The Nonlinear Impact of Economic Growth Pressure on Urban Land Green Utilization Efficiency—Empirical Research from China

Abstract

China’s unique economic growth target system exerts significant economic growth pressure (EGP) on local officials, leading to notable economic and environmental consequences for urban land use. Consequently, this system is theoretically expected to have a significant impact on urban land green utilization efficiency (ULGUE). This study investigates the invisible institutional factors that shape ULGUE within China’s distinct economic growth target system. The results indicate an inverted U-shaped relationship between EGP and ULGUE, and this nonlinear relationship is statistically significant in central, western, and northeastern cities but not in eastern cities. EGP influences ULGUE in a non-monotonic manner by affecting land marketization, green technology innovation, and industrial structure upgrading. Furthermore, environmental regulation and financial technology investment moderate the relationship between EGP and ULGUE. Heterogeneity analysis reveals that the inverted U-shaped relationship is more pronounced in resource-dependent cities and cities with stringent target constraints. This study contributes empirical evidence on the interaction between EGP and ULGUE while offering both theoretical insights and practical implications.

1. Introduction

Balancing economic growth and environmental conservation remains a significant global challenge in achieving sustainable urban development [1,2,3]. This is particularly urgent in China, which is experiencing rapid urbanization as a developing country [4]. According to National Bureau of Statistics data, the urbanization rate in China surged from 17.92% in 1978 to 64.72% in 2021 [5], far surpassing the global average. However, China’s economic growth remains heavily dependent on natural resource inputs, particularly urban land, resulting in significant inefficiencies in land utilization [6,7,8]. Given the constraints of limited arable land, promoting green and efficient land use has become essential for ensuring sustainable development [9].

Urban land green use efficiency (ULGUE) signifies the capacity to foster integrated development among urban society, the economy, and the environment [10,11]. In the early stages, numerous empirical studies focused on evaluating land use efficiency [12,13], developing measurement methods [14,15], and analyzing regional differences or dynamic evolution [11,16]. Additionally, research has examined the impact of ULGUE on economic and social factors, such as industrial structure [17], carbon emissions [18], and ecosystems [19,20]. More recently, scholars have increasingly focused on the key factors influencing the development of urban agglomerations [21,22]. These factors can be grouped into four primary dimensions: First, studies examine the effects of economic growth and population dynamics on ULGUE [23,24,25]. Second, research explores how policy and administrative factors impact ULGUE, including pilot policies [26,27], smart cities [28], the digital economy [29], land policies [30,31], and administrative levels [32]. Third, attention is given to how industrial structure and urban organization shape ULGUE [33,34,35]. Finally, researchers investigate the impact of technological advancements and strategic planning on ULGUE [36,37,38]. The above studies have empirically analyzed the key factors influencing ULGUE and have highlighted that enhancing ULGUE relies on advancements in economic development [39,40], the implementation of appropriate governance policies policy [12,41,42], and technological upgrading [25,35]. However, the influence of invisible institutional factors on ULGUE should not be overlooked. Given resource constraints, overcoming institutional barriers is a crucial prerequisite for achieving significant improvements in ULGUE. Despite this, the influence of China’s distinctive economic growth target system on ULGUE has not yet been thoroughly investigated.

The target responsibility system is a pivotal policy model in China, correlating economic growth targets with regional EGP [43,44]. Under EGP, governments prioritize strategies that yield immediate economic benefits [45], thereby affecting industrial structure, innovation, and investment [46]. Studies in this field can be categorized into two main strands: one examines the factors behind setting growth targets and their optimization [43,47], while the other explores the targets’ impact on social and economic development from both micro [48,49] and macro levels [50,51,52,53]. These studies provide insights into local governments’ resource allocation and its effects under EGP, although research on ULGUE is still limited.

Prior studies have predominantly examined ULGUE through the lenses of economic development and policy evaluation, highlighting the socioeconomic impacts of EGP in China. Nevertheless, a paucity of research has approached the ULGUE issue from China’s distinctive EGP perspective, particularly from an institutional standpoint. This study seeks to fill this research gap by offering new empirical insights into the relationship between EGP and ULGUE. To this end, we employed panel data spanning 271 cities between 2006 and 2021, leveraging the super-efficient SBM model to measure ULGUE and investigate the impact of EGP. We explore the mechanisms through which EGP affects ULGUE and examine how environmental regulation and financial technology input moderate the relationship between EGP and ULGUE. Furthermore, we investigate the heterogeneity of this relationship. The possible contributions of this research are outlined below:

• Unlike the literature that has focused on how economic development and policy factors affect ULGUE [33,54,55], this paper examines the institutional factors that impede ULGUE, which reveals the impact of local government political and economic behavior on urban land use [56].
• Existing studies have largely explored linear connections between EGP and economic, innovation, and environmental outcomes [45,53,57,58]. By contrast, our findings demonstrate an inverted U-shaped relationship between EGP and ULGUE, highlighting the dynamic nature of their linkage.
• This research delves into the intrinsic mechanisms and potential heterogeneity of EGP on ULGUE. It analyzes the mediating roles of land marketization, green technology innovation, and industrial upgrading alongside the moderating effects of environmental regulation and financial technology investment. Additionally, we compare the roles of different mediating variables and examine regional differences in the moderating effects of the moderating variables. This analysis enhances the theoretical understanding of how EGP influences ULGUE.

The remainder of this paper is structured as follows. Section 2 reviews the relevant literature and outlines the theoretical assumptions that form the foundation of our framework. Section 3 details the empirical model, including the key variables, their measurement methods, and data sources. Section 4 presents the empirical results, beginning with the benchmark regression analysis followed by a series of robustness checks. Section 5 examines the mechanisms through which EGP influences ULGUE and explores the heterogeneity of this relationship. Finally, Section 6 summarizes the theoretical insights and offers policy recommendations. The research framework is illustrated in Figure 1.

2. Theoretical Analysis and Research Hypotheses

2.1. EGP and Green Land Use

Under China’s “promotion tournament” and “yardstick competition” mechanisms [59,60], which are primarily driven by GDP targets, local officials frequently establish economic growth targets that deviate from their regions’ actual development conditions [51], as shown in Figure 2. This strategy aims to improve their political performance and secure advancement in the bureaucratic hierarchy. Scholars have argued that EGP affects local government behavior in two key ways: First, target setting imposes an internal constraint, referred to as “self-imposed pressure” [47,61], which motivates local governments to boost economic growth through increased investment. Second, target setting also creates an external constraint known as “top–down amplification” [43,60,62], which influences political incentives tied to officials’ performance evaluations and promotes competitive scaling among local governments. In theory, the extent of EGP influences both the orientation and intensity of local economic policies [57,63], as well as the expectations and perceptions of microeconomic agents regarding economic trends and policy direction [48,49].

