Can Local Industrial Policy Enhance Urban Land Green Use Efficiency? Evidence from the “Made in China 2025” National Demonstration Zone Policy

Abstract

As the fundamental physical carrier for human production and socio-economic endeavors, enhancing urban land green use efficiency (ULGUE) is crucial for realizing sustainable development. To effectively enhance urban land green use efficiency, this study systematically examines the intrinsic relationship between industrial policies and ULGUE based on panel data from 286 Chinese cities (2010–2022), employing an integrated methodology that combines the Difference-in-Differences (DID) model, Super-Efficiency Slacks-Based Measure Data Envelopment Analysis model, and ArcGIS spatial analysis techniques. The findings clearly demonstrate that the establishment of the “Made in China 2025” pilot policy significantly improves urban land green use efficiency in pilot cities, a conclusion that endures following a succession of stringent evaluations. Moreover, studying its mechanisms suggests that the pilot policy primarily enhances urban land green use efficiency by promoting industrial upgrading, accelerating technological innovation, and strengthening environmental regulations. Heterogeneity analysis further indicates that the policy effects are more significant in urban areas characterized by high manufacturing agglomeration, non-provincial capital/non-municipal status, high industrial intelligence levels, and less sophisticated industrial structure. This research not only provides valuable policy insights for China to enhance urban land green use efficiency and promote high-quality regional sustainable development but also offers meaningful references for global efforts toward advancing urban sustainability.

1. Introduction

1.1. Introduction

Land functions as the fundamental physical carrier for urban “production–living–ecological” spaces. Its green usage efficiency encapsulates a holistic assessment of the input–output dynamics among production elements and land use performance within the urban spatial context [1,2]. However, under conditions of high population density and constrained land supply, issues such as lagging improvements in land use efficiency and environmental quality—which are mismatched with rapid urban development—have become increasingly pronounced [3]. By 2023, China’s permanent resident urbanization rate had reached 66.16%. However, urban built-up areas accounted for only 0.6% of the national land area, while over 75% of urban land continued to operate at low to medium efficiency levels. This low-density “leapfrog” spatial expansion policy has adversely affected ecological integrity and land use efficiency, resulting in currently suboptimal ULGUE in China. This not only poses a significant barrier to green and sustainable development, but also exacerbates a series of issues including food security risks, resource wastage, and environmental pollution [4]. In this context, improving ULGUE is crucial for promoting green and high-quality economic development [5].

The objective of green land utilization is to maximize land use intensity and desired outputs while minimizing undesired outputs through the tripartite coupling of ecological, economic, and social systems. As the most energy-intensive and pollution-intensive sector, manufacturing requires China—the world’s largest manufacturing nation—to urgently transform and upgrade its industrial system into a resource-efficient and environmentally-friendly paradigm, which is critical for enhancing ULGUE [6]. The “Made in China 2025” (MIC 2025) initiative, as an incentive-based industrial policy, highlights the importance of innovation-driven and ecologically sound development, primarily by promoting intelligent and green manufacturing systems. It represents a significant national effort to drive manufacturing transformation and upgrading, and more importantly, thereby laying a solid real-economy foundation for enhancing ULGUE [2]. On the one hand, pilot cities concentrate on cutting-edge technological domains. By pursuing a strategy that balances technology introduction, absorption, and re-innovation with integrated and original innovation, these cities actively overcome key technological bottlenecks, thereby driving the iterative upgrading of the manufacturing technology system and providing solid technological support for ULGUE improvement [7]. On the other hand, this industrial policy sets specific requirements for pilot cities to promote energy-saving and environmental technology innovation, augment the proportion of renewable and low-carbon energy usage, transform and upgrade traditional industries, and strengthen environmental regulations [8]. Consequently, “MIC 2025” demonstration cities are not only frontiers for manufacturing transformation but also strategic pivots in China’s efforts to enhance ULGUE. In light of the pivotal role manufacturing plays in China’s economic trajectory, examining the effect of “MIC 2025” pilot city implementation on ULGUE bears significant practical relevance for advocating for sustainable growth under the new development paradigm.

The current literature pertinent to this study concentrates on three areas. Firstly, there is a focus on the measurement, evaluation, and evolution of ULGUE. Land use efficiency has long been a central research focus in academia. Methodologically, scholarly investigations have evolved through distinct developmental stages: beginning with single-indicator measurements, progressing to multi-indicator system constructions, and advancing to both parametric and non-parametric analytical approaches. While some researchers quantify land use efficiency through economic output per unit area, this unidimensional metric inadequately reflects the systemic complexity of land utilization [9] since the assessment of urban land use efficiency extends beyond purely economic outcomes to encompass social and ecological dimensions. Consequently, other scholars, building upon the total factor productivity framework, incorporate land as a core input factor alongside capital and labor and consider both economic and environmental benefits as outputs to measure land use efficiency [10]. Therefore, the Data Envelopment Analysis (DEA) model demonstrates strong applicability in evaluating the complex relationships inherent in ULGUE. Under the green development paradigm, scholars have increasingly emphasized “green” attributes—specifically, the reduction of undesirable outputs in economic activities. Consequently, only urban land use efficiency measurements that incorporate undesirable outputs reflect true efficiency. By further integrating pollution emissions and other undesirable outputs, this approach has evolved into the concept of green land use efficiency. The Super-SBM model has emerged as a primary measurement method because it not only accounts for undesirable outputs but also addresses the information loss problem caused by indivisible decision-making units. Essentially, it enhances traditional DEA models by incorporating undesirable outputs within an SBM framework. Based on this, scholars have systematically examined the spatiotemporal evolution trends and spatial differentiation characteristics of ULGUE in cities [11,12]. Secondly, other studies focus on analysis of the driving factors for ULGUE. The exploration of influencing factors has become a central focus in academic research. Determinants of ULGUE can be classified into two categories: One category is internal resource factors, including transportation infrastructure, regional integration, and industrial structure, etc. [13,14,15,16]. These factors stem either from the inherent development conditions of individual cities or from competitive advantages jointly formed by multiple cities. The other category is external environmental factors, including urban morphology, land finance, policy pilot programs, etc. [17,18,19,20,21,22,23,24], all of which originate from the development philosophies or institutional arrangements of government entities. Finally, research on the developmental effects of the industrial policy “MIC 2025” also represents a key focus in academic studies. Existing research predominantly focuses on evaluating the policy’s innovation-driven effects, such as its impacts on corporate R&D investment, innovation quality, innovation output, and Total Factor Productivity [7,8,25]. The existing literature provides fertile ground for exploration and broad research perspectives for this study, yet exhibits the following limitations: First, although studies on “MIC 2025” are relatively abundant, no research has specifically focused on the environmental benefits of the “MIC 2025” pilot policy, failing to conduct an in-depth city-level investigation into how this strategy affects the green utilization efficiency of urban land resources. Second, while substantial research on ULGUE has extensively discussed relevant urban policies, studies examining the relationship between manufacturing industrial policies and urban land green utilization efficiency remain notably scarce.

