Fiscal Decentralization, Environmental Regulation and High-Quality Economic Development

Abstract

Fiscal decentralization, as a policy with which to regulate the financial power relationships between central and local governments, undoubtedly has an impact on high-quality economic development, and in the current context of focusing on environmental quality its path may include environmental regulation, which is restricted by the degree of decentralization and directly affects high-quality economic development. Based on data from 30 Chinese provinces from 2010 to 2019, this paper empirically tests the moderating effect of environmental regulation on the relationship between fiscal decentralization and high-quality economic development through the use of an intermediary-effect model, a threshold model, and a quantile regression model. The following are the main conclusions: First, fiscal decentralization encourages high-quality economic development with a coefficient of 0.069, but environmental regulation dampens this effect, with a coefficient value of 0.0055 for the suppression effect; after removing endogenous effects, the conclusion remains valid. Second, the influence of fiscal decentralization has different manifestations under different environmental regulation intensities and different levels of high-quality economic development: fiscal decentralization has a larger regression coefficient when the intensity of environmental regulation exceeds 0.0108; it only shows a positive effect in regions with a higher quality of economic development (after the 25% quantile). The main contribution of this paper is the discussion of the impact of fiscal decentralization on high-quality economic development from the perspective of environmental regulation and revealing of a new path for fiscal decentralization to play its role, which is of certain significance in exploring the appropriate degree of decentralization and formulating environmental regulation policies.

1. Introduction

The Chinese government stated in 2017 that their economy had transitioned from a high-speed growth stage to a high-quality development stage; in 2022, it was again stated that promoting high-quality economic development is now the primary task for development. Pursuing higher quality has become the primary objective of China’s economic development [1,2]. The economy expands rapidly, but issues of balanced and complete development, as well as environmental protection, have become more worrying. In the face of environmental degradation, the government launched all-encompassing actions [3]. Policy systems have extraordinary implications for balancing economic growth and environmental protection; carrying out a new round of economic system reforms and achieving new economic development goals cannot be separated from mastering the policy system.

According to many studies, one of the key policy elements that affect economic growth is fiscal decentralization (FD) [4,5]. The goals of economic development have shifted over time, but the importance of FD in development has not. To achieve the new development goals, it is necessary to stimulate the vitality of local governments and urge them to take full advantage of their information advantages. In this way, in the pursuit of new development goals, it is especially important to investigate research perspectives of FD systems. Many studies have been conducted on the relationship between FD and economic expansion, but the results vary due to differences in index selection, measurement, and research methods. Moreover, most scholars discuss the relationship between FD and economic growth, and the conclusions in this regard have been very comprehensive; however, research on high-quality economic development has yet to be refined. The first question explored in this paper is how FD affects high-quality economic development, and this study is valuable in this research context.

Next, we discuss the path through which FD achieves high-quality economic development. High-quality economic development has rich connotations; one significant distinction between it and economic growth commonly found in traditional studies is whether the factor of environmental quality is taken into account [6]. From this, it is valuable to start from the factors affecting the environment in order to explore the influencing factors of high-quality economic development and the mechanisms of action behind them. Environmental regulation policies are common environment-influencing factors closely related to FD. In the world, the trend of FD is becoming more and more obvious [7]. Under this policy background, local governments with the singular goal of economic prosperity frequently choose to trade environmental degradation for economic expansion [8,9]; however, with prominent environmental problems, China has issued many environmental regulations and incorporated environmental quality into performance appraisals in recent years, compelling local governments to prioritize environmental quality [10]. This being the case, in the context of the current focus on environmental protection, will environmental regulation force local governments to prioritize environmental quality, thus controlling FD’s function in promoting high-quality economic development? This is the second issue explored in this paper, and there is currently no literature to refer to when discussing it. One of this paper’s potential marginal contributions lies in bringing high-quality economic development, environmental regulation, and FD into the same research framework, investigating the mechanisms of FD in high-quality economic development and its nonlinear features from the perspective of environmental regulation, providing a novel idea for the path analysis of FD affecting high-quality economic development. Because there is little literature that examines environmental regulation as a way in which FD can affect high-quality economic development, on the basis of the above theoretical analysis a comprehensive empirical test is carried out on the relationship between the three, which verifies the dynamic characteristics of the relationship between them and then draws comprehensive and targeted conclusions as well as recommendations.

The rest of this paper is organized as follows: Section 1 is a review of the literature; Section 2 examines the link between high-quality economic development, environmental regulation, and FD via the use of economic theory and presents the research hypotheses; Section 3 measures each province’s level of high-quality economic development and analyzes its spatial distribution as well as evolution characteristics on this basis; Section 4 tests the research hypothesis put forward earlier through an empirical analysis; and Section 5 summarizes the conclusions of this paper, gives policy recommendations, and provides an outlook for future research.

2. Literature Review

A growing number of studies are focusing on the issue of high-quality economic development, mainly covering its measurement [11,12,13], spatial distribution characteristics as well as time evolution patterns [2,14,15], and influencing factors [16,17,18]. Theoretical and practical advice for the high-quality development of regional economies can be obtained from a thorough study of the variables influencing this development and their mechanisms. There is much research in this field, covering a wide range of topics. High-quality economic development is rich in connotation and covers all aspects of political and economic life, while FD as an important fiscal system arrangement will undoubtedly affect the level of high-quality economic development.

2.1. Impact of FD

2.1.1. The Environmental Impact of FD

With the further implementation of the new development concept, to explore how FD affects environmental quality and, thus, high-quality economic development, which has become a hot issue, many scholars have used environmental influences as entry points for exploring the path of the role of FD [19,20,21]. As for how FD affects environmental quality, Tiebout proposed “the vote with the feet” mechanism in 1956, laying the groundwork for the first generation of FD theory. FD, according to this theory, is beneficial to raising environmental standards and, thus, promoting high-quality development for the following reasons: first, local governments will be motivated by the free flow of people to provide public goods, including environmental quality, more effectively [22]; second, local governments have more comprehensive information concerning regional environmental conditions and residents’ demand preferences than central governments, and can make decisions more efficiently [23]; and third, through the NIMBY effect, FD has sparked a race to the top between different regions [24] and has prompted governments to raise environmental standards [25,26].