Theoretically, EGP significantly influences urban land use efficiency. However, it pressures environmental protection [64,65,66]. Driven by EGP, local governments have adopted an investment-driven urbanization model, which often results in aggressive urbanization [67,68,69]. However, local officials, motivated by promotional incentives under the target responsibility system, may prioritize risky policies with negative consequences for public goods consumption and urban land sustainability [70,71]. Numerous quantitative studies have highlighted the detrimental effects of EGP on the urban environment under benchmark competition [45,50,53]. This paper examined the underlying mechanisms by which EGP influences ULGUE. To this end, we have delineated three distinct mechanisms: the direct effect mechanism, the mediation mechanism, and the moderation mechanism. In addition, we have formulated specific research hypotheses to guide our subsequent investigations (see Figure 3).

2.2. Main Effect Mechanism

EGP is closely tied to economic growth targets, driving land development and utilization activities aimed at boosting the GDP. In this process, local governments influence land redevelopment and utilization through scale effects, technological innovation, and resource allocation, all of which have a direct impact on ULGUE. Therefore, analyzing the impact of EGP on ULGUE requires a focused examination of these three dimensions.

First, the scale effect. Economies and diseconomies of scale affect the influence of EGP on ULGUE. EGP drives local authorities to foster economic growth and prioritize urban infrastructure, leading to economies of scale that improve land use efficiency [72,73,74]. However, increased EGP can also lead to reliance on land finance, concentrating land resources in inefficient firms and creating diseconomies of scale, which harms land use efficiency [75,76]. Second, the technological effect. EGP incentivizes local governments to market land to increase tax revenue, leading to increased R&D investment and accelerating local innovation resource agglomeration [77,78,79,80]. Nevertheless, focusing on short-term, quick-return investments can crowd out long-term innovation, lead to pollution and resource waste, and thus inhibit ULGUE [81,82,83]. Third, the resource allocation effect. EGP prompts local governments to employ a competitive bidding system to attract investors [84,85,86,87]. Although it will stimulate the transferee to increase marginal output, thereby improving and promoting ULGUE, excessive EGP leads to over-reliance on industrial investment, causing idle, wasted, or inefficient land use [88,89]. This situation will lead to the concentration of polluting industries, thereby reducing land use efficiency [90,91]. Overall, EGP can improve ULGUE, but beyond a critical threshold, it degrades ULGUE.

Hypothesis 1.

EGP and ULGUE present an inverted U-shaped relationship.

2.3. Mediation Mechanism

Several studies have identified land marketization, green innovation, and industrial upgrading as key mechanisms affecting ULGUE [92,93,94]. First, land marketization. The pursuit of economic growth has intensified fiscal pressure [95,96], prompting governments to prioritize urban land marketization through land transfers. Concurrently, the transfer of land has been identified as a pivotal factor in enhancing urban land utilization efficiency [92,97,98]. Second, green technology innovation. The government, recognizing the dynamic interplay between economic incentives and pressures, has been known to take a comprehensive approach, giving due consideration to innovation input and technological progress returns [99]. This has been shown to drive green technology innovation significantly [46], which is widely acknowledged as a pivotal mechanism enhancing ULGUE [100,101,102]. Third, industrial upgrading. Land use efficiency is closely linked to industrial land use in urban development [103]. In pursuit of the GDP target, the government has a propensity to allocate production factors, including land resources, to higher-income manufacturing and service industries, thereby promoting industrial structure advancement [91,104]. Industrial structure optimization is crucial in transforming urban land use patterns [33,35]. In summary, EGP influences ULGUE through land marketization, technological innovation, and industrial upgrading.

Hypothesis 2a.

EGP influences ULGUE through the channels of land marketization.

Hypothesis 2b.

EGP influences ULGUE through the channels of green technology innovation.

Hypothesis 2c.

EGP influences ULGUE through the channels of industrial upgrading.

2.4. Moderation Mechanism

Environmental regulation functions as a restraining force to protect nature and ensure green development. This includes government intervention in environmental issues, such as legislation, pollution permits, and impact assessments [105,106]. Environmental regulations affect ULGUE in two ways: First, reducing environmental damage through restrictions and assessments [107,108,109]. Second, impacting land development, potentially harming agriculture and ecosystems [110,111], while increasing compliance costs and hindering innovation [112]. Environmental regulation also moderates the nonlinear relationship between EGP and ULGUE. In periods of low economic pressure, local governments may enhance ULGUE through land transfers and urban planning [113]. Command-type environmental regulation can also trigger the Porter effect, compelling firms to invest in technological innovation to optimize resource allocation [63,114], thereby strengthening the positive impact of EGP on ULGUE. However, as economic pressure intensifies, land transfers may lead to inefficient land use, ultimately reducing ULGUE. In such circumstances, command-and-control environmental regulations may divert innovation resources and increase implementation costs, potentially leading to regulatory capture [52,115,116], thereby exacerbating the negative impact of EGP on ULGUE.

Hypothesis 3a.

Environmental regulation can regulate the impact of EGP on ULGUE.

In the context of rapid urbanization, governments can leverage incentive policies to address market failures and optimize resource allocation [117,118]. Support for science and innovation can modify input–output relationships through tax incentives, influencing land use patterns and technological changes [119] and affecting urban land use efficiency (ULGUE). Innovation support impacts ULGUE in two key ways: First, by improving the “risk–return” trade-off through fiscal and interest subsidies, which promote land use transformation [120,121], reduce production costs, minimize resource waste, and lower emissions [122], thus enhancing land use efficiency. However, this support can also induce a “rebound effect”, whereby reduced production costs lead to increased demand, higher energy consumption, and carbon emissions while potentially diminishing further innovation investments and harming resource allocation [123,124]. Second, by moderating the nonlinear relationship between EGP and ULGUE. Initially, economic growth stimulates ULGUE, but technological innovation subsidies may eventually “crowd out” further innovation [125], diminishing the impact of growth pressures on ULGUE. As competition intensifies, local governments may distort resource allocation and hinder ULGUE [57,126]. However, innovation support can mitigate these effects by fostering the integration of innovative factors, prioritizing high-tech industries, and promoting increased innovation investment and environmental protection.

Hypothesis 3b.

Financial technology input can regulate the impact of EGP on ULGUE.

3. Model Setting, Variables, and Data Sources

3.1. Model Setting

3.1.1. Baseline Model

This study aimed to examine the effects of EGP on ULGUE by establishing the following baseline model. In Formula (1), β₁ and β₂ represent the linear and nonlinear impact of EGP on ULGUE, respectively, while con denotes the control variables. The subscripts i and t indicate city and time, respectively. Additionally, γ_i captures the city-fixed effect, λ_t accounts for the time-fixed effect, and ε_it represents the random error term.

$$\mathrm{U}\mathrm{L}\mathrm{G}\mathrm{U}{\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\alpha }+{\mathrm{\beta }}_1\mathrm{E}\mathrm{G}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_2\mathrm{E}\mathrm{G}{{\mathrm{P}}^2}_{\mathrm{i}\mathrm{t}}+\sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\varphi }}_{\mathrm{k}}{\mathrm{c}\mathrm{o}\mathrm{n}}_{\mathrm{k},\mathrm{i}\mathrm{t}}+{\mathrm{\gamma }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