Urban land serves as a critical foundation for both urban economic development and the advancement of green eco-civilization. Enhancing the green utilization efficiency of urban land constitutes a systematically complex endeavor, requiring coordinated structural adjustments in socioeconomic systems and technological transformations. Thus, empowering green urban land utilization through industrial policies represents an imperative and crucial pathway aligned with contemporary development imperatives. Since the implementation of the “MIC 2025” pilot city program, local governments have actively responded to, supported, and promoted relevant policies, unleashing substantial economic and environmental dividends. Given that the conduct of these economic activities fundamentally relies on land as a basic resource, their cumulative effects are ultimately reflected in land use patterns and profoundly shape ULGUE. However, no existing literature has yet examined these two aspects within a unified analytical framework. As a result, the impact of “MIC 2025” demonstration city construction on ULGUE remains unclear.

This prompts the following critical questions: Does the industrial policy, exemplified by the “MIC 2025” pilot city initiative, affect ULGUE in these cities? If so, what are the underlying mechanisms? Furthermore, do the effects on ULGUE vary across pilot cities with different characteristics? How can the “MIC 2025” pilot program be refined to further enhance ULGUE?

To rectify these deficiencies in the literature, this study treats the “MIC 2025” pilot city construction as an exogenous policy shock. Therefore, we construct a “Government Policy Incentives–Market Entity Response–Environmental Performance Improvement” theoretical framework to analyze the impact of “MIC 2025” on ULGUE. The prospective marginal contributions of this paper are as follows: (1) This study shifts the analytical lens of regional industrial policy research toward environmental outcomes and explores the impact of the “MIC 2025” pilot policy on ULGUE, thereby offering an innovative viewpoint for evaluating the effectiveness of industrial policies. This approach not only addresses critical gaps in existing research but also offers practical guidance for the optimized implementation of pilot city initiatives and the green transformation of urban land use patterns. (2) This study further advances the understanding of the causal relationship between industrial policy and ULGUE. Specifically, it systematically elucidates and empirically tests the impact mechanisms of the “MIC 2025” demonstration policy from three key dimensions: industrial upgrading, technological innovation, and environmental regulation. In doing so, it opens the “black box” of how pilot policies shape urban land use patterns, while simultaneously offering actionable insights and an evidence-based foundation for cities aiming to enhance their green land use efficiency. (3) Drawing on four critical dimensions—manufacturing agglomeration level, city hierarchy, industrial intelligence level, and industrial structure—this study uncovers the heterogeneous impacts of the demonstration city policy on ULGUE across different urban contexts. By identifying these differential effects, it highlights priority pathways for cities to tailor policy design to local conditions and effectively enhance green land use performance.

1.2. Policy Background and Research Hypotheses

1.2.1. Policy Background

In May 2015, the State Council of China officially issued the “MIC 2025” strategy, aiming to achieve the strategic goal of becoming a global manufacturing power through a “three-step” roadmap and to build a modern industrial system centered on advanced manufacturing. This strategic document clearly outlined the objectives and requirements for establishing demonstration zones, particularly in terms of supplying innovative factors, cultivating an innovation-friendly environment, and improving the innovation system. In 2016, Ningbo became the first demonstration city under this initiative. Eventually, 12 cities and four urban agglomerations were included in the initial batch of national demonstration zones. In February 2018, the Office of the Leading Group for Building a Manufacturing Power of China released the Interim Evaluation Guidelines for National Demonstration Zones of “MIC 2025”, which further detailed the evaluation procedures and indicator systems. These included seven primary indicators and 29 secondary indicators, covering dimensions such as innovation-driven development, quality orientation, green development, structural optimization, talent strategies, organizational implementation, and the coordinated development of urban agglomerations. Subsequently, in order to systematically implement the policy and mobilize local governments to explore new models for manufacturing advancement, the government gradually expanded the construction of demonstration cities in multiple batches. As of now, the Ministry of Industry and Information Technology, the Chinese Academy of Engineering, and other relevant institutions have selected 30 cities (or urban agglomerations) as pilot cities between 2016 and 2017. The goal is to explore replicable and scalable experiences in manufacturing transformation. The Work Plan for Urban Pilot Demonstration of “MIC 2025”, issued by these institutions, explicitly stated that substantial progress should be achieved within three to five years. The policy development process is shown in Figure 1.

1.2.2. Direct Impact of “MIC 2025” on ULGUE

The construction of “MIC 2025” demonstration cities places strong emphasis on the development of intelligent and environmentally friendly manufacturing systems. It explicitly requires technical innovations in energy conservation and environmental preservation, a higher share of sustainable and low-carbon energy, the transformation of traditional industries, and the strengthening of green regulations [7]. This policy is expected to improve ULGUE by eliminating technological bottlenecks in industrial production, energy use, and pollutant emissions. Moreover, it encourages the reuse and efficient development of land, aligns land supply with industrial planning needs, and rationalizes development timelines.

The responsiveness of market entities proves pivotal to policy implementation. Their active engagement with the “MIC 2025” policy stems fundamentally from its precision alignment with enterprises’ inherent developmental needs, while its institutional innovations significantly mitigate the costs and risks associated with industrial transition and upgrading. The “MIC 2025” pilot policy restructures corporate utility functions, rendering industrial transition and upgrading a dominant strategy for micro-level actors. When the institutional rents provided by the policy (e.g., subsidies or market access) exceed transformation costs and its institutional design effectively reduces uncertainties, rational market entities become rationally compelled to embrace the policy orientation. Specifically, pilot governments first provide economic incentives—such as the leveraging effects of fiscal subsidies and targeted facilitation of financing channels—through implementing preferential policies and establishing special support funds. These measures effectively accelerate industrial transition and upgrading among market entities in demonstration cities, thereby promoting structural adjustments in land use-related economic systems and advancing green and clean development pathways [7]. Second, pilot governments vigorously advance institutional innovations to dismantle transition barriers. By proactively developing standards to reduce compliance costs and establishing regulatory sandboxes to permit trial-and-error approaches, they enable rapid refinement of policy implementation details to align with corporate realities. Simultaneously, guided by the Resource-Based View, strengthening technology-sharing platforms and restructuring talent supply systems cultivate sustained competitive advantages for micro-level actors. Demonstration cities under the “MIC 2025” initiative strategically coordinate industrial revitalization and upgrading through integrated advancements in innovation capacity building, traditional industry transformation, emerging industry cultivation, and industrial park development. This approach effectively drives emission and energy reduction among relevant enterprises while promoting green production systems. Consequently, production activities hosted within finite urban land resources achieve dual enhancements in output capacity and pollution abatement, thereby elevating the green utilization efficiency of urban land.

From an economic perspective, the successful realization of “MIC 2025” policy objectives fundamentally stems from its construction of an economic ecosystem centered on an innovation-driven core, grounded in market mechanisms, manifested through industrial clustering, and secured by resource optimization—where all elements form a virtually self-reinforcing cycle. Under its innovation-driven economic strategy, this policy establishes a dual-cycle integration of technological R&D and market transformation. Through market-government coordination mechanisms, it achieves optimized resource allocation, unlocks economies-of-scale benefits by leveraging industrial clustering effects, and ensures precision implementation via a systemic governance framework. From a management perspective, the successful realization of “MIC 2025” policy objectives is largely attributable to its adoption of a systems engineering approach that integrates top-level design with dynamic adaptation. This methodology has established a multi-tiered, network-based policy execution infrastructure. Thus, through the dual-track synergy of economic efficacy optimization and management mechanism innovation, operating within a cohesive incentive–constraint framework, the “MIC 2025” demonstration city development policy leverages land utilization as its core lever. This elicits proactive engagement from pilot cities’ market entities, ultimately serving as dual drivers—economic and environmental—for enhancing ULGUE. Thus, this paper proposes the following hypothesis:

H1. 

