Research on Enhancing Urban Land Use Efficiency Through Digital Technology

Abstract

Based on panel data from China’s prefecture-level cities spanning 2009–2023, this study thoroughly examines the impact of digital technologies on urban land use efficiency and its underlying mechanisms. Findings reveal that advancements in digital technologies significantly enhance urban land use efficiency. This conclusion remains robust after undergoing a series of stability tests and endogeneity treatments, demonstrating its reliability. Further heterogeneity analysis revealed regional variations and structural characteristics in the impact of digital technologies. The study found that digital technologies most significantly boosted land use efficiency in western regions and cities with higher levels of centralization. Meanwhile, in cities with higher levels of land industrialization and digital workforce capabilities, the positive impact of digital technologies is more pronounced. The analysis of intermediary mechanisms from both micro-level resource allocation and macro-level structural transformation perspectives reveals that digital technologies effectively enhance urban land use efficiency through four dimensions: increasing the number of startups, strengthening innovation support intensity, elevating green technology levels, and driving industrial structure upgrading. Additionally, the study examined synergistic mechanisms and found that government signaling and environmental policy intensity can all significantly amplify the enabling effects of digital technologies, providing multiple drivers for enhancing urban land use efficiency.

1. Introduction

Driven by both global competition and the pursuit of high-quality domestic economic development, land resources stand as a core element in national strategic development, with their utilization efficiency serving as a key indicator for assessing regional sustainable development capacity [1,2]. As a fundamental natural element supporting human survival and development practices, land’s resource attributes are intrinsically linked to human activities such as living and production [3]. Even Europe, as the world’s most developed continent, is not immune to land use challenges arising from climate change, rural depopulation, and land abandonment [4]. The Philippines similarly faces challenges of uneven land distribution and low utilization efficiency [5]. Pakistan’s forest coverage rate is even below 5%, a phenomenon closely linked to climate change and high urbanization rates [6]. From Western nations’ exploration of compact cities to developing countries’ efforts to combat land degradation, enhancing land use efficiency has become a core agenda item for global sustainable development. While achieving remarkable economic growth, China has also encountered issues such as extensive land use and encroachment on arable resources [7,8,9,10]. According to the National Comprehensive Index for Economical and Intensive Use of Construction Land (2023), China’s index reached 106.02 in 2022. Although this reflects a steady upward trend, the country still faces deep-seated contradictions such as regional development imbalances and difficulties in revitalizing existing land resources [11,12].

Against the backdrop of accelerating urbanization, enhancing land use efficiency cannot be achieved by a single entity or through isolated technological means. Rather, it constitutes a systemic endeavor involving multiple stakeholders, multiple levels, and multiple objectives. It urgently requires the collaborative participation of diverse ac-tors—including government, market, and society—alongside the synergistic effects of a multidimensional support system encompassing policy, technology, and institutional frameworks [13,14,15]. The rational allocation of production factors serves as the fundamental prerequisite for improving land use efficiency [16,17]. The spatial mismatch between population, capital, and land constitutes the core bottleneck constraining efficiency. Optimizing information flow to improve labor structure [18,19], upgrading industrial structure [20], and enhancing transportation infrastructure [21,22] represent key pathways to alleviate this contradiction. Nevertheless, the mismatch between population, natural re-sources, and land continues to impede efficient land utilization [23,24]. As sustainable development concepts deepen, research perspectives have shifted from solely pursuing economic efficiency toward synergistic development with ecological conservation. A pro-found interactive relationship exists between land use efficiency and the ecological environment [25,26]. Findings from Sutton et al. [27] underscore this relationship, highlighting the severe impacts of land degradation on ecosystems. Land degradation directly diminishes land productivity. Conversely, through ecological restoration and spatial use regulation, a coupled coordination mechanism between land use and ecological conservation can be established, achieving a win-win outcome for both economic and ecological objectives [28]. Policy frameworks guide the refinement of land use practices, serving as crucial external conditions for enhancing land use efficiency. As key external intervention forces, these policy systems exert a guiding influence throughout the entire process [29]. Land reform policies [30], industrial policies [31], environmental policies [32,33], and regional development policies [34,35] all influence land use efficiency to varying degrees. Compensation effects generated by policies such as urban expansion [36] and national key development zones [37] promote industrial upgrading and enhance the cross-regional mobility of production factors, thereby improving land use efficiency.

Previous literature has examined both positive and negative factors influencing land use efficiency from multiple perspectives. It is evident that technological progress has consistently served as a significant exogenous driver for enhancing land use, manifesting across multiple dimensions, including resource conservation, structural optimization, and strengthened governance capabilities [38,39,40]. With the gradual maturity of digital technology, scholars have begun investigating the pathways through which it affects land use efficiency. Current research primarily focuses on two perspectives: first, the market mechanism perspective, which examines the effects of digital technology on resource al-location and structural transformation. The adoption of advanced digital technology facilitates real-time land use monitoring and optimization of resource allocation [41], reduces market transaction costs [42], and promotes land resource utilization in a more valuable direction [43]. Brown et al. [44] argues that information technology provides a favorable opportunity for altering land use patterns. Zhang and Cao [29] employed a dynamic fuzzy set qualitative comparative analysis, finding that digital technological innovation interacts with social factors to enhance land use efficiency. Second is the policy orientation perspective, which primarily examines the institutional effects of policies related to digital technologies. Governments typically utilize geographic information to monitor land use and efficiency. Advances in digital technology enhance information transparency [45], while data sharing promotes public participation in policy formulation [46], thereby optimizing land utilization. Jiang et al. [47] employed the difference-in-differences approach to examine the impact of government digital transformation on land use efficiency, concluding that this effect primarily stems from industrial structure upgrading. Zhuo et al. [48] however, proposed that the improvement in land use efficiency is driven by innovation investment, the expansion of internet users, and the optimization of land management practices.

Amid the global wave of digital transformation, existing research is insufficient to fully explain the evolving patterns of efficient urban land use. This paper systematically investigates how digital technologies reshape urban land use efficiency, as well as their underlying mechanisms and boundary conditions. Centered on these core questions, the study unfolds across three dimensions: First, it expands research on the drivers of land use efficiency. Existing literature predominantly focuses on the impact of traditional technological progress and industrial restructuring on land use efficiency. This paper introduces digital technology as a core explanatory variable, examining its influence on urban land use efficiency within the context of the digital economy, thereby expanding the technological boundaries of this research field. Second, it deepens the identification of mechanisms through which digital technology affects land resource allocation. This study constructs a two-dimensional mechanism analysis framework including micro-resource allocation and macro-structural transformation, identifies four intermediary paths of new venture growth, science and technology expenditure input, green technology development and industrial structure upgrading, so as to reveal the internal logic of the role of digital technology and urban land more systematically. Third, this study introduces an institutional environment perspective, examining synergistic effects across two dimensions: government digital attention, and environmental policies. This aims to demonstrate the differentiated characteristics of digital technology’s land efficiency enhancement under varying institutional embeddings, providing more targeted policy references for improving urban digital governance capabilities and sustainable land resource utilization.

