Economic growth target and environmental regulation intensity: evidence from 284 cities in China

Management of economic growth targets is a universal measure employed by worldwide governments for macroeconomic regulation. This paper aims to empirically investigate the impact of economic growth targets set by governments of prefecture-level cities on the environmental regulation intensity. We extracted panel data on annual economic growth targets and environmental regulation indicators from the government work reports (2009–2016) of 284 China’s prefecture-level cities. The study concludes that an increase in economic growth target significantly weakens the intensity of environmental regulation. The conclusion still holds true after robustness tests, including changing measurement variables, regression samples, and conducting endogenous tests. The underlying reason for the inhibitory effect may be that in order to achieve economic growth targets, local governments prefer less stringent environmental regulations. They subsequently expand outputs in the short term by increasing the proportion of secondary industry in GDP, land transfer area, and fixed asset investment. Further research in this paper also finds that only cities with low economic development levels and low openness to the outside world experience the negative effect of a local government’s annual economic growth target on environmental regulation intensity.


Introduction
The increasingly serious ecological and environmental problems risk hindering future human development. Maintaining a pleasant living environment with blue sky, green land, green mountains, and clean water is a common human aspiration. As the world's largest developing country, China has undergone unprecedented economic growth over the past four decades. However, today, increasingly serious ecological damage and environmental pollution require China to strengthen environmental regulation, and subsequently it is confronted with a bottleneck for high-quality economic development. Environmental regulation is an important constituent of social regulation. In an attempt to coordinated economic and environmental development, governments regulate economic activities of enterprises and other entities by formulating corresponding environmental policies and measures (Oates et al. 1989; Kunce and Shogren 2005;Chen et al. 2018).
China's environmental regulation is closely related to its administrative systems, mechanisms, policies, and other factors. Economic growth target management is one such system and mechanism. An effective motivation tool for local governments and officials, it has played a critical role in China's rapid economic growth for more than four decades. Moreover, its presence can be found throughout China's entire political system, at all government levels, within all administrative departments and Party committees. As in many other countries, economic growth target management has become an important phenomenon in the process of China's macroeconomic growth. Since 1950, at least 49 economies have announced their economic growth targets (Xu and Liu 2017), including developed countries such as Germany, the UK, Japan, South Korea, and developing countries such as China and India. The UK sets and implements various top-down policy objectives as a core element of its government performance management system (Halpern 2015).
Of all the countries that enforce the economic growth target management system, China is the most highly motivated, always proactively proposing economic growth targets and reliably boosting its economy. Governments at all levels in China must announce economic growth targets in the Party Congress reports, outlining development planning, and government work reports. Such announced targets restrict and deeply affect behaviors of local governments and officials. In the context of "political centralization and economic decentralization," GDP growth rate is still the main means of assessing local governments and officials. To achieve growth targets, governments at all levels use all their powers and resources to foster economic growth. Environmental regulation is an important means of guiding or allocating various elements to certain industries or economic activities (Xu et al. 2018;Liu et al. 2019a, b). Xu and Liu (2017) used data from 49 economies that have consistently or regularly announced economic growth targets, which includes China. Their empirical research found the announced economic growth targets and subsequent economic growth rates to be positively correlated. On average, when the economic growth target changes by 1%, its corresponding subsequent economic growth rate also changes by about 1%.