The “MIC 2025” pilot policy has a positive effect on improving ULGUE.

1.2.3. Indirect Impact of “MIC 2025” on ULGUE

Industrial Upgrading Mechanism

The core of the “MIC 2025” pilot policy lies in promoting the transformation of the manufacturing industry. Therefore, on the one hand, pilot cities will commit to guiding the development of technology-intensive high-tech industries, modifying the ratio of conventional heavy industry, and improving the urban industrial structure. On the other hand, pilot cities will gradually shut down, merge, or transform pollution-intensive industries, guide manufacturing enterprises to “leverage specialized advantages through fission”, provide complete information technology services for manufacturing, and enhance intelligent manufacturing capabilities. This process promotes the optimization and enhancement of the manufacturing industry structure, achieving urban industrial upgrading.

The positive impact of industrial upgrading on ULGUE manifests through multidimensional pathways, with its core logic residing in the structural decoupling of land resource consumption from economic development and environmental pressures—achieved via industrial structure optimization and resource reallocation. Industrial upgrading intensifies land use efficiency by enabling high-value-added industries to displace lower-value counterparts, thereby preventing inefficient sectors from excessively occupying land resources. The phasing out of obsolete production capacities liberates land reserves while simultaneously accelerating multifunctional land utilization. This reduces industrial land dependence and diminishes corporate requirements for physical space. Simultaneously, industrial upgrading propels the agglomeration of producer services through the advancement of high-end manufacturing. By leveraging industrial linkage effects to optimize land use structures, pilot cities develop specialized industrial clusters that enhance land efficiency. Furthermore, the intensified interdependencies among production factors within these clusters substantially elevate economic output per unit of urban land [6]. Ultimately, industrial upgrading can drive the development of green and clean industries while effectively enhancing environmental governance and reducing pollutant emissions, thus providing strong support for improving ULGUE [26]. Thus, this paper proposes the following hypothesis:

H2. 

The “MIC 2025” pilot policy has a positive effect on improving ULGUE through industrial upgrading.

Technological Innovation Mechanism

The “MIC 2025” pilot policy highlights the enhancement of research and implementation of energy-efficient and eco-friendly technologies, fully implementing clean production, and transitioning urban development from being factor-driven to innovation-driven. Consequently, pilot cities attach greater importance to the pivotal role of green innovation, which in turn stimulates corporate green technological progress by providing platforms for technologically sophisticated enterprises and fostering industry-university-research collaboration. Specifically, the “MIC 2025” strategy explicitly calls for developing smart manufacturing and green manufacturing systems, which provides support for the manufacturing sector to overcome technical bottlenecks in industrial production, energy consumption, and waste discharge. Pilot cities intensify fiscal subsidies and technological investments in key fields such as intelligent manufacturing and high-end equipment so as to reduce the risks associated with innovation activities. Moreover, they strive to optimize the innovation environment through measures such as strengthening intellectual property protection, encouraging enterprises to conduct green R&D activities, and accelerating the enhancement of sustainable technological competencies in both enterprises and cities. In parallel, pilot cities place more emphasis on talent incentives and recruitment. By establishing and improving talent cultivation mechanisms, they aim to replenish human capital—the fundamental component of technological advances—thereby providing strong support for corporate green technological advancement, industrial transformation, and urban green development.

The positive impact of technological innovation on ULGUE is primarily manifested through pathways such as spatial restructuring, process optimization, mixed-use functions, and ecological restoration, achieving reduced consumption, intensified utilization, and enhanced sustainability of land resources. Technological innovation has significantly boosted land intensification levels, directly compressing production land use, enhancing spatial efficiency, and enabling the transition from passive load-bearing to active value creation in pilot areas’ land resources. Simultaneously, the green productivity generated by technological innovation has reshaped land use patterns. While green technologies enhance the ecological performance of land parcels, they reduce environmental burdens, thereby elevating ecological carrying capacity and enhancing land’s ecological value. Moreover, technological innovation reinforces land sustainability: clean production techniques lower pollution remediation costs, which reduces the land allocated for remediation and improves the ecological efficacy of land in pilot areas. Ultimately, the innovation-driven development model has become pivotal for cities to overcome the “resource curse” and “path dependency.” Technological innovation substantially reduces dependence on energy, labor, and other inputs in land use activities, enhancing factor productivity conversion rates, reducing pollutant emissions, and curbing resource wastage [21]. Thus, it not only enhances output capacity but also substantially mitigates pollutant emissions, which brings about substantial enhancements in both the economic and environmental performance of land-related production activities, ultimately contributing to enhanced ULGUE [21,25]. Therefore, this paper proposes the following hypothesis:

H3. 

The “MIC 2025” pilot policy has a positive effect on improving ULGUE through technological innovation.

Environmental Regulation Mechanism

Enhancing environmental oversight is regarded as an effective and indispensable measure for China to achieve pollution control and improve environmental quality. The “MIC 2025” pilot policy explicitly proposes to “strengthen green regulation” and “carry out green evaluations,” setting environmental sustainability objectives for the manufacturing industry in terms of emission reduction and consumption reduction. In response, demonstration cities have implemented stricter environmental supervision and regulatory frameworks, significantly intensifying regulatory pressure by setting higher objectives for energy conservation and decrease of emissions, imposing pollution discharge taxes and fees, enforcing stricter penalties for excessive emissions, and applying rigorous project approval standards. These measures have proven effective in eliminating outdated and excessive manufacturing capacity by constraining high-energy-consuming and high-emission projects, encouraging enterprises that meet environmental standards and adopt special environmental protection equipment, and thereby consolidating green development outcomes.

The positive impact of environmental regulation on ULGUE is primarily realized through a tripartite mechanism: compelling mechanisms, structural optimization, and ecological restoration. Its core logic lies in internalizing environmental external costs and reconstructing the dynamic equilibrium between economic and ecological dimensions of land use. Environmental regulation not only mitigates damage to land resources and ecosystems through rigid constraints such as restricting economic activities and strengthening environmental impact assessments, but also shifts environmental governance burdens onto heavy industries and resource-dependent enterprises. This facilitates the reallocation and flow of production factors—capital, land, and labor—among firms and even across sectors. On one hand, environmental regulation accelerates the phase-out and transformation of energy-intensive industries by imposing higher compliance costs on polluting sectors. This frees up inefficient industrial land for value-adding redevelopment, thereby enhancing the environmental carrying capacity of land resources. On the other hand, through rigid ecological spatial constraints, environmental regulation expedites contaminated site remediation while propelling green industry clustering. The establishment of new eco-industrial parks further intensifies land use efficiency. Therefore, environmental regulation effectively optimizes green land utilization, elevates per-unit economic output, and thereby achieves a win-win scenario for economic growth and sustainable land use—driving steady improvement in ULGUE [27]. Accordingly, this paper proposes the following hypothesis:

H4. 

