Do environmental target constraints of local government affect high-quality economic development? Evidence from China

The purpose of this paper seeks to investigate the impact that environmental target constraints imposed by local governments have on the quality of economic development. In this paper, the city-level economic high-quality development index was measured, and data on environmental target constraints were compiled from the governmental work reports submitted by 230 cities between the years 2004 and 2013. Using the DID model and the instrumental variable method, it is found that (1) when environmental performance is factored into the performance evaluation of governmental officials, environmental target constraints contribute significantly to the high-quality growth of the local economy and (2) environmental target constraints are heterogeneous across regions and have a greater promoting effect on cities in eastern China than in central and western China. These conclusions are supported by a number of robustness tests. Resultantly, this paper provides a novel perspective for understanding the relationship between environmental protection and the quality of economic development. In addition, it offers the government theoretical and empirical support to strengthen the development of an environmental protection system and promote high-quality economic development. Moreover, this study delivers crucial empirical evidence for developing countries to simultaneously achieve environmental pollution control and economic growth.


Introduction
The rapid expansion of China's economy over the past four decades as a result of the country's policy of opening up and reforming has been dubbed the "Chinese miracle" in the chronicles of the progression of world economic development. There are, however, a number of issues with China's high economic growth, including the overall low level of industry, the "hollowing out" of core basic technological innovations, the extensive regional income gap, and the widening inequality of public services. Thus, many academics examined the "Chinese style decentralization" system and promotion tournaments from a theoretical standpoint (Wu et al. 2020;Que et al. 2018;Zhao et al. 2022;Liu and Xiao 2018). China's economic growth has been more goal directed under the influence of the "Chinese decentralization" system, which has produced a variety of challenges for China's current high-quality development. In this context, the government asserted that "China's economic development has entered a new era." The economic development has shifted from high-speed development to high-quality development, and the focus of scholarly attention has shifted to high-quality development.
Numerous countries are confronted with the dual dilemma of economic development and environmental pollution control (Di Zhou et al. 2020;Egbetokun et al. 2020;Rao and Yan 2020). China's rapid economic growth has been at the expense of the environment, as has been the case in the majority of developing countries. China's environmental problems have become an urgent issue as a consequence of the explosive growth of heavy industry (Greenstone et al. 2021). Academic research on the relationship between environmental pollution and economic growth is primarily based on the "environmental Kuznets curve" and empirical studies of environmental pollution at various stages of economic development (Chien et al. 2021;Dinda 2004;Bo 2011;Cole et al. 1997;Wu and Tian 2012;Ahmad et al. 2021;Al Khars et al. 2022). By employing the moment-quantile regression approach, Anwar et al. (2021a) demonstrated the existence of the environmental Kuznets curve in BRICS countries. Results indicate that economic growth and financial development in BRICS countries increased carbon dioxide emissions across all quantiles. However, ICT reduces CO 2 emissions significantly only at lower emission quantiles (Anwar et al. 2021b). According to Haibing and Ahsan et al. (Anwar et al. 2022a), a 1% increase in renewable energy consumption, technological innovation, and institutional quality in G7 countries could reduce carbon emissions by 0.144%, 0.189%, and 0.257%, respectively (Anwar and Malik 2021).
China was still on the left side of the inflection point on the curve, the country's economic growth was still jeopardizing the environment, and environmental pollution was directly proportional to economic growth (Sun and Lin 2018). According to some scholars, economic growth and CO 2 emissions form an inverted U-shape Song et al. 2008). However, the relationship between haze pollution and economic growth in China is not a typical environmental Kuznets curve (EKC) with an inverted U-shape. Grouping regressions indicate that there are regional differences in the relationships between haze pollution and economic growth, as well as their tuning points. For instance, the central region exhibited a U-shaped relationship, while the eastern, western, and northeastern regions showed inverted N-shaped relationships. With the aggravation of environmental complications, environmental concerns have received increased attention and related policies have been introduced (Du et al. 2018). Some have discovered that the EKC in eastern and central China reveal an inverted U-shape, whereas in the west, they exhibit a positive U-shape (Lin and Jiang 2009;Yang and Yuan 2009). The performance reform of Chinese officials has gradually shifted from economic growth targets to comprehensive assessments including environmental targets, indicating that environmental targets will constrain government behavior to some degree and have a significant impact on economic development.
In this paper, we investigated how environmental goal constraints faced by local governments affect the high-quality development in China. Specifically, this paper analyzed data from 230 prefecture-level cities from 2004 to 2013 to identify environmental goal constraints local governments face in high-quality development. Nonetheless, a review of the literature revealed that the current literature focuses primarily on the "environmental Kuznets curve" to validate the relationship between environment and economic growth and the "Porter's hypothesis" regarding the relationship between environmental regulation and technological innovation. Environmental goals are granted little consideration by the typical Chinese local government. The impact of environmental accountability on economic activities, particularly the quality of economic development, has received scant attention in the literature. We have compiled a database of 230 prefecture-level cities with high-quality economic development indicators, environmental target assessments, and other economic data. Notably, our contribution to the previously published works consists of the following. First, in this paper, relevant data on environmental target constraints and government work reports from various provinces and municipalities were collected manually. Utilizing the official assessment system as a starting point, the traditional analysis of environmental regulations was deduced one step further, providing a new perspective for the study of environmental issues. Second, this paper describes environmental goal constraints from a uniquely Chinese perspective. It is predicated on the exogenous shock of incorporating environmental performance into assessments of governmental officials, leveraging cities with environmental goals as the experimental group, and employing the DID model and instrumental variable methods to examine the effect of local government environmental goal constraints on highquality economic development. Third, this paper enhances the theoretical foundation and empirical evidence of urban environmental governance and economic development from the aspect of the promotion of local government officials. Fourth, the uncertainty of our models and the sensitivity of our results are evaluated through a series of robustness tests.

