote economic innovation and growth. The policy aims to develop a replicable IP management and application model through policy pilots in selected cities to improve IP work nationwide. As China enters the stage of high-quality development, the demand for innovation-driven development increases, and IP becomes a key strategic resource. In order to realize the goal of becoming a “strong IP country”, the Chinese government has emphasized the importance of building an IP system as part of its national strategy in a number of important documents.

In 2011, the State IP Office of China (SIPO) formulated and released the evaluation methods for national pilot and demonstration cities of IP rights, detailing the basic conditions, application procedures, and responsibilities and capabilities required for the cities to apply for the program. In terms of specific conditions for policy implementation, cities or regions applying for the demonstration are required to achieve a certain level of IP protection and management, including efficient administrative and judicial protection mechanisms and a mature IP dispute resolution system. In addition, local governments are also required to attach great importance to IP protection and coordinate the promotion of IP. Regarding creating a model city, the relevant city needs to submit a detailed construction plan, which will be reviewed by the provincial government and submitted to the State IP Office (SIPO). The SIPO, together with the relevant departments, will carry out the selection, management, and acceptance and will select qualified cities to carry out demonstration construction for two years. Those cities that have passed the acceptance will be awarded the title of “Demonstration Zone”, which is valid for a period of time. The city that passes the acceptance will be awarded the title of “Demonstration Zone”, which is valid for three years and can be renewed after the expiration of the period. In terms of specific work, detailed provisions have been made for the duties of the executive unit, work support, financial service innovation, pilot demonstration of strong county projects, improvement of the public service system, strengthening of the work assessment, business training, increased guidance and support, and policy favoritism in a variety of aspects, forming a comprehensive system for IP rights.

The IPDC policy is an important measure taken by the Chinese government to enhance its IP governance capacity and realize its strategy of becoming a strong IP country. Since 2012, China has gradually established a number of IPDCs in batches, as shown in Table 1. 23 cities were established in 2012, 18 cities were approved in 2013, and a total of 6 batches containing 77 cities have been established as of 2019. The expanding scope of the IPDC policy provides a solid foundation for China’s IP-enhanced strategy and high-quality development, as well as a good policy impact for exploring the impact of IP protection on environmental quality.

Econometric Model Specification

The environmental impact of constructing national IP demonstration cities can be regarded as a policy experiment. The policy effect can be identified by the double difference model (DID) method in econometrics, which is unfolded mainly by comparing the research samples that have not been affected by the policy as well as those that have been affected by the policy before and after implementation to confirm the impact brought about by the policy. If the construction of IPR model cities can effectively promote the improvement of China’s environment, it indicates that IPR protection has a significant environmental improvement effect. In this paper, the 77 national IPR model cities established in batches are taken as the experimental group, and the rest are taken as the control group. The environmental improvement effect of IPR protection is assessed by calculating the double difference between the experimental group and the control group before and after implementing the IPR model city policy several times. Adopting the double difference model as the empirical method has the following advantages: (1) Policy shocks are exogenous variables, so they can be regarded as a “quasi-natural experiment”, avoiding causality reversal and thus reducing endogeneity problems. (2) By setting up dummy variables for regression analysis, the double-difference method can more accurately reflect the relationship between the occurrence and non-occurrence of policies and better predict the trend of the impact of IP protection on environmental improvement. (3) Compared with traditional empirical methods, the double difference model can effectively reduce the omission of variables, and the model form is simpler and easier to understand the inner principle. The double difference is a causal effect analysis method, which needs to consider four elements: shock event, treatment group, control group, and time and the classical construction is in the following form:

