Fiscal Decentralization, Environmental Regulation, and Green Technological Innovation: Evidence from China

Abstract

To achieve carbon peaking and neutrality, optimizing power allocation and fiscal responsibilities between China's central and local authorities is essential for balancing environmental and economic goals. This study employs fixed-effects panel regression and a threshold model to examine the relationships between fiscal decentralization, environmental regulations, and green technological innovation in 271 prefecture-level cities from 2007 to 2019. The results reveal that environmental regulations significantly advance green innovation. Fiscal decentralization modulates the impact of these regulations on green innovation, with regional variations. In central and western areas, fiscal decentralization inhibits the regulatory effects, whereas in the eastern region, it promotes them. Moreover, fiscal decentralization exhibits a threshold effect: below a certain threshold, it hinders regulatory impact, but beyond that threshold, it enhances it. These findings offer valuable insights for policy decisions and strategies to foster green innovation, providing a theoretical and empirical foundation for policymakers involved in environmental and fiscal planning.

1. Introduction

Low-carbon development in economic and social growth is vital in achieving high-quality growth, as stated in the 20th National Congress of the Communist Party of China. On 30 April 2021, at the 29th Collective Study Session of the Political Bureau on enhancing the construction of China’s ecological civilization in the new circumstance, General Secretary Xi Jinping emphasized the need to achieve long-term green development, build a sound economic system for low-carbon circular growth, and boost green economic and social transformation from all aspects. At the end of the year, the 6th Plenary Session of the 19th CPC Central Committee highlighted that “the central government is making unprecedented efforts to promote ecological civilization construction”. Moreover, according to the Central Economic Work Conference, promoting carbon peaking and carbon neutrality shows the country’s resolution for high-quality sustainable development and a solemn commitment to people around the world. “Guiding Opinions on Building a Market-oriented System for Green Technology Innovation” was published by the National Development and Reform Commission and the Ministry of Science and Technology in April 2019 [1], which clearly stated that China aims to establish an ecological-friendly system of technical innovation. This will be achieved through 6 tasks and 17 sub-tasks, including nurturing and strengthening the main bodies of green technological innovation and enhancing the guiding mechanisms for it. At the same time, a roadmap and timetable were designed, specifying the leading departments, major forms of achievements, and completion times.

In the past decades, China has witnessed remarkable economic achievements, attracting worldwide attention. But the extensive economic growth has resulted in high emissions, pollution, and other environmental issues, severely impacting people’s daily lives and normal societal functioning. In response to the increasingly intensified environmental pressures, China has continuously issued regulations to protect the ecological system, conducted energy revolutions, and encouraged green technological innovation to pursue sustainable economic growth.

The key to sustainable economic growth lies in green technology (e.g., Refs. [2,3]). Yet, the question of how enterprises, as primary agents of green innovation, respond to tightening environmental regulations remains critically unresolved [4]. The “cost-following” and “innovation compensation” theories offer two distinct explanations. The former suggests that in pursuit of profit maximization, enterprises are compelled to incur higher production costs due to strict environmental regulations, thereby constraining their green technological innovation. Conversely, the latter proposes that enterprises will enhance their innovation efforts and improve production efficiency as a means to cover extra expenses caused by environmental regulations. The debate between the “cost-following” and “innovation compensation” theories underscores the complexity of this response, pointing to the need for a nuanced understanding of the interplay between environmental regulations and green technological innovation.

Furthermore, China’s unique approach to fiscal decentralization introduces an additional layer of complexity, providing local governments with significant autonomy over their fiscal resources. This autonomy allows them to tailor environmental policies and innovation incentives to match regional economic conditions and development goals. By having the ability to craft and implement environmental strategies that align with local priorities, local governments can adopt a more tailored approach to environmental governance. This is crucial given the diverse economic landscapes and industrial bases across China’s regions. Such local autonomy enables authorities to prioritize investments in green technologies and innovations that address their specific environmental challenges and economic development objectives most effectively. To meet the unique needs of economic development, fiscal decentralization in China takes three steps [5], with 1978 and 1994 serving as the two dividing points. Generally speaking, fiscal decentralization stands for the tax-sharing reform in 1994, which redefined the taxation and expenditure rights of governments at all levels. Against this backdrop, despite possessing relatively independent taxation rights, local officials are also responsible for promoting local economic growth. Conflicts of interest may arise between the central and local authorities due to the current fiscal system [6], potentially affecting the policy effects of environmental regulations.

A thorough exploration of the correlation between fiscal decentralization, green technology, and environmental policies holds significant theoretical and practical value for strengthening policy implementation effectiveness and promoting sustainable economic development. By examining the dynamics of how environmental regulations affect green innovation under the framework of fiscal decentralization, this paper aims to reveal how environmental regulations affect green innovation through a fixed-effect model and measure the regulatory effects of fiscal decentralization using a moderating effect model, so as to supplement the existing literature and deepen our understanding of how fiscal and environmental policies coordinate to promote innovation. This paper also explores the threshold effects of fiscal decentralization on environmental regulations, offering a new analytical perspective. This helps better assess environmental policy effectiveness and identify conditions under which fiscal decentralization enhances or weakens the potential of environmental policy to stimulate green innovation. The major contributions of the paper are: (1) This study explores the impact of environmental policies on green technological innovation within the context of China’s fiscal decentralization and expands the theories of fiscal decentralization and environmental management in environmental economics. In particular, the study found fiscal decentralization modulates the ability of environmental policies to incentivize green innovation. This contribution is pivotal in refining our understanding of how fiscal and environmental policies can be harmonized to foster innovation. (2) The examination of threshold effects introduced by fiscal decentralization on environmental regulation efficacy presents a novel analytical lens. It deepens our understanding of how local governments can implement environmental policies tailored to local needs in a diverse economic context. In China, different regions differ significantly in terms of economic development, industrial bases, and environmental challenges, and local governments are able to formulate and implement environmental strategies according to their own specific conditions. The formulation and implementation of such strategies is of great significance for enhancing our capability to discern the intricate conditions under which fiscal decentralization promotes or ihibits the potential of environmental policies to stimulate green innovation. Through these specific contributions, this study not only provides valuable insights for the academic community but also provides practical guidance for policymakers in balancing the relationship between economic development and environmental protection, especially in a multi-tiered fiscal system of a large country like China.