2. Theoretical Foundations and Research Hypotheses

2.1. Impact of Digital Technologies on Urban Land Use Efficiency

In the context of the current global resource and environmental constraints and the accelerated transformation of urban high-quality development, improving land use efficiency has become an important goal of urban governance. Traditionally, the allocation of land resources has been largely limited by problems such as information asymmetry, fac-tor mismatch, and policy response lag, resulting in extensive land use and low efficiency [49]. With the development of the digital age, digital technologies (including big data, artificial intelligence, the Internet of Things, and blockchain) have become an important force in promoting urban economic development [41,50]. Digital technologies are profoundly transforming the methods and efficiency of urban land use by enhancing information transmission efficiency, optimizing resource allocation, and fostering innovation. In terms of digital infrastructure, its development has optimized the city’s information transmission network, effectively reducing the costs of information acquisition and flow. This enables more efficient utilization of land resources, fostering intensive land use. The application of digital technologies, along with the widespread use of big data and artificial intelligence, has enhanced the precision of government decision-making in urban planning, land approval, and land use control. This has made land allocation more scientific and rational, reducing inefficient land utilization. In terms of digital platforms, their development accelerates the digital industrialization process and fosters new business models, playing a pivotal role in enhancing land output efficiency. Concurrently, digital technologies enable real-time monitoring of land use and facilitate dynamic adjustments to land allocation. Based on the above analysis, this paper proposes the following research hypotheses:

Hypothesis 1 (H1):

Digital technologies can significantly enhance urban land use efficiency.

2.2. Mechanisms of Digital Technology’s Impact on Urban Land Use Efficiency

Although digital technologies exert a direct influence on land use efficiency, their impact pathways are not linear processes but are more prominently embedded in the in-direct mechanisms of resource allocation optimization and economic structural transformation [41,51]. According to theories from new institutional economics and entrepreneurial economics, the development of digital technologies has created new ground for business startups and the optimal allocation of resources [52]. First, the sharing of digital infrastructure and data resources lowers entry barriers for new ventures, enhances the dynamism of urban economies, and facilitates the reallocation of land resources toward higher-value-added uses. Second, digital technologies drive the aggregation of scientific and technological resources, prompting increased investment in R&D and technological expenditure by local governments and firms, thereby strengthening the synergistic allocation of capital, technology, and spatial resources. This further enhances the coordinated allocation of capital, technology, and spatial resources. By stimulating growth in startups and boosting technological spending, digital technologies optimize urban resource allocation structures, thereby indirectly improving land use efficiency.

Hypothesis 2 (H2):

Digital technologies enhance urban land use efficiency by optimizing resource allocation.

From the perspective of industrial economics and sustainable development theory, digital technologies can also drive improvements in land use efficiency by promoting the greening and upgrading of urban economic structures [37]. On one hand, digital technologies enhance green innovation capabilities and environmental governance tools, facilitating the widespread adoption and diffusion of green technologies [53]. This reduces environmental costs per unit of land use and improves green output efficiency. On the other hand, by driving industrial chain restructuring and upgrading, digital technologies pro-pel cities to transition from traditional industrial dominance toward high-end services and strategic emerging industries, significantly increasing economic output density per unit of land. Digital technology holds the potential to guide cities toward green technological advancement and industrial restructuring, thereby driving improvements in land use efficiency at the macro level.

Hypothesis 3 (H3):

Digital technologies enhance urban land use efficiency through structural transformation.

In summary, the transmission mechanism through which digital technology enhances urban land use efficiency is illustrated in Figure 1.

3. Materials and Methods

3.1. Empirical Model Construction

In order to test the impact of digital technologies on urban land use efficiency, this study employs a two-way fixed effects model for benchmark regression, setting the model as follows:

$$Gu{e}_{it}={\alpha }_0+{\alpha }_{1}Di{g}_{it}+{\alpha }_{2}Contro{l}_{it}+{\delta }_i+{\mu }_t+{\epsilon }_{it}$$

(1)

where $Gu{e}_{it}$ represents the dependent variable, denoting the land use efficiency of city i in year t; $Di{g}_{it}$ is the core explanatory variable, indicating the digital technology level of city i in year t; $Contro{l}_{it}$ encompasses all control variables, which in this study include government intervention (Gov), urbanization level (Urban), economic density (Eco), informatization level (Inf), and human capital level (Caph); ${\delta }_i$ represents individual fixed effects; ${\mu }_t$ denotes time fixed effects; ${\epsilon }_{it}$ is the random error term.

3.2. Data Sources

This study examines the impact of digital technologies on urban land use efficiency by analyzing 271 Chinese cities from 2009 to 2023. Patent data on digital technology adoption is sourced from patent databases, with additional cleaning applied to information such as patent classification codes. Other indicators primarily originate from the China Urban Construction Statistical Yearbook, China Urban Statistical Yearbook, and China Regional Economic Statistical Yearbook. Some data is obtained from the official websites of municipal-level statistics bureaus. Missing values are supplemented using linear interpolation, and severely missing data from certain prefecture-level cities is excluded.

3.3. Variable Descriptions

3.3.1. Dependent Variable

The dependent variable is urban land use efficiency (Gue). Urban land serves as the spatial foundation for urban development, and its utilization is defined as the allocation and use of land resources according to diverse user needs at specific developmental stages to achieve sustainable and balanced growth. Drawing on existing research [18,21,30], this paper constructs an evaluation index system for land use efficiency from an input-output perspective (as shown in

Table 1. Urban Land Use Efficiency.

Indicator Type	Primary Indicator	Indicator Description	Unit
Input	Land Input	Construction Land Area	km2
	Capital Input	Fixed Capital Stock	-
	Labor Input	Employment in the secondary and tertiary industries	10,000 person
	Energy Input	The product of the total energy consumption at the provincial level and the ratio of each city’s GDP to the GDP of its respective province	-
Expected Output	Economic Benefits	The sum of the value added of the secondary and tertiary industries	CNY 100 million
Unexpected Output	Pollution Output	Total Carbon Emissions	1,000,000

). This study selects land, labor, and economic factors as input elements. Construction land serves as the direct carrier for urban economic activities, and this indicator is treated as land factor input. Non-agricultural utilization of urban land is primarily driven by secondary and tertiary industries. Therefore, employment figures in these sectors serve as the labor input. Fixed capital stock is used to measure capital input. Considering that urban land primarily generates economic benefits, the value added of secondary and tertiary industries is selected as the expected output. Energy input is calculated as the product of provincial-level total energy consumption and each city’s GDP share relative to its province’s GDP. During the process of utilizing land for living and production, non-expected outputs are also generated. The carbon emissions indicator is selected as the non-expected output in the input-output model. Based on this indicator system, this paper employs a super-efficient SBM model to measure urban land use efficiency.

Analysis of China’s urban land use efficiency measurements from 2009, 2016, and 2023 reveals a steady overall improvement in efficiency levels throughout the observation period (Figure 2). From the perspective of spatial pattern evolution, spatial non-uniformity in land use efficiency persists and may exhibit increasingly complex patterns. Leading regions such as the eastern coastal areas are likely to maintain their advantage, while certain inland regions face challenges in enhancing efficiency [46,53].