In the performance appraisal and incentive system, which includes economic growth target management, local governments often pay attention to short-term economic effects, and deeply intervene in regional economic and social activities by decentralized management of the economy, society, ecology, and culture. Intervention includes changing the intensity of local environmental regulations, which generally has adverse effects on the environment. Li and Chen (2019) highlighted that the higher the industrial output value, the larger the total tax amount, and the larger the number of employees of the polluting enterprises, the greater their contribution to the local economy and the performance assessment of officials, and the higher the bargaining power of the polluters in the implementation of environmental regulation, so that they can be exempted from strict environmental regulation. This indicated that constraints of economic growth target impact on the degree of environmental regulation stringency of local governments. Therefore, this paper focuses on the effect of economic growth targets on the degree of environmental regulation stringency, and attempts to solve the following questions: (1) Do economic growth targets weaken environmental regulation stringency?
(2) If a negative effect exists, does it have heterogeneity? (3) What is the internal mechanism of the effect? In order to empirically test these problems, this paper collects data from more than 2000 government work reports of 284 prefecture-level cities in China between 2009 and 2016. This paper extracts data on government economic growth targets, constructs the environmental regulation stringency index, empirically tests the impact of local economic growth targets on the degree of environmental regulation stringency, analyzes a mechanism, and conducts a heterogeneity analysis.
Compared with previous studies, this paper may have innovations and contributions in the following aspects. Firstly, this paper considers the management of economic growth targets as an important system for restraining and motivating local governments and officials, and analyzes its effect on local government environmental regulations and its further effect on the environmental regulation intensity. When analyzing the impact of local governments and officials on environmental regulation, both the environmental federalism literature (Oates and Schwab 1988;Oates 1999;Kunce and Shogren 2005;Lai 2013) and the official "Promotion Tournament" literature (Besley and Case 1995;Tung and Cho 2001;Zhang et al. 2020;Chen and Gao 2020) emphasized that local governments and officials tended to "race to the bottom." That is, they continuously weakened environment regulation intensity in order to achieve rapid economic growth. Nevertheless, their studies lacked the analysis on economic growth target management. In effect, as a crucial mechanism that constrains and motivates local governments and officials, the management of economic growth targets changes the degree of environmental regulation intensity by affecting environmental regulation behaviors of local governments and officials.
Secondly, from the perspectives of economic growth target management and promotion and incentives of local officials, this paper discusses the relationship between economic growth target management and environmental regulation, by providing a new perspective for the study of environmental regulation, and introducing the literature of economic growth target management to the field of environmental economics. Although there exists much research on the management of economic growth objectives, most focuses on either the formulation of economic growth goals (Ma 2013;Xu and Liang 2014;Yu and Yang 2017;Wang and Huang 2019;, or the impact of economic growth goals on economic growth (Xu and Gao 2015;Xu and Liu 2017;Xu et al. 2018). Rarely has the impact of economic growth goals on noneconomic growth areas and working mechanisms been investigated.
Thirdly, while a large amount of literature has identified the assessment and evaluation mechanism of GDP supremacy as the direct cause of the behavior of many local governments and officials, most previous studies measured motivation and incentives based on the tenure, age, and promotion of officials (Zhou et al. 2017;Wang et al. 2018;Zhang and Chen 2018;Jin and Shen 2019). This paper attempts to take economic growth targets set by local governments as a new measurement to characterize local government and official responses to superior government assessment. This new measurement can comprehensively reflect the drivers of the behaviors of local officials. Additionally, most indicators that measure the degree of environmental regulation intensity are based on post extrapolation (Gray and Deliy 1996;Laplante and Rilstone 1996;Dasgupta et al. 2001;Zeng 2008;Rubashkina et al. 2015;Zhou et al. 2017). Following Chen et al. (2018), this paper collected government work reports and measured the degree of environmental regulation intensity by using the proportion of sentences containing environmental words to comprehensively investigate the environmental regulation of 284 Chinese prefecture-level cities. This quantitative measure, a pre-analysis indicator, is seldom used in environmental regulation intensity research and relevant research is still at the exploratory stage.
The following sections of this paper are as follows: the "Data and model" section establishes a model and elaborates on data collected; the "Empirical results" section conducts an empirical analysis; the "Mechanism analysis" section disc u s s e s a n d a n a l y z e s p o s s i b l e m e c h a n i s m s ; t h e "Heterogeneity analysis" section introduces heterogeneity tests; the final section concludes this study.

