How Does New Quality Productivity Impact Land Use Efficiency? Empirical Insights from the Central Plains Urban Agglomeration

Abstract

As a pivotal driver of high-quality development, new quality productivity (NQP) forms an indispensable synergistic relationship with land use efficiency (LUE) for achieving regional sustainability. Based on panel data from 29 prefecture-level cities in the Central Plains Urban Agglomeration (CPUA) from 2010 to 2023, this study integrates the entropy-weighted TOPSIS method, super-efficiency Slack-Based Measure (SBM) model, Malmquist index, and fixed-effects models to systematically explore the spatiotemporal evolution of NQP and its underlying impact mechanism on LUE. Key findings reveal: (1) The comprehensive NQP index of the CPUA increased from 0.280 to 0.828, exhibiting a “stepwise rise” trend, with a spatial pattern characterized by a “core–secondary–periphery” three-tier gradient distribution. Zhengzhou, as the core growth pole, played an innovative leading role, while peripheral cities (e.g., Handan, Hebi) remained constrained by resource-dependent economic structures, with NQP indices consistently below 0.2. (2) The average LUE in the study area increased from 0.917 to 1.031. Cities within Henan Province generally performed better than those in Hebei, Shanxi, and Anhui provinces. Total factor productivity grew at an average annual rate of 16.4%, with technological progress serving as the primary driver. (3) NQP exerts a significantly positive impact on LUE, yet with notable heterogeneity: large-scale cities enhanced intensive land use substantially through technological agglomeration and industrial upgrading; cities with scarce arable land and high economic development levels effectively leveraged NQP to boost LUE; in contrast, small cities, regions rich in arable land, and areas with low economic development have not established effective synergistic mechanisms, hindered by limited technological absorption capacity, path dependence, and factor bottlenecks. This study provides empirical support and actionable insights for optimizing land resource allocation and advancing coordinated development between NQP and LUE in similar urban agglomerations.

1. Introduction

The global economy is undergoing a profound transformation driven by technological revolution and industrial restructuring [1]. Within this context, China has proposed the strategic concept of new quality productivity (NQP), an advanced productivity paradigm formally introduced in late 2023 [2,3]. Characterized by high technology, efficiency, and quality standards, NQP represents an innovation-driven approach aimed at achieving substantial total factor productivity gains [4,5]. This paradigm marks a fundamental departure from traditional factor-driven growth models, recalibrating economic development logic and necessitating novel approaches to production factor allocation.

As the essential spatial carrier for production factors, land plays a pivotal role in the cultivation and materialization of NQP [6]. Theoretically, optimized land resource allocation can act as a catalyst, initiating cross-factor reforms and unlocking compounded effects among various production inputs [7,8], while the infusion of digital technologies and advanced knowledge propels land use patterns toward intensive, high-value-added activities [9,10]. Concurrently, land use efficiency (LUE) serves as a crucial indicator for assessing the coordination between socioeconomic development and ecological sustainability [11,12,13]. The synergy between NQP’s sustainable development principles and LUE’s pursuit of economic, social, and environmental benefits [14,15,16] underscores the critical importance of investigating their intrinsic linkage for fostering high-quality, sustainable economic development.

Despite the strategic importance, rigorous empirical research on the NQP-LUE nexus remains nascent. Existing literature can be broadly categorized into three streams. The first focuses on conceptualizing NQP itself, delving into its theoretical origins [3], scientific connotation [17,18], and strategic value [19,20,21], thereby laying a preliminary foundation for its conceptual edifice. A second, emerging stream of cross-disciplinary inquiry has begun to interrogate the relationship between land elements and productivity evolution under this new framework. Scholars posit that land is no longer a passive factor but an active platform integrating innovation [22]. For instance, Liu et al. [23] theorized that land resource allocation influences productivity efficiency primarily through three channels: industrial structure adjustment, technological innovation incentives, and ecological environment amelioration. Yu et al. [24] further contextualized this relationship by examining the co-evolution of NQP development and land resource allocation through historical, theoretical, and contemporary pragmatic lenses. Complementarily, studies also highlight the inverse constraint. Land resource misallocation—such as excessive allocation to low-efficiency traditional industries or disordered urban sprawl—significantly constrains the quality of urban economic development [7,25], thereby inhibiting the accumulation of innovation factors and impeding regional NQP cultivation. However, a third stream—rigorous quantitative empirical testing of the NQP-LUE relationship—is conspicuously underdeveloped, with extant work leaning heavily towards qualitative exposition [6,23,24,26].

