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Article

Evaluating the Economic Performance of Park City Policy—Based on the Penalized Version of Synthetic Control Method

by
Lingxiang Huang
,
Jianyuan Zhang
,
Xiang Wang
and
Zhu Chen
*
School of Economics and Management, Tianjin Chengjian University, Tianjin 300384, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(8), 3474; https://doi.org/10.3390/su17083474
Submission received: 6 March 2025 / Revised: 25 March 2025 / Accepted: 29 March 2025 / Published: 14 April 2025
(This article belongs to the Special Issue Sustainable and Smart City: Planning for Resilience)
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Abstract

:
Park city policy is an exploration of the construction of urban ecological civilization under the background of the new era of China. The evaluation of the economic performance is an important step to improve and popularize this policy. The article takes the implementation of the policy in Chengdu, China, as a quasi-natural experiment and adopts the penalized version of a synthetic control method to evaluate the economic performance of the policy. Firstly, the results show that park city policy improves economic performance by prompting the aggregation of labor factors and innovators, optimizing the structure of the local industries, and bringing an investment multiplier effect. Secondly, by establishing the control group, the penalized version of the synthetic control method is effective in overcoming the endogeneity and evaluating the economic performance of the policy. Thirdly, park city policy has significantly positive effects on both the economy and the industrial structure of Chengdu. Based on the result, the related suggestions are proposed.

1. Introduction

Park City policy in China is designed to treat the city as a park and focus on the residents’ experience in the construction program. In 2018, Chengdu was officially listed as the first Chinese pilot city to implement the park city policy. Since then, Cheng initiated the planning called the Chengdu General Program of Following the New Development Concept to Build a Park City as the Demonstration Area. In 2022, the program was approved by the State Council. Compared with park construction, which focuses on improving the living and working environment of urban residents, the program tries to set a more comprehensive model that accounts for the relationship between park construction and the whole issues of urban development, including the social, economic, and ecological aspects [1,2,3]. Therefore, an inclusive and accurate evaluation of the economic performance of park city construction is imperative to provide a reference for the next stage of urban construction in Chengdu and help other cities apply the park city model. Theoretically, park city policy shares the same principle with those programs that incorporate ecological concepts into city development. The principle has been globally recognized as an important issue in the realm of urban development. UNESCO proposed the concept of “eco-city” in 1971; Singapore built a “garden city” [4,5]; China proposed sanitary cities (1990), forest cities (2004), eco-cities (the 1980s), garden cities (1992), and ecological garden cities (2007). Park city policy further develops the principle because this policy prioritizes ecological construction as a means to drive comprehensive urban development, especially in both economic and ecological aspects [2,6,7,8,9,10,11].
As the policy is designed with the advanced concept, it is imperative to conduct research on a theoretical basis. In general, the literature that provides references for the economic performance of park city construction can be divided into two categories: one is the research on the relationship between park green space, green industry, and urban development [12]. Studies have shown that there is a coupled synergistic relationship between the layout of green spaces and the spatial distribution of urban population and infrastructure [13,14]. The development of smart cities, smart gardens, and other emerging industries will help to replace highly polluting and energy-consuming traditional industries and promote the green transformation of the urban economy [15,16,17]. The second category is the study of the impact of increased investment in public facilities on the economic performance of cities. The empirical results show that public facility investment can promote the upgrading of industrial structures through the transmission path of industrial linkage [18,19,20]. However, there is insufficient research on whether and how the construction of park cities affects the economic performance of cities.
Therefore, based on panel data of 17 cities from 2010–2022, in terms of both the scale and structure of the economy, this paper utilizes the corrective synthetic control method to construct a synthetic control group to analyze the impact of the implementation of park city in Chengdu, China, since 2018. The core research questions addressed in this study are as follows: Does the park city policy demonstrate a statistically significant promoting effect on urban economic development? Through what specific mechanisms does this policy intervention operate? Furthermore, under the context of limited sample availability during the initial phase of policy piloting, how can methodological innovations be employed to achieve precise identification and robust assessment of policy effects? This investigation seeks to systematically examine these critical issues through rigorous empirical analysis.
Compared with the existing literature, this paper may contribute to the following two aspects: First, this paper provides a theoretical analysis of the mechanism by which the park city policy affects economic performance and examines its impact on enhancing economic performance and optimizing the urban economic structure. Second, in view of the situation that the park city is in the early stage of exploration and the comparable sample is insufficient, the correction synthetic control method is used to achieve accurate economic performance estimation. Mechanism analysis, control group city screening, and economic performance results analysis can form the experience of park city construction. This research provides a reference for the promotion path of park city construction, a replicable analytical toolkit for sustainable urban transition studies, and theoretically enriches the “strong sustainability” discourse in urban economics.
This research theoretically enriches the “strong sustainability” discourse in urban economics and provides a reference for the promotion path of park city construction and a replicable analytical toolkit for sustainable urban transition.