Empirical model setting
In order to test the impact of economic growth targets on the degree of environmental regulation stringency, the following regression equation is set: where i denotes the city and t is the year. Renv denotes the index of environmental regulation stringency, and target is the economic growth target of a city. Taking account of the lag of the social impact of the government's economic policies, this paper adopts the practices of Wang et al. (2020) and Ren and Matsumoto (2020) to examine the effect of the lag period of independent variables on dependent variables. On this basis, we further take the lag phase of the economic growth target as an independent variable. X denotes other control variables, and ε is a random disturbance term. When carrying out the regression of Eq. (1), the fixed effect model was adopted for estimation. Coefficient β is the regression coefficient that raises the greatest concern in this paper. Its symbol and magnitude measure the impact of economic growth target on the environmental regulation intensity. If the coefficient β is significantly greater than 0, an increase in economic growth target leads to more stringent environmental regulation in the region. If the coefficient is significantly less than 0, an increase in economic growth target leads to less stringent environmental regulation. If the coefficient is neither significantly greater nor less than 0, economic growth targets have no effect on regional environmental regulation.

Data
This paper sampled China's prefecture-level cities from 2009 to 2016. Among them, there were 282 prefecture-level cities between 2009 and 2010, and 284 prefecture-level cities between 2011 and 2016.
The environmental regulation intensity index (Renv) is the dependent variable, and can be used to measure the degree to which the region attaches importance to the environment, or the degree of efforts taken to protect the environment. However, it is noteworthy that in empirical analysis, the intensity of environmental regulations is difficult to measure. As Busse (2004) and Lu (2009) highlighted, many empirical studies are limited due to the difficulty of obtaining relevant data on the intensity of environmental regulations and the relatively poor data quality.
Different researchers use different indicators: influential studies including Gray and Deliy (1996), and Laplante and Rilstone (1996), took the number of environmental audits as a measure of environmental regulation; Dasgupta et al. (2001) used pollution tax as an indicator of environmental regulation; Rubashkina et al. (2015) used data on Pollution Abatement and Control Expenditures (PACE) as a proxy for environmental regulation; Liu et al. (2019a, b) adopted the ratio of the investment in industrial pollution control to the industrial added value. These indicators are extrapolated indicators after the fact. Generally, market entities influence pollution and related behaviors by using the strength of environmental regulations as constraints for future actions, while inferring indicators after the fact is to infer the strength of past environmental regulations based on the results of subsequent pollution and other behaviors. This is obviously not satisfactory.
This paper adopts a more cutting-edge approach. Following a procedure adapted from Chen et al. (2018), this paper measures environmental regulation intensity with the proportion of sentences that contain environmental words, and obtains relevant data by manually collecting work reports of municipal governments in China. From the work reports, we select all sentences containing one or more of the following keywords as environment-related sentences: environment (huanjing); energy consumption (nenghao); pollution (wuran); emission reduction (jianpai); and environmental protection (huanbao).
A core independent variable is the economic growth target (target). Following the procedure detailed in  and Liu et al. (2019a, b), this study employs the economic growth rate targets announced in work reports of the prefecture-level city governments. Each city generally announces its targets for economic growth rates at the beginning of each year as approved by its people's congress. The economic growth target data in this article are manually collected from two channels. Firstly, the portal websites of the people's governments of provinces and prefecture-level cities, the main source of government work reports. Secondly, the prefecturelevel city yearbooks where work reports are published marked as "special publication." Following collection, this study further processes the data on economic growth targets. Those that are explicit numbers or followed by modifiers, i.e., "about," "around," "above," "higher," "minimum," and "not less than," are counted using the referred numbers. A mean value is used for the economic growth target interval.
In addition to core variables, this article takes a series of control variables into account in the empirical regression. We measure the level of economic development, consumption power, and openness of a region by selecting the following control variables: the real GDP per capita (lpgdp), proportion of retail sales of consumer goods in GDP (Rcum), and proportion of foreign direct investment (FDI) to GDP (Rfdi), respectively. Research by Zhou et al. (2015) indicated that changes in the industrial structure would cause changes in the driving force of economic growth. Therefore, we measure the industrial structure (stru) by the proportion of the secondary industry in GDP. Wang and Chao (2014) found that urbanization, an essential component of the national development strategy, was the driving force behind the stable and rapid development of China's economy because it accelerated the transformation of economic development mode. Consequently, this paper considers urbanization as a control variable and measures it by the ratio of the population in a municipal district to the total population of the city (Rub). Referring to the method adopted by Liu and Kong (2020), this paper selects the logarithmic form of industrial sulfur dioxide emissions (lSO2) as a control variable. The above data are from the "China City Statistical Yearbook." Table 1 summarizes the data sources of the main variables used in this paper. Table 2 provides the descriptive statistics of the variables. The minimum value of the economic growth target is 1.5 and the maximum value is 26; the minimum value of the environmental regulation index is −1.079 and the maximum value is 2.733.