Where Y_ct denotes the explanatory variable, D_c denotes the policy grouping dummy, T_c denotes the policy time dummy, D_c*T_c represents the interaction term between the two, γ, δ, and β denote the coefficients of each, and ε_ct is the random error term. The general double differencing method will cover two different sets of samples and two periods, with the first set of samples receiving no treatment in the first period, a specific treatment in the second period, and the second set of samples receiving no treatment in either period. Therefore, the case where individual c receives the treatment in period t is defined as the experimental group, i.e., D_ct=1, and the case where it does not receive the treatment is defined as the control group, i.e., D_ct=0. The period before the experimental group receives the treatment is defined as the baseline period, i.e., t=0, and the period after the treatment is notated as t=1. where for the experimental group of individuals, there is D_ct=1, for the control individuals, there is D_ct=0, and for all the individuals, c, there is D_c0=0. Constructing a double difference model makes it possible to identify the average treatment effect of a policy shock on the affected sample and thus assess the status of the change in the treatment outcome, Y_ct, when the policy impact occurs versus when it does not occur.

Accordingly, this paper constructs a fixed effects model for double difference estimation, which is set as follows:

Where the subscript c denotes the city, and t denotes the year. Pollution_ct is an explanatory variable representing the sulfur dioxide emissions of city c in year t, representing the environmental pollution of city c. IP_ct is a policy variable for the construction of IP demonstration cities, which is equal to the product of the policy dummy variable D_c and the time dummy variable T_c, and it takes the value of 1 if city c is a model city for IP rights in year t. Otherwise, it takes the value of 0. Otherwise, it takes the value of 0; X_ct represents a series of control variables at the city level; μ_c and μ_t represent city-fixed effects and time-fixed effects, respectively; and ε_ct is a random error term.

Variable Selection

Sample Selection and Data Sources

The city-related data mainly come from the Urban Statistical Yearbook and the hand-arranged statistical yearbook and statistical bulletin of each city. Based on the research needs, the study has screened the samples as follows: Firstly, the samples of four municipalities directly under the central government are excluded. Considering the differences in administrative levels and government behavior patterns between municipalities and other cities and the fact that some of the IPDCs are only part of the municipalities (e.g., Chaoyang and Haidian districts in Beijing), the inclusion of municipalities may affect the reliability of the results of this paper, so we exclude the samples of Beijing, Tianjin, Shanghai, and Chongqing. Secondly, we exclude the samples of prefecture-level cities that contain IP demonstration county-level city pilots; for example, Yiwu City, Zhejiang Province, was selected as the fourth batch of pilots in 2016, but Jinhua City, which is in charge of Yiwu City, was also selected in the list of the sixth batch of IPDCs in 2018. The same situation occurs in Qingdao, Suzhou, and Nantong, and so on. In order to avoid the influence of such city samples on the regression results, we have excluded such samples. Finally, this study also refers to the study of Nie et al. [55] to exclude the sample of prefecture-level cities that were selected in the sixth batch of the list in 2019 to avoid its impact on the policy evaluation results.

After screening, the research obtains the sample data of 243 prefecture-level cities, among which 49 cities have been successively selected as national IP demonstration cities, constituting the sample of the experimental group of this paper, and the remaining 194 cities constitute the sample of the control group of this paper. This study is based on macro-city-level data provided by the China City Statistical Yearbook and micro-firm-level data from the Foreign Direct Investment Approval Database aggregated to the city level, which provide valuable information for exploring the impact of IPR protection policies on environmental improvement and the intrinsic mechanism of impact. The first batch of national IPR demonstration cities was announced in 2012, and the data in the FDI Approval Database was last updated in 2017. In order to ensure that there are reasonable observation intervals before and after the implementation of this policy, this paper chooses 2007 as the starting period of the study so that the earliest batch of cities that have become IPR pilots will have a five-year observation period before treatment and a five-year observation period in the same year of the treatment as well as after the treatment. In addition, because the FDI approval database has not been updated in recent years, and the China Urban Statistical Yearbook does not have sufficiently complete statistics for some cities in remote areas, there are certain limitations in the availability and timeliness of data, which may limit our observation and analysis of longer-term trends, and our study is unable to fully capture the impact of recent policy changes on the environment. Despite certain shortcomings, we believe that the data over the time period analyzed can still provide valuable insights into understanding the drivers and pathways of environmental improvements from IP protection. Table 2 gives the results of the descriptive statistics.