2. Mechanism Analysis

The park city adheres to a people-oriented approach, highlighting the attribute of publicness. It emphasizes enhancing the spatial suitability of production and living ecosystems, as well as the integration of nature, economy, society, and humanity. This represents a highly harmonious and unified diversified urban development model that balances people, city, environment, and industry.
Specifically, the construction process of park city highlights the people-centered values, takes the people’s sense of gain and happiness as the fundamental starting point, leads the urban layout, and optimizes urban functions based on the system of regional parks, ecological corridors, and greenways. In addition, the development of park cities not only enhances the quality of the urban living environment but also fosters functional industries tied to ecological preservation, thereby facilitating the transformation and upgrading of urban industries.
The contribution of park city construction to the economic performance can be summarized as shown in Figure 1. This contribution can be achieved in three aspects: promoting the gathering of urban human resources, promoting industrial transformation, and investment multiplier effect.
First of all, the construction of park cities promotes the agglomeration of urban labor factors and human resources by enhancing the value of the living environment [21,22]. In the context of high-quality urban development, the construction of park cities provides a guarantee for a high-quality ecological environment and a high-level service supply for residents. As a crucial component of the park city concept, parks, and green spaces serve as multifunctional facilities that meet citizens’ needs for communication, fitness, sports, and art performances, thereby fostering social interaction, promoting a sense of community, and enhancing residents’ well-being [23,24,25]. Therefore, the construction of park cities can effectively promote the growth of labor factors and the introduction of innovative talents and then lay the foundation for the growth of both the quantity and quality of the urban economy [26,27].
Secondly, the construction of park cities promotes the development of green industries and the transformation of urban industries. Cities under the industrial civilization paradigm are driven by resource consumption, leading to the simultaneous generation of large amounts of waste and resource depletion alongside economic development [28]. The construction of park cities emphasizes minimizing resource and environmental consumption during industrial development while leveraging the positive impact of the urban ecological environment, thereby promoting the growth of green industries and facilitating the transformation and optimization of the urban industrial structure [29]. Specifically, the development of green industries will establish a high-quality development model characterized by resource conservation, efficient output, and reduced emissions. This can be achieved by strengthening independent innovation, transforming energy utilization methods, and upgrading renewable energy technologies and low-carbon technologies, thereby guiding the transformation of urban industries onto a high-quality development trajectory [30,31].
Thirdly, the investment in the construction of park cities has an investment multiplier effect on the economic development of the city. The construction of park cities is accompanied by a large amount of investment, which is an important engine to promote economic development [32,33]. On the one hand, investing in green ecological infrastructure, including municipal public facilities, parks, green spaces, and green buildings, can directly drive economic growth while further stimulating social consumption demand, thereby generating an investment multiplier effect [34]. On the other hand, investment in related industries such as ecological and environmental protection will bring about the vigorous development of vertical industrial chains and related industrial chains [35,36]. In addition, green investment aims to achieve an overall effect by enhancing the urban industrial and living environment, which is more conducive to improving the quality and welfare of urban development while ensuring a certain level of sustainability [37].