3.3.2. Core Explanatory Variables

The core explanatory variable in this study is digital technology (Dig). Drawing upon the research of Tao et al. [52], the number of key digital technology inventions is adopted as a proxy variable for the level of digital technology development. Patent data can better reflect substantive progress in digital intellectual property output and technological capabilities [54,55,56], while data from the National Intellectual Property Administration specifically capture national strategic priorities by covering digital industries and frontier technologies. The construction of this indicator is a rigorous, multi-step process. First, by integrating the National Bureau of Statistics’ Statistical Classification of the Digital Economy and Its Core Industries (2021) and the Reference Table for International Patent Classification and National Economic Industry Classification (2018), we strictly followed the Key Digital Technology Patent Classification System (2023) standard. We focused on selecting seven key digital technologies—including artificial intelligence, high-end chips, and quantum information—to identify and compile patents held by listed companies within these digital core technology domains. Subsequently, the identified patent data was aggregated by the two dimensions of “city-year.” Each aggregated value was incremented by 1 and then taken to its natural logarithm to serve as the measurement indicator for a city’s digital technology (Dig).

3.3.3. Mediating Variables

This paper selects the following two sets comprising four intermediary variables to examine micro-level resource allocation and macro-level structural transformation, respectively. (1) Market Entity Vitality (Entre): The number of newly established enterprises annually in each city serves as a proxy variable for market entity vitality. The emergence of new enterprises not only constitutes a core indicator for measuring market dynamism but also directly reflects the efficient allocation of land resources. This measurement method more sensitively captures the dynamic mechanism whereby digital technologies stimulate intensive land use by lowering entrepreneurial barriers and optimizing market allocation. (2) Government Behavior Guidance (Ins): Government orientation toward science and technology is measured by the proportion of local government R&D expenditure relative to general public budget expenditures. Fiscal expenditure structure directly reflects policy priorities for promoting technological innovation and digital governance. Furthermore, R&D expenditure can directly enhance land planning, monitoring, and management capabilities by supporting digital infrastructure development, effectively capturing the guiding effect of government actions on land use efficiency. (3) Technological Paradigm Shift (Gtech): This paper employs the number of authorized green invention patents to measure green technological innovation. This metric was selected for its ability to simultaneously characterize both the environmental attributes and the qualitative transformation of technologies. Its growth not only directly reflects the positive shift in production technology systems toward land-intensive and environmentally friendly approaches but also signifies a paradigm shift representing fundamental breakthroughs. (4) Industrial Structure (Ind): This paper employs the ratio of tertiary to secondary industry value-added to measure industrial structure upgrading. Land intensity varies significantly across different industries. An upward trend in industrial structure indicates that the regional economy as a whole is evolving toward a model characterized by lower land consumption and higher economic density.

3.3.4. Control Variables

Government Intervention (Gov): Measured by the ratio of local government general budget expenditures to regional GDP; Urbanization Level (Urban): Measured by the proportion of the non-agricultural population relative to the registered population; Economic Density (Eco): Measured by the ratio of regional GDP to the logarithm of administrative land area; Human Capital (Caph): Measured by the proportion of full-time students in regular higher education institutions relative to the year-end total population; Information Level (Inf): Measured by the average number of mobile phones per person. Descriptive statistics for all variables involved in the study are shown in

Table 2. Descriptive Statistics.

Variable Properties	Variable Name	Variable Symbol	Mean	Sd	Min	Max	N
dependent variable	Land Use Efficiency	Gue	0.887	0.0390	0.794	1	4065
Explanatory variable	Digital Technology	Dig	3.440	2.197	0	11.25	4065
Mediating variable	Market vitality	Entre	10.53	0.938	7.919	14.24	4065
	Government guidance	Ins	0.0170	0.0180	0	0.207	4065
	Green Technology Innovation	Gtech	2.644	1.810	0	9.373	4065
	Industrial Structure	Ind	2.302	0.144	1.831	2.846	4065
Control variables	Government intervention	Gov	0.198	0.102	0.0440	1.027	4065
	Urbanization Level	Urb	0.393	0.209	0.0750	1	4065
	Economic Density	Eco	7.260	1.308	2.806	12.06	4065
	Human capital	Caph	0.0200	0.0250	0	0.185	4065
	Level of informatization	Inf	1.057	0.760	0.134	10.17	4065

.

4. Empirical Analysis

4.1. Benchmark Regression

In order to examine the impact of digital technology on the efficiency of urban land use, this paper uses a two-way fixed effect model to regress Equation (1), and

Table 3. Benchmark Regression Results.

Variable	(1)	(2)
	Gue	Gue
Dig	0.006 ***	0.004 ***
	(9.490)	(5.668)
Gov		−0.018 *
		(−1.649)
Urb		0.018 ***
		(3.343)
Eco		0.036 ***
		(15.424)
Caph		−0.106 **
		(−1.996)
Inf		−0.002
		(−1.485)
_cons	0.866 ***	0.611 ***
	(394.377)	(34.125)
City	Yes	Yes
Year	Yes	Yes
N	4065	4065
R2	0.816	0.840

Note: *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

reports the benchmark regression results. Column (1) of Table 3 reports the model estimation results without the inclusion of control variables, and the coefficient estimation of digital technology is significantly positive. Column (2) re-estimates the results with the inclusion of control variables. The results show that the coefficient estimate of digital technology is 0.004, and it is significant at the 1% significance level. It is believed that each unit increase in digital technology is related to the increase in land use efficiency by 0.004 units. Although the coefficient estimates for digital technology are relatively small, its pervasive penetration and scale effects yield cumulative impacts that cannot be overlooked. The results indicate that, even when controlling for other potential influencing factors, advancements in digital technology still significantly promote improvements in land use efficiency, validating Research Hypothesis 1. Digital technology optimizes land resource allocation through means such as the Internet of Things and big data, thereby increasing output per unit of land. Additionally, digital technologies strengthen government land oversight and planning capabilities. By establishing digital platforms to facilitate intelligent industrial layout and enhance governmental land supervision and planning mechanisms, they elevate both the output efficiency and utilization efficiency of land resources. Against the backdrop of concurrent land resource scarcity and urban expansion pressures, digital tools provide technological support for refined land management. They effectively mitigate inefficiencies and waste in land utilization, demonstrating their positive role in advancing the intensive use of urban land.

4.2. Robustness Tests

First, we examine the replacement of the dependent variable’s measurement method. This study employs unit land output levels as a proxy variable, measuring land use efficiency by calculating the ratio of non-agricultural output to built-up area, and re-incorporates this metric into the model for estimation. Column (1) of

Table 4. Robustness Test Results.