Theoretical mechanism
Environmental goal restrictions may affect regional development, including regional environmental regulations, industrial policies, resource allocation, technological innovation, and fiscal expenditure structures. These changes may impact green TFP, industrial structure, innovation level, environmental pollution, and the quality of life for residents in highquality economic development. We demonstrate the effect of environmental target restrictions on regional high-quality development and the transmission mechanism from four different perspectives. country or region strengthens its environmental regulations, pollution-intensive firms lose their comparative advantage over other businesses and relocate to countries or regions with weak environmental regulations. Free trade has high environmental costs for heavily regulated regions. The relocation of pollutant-intensive imports is supported by the regional preference for such imports. Environmental constraints may influence where businesses invest, and they would establish operations in areas with lax environmental regulations to lower cost (Becker and Henderson 1997). Cai et al. (2016) used the "two controlled zones" policy adopted by the Chinese government in 1998 as a quasi-natural experiment and discovered that rigid environmental regulations had a negative impact on FDI entry. Due to the fact that developing countries have weaker environmental regulations than developed countries, polluting companies are transferred to developing countries, leading to degradation. According to the "pollution haven" hypothesis, in an open economy, international differences in environmental standards or the degree of trade and investment regulation will drive polluting industries across international borders and transform national or regional industrial structures. (Millimet and Roy 2016;Sun et al. 2017). Chen et al. revealed through empirical research on China that regional industries in China have a certain "pollution refuge" effect. Moreover, polluting industries tend to relocate to the central and western regions, where environmental regulations are relatively lenient (Guo and Tao 2009;Fu et al. 2011). When pollutionintensive companies leave the region due to environmental regulations, high-tech and service businesses may have more opportunities.
Second, from the perspective of enterprise entry, stringent environmental regulations create invisible green entry barriers and promote regional industrial structure optimization and adjustment. Strict environmental regulations increase the cost of entering the market for pollution-intensive businesses (Ollinger and Fernandez-Cornejo 1998). As a result, fewer pollution-intensive businesses in regions with strict environmental regulations enter the market. While the number of businesses entering clean industries, such as new high-tech industries and service industries, is on the rise, the proportion of these businesses is decreasing (Blair and Hite 2005;Cui and Ji 2011). The green entry barrier created by stringent environmental regulations can limit or even diminish the size of pollution-intensive businesses. When the government adopts rigorous environmental regulations, it is obligated to encourage and support clean enterprises, such as new high-tech industries and service industries, in order to enhance the level of the local ecological environment and avoid the economic decline caused by the reduction in the scale of pollution-intensive enterprises. Clean industries, such as the service industry, receive development opportunities and a substantial amount of capital and human capital inflows, which facilitate their scale expansion and rapid development, thereby increasing their proportion in the industrial structure. Finally, from the perspective of demand, environmental objective constraints will have an effect on consumers' consumption concepts and, consequently, on market demand, and the resulting shift in market demand will inevitably give rise to the adjustment of industrial structure. Consistent with the growth of the economy and the governmental promotion and publicity of environmental protection and the green consumption concept, consumers' awareness of environmental protection is on the rise. In daily life, consumers will reduce their consumption of polluting products and increase their consumption of green products Chen et al. 2018a, b). The change in the consumption concept is reflected in the adjustment of the consumption structure, which is then reflected in the market demand; the change in the final demand has an effect on the industrial structure.
In a broad sense, the effect of industrial environment target constraints on industrial restructuring is reflected in the proportional changes between manufacturing and service industries, with the proportion of service industries gradually increasing and the proportion of manufacturing industries gradually decreasing. On the other hand, it is also reflected in the changes in the proportion of the industrial value chain's low-end and high-end links, with the emphasis gradually shifting to the high-end of the value chain, which includes brand design, technological innovation, research and development, and marketing. The government formulates corresponding environmental regulation policies in accordance with environmental objectives. Moreover, it directly restrains the behavior of businesses and reduces environmental pollution and resource waste, thereby enhancing the quality of regional economic development and enhancing the living environment of residents. Additionally, the environmental regulation policy further promotes the high-quality development of the regional economy by optimizing regional industrial structure, bolstering regional technological innovation, and boosting regional green TFP.

Environmental target constraints, technological innovation, and high-quality economic development
First, environmental target constraints will encourage businesses to innovate and enhance regional technological innovation and foster high-quality regional development, despite the possibility that environmental regulations can increase production costs of firms and reduce their competitiveness. Porter (1991) argues, however, that firms can actually benefit from environmental protection policies since they encourage technological innovation without increasing costs. In contrast, the benefits of technological innovation can compensate for the higher costs, resulting in a net benefit that makes the business more competitive and advantageous in comparison. This is referred to as the "Porter hypothesis." In order to pursue immediate or short-term revenue and performance, manufacturers are not motivated to invest in innovation due to the time lag between revenue growth and technological innovation. While the strong environmental goal constraint of the government enables business managers to overcome this current preference behavior and increase their investment in technological innovation (Calel and Dechezleprêtre 2016;Ambec and Barla 2006;Bai and Song 2009). Anwar and Malik (2021) analyzed the macroeconomic determinants of CO 2 emissions in G7 countries and concluded that technological innovation can significantly reduce CO 2 emission levels (Anwar and Malik 2021). In order to meet the requirements of environmental regulations and improve the investment climate in the region, businesses will focus more on the improvement of production technology, process, and energy structure, the reduction of pollutant emissions, and the greening of production. Anwar et al. (2021c) demonstrated that under FMOLS, DOLS, and FE-OLS, when nonrenewable energy consumption increases by 1%, CO 2 emissions increase by 0.29%, 0.26%, and 0.30%, respectively, while renewable energy consumption increases by 1%. CO 2 emissions were reduced by 0.17%, 0.15%, and 0.17%, respectively (Anwar et al. 2021b). Consequently, environmental regulations will serve as a push-back mechanism to encourage businesses to invest more in R&D and advance technological innovation. The "compensating effect" of these technological innovations will mitigate the increase in enterprise costs caused by environmental regulations (Zhang 2019) and enhance the competitiveness of regional businesses.
The Porter hypothesis has been the subject of scholarly debate and research. After a more thorough and systematic analysis of the effect of environmental regulation on technological innovation, a comprehensive perspective was presented. They argue that the effect of environmental regulation on the comparative advantage of regional industries is not only the "cost-increasing effect" that raises the cost burden but also the "innovation-compensating effect" that compels businesses to innovate in response to environmental regulation. The ultimate effect is determined by which effect is greater (McCulligh 2018;Bertenthal 2021;Wang and Liu 2020;Shapiro and Walker 2018). Wang and Liu (2014) examined the effect of environmental regulation on the total factor productivity of Chinese industrial firms from 1998 to 2011 using a sample of Chinese industrial firms. The results indicate that environmental regulations and firms' total factor productivity have an "inverted N" relationship. When environmental regulations are weak, firms have lower environmental costs and less incentive to innovate, resulting in a decline in total factor productivity. As long as the environmental regulation is within a reasonable range, total factor productivity will rise when environmental regulation is increased to the extent that it can promote technological innovation. However, total factor productivity decreases when the intensity of environmental regulations exceeds the burden that firms can bear (Li et al. 2013;Dong et al. 2015;Chen 2016). Environmental regulations have an inverted U-shaped relationship with the quality of economic growth at the national level. Environmental regulations are currently on the left side of the inflection point, and an increase in environmental regulations can help improve technological innovation in China (Sun and Lin 2018).
Second, environmental target constraints enhance the regional green TFP and the quality of regional economic development (Tian and Feng 2022). In the previous model of development, economic growth was promoted at the expense of the environment in regions (Li and Lin 2016). The environmental goal constraint can effectively restrict polluting enterprise behavior, reduce pollution, and stimulate economic growth through innovation. Consequently, environmental goal constraints have a positive impact on green TFP (Cai et al. 2014;Bing and Wu 2008). However, the positive effect of environmental target constraint on green TFP is contingent upon the assumption that the intensity of regional environmental target constraint is compatible with the level of economic development at the local level. Otherwise, environmental target constraints inhibit local economic growth while reducing regional pollution (Ye and Peng 2011).