3. Materials and Methods

3.1. Study Area

The research area is Chengdu city, located in Sichuan Province in southwestern China, just tens of kilometers away from the central area of Sichuan’s provincial capital Chengdu, as shown in Figure 2 (here specifically referring to the surrounding areas not under the jurisdiction of Chengdu city). Chengdu covers a total area of approximately 14,335 square kilometers and serves as the provincial capital, political, economic, and cultural center of Sichuan Province. In recent years, despite the city’s continuous population growth, facing the challenges brought about by rapid urbanization, municipal authorities have adopted a series of measures to optimize urban development and address socio-economic issues arising from population density.
Although the area of parks and green spaces in Chengdu has been continuously expanded, it has not yet reached a higher standard of green space coverage per capita. According to the “Chengdu Park City Construction Planning Outline 2021–2035” and the “14th Five-Year Plan for Ecological and Environmental Protection in Chengdu”, the municipal government plans to further increase the area of urban green spaces, enhance their quality, and at the same time protect and make good use of existing green resources. Furthermore, Chengdu’s socio-economic development strategy up to 2035 aims to transform it into a green, ecological, and livable park city with fresh air, a serene environment, abundant green spaces, efficient energy management systems, widespread use of renewable energy, and a sustainable development model based on circular economy principles.

3.2. Penalized Version of Synthetic Control Method

In this paper, we simulate the “counterfactual” situation that China Chengdu has not implemented the park city policy based on the Penalized Version of Synthetic Control Method, so as to compare and study the effect of the park city policy [38,39]. In 2018, Chengdu’s park city plan was approved, which was the first time in China, and whether the event occurred or not is highly independent of the city’s characteristics, which can be seen as a quasi-natural experiment in the study of influencing economic performance. At present, the common quasi-natural experimental treatment method is the Difference-in-Differences (DID) method. When the government introduces a new policy, the DID method can be employed to evaluate its impact on specific groups or regions. Ideally, a control group that closely resembles the experimental group in terms of key characteristics is selected. By analyzing the changes in outcomes for both groups before and after the policy implementation, we can accurately identify and estimate the net effect attributable to the policy [40,41]. However, since Chengdu is the only treatment group sample in this study, this method cannot be implemented.
In contrast, the synthetic control method is more appropriate for this study. This method involves selecting identical experimental subjects within the same environment to establish both the treatment and control groups. The weight structure of the control group is then determined through data-driven approaches [42,43]. Like quasi-natural experimental methods such as the DID method, it also has the characteristics of avoiding subjective sample selection bias and policy endogeneity.
The Penalized Version of Synthetic Control Method used in this paper is an improvement of the synthetic control method, mainly by adding a “penalty term” [44,45], so that the weight of the synthetic region with large differences from the treatment group is reduced, and the weight of the synthetic region with small difference is increased, so as to solve the problem that the number of variables to be estimated may exceed the number of samples and the synthetic weight may not unique when constructing the “synthetic region”.

3.3. Date

3.3.1. Control Group Selection and Variable Selection

As a preparatory step for the Penalized Version of Synthetic Control Method, it is necessary to find a similar situation to the situation in China Chengdu as a source of control combinations into samples. In this paper, the areas that did not implement the park city policy but were once rated as national garden cities, such as Chengdu city, are combined as the “synthetic Chengdu”, with a high matching degree with Chengdu. The “synthetic Chengdu” is the control group. These cities include one municipality directly under the central government (Chongqing), three provincial capitals (Nanjing, Jinan, Changsha), and twelve prefecture-level cities (Jingmen, Zhuzhou, Yueyang, Suzhou, Wuxi, Ganzhou, Linyi, Zibo, Nanchong, Jiaxing, Shaoxing, Taizhou). None of these cities have implemented the park city policy from 2010 to 2022, which can more accurately verify the policy effect of Chengdu’s construction of a park city.
In the selection of policy-effect variables, considering that the most direct manifestation of policy impacts is on economic outcomes, this paper investigates both the dimensions of economic performance and economic structure. Relevant variables are thus chosen to quantitatively assess the policy effects. Specifically, this paper selects the GDP per capita as a proxy for economic performance and the proportion of the tertiary industry in the industrial structure as a proxy for economic structure, thereby evaluating the influence of the park city policy on urban economic development [46,47].
For the selection of predictive variables, considering the comprehensiveness and data availability, this paper mainly selects factors influencing urban development from three subsystems: economic, social, and ecological [48]. The predictive performance of the synthetic control group is assessed based on the fitted values derived from these three dimensions.
Specifically, in the economic subsystem, to evaluate economic potential, the labor productivity of the whole society is utilized as an indicator of a city’s production capacity [49,50,51]. In the social subsystem, the analysis is further subdivided into three sub-dimensions: demographic structure [52,53], public services [54], and labor employment [55]. Population dividend and labor supply are critical drivers of economic growth; the quality of public services influences human capital accumulation and residents’ consumption capacity, and the stability of the labor market determines overall social demand and economic resilience [56,57,58]. For demographic structure, the indicators include the natural population growth rate and the population growth rate [59]. For public services, the number of hospital beds per thousand people is adopted. For labor employment, the registered unemployment rate serves as the key metric [60]. In the ecological dimension, the analysis is divided into environmental foundation and environmental governance [61,62]. Environmental carrying capacity represents the physical constraint for sustainable urban development, while the efficiency of environmental governance mitigates negative externalities and enhances long-term economic performance [63]. Indicators for environmental foundation encompass per capita park green space area and green coverage rate of the built-up area, whereas urban sewage treatment rate is selected as the indicator for environmental governance [64,65]. All variables are shown in Table 1.