However, since the development of the second generation of FD theory based on the “rational person hypothesis”, the negative impact of FD on environmental quality has entered researchers’ field of vision [27]. The “rational person hypothesis” points out that when government officials and residents have a conflict of interest, government officials will inevitably choose to sacrifice public interests to maximize their interests, such as sacrificing the environment for economic development [28]. This position, however, is founded on the idea that economic growth is the only performance metric, which does not apply to the current situation in many countries, including China. In China, environmental quality standards are increasingly being included in performance evaluations, which will undoubtedly increase the emphasis on environmental quality in the region [29,30].

Some academics believe that FD will have an uncertain impact on environmental quality. Guo, Wen, Wu, Yue, and Fan [27] found that the decentralization of income has worsened pollution more than the decentralization of expenditure has; Zhao et al. [31] discovered that, in economically underdeveloped areas, negative effects of FD are more likely to appear, and Chen et al. [32] found that environmental quality will improve if local fiscal expenditure can be biased towards environmental governance expenditure.

2.1.2. The Impact of FD on High-Quality Economic Development

In China, FD not only resulted in the miracle of economic growth but also resulted in several serious problems [33]. Early research on FD concentrated on the impact of FD on economic growth. Some studies have found that FD has a positive [4] or negative [34,35] impact on economic development. Other studies have found that the relationship between them varies depending on the stage of development or geographical location [36,37]. Compared with the single-dimensional characteristics of economic growth, such as quantity or speed, the quality of economic growth, with wide connotations and closely related to the development theme of the times, is more realistic and instructive. Some research has studied the influence of FD on some factors that are important components of high-quality economic development, such as corruption [38,39], government behavior [40], income distribution [41], land issues [42], foreign investment inflows [43], and environmental innovation [21]. Some scholars have directly studied what impact FD will have on high-quality economic development, but have not reached a unanimous conclusion: Wang et al. [44] and Liu et al. [19] proved that there is a nonlinear link between them; Song et al. [45] in addition to Song, Du, and Tan [13] consider the relationship between them to be linear. As for the pathways through which FD affects the high-quality economic development of one of its components, the literature has examined factors such as residents’ expenditure preferences [44], environmental fiscal policy [20], government environmental preferences [19], and investment in environmental protection [46]. Many scholars believe that environmental regulation is another key avenue via which FD might influence environmental quality, particularly now that environmental quality is becoming increasingly important in performance evaluations [47,48,49]; however, there is no research on the role of environmental regulation as a pathway for FD to affect high-quality economic development.

2.2. The Impact of Environmental Regulation on High-Quality Economic Development

Some scholars put forward the notion that one source of competitive advantage is the distinction between environmental laws, which directly affects which regions [50] or countries [51] pollution-intensive industries will gather in. At present, there are few studies on the relationship between FD and environmental regulation. Some research supports the existence of synergy between them [47], but Chinese FD may distort the actual effect of environmental laws, resulting in a “green paradox” [52]. The reason for this could be due to China having long regarded GDP as the main criterion for examining local political achievements, which has led to the government having the propensity to emphasize economic expansion above environmental conservation, distorting the actual implementation of environmental laws [8,9].

Environmental regulation has the potential to resolve China’s dilemma between the environment and the economy [53]; however, environmental regulation affects both the environment and the economy, resulting in its complex role in high-quality development that is difficult to assess [54,55,56]. There are two opposing viewpoints on the specific impact of environmental regulation based on corporate behavior in academic circles: one is the “pollution refuge hypothesis”, which holds that environmental regulations will force enterprises to invest a lot of money in dealing with pollution problems, and this part of investment is called the “compliance cost”, which will eventually have a detrimental influence on enterprises’ productivity and comparative advantages [57]; the second is the “Porter Hypothesis”, which holds that because regulated enterprises face higher environmental governance costs, enterprises will be more inclined to implement innovative technologies to increase productivity from the technical level [58], which is the so-called “innovation compensation” effect [59]. Some studies also believe that various environmental regulations have various effects on the economy [60,61]. As for the relationship between economic growth and ecological efficiency, which are two aspects of high-quality development influenced by environmental regulations, some studies believe that it is nonlinear [62], whilst others believe that it is a positive correlation [63], and still others believe that this relationship will show heterogeneity under different conditions. For example, Liu et al. [64] point out that the relationship between them changes with a change in geographical location. The study by Lin et al. [65] demonstrates a U-shaped relationship between them: a negative link exists between the two when environmental regulation is less intense, while a positive correlation exists when environmental regulation is more intense.

To sum up, the relationship between FD, environmental regulation, and high-quality economic development is still unclear; there is no consensus in the academic community regarding the impact of FD or environmental regulation on quality economic development. Additionally, there is no literature that examines environmental regulation as a pathway to the role of FD in influencing high-quality economic development and discusses its mechanisms of action.

3. Theoretical Analysis and Research Hypotheses

Without the government’s policy support, economic development cannot enter a high-quality stage [13]; how the government participates in economic development is inextricably linked to fiscal expenditure. FD, as a policy for adjusting the balance of financial power between national and local governments, will undoubtedly influence high-quality economic development. It has been pointed out that FD is beneficial for governments in better providing public goods: on the one hand, this is due to local information advantages [66,67,68]; on the other hand, the theory of fiscal federalism suggests that local governments seek to maximize the interests of residents in their jurisdictions in order to gain their votes and that FD is conducive to increasing the spending power of local governments, which in turn improves the welfare of local residents [69]. It is worth noting that, in the context of Chinese-style FD, while government officials do not face electoral pressure, the mechanism of voting with one’s feet, which functions through population mobility, still exists; in order to attract and retain talent as well as investors, local governments tend to place more emphasis on improving social services [32].