Variable test
Before regression, a series of tests need to be performed on the variables. Firstly, Table 3 conducted a multicollinearity test to specifically calculate the VIF value of each explanatory variable. It can be found that among the explanatory variables, the VIF value of real GDP per capita (lpgdp) is the largest at 2.39, and the VIF value of the proportion of FDI (Rfdi) is the smallest at 1.18. The VIF values of all variables are less than 5, and the empirical model does not have serious multicollinearity problems. Secondly, the cross-section correlation test (Pesaran CD test) is performed on the panel data, the CD value is 10.306, and the corresponding P value is 0.000. It can be seen that the original hypothesis is rejected; that is, the data used in this paper is not sectional correlation.
Thirdly, we use the Fisher test as a panel unit root test. 1 As shown in Table 4, the P values of the eight variables are all 0, rejecting the null hypothesis; that is, the variables do not have unit roots, and the data is stable. 2 Benchmark result Table 5 reports the baseline regression results based on the empirical Eq.
(1). The coefficients of the lag period of the economic growth targets (L.target) in all five columns are significantly negative at the 1% level, which is consistent with the theoretical hypothesis that an increase in local economic growth targets weakens environmental regulation intensity. Specifically, column (1) of Table 2 has only one independent variable, the lag period of the economic growth target whose coefficient is −0.261, and statistically significant at the 1% level. Different control variables are introduced in columns (2) to (5) where coefficients on the lag period of economic growth targets are also significantly negative at the 1% level. The coefficient of the lag period of economic growth target in column (5) is −0.188, which means that for every 1% increase in the local economic growth target between 2009 and 2016, the level of environmental regulatory intensity will descend by roughly 0.188%. Table 5 demonstrates that higher economic growth targets weaken local environmental regulation intensity. This paper adopts the following four approaches to examine robustness of the empirical results: (1) transform core explanatory variables by changing the measurement variables of the economic growth target;

Robustness test
(2) transform dependent variables by changing the measurement variable of the intensity of environmental regulation; (3) transform regression sample; (4) consider endogeneity. Table 6 changes the measurement method of economic growth target for robustness test. Column (1) uses the "average of economic growth target indicators for two consecutive years" (logarithmic form) to measure economic growth targets (L.target2), and its coefficient is significantly negative at the 1% statistical level; column (2) adopts the "average of the economic growth targets in three consecutive years" (logarithmic form) to measure the economic growth target (L.target3), and its coefficient is −0.928 and passes the statistical test with a significance level at 1%. Though the measurement indicator of economic growth target is changed, the conclusion that an increase in economic growth targets reduces environmental regulation intensity is still valid, which is consistent with Table 5.