3.3.2. Data Source and Descriptive Statistics

The empirical study is based on the panel data of Chengdu and 16 control group cities from 2010 to 2022. The GDP per capita, the proportion of tertiary industry structure, the per capita area of park green space in the city, the green coverage rate of built-up areas, the municipal sewage treatment rate, the original data of labor productivity, natural population growth rate, population growth rate, number of hospital beds per 1000 people, and registered unemployment rate were obtained from the China Statistical Yearbook, provincial statistical yearbooks, and city statistical yearbooks. The descriptive statistical results for each variable are shown in Table 2.

4. Results

4.1. Optimal Weights and Correction Synthesis Control Results

Based on the principle of the Penalized Version of Synthetic Control Method, when selecting the weight index of the cities in the control group, the main factors of the synthetic control group should be as similar to that of Chengdu as much as possible before the implementation of the park city policy (2018) [65]. That is, the mean square error between the synthetic control group and Chengdu should be minimized. Cities and their weight coefficients are shown in Table 3. The urban economic performance of synthetic Chengdu is composed of nine cities, including Jingmen, Zibo, Taizhou, Chongqing, Shaoxing, Linyi, Wuxi, Jiaxing, and Jinan. The urban economic structure of synthetic Chengdu is composed of Nanjing, Jinan, Taizhou, and Chongqing.
Table 4 reports a comparison of predictors for Chengdu and synthetic Chengdu before 2018. It is evident that the actual value of the predictor variable in Chengdu exhibits a high degree of similarity to the composite value in synthetic Chengdu. Even in terms of policy effect variables, there is a close alignment. It demonstrates that the model based on the Penalized Version of Synthetic Control Method can effectively regulate the factors that may influence policy effectiveness, ensure the precision and objectivity of evaluations, and realistically verify the economic performance of the park city policy through synthetic Chengdu.

4.2. Analysis of Fitting Results Between Chengdu and Synthetic Chengdu

The impact of park city policies on urban economic performance is shown in Figure 3. Before the implementation of the park city policy, that is, on the left side of the vertical dotted line, the economic scale of Chengdu city and synthetic Chengdu was highly matched, and the growth trajectories of the two were close to coincide, indicating that the Penalized Version of Synthetic Control Method was accurate and effective. However, on the right side of the vertical dotted line, that is, after the implementation of the policy in 2018, the two gradually deviated, the economic performance of Chengdu was significantly ahead of the synthetic Chengdu, and the gap gradually widened. The difference between the two is the effect of the park city policy on the urban economy scale, which indicates that the implementation of the park city policy can promote the improvement of the performance of the urban economy. The policy effect initially increases before stabilizing.
The impact of the park city policy on the urban economic structure is shown in Figure 4. Before the implementation of the park city policy, the economic structure of Chengdu and synthetic Chengdu was relatively close. The gap between the two was greatly expanded immediately after the implementation of the park city policy, and the optimization trend of Chengdu’s economic structure was significantly faster than that of synthetic Chengdu. The difference between the two shows the impact of the park city policy on the urban economic structure. This indicates that the implementation of the park city policy can optimize the industrial structure of the city, and compared with the increase in economic performance, this effect is more direct and significant in the initial stage, with immediate effect and long-term stability. However, the optimization effect of this policy on the economic performance and structure is also facing the challenge of insufficient stamina, and there is stagnation compared with the continuous improvement of the economic scale. As PGDP keeps increasing and TlR remains at the same level after the outbreak of COVID-19, the model is stable and not sensitive to external shock.