As a comprehensive consideration of the meaning of high-quality economic development, this study examines how FD affects high-quality economic development in terms of five aspects: innovative development, coordinated development, green development, efficient development, and stable development. In the context of FD, from the standpoint of innovation, local governments with greater fiscal spending power will enhance investment in scientific research and build innovation systems to encourage economic growth, ultimately improving the innovation efficiency of a region [70]; from the standpoint of coordination, to attain the aim of economic development, local governments will strengthen infrastructure construction, which helps to promote coordinated regional development, vigorously promote urbanization, strengthen the driving role of cities in the countryside, and reduce the disparity between urban and rural areas; and from the standpoint of green development in the past, under the backdrop of economic growth as a single assessment standard, FD may cause environmental damage [15]. However, because environmental quality and environmental governance capability are now considered in performance evaluations, local governments will boost environmental spending in order to enhance the environment [29]. From the standpoint of efficiency, local governments have more comprehensive and faster access to local information than central governments do [23]. FD gives local governments more fiscal power, which allows them to fully exploit their information advantages, seize opportunities, and reduce efficiency losses caused by information asymmetry. From the standpoint of stable development, some studies stand on environmental protection, pointing out that FD will give local governments more power in financial expenditure, such that local governments with local information advantages and financial power will implement more appropriate environmental protection policies according to local conditions and strengthen environmental protection, which will be conducive to sustainable economic development [23] and strengthen economic stability. Hypothesis 1 is offered based on the preceding analysis.

Hypothesis 1.

FD enables local governments to seize opportunities in technological innovation, regional coordination, environmental protection, information dissemination, economic stability, etc., and have higher autonomy in action, thus comprehensively promoting high-quality economic development.

Environmental quality is an important component of high-quality economic development; environmental regulation is restricted by the level of regional decentralization and directly affects environmental quality [9,47,63], which is the “bridge” connecting FD with high-quality economic development. Therefore, we have reason to speculate as follows: FD has an indirect impact on high-quality economic development through various paths, and environmental regulation is one of them. This paper explores the process of FD influencing high-quality economic development through environmental regulation in two steps: firstly, how FD affects environmental regulation; secondly, how environmental regulation affects high-quality economic development. From the perspective of FD affecting environmental regulation, FD has stimulated competition among local governments, encouraged local governments to destroy the natural environment in exchange for economic development, relaxed environmental controls, and seen an “environmental race to the bottom” [27,28]. At this time, strengthening decentralization will result in extensive economic growth: total economic volume will increase [5], but the impact on high-quality economic development through the role of environmental regulation will be negative. However, with the current emphasis on environmental protection, the FD system provides new incentives to governments, and the government’s response to this incentive undoubtedly includes the adjustments made in environmental management regulations [61].

From the perspective of environmental regulation affecting high-quality economic development, the influence of environmental regulation on economic growth is lagging behind and usually shows nonlinear characteristics [65]; Porter’s hypothesis explains this phenomenon in detail [58,59]. The promotion of Porter’s hypothesis proposes the classification of the role of environmental regulation into two distinct aspects: a negative impact, similar to the initial “compliance cost”, and a positive impact, similar to the later “innovation compensation”. China’s environmental regulation process is gradual, with a weak initial policy [27,28]. First of all, it is unwise to invest heavily in the research of new technologies because the pollution cost of enterprises is so low. Secondly, increasing investment in environmental protection and innovative R&D is costly, in addition to the benefits that it brings being difficult to see in a short amount of time [57]. As a result, in the early stages of policy, the negative influence of environmental regulation is more pronounced; however, with the government’s enforcement of environmental regulations exceeding a certain threshold, firstly, due to the strict policy, the cost of pollution is rising continuously, and so enterprises will regulate their production behaviors and avoid pollution costs. Secondly, the benefits brought via investment in environmental protection and innovative R&D in the early stage of an enterprise are constantly emerging over time, which reduces pollution and improves the production efficiency of an enterprise [58]; the negative influence of environmental regulation weakens or produces a positive influence. Following the analysis presented earlier, Hypothesis 2 is proposed.

Hypothesis 2.

FD will affect high-quality economic development through environmental regulation. Specifically, FD promotes environmental regulation, which, in turn, suppresses FD’s role in promoting high-quality economic development. However, with the strengthening of FD and environmental regulation, this suppression effect will be weakened because of the “innovation compensation” effect. As a whole, the influence of FD has a nonlinear feature of “increasing marginal effect”.

4. Measurement and Analysis of High-Quality Economic Development

4.1. Measurement of High-Quality Economic Development

The connotations of “high-quality economic development” are richer than those of “economic growth”. In addition to economic expansion, high-quality economic development should also consider peoples’ livelihoods, ecological civilization, and coordinated development. In the existing literature, there are two primary methods for measuring high-quality economic development: One approach is to measure it directly using green total factor productivity [13]. This method is widely used, but the measurement results fluctuate greatly and the measurement dimension is relatively single. The second is to build an index system according to meaning [6]. Obviously, on the premise of meeting the research needs, the index obtained by the second measurement method can be closer to the rich connotations of the measured object; therefore, this paper uses the second measurement method to establish a multidimensional index system to measure the level of high-quality economic development.

For the selection of the secondary indicators, based on the new development concept, this paper refers to previous studies [6] in order to divide the measured object into five dimensions: efficient development, stable development, coordinated development, green development, and innovative development. For the selection of the tertiary indicators, based on the measurement method used by Wei and Li [71] and the principles of the easy availability of statistical data, 17 tertiary indicators were finally selected, the complete construction of the index system is shown in Appendix A

Table A1. Index system composition.

Primary Index	Secondary Index	Third Index	Index Attribute
High-quality development	Efficient development	Capital productivity	+
		Labor productivity	+
		Land productivity	+
	Stable development	Producer price index	−
		Consumer price index	−
		Employment stability	−
	Coordinated development	Advanced industrial structure	+
		Rationalization of investment structure	+
		Openness of economic structure	+
	Green development	Wastewater discharge per unit of GDP	−
		Emissions per unit of GDP	−
		Solid waste emissions per unit of GDP	−
		Forest coverage rate	+
		Green coverage rate of built-up areas	+
	Innovative development	R&D funding intensity	+
		Percentage of technology market turnover	+
		High-tech revenue	+

. In this paper, the secondary indicators have been assigned an average weight, while the tertiary indicators have been assigned a weight via the use of the entropy weight method. This has led to the calculation of a comprehensive index for high-quality economic development, the results of the high-quality economic development index measurement for each of the 30 provinces over the 10-year period are presented in Appendix A

Table A2. Calculation result of high-quality economic development index.