Consequently, significant research gaps persist: (1) A lack of a systematic, context-specific quantitative assessment framework for NQP. While some studies have attempted to measure NQP, a systematic and widely recognized evaluation framework applicable to land use contexts is still lacking. (2) An unclear “black box” regarding the precise transmission mechanisms (e.g., technological progress, factor allocation) through which NQP influences LUE. (3) Insufficient exploration of the spatial and conditional heterogeneity of this impact. It is plausible that the impact of NQP on LUE varies significantly across regions with different economic development levels, city sizes, or resource endowments (e.g., arable land resources). However, this spatial and conditional heterogeneity has received scant attention, limiting the generalizability of findings and the formulation of targeted, place-based policies.

The Central Plains Urban Agglomeration (CPUA) presents an ideal empirical setting to address these research gaps. Situated in China’s strategic inland hinterland, the CPUA serves as a vital economic nexus and a testing ground for national regional development strategies. Characterized by a midtier level of economic development nationally, the CPUA embodies the classic dual challenge faced by many developing regions: the imperative to accelerate economic growth while simultaneously ensuring sustainable land resource management and efficiency enhancement [27,28]. In an era of rapid urbanization, the escalating demand for land resources intensifies the tension between growth objectives and resource constraints [29,30]. Within this context, reconciling the advancement of NQP with the enhancement of LUE emerges as a pressing and complex issue demanding immediate scholarly and policy focus.

To bridge these critical gaps, the primary objective of this study is to empirically investigate the impact mechanism of NQP on LUE within the CPUA and to unravel the heterogeneity of this relationship. Compared to previous studies, this research offers distinct advantages and innovations: (1) Methodologically, it integrates the entropy-weighted TOPSIS method, super-efficiency SBM model, Malmquist index, and fixed-effects models to construct a comprehensive “measurement–evolution–mechanism–heterogeneity” analytical chain, enabling a more rigorous and multi-faceted test of the NQP-LUE nexus. (2) In terms of content, it moves beyond theoretical postulation to provide the first empirical validation of the NQP-LUE relationship in the CPUA, explicitly tests technological progress as a key mediating pathway, and systematically examines heterogeneity based on city scale, arable land endowment, and economic development level. (3) From a geographical perspective, it focuses on a strategically important yet underexplored urban agglomeration, offering place-based insights that complement studies focused on coastal or more developed regions.

The remainder of this paper is structured as follows: Section 2 elaborates the theoretical analysis and research hypotheses. Section 3 describes the study area and data sources. Section 4 details the methodology and indicator systems. Section 5 presents the empirical results, including spatiotemporal evolution, impact mechanisms, and heterogeneity analysis. Section 6 discusses the findings in relation to existing literature. Finally, Section 7 presents the conclusions and policy implications, and discusses research limitations along with future directions.

The findings of this study are expected to bridge the critical gap between theoretical postulation and empirical evidence regarding the NQP-LUE nexus. By clarifying the impact mechanisms and conditional heterogeneities, this research will provide a robust empirical foundation and actionable insights for optimizing land resource allocation and fostering targeted NQP development within the CPUA and other comparable regions, thereby supporting their transition toward sustainable and high-quality development trajectories.

2. Theoretical Analysis and Hypotheses

As an advanced productivity paradigm centered on innovation, new quality productivity (NQP) fundamentally reshapes the input–output dynamics of production factors, thereby establishing a profound theoretical connection with land use efficiency (LUE). Within the framework of Chinese-style modernization, where sustainable development is paramount, the efficient allocation of the finite resource of land becomes critically important [24]. NQP, with technological innovation as its core driver, recalibrates the allocation mechanisms of production factors and propels industrial transformation and upgrading [11,31]. This process offers a novel theoretical lens and actionable pathways for understanding and enhancing LUE. Grounded in the intrinsic linkage between NQP and LUE, this study proposes the following hypotheses to systematically investigate the impact relationship, transmission mechanisms, and underlying heterogeneity.

H1. 

NQP exerts a significant positive influence on LUE.

The proposed positive impact of NQP on LUE is theorized to operate through three interconnected mechanisms, forming the core of our analytical framework:

(a)

Factor configuration optimization through agglomeration economies. NQP, characterized by the integration of high-end knowledge, advanced technology, and data, transcends traditional factor inputs subject to diminishing returns. It fosters synergistic interactions among these advanced factors, which tend to geographically agglomerate in innovation hubs. This agglomeration reduces transaction costs, accelerates knowledge spillovers, and enables more efficient matching of factor inputs—including land—to their most productive uses. Consequently, the land input–output relationship is reconfigured, elevating output per unit of land area.