The “MIC 2025” pilot policy has a positive effect on improving ULGUE through environmental regulation.

The mechanism of the direct and indirect impacts of “MIC 2025” on ULGUE is illustrated in Figure 2.

2. Materials and Methods

2.1. Model Construction1

This study evaluates the relationship between the “MIC 2025” pilot city policy and ULGUE. Given that the policy was implemented in different pilot cities in 2016 and 2017, a DID model is adopted to conduct the empirical estimation. The DID model is shown in Equation (1):

$${\mathrm{ULGUE}}_{it}={\alpha }_0+{\alpha }_{1}Di{d}_{it}+{\displaystyle \sum \rho x_{it}}+{\mu }_i+{\lambda }_t+{\epsilon }_{ it}$$

(1)

In Equation (1), ULGUE_it represents the green land use efficiency of city $\mathrm{i}$ at time $\mathrm{t}$; Did_it represents the interaction term between a time dummy variable indicating the policy implementation period and a group dummy variable identifying pilot cities. The coefficient of this interaction term is the parameter of primary interest, as it captures the causal effect of the demonstration city policy on ULGUE_it; ${\alpha }_0$ is the intercept term; x_it is the set of control variables; ρ is the corresponding coefficient; μ_i is the regional fixed effect; ${\lambda }_t$ is the time fixed effect; and ${\epsilon }_{it}$ is the random disturbance term.

2.2. Variable Definitions

2.2.1. Core Explanatory Variable

This study defines the “Made in China 2025” pilot demonstration city policy as the core explanatory variable, constructed as an interaction term (Did_it = Treat_i × Post_t). Specifically, for the inter-group dummy variable (Treat_i), cities under pilot are designated as the treatment group and assigned a value of 1; other cities are considered the control group and assigned a value of 0. For the time dummy variable (Post_t), years before policy implementation are assigned a value of 0 and years after implementation are assigned a value of 1.

2.2.2. Dependent Variable

ULGUE_it reflects the integrated performance of input and output systems within the urban land use framework, with land as the core production factor mapped onto urban space. While it considers economic output, it places even greater emphasis on environmental outcomes, thus highlighting features such as resource use efficiency and ecological sustainability. Consistent with the existing literature, this study develops a system of indexes comprising three distinct dimensions—input, desirable output, and undesirable output—and evaluates it using an SBM model incorporating undesirable outputs [28,29]. The particular implications of each metric are presented 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) [30] Zhao et al. (2018) [31] Ding et al. (2022) [32] Liu et al. (2025) [29]
	Labor	Workforce in the second	10 thousand persons
	Capital	Fixed capital stock	100 million Yuan
	Energy	Total annual electricity consumption in the city	10,000 kwh
Expected outputs	Economic gains	Secondary and tertiary sector value creation	100 million Yuan	Xie et al. (2021) [5] Gu et al. (2023) [33]
	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 dust emissions; Carbon emissions	10 thousand tons	Zhao et al. (2018) [31] Zhou et al. (2024) [2] Ma et al. (2024) [34] Niu et al. (2023) [18]

. The architectural logic of the input–output index system for ULGUE is illustrated in Figure 3.

This research calculates ULGUE in China from 2010 to 2022 and plots the results through ArcGIS 10.2 software. Because of text restrictions, only 2010, 2016, and 2022 are selected for display (as shown in Figure 4). ULGUE is classified into low, medium, high, and high-value areas based on the natural fracture method. It can be seen that with the passage of time, the ULGUE in prefecture-level cities in China is rising, revealing that ULGUE has been significantly enhanced during the research period.

From 2010 to 2016, the overall spatial pattern of ULGUE across the country did not change significantly compared to 2010. In the western and northeastern regions, some high-efficiency cities experienced declines, with only a few maintaining high efficiency levels. These regions overall exhibited a stagnant development trend. The eastern region saw an improvement in efficiency levels, and the disparities between cities gradually narrowed. However, some high-efficiency cities there also declined, and most cities showed minimal changes relative to 2010. The central region demonstrated a relatively balanced development trend, with modest increases and limited specific numerical changes in magnitude. This indicates that the process of urbanization and industrialization is often accompanied by greater environmental pressures, leading to more extensive land use patterns.

From 2016 to 2022, all cities within the “MIC 2025” pilot policy demonstration zones entered the high-efficiency tier, demonstrating the policy’s remarkable effectiveness in pollution control. This indicates that these areas have achieved a coordinated balance between environmental protection and economic development in their land use systems. Efficiency values in the western and northeastern regions also rose compared to the previous period, signaling new progress in the transformation and upgrading of high-pollution industries. The spatial distribution of efficiency values in the eastern and central regions became more balanced relative to 2016, with most low-value cities showing significant improvement. This suggests that, driven by industrial policies promoting manufacturing upgrades, urban land development and utilization have become more rationalized. These trends reflect a shift during this period from rapid growth in individual cities to coordinated regional development, marking a gradual transition into a stable development stage. Most cities have effectively managed to ensure economic growth while simultaneously balancing environmental protection and the efficient, intensive utilization of natural resources.

2.2.3. Control Variables

Relying on current studies, this research includes a series of control variables that may influence urban green development efficiency [28,29]. These variables are as follows. ① Level of Economic Development (PGDP): Evaluated by the natural logarithm of real GDP per capita, reflecting the overall economic strength of a city. ② Degree of Openness (OPD): Calculated using the natural logarithm of actual utilized foreign direct investment (FDI), which captures a city’s integration into the global economy. ③ Urbanization Rate (UR): Expressed as the ratio of the urban population, serving as a proxy for the degree of urbanization. ④ Infrastructure Level (INF): Quantified by the logarithm of the ratio between the urban road network length and the area of developed urban land, which indicates the quality and extent of urban infrastructure. ⑤ Intensity of Government Intervention (GOV): Measured by the ratio of general public budget expenditure to GDP, capturing the scale of fiscal influence exerted by local governments. ⑥ Population Density (PD): Defined as the number of residents per unit of land area, representing urban crowding and land use pressure.

2.2.4. Data Description

Regarding availability of data and consistency, this research used a panel of 286 prefecture-level cities in China, covering the period from 2010 to 2022 as the research sample. The data sources primarily include the China Land and Resources Statistical Yearbook, China City Statistical Yearbook, and official documents or websites such as the EPS database.

To ensure data reliability and comparability, several preprocessing steps are undertaken. First, missing values are interpolated linearly. Second, continuous variables are transformed using natural logarithms to reduce skewness. Third, all continuous variables are winsorized at the 1% level to mitigate the influence of outliers. Descriptive statistics for the main variables are summarized in

Table 2. Descriptive statistics.

VARIABLES		Obs	Mean	Std.Dev.	Min	Max
ULGUE	Urban land green utilization efficiency	3718	0.813	0.129	0.412	1.296
Did	“Made in China 2025” pilot demonstration city construction	3718	0.054	0.225	0	1
PGDP	Economic Development level	3718	10.703	0.596	8.916	12.309
lnopd	Opening degree	3718	5.492	1.592	2.117	8.268
UR	Urbanization rate	3718	54.689	15.195	20.891	90.075
INF	Infrastructure level	3718	23.677	14.029	5.107	58.319
PD	Population density	3718	0.0421	0.0349	0.0001	0.441
GOV	Government intervention intensity	3718	11.416	0.637	9.714	12.838

.