City	2019	2018	2017	2016	2015	2014	2013	2012	2011	2010
Beijing	0.8608	0.8172	0.8255	0.7673	0.7218	0.7124	0.7273	0.7248	0.7916	0.7539
Tianjin	0.4356	0.4129	0.3590	0.3579	0.3406	0.3251	0.3119	0.3056	0.3303	0.3060
Hebei	0.2356	0.1977	0.1912	0.1545	0.2065	0.1991	0.1796	0.2018	0.2061	0.1912
Shanxi	0.2317	0.2541	0.2323	0.1887	0.2052	0.2057	0.1897	0.1770	0.1790	0.1623
Inner Mongolia	0.1922	0.2187	0.2111	0.1987	0.1690	0.1749	0.1734	0.1786	0.2146	0.2333
Liaoning	0.3814	0.3423	0.3663	0.3017	0.2256	0.2066	0.2200	0.2156	0.2387	0.2382
Jilin	0.2335	0.1942	0.2142	0.2244	0.1993	0.1683	0.1818	0.1856	0.2160	0.2233
Heilongjiang	0.2071	0.2210	0.2766	0.3015	0.2726	0.2697	0.2315	0.2282	0.2192	0.2183
Shanghai	0.5686	0.6064	0.5236	0.4834	0.4491	0.4365	0.4591	0.4619	0.4948	0.4492
Jiangsu	0.3018	0.3044	0.2953	0.2705	0.2508	0.2606	0.2864	0.2997	0.3163	0.3016
Zhejiang	0.3200	0.3280	0.2861	0.2508	0.2283	0.2315	0.2628	0.2750	0.2904	0.2930
Anhui	0.2343	0.2457	0.2102	0.1646	0.1715	0.1748	0.1750	0.1813	0.2011	0.1878
Fujian	0.2731	0.2978	0.2477	0.2046	0.1890	0.2064	0.2313	0.2344	0.2598	0.2712
Jiangxi	0.2476	0.2343	0.2014	0.1705	0.1823	0.1514	0.1973	0.2202	0.2207	0.2310
Shandong	0.2855	0.2482	0.2542	0.2346	0.2183	0.2232	0.2384	0.2370	0.2712	0.2304
Henan	0.1946	0.1870	0.1798	0.1407	0.1404	0.1471	0.1469	0.1563	0.1673	0.1817
Hubei	0.2794	0.2968	0.2674	0.2188	0.2180	0.2135	0.2027	0.1778	0.2084	0.2152
Hunan	0.3088	0.3026	0.2486	0.1981	0.1715	0.1776	0.1894	0.1976	0.2158	0.2507
Guangdong	0.3807	0.4213	0.3857	0.2834	0.2605	0.2719	0.3144	0.3337	0.3741	0.3697
Guangxi	0.2185	0.2133	0.1911	0.1555	0.1397	0.1569	0.1713	0.1708	0.1824	0.1936
Hainan	0.3896	0.3552	0.3190	0.3247	0.2902	0.2700	0.2569	0.2704	0.2947	0.3221
Chongqing	0.2364	0.2560	0.2074	0.1771	0.1827	0.2049	0.2001	0.1969	0.2266	0.2151
Sichuan	0.2783	0.3048	0.2356	0.1884	0.1684	0.1623	0.1758	0.1835	0.1978	0.1987
Guizhou	0.2268	0.2430	0.1712	0.1615	0.1554	0.1448	0.1786	0.1583	0.1858	0.1599
Yunnan	0.2499	0.2569	0.1897	0.1667	0.1670	0.1732	0.1736	0.1737	0.2001	0.1667
Shaanxi	0.2488	0.2576	0.2433	0.2387	0.2360	0.2381	0.2435	0.2137	0.2275	0.2214
Gansu	0.2111	0.2234	0.2498	0.2295	0.2120	0.1676	0.1708	0.1836	0.1662	0.1883
Qinghai	0.1414	0.1255	0.1245	0.1153	0.1389	0.1384	0.0962	0.1150	0.1211	0.1238
Ningxia	0.2053	0.1779	0.1620	0.1034	0.1206	0.1240	0.1217	0.1301	0.1058	0.1279
Xinjiang	0.1803	0.1662	0.1304	0.1746	0.1947	0.1336	0.1085	0.1183	0.1142	0.1516

.

4.2. Spatial Distribution and Evolution Characteristics of High-Quality Economic Development Level

To examine the spatial distribution and evolution features of China’s high-quality economic development level, this article visualizes the high-quality economic development indicators calculated above and draws topographic maps of four years: 2010, 2013, 2016, and 2019, as shown in Figure 1. The darker the color of an area on the map the higher the index value of the region, and a blank indicates the lack of data for a region.

In terms of spatial distribution characteristics, it is clear that the high-quality economic development levels of each province in China are quite different in the four years. Generally speaking, Beijing, Shanghai, and Guangdong are in the first echelon each year and have a high level of development; the index values of the southeast coastal areas and the central areas are higher, with certain aggregation distribution patterns; and the western region’s index value is relatively low, with a ladder-shaped distribution from large to small in the east, middle, and west. This observation is consistent with China’s economic development in recent years, and also with the research results of existing literature [11,14,15].

As for the time evolution characteristics, it can be seen that, over time, the high-quality economic development level of each province in China has an overall trend of improvement, and the development differences between different regions have a trend of first appearing and then narrowing. In 2010, the country’s overall level of development was low, and the gap between the various areas was not very noticeable. Beijing, Shanghai, and Guangdong were far ahead in terms of development, followed by a few southeast coastal cities, while the central and western areas were at a relatively low level. In 2013, the development level of the whole country was significantly enhanced, and the difference in development level between different regions gradually appeared. The overall spatial pattern had not changed, and the southeast coastal areas were still in the first echelon, but some central areas had also entered a higher level of development. In 2016, the national development level improved significantly compared with 2010 and 2013, but regional differences still existed. In 2019, the whole country entered a high level of development, except for a few areas. As far as regional differences are concerned, the overall appearance of the ladder-shaped distribution did not change; however, the whole central region was almost at the same level of development, and the gap with the southeast coastal regions had also narrowed. Generally speaking, in terms of time evolution, it shows the characteristics of improving the overall level first and then narrowing the regional differences [14].