The remaining chapters of this article are organized below. Section 2 reviews and analyzes existing studies. Section 3 describes the model and variable sources. Section 4 presents the empirical results. Section 5 concludes with policy significance.

2. Literature Review and Theoretical Analysis

Studies concerning green innovation and environmental regulations have not reached a unified conclusion. Scholars have roughly divided it into four types. The promotion effect argues that stringent environmental policies can spur companies to innovate, leading to improvements in green technology and processes. This is exemplified by industries that have shifted towards more sustainable practices to not only comply with regulations but also to gain a competitive edge through eco-friendly products and services (e.g., Refs. [7,8,9,10,11]). Conversely, the inhibition effect suggests that overly stringent regulations can burden companies, particularly small and medium-sized enterprises (SMEs), with high compliance costs. This can inhibit their ability to invest in research and development (R&D) for green innovations due to limited resources (e.g., Refs. [12,13,14,15]). Moreover, a non-linear relationship between environmental regulations and green innovation is observed (e.g., Refs. [16,17,18]), indicating that the impact of regulations can vary based on factors such as industry characteristics, the firm’s initial environmental performance, and the technological capability to innovate. Lastly, the uncertain effect highlights that the influence of environmental regulations on innovation is not always clear-cut and can vary greatly depending on various external and internal factors, including market demand, technological pathways, and government policy consistency [19]. Overall, environmental regulations can affect green technology negatively and positively, which are driven by the cost effect [20] and the innovation compensation effect [21], respectively. These two effects together determine the final influence of eco-friendly rules on green innovation [22]. This duality suggests that while regulations may increase production costs, discouraging green technology investment, and they also enhance market competitiveness and efficiency for innovative firms. Specifically, the key mechanisms include the following aspects. First, environmental regulations require businesses to invest in pollution control, increasing operational costs and potentially limiting funds for green innovation. However, environmental regulations also provide an innovation offset mechanism, enhancing the production benefits of adopting green technologies and offering competitive advantages in the market. These regulations may boost market demand for green products and services, encouraging companies to expedite green technological innovation. Second, environmental regulations can foster closer collaboration among companies, research institutions, and government agencies, including technology sharing, joint R&D, and resource integration, thereby accelerating innovation and dissemination of green technologies. This collaboration enhances knowledge and technology flow, strengthening the innovation network and offering a broader opportunity for green technology growth. Third, the government is also a key to achieving green innovation. Governmental support through funding, tax incentives, R&D subsidies, and other motivational measures reduces the economic risks for businesses engaging in the R&D of green technology and stimulates market demand, thereby facilitating its study, advancement, and commercialization.

Accordingly, this research puts forward hypothesis 1 (H1):

H1: 

Environmental regulations greatly facilitate the upgrading of green technology.

There are conflicts concerning how fiscal decentralization affects green innovation. Supporters for fiscal decentralization suggest that local governments may promote technological innovation capabilities to enhance competitiveness, thus facilitating green innovation (e.g., Refs. [23,24,25,26,27]). However, some local officials who value personal achievement are always short-term-focused and narrow-minded, which impedes green innovation (e.g., Refs. [24,28]).

However, there is limited research on the regulatory effect of fiscal decentralization on environmental law enforcement and green technology innovation. Scholars have mostly examined its influence on green innovation under the current taxation and fiscal policies of the fiscal decentralization framework (e.g., Refs. [25,29]), fiscal expenditure (e.g., Refs. [30,31,32]), and subsidies (e.g., Refs. [31,33,34,35,36,37]). Studies on the influence of fiscal environment policies on corporate green innovation are still not sufficient.

Within a politically centralized framework, local officials are required to fulfill the tasks entrusted by higher authorities while pursuing higher political achievements. As ecological degradation intensifies and economic paradigms shift, eco-environmental performance is increasingly becoming a critical tool for government evaluation. This compels local governments and businesses to focus on green technological innovation as an effective means of environmental management, internalizing pollution costs to foster green economic growth. Simultaneously, under the pressure of eco-environmental performance assessments, local governments manage to stimulate the innovation of green technology by strengthening environmental regulatory policies. However, there are slight differences among local authorities due to varying degrees of fiscal decentralization, local economic foundations, and the uneven enforcement intensity of environmental regulations. Therefore, the regulatory effect of fiscal decentralization varies regionally, as illustrated below.

Firstly, local governments in different regions are allowed to issue tailored environmental regulations, resulting in varying degrees of support for environmental protection and green technological innovation.

Secondly, fiscal decentralization might stimulate competition among local governments, prompting them to implement stricter environmental regulations to attract investments and industrial development. These competitions can drive innovative environmental technologies and solutions to meet the heightened standards.

Thirdly, local governments are allowed to allocate fiscal resources autonomously, promoting innovative resource allocations in environmental protection and green technology innovation. This includes allocating more funds towards green technology research, industry upgrades, and environmental projects, stimulating relevant innovations.