Baseline Model Test

The study adopts a double difference model to test the net effect of the IPDC policy on the degree of urban environmental load; specific regression results are Table 3, Column (1) and Column (2) results show that, in the uncontrolled time fixed effects and city fixed effects, gradually adding control variables after the establishment of IPDCs suppressed the urban sulfur dioxide emissions at the 1% level of significance. Columns (3) and (4) gradually add year-fixed effects and city-fixed effects on the basis of the previous two columns, which shows that the IPDC policy still significantly negatively affects urban sulfur dioxide emissions and reduces urban environmental pollution. This verifies hypothesis 1.

Parallel Trend Test

The premise of using the double difference model is to require that the experimental group and the control group have a similar trend before the implementation of the policy, meet the common trend assumption, and have a significant increase or decrease after the implementation of the policy. This paper refers to the event study method, sets IPDCs as the experimental group and other cities as the control group, takes the year before the city is listed as IPDCs as the base period, and sets the 0 and 1 dummy variables before and after the implementation of the policy as the first 5 periods, the first 4 periods, the first 3 periods, the first 2 periods, the current period, the second 1 period, the second 2 periods, the second 3 periods, the third 4 periods, and the fifth 5 periods of the implementation of the policy, respectively. In Fig. 1, the regression coefficients of the first 5 periods of policy implementation contain 0 values in the 95% confidence interval; the confidence interval of the regression coefficients of the fourth period and after the implementation of the policy no longer contain 0 values, and the size of the coefficients increases with the incremental increase in time, indicating that IPDCs passed the test of the parallel trend, and at the same time the effect of the implementation of the policy effect on the improvement of the quality of the urban environment, there is a three-year lag, and there is some continuity. There is a certain degree of continuity with the passage of time to promote the effect and gradually enhance it.

Fig. 1.

Parallel trend test.

Robustness Tests

To enhance the robustness of the benchmark regression, the study performs a series of robustness tests, such as replacing the explanatory variables, sample shrinkage treatment, propensity score matching method, and changing the sample range.

Mechanism Analysis

In the above analysis, the study believes that the IPDC policy will affect environmental quality through technological innovation, foreign investment, and industrial structure upgrading; therefore, the study will empirically test the above two mechanisms.

(1) Green Technological Innovation

IP model city policies can promote green technological innovation by enterprises. Improvements in factor resource inputs, product cleanliness, and energy-saving and emission-reduction efficiency can help reduce environmental pollution. This paper uses green patent applications and authorizations to measure the level of green technological innovation in cities. Table 5 shows that the estimated coefficients of IP demonstration cities are all significantly positive, indicating that the IP demonstration city policy promotes urban green technological innovation, proving that implementing the IP demonstration cities policy provides support and incentives for urban green technological innovation. Previous studies have found that technological development is significantly biased toward low carbon, which helps to improve product cleanliness, energy saving, and emission reduction efficiency and helps to change China’s energy-dependent economic development model. Therefore, innovation in green technology is important in reducing urban environmental pollution. In addition, the test of calculating the urban innovation environment was done by referring to Kou’s method [56].

(2) Foreign investment structure and industrial structure upgrading

The study uses the FDI enterprise registration database of the Foreign Enterprise Information Query Platform of the Ministry of Commerce of China to construct indicators for optimizing the structure of foreign investment. This database contains more than 2 million records of FDI registrations. Based on this database, we can know the investment time, registration amount, and investment source of each foreign investment in China. On this basis, this paper matches the FDI enterprise registration database with the list of enterprises captured on the platform of “Tianyiha” through the enterprise name. Then, it obtains the province and city where the enterprise is located and the industry category to which the enterprise belongs. Referring to the methods of Gan [57] and Yuan [58] to construct the foreign investment structure optimization index, the construction method is shown in Equation (3) and Equation (4):

Fig. 2.

Placebo test.