5. Empirical Results

5.1. Model Settings

To examine Hypothesis 1 and Hypothesis 2, the baseline regression model set in this paper is shown in Equation (1):

$$e{q}_{it}={\beta }_0+{\beta }_{1}f{d}_{it}+{\beta }_{2}ed{u}_{it}+{\beta }_{3}e{x}_{it}+{\beta }_{4}in{d}_{it}+{\beta }_{5}urba{n}_{it}+{\epsilon }_{it},$$

(1)

In Equation (1), eq represents high-quality economic development, fd represents FD, edu represents the number of years of education per capita and controls for the impact of human resources, ex is the level of opening to the outside world and controls for the impact of the open environment, ind is the level of industrial development and controls for the impact of industrial development, urban means urbanization level and controls for the influence of a region’s total construction level on high-quality development, β stands for the coefficient of regression, and ε_it stands for the random disturbance term.

To examine Hypothesis 2, the intermediary-effect model established in this paper is shown in Equations (2) and (3). The model established by Baron and Kenny (1986) can test the intermediary influence between variables:

$$er{s}_{it}={\gamma }_0+{\gamma }_{1}f{d}_{it}+{\gamma }_{2}ed{u}_{it}+{\gamma }_{3}e{x}_{it}+{\gamma }_{4}in{d}_{it}+{\gamma }_{5}urba{n}_{it}+{\epsilon }_{it},$$

(2)

$$e{q}_{it}={\alpha }_0+{\alpha }_{1}er{s}_{it}+{\alpha }_{2}ed{u}_{it}+{\alpha }_{3}e{x}_{it}+{\alpha }_{4}in{d}_{it}+{\alpha }_{5}urba{n}_{it}+{\epsilon }_{it},$$

(3)

In the equations, ers represents environmental regulation. If both γ₁ and α₁ are significant, then the intermediary influence of environmental regulation holds. On this basis, if this effect is a mediation effect, the positive and negative properties of γ₁ and α₁ are consistent, and the mediation effect is γ₁α₁. If this effect is a suppression effect, the positive and negative properties of γ₁ and α₁ are opposite, and the suppression effect is γ₁α₁, which means that environmental regulation weakens the influence of FD on high-quality development [47].

Furthermore, to test whether the intermediary effect of environmental regulation is complete, this paper establishes a regression model, as shown in Equation (4):

$$e{q}_{it}={\phi }_0+{\phi }_{1}f{d}_{it}+{\phi }_{2}er{s}_{it}+{\phi }_{3}ed{u}_{it}+{\phi }_{4}e{x}_{it}+{\phi }_{5}in{d}_{it}+{\phi }_{6}urba{n}_{it}+{\epsilon }_{it},$$

(4)

In this model, environmental regulation, an intermediary variable, is introduced into baseline regression as a control variable and observes whether the relationship between FD and high-quality economic development still exists after controlling for the intermediary variable. If the intermediary effect is complete, then φ₁ is not significant but φ₂ is significant; if the intermediary effect is incomplete, in other words, on the one hand, the core explanatory variable can directly affect the core explained variable, on the other hand, it can also have a positive or negative impact on the core explained variable through the intermediary effect of the intermediary variable, then φ₁ and φ₂ are both significant [47].

The intermediary-effect model can only verify whether the intermediary effect is tenable, but cannot reveal how environmental regulation regulates the relationship between FD and high-quality development, which is a complex dynamic process. To further verify Hypothesis 2, this paper establishes a panel threshold model (5) by utilizing the environmental regulation intensity, er, as the threshold variable:

$$e{q}_{it}={\lambda }_0+{\lambda }_{1}f{d}_{it}\times I(ers\le \theta )+{\lambda }_{2}f{d}_{it}\times I(ers>\theta )+{\lambda }_{3}ed{u}_{it}+{\lambda }_{4}e{x}_{it}+{\lambda }_{5}in{d}_{it}+{\lambda }_{6}urba{n}_{it}+{\epsilon }_{it}$$

(5)

In Equation (5), I (·) is an indicator function. If the bracketed conditions are satisfied, 1 will be taken; otherwise, 0 will be taken.

5.2. Variable Selection and Data Sources

FD is the core explanatory variable of this empirical study, and its intensity determines the power of local governments to control financial resources independently. In this paper, the ratio of fiscal expenditure in provincial budgets to fiscal expenditure in the central budget is selected to measure the degree of FD, and the numerator as well as denominator of this formula are standardized with per capita, so as to control for the influence of population factors on the allocation of financial resources. There are some shortcomings in this indicator—in a study on a single country, many regions are faced with a single central government, so this indicator cannot reflect regional differences well; however, this indicator can indicate the intertemporal variations in a country’s financial connection between its central and local governments, and it is still the most widely used indicator in current research.

At the moment, the academic community has yet to agree on a method for measuring environmental regulation. Existing indicators are generally focused on the amount of sewage discharged, pollution control investment, environmental quality, and other factors. This paper draws lessons from previous measurement methods [72] and constructs an index system with the discharge of three wastes (industrial wastewater, ${\mathrm{S}\mathrm{O}}_2$, and smoke) per unit output value. The environmental regulation intensity index obtained by this method is a negative indicator. The higher the index value, the lower the intensity of environmental regulation. To avoid serious errors in the regression results of the model resulting from missing variables, this paper includes four control variables in the regression model with which to control for urban characteristics and economic-related variables, namely years of education per capita (edu), the degree of openness (ex), the degree of industrial development (ind), and the degree of urbanization (urban).

The panel data of 30 provinces (excluding Tibet) in the Chinese mainland from 2010 to 2019, which come from the National Bureau of Statistics and EPS database, are utilized in the empirical section of this paper. To reduce the impact of dimensional differences, some data are standardized in this paper, and per capita education years are logarithmic to reduce the impact of heteroscedasticity.

Table 1. Descriptive statistics results of main variables.