Finally, fiscal decentralization motivates local governments to actively monitor and implement environmental regulations rigorously, but the extent of this supervision and implementation may vary across regions, thereby affecting green innovation and environmental protection.

In light of the above discussion, hypothesis 2 (H2) is proposed.

H2: 

Fiscal decentralization modulates the influence on green innovation and environmental law enforcement, with regional variations.

The impact of environmental law enforcement on green innovation is modulated by fiscal decentralization in a non-linear way. Initially, within a certain range, it encourages local governments to be fully engaged in the environmental sector and drive green technology innovation. However, once exceeding this threshold, local authorities tend to compete with one another for better political achievements, which may cause the relaxation of environmental regulations to attract businesses and investments. This, coupled with reduced regulatory pressure and asymmetrical information, can weaken environmental policies and hinder green innovation. Additionally, a lack of coordination and cooperation in environmental policies among different local governments can create inconsistency and uncertainty for businesses as they strive to predict and adapt to them, thus reducing their investments in green technology innovations.

Under fiscal decentralization, local governments prioritize their own interests, often favoring industries that are highly polluting and economically beneficial. This can lead to lower environmental supervision standards during the bidding process for businesses and negatively affects the degrees of green innovation in some regions. Moreover, affected by the fixed tenure of government officials, they might also opt for short-term benefits over long-term environmentally friendly projects such as green technology innovations. However, local governments emphasizing sustainable environmental development may favor businesses with advanced technology and lower pollution levels and even elevate environmental supervision standards to prevent highly polluting firms from entering the local market. Moreover, local governments might better allocate resources, including funds and manpower, toward high-tech, low-pollution industries, promoting green innovation.

Therefore, hypothesis 3 (H3) is proposed.

H3: 

Fiscal decentralization exhibits a non-linear regulatory effect. When exceeding a threshold, fiscal decentralization may prevent environmental regulations from promoting green innovation, and vice versa.

3. Model Construction, Description of Variables and Data Sources

3.1. Model Construction

This study establishes a fixed-effect model, where ${\mathrm{GTI}}_{\mathrm{it}}$ is the degree of green innovation, ${\mathrm{ER}}_{\mathrm{it}}$ is the strength of environmental regulation, ${\mathrm{FD}}_{\mathrm{it}}$ is the degree of fiscal decentralization, ${\mathrm{X}}_{\mathrm{it}}$ refers to the control variable, ${\beta }_0$ is the intercept term, ${\lambda }_{\mathrm{i}}$ is the individual effect of the i-th prefecture-level city, and ${\epsilon }_{\mathrm{it}}$ is the error term.

$${\mathrm{GTI}}_{\mathrm{it}}={\beta }_0+{\beta }_1{\mathrm{ER}}_{\mathrm{it}}+{\beta }_2{\mathrm{FD}}_{\mathrm{it}}+\sum _{\mathrm{k}=1}{\gamma }_{\mathrm{k}}{\mathrm{X}}_{\mathrm{it}}+{\lambda }_{\mathrm{i}}+{\epsilon }_{\mathrm{it}}$$

(1)

We add ${\mathrm{ER}}_{\mathrm{it}}\times {\mathrm{FD}}_{\mathrm{it}}$—the interaction term of environmental regulation and fiscal decentralization. This approach enables us to study how fiscal decentralization moderates environmental regulation and green innovation. In a fiscal decentralization framework, local governments are given significant autonomy to make decisions on resource allocation, which may prompt them to make trade-offs between pursuing short-term economic development and committing to long-term environmental protection. Therefore, by examining the interaction between ER and FD, we can better understand the actual impact of environmental regulation on green technology innovation under fiscal decentralization.

$${\mathrm{GTI}}_{\mathrm{it}}={\beta }_0+{\beta }_1{\mathrm{ER}}_{\mathrm{it}}+{\beta }_2{\mathrm{FD}}_{\mathrm{it}}+{\beta }_3\left({\mathrm{ER}}_{\mathrm{it}}\times {\mathrm{FD}}_{\mathrm{it}}\right)+\sum _{\mathrm{k}=1}{\gamma }_{\mathrm{k}}{\mathrm{X}}_{\mathrm{it}}+{\lambda }_{\mathrm{i}}+{\epsilon }_{\mathrm{it}}$$

(2)

Taking the interaction term ${\mathrm{ER}}_{\mathrm{it}}\times {\mathrm{FD}}_{\mathrm{it}}$ as the core variable and ${\mathrm{FD}}_{\mathrm{it}}$ as the threshold variable, we study whether the regulatory effect of fiscal decentralization has a threshold.

$$\begin{gathered} {\mathrm{GTI}}_{\mathrm{it}}={\beta }_0+{\beta }_1\left({\mathrm{ER}}_{\mathrm{it}}\times {\mathrm{FD}}_{\mathrm{it}}\right)\mathrm{I}\left({\mathrm{FD}}_{\mathrm{it}}<{\mathrm{r}}_1\right)+{\beta }_2\left({\mathrm{ER}}_{\mathrm{it}}\times {\mathrm{FD}}_{\mathrm{it}}\right)\mathrm{I}\left({\mathrm{r}}_1\le {\mathrm{FD}}_{\mathrm{it}}<{\mathrm{r}}_2\right)+\cdots + \\ {\beta }_{\mathrm{n}}\left({\mathrm{ER}}_{\mathrm{it}}\times {\mathrm{FD}}_{\mathrm{it}}\right)\mathrm{I}\left({\mathrm{FD}}_{\mathrm{it}}\ge {\mathrm{r}}_{\mathrm{n}}\right)+\sum _{\mathrm{k}=1}{\gamma }_{\mathrm{k}}{\mathrm{X}}_{\mathrm{it}}+{\lambda }_{\mathrm{i}}+{\epsilon }_{\mathrm{it}} \end{gathered}$$