Environmental target constraints, resource allocation, and economic quality development
Environmental target constraints can indirectly adjust resource allocation and reduce regional and rural-urban income disparities. Based on the "environmental Kuznets curve" and the "pollution halo hypothesis," environmental goal constraints reduce economic and income disparities. When regional environmental regulations are stringent, enterprises with high pollution output will relocate to rural suburbs and regions with relatively lax environmental regulations. The left side of the inverted "U" curve represents the economic development of rural areas and places with lax environmental regulations. These regions' economic development is typically lagging. The transfer of pollutionintensive industries to less developed regions results in the introduction of new technologies and the creation of a large number of jobs, which drives the economic and technological development of less developed regions. Although on a short-term basis, the expansion of scale will result in the overexploitation of resources and the worsening of pollution in less developed regions. However, in the long run, as the level of local economic development keeps progressing, the government will gradually raise environmental regulations, which will prompt these regions to accelerate research and development of environmental protection technologies.
The transfer of industries from developed regions with environmental target constraints to less developed regions with looser environmental target constraints optimizes the allocation of factors between urban and rural areas and between developed and less developed regions. In this context, less developed regions experience a heightened rate of economic growth. First, it has the effect of industrial agglomeration. Transferring industries to less developed regions with less stringent environmental regulations frequently results in the formation of clusters of local industries. Typically, the industry undertaking party has a tenuous industrial foundation and an unclear industrial development direction. Following the transfer of an industry's enterprises to a less developed region, the industry in that region experiences rapid expansion. Additionally, it encourages the joint development of the industry's peripheral products as well as its upstream and downstream products, which has a certain positive influence on the scale and clustering of regional industries. Second, on the industrial undertaking side, the industrial transfer creates jobs. When investment attraction yields new industries, labor force demand will inevitably increase. Likewise, labor cost, which creates new jobs for the industrial undertaking party, is an important factor for many industrial transfer enterprises in selecting the industrial undertaking party. Finally, the industrial transfer can contribute to capital accumulation for the party undertaking the industry. In exchange for an influx of capital, the transferred industries are better able to utilize the comparative advantages of environmental resources and labor resources, as well as the resource advantages of the industry undertaking party. This can change the plight of the industry undertaking party's lagging economic development and provide capital for its economic expansion. Therefore, the transfer of pollution-intensive enterprises to less developed regions as a result of rigorous environmental regulations brings capital, technology, and job growth to less developed regions and drives the development of the local economy, thereby reducing the economic gap between less developed and developed regions.
In addition to their redistribution between regions, elements are optimally distributed between industries. Polluting businesses are either removed from the market or forced to relocate as a result of the governmental formulation of environmental regulation policies and corresponding industrial policies, which are imposed by environmental objectives. Concurrently, new high-tech industries and services, as well as other clean enterprises, will be encouraged and supported in order to improve the local ecological environment and avoid the decline in economic development caused by the downsizing of pollution-intensive enterprises. The development opportunities of clean industries, such as the service industry, and the influx of large amounts of capital and human capital will result in their expansion and rapid development, thereby increasing their share of the industrial structure. Consequently, environmental target constraints can promote the optimal allocation of factors in different industries and between different regions, encourage coordinated regional development, narrow the income gap, and augment the living standards of residents in lagging regions, thus enhancing the quality of regional economic development.