3. Results

3.1. Baseline Regression

Table 3. Baseline regression.

VARIABLES	(1)	(2)	(3)
	ULGUE	ULGUE	ULGUE
Did	0.151 *** (0.022)	0.134 *** (0.036)	0.129 *** (0.018)
PGDP			0.376 *** (0.028)
lnopd			0.291 *** (0.077)
UR			−0.015 ** (0.007)
INF			0.194 ** (0.092)
PD			−0.168 * (0.094)
GOV			0.132 *** (0.041)
City FE	N	Y	Y
Year FE	N	Y	Y
N	3718	3718	3718
R2	0.617	0.533	0.695

Notes: 1. *, **, and *** denote significance at the 10%, 5%, and 1% levels; 2. Robust standard errors clustered at the city level are reported in parentheses.

displays the baseline regression results. Specifically, Column (1) reports the estimates without incorporating control variables, city fixed effects, or time fixed effects. Column (2) adds city and time fixed effects to control for unobservable heterogeneity across time and regions. Column (3) further incorporates an extensive array of control variables to mitigate possible confusing influences.

Across Columns (1) to (3), the estimated coefficient of the “MIC 2025” pilot policy on ULGUE remains statistically significant and positive at the 1% level. Notably, Column (3) reports a coefficient of 0.129, suggesting that the execution of the pilot policy significantly enhances ULGUE. This finding provides robust empirical support for Theoretical Hypothesis 1.

3.2. Parallel Trend Test

The trajectories of ULGUE in both treatment and control groups should exhibit similarity prior to the policy intervention. In light of the fact that pilot cities were designated in two batches (2016 and 2017), this research will use an event study methodology to analyze dynamic effects over a ten-year window—five years before and five years after policy implementation. The specification is given as follows:

$${\mathrm{ULGUE}}_{it}={\alpha }_0+{\displaystyle {\sum }_{k=-5}^5{\beta }_k\times D_{i,t_0+k}}+{\alpha }_2{x}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

where $D_{i,t_0+k}$ is the policy period dummy variable, with t₀ denoting the policy implementation year and k = t − t₀, k = −5, −4, −3, −2, −1, 0, 1, 2, 3, 4, 5. Negative values of k indicate $\left|\mathrm{k}\right|$ years before pilot city implementation, while positive values indicate k years after policy implementation. This analysis uses the policy enactment year as the base period. As illustrated in Figure 5, prior to policy implementation, there is no statistically significant disparity in ULGUE between pilot and non-pilot cities. Moreover, the estimated coefficients become statistically significant starting from the first year of policy being implemented and exhibit a progressively increasing trend over time. This pattern indicates that the “MIC 2025” pilot policy not only has a significant positive effect but also produces a continuously strengthening impact on ULGUE.

3.3. Robustness Tests

3.3.1. Placebo Test

To mitigate the possible disruption from unobservable factors, this study conducts a placebo test by randomly generating a set of pseudo pilot city samples, based on the number of actual pilot cities. A dummy variable $\Psi$ is constructed, and its regression coefficient is tested for statistical significance. This process is repeated 1000 times to evaluate the strength of the findings. As shown in Figure 6, the distribution of $\Psi$ is centered near 0 and follows a normal distribution. Notably, the estimated coefficients of the real treatment group and the “pseudo” treatment group differ significantly, suggesting that the randomly sampled fictional group does not have a significant impact on ULGUE.

3.3.2. PSM-DID

Given that the selection of “MIC 2025” pilot cities was based on voluntary applications and subsequent evaluations by the government, the process was influenced by factors such as the economy, geography, and manufacturing development. This non-random assignment raises concerns regarding potential selection bias. This research utilizes the Propensity Score Matching approach in conjunction with a DID model to tackle this issue. Columns (1) and (2) of

Table 4. Robustness test 1.

VARIABLES	(1)	(2)	(3)	(4)
	ULGUE	ULGUE	ULGUE	ULGUE
Did	0.121 *** (0.035)	0.116 *** (0.027)	0.212 *** (0.046)	0.135 *** (0.015)
CV	Y	Y	Y	Y
City FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
N	3538	3536	3718	3666
R2	0.451	0.496	0.583	0.662

Notes: 1. *** denote significance at the 1% levels; 2. Robust standard errors clustered at the city level are reported in parentheses.

disclose the findings using radius matching and kernel density matching. The coefficient of the “MIC 2025” pilot policy continues to exhibit a markedly beneficial effect at the 1% significance level across both matching methods.

3.3.3. Double Machine Learning

To obtain more robust unbiased estimates of the “MIC 2025” pilot policy, this paper re-estimates using the double machine learning method [35]. Specifically, the sample splitting ratio is set at 1:3 and the neural network algorithm is adopted for the prediction process. Column (3) of Table 4 demonstrates that the coefficient for pilot city designation continues to exhibit a markedly beneficial effect at the 1% significance level, further confirming the strength of the findings after mitigating potential estimation bias.

3.3.4. Sub-Sample Regression

Municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing) hold unique political status and policy privileges not available to other cities, which may distort the accuracy of policy effect estimates. This study re-evaluates the results by removing these four municipalities to solve this concern. Column (4) of Table 4 presents the results after removing these municipal samples. The coefficient for the “MIC 2025” pilot policy continues to exhibit a markedly beneficial effect at the 1% significance level, which supports the conclusion that the unique political status of these municipalities does not affect the core consequences of this paper.

3.3.5. Excluding Contemporaneous Policy Interference

During the implementation of the “MIC 2025” pilot policy, other simultaneous policy initiatives may also exert influence on ULGUE. To account for this, this study considers three representative concurrent policies: smart city, low-carbon city, and innovative city pilot programs [17,18,22]. These initiatives may have independent effects on ULGUE and thus pose a risk of confounding the estimated impact of the “MIC 2025” initiative. Accordingly, dummy variables for all four policies are integrated into the baseline regression model. As shown in Columns (1)–(3) of

Table 5. Robustness test 2.

VARIABLES	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	ULGUE	ULGUE	ULGUE	ULGUE	ULGUE	Did	ULGUE
Did	0.115 *** (0.029)	0.099 *** (0.016)	0.109 *** (0.026)	1.153 (2.1371)	2.094 (3.057)		0.121 *** (0.006)
Smart City	0.034 *** (0.010)
Low-Carbon City		0.068 *** (0.013)
Innovative City			0.062 *** (0.011)
IV						0.066 *** (0.013)
CV	Y	Y	Y	Y	Y	Y	Y
City FE	Y	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y	Y
N	3718	3718	3718	3718	3718	3718	3718
Kleibergen-Paap rk LM statistic							6.792 *** (0.016)
Kleibergen-Paap rk Wald F statistic							52.663
R2	0.615	0.579	0.593	0.602	0.471	0.792	0.712

Notes: 1. *** denote significance at the 1% levels; 2. Robust standard errors clustered at the city level are reported in parentheses.