(b)

Technological permeation and empowerment mitigating spatial mismatch. At the technological level, advancements such as Artificial Intelligence, Big Data, and the Internet of Things enable the development of smart land management systems. These technologies address classic spatial mismatch problems by improving information flows and analytical capabilities. They facilitate precise matching of land supply and demand, mitigate allocation distortions stemming from information asymmetry, and support data-driven decision-making. This technological empowerment directly reduces land idling and misuse, thereby enhancing utilization precision and overall efficiency.

(c)

Industrial structure transformation and upgrading guided by locational advantage. NQP drives industries toward high-end, intelligent, and green development. This shift embodies a structural transformation of the regional economy. Land resources are progressively reallocated from low-value-added, extensive-use sectors to high-value-added, intensive-use sectors that benefit from or even require proximity to NQP clusters (e.g., R&D centers, advanced manufacturing). This process optimizes the spatial structure of land use in accordance with evolving regional comparative advantages, lifting aggregate land use efficiency.

H2. 

Technological progress serves as a primary transmission pathway through which NQP enhances LUE.

Technological progress is the hallmark of NQP and is posited as a central channel mediating its effect on LUE. This mediation manifests in several key areas:

(a)

Land management and decision-making. The application of smart land management systems, leveraging cloud computing and Internet of Things, allows for real-time monitoring and sophisticated analysis of land resource data. This provides a scientific foundation for land-use planning and management, minimizing inefficiencies and errors in decision-making that lead to land waste.

(b)

Production process transformation. Technological progress revolutionizes production methods across sectors. In industry, intelligent manufacturing and automation increase production efficiency while reducing the consumption of land and other inputs per unit of output. In agriculture, modern planting techniques and precision farming enhance crop yields and land productivity.

(c)

Industrial structure facilitation. As a key driver of industrial upgrading, technological innovation catalyzes the emergence of new industries and the high-end transformation of traditional ones. This guides land resources toward more efficient and productive sectors, optimizing the macro-level structure of land use and ultimately boosting overall LUE.

Synthesizing the aforementioned analysis, the postulated mechanism underlying the effect of new quality productivity on land use efficiency is graphically summarized in Figure 1.

3. Study Area and Data Sources

3.1. Study Area

The Central Plains Urban Agglomeration (CPUA), designated as a national-level urban cluster, is predominantly situated within Henan Province while incorporating adjacent regions from Shanxi, Shandong, Hebei, and Anhui provinces (Figure 2). CPUA boasts a superior geographical location, serving as an important hub connecting urban agglomerations like Beijing–Tianjin–Hebei, Yangtze River Delta, Chengdu–Chongqing, and the Guanzhong Plain. As of 2023, the permanent population of the CPUA exceeded 160 million, with an economic output accounting for over 80% of Henan Province, making it a core bearing area for the central region’s rise strategy [32]. The area has prominent natural resource endowments, vast arable land area, grain output accounting for over one-tenth of the national total, and abundant mineral resources such as coal and bauxite, laying the foundation for traditional industrial development [33,34]. In recent years, with the advancement of the national “dual circulation” strategy and the ecological protection and high-quality development planning of the Yellow River Basin, the CPUA has become a forefront for cultivating new quality productivity (NQP). However, rapid urbanization and industrialization have intensified the imbalance between land supply and demand [35]. The expansion of construction land and the protection of arable land form rigid constraints [28]. Core cities like Zhengzhou and their adjacent areas exhibit typical problems such as disordered urban spatial sprawl and low allocation efficiency of industrial land, while some counties in eastern and southern Henan still exhibit extensive tendencies in land development and utilization. In contrast, some areas within the urban agglomeration, through industrial upgrading, industry–city integration, and ecological restoration, have gradually explored an “intensive, efficient, and multi-functional” land use model. Against this background, systematically exploring the mechanism of NQP’s impact on LUE in the CPUA has important practical significance for optimizing territorial spatial layout and promoting regional high-quality development.