Variable	(1)	(2)	(3)	(4)
	Replace the Dependent Variable	Shorter Sample Interval	Replace Explanatory Variables	Tail Trimming
Dig	0.030 ***	0.002 ***		0.003 ***
	(2.946)	(3.310)		(4.676)
L.Dig			0.003 ***
			(5.172)
Gov	−0.250	−0.013	−0.001	−0.030 ***
	(−1.629)	(−0.890)	(−0.130)	(−2.632)
Urb	−0.296 **	0.026 ***	0.019 ***	0.019 ***
	(−2.495)	(3.034)	(3.393)	(3.590)
Eco	1.080 ***	0.041 ***	0.041 ***	0.033 ***
	(25.836)	(12.299)	(17.444)	(13.565)
Caph	−4.383 ***	−0.043	−0.120 **	−0.163 ***
	(−4.674)	(−0.687)	(−2.256)	(−2.888)
Inf	−0.105 ***	−0.002	−0.001	−0.003
	(−3.370)	(−1.581)	(−1.055)	(−1.242)
_cons	−5.889 ***	0.580 ***	0.571 ***	0.643 ***
	(−18.142)	(22.781)	(30.925)	(35.034)
City	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
N	4065	2710	3794	4065
R2	0.989	0.878	0.859	0.837

Note: **, and *** indicate significance at the 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

reports the results of this robustness test. The regression results indicate that even after replacing the dependent variable, the estimation still shows that digital technologies significantly promote improvements in land use efficiency. This further confirms that the impact of digital technologies on land use efficiency is independent of specific indicator construction methods, indicating the robustness of the baseline regression results.

Second, the sample period was adjusted. Considering that 2009 was still within the financial crisis period and that non-economic shocks emerged starting in 2020, the study sample was shortened to 2010–2019 to control for extreme exogenous shocks and focus on the effective period of digital technology. Column (2) in Table 4 reports the results. The results indicate that the coefficient estimates for digital technology remain significantly positive. This indicates that the positive impact of digital technology on land use efficiency remains stable across different time periods, unaffected by economic fluctuations during specific periods, further confirming the robustness of the benchmark regression conclusions.

Third, using the lagged explanatory variable. When employing the lagged explanatory variable, the innovation coefficient for the lagged variable in column (3) is 0.003, significant at the 1% level. This indicates that the impact of digital technology on land use efficiency exhibits a certain degree of lag effect, yet it still maintains a significant positive promoting role, further validating the robustness of the benchmark results.

Fourth, tail trimming. All continuous variables were trimmed at the 1% and 99% percentiles to mitigate the potential impact of extreme values on estimation results. Specifically, the 1% and 99% percentiles were used as upper and lower bounds, with observations outside this range trimmed to the respective boundary values. This method effectively reduces the adverse effects of outliers on parameter estimation while preserving sample integrity and overall trends. The coefficient of digital technology in row (4) remains significantly positive at the 1% level. This demonstrates that the benchmark regression conclusions are not driven by outliers, further confirming the robustness of the benchmark findings.

4.3. Endogeneity Tests

Although the benchmark regression established a positive correlation between digital technologies and urban land use efficiency, which withstood robustness tests, these findings could still be biased by endogeneity. To address potential endogeneity issues, this paper employs the instrumental variables (IV) method for endogeneity testing. This study employs the interaction term between terrain undulation and broadband internet users per 100 people (IV) as an instrumental variable. Topographic roughness, as a natural geographical factor, facilitates the reduction in digital technology development and application costs in flatter terrain. This factor remains strictly exogenous in the short term and does not directly impact contemporary urban land use efficiency. The number of internet broadband users per 100 people reflects the prevalence and application level of digital technology in a region. Therefore, the interaction term between these two variables serves as an effective instrumental variable. The 2SLS estimation results are shown in

Table 5. Results of Endogeneity Tests.

Variable	IV1
	First-Stage	Second-Stage
Dig		0.036 ***
		(6.817)
IV1	−0.004 ***
	(−8.74)
Gov	0.404	−0.039 ***
	(1.15)	(−3.130)
Urb	−0.005	0.018 ***
	(−0.03)	(2.818)
Eco	0.673 ***	0.015 ***
	(11.59)	(3.364)
Caph	3.412 **	−0.225 ***
	(2.32)	(−3.170)
Inf	−0.052	0.001
	(−1.32)	(0.386)
Anderson LM		80.69
(statistic p-value)		[0.000]
C-D Wald F		76.44
(10% threshold)		(16.38)
City	Yes	Yes
Year	Yes	Yes
N	4065

Note: **, and *** indicate significance at the 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

. The first-stage regression results indicate that the coefficients of the instrumental variable (IV) is highly significant at the 1% level. Furthermore, both the Anderson LM statistic and the Cragg-Donald Wald F statistic pass the significance test, strongly rejecting the null hypothesis of “weak instruments.” This confirms the existence of a strong correlation between the instrumental variables and the endogenous variable. The second-stage estimation results are crucial for testing endogeneity. After controlling for endogeneity, the core coefficient estimates for digital technology (Dig) were 0.404, remaining statistically significant at the 1% level. This outcome fully aligns with the benchmark regression findings, indicating that after effectively mitigating endogeneity biases such as omitted variables and reverse causality, the positive impact of digital technology on urban land use efficiency remains robust.

4.4. Heterogeneity Analysis

4.4.1. Location and Structural Heterogeneity

Table 6. Spatial and Structural Heterogeneity.

Variable	Regional Heterogeneity			Urban Structural Heterogeneity
	Eastern	Central	Western	Highly Centralized	Low Centralization
Dig	−0.001	0.001	0.005 ***	0.004 ***	0.003 **
	(−1.345)	(0.949)	(3.879)	(5.417)	(2.092)
Gov	−0.006	−0.025	−0.055 ***	−0.030 **	0.004
	(−0.377)	(−1.403)	(−3.167)	(−2.252)	(0.214)
Urb	0.011 **	0.022 **	−0.011	0.026 ***	−0.014
	(2.320)	(2.403)	(−0.586)	(5.504)	(−0.935)
Eco	0.050 ***	0.032 ***	0.049 ***	0.034 ***	0.037 ***
	(18.585)	(8.176)	(8.112)	(11.791)	(9.080)
Caph	−0.291 ***	−0.071	0.340 ***	−0.071 *	−0.042
	(−3.539)	(−0.882)	(3.524)	(−1.754)	(−0.343)
Inf	0.001	−0.003	−0.008 *	0.001 *	−0.013 ***
	(0.903)	(−0.593)	(−1.674)	(1.901)	(−2.953)
_cons	0.504 ***	0.651 ***	0.569 ***	0.623 ***	0.626 ***
	(21.564)	(21.579)	(13.961)	(28.056)	(21.784)
City	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes
N	1485	1365	1200	2406	1634
R2	0.919	0.828	0.811	0.869	0.823

Note: *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

presents the estimated results for urban location and structural heterogeneity. From a regional perspective, the impact of digital technologies on urban land use efficiency exhibits significant differences across the eastern, central, and western regions. The impact of digital technology is not significant in the eastern regions, likely because their higher level of economic development comes with widespread digital adoption and land use efficiency approaching saturation, leaving limited room for further gains through digital means and aligning with the principle of diminishing marginal returns. This implies that for the developed eastern regions, future improvements in land use efficiency cannot rely solely on technological solutions. Instead, they require deeper institutional innovation and systemic restructuring. The central region also showed no significant impact, suggesting that the area is undergoing a transitional phase in economic transformation and digital technology adoption, with the integration of digital technologies and land use yet to be fully realized. Digital technologies in western regions have demonstrated a significant positive impact, aligning with the “latecomer advantage” theory in economics. Traditional infrastructure and land use in western regions have lagged behind, yet digital technology, as a transformative tool, can deliver more pronounced resource optimization effects. As an emerging technology, digital solutions enable these areas to bypass conventional pathways and directly leverage digital platforms for daily life and production, thereby mitigating land use inefficiencies to a significant extent. Consequently, the marginal benefits of digital technology are particularly pronounced in western China.