, after controlling for the influence of the smart city, low-carbon city, and innovative city policies, the coefficient of the “MIC 2025” pilot policy continues to exhibit a markedly beneficial effect at the 1% significance level, thereby reinforcing the strength of the empirical findings.

3.3.6. Counterfactual Test

Prior to the official launch of the pilot policy, China had already implemented several regional industrial upgrading initiatives aimed at promoting economic restructuring and enhancing industrial competitiveness. These earlier reforms may have had a preemptive influence on ULGUE, potentially confounding the effect of the subsequent “MIC 2025” policy. To rule out such a possibility, this study conducts counterfactual tests by adjusting the policy timing and the sample window as follows: First, the timing of implementation is artificially advanced to 2011 or 2012 (Model (4) in Table 5). Second, the sample is restricted to the period from 2010 to 2015, assuming policy implementation in 2012 or 2013 (Model (5) in Table 5). In both cases, the estimated coefficients for the “MIC 2025” policy are statistically insignificant, thereby providing additional validation for the accuracy and credibility of the original estimation results.

3.3.7. Endogeneity Treatment

To mitigate the impact of endogeneity concerns, this study employs an instrumental variable approach for empirical testing. Specifically, we construct the instrument using the share of high-technology industries in cities (2014). A higher value of this indicator signifies superior foundational manufacturing capabilities at the urban level. Crucially, cities with advanced manufacturing advantages exhibit higher probability of selection as pilot cities under the “MIC 2025” policy, thereby satisfying the relevance condition for instrument validity. On the other hand, the 2014 urban high-technology industry share serves as a predetermined historical variable that exhibits no immediate causal link with current-stage urban land green utilization efficiency, thus satisfying the exogeneity criterion for instrument validity. Given that the 2014 urban high-technology industry share constitutes cross-sectional data, this study introduces the growth rate of central government media coverage frequency regarding “MIC 2025”. We then adopt the interaction term between the 2014 high-technology industry share and the “MIC 2025” coverage growth rate as the instrumental variable (IV) to address potential bias. Columns (6) and (7) in Table 5 present the results of the two-stage regression tests. The findings demonstrate that after addressing endogeneity, the “MIC 2025” pilot policy significantly enhances ULGUE in designated pilot zones.

3.3.8. Goodman–Bacon Decomposition

The TWFE-DID estimator is essentially a weighted average of all possible 2 × 2 DID estimators in the sample. The 2 × 2 DID estimator comparing the later-treated group (Group T) and the earlier-treated group (Group C) may be biased. This is because the earlier-treated group is serving as the control group for the later-treated group, yet having already been exposed to the policy intervention, it no longer constitutes an ideal control group. When the weights assigned to such 2 × 2 DID estimators with “bad” controls are low, the staggered DID estimator is more likely to accurately identify the treatment effect [36]. Accordingly, this study conducts a decomposition analysis [36]. The decomposition results, presented in

Table 6. Robustness test 3.

Later Group T VS. Earlier Group C	0.051	−0.324
Earlier Group T VS. Later Group C	0.045	0.086
T VS. Never Treated	0.904	0.139

, demonstrate the following critical patterns: the 2 × 2 DID estimate involving “contaminated controls” is 0.324 with a weight of merely 5.1%. This indicates that such estimators may understate the true policy effect in baseline regression results. Nevertheless, given its minimal weight, this component likely exerts negligible distortion on the core findings of this study.

3.4. Mechanism Tests

Existing research has confirmed the beneficial impacts of industrial upgrading, technical advancement, and environmental regulation on ULGUE [6,22,34]. To further explore the underlying transmission pathways, this section investigates how the “MIC 2025” pilot policy affects three specific mechanisms—industrial upgrading, technological innovation, and environmental regulation—with the goal of uncovering the causal logic by which the pilot city initiative enhances ULGUE. To this end, the following empirical model is constructed:

$${\mathrm{Channel}}_{it}={\alpha }_0+{\alpha }_{1}Di{d}_{it}+{\displaystyle \sum \rho x_{it}}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

where Channel denotes the mediating variables relevant to this study’s mechanisms:

① Industrial structure upgrading. Following GDP contribution criteria, this paper divides industries into three sectors, treating each sector’s value-added share of GDP as a constituent of a spatial vector, thereby constructing a three-dimensional industrial structure spatial vector X₀ = (x_0,1,x_0,2,x_0,3). The basic unit vector set X₁ = (1,0,0), X₂ = (0,1,0), X₃ = (0,0,1) is selected as reference vectors to sequentially calculate the angles between X₀ and each ${\vartheta }_j=\left(1,2,3\right)$:

$${\vartheta }_j=\arccos \left[{\displaystyle \frac{{\displaystyle {\sum }_{i=1}^3\left(x_{j,i}\cdot x_{0,i}\right)}}{{\left({\displaystyle {\sum }_{i=1}^3{x}_{j,i}^2}\right)}^{1/2}\cdot {\left({\displaystyle {\sum }_{i=1}^3{x}_{0,i}^2}\right)}^{1/2}}}\right]$$

(4)

In Equation (4), x_j,i denotes the i-th component of the basic unit vector group X_j (j = 1,2,3); x_0,i represents the i-th component of the vector X₀.

$$IS={\displaystyle \sum _{j=1}^3{W}_j\times {\vartheta }_j}$$

(5)

In Equation (5), W_j is the weight of ${\vartheta }_j$; IS represents the degree of industrial structure optimization and upgrading, with its numerical value indicating the level of industrial structure optimization and enhancement. Combining the monotonically decreasing property of the arccosine function, a larger ${\vartheta }_j$ and IS indicate an enhanced level of industrial structure optimization and advancement [6].

② Technological innovation capability. The number of local patent applications serves as a proxy for regional technological advancement, reflecting the tangible outcomes of workers’ general intelligence in the digital era. Patents systematize and make explicit otherwise dispersed and tacit knowledge, thereby fueling urban innovation. In this study, we employ the natural logarithm of regional patent applications (Lninv) as the proxy measure.

③ Environmental regulation intensity. This is measured using the degree of command-and-control-based environmental regulation intensity (ER) [34]. The principal explanatory variable in this study is ${\alpha }_1$, which captures the effect of the “MIC 2025” pilot city construction on each of the mediating variables.

As indicated in Column (1) of

Table 7. Analysis of influence mechanisms.

VARIABLES	(1)	(2)	(3)
	IS	LnINV	ER
Did	0.253 *** (0.029)	0.197 *** (0.018)	0.138 *** (0.041)
CV	Y	Y	Y
City FE	Y	Y	Y
Year FE	Y	Y	Y
N	3718	3718	3718
R2	0.735	0.628	0.653

Notes: 1. *** denote significance at the 1% levels; 2. Robust standard errors clustered at the city level are reported in parentheses.

, the regression coefficient for industrial structure upgrading is 0.253, which is significantly positive at the 1% level. This suggests that the construction of “MIC 2025” pilot cities has significantly promoted industrial structure upgrading, thereby improving ULGUE and confirming Theoretical Hypothesis 2.

Column (2) shows that the regression coefficient for technological innovation capability is 0.197, also significantly positive at the 1% level. This implies that the pilot city initiative effectively enhances technological innovation, which in turn improves ULGUE, thus validating Theoretical Hypothesis 3.