Environmental target constraints, government behavior, and economic quality development
Environmental target constraints can influence government behavior, which in turn influences the formulation of environmental regulation policies, industrial policies, and the adjustment of government fiscal expenditures. Due to structural effects, when local governments are constrained by environmental goals, they will promote local industrial transformation and upgrading through industrial policies such as innovation support and technological transformation support. Local governments have a strong incentive to develop the local economy under the system of political centralization, fiscal decentralization, and promotion incentives (Zhou 2004). The traditional model of promoting rapid economic growth in China by relying on high energy consumption, the "demographic dividend," and the development of capital-intensive industries is no longer viable (Jin 2009). In contrast to business industries, the government has access to holistic information and can guide industrial development and promote industrial upgrading by formulating appropriate industrial policies (Lin et al. 2010). Under the "double constraint" of economic growth and environmental goals, industrial restructuring and industrial upgrading are viable options for local governments (Zhang and Chen 2013). Local governments will, therefore, encourage the transformation of resource-intensive industries into knowledge-and technology-intensive industries (Zhang et al. 2009). Since local governments have the authority to formulate industrial policies, local officials will introduce policies to promote the growth of high-tech industries and environmentally friendly technological innovation. And this would facilitate the "greening" of traditional pollutant-intensive industries in order to meet assessment requirements under the assessment incentive (Flynn et al. 2015;Shu et al. 2015;Vásquez-Urriago et al. 2016). Maseko et al. (2011) and Shao (2015) have demonstrated that government support for innovation policies does play a significant role in promoting the technological innovation of businesses. However, technological innovation encourages industrial transformation and the upgrading of firms in particular industries. Moreover, changes in demand structure and labor productivity brought about by technological innovation are also significant drivers of regional industrial structure upgrading (Greunz 2004). Compared across industries, medium-and high-technology industries do not necessarily have the highest labor productivity growth. Nevertheless, their technological innovation activities contribute to the growth of labor productivity in their own industries and to the transformation of mediumand high-technology industries into relatively high productivity growth industries, thereby optimizing regional industrial structure (Varum et al. 2009).
Under the constraint of local environmental objectives, local officials adjust the fiscal expenditure structure of local governments, thereby promoting the transformation and upgrading of local industries. On the one hand, government subsidies are a significant part of fiscal spending and the most direct way for the government to "lend a hand" to the transition economy (Frye and Shleifer 1997). Due to China's traditionally GDP-oriented promotion incentive system, imperfect market economy, inadequate legal system, and non-transparent subsidy process, state-owned enterprises and loss-making enterprises used to receive a disproportionate amount of fiscal subsidies, wasting public funds (Kong et al. 2013). In addition, this distorted fiscal spending led to overcapacity (Geng et al. 2011). Local governments, under the "double constraint" of economic growth goals and environmental goals, have a clear tendency to provide more fiscal subsidies, tax preferences, and administrative approvals to low-pollution, high-output, technologically innovative industrial and service enterprises following a binding assessment of their environmental goals in addition to preferential policies such as tax breaks, R&D subsidies, and land policies to promote their development (Hamberg 2007;Feldman 2009;Wei et al. 2013). Lu et al. (2014) conducted an empirical study on the performance of industrial innovation subsidies provided by the Chinese government to strategic emerging industries since 2010. The results demonstrated that the government's innovation subsidies for strategic emerging generations promoted the growth of businesses substantially. On the other hand, the academic community has paid significant attention to the positive impact of science and education expenditures on industrial upgrading. Government spending on science and education, according to , influences the development of tertiary industries by changing total factor productivity and the accumulation of capital-labor production factors in each industry. Chu and Jian (2014) concluded that increased financial investment in education will assist in adjusting and optimizing the industrial structure, which will in turn promote the industrial structure of a country to become more advanced and rationalized. Government spending on science and technology has a direct impact on technological advancement and enterprise innovation capacity. Increasing government spending on science and education encourages industrial transformation and upgrading Liu and Xu 2014;Shang and Tao 2015). In addition, the government's explicitly biased fiscal behavior also serves as a "directional inducement" and promotes industrial transformation and upgrading through a "rent creation" mechanism (Shi and Kong 2012).
Under the constraints of environmental goals, the government will formulate relatively strict environmental regulation policies and environmentally friendly industrial policies in light of the preceding discussion. In addition, the government revises the structure of its fiscal expenditures to ramp up support for new high-tech industries and environmentally conscious service industries. Government actions have a significant impact on business decisions. To compensate for the impact of environmental target constraints on corporate costs through the improvement of production processes and innovation in production technology, polluting companies increase their investments in science and technology R&D. Simultaneously, polluting companies with insufficient innovation capacity are compelled to cease operations or relocate, giving rise to optimal resource allocation within the industry and between regions. These corporate and government actions will promote the optimization and modernization of industrial structures in regions with stringent environmental target constraints. This may significantly raise the innovation capacity of businesses, enhance the green TFP of the region, rectify environmental pollution issues gradually, optimize the living environment of residents, gradually enhance the quality of life, and ultimately lead to the achievement of high-quality regional economic development.

Data
From 2004 to 2013, 230 prefecture-level cities in 23 provinces of China are selected for this study, and macroeconomic data are obtained from the China Regional Economic Statistical Yearbook, China Statistical Yearbook, China City Statistical Yearbook and China Industry Business Performance Data. 2004-2013 was selected as the sample period for a variety of reasons, including the length of the investigation, the availability of data, and the size of the sample. First and foremost, this paper considers the 2007 Responsibility Statement for the Reduction of the Total Amount of Major Pollutants in the 11th Five-Year Plan 1 to be an exogenous impact. Since the implementation of a policy typically takes time to take effect, it is necessary to select the sample cities for a long-term survey after the policy has been implemented in order to accurately evaluate its effect. Second, the selection of three years prior to 2007 can ensure a sufficient sample size for more reliable results; therefore, the year 2004 was selected as the starting point. Third, the China Industry Business Performance Data database was only updated through 2013, which was chosen as the final year due to the availability of data. Chongqing, Qinghai, Hainan, Inner Mongolia, Guangxi Zhuang Autonomous Region, Tibet Autonomous Region, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region, Hong Kong Special Administrative Region, and Macao Special Administrative Region are excluded from the sample of prefecture-level cities due to pronounced data deficiencies. From 2004 to 2013, this study utilized panel data from 230 prefecture-level cities in China. The authors manually calculated the data on highquality economic development and manually collected the data on environmental target assessment from government work reports of various provinces and municipalities from the previous years. It should be clarified that only if the government work report expressly proposes a numerical target for controlling industrial pollutant emissions, such as a 5% reduction in sulfur dioxide emissions, should the target be fulfilled. This paper will conclude that environmental goals in that year constrained the local government. The value 1 is assigned to municipalities with environmental target constraints, while the value 0 is assigned to municipalities without such constraints.

Indicator measures
A systematic and scientific index system plays a crucial role in predicting, evaluating, and judging the implementation effect of policies related to the high-quality development stage. Existing literature on high-quality development has established a relatively comprehensive index system for measuring high-quality development. In this paper, we have compiled the index system for measuring high-quality development from the current literature, with principal component analysis and equal weight method, as the primary methods. The primary indicators are the structure of economic growth, the stability of economic growth, the welfare change, and the distribution of economic growth, as well as the cost of resource utilization and the ecological environment (Cao and Ren 2011).
This paper establishes the following indicators for gauging the quality of economic development with reference to the pertinent literature.
(1) Industrial development. China's economy is in a period of transition from high growth to high-quality development, accompanied by a shift from an industrial structure dominated by the secondary sector to one dominated by the tertiary sector. Consequently, this paper incorporates the level of industrial development into the development of a system of superior economic development. According to the classification of factorintensive industries, the manufacturing industry can be categorized as labor-intensive, capital-intensive, or technology-intensive (Chen and Xu 2012). Indicators of industrial development include the percentage of tertiary industry output value, the percentage of productive service industries, and the percentage of technology-intensive manufacturing industries.
(2) Technology innovation. Technology innovation is the predominant driver of economic development and the foundation for constructing a modern environmental system. Accordingly, incorporating innovation indicators into the system of high-quality economic development is essential. The China City and Industry Innovation Power Report 2017 uses 230 city innovation indexes from 2004 to 2013 to describe technological innovation in this paper. (3) Green TFP. The endogenous growth theory perceives TFP as a potent instrument for explaining regional income and economic disparity, which is more suitable for sustainable economic development, allowing an increasing number of studies to employ it to explain regional economic disparity (Wu 2009;Hu and Yang 2011). The green TFP index is measured in this paper utilizing the Malmquist-Luenberger index derived from the SBM model. The selected input indicators represent the two primary factors of capital and labor. The capital stock is calculated using the perpetual inventory method, while the labor in the input index is based on the data of all employees in the economy. Output indicators include desired output and non-desired output, with desired output represented by the regional GDP (billion yuan) of each prefecture-level city between 2004 and 2013. In this paper, the industrial wastewater emissions of each municipality represent wastewater emissions (million tons). The sulfur dioxide emissions of each prefecture-level city serve as a proxy for exhaust gas emissions (million tons). (4) Resident life. The ultimate objective of high-quality economic development is a return to the standard level of living for the nation; therefore, residents must be placed at the forefront of high-quality economic development. Here, the standard of living of the locals is incorporated into the system of high-quality economic development. The quantity and quality of social public goods such as education, medical care, and social secu-rity are sufficient to meet the needs of all citizens. As a corollary, this paper employs three metrics-GDP per capita (yuan/person), education expenditure per capita (yuan/person), and hospital beds per capita (number of beds/person)-to assess the quality of life among residents. (5) Environmental indicator. The construction of an ecological civilization is a necessary condition for highquality development, and the achievement of high-quality development is contingent upon a healthy ecological environment. The system of China's high-quality economic development, therefore, encompasses environmental indicators. In this paper, the comprehensive utilization rate of industrial solid waste, sulfur dioxide removal rate, and PM2.5 data are selected to illustrate environmental indicators. The sulfur dioxide removal rate is obtained from sulfur dioxide removal/ (sulfur dioxide removal + sulfur dioxide emission). In this paper, the PM2.5 index is treated as a negative indicator, as it represents an inverse impact.
The comprehensive evaluation index system is shown in Table 1.