3.2. Data Sources and Processing

Based on the geographical scope and administrative divisions of the CPUA, and considering data availability and research object integrity, this study ultimately selected 29 prefecture-level cities within the region as research samples. Jiyuan City was temporarily excluded from the research scope due to severe lack of relevant data. The selected 29 samples cover the main carriers of economic activities in the CPUA and can objectively reflect the overall characteristics of the regional NQP level and LUE. The research period was set from 2010 to 2023. Data types included urban land use data, socio-economic data, and environmental pollution data. Among them, land use data were sourced from the Wuhan University China 1985–2023 Annual Land Use Dataset (CLCD) (https://zenodo.org/records/12779975) (accessed on 2 June 2025), with a spatial resolution of 30 m. Vector data came from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/) (accessed on 10 June 2025). Socio-economic and environmental data were mainly sourced from the 2011–2024 China City Statistical Yearbook, China Urban Construction Statistical Yearbook, China Environmental Statistical Yearbook, and various cities’ national economic and social development bulletins (https://www.stats.gov.cn/) (accessed on 10 June 2025), as well as the China Environmental Status Bulletin (https://www.mee.gov.cn/hjzl/sthjzk/) (accessed on 10 June 2025). A small amount of missing data was supplemented using linear interpolation to ensure data continuity and integrity.

4. Materials and Methods

4.1. Indicator System Construction

4.1.1. NQP Indicator System

New quality productivity (NQP) signifies an advanced productive paradigm, characterized by innovation-driven transformation that transcends conventional development models [4,9]. It encompasses three core dimensions grounded in technological breakthroughs and cross-domain integration [3,36]: (1) the new-quality labor force, focusing on human capital advancement through input–output metrics reflecting labor quality and efficiency; (2) the new-quality labor objects, centered on ecological and industrial modernization, evaluating the sustainability of production targets and industrial upgrading; and (3) the new-quality labor means, emphasizing regional innovation capacity and infrastructure development as technological and material foundations for production. Notably, infrastructure in this framework is divided into digital and traditional subtypes: digital infrastructure (e.g., broadband access, telecommunication services) provides the basis for data flow and digital production, while traditional infrastructure (e.g., railway land, highway mileage) ensures the efficient circulation of people and logistics required for factor agglomeration. This dual classification addresses the limitation of existing studies that overemphasize digital infrastructure while neglecting the foundational role of traditional infrastructure in NQP cultivation, reflecting the systematicness and novelty of the indicator system. Building on this three-dimensional framework, we construct an integrated evaluation system comprising 23 systematically designed indicators to comprehensively capture the multidimensional attributes of NQP and assess regional progress toward modernized productivity. The detailed indicator system is presented in

Table 1. Evaluation index system of new quality productivity.

Target Layer	Criterion Layer	Indicator Layer	Measurement Method	Unit
New quality labor force	New quality human capital input	Education investment	Education expenditure/fiscal expenditure	%
		R&D personnel quantity	R&D personnel	number
	New quality human capital output	Human capital level	College students/resident population	%
		Employee personal capability	Average wage of employed staff	Yuan
		Employee high-quality level	Regular higher education institutions	number
New quality labor objects	Ecological environment	Green development	Comprehensive utilization rate of general industrial solid waste	%
			Centralized treatment rate of urban sewage treatment plants	%
			Green invention applications	number
			Green utility model applications	number
	Emerging industries	Informatization	R&D enterprises	number
		Digitalization and intelligence	E-commerce active enterprises	number
			Artificial intelligence enterprises	number
		Networking	Industrial internet enterprises	number
			Satellite internet enterprises	number
		Future industries	Robot installation density	number
New quality labor means	Technology R&D and Innovation	Tech innovation and development	Total R&D expenditure	10,000 Yuan
			Science and technology investment/fiscal expenditure	%
		Innovation output	Patent applications	number
	Infrastructure	Digital infrastructure	Broadband access subscribers	number
			Total telecommunication service volume	10,000 Yuan
		Traditional infrastructure	Railway transportation land area	m2
			Highway route mileage	km
			Urban per capita road area	m2/person

.

4.1.2. LUE Indicator System

The assessment of urban land use efficiency (LEU) requires an integrated evaluation of economic outputs and ecological impacts. Grounded in established theory and principles of validity and data accessibility [37,38,39], this study constructs a comprehensive system (

Table 2. Evaluation index system of land use efficiency.

Dimension	Sub-Dimension	Indicator Type	Indicator	Unit
Urban land use efficiency	Input indicators	Capital input	Growth rate of fixed asset investment	%
		Land input	Urban built-up area	km2
		Labor input	Employment share in secondary and tertiary sectors	%
		Technology input	Expenditure on science and technology in fiscal budget	10,000 Yuan
	Desirable output indicators	Economic output	Value added of urban secondary and tertiary industries	10,000 Yuan
	Undesirable output indicators	Environmental negative effect output	Industrial wastewater discharge	10,000 Tons
			Municipal solid waste treatment volume	10,000 Tons

). The input dimension incorporates four production factors: urban built-up area (land), fixed-asset investment (capital), employment share in secondary/tertiary sectors (labor), and science/technology expenditure (technology). For outputs, value-added from secondary/tertiary industries serves as the desirable economic output, while industrial wastewater discharge and municipal solid waste treatment volume are included as undesirable environmental outputs, aligning with the Environmental Kuznets Curve framework [40,41]. This dual-type output design enables a balanced assessment of economic and environmental performance in land use.