3.1.2. Mediating Effect Model

Based on the theoretical analysis above, we investigated the pathways through which EGP influences ULGUE, focusing on land marketization, green technology innovation, and industrial structure upgrading, referring to Gelbach’s mechanism setting method [127]. In Formula (3), MED_it denotes the land marketization, green technology innovation, and industrial structure upgrading of city_i in year_t; con denotes a set of control variables affecting the mediating variables; and β₁ is our parameter of interest, which measures the impact of EGP on mediating variables. All variables maintain consistent operational definitions with those specified in Formula (1).

$${\mathrm{M}\mathrm{E}\mathrm{D}}_{\mathrm{i}\mathrm{t}}=\mathrm{\alpha }+{\mathrm{\beta }}_1{\mathrm{E}\mathrm{G}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_2\mathrm{E}\mathrm{G}{{\mathrm{P}}^2}_{\mathrm{i}\mathrm{t}}+\sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\varphi }}_{\mathrm{k}}{\mathrm{c}\mathrm{o}\mathrm{n}}_{\mathrm{k},\mathrm{i}\mathrm{t}}+{\mathrm{\gamma }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{it}}$$

(2)

$${\mathrm{U}\mathrm{L}\mathrm{G}\mathrm{U}\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\alpha }+{\mathrm{\beta }}_1{\mathrm{E}\mathrm{G}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_2\mathrm{E}\mathrm{G}{{\mathrm{P}}^2}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_3\mathrm{M}\mathrm{E}{\mathrm{D}}_{\mathrm{it}}+\sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\varphi }}_{\mathrm{k}}{\mathrm{c}\mathrm{o}\mathrm{n}}_{\mathrm{k},\mathrm{i}\mathrm{t}}+{\mathrm{\gamma }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

3.1.3. Moderating Effect Model

As mentioned above, environmental regulation and financial technology input can both adjust the inverted U-shaped impact of EGP on ULGUE. The model is structured as shown in Formula (4), where MOD_it−1 denotes environmental regulation and financial technology input of city_i in year_t. All variables maintain consistent operational definitions with those specified in Formula (1).

$$\begin{array}{l}{\mathrm{U}\mathrm{L}\mathrm{G}\mathrm{U}\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\alpha }+{\mathrm{\beta }}_1\mathrm{E}\mathrm{G}\mathrm{P}\mathrm{i}\mathrm{t}+{\mathrm{\beta }}_2\mathrm{E}\mathrm{G}{{\mathrm{P}}^2}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_3({\mathrm{M}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{t}-1}\times {\mathrm{E}\mathrm{G}\mathrm{P}}_{\mathrm{i}\mathrm{t}})\\ + {\mathrm{\beta }}_4({\mathrm{M}\mathrm{O}\mathrm{D}}_{\mathrm{i}\mathrm{t}-1}\times \mathrm{E}\mathrm{G}{{\mathrm{P}}^2}_{\mathrm{i}\mathrm{t}})+\mathrm{M}\mathrm{O}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-1}+{\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{n}}}{\mathrm{\varphi }}_{\mathrm{k}}{\mathrm{c}\mathrm{o}\mathrm{n}}_{\mathrm{k},\mathrm{i}\mathrm{t}}+{\mathrm{\gamma }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}\end{array}$$

(4)

3.2. Variables

3.2.1. Dependent Variable

Drawing on existing research [29,76], we adopted the super-efficiency SBM model that includes unexpected outputs to measure the value of ULGUE, that is, we constructed an indicator system with three dimensions: input, expected output, and unexpected output in indicator selection. Specifically, (1) following the approach of Zhao et al. (2018) [128], the input dimension includes land, capital, and urban labor. Land input is represented by the constructed urban space, labor input by the workforce in the secondary and tertiary sectors, and capital input by the stock of fixed capital, which is calculated using the perpetual inventory method, with 2006 as the base year [129]. (2) Following Xie et al. (2021) [19], the expected output dimension encompasses three indicators: the secondary and tertiary sector value creation, the urban resident income capacity, and the urban green space ratio. The secondary and tertiary sector value creation and the urban resident income capacity are adjusted to constant 2006 prices. (3) Regarding undesirable outputs, drawing on the work of Zhou et al. (2024) [29], industrial effluent discharge, sulfur dioxide (SO₂) emissions, and industrial smoke emissions were selected as indicators of non-desirable outputs, based on pollution levels. The specific definitions of these indicators are provided in

Table 1. The input–output index system of ULGUE.

Layer of Criteria	Factors	Indicators	Unit	References
Input indicators	Land	Constructed urban space	square kilometer	Xue et al. (2022) [40] Zhao et al. (2018) [128] Ding et al. (2022) [130]
	Capital	Fixed capital stock (based on 2006, calculated using the perpetual inventory method)	100 million Yuan
	Labor	Workforce in the secondary and tertiary sectors	10 thousand persons
Expected outputs	Economic gains	Secondary and tertiary sector value creation	100 million Yuan	Xie et al. (2021) [19] Gu et al. (2023) [131]
	Social gains	Urban resident income capacity	Yuan
	Environmental gains	Urban green space ratio	square meter per person
Non-expected outputs	Negative impact on the environment	Industrial SO2 emissions Industrial wastewater discharge Industrial SO2 emissions	10 thousand tons	Zhao et al. (2018) [128] Zhou et al. (2024) [29] Ma et al. (2024) [41]

.

3.2.2. Key Explanatory Variable

Referring to relevant research [94], EGP is quantified as the ratio of the GDP growth target outlined in each prefecture-level city’s annual government work report to the actual GDP growth rate of the preceding year. A higher ratio typically signifies stronger EGP. Since the GDP growth target in the annual government work report is not clearly defined by specific numbers, this study used precise figures to represent the target. It added modifiers such as “approximately”, “around”, “higher than”, “above”, “lowest”, and “not less than”. For modifiers such as “approximately”, “lowest”, and “not less than”, specific numbers were used; for ranges, the average value was employed.

3.2.3. Mediating Variables

(1) Land marketization (lnLM). To evaluate the extent of marketization in land use, this study adopted the proportion of land area transacted through tender, auction, and listing relative to the total transferred land area as a key indicator [92]; (2) Green innovation level (lnGI). The level of green innovation was quantified by the number of green invention patents granted per 10,000 people [132,133]; (3) Industrial structure upgrading (Ind). The degree of industrial structure upgrading is represented by the share of the secondary industry’s value-added contribution to GDP [134].

3.2.4. Moderating Variables

(1) Environmental regulation (ER). Drawing on prior studies [135,136] and considering city-level data availability, we constructed a comprehensive metric to assess ER. Specifically, three key indicators were selected: the centralized treatment rate of sewage facilities, the harmless disposal rate of household waste, and the comprehensive utilization rate of municipal solid waste. These variables were integrated using the entropy method, yielding a single measure of ER intensity. (2) Financial technology input (FTI). Referring to the existing literature [137], we operationalized technological investment intensity as the proportion of scientific research expenditure within total fiscal budgets. It is posited that escalating governmental support in these areas is instrumental in bolstering regional innovation capabilities.