Measure methods and data source
To enhance the objectivity and rationality of the results in measuring the level of high-quality economic development, this paper utilizes principal component analysis on five second-tier indexes to calculate a comprehensive score of high-quality economic development. The indexes include industrial development, green TFP, technological innovation, quality of life for residents, and an environmental index. Considering that the third-grade indexes for industrial structure, standard of living, and environmental index have multiple tertiary indicators, this paper first calculates the corresponding second-tier index scores for each of these three second-tier indexes. This is achieved by conducting a principal component analysis for each of the industrial structures, resident standard of living, and environmental index second-tier indexes. Thereafter, the five second-grade indexes of industrial structure, green TFP, innovation level, residents' living standard, and environmental index are reanalyzed utilizing the principal component analysis method to generate an economic quality development level score.
Since several index data have missing values, this paper uses the average value of the data before and after the missing value by two years. In Baiyin, Tianshui, Wuwei, and Pingliang, the missing proportion of tertiary industry output value in 2007 is replaced with the average value of the two preceding years. The descriptive statistics for the variables are displayed in Table 2.

The model
Taking into account the influence of other factors on the level of high-quality economic development, and based on the analysis of the relationship between environmental target constraints and the level of high-quality economic development presented previously. In addition, in December 2006, the State Council issued the official Decision on Implementing the Scientific Outlook on Development and Further Strengthening Environmental Protection. It proposed incorporating the performance of cadres' environmental target constraints as a criterion for their appointment, selection, and rewards and punishments. In 2007, following the signing of the Eleventh Five-Year Total Reduction of Major Pollutants Target Responsibility by the Ministry of Environmental Protection, environmental target constraints began to be formally included in Responsibility Book was a relatively exogenous shock, and local governments' self-imposed pressure in response to this shock was inconsistent. Some local governments included environmental target constraints in the government work report and explicitly listed it as an annual performance objective, while others did not. Model (1) is therefore constructed to introduce an exogenous shock in 2007. In this study, the "experimental group" consists of municipalities that have stated numerical targets for reducing industrial pollutant emissions in their government work reports. Municipalities that do not explicitly state their numerical targets for industrial pollutant emission reduction in the government work report constitute the "control group." In this paper, a quasi-natural experiment is conducted to determine whether local governments include environmental target constraints in their annual government work reports, with the following model in place: where the explanatory variable Score it denotes the local municipality i in the t year economic quality development level score; i and t represent the municipality and year, respectively; and the main explanatory variable P is a dummy variable; if the municipality i is influenced by the policy in the sample interval, then it takes the value of 1; otherwise, it is 0. X it is a set of influencing factors that affect the environmental target constraints. d i is a municipality fixed effect to capture other non-time-varying unobservable characteristics of the municipality. it is a random error term.
T is a dummy variable representing the exogenous shocks in 2007, which when t ≥ 2007 takes the value of 1 and 0 (1) otherwise, where the crossover term P × T is used to observe the response of the results after exogenous shocks.

Variable
Independent variable: economic quality development. In this paper, the economic quality development is captured by the economic quality development score ( Score ) measured by principal component analysis above. Dependent variable: environmental goal constraints. The main explanatory variables are the dummy variable of whether the prefecture-level city receives environmental constraints (P) and the time dummy variable (T) and the interaction term between the two (P×T). In this paper, we manually collect the government work reports of each municipality in the current year to see if there are clear and specific target requirements for environmental goals. In particular, only if the government work report explicitly puts forward numerical targets for controlling industrial pollutant emissions, such as a 5% reduction in sulfur dioxide emissions, will this paper conclude that the local government was constrained by environmental targets in that year. Municipalities with clear environmental target constraints are assigned a value of 1, otherwise 0.
Control variables: according to existing studies, there are still many factors that have a significant impact on the quality development of the economy. Therefore, this paper will also control for these factors as follows: fiscal decentralization degree ( fd ), expressed by the ratio of fiscal budget revenue to fiscal budget expenditure; the level of economic development ( agdp 2 ), using the per capita GDP to control the possible non-linear effects of the level of economic development; foreign investment ( fdi ), expressed as actual foreign investment used in the year compared to regional GDP; the level of urbanization ( popden ), and expressed as the logarithm of population density. Table 3 shows the results of descriptive statistics of the variables.

The baseline regression
The paper first empirically investigates the effect of environmental target constraints on economic high-quality development. Table 4 shows the basic regression results.
Columns (1) and (2) are the basic regression of model (1) environmental target constraint cross-term to high-quality economic development. With or without the addition of control variables, the results show that the regression coefficient of variable cross virtual terms (P×T) is significant at the 1% level, and it is positive. When control variables are added, the regression coefficient of the cross virtual term (P×T) is 0.411. In other words, when other factors reflecting regional economic differences are controlled, incorporating environmental target constraints into government assessment will greatly improve the regional economic high-quality development level by 0.411. Columns (3) and (4) are regression results excluding samples that have been incorporated into environmental target constraints before 2007. It can be seen that after removing the samples of cities that have been included in environmental target constraints before 2007, environmental target constraints in this region still have a significant positive effect on high-quality economic development. More specifically, the results show that for cities that did not implement environmental target constraints before 2007, the inclusion of environmental target constraints will increase the economic quality level by 0.072. In conclusion, the basic regression results in Table 4 show that the implementation of environmental target constraints can promote the high-quality development of the local economy. These results support recent studies like Shang et al. (2022) and Shuai and Fan (2020).