Transform explained variable
The measure of environmental regulation intensity is changed in Table 7. The environmental protection words per capita   (1) to measure the environmental regulation intensity. The regression coefficient of the lag period of economic growth target (L.target) is −0.205 and passes the statistical test with a significance level of 1%. This indicator is built around the frequency of environmental protection words. Therefore, we must find other recognized, commonly used, and more reasonable environmental regulatory indicators. Column (2) adopts the comprehensive environmental regulation index of Hao and Zhang (2016) and , which is calculated from industrial wastewater emissions, industrial SO 2 emissions, and industrial smoke and dust emissions. The index data is a provincial panel data. The second column is the regression using provincial panel data (panel data for 30 provinces from 2009 to 2016). At this time, the coefficient of the lag period of the economic growth target (L.target) is significantly negative at the 1% statistical level. The explained variable in column (3) remains the comprehensive index of environmental regulation, but uses the prefecture-level city panel data. Specifically, the comprehensive index of environmental regulation of different cities in the same province in the same year is regarded as the same, and the data of the remaining variables such as Eq. (1). At this time, the coefficient of the lag period of the economic growth target (L.target) is also significantly negative at the 5% statistical level.
In order to proxy the strength of environmental regulations, column (4) uses the environmental performance index jointly released by the Yale Center for Environmental Law & Policy and the Center for International Earth Science Information Network (CIESIN). The index is a variable proxy of the environmental regulations stringency and commonly used in the study of transnational environmental regulations (Chung and Seong 2013;Adeel-Farooq et al. 2018). Therefore, this index focuses on a series data from the perspective of time at a national scale. At this time, the lag period of the economic growth target (L.target) is significantly negative at the 5% statistical level. This is in accordance with the conclusion we draw from the results in Table 5. Though an alternative indicator is adopted to measure the environmental regulation intensity, the conclusion that an increase in economic growth targets reduces environmental regulation intensity still holds true.

Transform regression sample
Data on economic growth targets is missing to different degrees in sampled prefecture-level cities. Only 37 cities Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit reported annual economic growth targets between 2009 and 2016, with a sample size of 592, accounting for 13.1%. The sample size of cities with partially available economic growth target data is 3901, accounting for 86.3%; the growth target data of Jiamusi, Bijie, and Tongren is all missing, with a total sample size of 28, accounting for 0.62%. Column (1) of Table 8 reports the regression results of a sample of cities with missing target data. The regression coefficient on the lag period of the economic growth target (L.target) is −0.180 and statistically significant at the 1% level. Column (2) further examines the samples of prefecturelevel cities with 4 or more economic growth targets. The results show that the regression coefficient on the lag period of the economic growth target (L.target) is −0.188, statistically significant at the 1% level. There is almost no difference in the regression results of the cities with missing growth target data. Column (3) examines a sample of cities with 6 or more economic growth targets. The regression coefficient on the lag period of the economic growth target (L.target) is −0.178, also statistically significant at the 1% level.
The above analysis demonstrates that regardless of sample ranges, results are still in accordance with the theoretical hypothesis. Between 2009 and 2016, China's local governments promoted economic growth in a way that negatively impacted on environmental regulation intensity. Specifically, for every 1% increase in the economic growth target, the environmental regulation intensity index will significantly decrease by about 0.18%.

Endogenous
The lag period of economic growth targets is taken into account in the regression model. We believe that economic growth targets are established prior to environmental regulation behaviors. Overall, local governments weaken environmental regulation intensity to improve fixed asset investment and land transfer areas, which enables the promotion of economic growth in order to achieve the established growth targets. However, an endogenous problem cannot be ruled out because economic growth targets may be established to assess the intensity of potential environmental regulations.
This paper uses instrumental variables to investigate the abovementioned endogenous problem. Specifically, provincial economic growth targets are regarded as an instrumental variable of prefecture-level economic growth targets (Liu et al. 2019a, b; Pan 2019), given that lower-level governments in China under the system of official promotion tournaments tend to formulate economic growth targets higher than those proposed by higher-level governments (Zhou et al. 2015), and that economic growth targets set by higher-level governments are expected to be less affected by those set by lower-level governments. Table 9 reports the two-stage IV estimation results with the provincial economic growth targets as an instrumental variable of prefecture-level economic growth targets. Column (2) reports the regression results of the first stage. The regression coefficient on the instrumental variable, provincial economic growth target is 0.308 and passes the statistical significance test of 1%. This suggests a significantly positive correlation between economic growth targets and the instrumental variable. Column (1) reports the basic results of the second stage, IV estimation. The regression coefficient on the growth target is −0.280 and passes the statistical significance test of 5%. The above results imply that the benchmark results still exist when endogeneity is taken into account, which corroborates the robustness of the basic conclusions.