(3)

3.2. Description of Variables

3.2.1. Explained Variable: Green Technology Innovation

In addressing the challenge of measuring green technology innovation, a field characterized by its intricate blend of general technological advancement and specific environmental considerations, this study adopts a novel approach. Recognizing the inherent difficulty in quantifying such a multifaceted phenomenon—owing to divergent research methodologies, experimental constraints, and the scarcity of comprehensive statistical data—we draw on the measurement method proposed by Lv et al. [38]. This method offers a robust and clear indicator of green innovation by utilizing the total number of eco-friendly patents per 10,000 people. This indicator encompasses both green invention patents and green utility model patents, thereby providing a comprehensive measure of a region’s contribution to environmentally sustainable technological advancement. By employing this specific metric, our study not only navigates the complexities associated with evaluating green technological innovation but also establishes a clear, standardized criterion for assessing progress in this critical area.

3.2.2. Regulatory Variable: Fiscal Decentralization

Different from its fluctuation reflected by different decentralization methods, fiscal decentralization has a certain impact on empirical cases. We investigate the impact of the matching degree of administrative and financial power on green technology innovation, drawing on You et al. [28]. In order to quantify the extent of fiscal decentralization, this paper selects “fiscal freedom” as an indicator, which is measured by the ratio of revenues to expenditures in a city’s budget. Specifically, fiscal freedom refers to a city’s ability to cover its expenditures with its own revenues without relying on fiscal transfers from higher levels of government. Theoretically, the higher this ratio is, the greater the fiscal independence of the local government and the higher the level of fiscal decentralization. Fiscal freedom was chosen as an indicator of fiscal decentralization because, compared to other possible indicators, it is able to better combine the revenues and expenditures of local governments, has a high degree of data continuity, and is easy to collect and compare. In addition, fiscal freedom, as an indicator, can visually reflect the autonomy and flexibility of local governments in managing their financial resources, which is particularly crucial for promoting green technological innovation. Green technological innovation often requires local governments to have greater autonomy in terms of financial inputs and the formulation of tax incentives to meet the specific needs of local environmental protection and sustainable development.

3.2.3. Core Explanatory Variables: Environmental Regulation

Environmental regulations should be evaluated by the means of implementing the regulation and its effects. Regulatory methods in China are command-and-control, market-incentive, and public participation environmental regulations. The first type involves imposing administrative penalties for environmental pollution; the market-incentive type is characterized by a system of tradable environmental licenses that manage pollution control intensity, environmental protection taxes, and pollution discharges. Regulation involving public participation mainly features a citizen petition system. Previous studies have demonstrated that public participation in environmental petitions in China is still in its infancy. The efficiency of environmental policies is often determined according to the amount of discharged pollution, including wastewater, exhaust gases, and solid waste generated. There are many measurement methods used for implementing environmental regulations. Considering the available statistics and the comprehensive nature of the index system, this study draws on the measurement methods used by Fu and Li [39] for measuring the wastewater, sulfur dioxide, and smoke (powder) discharges in each prefecture-level city. The emission of these three types of pollutants is linearly normalized and then assigned respective weights to form a comprehensive index of pollution emissions that is practical for evaluating the effects of environmental regulation. The specific equation used to calculate these measurements is provided in Equation (4) below.

$${\mathrm{ER}}_{\mathrm{i}}=\frac{1}{3}\stackrel{3}{\sum _{j=1}}\left(\frac{{\mathrm{EM}}_{\mathrm{ij}}}{\mathrm{mean}\left({\mathrm{EM}}_{\mathrm{ij}}\right)}\times \frac{{\mathrm{EM}}_{\mathrm{ij}}-min\left({\mathrm{EM}}_{\mathrm{j}}\right)}{max\left({\mathrm{EM}}_{\mathrm{j}}\right)-min\left({\mathrm{EM}}_{\mathrm{j}}\right)}\right)$$

(4)

3.2.4. Control Variables

Other variables can also affect green innovation. To improve the robustness of this model, several control variables have been included: economic growth, assessed by the regional per capita gross development product (PGDP); openness to the global economy (PFDI), following Ye et al. [40], which is assessed according to the ratio of the output of foreign-invested enterprises to that of secondary industry; educational expenditures (EDU), which are represented by the amount of local financial educational expenditures; infrastructure (INFRA), represented as the per capita road area of the region; and industrial structure (SR), measured by the coupling degree between the labor factor input structure and the output structure of three industries following Xiao et al. [41]. It is calculated using Equation (5), where ${\mathrm{Y}}_{\mathrm{it}}$ refers to the total industrial value of industry i in year t, ${\mathrm{Y}}_{\mathrm{t}}$ is the total output value in year t, ${\mathrm{L}}_{\mathrm{it}}$ is the labor input of industry i in year t, and ${\mathrm{L}}_{\mathrm{t}}$ is the total labor force in year t.

$$\mathrm{SR}=\stackrel{3}{\sum _{i=1}}\frac{{\mathrm{Y}}_{\mathrm{it}}}{{\mathrm{Y}}_{\mathrm{t}}}\times \ln \left(\frac{{\mathrm{Y}}_{\mathrm{it}}}{{\mathrm{Y}}_{\mathrm{t}}}/\frac{{\mathrm{L}}_{\mathrm{it}}}{{\mathrm{L}}_{\mathrm{t}}}\right)$$

(5)

The variables in the mentioned models are listed in

Table 1. Variable definitions and measurement methodologies.