3.2.5. Control Variables

Building upon existing empirical studies concerning determinants of ULGUE, we selected six control variables: (1) Economic development level (PGDP). Urban land use efficiency is closely linked to economic growth, which can enhance output per unit of land area under stable conditions [138]. Following established methodologies [21], we used the logarithm of per capita GDP to indicate a city’s affluence. (2) Infrastructure level (INF). Infrastructure significantly shapes urban structure and land use. The level of infrastructure development was quantified through the logarithmic transformation of the proportion between urban road network length and the area of developed urban land [139]. (3) Population density (DEN). Urban land expansion is strongly correlated with population urbanization [140]. This variable was operationalized as the logarithmic transformation of the resident population per spatial unit within urban boundaries [141]. (4) Government intervention intensity (GOV). In China, the government heavily funds public infrastructure projects, which are crucial in shaping urban expansion and development [92]. The extent of governmental involvement was measured by the proportion of urban fiscal spending relative to GDP [142]. (5) Opening degree (opd). Foreign investment significantly influences urban development [143]. We measured openness using the scale of foreign direct investment [92]. (6) Urbanization rate (UR). Urbanization has driven an unparalleled expansion of urban boundaries, significantly influencing the efficiency of urban land use [144]. Drawing on the relevant literature [145,146], we incorporated the urbanization rate as a control variable in our analysis. To reduce the impact of extreme values, all continuous variables were adjusted using winsorization at the 1st and 99th percentiles.

3.3. Data Sources

Following previous studies [53,57], we systematically compiled policy-related data from authoritative government documents and official portals to establish a comprehensive panel dataset on EGP. Following established research practices [21,142], urban centers exhibiting substantial data deficiencies were excluded from the analysis to ensure data reliability. The research encompasses a sixteen-year timeframe (2006–2021), incorporating panel data from 271 prefecture-level municipalities across China. The data sources primarily include the China Land and Resources Statistical Yearbook, China City Statistical Yearbook, China Energy Statistical Yearbook, the China Research Data Services Platform, and official documents or websites such as the EPS database. We supplemented a small amount of missing data to address observations that were missing for specific years through interpolation. The descriptive statistics are provided in

Table 2. Summary statistics of variables.

Variable		Symbol	Obs	Mean	Std. Dev.	Min	Max
Dependent variable	Urban land green utilization efficiency	ULGUE	4336	0.713	0.110	0.513	1.028
Explanatory variable	Economic growth pressure	EGP	4336	0.452	0.151	0.000	1.000
		EGP2	4336	0.262	0.148	0.000	1.000
Mediating variables	Land marketization	lnLM	4336	6.303	0.985	3.378	8.301
	Green innovation	lnGI	4336	4.148	1.645	1.386	7.252
	Industrial structure upgrading	Ind	4336	45.404	10.151	26.180	59.430
Moderating variables	Environmental regulation	ER	4336	0.008	0.001	0.006	0.010
	Financial Technology Input	FTI	4336	0.014	0.010	0.003	0.034
Control variables	Opening degree	lnopd	4336	5.450	1.660	2.086	8.203
	Urbanization rate	UR	4336	52.348	15.029	19.770	89.600
	Economic Development level	PGDP	4336	10.537	0.670	8.849	11.954
	Population density	DEN	4336	5.838	0.847	2.916	7.200
	Government intervention intensity	GOV	4336	10.708	0.512	9.521	11.636
	Infrastructure level	INF	4336	23.582	14.240	4.976	57.557

.

Figure 4 illustrates China’s EPG and ULGUE for the years 2006, 2011, 2016, and 2021. Each data point on the map represents a specific city’s EGP, with its geographical coordinates determined through the A Map API geocoding system based on the location of municipal government offices. The color-coded regions illustrate the varying levels of ULGUE across different cities, providing a visual representation of spatial patterns and temporal changes in land use performance.

4. Results

4.1. Benchmark Regression

Before conducting regression analysis, we used Stata’s Collin command to examine the correlation matrix for multicollinearity and calculated the VIF for each variable. The results showed that all VIF values were below 5, indicating no multicollinearity. Columns 1–2 of

Table 3. Benchmark regression.

	(1)	(2)	(3)	(4)	(5)	(6)
	ULGUE	ULGUE	Eastern Region	Central Region	Western Region	Northeastern Region
EGP	0.072 ***	0.067 ***	0.040	0.079 **	0.193 ***	0.101 *
	(3.720)	(3.471)	(1.158)	(2.265)	(3.246)	(1.686)
EGP2	−0.060 ***	−0.057 ***	−0.021	−0.078 **	−0.172 *	−0.103 **
	(−3.183)	(−3.032)	(−0.538)	(−2.283)	(−3.165)	(−2.044)
lnopd		0.002 **	0.005 **	0.002	0.004 ***	−0.002
		(2.201)	(2.164)	(0.937)	(2.973)	(−0.681)
UR		−0.000 ***	−0.001 ***	−0.000	−0.000	−0.001 *
		(−3.411)	(−3.785)	(−1.225)	(−0.517)	(−1.845)
PGDP		−0.008 *	−0.017 **	0.001	−0.006	0.031
		(−1.649)	(−2.236)	(0.171)	(−0.379)	(1.618)
DEN		0.001	−0.028	−0.001	0.002	0.085
		(0.106)	(−1.050)	(−0.090)	(0.498)	(1.079)
GOV		−0.004	−0.023	−0.012	0.058 ***	−0.070 *
		(−0.483)	(−1.492)	(−1.034)	(3.138)	(−1.908)
INF		0.000	0.012 **	0.173	−0.000	0.001
		(0.017)	(2.164)	(1.306)	(−0.856)	(0.335)
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
N	4336	4336	1230	1185	1185	480
R2	0.915	0.915	0.924	0.902	0.911	0.949
F	11.433	3.443	5.882	1.364	4.107	2.371

Note: *** p < 0.01, ** p < 0.05, * p < 0.10.

show the regression results, indicating that EGP exhibits a significant positive coefficient on ULGUE (β = 0.067, p < 0.01), while EGP² has a negative association with ULGUE (β = −0.057, p < 0.01). These findings suggest an inverted U-shaped relationship between EGP and ULGUE, confirming Hypothesis H1.

To gain deeper insights into regional disparities, we categorized the sample cities into four major regions: eastern, central, western, and northeastern. We then applied grouped regression analysis to examine how the impact of EGP on ULGUE varied across these regions. The specific results are presented in columns (3) to (6) of Table 2. The inverted U-shaped relationship between EGP and ULGUE is primarily observed in the central, western, and northeastern regions, where economic development remains relatively underdeveloped. By contrast, no significant relationship is found between EGP and ULGUE in the more economically advanced eastern region. This phenomenon can be attributed to two key factors. First, differences in economic foundations. Compared to the eastern region, which has a higher level of economic development, the central, western, and northeastern regions experience slower economic growth, weaker fiscal capacity, and underdeveloped infrastructure [147,148]. These economically weaker regions bear relatively heavy EGP, often engage in short-term economic activities, and are more vulnerable to fluctuations in ULGUE. Second, variations in development models. The eastern region’s growth is largely driven by technological innovation and industrial upgrading, following an intensive development approach that emphasizes efficiency improvements, and resource optimization [149]. By contrast, the central, western, and northeastern regions rely heavily on natural resource extraction, resource-intensive development, and economic expansion through scale rather than efficiency [150]. The northeastern region, in particular, remains dominated by traditional industries and has historically prioritized GDP growth over high-quality development. The significant relationship between EGP and ULGUE becomes more apparent within this context. These findings align with those of Zhang et al. [94].