Grouped estimates based on urban centralization levels indicate that digital technologies enhance land use efficiency in both highly centralized and less centralized cities, though the effect is more pronounced in highly centralized urban areas. Highly centralized cities typically exhibit economies of scale and agglomeration effects. This heterogeneous outcome highlights the synergistic amplification between agglomeration economies and digital technologies. In central cities, where population, capital, and information are highly concentrated, there is an inherent strong demand for intensive land use. Moreover, the application of digital technologies alleviates traffic congestion through intelligent transportation systems, enhancing land accessibility. By optimizing urban spatial layouts through digital management and precisely allocating public service facilities, these technologies further unleash the positive externalities of agglomeration economies. In less centralized cities, while digital technologies can also improve land use efficiency, their marginal benefits are relatively lower due to smaller urban scales and more dispersed economic activities.

4.4.2. Heterogeneity in Factor Inputs and Allocation

Table 7. Heterogeneity in Factor Inputs and Allocation.

Variable	Land Industrialization		Digital Workforce Level
	(1)	(2)	(3)	(4)
Dig	0.000	0.004 ***	0.001	0.005 ***
	(0.202)	(5.508)	(0.960)	(5.943)
Gov	−0.033 **	−0.012	−0.019	−0.022
	(−2.133)	(−0.972)	(−0.908)	(−1.531)
Urb	0.004	0.022 ***	0.021 **	0.013 **
	(0.700)	(3.363)	(2.162)	(2.013)
Eco	0.053 ***	0.033 ***	0.046 ***	0.033 ***
	(23.180)	(11.168)	(12.082)	(10.578)
Caph	−0.130 **	−0.281 ***	−0.038	−0.281 ***
	(−2.113)	(−3.650)	(−0.495)	(−3.152)
Inf	0.003 ***	−0.005	0.001	−0.006 ***
	(4.619)	(−1.541)	(0.729)	(−2.613)
_cons	0.459 ***	0.646 ***	0.549 ***	0.641 ***
	(21.366)	(29.924)	(18.075)	(27.979)
City	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
N	966	3084	1231	2805
R2	0.944	0.820	0.867	0.845

Note: **, and *** indicate significance at the 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

presents the estimated results for factor inputs and allocation heterogeneity. The industrialization of land use is often accompanied by adjustments in industrial structure and changes in land utilization patterns. At higher stages of industrialization, the application of digital technologies can better integrate with industrial production processes, enabling intelligent and automated production and enhancing land output efficiency. This reveals that the effective application of digital technologies requires a mature industrial system as its foundation. Regions with high industrialization levels typically possess more robust industrial ecosystems and advanced technological foundations, enabling digital technologies to deeply integrate with existing industrial systems. Through industrial internet and smart factories, they achieve intensive land use. Conversely, in less industrialized stages, digital technologies lack interfaces for deep integration. Their impact may be more evident in optimizing land resource allocation and enhancing infrastructure construction efficiency, while struggling to reach the core aspects of land utilization. This reflects how digital technology, as an innovation factor, can propel land industrialization to higher levels and enhance land use efficiency. It also demonstrates that the role of digital technology must align with the local stage of industrial development.

Digital human capital, as a crucial production factor, significantly influences the effect of digital technology on land use efficiency. Regions possessing elevated digital human capital can more effectively comprehend and implement digital technology, maximizing its benefits for land utilization. This reveals the complementary relationship between technology and skills. Higher digital human capital translates to stronger technological absorption and innovative application capabilities, enabling the conversion of digital technologies into tangible improvements in land use efficiency. Conversely, in regions with lower digital human capital, the application of digital technology may be constrained by talent shortages, resulting in relatively weaker effects on improving land use efficiency. This indicates that the successful application of digital technologies requires not only hardware investment but also corresponding talent support. The results on factor input and allocation heterogeneity underscore the importance of synergizing digital technologies with local factor conditions. While advancing digital technology adoption, it is essential to strengthen talent development and industrial infrastructure construction to foster a virtuous cycle among technology, talent, and industry.

4.5. Mechanism Verification

This paper constructs an intermediary effect model based on two major economic mechanisms—“micro-level resource allocation” and “macro-level structural transformation”-to reveal the intrinsic pathway through which digital technologies enhance land use efficiency.

4.5.1. Micro Resource Allocation Mechanism

The micro-level resource allocation mechanism emphasizes that digital technologies promote intensive land use by optimizing the efficiency of factor allocation at both market and government levels. This paper starts from two micro-subjects: one is the channel of market subject proliferation measured by the logarithm of the number of new enterprises (Entre). The model estimation results in Column (1) of

Table 8. Mechanism Verification Results.

Variable	(1)	(2)	(3)	(4)
	Entre	Insu	Gtech	Ind
Dig	0.048 ***	0.002 ***	0.526 ***	0.004 ***
	(5.681)	(5.735)	(29.082)	(2.826)
Gov	−0.004	−0.000	−0.325	−0.081 ***
	(−0.034)	(−0.058)	(−1.203)	(−3.673)
Urb	−0.055	−0.004	0.194	0.024 *
	(−0.684)	(−1.418)	(1.205)	(1.931)
Eco	0.439 ***	0.012 ***	0.196 ***	0.006
	(14.480)	(11.643)	(3.336)	(1.213)
Caph	1.959 **	0.099 **	0.301	−0.341 ***
	(2.305)	(2.112)	(0.288)	(−2.933)
Inf	−0.051 *	−0.003 **	−0.076 ***	0.032 ***
	(−1.779)	(−2.278)	(−2.598)	(7.814)
_cons	7.216 ***	−0.075 ***	−0.532	−0.081 ***
	(30.838)	(−9.019)	(−1.165)	(−3.673)
City	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
N	4065	4065	4065	4065
R2	0.933	0.762	0.938	0.925

Note: *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

show that the coefficient estimate for digital technology is 0.048, significant at the 1% level. The application of digital technology has significantly reduced the fixed costs and information search costs associated with entrepreneurship, eliminating numerous operational barriers for micro-enterprises and decreasing initial dependence on land resources. The emergence of new ventures, particularly technology-based enterprises, compels the market to transfer inefficiently used land to these more productive entities, thereby enhancing overall urban land utilization efficiency. Second, the government behavior guidance channel is measured by the proportion of government science and technology expenditure (Ins). The coefficient estimates for digital technology are significantly positive, indicating that advancements in digital technology send a strong signal to governments about industrial upgrading. This prompts governments to adjust their fiscal expenditure structures, significantly increasing support for scientific and technological innovation. The government’s strategic R&D investment guides land, capital, and other factors to tilt toward high-tech fields and optimizes the directional allocation of resources. This targeted allocation avoids locking land expansion into low-end industries, thereby enhancing the macro-level returns on land investment at its source. The above findings validate Research Hypothesis 2, demonstrating that digital technologies improve land use efficiency at the micro level through dual pathways: stimulating market vitality and optimizing government actions. This mechanism reflects how digital technologies simultaneously strengthen the decisive role of the market in resource allocation and enhance the government’s capacity for precise regulation, forming a mutually empowering dynamic that collectively drives improvements in land use efficiency.