4.3. Analysis of the Effect of Park City Policy

To more intuitively observe the impact of the park city policy on the urban economic performance and economic structure, this paper uses the difference between the GDP per capita and the tertiary industry structure of Chengdu and synthetic Chengdu before and after the implementation of the park city policy. Figure 5 shows the trajectory of the policy impact effect on Chengdu’s economic performance over time. From 2010 to 2018, the difference in GDP per capita fluctuated slightly within the range of −2423.40 yuan to 2063.01 yuan. However, the fluctuation range increased significantly after 2018; the maximum value appeared in 2020, as high as 8384.76 yuan. After 2020, although the difference fell slightly, it was still higher before the implementation of the policy. This shows that the construction of park city in Chengdu has significantly improved the economic performance of the place, and the policy effect is sustainable.
For the urban economic structure, the impact of the park city policy is more significant and direct. As shown in Figure 6, the difference in Chengdu’s economic structure increased sharply from −1.49% in 2018 to about 8.92% in 2019 and then plateaus, indicating that Chengdu’s implementation of the park city policy has a strong role in the optimization and upgrading of the city’s economic structure. Although the difference did not increase significantly in subsequent years, showing a certain lack of stamina, it is enough to prove the positive role of the policy in promoting the transformation of Chengdu to a more advanced and reasonable economic structure.

5. Robustness Test

5.1. Placebo Test

In order to further verify that the urban economic development before and after the implementation of the park city policy is affected by the park city policy but not by the selection range of the city sample in the control group or other external unobservable factors, the robustness test is carried out by using the placebo test and the ranking test. The placebo test is based on the principle that a sample is randomly selected as a placebo city in the control group, assuming that the placebo city also implemented the park city policy in 2018. The sample is synthesized by the Penalized Version of Synthetic Control Method, and the policy effect of the treatment group in the “counterfactual” state is estimated. The results were compared with the policy effect of the experimental group to see if there was any difference between the two [66,67].
In this paper, two control group cities that did not implement the park city policy were respectively selected for the same analysis as above. If there is a large gap between the policy effect of this city and the synthetic city, and the policy effect is similar to that of Chengdu, it means that the correction synthetic control method cannot prove that the implementation of the park city policy has an impact on urban economic development. In addition, the weights of the cities in the control group are displayed in the weight matrix constructed by the Penalized Version of Synthetic Control Method, and the larger the weight coefficient, the closer the economic and social characteristics of the city are to Chengdu. Conversely, a weight coefficient of zero indicates that it is the least similar to Chengdu. Therefore, it is reasonable for the control group to transform the city with the largest weight coefficient of Chengdu and the city with zero weight coefficient as placebo cities.
Jingmen city with the largest weight and Zhuzhou city with zero weight are selected as the control group to verify that the gap in urban economic performance before and after 2018 was affected by the park city policy, so as to exclude accidental factors. As can be seen from Figure 7a, the placebo test results for Jingmen city showed that the policy effect of Jingmen city in 2018–2019 was positive, decreased sharply in 2020, and then increased, and continued to grow. Compared with Chengdu, there are significant differences in the effects of the policy. As can be seen from Figure 7b, the placebo test results of Zhuzhou city show that the economic performance of Zhuzhou city changes along the development path of synthetic Zhuzhou city, and there is no sudden change in the fitting situation of Zhuzhou city around 2018, so Zhuzhou city does not have the same policy effect as Chengdu city.
Similarly, for the economic structure of the city, Chongqing with the largest weight in the original synthetic control group and Wuxi with zero weight were selected as placebo cities. As shown in Figure 8a, the policy effect of Chongqing was negative after 2018 and continued until 2022, which is different from that of Chengdu. As can be seen from Figure 8b, there is a continuous negative effect in Wuxi after the implementation of the policy, which is significantly different from that in Chengdu. In summary, the conclusions of this paper based on the Penalized Version of Synthetic Control Method are robust and credible and objectively prove that the construction of park cities has improved the level of urban economic development, rather than other accidental factors.