Finally, Column (3) shows a regression coefficient of 0.138 for environmental regulation intensity, which is also markedly positive at the 1% significance level. This signifies that the establishment of pilot cities significantly strengthens environmental regulation, thereby contributing to improved ULGUE and verifying Theoretical Hypothesis 4.

3.5. Heterogeneity Analysis

3.5.1. Heterogeneity in Manufacturing Agglomeration Levels

Manufacturing industries typically expand through agglomeration to minimize production and transaction costs while attaining economies of scale and scope, thereby forming an “oasis effect.” Moreover, urban areas characterized by significant manufacturing concentration are more prone to encounter economies of scale and knowledge dissemination, further enhancing the self-clustering of assets, including human capital, financial resources, and technological advancements. As a result, the influence of the pilot policy on ULGUE may vary across cities with different levels of manufacturing agglomeration. Based on this premise, this study calculates the level of manufacturing agglomeration, dividing them into high- and low-agglomeration groups using the sample mean [26]. A dummy variable (Level) is defined, assigning 1 to cities above the mean and 0 otherwise. The interaction term between this variable and the pilot policy is then included in the regression model.

As shown in Column (1) of

Table 8. Heterogeneity analysis.

VARIABLE	(1)	(2)	(3)	(4)
	ULGUE	ULGUE	ULGUE	ULGUE
Did×Level	1.089 *** (0.053)
Did×city		1.371 *** (0.076)
Did×Intel			1.295 ** (0.621)
Did×Ind				1.177 *** (0.162)
CV	Y	Y	Y	Y
City FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
N	3718	3718	3718	3718
R2	0.596	0.623	0.778	0.639

Notes: 1. **, and *** denote significance at the 5%, and 1% levels; 2. Robust standard errors clustered at the city level are reported in parentheses.

, the interaction term (Did × Level) is markedly positive at the 1% significance level, indicating that the construction of pilot cities exerts a more substantial positive effect on ULGUE in cities with high manufacturing agglomeration. This is possibly because such cities, by leveraging advantages in economic scale, public service provision, information networks, and transportation infrastructure, can attract more producer services. This agglomeration facilitates the industrial transformation and upgrading prompted by the pilot policy, stimulates green technological advancement, and enhances ULGUE. In addition, highly agglomerated cities tend to enforce stricter environmental protection policies and adopt greener, energy-efficient production technologies as a means of differentiation for manufacturing firms. This, in turn, lowers both energy consumption and marginal pollution control costs. Conversely, cities with low manufacturing agglomeration—limited by economic scale, infrastructure, and innovation resources—cannot fully leverage the pilot policy to narrow development gaps in the short term, resulting in a relatively weaker improvement in ULGUE.

3.5.2. Heterogeneity in City Hierarchy

Differences in administrative hierarchy lead to disparities in population size, talent concentration, financial support, and infrastructure development, thereby resulting in heterogeneous effects of pilot city construction on ULGUE. To explore this, cities are grouped into non-municipal and non-provincial capitals versus municipalities and provincial capitals, based on administrative level [17]. Specifically, a city hierarchy dummy variable (city) is set, where non-municipal non-provincial capital cities are assigned 1 and municipalities provincial capitals are assigned 0. The interaction term between this variable and the policy (Did × city) is further constructed for regression.

As indicated in Column (2) of Table 8, the coefficient of the interaction term is markedly positive at the 1% significance level, indicating that the pilot strategy exerts a more significant enhancing effect on ULGUE in non-municipal and non-provincial capital cities. One possible explanation is that, compared with municipalities or provincial capitals, these cities often have lower levels of manufacturing technology and rely more on extensive resource development. The support from the pilot policy thus plays a more prominent role in accelerating industrial upgrading and green transformation, thereby delivering a stronger impact on urban green development. By contrast, in municipalities and provincial capitals—where manufacturing is already advanced and innovation capabilities are stronger—the policy support tends to serve more as an “icing on the cake,” yielding a relatively marginal improvement in ULGUE.

3.5.3. Heterogeneity in Industrial Intelligence Level

As a key technological element of the integration between the new technological revolution and manufacturing, industrial intelligence opens up new possibilities for green transformation by promoting energy conservation and emission reduction. This suggests that pilot city construction may have heterogeneous effects on ULGUE depending on cities’ levels of industrial intelligence [37]. Therefore, this study calculates each city’s industrial intelligence level for the year preceding policy implementation and classifies cities into high and low groups based on the sample mean. A dummy variable (Intel) is set to 1 for cities above the mean and 0 otherwise. The interaction term (Did×Intel) is then included in the regression.

Column (3) of Table 8 indicates the coefficient of the interaction term is markedly positive at the 1% significance level, signifying that pilot city construction has a first-mover advantage in improving ULGUE, exhibiting elevated levels of industrial intelligence. This advantage may stem from the accumulated deployment of smart factories, intelligent technologies, and advanced equipment, all of which facilitate the diffusion of green innovation. Conversely, cities with low industrial intelligence development or in the initial stages face high costs in acquiring information and feeding back knowledge for R&D. Moreover, the low density of smart devices constrains technology diffusion, thus limiting the potential benefits of the pilot policy on their ULGUE.

3.5.4. Heterogeneity in Industrial Structure

Variations in initial regional industrial structure may also lead to differentiated policy effects on ULGUE. To examine this, this study employs the ratio of secondary to tertiary industry added value as a proxy for a city’s industrial structure [32]. A dummy variable (Ind) is defined as 1 for cities above the sample mean and 0 for those below. The interaction term (Did × Ind) is then included in the regression.

As indicated in Column (4) of Table 8, the coefficient of the interaction term is markedly positive at the 1% significance level, signifying that the pilot policy exerts a stronger effect on cities with relatively low levels of industrial structure development. This may be because these cities are typically dominated by traditional manufacturing, which heavily relies on policy intervention to undergo transformation and upgrading. Under the guidance of the “MIC 2025” policy, such cities are more likely to initiate technological modernization and digital–intelligent transformation, thereby improving resource efficiency and advancing ULGUE. Additionally, cities with lower industrial structures face greater pressure for green development, which may further amplify the effects of the policy. Conversely, cities with more advanced industrial structures—often service-oriented and knowledge-intensive—rely less on transformation policies, resulting in diminished marginal returns in terms of ULGUE.

4. Discussion

In recent years, heightened global attention to sustainable development has amplified the visibility of environmental benefits in manufacturing growth. Consequently, assessing the environmental impacts of industrial policies targeting the manufacturing sector has become an imperative research agenda. However, existing studies predominantly focus on economic and innovation metrics—such as R&D inputs, innovation caliber, innovative outputs, and total factor productivity—while the critical nexus between such policies and enhanced ULGUE remains underexplored [7,8,25]. To address this research gap, this study pioneers a causal identification strategy establishing the relationship between the “MIC 2025” industrial policy and ULGUE, thereby providing a significant contribution to the extant literature. The analysis above reveals that the “MIC 2025” industrial policy serves as a causal driver of enhanced ULGUE, operating primarily through three pathways: industrial upgrading, technological innovation, and environmental regulation. Notably, the policy’s impact on ULGUE exhibits statistically heterogeneous effects contingent upon regional variations in manufacturing sophistication, city tiers, industrial IoT adoption, and industrial structure advancement.