Variable	Meaning	Measurement Methodology
GTI	Green Technology Innovation	Number of green invention patents + number of green utility model patents/population
ER	Environmental Regulation	Refer to Equation (4)
FD	Fiscal Decentralization	Fiscal revenue/fiscal expenditure
PGDP	Level of Economic Development	GDP/population
PFDI	Level of Openness	Output value of foreign-invested enterprises/output value of secondary industry
EDU	Education Expenditure	Local financial education expenditure
SR	Industrial Structure	Refer to Equation (5)
INFRA	Infrastructure	Road area/population

Note: GDP = gross domestic product; PGDP = per capita GDP.

with a description of how they are measured.

3.3. Data Sources

This paper collects data from the China Regional Statistical Yearbook (2007 to 2014) [42], the China Environmental Statistical Yearbook (2007 to 2014), the China Urban Statistical Yearbook (2007 to 2019), the Chinese Research Data Services Platform, and the EPS China Macro Economy Database. Some missing values were filled in by querying the yearbooks or annual reports of 271 prefecture-level cities, and interpolation methods were also used. To prevent extreme values from affecting the regression results, two-sided Windsor tail processing at a 2.5% level was adopted in this study. Logarithmic processing was carried out to reduce the interference of the varying dimensions and orders of magnitude of the variables on the regression results. Centralized processing of the interaction between environmental regulation and fiscal decentralization was conducted (see

Table 2. Variable description and statistics.

Variable	N	Mean	SD	p50	Min	Max
GTI	3523	4.758	1.734	4.654	1.386	8.581
ER	3523	0.145	0.2	0.069	0	0.906
FD	3523	0.38	0.147	0.369	0.133	0.67
PGDP	3523	10.51	0.641	10.51	9.199	11.790
FDI	3523	0.745	0.869	0.408	0	3.595
EDU	3523	12.68	0.869	12.7	10.87	14.46
SR	3523	0.223	0.145	0.206	0.011	0.546
INFRA	3523	7.007	0.951	6.917	5.226	9.203

Note: SD = standard deviation; FDI = foreign direct investment.

).

Multicollinearity between variables may distort regression results. Therefore, we employed a multicollinearity test to increase the effectiveness and accuracy of the regression results. It is indicated that the variance inflation factor (VIF) values of all variables are below 10, exhibiting no sign of multicollinearity (see

Table 3. Multicollinearity test results.

Variable	VIF	1/VIF
ER	1.860	0.538
FD	3.380	0.296
PGDP	2.650	0.377
FDI	2.380	0.421
EDU	2.430	0.412
SR	1.580	0.634
INFRA	3.260	0.306
ER × FD	2.160	0.463
Mean VIF	2.460

Note: VIF = variance inflation factor.

).

4. Empirical Results and Discussion

4.1. Benchmark Regression

The Hausman test was applied to the random-effects models, fixed-effects model, and mixed ordinary least squares, demonstrating that the p-values were less than 0.1. Considering the existence of individual heterogeneity, a fixed-effects model was adopted for the regression analysis to more accurately control for unobserved individual-specific effects. The regression result of Model 1 is illustrated in

Table 4. Regression results (Model 1).

GTI	National Level	Eastern	Central	Western
ER	0.2122 ***	0.1827 **	0.4207 ***	0.0068
	(3.1879)	(2.2842)	(3.4732)	(0.0400)
FD	−2.3557 ***	−2.5194 ***	−2.4247 ***	−1.6062 ***
	(−13.1030)	(−10.0392)	(−8.7257)	(−3.6342)
PGDP	0.8335 ***	0.4364 ***	1.3968 ***	0.5386 ***
	(15.3586)	(5.6952)	(14.4213)	(6.1185)
FDI	−0.1688 ***	−0.0135	−0.2523 ***	−0.3430 **
	(−3.9111)	(−0.2706)	(−3.4716)	(−2.4771)
EDU	0.7944 ***	0.9059 ***	0.5843 ***	0.8929 ***
	(22.3196)	(18.7034)	(9.1549)	(14.2959)
SR	−0.1928	−0.7545***	0.1252	0.0902
	(−1.3511)	(−3.0887)	(0.5654)	(0.3743)
INFRA	0.4574 ***	0.7005 ***	0.2663 ***	0.5299 ***
	(11.9890)	(10.0455)	(4.7693)	(8.1132)
Constant	−16.2456 ***	−14.6353 ***	−18.2837 ***	−15.8572 ***
	(−66.1716)	(−32.2774)	(−49.5830)	(−35.1385)
Adjusted R2	0.7996	0.8339	0.8292	0.7560
N	3523	1287	1261	975

Note: T statistics appear in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

.

According to this, the coefficient of environmental regulation in the regression exhibits a positive result at a significance level of 1%, which means environmental regulations and green technology present a positive correlation. The regression coefficient of fiscal decentralization is negative, which indicates that the main evaluation standard for economic performance, namely GDP, hinders green technological innovation. This may be due to the fact that fiscal decentralization has increased economic competition between local governments, leading to a focus on short-term economic growth at the expense of long-term environmental investment. The coefficients of economic development, educational expenditures, and infrastructure present positive results at a significance level of 1%, showing that these three may effectively promote green technology innovation. The coefficient of economic openness is negative at the 1% level, which does not coincide with our expectations. We speculate that this may be due to the low preference of foreign investors for investment in green technologies, or due to competitive pressures in open economic conditions that lead firms to focus more on short-term profits than on long-term environmental sustainability.