Mechanism analysis
The above empirical results enable us to conclude that an increase in local economic growth targets will significantly reduce environmental regulation intensity. In this section, we attempt to explore the functional mechanism. We believe the significant impact of economic growth targets on environmental regulation intensity can be explained by the less stringent Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit environmental regulation promoting outputs in the short term and increasing economic growth. Obviously, less strict environmental regulation can improve the proportion of the secondary industry in GDP, expand land transfer areas, and increase the growth rate of fixed asset investment. These are important short-term economic growth measures. This paper uses the real GDP growth rate, proportion of secondary industry added value in GDP, land transfer area, and the fixed asset investment growth rate as dependent variables in order to further verify the mechanism; the environmental regulation intensity index is used as an independent variable for empirical testing.
Column (1) of Table 10 takes the real GDP growth rate (logarithmic form) as a dependent variable, and the environmental regulation intensity index (logarithmic form) as an independent variable. The coefficients on environmental regulation intensity (Renv) are negative, and are statistically significant at 10%. The coefficient on environmental regulation intensity is −0.207. When the intensity of environmental regulations is reduced by 1%, the real GDP growth rate will increase by 0.207%. This importantly explains why an increase in economic growth targets leads to a drop in the environmental regulation intensity. In order to achieve higher economic growth targets, local governments are more likely to employ their power and resources to weaken environmental regulation intensity and achieve a relatively high economic growth rate in the short term.
Under the Chinese "political centralization and economic decentralization" system, local governments have the authority to intervene in economic activities of market entities, such as intervening in business operations of state-owned enterprises (SOEs) and adjusting mineral and energy prices, as well as other resource factors, with the aim of significantly increasing second industrial outputs, thereby boosting economic growth. According to Lin and Su (2007), China's economic growth in recent decades has been driven mainly by capital and land inputs with low interest rates and low energy prices, resources, and other factors. The unreasonable factor price system was the root of China's factor-driven economic growth model. Xu et al. (2007) highlight that competition among Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit provincial officials has led them to vigorously develop second industries to increase economic growth. For this reason, column (2) of Table 10 takes the proportion of second industry added value to GDP as a dependent variable, and the environmental regulation intensity index as a core independent variable. Similarly, the coefficient on environmental regulation intensity (Renv) is statistically significant at the 1% level, suggesting less stringent environmental regulation significantly increases the proportion of the secondary industry, which can achieve short-term high economic growth rates. Land resources are the most basic production factor for economic development, and local governments in China play a decisive role in the supply of industrial land. Local governments regard the efficient allocation of industrial land as an important means of promoting local economic development and GDP growth (Yang et al. 2020). Zhang et al. (2013) pointed out that as land was an important resource controlled by local governments, local officials may sell large amounts of land for the purpose of economic development.
In reality, stringent environmental regulation restricts the use of land resources by local governments, which in turn affects the rate of local economic growth. Less stringent environmental regulation allows local governments to utilize land resources for the construction of new projects, including projects that cause environmental pollution. From this perspective, the intensity of environmental regulations is closely related to the land area sold by local governments. Therefore, this paper takes the area of land transfer (logarithmic form) as a dependent variable and the environmental regulation intensity as a core independent variable to further test the impact of the intensity of environmental regulations on the area of land transfer (see column (3) of Table 6). The coefficient on environmental regulation intensity (Renv) is −0.054, statistically significant at the 10% level. These results suggest that less stringent environmental regulation causes an increase in the area of land for sale, thereby boosting economic growth. In accordance with our findings, the research of Hu and Lv (2019) found that raising economic growth targets remarkably expanded the area of land transfer. Hence, it can be concluded that local governments intentionally set higher economic growth targets to weaken environmental regulation stringency so as to increase the area of land transfer within their jurisdiction and to achieve short-term expansion of outputs.
Fixed asset investment is the main driving force for China's economic growth. The Nobel Prize winner economists, Paul Krugman and Alwyn Young from the London School of Economics, believe that China's economic boom is not surprising since its growth model is "high input and low efficiency." Although the GDP growth rate is high, the total factor growth rate is only 2 to 3%, equal to the long-term total factor productivity in developed countries. When establishing high economic growth targets, Chinese local governments have a strong internal driving force to increase fixed asset investment on a large scale in order to achieve such targets. Liu et al. Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit (2019a, b) also argued that local governments used their power and resources to increase investment in fixed assets so as to achieve economic growth targets. However, environmental regulations restrict local governments from increasing fixed asset investment. In order to improve the growth rate of fixed asset investment, local governments will intentionally weaken the intensity of environmental regulations to be exempt from constraints on fixed investment increases. From this perspective, the growth rate of local government fixed investment is closely related to environmental regulation intensity. Therefore, this paper takes the growth rate of fixed asset investment (logarithmic form) as a dependent variable and the environmental regulation intensity as the core independent variable to further empirically test the influence of environmental regulation intensity on fixed asset investment. The empirical test results demonstrate that whether control variables are added or not, the regression coefficients on the degree of environmental regulation intensity (Renv) are statistically significant at the 5% level (see column (4) of Table 6), suggesting that weakened intensity of environmental regulations significantly increases investment in fixed assets, as is consistent with the research results made by Oates and Schwab (1988) and Zhang (2005). Hence, it can be concluded that local governments faced with high economic growth targets intentionally weaken the intensity of environmental regulation so as to increase the fixed asset investment in the jurisdiction for short-term expansion of outputs.