Furthermore, following the approach of Lind et al. (2010) [151], we used Stata’s Utest command to verify the inverted U-shaped relationship. The results indicated that the extreme point is 0.589, which falls within the observed range of EGP values, confirming the nonlinear pattern. At the same time, the slope of the low EGP (EGP = 0) curve is 0.067 (β = 0.067, p < 0.01), and the slope of the high EGP (EGP = 1) curve is −0.047 (β = −0.047, p < 0.01), which further proves that the inverted U-shaped curve relationship is established. To visually illustrate the curve and its extreme point, a graphical representation is provided in Figure 5. When the EGP level is low (e.g., below the threshold of 0.589), the positive impact of the first-order term prevails, meaning that each 1-unit increase in EGP significantly enhances ULGUE. However, as EGP increases beyond 0.589, the negative influence of the second-order term intensifies, leading to a gradual reduction or even reversal in the marginal contribution of EGP. The threshold of 0.589 represents the point at which ULGUE reaches its theoretical optimal value.

4.2. Analysis of Mediating Effect

Building upon the established analytical framework, we enhanced Model 1 by incorporating mediating variables as additional controls to verify the robustness of our baseline findings. Column (1) of

Table 4. Impact of mediating variables on the nexus of EGP and ULGUE.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	ULGUE	lnLM	ULGUE	lnGI	ULGUE	Ind	ULGUE
EGP	0.054 ***	0.885 **	0.053 ***	0.785 ***	0.052 ***	5.356 **	0.053 ***
	(3.268)	(2.480)	(3.193)	(2.654)	(3.151)	(2.199)	(3.182)
EGP2	−0.050 ***	−0.706 **	−0.049 ***	−1.003 ***	−0.047 ***	−5.140 **	−0.049 ***
	(−3.079)	(−2.176)	(−2.997)	(−3.373)	(−2.926)	(−2.186)	(−2.995)
lnopd	0.002 ***	0.020	0.002 ***	0.010	0.002 ***	0.045	0.002 ***
	(3.411)	(1.609)	(3.357)	(0.967)	(3.382)	(0.301)	(3.408)
UR	−0.000 ***	0.005	−0.000 ***	0.005 ***	−0.000 ***	−0.004	−0.000 ***
	(−4.036)	(1.448)	(−4.009)	(2.733)	(−4.170)	(−0.229)	(−4.017)
PGDP	−0.008 **	0.350 ***	−0.009 **	0.531 ***	−0.010 **	13.005 ***	−0.012 ***
	(−2.179)	(5.098)	(−2.400)	(9.772)	(−2.501)	(15.764)	(−3.067)
DEN	−0.008 *	0.001	−0.008 *	0.029	−0.008 *	0.577	−0.008 *
	(−1.742)	(0.009)	(−1.705)	(0.401)	(−1.768)	(0.969)	(−1.778)
GOV	0.000	0.841 ***	−0.002	0.208 *	−0.001	6.476 ***	−0.002
	(0.002)	(4.924)	(−0.274)	(1.896)	(−0.080)	(6.249)	(−0.259)
INF	0.000	0.001 ***	0.000	−0.000	0.000	0.008 ***	0.000
	(1.227)	(2.986)	(1.021)	(−0.255)	(1.224)	(3.731)	(1.147)
lnLM			0.071 **
			(2.116)
lnGI					0.005 **
					(2.017)
Ind							0.002 **
							(2.357)
_cons	0.831 ***	−6.907 ***	0.846 ***	−4.164 ***	0.842 ***	−16.837 **	0.876 ***
	(10.510)	(−3.485)	(10.633)	(−3.276)	(10.602)	(−14.271)	(10.924)
R2	0.945	0.796	0.945	0.964	0.945	0.947	0.945
F	6.125	10.411	5.829	17.501	5.902	63.264	6.179
Inflection point	0.5844		0.5738		0.5931		0.5867

Note: *** p < 0.01, ** p < 0.05, * p < 0.10.

displays the baseline empirical results, while column (2) presents the results with lnLM included as a control variable. The findings show that lnLM exhibits a substantial positive impact, indicating that it substantially improves the efficiency of ULGUE. In this context, the coefficient for EGP is significantly positive, while the coefficient for EGP² reveals a substantial negative association. The inflection point for EGP is 0.584, as shown in Table 4. Thus, even after including lnLM, the relationship between EGP and ULGUE remains unchanged.

Column (3) is the result of the regression analysis with lnLM as the dependent variable and EGP as the independent variable. The coefficient of EGP is positive and significant, while the coefficient of EGP² is negative and significant, indicating an inverted U-shaped relationship between EGP and land marketization. This pattern suggests that moderate EGP stimulates land marketization [152], while excessive EGP leads to intergovernmental competition that may hinder land marketization. Thus, EGP’s effect on land marketization is a transmission mechanism through which EGP ultimately influences ULGUE, supporting Hypothesis 2a.

Columns (4) and (6) show the regression outcomes with lnGI and Ind as extra control variables in Model 1. The positive and significant coefficients for both lnGI and Ind indicate that these factors also enhance ULGUE. The coefficient for EGP stays significantly positive, while the coefficient for EGP² continues to be significantly negative. The inflection points for EGP, as presented in columns (5) and (7), are 0.563 and 0.587, respectively. These findings suggest that adding lnGI and Ind causes a slight rightward shift in the inflection point, implying that these variables contribute to extending the favorable impact of EGP on ULGUE.

Columns (5) and (7) further display the regression results with lnGI and Ind as dependent variables and EGP as the independent variable. The significant positive coefficients for EGP and the negative coefficients for EGP² demonstrate an inverted U-shaped relationship between EGP and both lnGI and Ind. These findings align with the previous literature and support the argument that EGP’s impact on lnGI and Ind functions as additional transmission mechanisms through which EGP ultimately affects ULGUE, thereby validating Hypotheses 2b and 2c.

Assessing the magnitude of the mediation effect provides a deeper understanding of the relative contributions of land marketization, green technology innovation, and industrial structure upgrading to ULGUE. To evaluate the mediation effect sizes and examine the impact of each mechanism on ULGUE, we employed the Bootstrap method. The results, presented in

Table 5. Bootstrap test results.

	lnLM	lnGI	Ind
Cl Lower	0.003	0.001	−0.007
Cl Upper	0.094	0.040	0.046
Point Estimate	0.052	0.012	0.001

, show that the point estimate of lnLM, based on the average coefficient of the mediating variable from 1000 bootstrap iterations, is the largest, indicating that the mediation effect is the strongest. This finding can be attributed to China’s land-based fiscal system, which has been a key institutional factor influencing urban land use over recent decades and has played a pivotal role in driving the country’s economic growth [153,154].

4.3. Analysis of Moderating Effects

The model was extended by including interaction terms between EGP and ER, as well as EGP and FTI. The regression results in column 1 of

Table 6. The impact of ER on the relationship between EGP and ULGUE.