5.2. Ranking Test

The ranking test method requires that the synthetic control cities need to have a good fitting effect before the implementation of the policy, that is, the root mean square prediction error is required to be small [68,69]. If the root mean square error prediction before the implementation of a city policy is relatively large, it means that the fitting of the city policy before the implementation is not ideal, which leads to the insufficient effectiveness of using the GAP (difference between the control group and the treatment group) after the implementation of the city policy. Therefore, in the ranking test, when the composite control object of the control group is not satisfactory before the implementation of the policy, the confidence level of the composite control object of the city is not satisfactory, and the situation of the city is no longer analyzed.
For the urban economic performance as the policy effect variable, this paper takes the root mean square prediction error of the control group of cities more than five times that of Chengdu as the exclusion criterion to ensure the feasibility of the ranking test, and the results show that five cities, Changsha, Nanjing, Suzhou, Wuxi, and Nanchong, are excluded. Figure 9 shows the distribution of the difference in the development of urban economic performance after excluding the five cities with poor fitting before the implementation of the policy, with the solid line representing the difference between Chengdu and the synthetic Chengdu, and the dotted line showing the difference in the results of the control group city test. It can be seen that before 2018, the gap between the economic performance of Chengdu and other cities in the control group was not large, but after 2018, the gap between Chengdu and other cities in the control group gradually widened. After this year, it is significantly larger than most cities, which indicates that the park city policy has a certain impact on the economic performance development of Chengdu. According to the above ranking test analysis, it can be seen that the park city policy has promoted the improvement of Chengdu’s economic performance, and the probability is only 1/12, which is similar to the significance level in statistical inference 10% significant level.
For urban economic structure as a policy effect variable, this paper takes the root mean square prediction error of the control group of cities more than five times that of Chengdu as the exclusion criterion to ensure the feasibility of the ranking test. The results show that only Jingzhou is excluded, and Figure 10 shows the distribution of the difference in the economic structure of each city. The ranking results show that the cities in the control group do not have the same policy treatment effect as Chengdu, and the difference between all the cities in the control group is smaller than that of Chengdu, and the policy effect is obvious, that is, the implementation of the park city policy to optimize the urban economic structure in Chengdu is real and significantly better than the economic structure of other cities in the same period. In the same way, the implementation of park city policies promotes the optimization of urban economic structure at the level of 10%.

6. Discussion and Conclusions

6.1. Discussion

This paper delves into the impact pathways of the park city policy and employs the Penalized Version of Synthetic Control Method to measure the economic effects brought about by the implementation of the park city policy in Chengdu. The research findings indicate that the park city policy has positive impacts on both economic performance and economic structure. However, to draw a comprehensive conclusion on this topic, several issues remain worthy of further discussion.
Firstly, in this study, the synthetic control method performs well in the evaluation of the policy implemented at the early stage. The advantage of this method is that it can adapt to the situation where the quantity of the treated samples is remarkably circumscribed while overcoming the issues of endogeneity and selective bias. Moreover, from the perspective of the implementation effect, the result withstands the shock of the COVID-19 outbreak and passes the robustness check. Comparatively, the methods with a similar function, mainly DID and propensity matching score method (PSM), cannot satisfy the above requirements. DID requires a balance between the samples affected by the policy and the ones not. Additionally, PSM is used to avoid selective bias based on the premise that the number of the required sample is sufficient.
Secondly, the synthetic control method is not without flaws, of course. In the process of constructing the control group, there are inevitably subjective factors. Such subjectivity may lead to a certain deviation between the synthetic results and the actual situation. Therefore, we cannot fully rely on the results obtained from the current empirical research but need to keep a close eye on it. As the data volume continues to increase, we should adopt more comprehensive measurement methods to re-evaluate the economic effects of the park city policy in order to obtain more accurate and objective research conclusions.
Thirdly, the initiative to develop Chengdu as a park city represents a pioneering effort in China, driven by its distinctive environmental characteristics and rich historical heritage. However, replicating Chengdu’s success in other cities presents numerous challenges. From an economic perspective, the varying dominant industries and development levels across cities result in significant disparities in resource allocation capabilities and prioritization needs for park city construction. In terms of ecological conditions, regional differences in natural environments necessitate distinct models, priorities, and sequences for park city development. Each city operates within its own strategic framework, and integrating the concept of a park city into these diverse systems requires further in-depth exploration and research.
Lastly, after the implementation of the park city policy, it is evident that both economic performance and economic structure have exhibited substantial improvements, thereby validating the policy’s initial positive effectiveness. However, when we focus on the long-term effects of the empirical results, we find that this positive effect shows a gradually weakening trend. Compared with the control group that did not implement the policy, the gap between the two gradually stabilizes. This phenomenon implies that there is still much room for improvement in the sustained influence of the park city policy, and its ability to maintain long-term momentum needs to be further enhanced to ensure that the policy can continuously drive economic development in the long term.