Regional heterogeneity
China is a vast country with huge differences in economic development between regions, and regional development imbalances may also have an impact on the effect of the policy, often the eastern region tends to be more developed.
In order to further examine the impact of regional heterogeneity on the policy effect, our study regresses model (1) based on the different provinces and the geographical factors of China classified into two parts, the eastern and the central and western parts, respectively, and the results are shown in Table 5. Columns (1) and (2) represent the sample regression results of model (1) from eastern regions, while columns (3) and (4) represent the sample regression results of central and western regions. As can be seen from the table, whether control variables are added or not, the coefficient of cross virtual term (P×T) is significant and positive at the 1% level. When control variables were added, the regression coefficient of the cross virtual term (P×T) was 0.815. In other words, when other factors reflecting regional economic differences are controlled, the inclusion of environmental target constraints in government assessment will greatly improve the regional economic high-quality development level by 0.411 in the eastern region. While the sample in the central and western regions is significant and positive at the 5% level, the coefficient is much smaller than that in the eastern region. This means that after adding control variables, the inclusion of environmental target constraints in government assessment in central and western China will increase the regional economic quality development level by 0.154. These results support the study Shuai and Fan (2020). It indicates that the environmental target constraints in the east have a greater impact on high-quality economic development. The reason for this is that the developed regions and cities in the eastern part are developing faster and are nearing the inflection point of the inverted "U"-shaped environmental Kuznets curve. In contrast, the less developed regions and cities in the central and western parts, where economic development is relatively backward, still regard economic development as their top priority, so the impact of environmental target constraints on high-quality development in these regions is weaker than that in the developed regions in the east.

Urban economic development level heterogeneity
To further examine the impact of cities with different levels of economic development on the policy effects, the paper uses the average GDP per capita of the 230 cities in 2013 as the criterion and divides cities into two groups of above average and below average for heterogeneity analysis. The results are in Table 6. Columns (1) and (2) listed environmental target constraints on 2013 per capita GDP higher than middle-income countries. It can be seen from the regression results that the coefficient of cross virtual term (P×T) is significant and positive at the 1% level for the samples from the eastern region regardless of whether control variables are added. When control variables are added, the regression coefficient of the cross virtual term (P×T) is 0.519. In other words, when other factors reflecting regional economic differences are controlled, the more developed regions will greatly improve the regional economic high-quality development level by 0.519 when environmental target constraints are included in government assessment. Columns (3) and (4) list the sample of countries with GDP per capita lower than middle-income countries in 2013, which is significant and positive at the 5% level. But the coefficient is much smaller than that in developed areas. This means that after adding control variables, the inclusion of environmental target constraints in government assessment in central and western China will increase the regional economic quality development level by 0.151.
The above results indicate that the inclusion of environmental target constraints in more economically developed regions has a greater impact on high-quality economic development. Because the eastern region is more developed than the western region, the heterogeneity test results of urban income level are consistent with the regional heterogeneity test results.

Urban industrialization level heterogeneity
The more industrialized cities are facing serious environmental problems, the more motivated they are to set environmental targets. For the heterogeneity test of urban industrialization level, this work uses the urban industrial share as a measure of urban industrialization level and regresses the cities into those with industrialization level below 50% and above 50% in 2013, respectively, and the results are shown in Table 7. The first and second columns are regressions for cities with industrial share below 50% in 2013, and the third and fourth columns are regressions with industrial share above 50% in 2013. It can be seen from columns (1) and (2) that the coefficient of cross virtual term (P×T) is not significant in the samples of urban areas with industrialization level below 50%, no matter whether control variables are added or not. In other words, for cities with a lower level of industrialization, the inclusion of environmental target constraints has no significant impact on the high-quality economic development of the city. Columns (3) and (4) are listed as samples of cities with an industrial share greater than 50%, which is significant and positive at the 1% level. After adding control variables, the coefficient of cross virtual terms (P×T) is 0.687. This means that after adding control variables, cities with higher industrialization level will increase the high-quality economic development level of the region by 0.687 when environmental target constraints are included in government assessment. The above results show that there is no significant relationship between environmental target constraints of cities with low industrialization and high-quality development, while cities with a relatively high industrial proportion have a significant promoting effect on high-quality development after they are included in environmental target constraints. This paper concludes that cities with a high industrial share are more influenced by environmental target constraints; environmental target constraints have a greater role in promoting industrial transformation and upgrading, industrial innovation, and optimal resource allocation in these cities, which promote high-quality economic development. In contrast, for cities with a low industrial share, the environment Table 6 Empirical results of the impact of environmental target constraints on cities with different levels of economic development ***, **, and * represent passing the coefficient significance test at 1%, 5%, and 10% significance levels, respectively. Robust standard errors are in parentheses  is relatively good, and the binding force and influence of environmental target constraints on these cities are relatively weak and thus do not have a significant contribution to highquality economic development.