Furthermore, to examine the existence of whether regional heterogeneity affects environmental regulation, the selected data are classified according to geographic regions as eastern, central, and western cities, with the corresponding regression analysis shown in Table 4. The coefficients of these three subsamples are positive. Those of the eastern and central cities are at a significance level of 5% and 1%, respectively. This result may point to the importance of sample size for statistical inference while also suggesting that more samples are needed in the west to validate this trend. However, the coefficient for the western region is not significant. This may be due to the relatively small number of observations in the regression. Moreover, the coefficients of the fiscal decentralization variables in the three regions are all negative at a significance level of 1%, which echoes the national-level outcome and indicates the robustness of estimation results at the national level. This further verifies H1.

The interaction term of environmental regulation and fiscal decentralization was introduced, as presented in

Table 5. Regression results (Model 2).

GTI	National Level	Eastern	Central	Western
ER	0.3221 ***	0.1027	0.6107 ***	0.3124 *
	(4.1775)	(1.0579)	(4.4200)	(1.6637)
FD	−2.2354 ***	−2.5959 ***	−2.2000 ***	−1.2417 ***
	(−12.1094)	(−10.1289)	(−7.6338)	(−2.7550)
PGDP	0.8180 ***	0.4424 ***	1.3738 ***	0.4881 ***
	(15.0129)	(5.7682)	(14.1772)	(5.4871)
FDI	−0.1702 ***	−0.0170	−0.2745 ***	−0.3595 ***
	(−3.9468)	(−0.3412)	(−3.7668)	(−2.6048)
EDU	0.7995 ***	0.8993 ***	0.5855 ***	0.8970 ***
	(22.4566)	(18.4976)	(9.2012)	(14.4053)
SR	−0.2072	−0.7499 ***	0.0988	0.0079
	(−1.4527)	(−3.0711)	(0.4468)	(0.0329)
INFRA	0.4496 ***	0.7154 ***	0.2595 ***	0.5362 ***
	(11.7675)	(10.1551)	(4.6561)	(8.2364)
ER × FD	−0.0172 ***	0.0118	−0.0308 ***	−0.0482 ***
	(−2.8156)	(1.4554)	(−2.8303)	(−3.6669)
Constant	−16.2205 ***	−14.6331 ***	−18.2237 ***	−15.7843 ***
	(−66.0969)	(−32.2876)	(−49.4879)	(−35.1748)
Adjusted R2	0.8001	0.8342	0.8303	0.7594
N	3523	1287	1261	975

Note: T statistics appear in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

.

The coefficients of national environmental regulation and the interaction term are positive and negative, respectively, at a significance level of 1%. This implies that fiscal decentralization inhibits the positive influence of environmental policies on green innovation at the national level. This result shows obvious regional differences, with estimation results shown in Column 2. The coefficient of the eastern region is positive but not significant, which may be attributable to two reasons. First, the economy in the east is relatively high. Thus, the green development trend is positive, and fiscal decentralization shows a relatively low regulatory effect. Second, the influence of environmental policies is inconsistent, resulting in insignificant final results. Those of central and western economies are negative at a significance level of 1%, which means that in the central and western cities, the current fiscal decentralization and environmental policies hinders green innovation. The reason for such situations may be the relatively low financial freedom, which intensifies local economic competitiveness. However, this kind of competition hinders the improvement of green technology. Overall, the outcomes of cities in the middle and west of China are in line with those in the national-level regression, while those of the eastern region are not. This confirms the existence of regional heterogeneity, verifying H2.

4.2. Robustness Tests

4.2.1. Endogeneity

There may be a two-way causal correlation between green technological innovation and environmental policies. Coupled with the influence of measurement errors and other unobservable factors, this might result in endogeneity in environmental regulation. Therefore, with reference to Lin et al. [43], environmental valuation with a lag of one period is chosen as an instrumental variable.

Table 6. Instrumental variable regression results.

	Model 1		Model 2
	ER First Stage	GTI Second Stage	ER First Stage	GTI Second Stage
L.ER	0.5164 ***		0.4062 ***
	(30.0209)		(25.6565)
ER		0.6616 ***		0.9300 ***
		(4.7098)		(5.0320)
FD	0.0396	−2.4765 ***	−0.2079 ***	−2.1761 ***
	(0.9051)	(−13.4254)	(−5.1985)	(−11.0845)
PGDP	−0.0337 **	0.8599 ***	0.0022	0.8237 ***
	(−2.4490)	(14.8033)	(0.1819)	(14.0831)
FDI	−0.0023	−0.1322 ***	0.0029	−0.1380 ***
	(−0.2150)	(−2.9488)	(0.3039)	(−3.0727)
EDU	0.0072	0.8997 ***	−0.0009	0.9080 ***
	(0.7586)	(22.5367)	(−0.1100)	(22.6200)
SR	0.0593 *	−0.2252	0.0772 **	−0.2636 *
	(1.7131)	(−1.5376)	(2.5043)	(−1.7874)
INFRA	−0.0022	0.4028 ***	0.0130	0.3843 ***
	(−0.2264)	(10.0454)	(1.5347)	(9.4862)
ER × FD			0.0319 ***	−0.0401 ***
			(27.8097)	(−4.4726)
Adjusted R2	0.1783	0.793	0.3477	0.792
N	3253	3253	3253	3252

Note: T statistics appear in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the results of instrumental variable regression. The F-value of the CDW test on Model 1 is 901.25 and that of Model 2 is 658.25, both of which are higher than the 10% critical value (16.38). Thus, the effectiveness of the instrumental variable method is proved. The results are recorded in Table 6, which conforms with the benchmark regression, proving its robustness.