6.2. Conclusions

This paper analyzes the mechanism through which the park city policy impacts urban economic development. Taking the implementation of the park city policy in Chengdu in 2018 as a quasi-natural experiment and based on panel data from 17 cities in China from 2010 to 2022, this study explores the effects of the park city policy on Chengdu’s economic development level from multiple dimensions using the Penalized Version of Synthetic Control Method. The main findings and recommendations are as follows:
(1)
Urban layout optimization and high-quality development drive. The construction of park cities reconfigures urban layouts via an integrated greenway system, enhancing environmental quality and functional efficiency. This attracts talent aggregation, facilitates industrial upgrading, and amplifies investment multiplier effects, thereby improving economic performance. To promote park city policies effectively, they should be tailored to the developmental stages and characteristics of individual cities. By leveraging the unique environmental features and industrial foundations of each city, refined construction strategies can optimize urban layouts and industrial patterns, fostering local technological innovation and industrial upgrading to achieve high-quality development goals.
(2)
Scientific assessment methods and promotion support. The bias-corrected synthetic control method addresses the issue of insufficient sample sizes during the early stages of policy implementation, confirming that the positive impacts on economic scale and industrial structure optimization in Chengdu are non-random. When promoting the park city model, it is essential to adopt scientific evaluation methods (e.g., the synthetic control method) to identify comparable city samples. Establishing a dynamic monitoring mechanism ensures robust data support for verifying policy effectiveness and facilitating national-scale promotion while mitigating endogenous biases.
(3)
Multi-dimensional development path exploration. Park city policies leverage green spaces to guide functional zoning and industrial agglomeration, yielding stable positive effects on economic scale and structural optimization. Cities should explore development paths suited to their specific conditions by integrating green space systems with local geographical contexts and strategically planning characteristic industries. Strengthening coordination across the upstream and downstream segments of industrial chains and promoting specialized upgrades in service sectors fosters a new urban development paradigm characterized by “environment-industry-service” synergy.
(4)
Effectiveness improvement and experience replication mechanism. The policy’s positive effects are both significant and robust. To expand its influence, case studies should be systematically analyzed and refined. A dual-track approach combining precise policy implementation with replicable model dissemination is recommended. On one hand, realistic and context-specific construction plans should be formulated; on the other hand, cross-regional demonstration projects can broaden the coverage of green benefits, accelerating the nationwide transition toward green and sustainable development.