Endogeneity issue
As shown in Figure 1, none of the estimated coefficients fluctuates around 0 prior to the implementation of the policy. This indicates that model (1) does not adhere to the double difference model's fundamental assumptions. Different provinces, cities, and autonomous regions are in varying stages of economic development and environmental dynamics as a result of the significant economic disparity between China's regions and the influence of complex geographical factors. Despite the 2007 agreement signed by the Ministry of Environmental Protection, each prefecture-level city has made its own decision on whether to include environmental targets in the evaluation of government work reports in response to its unique economic and environmental characteristics. Therefore, while the "Eleventh Five-Year Plan" Responsibility for Total Reduction of Major Pollutants signed between the Ministry of Environmental Protection and each province, municipality, autonomous region, and municipality directly under the Central Government is an exogenous shock, the selection of the "experimental group" and "treatment group" is not. This paper considers the use of instrumental variables as a solution to this endogenous problem. A good instrumental variable should exhibit both a strong correlation with endogenous variables and exogeneity that is independent of the residual term. In terms of correlation, this paper argues, first, that the response of municipalities to the 2007 EPRs in establishing environmental target constraints is essentially a competition between various government officials regarding their ability to govern. The EPRs have shifted the traditional "GDP-only" evaluation of economic growth by government officials from "quantity" to "quality." The top-down assessment strategy has prompted local officials to improve the government's performance evaluation by focusing on the environment. Given that there is a finite number of provincial promotion slots available, the number of prefectures directly correlates to the quality of the provincial officials. In contrast, the number of promotional positions is roughly the same regardless of the number of prefectures; therefore, in provinces with a smaller number of prefectures, the likelihood of officials being promoted increases dramatically as the number of competitors decreases. The competition for promotion is moderate, and its inclusion in the assessment of environmental goal constraints is minimal. The sample period for this paper is 2004-2013, during which time the number of prefecturelevel cities in each province remains essentially unchanged. Moreover, the division of prefecture-level cities within each province is political and determined by the central government and is unaffected by the economic quality growth of each prefecture-level city. Therefore, a fixed number of prefectures within a province has no direct effect on the economic quality development of each prefecture. To satisfy the exogeneity requirement, the number of prefectures in the province where the prefecture is located is chosen as the instrumental variable.
Besides, this paper seeks instrumental variables from China's distinctive geographic environment. First, as a consequence of China's historical development, rivers play a crucial role in the industrial layout and industrial development of a city in terms of transportation and water and energy sources, culminating in a close relationship between the carrying capacity of rivers and the industrial development of society. Consequently, cities near large rivers are typically found in industrially developed regions due to their unique geographical setting and advanced water transportation. In turn, China's environmental pollution issues are closely related to industrial development and are rapidly exacerbated as the heavy industry develops (Jin et al. 2011). Consequently, the governmental approach to environmental issues frequently begins with industrial treatment. The more developed the industrial base, the greater the environmental challenges faced by cities, and the greater their motivation to establish environmental goals. Second, environmental management typically places emphasis on water and air pollution at the local level. Compared to water pollution, air pollution is less palpable, and its short-term effects on residents' health are not as severe as those of water pollution. Additionally, as the public is less sensitive to air pollution, they tend to be more concerned with the water quality of rivers and lakes, as well as the safety of their drinking water, out of concern for their own health. Moreover, many industries (fisheries, forestry, etc.) have specific river and lake water quality requirements. Once there is untreated wastewater discharge, tap water pollution, etc., public health and individual interests will be directly threatened. These are the issues that residents express the strongest concerns about and actively engage in discussions with high initiative (Yu 2019). The management of water pollution is the focal point of environmental management and public attention, which places additional pressure on local governments to treat pollution. Likewise, when local governments set environmental target constraints, they would consider not only the desires of higher governments but also the immediate sentiments of the public and the influence of public opinion. Therefore, it prompts the government to include environmental target assessment in the government work report in order to actively address the local environmental pollution issue. Finally, the economic quality development of each prefecture-level city has no effect on rivers, which are objective geographical factors. The number of large rivers flowing through the city at the level of a prefecture is selected as an instrumental variable to also satisfy the exogeneity requirement. In this paper, the sample consists of the sixty large rivers listed in the Yearbook of the People's Republic of China. As the second instrumental variable, the number of major rivers flowing through each city is manually tallied.
Utilizing either the number of municipalities in a province or the number of waterways as instrumental variables alone is insufficient due to the presence of fixed effects in the sample of this paper. This paper refers to Nunn and Qian's (2014) approach of setting instrumental variables and constructs the interaction term between the number of prefecture-level cities/number of rivers flowing in the province where the prefecture-level city is located and the national environmental target constraint values at different periods as the instrumental variables for the prefecture-level city's economic growth target constraint. Among these environmental target constraint values, sulfur dioxide emission reduction targets and chemical oxygen demand reduction targets comprise the majority. The primary objective for the period of the Tenth Five-Year Plan (2000-2005 is to reduce emissions of major pollutants such as sulfur dioxide and chemical oxygen demand both by 10%, by 2005 compared to 2000. The objective for the "Eleventh Five-Year Plan" period (2005Plan" period ( -2010 is to reduce emissions of major pollutants such as sulfur dioxide and chemical oxygen demand by 10% from 2000 to 2010. The objective for the "Twelfth Five-Year Plan" period (2010-2015 is to reduce emissions of major pollutants such as sulfur dioxide and chemical oxygen demand by 10% compared to 2000 by 2015. The target value for 2015 is an 8% reduction in sulfur dioxide, chemical oxygen demand, and other major pollutant emissions compared to 2010. The setting of the environmental target assessment for the prefecture-level municipalities included in that year is unaffected by the variation in the environmental target constraint values among them. This paper considers the interaction term between the number of prefecture-level municipalities/number of large rivers flowing in the province and the national environmental target constraint values in various periods as an appropriate instrumental variable for the prefecture-level municipalities' environmental target constraint. In this paper, based on the above selection of instrumental variables, the instrumental variables were firstly regressed, and the results are shown in Table 8. Columns (1) and (2) are regression for instrumental variable 1, and columns (3) and (4) are regression for instrumental variable 2. The results are both significant and positive at the 1% level. When control variables were added, the regression coefficients of the cross virtual terms (P×T) were 1.785 and 1.611, respectively. In other words, when other factors reflecting regional economic differences are controlled, incorporating environmental target constraints into government assessment will significantly improve the level of high-quality regional economic development. This shows that the regression results in this paper are still robust. Secondly, the two instrumental variables are tested for weak instrumental variables, and the results of the RKF test show that the instrumental variables selected in this paper are correlated with the endogenous variables; i.e., the hypothesis of "weak instrumental variables" is rejected. Therefore, the instrumental variables selected in this paper are valid. Finally, the Hausman test was conducted to determine whether there were endogenous explanatory variables in the original model, and the p value of the DWH test was 0. It can be assumed that the environmental target constraint is an endogenous explanatory variable, so it is necessary to use the instrumental variable method. Although the regressions in the previous section of this paper largely confirm the impact of environmental target constraints on high-quality economic development, there are a series of other factors that may affect the robustness of the paper's conclusions. In the following, we use a series of tests to exclude the interference of these factors.