4.2.2. Substitution of Core Explanatory Variables

Drawing from Hao and Zhang [44], the core explanatory variables are replayed by the ratio of green coverage in built-up areas to ensure the robustness of the empirical outcomes. Compared to the variables of enterprise cost, this indicator is more closely related to environmental protection and less closely related to green technology innovation.

Table 7. Replacement variable regression (Model 1).

GTI	National Level	Eastern	Central	Western
ER2	0.4046 ***	0.3398 **	0.4512 **	0.5122 **
	(2.6634)	(2.3815)	(2.3057)	(2.2344)
FD	−2.3655 ***	−2.5149 ***	−2.4639 ***	−2.0537 ***
	(−6.8804)	(−10.0283)	(−8.8539)	(−4.4691)
PGDP	0.8223 ***	0.4331 ***	1.3850 ***	0.6667 ***
	(8.4074)	(5.6553)	(14.2093)	(5.8960)
FDI	−0.1677 *	−0.0119	−0.2419 ***	−0.5835 ***
	(−1.9399)	(−0.2383)	(−3.3245)	(−3.5804)
EDU	0.7892 ***	0.8980 ***	0.5749 ***	0.8072 ***
	(13.5299)	(18.5765)	(8.9944)	(10.4289)
SR	−0.1993	−0.7431 ***	0.1318	−0.1705
	(−0.9320)	(−3.0484)	(0.5930)	(−0.6143)
INFRA	0.4499 ***	0.6982 ***	0.2618 ***	0.4952 ***
	(5.4922)	(10.0076)	(4.6510)	(6.3970)
Constant	−16.7500 ***	−15.1160 ***	−18.8163 ***	−16.5767 ***
	(−30.7217)	(−29.4511)	(−39.6712)	(−30.7957)
Adjusted R2	0.8152	0.7376	0.8283	0.8340
N	3523	1287	1261	975

Note: T statistics appear in parentheses; * p < −0.1, ** p < 0.05, *** p < 0.01.

and

Table 8. Replacement variable regression (Model 2).

GTI	National Level	Eastern	Central	Western
ER2	0.4152 ***	0.0193	0.2686	1.1853 ***
	(3.8720)	(0.0295)	(1.2650)	(4.1599)
FD	−2.3809 ***	−2.2626 ***	−2.5469 ***	−2.3564 ***
	(−13.2485)	(−4.2880)	(−9.0835)	(−9.2808)
PGDP	0.8148 ***	0.6703 ***	1.3773 ***	0.4249 ***
	(14.9959)	(5.9216)	(14.1437)	(5.5704)
FDI	−0.1672 ***	−0.5681 ***	−0.2343 ***	0.0033
	(−3.8786)	(−3.4619)	(−3.2205)	(0.0661)
EDU	0.7914 ***	0.8021 ***	0.5785 ***	0.9036 ***
	(22.2896)	(10.3278)	(9.0632)	(18.7663)
SR	−0.1968	−0.1704	0.1200	−0.7363 ***
	(−1.3807)	(−0.6141)	(0.5404)	(−3.0339)
INFRA	0.4478 ***	0.4968 ***	0.2509 ***	0.6903 ***
	(11.7246)	(6.4148)	(4.4469)	(9.9341)
ER2 × FD	−1.4774 **	−2.9454	−5.0665 **	−5.1900 ***
	(−2.2853)	(−0.8061)	(−2.1949)	(−3.4229)
Constant	−16.6874 ***	−15.5723 ***	−18.3278 ***	−16.7576 ***
	(−56.8428)	(−11.4723)	(−35.0300)	(−23.9135)
Adjusted R2	0.8003	0.7371	0.8287	0.8355
N	3523	1287	1261	975

Note: T statistics appear in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

show the regression results after the substitution. The regression coefficients of core explanatory variables and the interaction terms are in line with previous ones, confirming the robustness of the benchmark regression.

4.3. Threshold Effects of Fiscal Decentralization

This research further estimates the optimal range of fiscal decentralization for governments at all levels to investigate its regulating effect. Further, it examines whether the regulatory effect of fiscal decentralization on green technology has a threshold effect. In light of Peng et al. and Bi et al. [45,46], the researcher takes fiscal decentralization as the threshold variable and its interaction with environmental regulation as the core variable with a panel threshold model, namely Model 3.

Table 9. Bootstrapping tests of the threshold effects of fiscal decentralization.

		Threshold Value	F-Value	p-Value	10%	5%	1%
Fiscal Decentralization	Single Threshold	0.4248	89.4700	0.0000	43.3785	47.0845	62.0496
	Double Threshold	0.1836	27.8100	0.2100	32.3385	37.2015	44.4993

and

Table 10. Threshold regression results.

	Coefficient	t-Value
ER × FD (FD < 0.4248)	−0.0411 ***	(−7.0579)
ER × FD (FD > 0.4248)	0.0229 ***	(3.3221)
PGDP	0.7316 ***	(13.4032)
FDI	−0.2507 ***	(−5.7920)
EDU	0.8418 ***	(23.6042)
SR	−0.0777	(−0.5399)
INFRA	0.4869 ***	(12.6262)
Constant	−16.9068 ***	(−71.4205)
N	3523	3523

Note: T statistics appear in parentheses; * p < −0.1, ** p < 0.05, *** p < 0.01.

present the test results.