Second, existing research on the mediating mechanisms through which “MIC 2025” influences economic development has primarily focused on talent agglomeration, fiscal support, and industrial restructuring. However, its dual catalytic effects—technological innovation spillovers and environmental regulatory stringency—on urban environmental quality have been persistently overlooked in the literature. Compared to its widely discussed economic growth effects, “MIC 2025” holds greater strategic significance for advancing the national green development agenda. The triple transmission mechanisms—industrial upgrading, technological innovation, and environmental regulation—revealed by this study not only expand current understanding of MIC 2025’s impact pathways on ULGUE and other ecological factors (e.g., carbon intensity, pollution remediation), but fundamentally deepen the comprehension of their underlying interdependencies.

Third, akin to prevailing methodologies in land and urban studies, this research categorizes cities by administrative tiers to examine heterogeneous ULGUE impacts of “MIC 2025”. However, our theoretical framework reveals that inter-city variations in manufacturing agglomeration intensity, industrial intelligence levels, and industrial structure advancement exert substantially greater influence than administrative hierarchies on MIC 2025’s land efficiency outcomes. Consequently, heterogeneity analyses stratified by these industrial capacity dimensions offer superior policy relevance when evaluating MIC 2025’s ULGUE effects.

This research not only broadens the analytical horizon of the “MIC 2025” policy effects but also provides actionable insights for enhancing both policy implementation efficiency and urban land green utilization. It should be noted, however, that several methodological limitations warrant acknowledgment. (1) This study adopts established methodologies to measure ULGUE, yet inherent measurement limitations warrant acknowledgment. Constrained by data availability, our assessment of undesirable outputs excluded service-sector pollution emissions, thereby precluding a more granular characterization of ULGUE. Consequently, developing precision-oriented methodological innovations for quantifying land green efficiency represents a critical avenue for future research. (2) This research concentrates on the effect of the single “MIC 2025” policy. However, multiple overlapping policies—such as low-carbon cities and innovative cities—were simultaneously implemented between 2010 and 2022. Although potential interference from these policies was carefully excluded, their joint impacts were not explicitly assessed. Future research could employ a computable general equilibrium (CGE) model to investigate the synergies or trade-offs of policy portfolios. (3) This study relies on prefecture-level city data, which limits granularity. Future investigations could benefit from higher-resolution data, such as county-level analysis, to yield more detailed and policy-relevant insights.

5. Conclusions

The “MIC 2025” pilot policy aims to establish a modern industrial system centered on advanced manufacturing—an objective that aligns perfectly with enhancing ULGUE as a core sustainability target. As a pivotal industrial development strategy under China’s new development paradigm, this policy provides robust support for both intensive land use and ecological civilization development. Utilizing panel data from 286 prefecture-level and higher cities in China spanning from 2010 to 2022, this research measures ULGUE using the SBM model and adopts a multiphase DID model to systematically assess the impact of the “MIC 2025” demonstration city policy on ULGUE. This study’s key findings can be summarized as follows: (1) Empirical research demonstrates that the establishment of “MIC 2025” demonstration cities significantly enhances the ULGUE of pilot cities. This finding remains robust under various alternative specifications and robustness checks. This demonstrates that beyond its documented economic outcomes—such as driving economic growth and fostering innovation—the “MIC 2025” industrial flagship policy unleashes substantial environmental co-benefits by catalyzing regional green transitions. (2) Further analysis of the mechanisms indicates that the demonstration city policy promotes ULGUE primarily through three channels: industrial upgrading, technological innovation, and environmental regulation. This outcome not only validates the theoretical framework established in this study, but crucially provides empirical quantification of the green development effects induced by the “MIC 2025” pilot policy, translating abstract sustainability concepts into actionable guidance for eco-industrial transformation. (3) Analysis of heterogeneity demonstrates that the policy consequences are particularly evident in urban areas. characterized by high levels of manufacturing agglomeration, non-provincial-capital status, higher degrees of industrial intelligence, and relatively underdeveloped industrial structures. This confirms that implementing the “MIC 2025” pilot policies with tailored approaches based on city-specific characteristics holds significant practical value. Policymakers play a crucial role in enhancing ULGUE. By creating a policy environment conducive to manufacturing upgrading, decision-makers can make substantial contributions to the sustainable development of cities. To continuously enhance ULGUE and refine manufacturing industrial policies, this paper proposes the following policy recommendations:

(1) Continue advancing the construction of demonstration cities, strengthen policy support, and foster exemplary models in which manufacturing transformation and upgrading serve as key drivers of improvements in ULGUE. First, systematically summarize successful practices in pilot cities, with a focus on optimizing innovation frameworks and institutional mechanisms. Identify and disseminate replicable pathways for manufacturing transformation and expand the pilot scope in a steady and orderly fashion. For regions where the policy has yielded significant effects, recognition and incentives should be enhanced to fully mobilize their enthusiasm for transformation. Conversely, regions with limited policy impact should be the focus of remedial efforts, with context-specific strategies adopted to effectively unlock the potential of the policy. Second, continuously optimize and implement policy instruments supporting “MIC 2025” demonstration cities—include tax incentives, government subsidies, land use planning, and funding facilitation—and integrate land use-related indicators into performance evaluation systems. Furthermore, accelerate the transition of manufacturing toward digitalization and intelligence to establish a solid institutional foundation for improving ULGUE.

(2) Explore multi-dimensional pathways to enhance ULGUE and fully harness the green dividends of pilot policies. First, local governments should strengthen technical support for enterprises by improving the business environment, offering R&D subsidies in areas such as energy conservation, emission reduction, efficiency enhancement, circular resource utilization, and pollution control. Moreover, efforts to promote industrial upgrading, stimulate technological innovation, reinforce environmental regulation, and implement detailed land use plans should be intensified to ensure sustained improvements in ULGUE. Second, policy design and institutional arrangements during the construction of demonstration cities should prioritize the three key mechanisms—industrial upgrading, technological innovation, and environmental regulation. This includes accelerating the phase-out of outdated production capacity, promoting low-carbon transformation in high-energy and high-emission industries, expanding the large-scale development of new energy, encouraging enterprises to increase their reliance on clean energy, and optimizing energy consumption structures to continuously boost ULGUE.

(3) Recognize and accommodate the differences across cities in manufacturing agglomeration levels, administrative hierarchy, industrial intelligence, and industrial structure. Policies should be tailored to local conditions to explore diversified pathways for manufacturing transformation. Local governments should leverage local resource endowments and industrial cluster conditions to refine the “MIC 2025” demonstration city strategy. First, pilot cities should actively promote the optimization of manufacturing structures, accelerate the development of high-tech industries under the dual guidance of policy and market forces, foster manufacturing agglomeration, and drive high-quality and green-oriented transformation of urban manufacturing. Second, efforts should be made to promote the industrial internet, construct smart factories, deploy intelligent equipment, and boost R&D investments in industrial intelligence. Meanwhile, cultivating new business models and applications in industrial intelligence will provide fresh momentum for improving ULGUE.