Urban environment heterogeneity analysis
Although the basic regressions in the previous section have basically confirmed the impact of environmental target constraints on high-quality economic development, there are some other possible factors that affect the robustness of this paper. For example, the environmental target constraint policy may not have a significant impact on cities with good environmental conditions such as Lishui, and we remove these disturbances by testing city heterogeneity below.
In this paper, 12 cities including Kunming, Lishui, Huizhou, Zhuhai, Taizhou, Fuzhou, Xiamen, Guiyang, Zhongshan, Yantai, Qingdao, and Shenzhen are excluded by referring to previous years' environmental quality reports. Table 9 reports the above regression results. Columns (1) and (2) are full samples, while columns (3) and (4) are samples that have environmental target constraints before 2007 removed. It can be seen from columns (1) and (2) that the coefficient of cross virtual terms (P×T) is significant and positive regardless of whether control variables are added. After adding control variables, the coefficient of the cross virtual term (P×T) is 0.386. In other words, after excluding the cities with higher environmental quality, the inclusion of environmental target constraints in the remaining cities increased the city's economic high-quality development level by 0.386. Columns (3) and (4) are excluded from the sample of cities that were constrained by environmental targets before 2007, which is significant and positive at the 10% level. After adding control variables, the coefficient of cross virtual term (P×T) is 0.071. This means that after adding control variables, the inclusion of environmental target constraints in government assessment will increase the high-quality economic development level of this region by 0.071. This is consistent with the baseline results. This means that the policy effect of environmental target constraint is still very robust.

Elimination of interference policy
In fact, the promotion of officials to environmental target constraints has been accompanied by the promotion assessment of economic growth and the tenure-based assessment of environmental protection "five-year planning goals." Local governments, under pressure from the assessment of economic growth targets, may sacrifice the environment in exchange for economic development or make trade-offs in environmental management in their jurisdictions (Zheng 2016); for the other accompanying assessment of environmental protection "five-year planning goals," the implementation of the assessment targets must also rely on local officials. With limited tenure and multiple objectives, local governments may implement short-term environmental management measures to meet a certain tenure assessment (Lai and Zheng 2016). Then whether these two may interfere or affect the robustness of the study findings is to be further tested. The "two controlled zones" 2 policy was put forward earlier in 1998. If the policy effect of environmental target constraint is caused by the "two controlled zones" policy, then the effect of the policy on the SO 2 removal rate should be different between the cities in the "two controlled zones" and the cities not in the "two controlled zones." In this paper, the samples of cities not in the two controlled zones are excluded, as shown in Table 10. (1) is listed as the result of no control variable added, and (2) is listed as the result of adding control variable. The coefficients are both significantly positive at the 1% level. This shows that after considering other factors affecting economic development, for cities in the "two controlled zones," the inclusion of environmental target constraints will increase the level of high-quality economic development by 0.542. This also means that the policy effect of environmental target constraint will not be affected by these two types of interference policies.

Excluding systematic changes in macro factors
Environmental goal assessment may also bring about changes in the macro-systemic environment. For example, the environmental and financial status of each province in China varies, and the structure of central and provincial financial allocations will change after the environmental goal assessment. If this factor is important enough, it will have an impact on the reliability of the conclusions of the analysis in this paper. We capture this systematic macrovariation by further introducing province fixed effects, which are used to portray the effects of non-observable macro-systemic factors due to policies, and province-year interaction effects, which portray the effects of readily variable macrosystemic factors in terms of dynamic trends. The first two columns in Table 11 show the estimation results of model (1) after controlling for province effects, while the third column reports the estimation results after the province-year interaction effects. Compared with the result of main regression, there is no significant change. In other words, changes in the macro environment did not influence the results, and the results are reliable.

Conclusion and policy
As an emerging economy, China's rapid economic growth has garnered global recognition. Similar to other emerging economies, China's rapid economic growth has not taken into account negative environmental externalities. Evidently, the central government has taken note, as it has issued a series of environmental assessment documents to local governments and called for China's economic transformation into a phase of high-quality development.
Based on this background, this paper puts the perspective of government behavior and raises the following question: will the environmental goals of local governments have an impact on the quality of economic development? First and foremost, this paper provides theoretical support for this study by analyzing the impact mechanism of environmental target constraints on high-quality economic development. Second, this paper utilizes principal component analysis to measure the high-quality development level index of China's economy, DID model, and instrumental variable method to test the impact of local government environmental policy changes on the quality of local economic development and heterogeneity analysis to conclude. Finally, by subjecting the findings to a robustness test, the confidence in the results is validated and the subsequent policy recommendations are put forward accordingly. First, in general, the inclusion of environmental target constraints by local governments has a significant effect on promoting the quality of economic development. After the implementation of environmental target constraints, local governments promote the transformation and upgrading of local industrial structure and form the reverse force mechanism of enterprise technological innovation, so as to improve the high-quality development level of the local economy. Secondly, the influence of environmental target constraints on the high quality of local economic development has regional heterogeneity. The environmental target constraint effect in eastern China is greater than that in central and western China. The developed cities have a greater impact than their less developed counterparts. A high industrial proportion has a greater effect than a low industrial proportion. Due to the large regional development gap between the eastern region and other developed regions, as well as the high level of urban economic development, China's environmental Kuznets curve is near the inverted "U"-shaped environmental Kuznets curve inflection point. However, western and other underdeveloped regions and cities continue to prioritize economic development. The influence of environmental constraints on this region's high-quality development is weaker than in the east and other developed regions.
According to the above findings, this study offered the following policy implications. First, the central government should replace the "only GDP" assessment system of local officials at the macro level with a green assessment system that connects environmental protection and political performance. The objective is to avoid the phenomenon of "race to the bottom" in the environmental aspect of local governments and to achieve genuine high-quality economic development. Second, when developing assessment indicators, the government should account for the natural conditions and industrial development status of the region. Furthermore, environmental target constraints should be tailored to the context of economic development instead of using a "one-size-fits-all" approach. In developed regions such as eastern China, market-oriented incentive policies should be implemented to restrict environmental targets, give market mechanisms full play, and complete mandatory assessments of environmental targets through market-based means such as emission permit trading and pollutant discharge fees. In less developed regions, such as central and western China, it is necessary to increase support for local governmental investments in environmental governance and assist businesses with investments in environmental governance infrastructure and technology upgrades. This will introduce and promote green, energy-saving, and businesses with environmental awareness, leading to a "win-win" scenario between environmental protection and economic development.
Finally, the preceding conclusions and policy recommendations are based on the situation in China and provide lessons for other developing countries, such as Vietnam, India, and Thailand. Rapid economic growth in developing nations may result in environmental damage. Considering the current global climate deterioration and the entry into force of the Paris Agreement, developing countries face the dual pressure of environmental protection and economic development. This article provides another perspective for developing countries that environmental protection and economic development can coexist and provides empirical evidence for other developing countries to carry out coordinated development of environmental protection and economic development.