4.5.2. Macro-Structural Transformation Mechanism

The macro-structural transformation mechanism emphasizes how digital technologies reduce growth’s dependence on land by driving fundamental changes in economic structures and technological paradigms. First, the technological paradigm shift channel, measured by the logarithm of green inventions (Gtech). As shown in Column (3) of Table 8, the coefficient estimate for digital technology in this model is 0.562 and is significantly positive. This result indicates that digital technology empowers R&D and significantly promotes green technological innovation. This signifies that digital technology drives the transformation of land use paradigms toward sustainability. Digital technology provides critical support for the R&D and application of green technologies. The application of green technologies propels land use toward energy-saving, emission-reducing, and circular symbiotic paradigms, elevating land’s sustainable value and unifying economic and ecological efficiency. Second, the channel of industrial structure upgrading as measured by the industrial structure index (Ind). A higher value of this index (where the tertiary sector holds the highest weighting) indicates a more advanced economic structure. Table 8 Column (4) reports the estimated results of this mechanism, indicating that digital technology, as a general-purpose technology, significantly promotes the upgrading of industrial structure. This is because digital technology has given rise to digital economy sectors with lower land dependency, such as e-commerce and the platform economy. Simultaneously, it propels the transformation of traditional services into modern services, fundamentally reducing the economy’s reliance on land inputs for growth and enabling intensive growth. Consequently, as industrial upgrading progresses, the decoupling trend between economic growth and land consumption becomes increasingly pronounced. The above findings confirm Research Hypothesis 3, demonstrating that digital technologies reduce development’s dependence on land at the macro level by driving green innovation and industrial restructuring. This mechanism indicates that digital technologies serve not only as efficiency tools but also as catalysts for transforming growth patterns. By promoting technological innovation and structural optimization, they enhance land use efficiency, aligning with the endogenous requirements of sustainable development and green growth.

In summary, digital technologies contribute to enhancing urban land use efficiency through a multifaceted mechanism operating at two levels and via four specific channels: not only by optimizing micro-level resource allocation efficiency (stimulating entrepreneurship and guiding government actions), but also by driving macro-level structural transformation (promoting green technologies and industrial upgrading).

5. Further Analysis

5.1. Further Analysis: Synergistic Mechanisms of Digital Technology’s Empowering Effects

The aforementioned benchmark regression and mechanism verification confirm the significant role of digital technologies in enhancing urban land use efficiency and their transmission pathways. However, the full realization of digital technology’s efficacy does not occur in a vacuum; it is profoundly shaped and regulated by external policy environments and infrastructure conditions. This study examines the role of institutional environments in enhancing land use efficiency through digital technologies. It introduces two moderating variables—Government Digital Attention (Atdig) and Environmental Policy Intensity (Envip)—from governmental and environmental perspectives, respectively. These variables interact with the core explanatory variable, Digital Technology Level, in regression analyses. The aim is to reveal how institutional environments shape the pathways and efficacy boundaries of digital technologies in improving land use efficiency.

5.1.1. The Modulating Effect of Government Digital Attention: Signal Transmission and Resource Allocation

Based on information economics and government behavior theory, this study introduces the Government Digital Attention (Atdig) variable to examine whether strategic government emphasis and policy signals can amplify the enabling effects of digital technologies. The signals of digital focus conveyed by the government through policy documents and development plans not only reduce market uncertainty and guide the allocation of production factors toward the digital sphere but also strengthen the digital consensus between government and enterprises, creating a more favorable institutional environment for the research, development, and application of digital technologies. Therefore, by conducting a textual analysis of the full texts of government work reports from various cities, we calculated the total word frequency associated with keywords such as digital economy, artificial intelligence, big data, and Internet Plus. This word frequency serves as a metric for assessing the level of digital focus within government policies. We anticipate that the high level of government attention to digitalization will complement digital technologies, amplifying their positive impact on enhancing land use efficiency. Column (1) results in

Table 9. Moderating Effects.

Variable	(1)	(2)
	Atdig	Envip
Dig	0.002 ***	0.002 ***
	(2.899)	(3.449)
Atdig	−3.479 ***
	(−6.340)
Envip		−0.008 ***
		(−3.687)
c.Dig##c.Atdig	1.043 ***
	(10.403)
c.Dig##c.Encip		0.002 ***
		(7.046)
Gov	−0.011	−0.001
	(−0.968)	(−0.085)
Urb	0.017 ***	0.019 ***
	(2.943)	(3.296)
Eco	0.036 ***	0.041 ***
	(15.346)	(17.327)
Caph	−0.162 ***	−0.116 **
	(−3.025)	(−2.195)
Inf	−0.001	−0.003 ***
	(−1.391)	(−3.026)
_cons	0.617 ***	0.576 ***
	(34.410)	(31.238)
City	Yes	Yes
Year	Yes	Yes
N	3955	3780
R2	0.843	0.860

Note: **, and *** indicate significance at the 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

show that the interaction term between digital technology (Dig) and government digital attention (Atdig) (c.Dig##c.Atdig) is significantly positive at the 1% level. This indicates that government digital attention significantly amplifies the positive impact of digital technology on land use efficiency. In cities where governments prioritize digital development, the application of digital technology benefits from clearer policy expectations, more robust resource support (such as fiscal subsidies and data openness), and more efficient administrative services. Consequently, it can more seamlessly integrate into all stages of land planning, management, and monitoring, ultimately driving more effective intensification and intelligent transformation of land use. The above conclusions are visually presented in Figure 3a. Figure 3a shows that when government digital attention is low, the marginal curve of digital technology’s impact on land use efficiency is relatively flat. However, when government digital attention reaches higher levels, the marginal effect curve steepens and consistently remains above the zero line. These results also indicate that enhanced government policy signals can significantly expand the scope of digital technology’s contribution to land use efficiency, validating the critical guiding role of government actions in the implementation of digital technologies.

Cities with higher land use efficiency may possess stronger governance capabilities, making them more inclined to emphasize the digital economy in their policies. However, the word frequency analysis method employed in this study to measure this variable—based on government work reports—is subject to annual fluctuations and significant influence from higher-level policy directives, which somewhat weakens its endogeneity. Furthermore, the model incorporates economic development and urbanization levels as control variables while fixing the city variable, thereby mitigating bias arising from omitted variables.