The threshold bootstrap test shows that the single threshold is significant at the level of 1%, which is completely different for the double threshold. This indicates the interaction term has a single threshold, namely 0.4248. When fiscal decentralization is less than 0.4248, there is a negative regression coefficient of the interaction term at the same significance level. Therefore, a negative regulatory correlation between environmental regulations and green innovation is proved under fiscal decentralization. When it is higher than 0.4248, the coefficient of the interaction term appears to be positive, resulting in a positive regulatory effect.

When fiscal decentralization is relatively low, the economic competition between regions becomes more intense. This may bring out the rapid economic development of a region. When there is a higher level of fiscal decentralization, it boosts the positive influence of environmental regulations on green technological innovation by granting local governments control over fiscal aspects. This empowers them to enhance environmental policies; promote balanced development, ecological conservation, and green growth; and attract more resources, thereby positively influencing economic and social progress and affirming H3.

However, both sides have their drawbacks. When fiscal decentralization is low and administrative and financial power are mismatched, local governments cannot ensure that they will invest funds in green innovation based on specific local conditions. Due to enormous financial pressures, local governments are forced to focus their finances on promoting the local economy and ignore environmental protection, which greatly reduces the quality of local environmental regulation enforcement. Similarly, the economic incentives behind high fiscal decentralization, showing as high taxation, may result in corruption and even the conceal of pollution information. This kind of rent-seeking behavior may cause local government agencies to focus more on short-term benefits, decreasing the implementation intensity of environmental protection strategy.

5. Conclusions and Policy Implications

5.1. Conclusions

Under the fiscal decentralization framework, this research employs the panel data of prefecture-level cities in China for 2007–2019 to investigate the impact of environmental regulations on green technologies. The major findings are concluded below. First, the innovation of green technologies can be significantly boosted by environmental regulations. Second, fiscal decentralization is crucial in regulatory environmental regulations and green innovation. National-level fiscal decentralization inhibits green technology innovation. Third, its regulatory effects exhibit regional heterogeneity, which proves the significant negative moderation in the middle and west of China. Fourth, the interaction term of environmental regulation and fiscal decentralization shows a single threshold effect. Fiscal decentralization has a positive regulatory effect at a higher degree, in which environmental regulations can promote green innovation. When that degree is low, environmental regulations may significantly hinder green innovation. The theoretical significance of this study lies in its comprehensive examination of the intricate interplay between environmental regulations, fiscal decentralization, and green technology innovation. By identifying the conditions under which environmental regulations can either foster green innovation, this research contributes to a deeper understanding of the mechanisms driving sustainable technological progress. Furthermore, the findings offer critical insights into the design of environmental policies, suggesting that the effectiveness of such regulations is contingent upon the fiscal structure within which they are implemented and the specific regional contexts.

5.2. Suggestions

First, it is crucial to establish and refine various environmental regulations to effectively boost the innovation of green technologies. In economically advanced regions like the eastern area, local governments should implement policies promoting cleaner production. In contrast, in developing regions, such as the central and western areas, local governments should encourage enterprises to invest more in the research and development of green technology through a subsidy system. This kind of financial support may convey a positive message of government encouragement and attract investment in green technology innovation projects. Adopting such a gradual increase in regional environmental regulation intensity is preferable to an indiscriminate “one-size-fits-all” approach.

Second, decision-makers should clarify the responsibility of governments at all levels in environmental pollution control and optimize the performance evaluation system for local officials. It is essential for governments to guide the local government’s fiscal expenditure preferences and structure by optimizing the well-designed environmental regulatory system. This will augment environmental pollution control measures and enhance the innovation of local green technology. Integrating regional green technology into the development objective function can foster coordinated green development between pollution control and technological innovation. A rigorous scientific policy evaluation system should prioritize social welfare indices, such as ecological protection and the innovation of local green technology. The pollution supervision mechanism should incentivize local governments to prioritize environmental protection and promote economic and social efficiency, which can effectively address deficiencies in China’s financial management system. Further improvements should be made in preparing local natural resource balance sheets by meticulously recording changes in local environmental assets and liabilities and establishing an auditing system for leading cadres leaving the office.

Third, our findings highlight the imperative of calibrating fiscal decentralization within an optimal range to ensure that administrative authority is effectively matched with financial responsibilities, thereby amplifying local governments’ fiscal autonomy. This optimal range, identified through our analysis, suggests that when fiscal decentralization measures above a threshold level of 0.4248, it fosters a conducive environment for green technological innovation by enhancing the positive impacts of environmental regulations. Adjusting fiscal decentralization to an optimal level is crucial to align administrative power with financial responsibilities, enhancing local governments’ financial autonomy. This adjustment allows local governments to possess an informational advantage in their regions, enabling the continuous adjustment and improvement of their environmental protection policies. Strengthening the coordination between fiscal and environmental policies will unlock the full potential of fiscal decentralization, which can maximize their benefits, with targeted environmental regulations and financial incentives complementing each other based on regional and industrial specificities.

Fourth, utilizing big data technology for supervision is imperative, as well as actively developing comprehensive demonstration zones for green technology innovation. This approach allows for the instant sharing of information between central government and local authorities, enhancing monitoring and creating an effective supervision system. By consolidating local environmental data in a centralized database, the central government can make more informed decisions and supervise more effectively. This will also decrease the possibility that private information will collude with polluting enterprises. Areas with a strong industrial developmental foundation that adopt green manufacturing early should be selected as comprehensive national-level demonstration zones for green technology innovation. These zones should comprise a green manufacturing system consisting of green factories, parks, supply chains, and design products. Upon completion, the guiding, leading, agglomeration, and radiation of such green-technology-innovation demonstration areas should be fully leveraged through publicity to promote their advanced experience.