	(1)	(2)	(3)	(4)	(5)
	ER	Eastern Region	Central Region	Western Region	Northeastern Region
EGP	0.100 ***	0.109 ***	0.119 ***	0.300 ***	0.027
	(4.803)	(2.778)	(2.892)	(4.308)	(0.479)
EGP2	−0.090 ***	−0.100 **	−0.121 ***	−0.274 ***	−0.022
	(−4.475)	(−2.397)	(−3.062)	(−4.295)	(−0.456)
ER	−3.353 **	−7.854 **	−6.056 *	−9.268 ***	−5.210
	(−2.386)	(−2.075)	(−1.842)	(−2.637)	(−0.998)
ER×EGP	22.235 ***	45.549 **	27.406 *	46.281 ***	16.156
	(3.119)	(2.554)	(1.952)	(3.068)	(0.796)
ER×EGP2	−20.838 ***	−41.530 **	−26.148 *	−42.892 ***	−13.737
	(−3.011)	(−2.217)	(−1.929)	(−2.928)	(−0.705)
Fixed effects	YES	YES	YES	YES	YES
Control variables	YES	YES	YES	YES	YES
_cons	0.841 ***	1.417 ***	0.833 ***	0.106	0.498
	(7.691)	(5.954)	(5.582)	(0.367)	(0.794)
R2	0.915	0.924	0.901	0.910	0.960
F	4.374	5.408	1.439	3.738	3.627

Note: *** p < 0.01, ** p < 0.05, * p < 0.10.

indicate that the interaction term between EGP and ER exerts a significantly positive influence on ULGUE (β = 22.235, p < 0.01). By contrast, the interaction of EGP² with ER exhibits a significant negative impact (β = −20.838, p < 0.01). These findings indicate that ER exerts a significant moderating influence. Similarly, the regression results presented in column 1 of

Table 7. The impact of FTI on the relationship between EGP and ULGUE.

	(1)	(2)	(3)	(4)	(5)
	FTI	Eastern Region	Central Region	Western Region	Northeastern Region
EGP	0.075 ***	0.143 ***	0.084 **	0.323 ***	0.383 ***
	(3.676)	(3.310)	(2.256)	(3.901)	(3.380)
EGP2	−0.064 ***	−0.146 ***	−0.082 **	−0.305 ***	−0.322 ***
	(−3.236)	(−3.082)	(−2.280)	(−4.016)	(−3.307)
FTI	0.360	0.696	0.311	5.587 ***	7.816 ***
	(1.282)	(1.480)	(0.496)	(3.836)	(3.082)
FTI×EGP	−3.611 ***	−5.930 ***	−5.460 **	−18.495 ***	−29.972 ***
	(−2.658)	(−2.698)	(−2.034)	(−2.997)	(−2.992)
FTI×EGP2	4.129 ***	7.891 ***	7.896 ***	18.663 ***	25.677 ***
	(2.866)	(3.147)	(2.583)	(2.818)	(2.685)
Fixed effects	YES	YES	YES	YES	YES
Control variables	YES	YES	YES	YES	YES
_cons	0.822 ***	1.411 ***	0.791 ***	0.010	−0.424
	(7.498)	(6.043)	(5.541)	(0.036)	(−0.778)
R2	0.915	0.921	0.904	0.919	0.961
F	4.384	4.049	3.631	6.275	4.095

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

indicate that the interaction term of EGP and FTI significantly and negatively impacts ULGUE (β = −3.611, p < 0.01). By contrast, the interaction term of EGP² and FTI demonstrates a significantly positive effect (β = 4.129, p < 0.01). This indicates that FTI also exerts a significant moderating influence, confirming Hypothesis H3a and H3b.

To further investigate the heterogeneity of moderating variables across different regions, we divided the sample cities into four regions. Based on the baseline regression results from earlier, we observed no significant inverted U-shaped relationship between EGP and ULGUE in the eastern region, whereas such a relationship is significant in the western, central, and northeastern regions. After incorporating the moderating effect of ER, as shown in columns (2) to (5) of Table 6, a significant inverted U-shaped relationship emerged in the eastern region, while this relationship became insignificant in the northeastern region. This suggests that the moderating effect of ER on the EGP–ULGUE relationship is strongest in the eastern region and weakest in the northeastern region. This finding may be explained by the relatively strong economic foundation in the eastern region. ER can encourage firms to invest more in pollution control and green technology transformation, thereby alleviating economic pressure and improving land use efficiency [155]. Conversely, in the northeastern region, which has a relatively single industrial structure and a high proportion of traditional industries, the short-term economic pressure from ER is relatively large, potentially hindering improvements in urban land use efficiency [156]. When the moderating effect of FTI is added, as presented in columns (2) to (5) of Table 7, the coefficients of the squared interaction term of FTI and EGP are notably larger in the northeastern and western regions, while they are relatively smaller in the eastern and central regions. This suggests that the moderating effect of FTI is stronger in regions with lower levels of economic development. A potential explanation is that in regions with lower economic development, resource allocation efficiency tends to be lower. Investment in science and technology can improve production efficiency and total factor productivity, yielding higher marginal benefits, alleviating economic growth pressure, and reducing its negative impact on urban land use.

In addition, we drew a visual picture to present the moderating effect of these two variables. As shown in Figure 6a, ER moderates this relationship by amplifying both the positive and negative effects of EGP on ULGUE. Conversely, Figure 6b indicates that FTI weakens these effects. Theoretically, when EGP is low, command-based environmental regulations can trigger the Porter effect, prompting enterprises to invest in technological innovation and optimize resource allocation [135]. This process enhances the positive impact of EGP on urban land use, aligning with the findings of Yuan et al. (2020) [157] and Li et al. (2019) [158]. By contrast, government subsidies for science and technology may reduce enterprise costs, potentially weakening their motivation to achieve environmental goals [159,160]. This reduction in incentives increases the risk of adverse selection, thereby diminishing the role of economic growth pressure in improving ULGUE, consistent with the findings of Liang et al. (2022) [161]. As economic growth pressure intensifies, overly stringent environmental regulations may hinder innovation investment, negatively affecting land ecological efficiency and exacerbating EGP’s adverse impact on ULGUE [63,106] However, government investment in science and technology can mitigate these effects by fostering innovation, prioritizing high-tech industries, and promoting environmental protection [162].

4.4. Endogeneity Discussion

To address potential endogeneity issues, this study used the instrumental variable method for empirical analysis. Endogeneity of the explanatory variables can arise from two primary sources. First, reverse causality may occur, as environmental pollution or land resource misallocation can drive the need for environmental protection, thereby influencing government decision-making and related economic planning. Second, omitted variable bias could arise in Model 1. Although this study included several control variables based on a comprehensive literature review, some omitted variables may still pose an issue. To alleviate these concerns, we selected two instrumental variables.