5.1.2. Regulatory Effects of Environmental Policy: Green Constraints and Technological Induction

Building upon the Porter Hypothesis in environmental economics, this study examines the moderating role of environmental policy intensity (Envip). Stringent environmental regulations compel cities to pursue new models of sustainable development. Digital technologies serve as crucial tools for achieving green and intensive development. Environmental policies will generate “inducement demand” for digital technologies, creating policy synergies between the two in promoting green and low-carbon land use patterns. Therefore, this paper selects key air pollution control zones to evaluate environmental policies. If a city is located within a key air pollution control zone, it is assigned a value of 1; otherwise, it is assigned a value of 0. The estimation results in Table 9 indicate that the coefficient for the interaction term between digital technology and environmental policy intensity (Envip) (c.Dig##c.Encip) is 0.002, significant at the 1% level. Figure 3b illustrates the differential moderating effects of environmental regulation policies, consistent with the results estimated in Table 9. When cities are not designated as key air pollution control zones, the marginal effect of digital technologies on land use efficiency is relatively small. Conversely, when cities are classified as key air pollution control zones, the marginal effect significantly increases. These findings demonstrate that stringent environmental regulations can indeed stimulate induced demand for digital governance technologies, fostering a virtuous cycle between green development and technological empowerment. This demonstrates that environmental policy can indeed form a positive complementarity with digital technology: the implementation of environmental policy requires the support of digital technology, while the application of digital technology also contributes to better achieving the objectives of environmental policy.

The policy for key air pollution control zones is uniformly delineated by the Ministry of Ecology and Environment. The implementation areas of this policy are determined by urban air quality rather than urban land use efficiency. Furthermore, the model incorporates urbanization levels and economic development levels as control variables, thereby further eliminating potential selection bias arising from policy interventions driven by urban development.

5.1.3. Analysis of Heterogeneity in Moderating Effects

Given the reality of uneven regional development in China, this study further divides the sample into three sub-samples—Eastern, Central, and Western regions—based on geographic location to examine regional variations in the enabling effects of digital technologies. Heterogeneity tests were conducted separately for the moderating effects of government digital attention and environmental policies. The results are presented in

Table 10. Test for Heterogeneity of Moderating Effects.

Variable	Atdig			Encip
	Eastern	Central	Western	Eastern	Central	Western
Dig	−0.001	0.000	0.002	−0.001	−0.000	0.003 ***
	(−0.864)	(0.132)	(1.158)	(−1.313)	(−0.483)	(2.708)
Atdig	−0.363	−1.495 **	−5.184 ***
	(−0.429)	(−2.111)	(−4.718)
Encip				−0.005 **	−0.015 ***	−0.008
				(−2.214)	(−4.200)	(−0.951)
c.Dig##c. Atdig	0.132	0.540 ***	1.649 ***
	(1.028)	(3.817)	(6.462)
c.Dig##c.Encip				0.001 ***	0.002 ***	0.003 **
				(3.610)	(3.655)	(2.384)
Gov	0.004	−0.020	−0.055 ***	−0.016	−0.007	−0.034 *
	(0.219)	(−1.098)	(−3.134)	(−0.963)	(−0.379)	(−1.796)
Urb	0.012 **	0.024 ***	−0.011	0.013 ***	0.024 **	−0.009
	(2.476)	(2.605)	(−0.534)	(2.649)	(2.530)	(−0.441)
Eco	0.048 ***	0.032 ***	0.047 ***	0.050 ***	0.038 ***	0.055 ***
	(16.934)	(8.280)	(7.618)	(19.246)	(9.749)	(8.525)
Caph	−0.279 ***	−0.120	0.171 *	−0.235 ***	−0.105	0.249 ***
	(−3.322)	(−1.393)	(1.826)	(−2.785)	(−1.297)	(2.672)
Inf	0.000	−0.004	−0.007	0.000	−0.007	−0.008 *
	(0.100)	(−0.833)	(−1.509)	(0.293)	(−1.486)	(−1.697)
_cons	0.519 ***	0.651 ***	0.593 ***	0.503 ***	0.615 ***	0.526 ***
	(21.164)	(21.655)	(14.289)	(22.175)	(20.308)	(11.885)
City	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
N	1428	1350	1164	1386	1260	1120
R2	0.918	0.830	0.818	0.924	0.840	0.834

Note: *, **, and *** indicate significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses.

.

The moderating effect of government digital attention (Atdig) exhibits an increasing trend from east to west. In eastern regions, the interaction coefficient is 0.132 and not statistically significant. In contrast, the coefficients for central and western regions are significantly positive at the 1% level, with values of 0.540 and 1.649, respectively. This indicates that the moderating effect of government digital attention is more pronounced in economically underdeveloped areas. In the western region, where digital infrastructure is relatively weak and the institutional environment remains underdeveloped, the government’s digital focus can more effectively serve as a signal enhancer and resource guide. Through top-down policy support, it can rapidly compensate for market mechanism deficiencies, significantly amplifying the role of digital technology in promoting land use efficiency.

The moderating effect of environmental policy intensity (Envip) also exhibits regional heterogeneity. The interaction term coefficients for eastern, central, and western regions are all significantly positive, with relatively minor differences in intensity. The interaction coefficients for these three regions all fall below 0.1, with the western region exhibiting the largest coefficient value at merely 0.003. Combined with the finding that the environmental policy intensity variable itself is insignificant in the western region, this indicates that the Porter effect of environmental policy intensity in the west relies more heavily on synergies with digital technology. This highlights the unique value of technology-driven environmental governance in ecologically fragile areas.

6. Discussion, Conclusions and Policy Implications

6.1. Discussion

Against the dual backdrop of accelerating global digital transformation and intensifying resource and environmental constraints, achieving intensive and efficient utilization of urban land resources has become a critical issue for modernizing urban governance systems worldwide [9,57,58]. Drawing on data from China’s prefecture-level cities between 2009 and 2023, this study adopts digital technology as its entry point. It conducts a comprehensive analysis centered on the logical framework of “urban land use efficiency—impact effects and mechanisms of digital technology—pathways to enhance digital technology’s enabling effects.” The study confirms that digital technologies significantly enhance urban land use efficiency, as confirmed by robustness and endogeneity tests. This conclusion complements existing research on the enabling effects of digital technologies [43,47] while enriching studies on pathways to improving land use efficiency [30,59]. This study extends the digital dividend theory proposed in [9], demonstrating that efficiency gains from digital technologies extend beyond labor productivity to encompass natural resource management [60,61].

This study further reveals how digital technologies jointly influence urban land use through two pathways: micro-level resource allocation and macro-level structural transformation. Previous research has explored how digital technologies lower barriers to entrepreneurship and accelerate knowledge spillovers, thereby stimulating market vitality [62], while macro-level studies have primarily focused on how technological progress drives economic development and transformation [63,64]. Future research may further explore this topic from a nonlinear perspective, delving deeper into the multifaceted enabling effects of digital technology. This study integrates macro and micro perspectives within a unified analytical framework. Mechanism analysis indicates that digital technologies optimize resource allocation by stimulating new business incubation and enhancing innovation support. At the macro level, they achieve structural transformation in urban development by elevating green technologies and promoting industrial upgrading, thereby demonstrating that digital technologies not only optimize land resource utilization but also guide urban economic structures toward more sustainable models.