Heterogeneity analysis
So far, the empirical test results of this paper are consistent with the theoretical hypothesis expectations. From 2009 to 2016, the increase in economic growth targets set by local governments significantly reduced the degree of environmental regulation stringency. The purpose of weakening the intensity of environmental regulations is to expand outputs in the short term, specifically to increase the proportion of the secondary industry in GDP, the area of land transferred by the government, and investment in fixed assets. This section will further explore whether there is heterogeneity for the effect of economic growth targets on environmental regulation stringency under different conditions, aiming to further reveal the underlying mechanism.

The heterogeneity of urban development
The degree of urban development is measured by real GDP per capita. According to the size of real GDP per capita, samples are divided into two groups and regression is performed to both groups. Cities with a real GDP per capita higher than the mean value are regarded as developed while those lower than the mean value are regarded as less developed. Column (1) of Table 11 presents samples of less developed cities where the regression coefficient on the lag period of the economic growth target (L.target) is −0.227 and statistically significant at the 5% level. Column (2) of Table 11 shows samples of developed regions where the coefficient on the lag period of the economic growth target (L.target) is insignificant. The above results indicate that only in undeveloped areas does a fall in the degree of environmental regulation stringency result from an increase in economic growth targets.

The heterogeneity of openness
The foreign direct investment (FDI) as a percentage of local GDP is used to measure a city's openness to the outside world. Samples are separated into two groups by comparing the proportion of FDI with the mean value, and regression analysis is performed to both groups. Column (1) of Table 12 presents samples whose FDI proportion is greater than the average value, and their coefficient on the lag period of the economic growth target (L.target) is statistically significant at the 1% level; column (2) shows samples whose FDI proportion is less than or equal to the average value, and their coefficient on the lag period of the economic growth target (L.target) is insignificant. Accordingly, the hypothesis that a rise in economic growth targets set by local governments induces less stringent environmental regulations only holds true in less-open cities.
In summary, analysis of the heterogeneity of urban development and openness of the sampled cities has revealed that negative effects of economic growth targets on environmental regulation stringency mainly exist in cities with relatively lowlevel economic development and relatively low-level openness. In cities where the economy is less developed and marketization of economic development is at a relatively low level, local governments tend to more actively use various government resources and approaches, including the environmental regulations emphasized in this paper, to stimulate companies to expand production capacity and to achieve high economic growth targets.