Author Contributions

Conceptualization, L.H. and Z.C.; methodology, L.H. and Z.C.; software, X.W.; validation, L.H. and Z.C.; resources, L.H. and Z.C.; data curation, J.Z. and X.W.; writing—original draft preparation, L.H., Z.C., J.Z., and X.W.; writing—review and editing, L.H., Z.C., and J.Z.; supervision, Z.C. and J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Key Project of Tianjin Philosophy and Social Sciences Planning: “Research on the Optimization of Land Supply in Tianjin under the Coordinated Development of the Beijing-Tianjin-Hebei Economy” (TJYJ20-007).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The influencing mechanism of the economic performance of park city policies.
Figure 1. The influencing mechanism of the economic performance of park city policies.
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Figure 2. Study area.
Figure 2. Study area.
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Figure 3. Comparison of the economic performance of Chengdu City and synthetic Chengdu.
Figure 3. Comparison of the economic performance of Chengdu City and synthetic Chengdu.
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Figure 4. Comparison of the economic structure of Chengdu and synthetic Chengdu.
Figure 4. Comparison of the economic structure of Chengdu and synthetic Chengdu.
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Figure 5. The policy effect of park cities on economic performance.
Figure 5. The policy effect of park cities on economic performance.
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Figure 6. The policy effect of park cities on economic structure.
Figure 6. The policy effect of park cities on economic structure.
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Figure 7. Placebo test for the development of urban economies scale. (a) Jingmen; (b) Zhuzhou.
Figure 7. Placebo test for the development of urban economies scale. (a) Jingmen; (b) Zhuzhou.
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Figure 8. Placebo test for the development of urban economic structure. (a) Chongqing; (b) Wuxi.
Figure 8. Placebo test for the development of urban economic structure. (a) Chongqing; (b) Wuxi.
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Figure 9. Distribution of the difference in economic performance of each city.
Figure 9. Distribution of the difference in economic performance of each city.
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Figure 10. Distribution of differences in the economic structure of each city.
Figure 10. Distribution of differences in the economic structure of each city.
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Table 1. Variables.
Table 1. Variables.
VariableStandard TierFeature LayersIndicator LayerVariable
Symbol		Unit
Policy
Effect
variables		Park City Construction PerformanceThe size of the city’s economyGDP per capitaPGDPYuan
The economic structure of the cityProportion of tertiary industry structureTIR%
predictor variablesEconomic subsystemEconomic potentialLabor productivity of the whole societyLPYuan/person
Social subsystemDemographic structureNatural population growth rateNPGR%
Population growth ratePGR%
Public servicesNumber of hospital beds per 1000 peopleHBCbed/thousand persons
Labor and employmentRegistered unemployment rateRUR%
Ecological subsystemEnvironmental FundamentalsThe per capita area of park green space in the cityPGSAm2/person
Green coverage of built-up areasGCR%
Environmental FundamentalsMunicipal sewage treatment rateSTR%
Table 2. Descriptive statistics of variables.
Table 2. Descriptive statistics of variables.
VariableSample SizeMeanStandard
Deviation		MinimumMaximum
PGDP22179,339.50041,870.08012,839.220199,017.000
TIR22147.1437.83730.10066.400
LP221213,831.900194,980.2004746.9271,098,564.000
NPGR2213.7253.400−3.80022.400
PGR2210.0200.181−0.6851.683
HBC2215.9401.7382.19011.470
RUR2212.7400.7771.0804.800
PGSA22113.8592.9017.58021.600
GCR22142.1042.76533.43049.870
STR22192.6718.09245.68099.520
Table 3. The optimal weight matrix constructed by the Penalized Version of Synthetic Control Method.
Table 3. The optimal weight matrix constructed by the Penalized Version of Synthetic Control Method.
Policy Effect VariablesCityWeight
PGDPJingmen0.360
Zibo0.221
Taizhou0.192
Chongqing0.108
Shaoxing0.054
Linyi0.037
Wuxi0.021
Jiaxing0.007
Jinan0.001
TIRChongqing0.559
Nanjing0.258
Jinan0.151
Taizhou0.033
Table 4. Comparison of predictors.
Table 4. Comparison of predictors.
VariablePGDP (yuan)TIR (%)
True ValueComposite
Value		True ValueComposite
Value
LP116,397.000170,019.000116,397.000120,749.000
NPGR2.8753.8982.8752.894
PGR0.0520.0110.0520.014
HBC7.3844.8927.3845.056
RUR2.9663.0572.9663.066
PGSA13.75013.76013.75014.950
GCR39.61040.11039.61041.490
STR92.63090.91092.63092.200
PGDP (2012)54,211.00052,148.000
PGDP (2015)60,643.00062,706.000
TIR (2011) 50.10049.310
TIR (2012) 49.50049.560
TIR (2016) 53.40053.820
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Huang, L.; Zhang, J.; Wang, X.; Chen, Z. Evaluating the Economic Performance of Park City Policy—Based on the Penalized Version of Synthetic Control Method. Sustainability 2025, 17, 3474. https://doi.org/10.3390/su17083474

AMA Style

Huang L, Zhang J, Wang X, Chen Z. Evaluating the Economic Performance of Park City Policy—Based on the Penalized Version of Synthetic Control Method. Sustainability. 2025; 17(8):3474. https://doi.org/10.3390/su17083474

Chicago/Turabian Style

Huang, Lingxiang, Jianyuan Zhang, Xiang Wang, and Zhu Chen. 2025. "Evaluating the Economic Performance of Park City Policy—Based on the Penalized Version of Synthetic Control Method" Sustainability 17, no. 8: 3474. https://doi.org/10.3390/su17083474

APA Style

Huang, L., Zhang, J., Wang, X., & Chen, Z. (2025). Evaluating the Economic Performance of Park City Policy—Based on the Penalized Version of Synthetic Control Method. Sustainability, 17(8), 3474. https://doi.org/10.3390/su17083474

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