Following the approach of related studies [94,163], the following two instrumental variables were chosen: (1) Economic growth target of the province in which each city is located (Target i). Under the “promotion tournament” mechanism, local officials often inflate economic growth targets set by higher authorities to signal their capabilities, which satisfies the relevance condition. Additionally, the correlation between a city’s ULGUE and provincial economic growth target is minimal, fulfilling the homogeneity requirement. (2) The mean economic growth target of all prefecture-level cities in the same province, excluding the city being analyzed (Target ii). According to the “yardstick competition” mechanism, local governments in the same province often compete to meet economic growth targets, satisfying the relevance condition. Moreover, the economic growth targets set by other cities are not expected to have a direct effect on a city’s ULGUE, thus meeting the homogeneity requirement. The test results in

Table 8. Endogeneity test.

	(1)	(2)	(3)	(4)	(5)	(6)
	EGP	EGP2	ULGUE	EGP	EGP2	ULGUE
	First Stage		Second Stage	First Stage		Second Stage
EGP			0.727 ***			0.202 ***
			(0.157)			(0.066)
EGP2			−0.746 ***			−0.208 ***
			(0.158)			(0.064)
Target i	1.252 ***	1.190 ***
	(0.146)	(0.139)
Target i2	3.721 ***	1.922 *
	(1.087)	(1.074)
Targe ii				4.167 ***	4.608 ***
				(0.263)	(0.269)
Target ii2				40.639 ***	14.749 **
				(6.749)	(6.762)
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Anderson canon. corr. LM statistic			48.958 ***			137.921 ***
Cragg-Donald Wald F statistic			25.083			79.515
R2			0.564			0.661
F	45.81	40.97	170.49	162.41	142.93	230.64

Note: *** p < 0.01, ** p < 0.05, * p < 0.10.

, as shown in columns 1–6, indicate that Target 1 and Target 2 are highly correlated with EGP. Moreover, EGP continues to exhibit an inverted U-shaped relationship with ULGUE, confirming the robustness of the benchmark regression results.

4.5. Further Robust Analysis

To enhance the reliability of the benchmark regression findings, a series of robustness checks were performed: (1) Adjustment of the study period. The analysis timeframe was narrowed to 2009–2019 to mitigate potential distortions caused by the 2008 financial crisis and the 2020 COVID-19 pandemic, significantly influencing economic and social dynamics. (2) Exclusion of central cities. Cities with elevated political and economic prominence, such as municipalities, sub-provincial cities, and provincial capitals, were omitted from the sample. Their unique status could introduce estimation biases into the model. (3) Bilateral trimming of variables. Given this study’s extensive data and broad period, extreme values could skew the results, so variables were shrunk by 1% on both ends for testing. (4) The newly explained variable. Based on a prior study [46], the measurement of the core explanatory variable, EGP, was recalibrated to mirror the discrepancy between the economic growth target and the preceding year’s actual economic growth rate. (5) SGMM model. The SGMM model was employed to estimate the temporal dynamics inherent in the panel data. This approach addressed potential endogeneity and serial correlation issues. The outcomes of these robustness tests are detailed in columns (1) to (5) of

Table 9. Robustness test.

	(1)	(2)	(3)	(4)	(5)
	Shorten Research Period	Remove Municipalities	Shrink Variables	New Explained Variable	SGMM Model
L.eff3					0.947 ***
					(16.778)
EGP	0.089 ***	0.069 ***	0.067 ***	0.010 **	0.048 **
	(3.178)	(2.823)	(2.787)	(2.394)	(2.059)
EGP2	−0.084 ***	−0.059 ***	−0.057 **	−0.003 **	−0.045 **
	(−3.259)	(−2.629)	(−2.582)	(−2.271)	(−2.063)
lnopd	0.002 **	0.002	0.002 *	0.002 *	−0.001
	(1.976)	(1.614)	(1.734)	(1.789)	(−1.101)
UR	−0.000	−0.000 **	−0.000 ***	−0.000 **	−0.000
	(−1.518)	(−2.457)	(−2.630)	(−2.573)	(−0.832)
PGDP	−0.006	−0.008	−0.008	−0.008	0.003 *
	(−0.659)	(−1.150)	(−1.115)	(−1.108)	(1.895)
DEN	−0.032 **	0.000	0.001	0.001	0.000
	(−2.116)	(0.035)	(0.071)	(0.072)	(0.045)
GOV	−0.002	−0.004	−0.004	−0.004	−0.001
	(−0.110)	(−0.324)	(−0.327)	(−0.342)	(−0.093)
INF	0.000	0.000	0.000	−0.000	−0.000 *
	(0.541)	(0.061)	(0.019)	(−0.039)	(−1.782)
_cons	0.897 ***	0.723 ***	0.718 ***	0.733 ***	0.007
	(4.869)	(5.195)	(5.199)	(5.368)	(0.062)
AR(1)					0.000
AR(2)					0.191
Hansen test					0.641
R2	0.578	0.666	0.667	0.667
F	100.945	133.398	137.030	133.483	1.66 × 105

Note: ***p < 0.01, **p < 0.05, *p < 0.10.

. After conducting these tests, the relationship between EGP and ULGUE remained significantly inverted U-shaped, further confirming the robustness of the benchmark regression results.

Additionally, we have added Figure 7 in the robustness analysis section, where panels (a)–(e) respectively illustrate the inverted U-shaped relationships discussed in the robustness analysis.

5. Heterogeneity Analysis

5.1. Impact of EGP on ULGUE Under Different Resource Endowments

The resource endowments of Chinese cities exhibit significant variation, leading to substantial differences in the economic goals set by local governments. To explore these differences, we categorized the sample into resource-based and non-resource-based cities. As shown in columns (1) and (2) of Table 8, the estimated coefficient of the core explanatory variable in non-resource-based cities is 0.048. However, this coefficient increases in resource-based cities from 0.048 to 0.074, higher than that of non-resource-based cities. Moreover, all results pass the 1% significance level. This suggests that resource-based cities demonstrate a more pronounced sensitivity to EGP, resulting in a more substantial influence on ULGUE than non-resource-based cities.

This phenomenon can likely be traced to the entrenched path dependency in the land use and industrial frameworks of these cities. First, regarding land use structure, resource-dependent cities frequently face challenges related to land use, including an excess of industrial land and large expanses of underutilized or inefficiently managed areas [164,165]. As growth pressures intensify, land resources in these cities are primarily dedicated to facilitating the expansion of resource-driven industries [166], deepening the imbalance in land use and diminishing the effectiveness of green land utilization. By contrast, non-resource-based cities prioritize a more balanced land allocation for multiple functions, such as residential, commercial, industrial, and public services [164]. This approach helps to minimize land resource waste, reducing the negative impacts on green land efficiency. Second, in terms of industrial structure, non-resource-based cities typically boast a more varied industrial foundation [167], which allows them to mitigate risks by leveraging the complementarity and synergy between different industries amid economic growth pressures. By contrast, resource-based cities’ economies heavily rely on extracting and processing natural resources [33]. This reliance on non-renewable resources leads to a resource-dependent industrial structure [168,169], making it difficult for these cities to quickly adjust their industrial composition in response to economic growth, which in turn hampers ULGUE. Therefore, resource-based cities face greater EGP than non-resource-based counterparts, owing to the rigid nature of their resource-driven development models and industrial structures [164,170]. Their reliance on large-scale resource exploitation and uncontrolled land expansion exacerbates inefficiencies in ULGUE, leading to more severe impacts.