Conclusion and review
Environmental regulation is crucial to address environmental pollution and achieve sustainable economic and social development. For local governments, environmental regulation stringency is influenced by environmental protection standards formulated by the central government, and also closely related to the regulation enforcement by local governments. The management system of economic growth targets is an important factor affecting the environmental regulation enforcement by local governments. In the performance appraisal and incentive system that centers economic growth target management, local governments often devote attention to the Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit Numbers in parentheses are robust standard errors; ***, **, and * denote that variables are statistically significant at 1%, 5%, and 10%, respectively; N is the number of observations and R 2 is the goodness of fit short-term economic benefits, and make use of the decentralization of economic, social, ecological, and cultural management to deeply intervene in regional economic and social activities, including weakening the intensity of local environmental regulation, so as to achieve rapid economic growth. This paper uses panel data between 2009 and 2016 from the "China Urban Statistical Yearbook" and urban government work reports in order to firstly analyze the impact of economic growth goals set by governments on economic and social development. It then constructs the index of environmental regulation stringency, and empirically explores the impact of economic growth targets on environmental regulation stringency. This paper observes that high economic growth targets give rise to less stringent environmental regulation. After the robustness tests of changing measurement variables, regression samples, and considering endogeneity, the conclusion is still valid, indicating that the empirical test results are robust. This paper points out that local governments aimed at achieving economic growth goals weaken environmental regulation intensity and expand outputs in the short term, which is manifested in expanding the proportion of the secondary industry in GDP, the area of land transfer, and the investment in fixed assets. It is also observed that the hypothesis that an increase in economic growth targets weakens environmental regulation stringency only takes effect in economically underdeveloped cities with relatively low openness. Such findings enable this paper to derive the following policy recommendations.
Firstly, this paper recommends dilution of the economic growth target assessment, and establishment of the government target management system of sustainable development. The political assessment mechanism with GDP growth rate as the core assessment indicator makes officials at all levels pursue short-term economic growth. This paper has observed that in order to achieve economic growth goals, local governments reduce the intensity of environmental regulations, increase the proportion of the secondary industry in GDP, the area of land transferred by the government, and invest in fixed assets. The development model mentioned above is unsustainable. This paper has identified that when assessing administrative officials, the economic growth target achievement should change from a "hard constraint" to a "soft constraint." The economic growth target should be set within a reasonable range to better match the actual local development situation. Additionally, a multi-dimensional performance appraisal mechanism should be introduced, instead of blindly encouraging local governments and administrative officials to achieve high economic growth. Government work should focus on shifting from chasing economic growth to new development orientations such as concept of innovative, coordinated, green, open, and shared development. China has been actively exploring this since the 18th National Congress of the Communist Party in 2012.
Secondly, this paper recommends regulating the aggressive competition between regions, with an emphasis on balancing regional development. Such extreme competition among regions is rooted in the "promotion tournament," which sees regions competing with each other and racing to the bottom. For example, as reflected in this paper, the intensity of environmental regulations is reduced in order to achieve high economic growth. The following are therefore necessary: (1) build a platform for communication among officials from different regions under the pattern of "a game of chess in the whole country"; (2) strengthen inter-provincial cooperation and coordination of interests; (3) further explore high-quality models of inter-regional competition and cooperation to better build an ecological civilization.
Thirdly, this paper recommends changing the economic growth model and exploring diversified ways to promote economic growth. Under pressure to complete economic growth targets, local officials blindly reduce the intensity of environmental regulations and pursue short-term output, which also stems from the lack of means to promote economic growth. The empirical evidence of this paper also shows that in developed regions with greater economic growth, the effect of increasing economic growth targets and reducing the intensity of environmental regulations does not exist. Therefore, to avoid the "race to the bottom" of environmental regulations, it is also necessary to explore diversified means of promoting economic growth, such as continuing to deepen the reform of the commercial system, promoting "mass entrepreneurship and innovation," and promoting the high-quality development of enterprises.

Declarations
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