Variable Type	Variable	Observations	Mean	Std. Dev.	Min	Max
Explained variable	eq	300	0.249	0.125	0.0962	0.861
Explanatory variable	fd	300	1.007	0.416	0.542	2.191
	ers	300	0.525	0.533	0	2.585
Control variable	ex	300	0.284	0.307	0.0128	1.464
	ind	300	1.264	0.702	0.527	5.234
	urban	300	0.570	0.126	0.338	0.898
	edu	300	2.201	0.0983	1.912	2.548

displays descriptive statistics for each variable index value in this paper.

5.3. Baseline Regression Analysis

This study first verifies the impact of FD on high-quality economic development. After the Hausman test, this paper chooses a fixed-effects panel model in order to regress Equation (1);

Table 2. Baseline regression results.

	eq	eq
fd	0.044 **	0.069 ***
	(0.022)	(0.023)
edu		−0.029 **
		(0.093)
ex		0.028 **
		(0.025)
ind		−0.058 ***
		(0.014)
urban		−0.125
		(0.113)
Constant	0.248 ***	0.399 **
	(0.023)	(0.195)
Observation	300	300
City-fixed effect	Yes	Yes
Year-fixed effect	Yes	Yes
R-sq	0.523	0.570

Note: **, p < 0.05; ***, p < 0.01.

displays the regression findings. The coefficients of FD are significantly positive regardless of whether control variables are included in the regression, indicating that FD could foster high-quality development. Hypothesis 1 holds. According to this paper’s speculation, the possible reasons for this phenomenon are as follows: with the implementation of the new development concept, China no longer puts economic expansion as its top priority, as has been the case over the past few decades; instead, it now places more emphasis on comprehensive, coordinated, and stable development, and it has included key measures that represent the quality of development in its performance evaluation. Under the FD system, local governments are given more powers and responsibilities, which will undoubtedly further standardize development behavior, grasp the advantages of information, and foster high-quality economic development.

As for the control variable regression results, the regression coefficient of per capita education years was negative and passed the significance test under a confidence level of 5%. This result may seem illogical, but considering the current development situation in China, it is reasonable. The upgrading of human capital structures necessitates the creation of corresponding jobs, and the creation of these jobs is bound to be accompanied by technological progress and industrial upgrading, which also have higher and higher demands for workers’ education levels, forming a spiraling upward trend. Peoples’ education levels in China are rising as a result of the expansion of higher education; in contrast, the upgrading and transformation of China’s industrial structure have not been completed, and the jobs provided by society cannot match human capital with a high educational background. The problem of overeducation is becoming more visible. This is undoubtedly a misallocation of resources, leading to the inefficiency of development. The regression coefficients of ex are significantly positive in all three models, which is consistent with experience and logic.

5.4. Empirical Test of the Indirect Effects of Environmental Regulation

The new development concept emphasizes the importance of ecological civilization building in development, and it necessitates the development as well as execution of stricter environmental regulations. GDP as the primary index performance measurement criteria is changing, and ecological civilization is gradually being included in the evaluation scope, which will undoubtedly foster high-quality economic development; however, the long-standing competition model of the “GDP championship” has far-reaching implications. Simultaneously, the beneficial influence of environmental regulation has a time lag, and this beneficial influence is hardly reflected in the early stage of policy implementation. Under such a complicated background, how does environmental regulation contribute to the correlation between FD and high-quality economic development? To explore the problems raised above, this paper selects fixed-effects panel models to regress Equations (2) and (4), in addition to a random-effects panel models to regress Equation (3) via the Hausman test.

Table 3. Intermediary effect regression results.

	eq	ers	eq
fd		−0.263 **	0.066 ***
		(0.125)	(0.023)
ers	0.021 **		0.019 **
	(0.009)		(0.009)
edu	−0.034	−0.691 *	−0.050
	(0.094)	(0.390)	(0.093)
ex	0.006	−0.294 **	0.019
	(0.025)	(0.143)	(0.025)
ind	−0.053 ***	−0.059	−0.060 ***
	(0.014)	(0.063)	(0.014)
urban	−0.017	0.991 **	−0.102
	(0.111)	(0.431)	(0.113)
Constant	0.413 **	1.904 ***	0.429 **
	(0.197)	(0.733)	(0.194)
Observations	300	300	300
City-fixed effect	Yes	Yes	Yes
Year-fixed effect	Yes	Yes	Yes
R-sq	0.564	0.432	0.578

Note: *, p < 0.1; **, p < 0.05; ***, p < 0.01.

displays the results.

In the regression results of Equation (2), the coefficient of the negative indicator of environmental regulation is positive under a confidence level of 5%, i.e., strengthening environmental regulation will inhibit high-quality economic development, which is generally in line with expectations. Although the formulation of stricter environmental laws and the improvement of environmental law enforcement standards can improve environmental quality as well as encourage technological innovation, considering the fact that China has been relying on extensive development to achieve rapid economic expansion in the past decades, which has made the resource-intensive and pollution-intensive industries with high energy consumption occupy a dominant position in the overall economic layout of China. In this context, strengthening environmental regulation through the government will undoubtedly restrain the expansion of the economic aggregate. Previous studies have proven that, in the promotion of high-quality development, the intensity of environmental regulation can only have a positive impact if it is within a certain threshold. This also explains the regression result in this study.

From the regression results of Equation (3), it can be seen that the coefficient of fd is negative. As mentioned above, environmental governance is becoming increasingly significant in performance appraisals, FD brings responsibilities while delegating power, and local governments are facing severe assessments. In this way, FD has effectively inspired local governments to formulate stricter environmental regulations and implement them.

From the above analysis, it can be concluded that FD will simultaneously foster high-quality economic development and strengthen the intensity of environmental regulation, while high-quality economic development will be hampered by environmental regulations. It is preliminarily judged that the indirect effect value of FD on high-quality economic development through environmental regulation is −0.0055(${\gamma }_1{\alpha }_1$), which is opposite to ${\beta }_1$ in positive and negative aspects and shows a suppression effect. The regression coefficient of FD is 0.044. After controlling for the suppression effect of environmental regulation, the total impact coefficient of FD should be 0.0495.

From the regression results of Equation (4), it can be seen that the regression coefficients of FD remain positive after correcting for the indirect influence of environmental regulatory intensity. That is, FD has an impact on high-quality economic development not only through environmental regulation alone, which is in line with empirical facts, and side-by-side verifies the robustness of this set of empirical results.