Where y_ct,m=3 indicates the number of new FDIs in the tertiary industry in region c in period t, and y_ct,m=2 indicates the number of new FDIs in the secondary industry in region c in period t, which reflects the size of the proportion of the service industry in FDI and the degree of upgrading of the city’s foreign investment structure.

Among them, y_cmt indicates the proportion of the new number of FDIs in industry m in region c in period t to the regional total, reflecting the evolutionary relationship of the three major industries of FDI from the dominant position of the primary industry to the dominant position of the secondary and tertiary industries gradually.

In this paper, we refer to Gan [57] and construct the industrial structure upgrading index by utilizing the ratio of the tertiary industry’s added value to the secondary industry’s added value. The above indicators are added in Equation (2) for regression.

The coefficients of Columns (1) to (2) in Table 6 are significantly positive, indicating that the IPR model city policy can promote the optimization of foreign investment structure. The number of foreign investment introductions in the use of services shows similar results. In addition, the results in Columns (3) and (4) of Table 6 have significantly positive coefficients, indicating that the IPR model city policy can promote the overall industrial structure upgrading. Studies have shown that the optimization of foreign investment structure and industrial structure upgrading can force local enterprises to improve the efficiency of resource allocation, continuously adjust their production and operation, and shift from high-energy-consuming and high-polluting industries to high-tech direction, effectively reducing the pollution intensity [49]. Combining the regression results and previous studies, it can be concluded that the optimization of foreign investment structure and upgrading industrial structure are still important factors influencing China’s low-carbon development. The IPR model city policy effectively promotes the inflow of foreign investment in the service industry, the transformation of industrial structure in the direction of low-energy modernization, and the rapid development of clean and green industries. This verifies hypothesis 2.

Heterogeneity Tests

Recommendations

These findings provide the following policy insights for China to utilize the IP model city policy to achieve low-carbon development goals:

First, the government expanded the list of pilot cities by optimizing the city screening criteria and strengthened the assessment and incentive mechanisms for IPR legislation and enforcement personnel to enhance the intensity of legislation and enforcement. It emphasizes the importance of IP protection when formulating environmental governance policies such as pollutant emission standards.

Second, emphasize the role of green technology innovation capacity in low-carbon development. At the level of policymakers, set up special funds and innovation platforms to support enterprises, universities, and scientific research institutions to carry out green technology research and development. At the level of industrial policies, prioritize the development of energy, chemical, and other industries that apply renewable energy and clean production technologies and promote the upgrading of green technologies to achieve low-carbon transformation.

Third, promote the introduction of high-quality foreign investment to help upgrade the industrial structure. At the level of policymakers, improve the investment environment, optimize the structure of foreign investment, and guide foreign investment in high-tech, modern service industries, and other green industries. Encourage the transformation and upgrading of strategic emerging industries such as information technology, high-end equipment manufacturing, and traditional industries. At the level of local industries, high-tech enterprises and service enterprises should actively cooperate with foreign investment, introduce advanced technology and management experience, and realize the green transformation of industries.

Fourth, the policy of model cities for IP rights should be implemented according to regional conditions. Developed regions continue to strengthen IP protection and set up domestic low-carbon demonstration benchmarks through sustainable technology development and industrial evangelization. Less developed regions focus on strengthening the awareness of IP protection in leading industries such as agriculture and equipment manufacturing and promoting the research and development of green technologies and their application. Local governments will support foreign-funded enterprises in countries with more stringent environmental standards to attract high-quality foreign investment inflows. Encourage high-quality foreign-funded enterprises to establish clean energy R&D centers in China, strengthen the spillover effect of foreign investment, promote industrial structure upgrading, and realize low-carbon economic transformation.

This paper still has some limitations. First, this paper is based on the FDI approval database, which is limited in its availability and timeliness of the data, and the study cannot fully capture the environmental impact of the recent changes in IPR protection policies. Future research can further validate the findings of this study by expanding the timeframe and sources of data and exploring possible changes over different time periods. Second, this paper is based on China’s “Model IPR City” policy. When similar studies are generalized to other countries in the future, the differences in policies and institutions of different countries should be taken into account.