4.2. Super-Efficiency SBM Model

To accurately measure LUE while accounting for undesirable outputs and input/output slacks, this study employs the non-radial, non-oriented Super-Efficiency Slack-Based Measure (SBM) model [42,43]. This model overcomes the limitations of traditional radial DEA models by directly incorporating slack variables into the efficiency score, allowing for a more precise assessment of efficiency when economic and environmental outputs are considered simultaneously [12]. The super-efficiency variant enables further discrimination among efficient decision-making units (DMUs). The specific formulation is presented in Equations (1) and (2).

$${\rho }^{\ast }={\displaystyle \frac{1-\frac{1}{m}{\displaystyle {\sum }_{i=1}^m\frac{s_i^{-}}{x_{i0}}}}{1+\frac{1}{s_1+s_2}\left({\displaystyle {\sum }_{r=1}^{s_1}\frac{s_r^g}{y_{r0}^g}}+{\displaystyle {\sum }_{r=1}^{s_2}\frac{s_r^b}{y_{r0}^b}}\right)}}$$

(1)

$$s.t.\left\{\begin{array}{l}{x}_k=X\lambda +s^{-},y_k^g=Y^g\lambda -s^g,z_k^b=Z^b\lambda +s^b\\ s^{-}\ge 0,s^g\ge 0,s^b\ge 0,\lambda \ge 0\end{array}\right.$$

(2)

where m, s₁, and s₂ represent the number of input, desirable output, and undesirable output factors, respectively. s⁻, s^g, and s^b denote the slack vectors for inputs, desirable outputs, and undesirable outputs, while x, y^g, and z^b correspond to the actual values of inputs, desirable outputs, and undesirable outputs. λ is the weight vector. The objective function is a strictly monotonically decreasing with respect to the input slack s⁻, the desirable output slack s^g, and the undesirable output slack s^b. This function directly yields the efficiency score for a decision-making unit (DMU). The efficiency score reaches its optimum (${\rho }^{\ast }=1$) only when all three slack variables are simultaneously zero, indicating that the DMU is fully efficient. Conversely, a score in the range $0<{\rho }^{\ast }<1$ signifies inefficiency, revealing potential for improvement through a reduction in input excesses or output shortfalls.

4.3. Entropy Weight–TOPSIS

The Entropy Weight–TOPSIS method is used to synthesize the multi-indicator NQP data into a comprehensive index. The entropy weight method objectively assigns weights based on the informational utility (degree of variation) of each indicator across samples. Subsequently, the TOPSIS technique calculates the relative closeness of each city in a given year to the ideal and negative-ideal solutions, resulting in a comparable NQP score ranging between 0 and 1 [44,45].

4.4. Malmquist Index Model

While the super-efficiency SBM model provides a static assessment of relative efficiency across decision-making units within a single time period, it lacks the capacity to track efficiency evolution over time. To address this temporal dimension, Färe et al. [46] enhanced the DEA framework by developing the Malmquist productivity index, which captures dynamic efficiency changes between two consecutive periods. This approach enables the measurement of total factor productivity (TFP) fluctuations by calculating the geometric mean of efficiency changes relative to the technological frontiers of both periods. The index’s decomposition capability further distinguishes between efficiency catch-up (technical efficiency change) and frontier shift (technological change) components, offering valuable insights into the sources of productivity growth. The fundamental formulation is expressed as follows:

$$TFP=M\left(x^{t+1},y^{t+1},x^t,y^t\right)=\sqrt[2]{{\displaystyle \frac{D^t\left(x^{t+1},y^{t+1}\right)}{D^t\left(x^t,y^t\right)}}\times {\displaystyle \frac{D^{t+1}\left(x^{t+1},y^{t+1}\right)}{D^{t+1}\left(x^t,y^t\right)}}}$$

(3)

$$Effch={\displaystyle \frac{D^{t+1}\left(x^{t+1},y^{t+1}\right)}{D^t\left(x^t,y^t\right)}}$$