5.5. Robustness Test

To further assess the robustness of the model, two robustness tests are conducted in this paper as follows: (i) Replacing the calculation of the core explanatory variable, FD. The ratio of fiscal revenue in provincial budgets to fiscal revenue in the central budget is used to assess the degree of FD of regions, and it is included in Equations (1) as well as (4) and regressed with the help of the fixed-effects panel model; the results are shown in columns 1 and 2 of

Table 4. Robustness test results.

	eq	eq	eq	eq
fd	0.060 **	0.056 **	0.078 ***	0.070 **
	(0.024)	(0.023)	(0.030)	(0.030)
ers		0.028 ***		0.023 **
		(0.010)		(0.009)
lnedu	−0.019	−0.045	−0.018	−0.047
	(0.095)	(0.094)	(0.102)	(0.101)
ex	−0.033	−0.044 *	−0.022	−0.039
	(0.026)	(0.026)	(0.036)	(0.036)
ind	−0.047 ***	−0.050 ***	−0.052 ***	−0.052 ***
	(0.015)	(0.014)	(0.016)	(0.016)
urban	−0.052	−0.030	−0.255 **	−0.219 *
	(0.116)	(0.114)	(0.129)	(0.128)
Constant	0.356 *	0.392 **	0.400 *	0.439 **
	(0.199)	(0.197)	(0.209)	(0.207)
City-fixed effect	Yes	Yes	Yes	Yes
Year-fixed effect	Yes	Yes	Yes	Yes
Observations	300.000	300.000	260.000	260.000
R-sq	0.520	0.535	0.515	0.529

Note: *, p < 0.1; **, p < 0.05; ***, p < 0.01.

. (ii) Excluding special samples. After excluding the data of four municipalities directly under the central government—Beijing, Tianjin, Shanghai, and Chongqing—Equations (1) and (4) are regressed with the help of the fixed-effects panel model, and the results are shown in columns 3 and 4 of Table 4.

The results show that the positive and negative regression coefficients of the core explanatory variables, FD and ers, are consistent with the previous regression results and pass the 5% significance test; the previous empirical results are robust.

5.6. Endogenous Treatment: Based on a Three-Stage Least Squares Method

This paper employs an intermediary effect analysis and uses environmental regulation as the explanatory variable to explain the changes in high-quality economic development; however, the interaction between the two is mutual, and high-quality development as an interpreted variable may in turn affect the environmental factors. In other words, there is an endogenous relationship between them; however, previous empirical analyses ignored this fact, which may result in inconsistent regression results. To test whether endogeneity has influenced the above research results, this paper regards these two variables as endogenous variables and constructs simultaneous equations, as shown in Equations (6) and (7):

$$er{s}_{it}={\theta }_0+{\theta }_{1}f{d}_{it}+{\theta }_{2}e{q}_{it}+{\theta }_{3}gd{p}_{it}+{\epsilon }_{it},$$

(6)

$$e{q}_{it}={\rho }_0+{\rho }_{1}f{d}_{it}+{\rho }_{2}er{s}_{it}+{\rho }_{3}ed{u}_{it}+{\rho }_{4}e{x}_{it}+{\rho }_{5}in{d}_{it}+{\rho }_{6}urba{n}_{it}+\epsilon ,$$

(7)

Assuming that environmental regulation is an endogenous variable, which is affected by FD and economic aggregate, the influencing factor equation is shown in Equation (6). Equation (7) is a model of influencing factors for high-quality economic development. Under the influence of the endogeneity problem, to estimate the unbiasedness and validity of the results, the equations are estimated using a three-stage least squares approach, and

Table 5. Results of the three-stage least squares regression.

	(Equation (6))	(Equation (7))
	ers	eq
fd	−0.158 *	0.028 **
	(0.086)	(0.014)
ers		0.066 ***
		(0.014)
eq	−1.035 ***
	(0.307)
gdp	−1.353 ***
	(0.133)
edu		0.270 ***
		(0.064)
ex		0.241 ***
		(0.019)
ind	1.844 ***	0.088 ***
	(0.105)	(0.007)
urban		−0.223 ***
		(0.067)
R-sq	0.439	0.785

Note: *, p < 0.1; **, p < 0.05; ***, p < 0.01.

displays the results. The regression coefficients of the main variables are highly congruent with the above empirical results; therefore, the previous research conclusions are robust.

5.7. Heterogeneity Test

5.7.1. Threshold-Effect Test

The intermediary effect analysis above has verified the moderating effect of environmental regulation. Does this adjustment have threshold characteristics? This section will empirically test this conjecture through the single-threshold-effect model proposed by Hansen, and the regression results are displayed in

Table 6. Threshold effect regression results.

Variable	ers	edu	ex	ind	urban	Fd (ers < 0.0108)	Fd (ers > 0.0108)
coefficient	0.036 ***	−0.254 ***	0.041	−0.030 **	−0.030	0.087 ***	0.045 *
	(0.010)	(0.068)	(0.030)	(0.012)	(0.077)	(0.030)	(0.026)

Note: *, p < 0.1; **, p < 0.05; ***, p < 0.01.

.

High-quality economic development is consistently positively impacted by FD, but when the ers value is less than or equal to 0.0108 the regression coefficient of FD is larger and the degree of positive impact is higher. In other words, the stricter the environmental laws and regulations the greater the enforcement, and the greater the role of FD in fostering high-quality economic development, showing the feature of an “increasing marginal effect”. This empirical result supports the results of the previous analysis in this paper: high-quality economic development includes many aspects, and different aspects have different responses to changes in environmental regulations, which lead to the complexity of the final result. When the policing force of environmental regulation surpasses a specific threshold, it can yield considerable benefits to high-quality development; this positive effect is also manifested in the weakening of the suppression effect. Hypothesis 2 is confirmed.

5.7.2. Heterogeneity Test of Development Level

The baseline regression model used in this study is the mean regression model, which provides regression results that only describe the mean influence of independent variables on the dependent variable; it is difficult to reflect the entire picture of condition distribution and heterogeneity among different individuals. In 1978, Koenker and Passett put forward the quantile regression method, which does not make any assumptions about the distribution of error terms and can better describe individual heterogeneity. In this study, five commonly used quantiles (10%, 25%, 50%, 75%, and 90%) were chosen and employed the random effect panel quantile model to examine the data.