5.2. The Impact of EGP on ULGUE Under Different Target Constraint Features

Based on the specific content of government work reports, economic growth targets are accompanied by adverbial modifiers, with varying degrees of constraint implied by different modifiers. These modifiers influence the government’s strategies to promote growth and address economic challenges, affecting ULGUE. Based on the existing literature, we classified the economic growth targets according to their constraint characteristics. Targets modified by adverbs such as “left and right”, “up and down”, and “between” were considered soft constraints, whereas those modified by adverbs such as “reach…%”, “for…%”, “ensure”, “above”, and “strive to” were classified as strong constraints. We then explored the heterogeneous effects of EGP on ULGUE across varying levels of constraints. As shown in columns (3) and (4) of

Table 10. Heterogeneity analysis of the impact of EGP on ULGUE.

	(1)	(2)	(3)	(4)
	Non-Resource-Based Cities	Resource-Based Cities	Softly Constrained Targets	Strongly Constrained Targets
EGP	0.048 **	0.074 **	0.083	0.081 ***
	(2.166)	(2.251)	(1.510)	(3.712)
EGP2	−0.040 *	−0.065 **	−0.078	−0.067 ***
	(−1.807)	(−2.124)	(−1.565)	(−3.122)
Control variables	YES	YES	YES	YES
City FE	YES	YES	YES	YES
Year FE
_cons	0.837 ***	0.925 ***	0.767 ***	0.624 ***
	(6.657)	(4.482)	(6.168)	(2.919)
Inverted U-curve extreme point	0.596	0.567		0.601
Slope of left and right end points of the curve	0.048 −0.032	0.073 −0.056		0.081 −0.054
R2	0.933	0.898	0.919	0.947
F	4.920	1.284	3.556	3.852

Note: *** p < 0.01, ** p < 0.05, * p < 0.10.

, when the growth target is categorized as a soft constraint, the coefficient of the key independent variable is 0.083, which is not statistically significant. However, as the constraint level increases to strong constraints, the estimated coefficient changes from 0.081 to −0.067, with both results passing the 1% significance level.

A possible explanation for this pattern lies in the strong link between target pressure and the performance appraisal mechanism. According to the political promotion tournament theory, local economic performance is a key determinant of officials’ career advancement, with evaluations based on comparative rather than absolute performance [171,172]. Facing the pressure of stringent constraints, local government officials tend to adopt more aggressive economic stimulus measures, such as large-scale investment and industrial expansion [49,50], to meet GDP growth targets. This development approach often leads to extensive land use and structural imbalances, significantly affecting urban land use. By contrast, when EGP operates as a soft constraint, local government officials have greater flexibility in implementing growth policies, allowing them to better balance economic development with urban environmental protection. under hard constraints, and the impact of EGP on ULGUE becomes more pronounced and intensifies over time.

6. Conclusions and Policy Implications

6.1. Conclusions

This study systematically investigated the influence of EGP on ULGUE by analyzing panel data from 271 prefecture-level and higher cities in China 2006 to 2021. The main findings are summarized as follows: (1) The baseline regression analysis reveals an inverted U-shaped relationship between EGP and ULGUE, a result that remains robust after addressing endogeneity concerns through various tests. Furthermore, from a regional standpoint, the inverted U-shaped relationship between EGP and ULGUE is primarily observed in the central, western, and northeastern regions. By contrast, no significant correlation is observed between EGP and ULGUE in the eastern region. (2) Mediation effect analysis reveals that EGP impacts ULGUE by influencing the allocation of land marketization, green technology innovation, and industrial structure upgrading. Among these mechanisms, the mediating effect of land marketization is the strongest. (3) The moderating effect test indicates that environmental regulation enhances the inverted U-shaped relationship between EGP and ULGUE, making the curve steeper. This moderating effect is most pronounced in eastern China. By contrast, financial technology input weakens the inverted U-shaped relationship between EGP and ULGUE, resulting in a flatter curve. This effect is more pronounced in western and northeastern China. (4) The extended analysis further reveals several key findings. From the perspective of resource endowment differences, EGP significantly impacts ULGUE in resource-based cities. Regarding the characteristics of economic growth target constraints, under strict economic growth targets, the influence of EGP on ULGUE is significantly amplified.

6.2. Policy Implications

• Reasonable regulation of economic growth targets. The study indicates that excessive EGP can hinder urban land use efficiency. Therefore, local governments should establish targets that align with their respective stages of economic development. During the initial phases of industrial advancement, adopting more growth objectives could be justified to optimize ULGUE. Conversely, in the post-industrial phase, the focus should shift from rapid expansion to economic quality, mitigating the negative impact on urban land use. Moreover, economic targets should be integrated with sustainable land use strategies, incorporating indicators that emphasize environmentally friendly land use to counterbalance the adverse effects of EGP.
• To mitigate the adverse effects of EGP, local governments should optimize the land transfer system, prioritize technological and industrial upgrading, and emphasize the role of regulation and incentives. This includes improving the land property rights system, establishing an open and transparent land transaction market, and optimizing the land supply mechanism. Governments should accelerate the aggregation of high-level innovation resources, enhance regional talent systems, and cultivate a culture of innovation. Strengthening policy guidance can further promote the growth of emerging industries while phasing out high-input, high-pollution, and low-efficiency industries. In particular, technological and industrial upgrading should play a central role in this process. Furthermore, adequate environmental supervision plays a pivotal role in enhancing the beneficial influence of EGP on urban land development. Fintech inputs can also help alleviate its adverse effects. Therefore, enhancing environmental regulations, establishing a comprehensive monitoring system, and increasing fiscal support for green industries are essential. Specifically, while the eastern region should focus on strengthening environmental supervision, the western and northeastern regions should emphasize investments in science and technology to drive sustainable development.
• Scientific evaluation of local conditions and differentiating policy formulation. The research underscores the critical need to consider regional differences and policy goal-setting approaches in policy evaluation. Resource-based cities should avoid excessive dependence on resource exploitation and prioritize sustainable urban land use by accounting for local resource availability and environmental capacity. Furthermore, compared to the eastern region, the central, western, and northeastern regions should focus on optimizing economic structures, setting growth targets that align with their developmental foundations, and ensuring a balance between ecological conservation and economic development. Additionally, when formulating economic growth targets, municipal governments should consider adopting a flexible “soft constraint” approach to enable more adaptive and diversified policy measures.

6.3. Limitations

To conclude, we discuss the limitations of this research and suggest directions for future exploration. First, ULGUE is a complex, multidimensional concept, with indicators differing significantly across studies. Owing to constraints in data accessibility, this study utilized only a set of commonly used indicators. Future research could incorporate multidimensional expected outputs, such as residents’ quality of life, to enhance the theoretical depth and expand the analytical scope of green urban land use efficiency. Second, ULGUE encompasses diverse categories, such as agricultural ULGUE and forestry ULGUE. Examining the impact of EGP on these distinct types of ULGUE would provide valuable insights. Future studies should explore this aspect to better understand ULGUE across various sectors and from diverse analytical perspectives. Future research should consider additional factors, such as informal environmental regulations, to improve the robustness and accuracy of the findings.