The enabling effects of digital technologies exhibit significant spatial and urban heterogeneity. In western regions and highly centralized cities, the marginal benefits of digital technologies are more pronounced. The former confirms that late-developing cities can more readily achieve leapfrog development when adopting new technologies [65]. The latter benefits from amplified technological externalities within mature industrial and population networks characteristic of highly centralized cities [66,67]. Furthermore, cities with higher levels of industrialization and digital human capital are better positioned to achieve efficient resource integration and allocation through digital technologies, as stronger foundational capabilities and human capital enable more effective capture of the dividends brought by new technologies. The conclusions drawn from this heterogeneity analysis provide a theoretical basis for formulating differentiated policies.

The success of technological transformation often hinges on the support of institutional and policy environments [68]. This study emphasizes that the synergistic effects of government and environmental policies are crucial for unleashing the enabling effects of digital technologies. Research findings indicate that both strategic government focus and stringent environmental regulations can generate significant synergistic effects with digital technologies. This indicates that digital technology empowerment is not a purely spontaneous market process; institutional and policy support is essential for maximizing technological efficacy [69]. Therefore, to maximize the positive role of digital technologies in enhancing land use efficiency, it is imperative to establish a comprehensive governance system that fosters multi-stakeholder coordination and aligned efforts.

6.2. Conclusions

Against the dual backdrop of accelerating global digital transformation and intensifying resource and environmental constraints, achieving intensive and efficient utilization of urban land resources has become a critical issue for modernizing urban governance systems worldwide. Based on data from China’s prefecture-level cities from 2009 to 2023, this paper takes digital technology as an entry point to comprehensively examine its influence on urban land use efficiency, following the logical framework of “urban land use efficiency—impact effects and mechanisms of digital technology—pathways to enhance digital technology’s enabling effects.” The core conclusions are summarized as follows: (1) Digital technologies exert a significant and robust positive effect on overall urban land use efficiency, a finding confirmed by a series of robustness checks and endogeneity tests. This indicates that amid the digital wave, digital technologies have become a key factor in enhancing cities’ sustainable development capabilities. (2) The enabling effects of digital technologies exhibit significant heterogeneity. They have the greatest impact on enhancing land use efficiency in western regions and highly centralized cities, likely because these areas achieve higher marginal returns following digital technology penetration. Simultaneously, cities with higher levels of land industrialization and digital human capital are better positioned to leverage digital technologies for efficient resource integration and allocation, thereby amplifying their positive influence on land use efficiency. (3) Digital technologies influence urban land use through two interrelated pathways: “micro-level resource allocation” and “macro-level structural transformation.” At the micro level, digital technologies optimize resource allocation by stimulating new business incubation and enhancing innovation support. At the macro level, they drive structural shifts in urban development by advancing green technologies and promoting industrial upgrading. This demonstrates that digital technologies not only optimize existing resources but also generate incremental value for cities. (4) The synergistic effects of government digital attention and environmental policies are crucial for realizing the enabling impact of digital technologies. Both strategic government focus and stringent environmental policies can form positive synergies with digital technologies, thereby enhancing urban land use efficiency.

6.3. Policy Recommendations

Based on the findings above, this paper offers the following key policy implications for government decision-makers. These recommendations aim to promote the continuous improvement of urban land use efficiency through the effective utilization of digital technologies, thereby achieving high-quality and sustainable urban development.

6.3.1. Deepen the Application of Digital Technologies to Expand the Technical Boundaries for Enhancing Land Use Efficiency

Governments at all levels should regard digital technology as a key driver for enhancing urban land use efficiency. To promote the deep integration of “digital technology and land management,” government departments should accelerate the systematic application of digital technology in land administration. This includes establishing city-level land data platforms, strengthening remote sensing monitoring, and integrating GIS systems to achieve precise identification and dynamic control over land use status, efficiency, and structure. For new district development or urban renewal projects, digital twin models should be employed to predict land resource utilization efficiency, thereby optimizing land use plans. Concurrently, digital governance capabilities must extend to grassroots levels, strengthening data-driven mechanisms for planning approvals, land use conversions, and supply-demand matching. This will facilitate the transition from extensive land use to refined, intelligent management. Such measures are not only necessitated by technological evolution but also represent a strategic choice to address the tension between high-quality urban development and resource constraints.

6.3.2. Develop Differentiated Strategies to Optimize Regional Synergies in Digital Technologies

Policy formulation should avoid a one-size-fits-all approach and instead adopt differentiated development strategies tailored to local conditions. For western regions, governments should increase investment in digital infrastructure and provide targeted policy support to fully unlock the immense potential of digital technologies in these areas. For highly centralized cities, the focus should be on optimizing existing land resources through digital technology, by developing, for instance, digital government services and smart transportation systems, thereby addressing the efficiency bottlenecks caused by high-density development. Simultaneously, cities with high levels of land industrialization should be encouraged to deepen their “digital+” transformation, guiding traditional manufacturing industries toward digital upgrades and promoting efficient, intensive use of industrial land.

6.3.3. Strengthen the Multidimensional Role of Digital Technologies and Ensure the Smooth Transmission of Technological Dividends to Enhance Land Efficiency

First, foster a dynamic environment for startups to stimulate innovation. The government should encourage the growth of startups by introducing supportive policies such as tax incentives, designating dedicated land parcels, and streamlining approval processes. Particularly for innovation sectors critical to land use efficiency, it should provide cost-effective, well-located spaces to attract firms exploring pioneering land use models. Second, increase the intensity of government investment in science and technology to foster technological innovation in land use. Guide government science and technology funding toward the research and development of digital technologies that enhance land use efficiency, such as land efficiency simulation and assessment, high-precision remote sensing technology, and geographic information systems. Third, promote green technology innovation to enhance resource efficiency. This involves advancing the development and application of technologies such as green buildings, intelligent management systems, and ecological restoration. For instance, digital energy management systems can be deployed in the industrial sector to reduce energy consumption per unit of output, while smart agricultural technologies should be adopted in agriculture to minimize land resource waste. Fourth, guide industrial structure upgrades to unlock structural dividends. Align with industrial policies to establish incentive mechanisms prioritizing land allocation for digital industries such as high-tech services, the platform economy, and smart manufacturing. Implement dynamic assessments of industrial land use, phase out high-pollution, low-output projects, and mandate continuous improvements in land utilization efficiency.

6.3.4. Establish a Synergistic Policy Framework to Enhance the Adaptability of Digital Technologies in Empowering Land Use Efficiency

Policy-making should adopt a tailored and systematic approach to build a diversified institutional support system. This is crucial for enhancing the effectiveness and adaptability of digital technologies in land governance and for creating synergistic effects across multiple policy domains. In terms of government signal guidance, the government should transmit positive digital development signals to the market through clear planning and policy guidance, guide social capital to flow to the fields of digital technology innovation and application, and create a favorable business environment. The government should also continue investing in new digital infrastructure such as 5G networks, data centers, and the Internet of Things to provide robust infrastructure support for the widespread application of digital technologies. Concurrently, government efforts should extend to fostering industry–academia–research collaboration, facilitating the establishment of digital innovation laboratories and technology transfer centers. Regarding environmental policy intensity, environmental policies should be integrated with digital technology development. For instance, financial subsidies or tax breaks could be provided to enterprises adopting digital technologies for emission reduction or resource recycling that can transform environmental constraints into drivers for digital innovation, thereby achieving simultaneous improvements in efficiency and ecological sustainability.