Table 7. Quantile regression results.

	q10	q25	q50	q75	q90
	eq	eq	eq	eq	eq
fd	−0.03 ***	−0.02 **	0.15 ***	0.26 ***	0.38 ***
	(−2.79)	(−2.45)	(7.37)	(11.09)	(12.30)
ers	0.01	0.02 **	0.03 **	0.04 **	0.03
	(1.47)	(2.50)	(2.03)	(2.24)	(1.42)

Note: **, p < 0.05; ***, p < 0.01.

presents the results.

As shown in Table 7, in the provinces with a higher quality of economic development, i.e., high-quantile provinces, FD has a significant beneficial influence on high-quality development; however, in the provinces with low-quality economic development, i.e., low-quantile provinces, the impact of FD on high-quality development is significantly negative. The regions with lower economic development levels are more likely to invest in pollution-intensive industries that provide faster economic benefits while ignoring fields such as technology research and development, with slow effects and long cycles. This kind of investment layout can enable the region to achieve the goal of rapid GDP growth, but at the same time it also brings many problems, such as the overconstruction of infrastructure, environmental pollution, and low production efficiency. Local governments will have more power as a result of FD, which will magnify the impact of government behavior and investment decisions, resulting in a significantly negative regression coefficient for low-quantile provinces. The regional development pattern has gradually shifted to the right track, the extensive development model has been abandoned, and more government investment has flowed into the field of public service construction, which is beneficial to the improvement of ecological civilization and peoples’ livelihoods, such that the regression coefficient is significantly positive in provinces with a high quantile.

The regression coefficients of ers are significantly positive for all cases except extreme ones (at 10% and 90% loci). The coefficients are smaller in the provinces with low quantiles, and then gradually increase with an increase in the quantiles. Combined with previous research results, this paper suggests that the potential causes could be as follows: The western region has a lower quality of economic development, i.e., it is a “low-quantile region”. Previous studies have pointed out that the western region is at the end of the pollution transfer chain and that the overall pollution level is high. Enterprises are facing severe pollution punishment measures and have to standardize their operations to reduce pollution levels. Stated differently, the positive impact brought by environmental regulation is more obvious in the western regions, while the negative impact is further covered-up.

6. Conclusions and Policy Recommendations

6.1. Conclusions

After conducting a comprehensive evaluation of high-quality economic development level based on panel data from 30 Chinese provinces from 2010 to 2019, this paper examined the effects of FD and environmental regulation on high-quality economic development and the heterogeneity of such effects under different conditions, providing new ideas with which to explore the path of the role of FD in influencing high-quality economic development. This paper empirically tested the relationship amongst the aforementioned three variables via the utilization of a fixed-effect panel model as well as an intermediary-effect model and evaluates FD’s heterogeneous influence from multiple perspectives by applying the threshold-effect model and quantile regression. The study revealed the following main results:

First, the high-quality economic development level in each region of China displayed an ascendant tendency during the inspection period. The differences in development levels between different provinces first gradually appeared over time and then shrunk. In terms of spatial distribution, the southeast coastal region ranked highest for high-quality economic development, and the central regions followed, with the western region coming in last.

Second, FD fostered high-quality economic development; this conclusion holds even after accounting for endogenous effects. In addition, the level of openness also contributed to high-quality economic development. The level of education per capita, on the other hand, was not conducive to high-quality economic development, which may have been caused by the mismatch between the industrial structure and the education of the workforce.

Third, environmental regulation had a suppression effect on the process of FD fostering high-quality economic development, but as the intensity of environmental regulation increased this suppression effect had a nonlinear feature of an “increasing marginal effect”, which led to the nonlinear characteristic of an increasing marginal effect of FD on the quality development of the economy from the overall perspective.

Fourth, the role of FD varied according to the level of development: in provinces that had more developed levels it fostered high-quality economic development, while in less developed provinces the opposite was true.

6.2. Policy Recommendations

First, the degree of FD should be increased, and power should be decentralized to local governments to allow them to fully exploit their information advantages. Furthermore, improving the level of openness has the same effect. Improving the per capita education level has the opposite effect, which may be due to a mismatch between the industrial structure and labor force education.

Second, the government should continue to advocate for environmental regulation and standardize the implementation process. Especially in some provinces with lower levels of development, the negative impact of environmental regulation on development should be vigorously promoted and turned into a positive impact. Simultaneously, the opportunity should be taken to vigorously promote green technology innovation, improve green total factor productivity, drive the transformation and upgrading of traditional industries with innovations in green technology, and help advance the industrial structure as well as high-quality economic development.

Third, in the process of developing FD policies, we should consider not only the role of FD but also the regional status quo and the effects of other policies from a broader perspective in order to achieve better policy effects by formulating a policy mix according to local conditions. In reality, policy effects are complex. FD has a positive impact on high-quality economic development in general, but if FD is strengthened it will bring about environmental degradation and other problems, which will cause policy inefficiency. Environmental regulation is undoubtedly beneficial to the construction of ecological civilization, but its effects have a time lag, and effects on high-quality economic development are also gradually revealed. Therefore, the decentralization of government should be complemented by appropriate environmental regulation policies, such that the maximum advantages of both policies can be brought into play and help high-quality economic development.

6.3. Future Research

The empirical study in this paper is based on data from China, where FD policies are different from those under a federal system and have their own particularities. Therefore, although the findings based on a large amount of data in this paper are somewhat generalizable and some of the policy recommendations obtained can be extended to regions outside of China, their contribution in a global context is still insufficient. Extending the research perspective and empirical data beyond China to enrich the global contribution of this study is an important next step in our research.

In the future, it is also possible to refine our study by dividing FD into fiscal revenue decentralization and expenditure decentralization, as well as by classifying environmental regulation by policy type in order to explore what impacts different decentralization policies and environmental regulation policies have on high-quality economic development, so as to propose more comprehensive and targeted conclusions in addition to policy recommendations. In addition, this study measures the level of high-quality economic development by constructing an indicator system, and it would be meaningful to explore how FD affects different secondary indicators under the primary indicator of high-quality economic development, which can also achieve the same purpose.