(4)

$$Tech=\sqrt{{\displaystyle \frac{D^t\left(x^{t+1},y^{t+1}\right)}{D^{t+1}\left(x^{t+1},y^{t+1}\right)}}\times {\displaystyle \frac{D^t\left(x^t,y^t\right)}{D^{t+1}\left(x^t,y^t\right)}}}$$

(5)

In the methodological framework, TFP can be decomposed into the Technical efficiency change index (Effch) and the Technological progress change index (Tech). The former represents the improvement degree of resource allocation efficiency of DMUs from period t to t+1; the latter indicates the dynamic shift in the production frontier, that is, the driving effect of technological innovation on land use efficiency. Here, x^t, y^t and x^t+1, y^t+1 represent the input–output vectors of DMUs in period t and t+1, respectively, while D^t(x^t, y^t) and D^t+1(x^t+1, y^t+1) represent the distance functions relative to the technological frontiers of periods t and t+1. Specifically, D^t(x^t, y^t) and D^t(x^t+1, y^t+1) measure the distance between DMUs and the production frontier in period t. The interpretation of the index follows these principles: TFP > 1 indicates significant productivity improvement in period t+1 compared to period t; TFP < 1 reveals a declining trend in factor productivity; and TFP = 1 suggests maintained productivity levels, with DMUs demonstrating stable performance relative to the evolving production frontier.

4.5. Fixed Effects Model

To empirically examine the impact of new quality productivity on land resource use efficiency, we establish the following regression model:

$$\begin{array}{ll}Land\_use\_efficienc{y}_{i,t}=& {\beta }_0+{\beta }_{1}New\_quality\_productivit{y}_{i,t}\hfill \\ & +{{\displaystyle {\sum }_k{\beta }_{k}Controls}}_{i,t}+Yea{r}_{FE}+Cit{y}_{FE}+{\epsilon }_{i,t}\hfill \end{array}$$

(6)

where the subscripts i and t denote city and year, respectively. The dependent variable, Land_use_efficiency_i,t, represents the land use efficiency score of city i in year t. The core independent variable, New_quality_productivity_i,t, quantifies the NQP level through a comprehensive index constructed via the entropy method.

The selection of control variables integrates theoretical foundations with regional characteristics. Recognizing that urban land use efficiency is determined through multidimensional pathways and accounting for the CPUA’s distinctive position as both a major agricultural zone and strategic grain production base, this study extends established research [38,47,48,49] to develop a comprehensive control variable system (Controls_i,t). This system captures fundamental economic, technological, and structural dimensions through seven indicators: economic output (X₁), research and development investment (X₂), human capital formation (X₃), governmental influence measured by the ratio of local fiscal expenditure to regional GDP (X₄), demographic scale (X₅), agricultural land endowment (X₆), and primary sector composition (X₇). The empirical specification incorporates city fixed effects (City_FE) to account for time-invariant heterogeneity across cities and year fixed effects (Year_FE) to control for common temporal shocks. The disturbance term ε_i,t captures remaining unobserved determinants. This methodological design ensures robust identification of NQP effects while maintaining theoretical coherence with the region’s socio-economic and agricultural particularities.

To ensure estimation robustness, we conducted multicollinearity diagnostics prior to estimation. The mean variance inflation factor (Mean VIF = 4.98) remains below the conventional threshold of 5, demonstrating the absence of severe multicollinearity and validating the reliability of subsequent parameter estimates. This methodological approach ensures that the identified relationship between NQP and LUE reflects genuine economic causality rather than spurious correlation.

5. Results

5.1. Spatiotemporal Evolution of NQP

To elucidate the spatial differentiation patterns of new quality productivity (NQP) within the Central Plains Urban Agglomeration (CPUA), we employed the natural breaks classification method in ArcGIS 10.8 to categorize NQP levels into five distinct tiers. As illustrated in Figure 3, the NQP development in the CPUA demonstrated pronounced temporal phasing and spatial heterogeneity during the 2010–2023 period. The composite NQP index exhibited a “stepwise growth” trajectory, ascending from 0.280 to 0.828 with an average annual growth rate of 6.5%, reflecting the region’s progressive transition toward innovation-driven development.

The evolutionary process unfolded through two distinctive phases characterized by different growth dynamics. The initial accumulation phase (2010–2015) witnessed moderate growth at 4.2% annually, during which Zhengzhou emerged as an early innovation pole leveraging its provincial capital advantages. The city’s NQP index increased from 0.280 to 0.363, representing a 29.6% growth that substantially exceeded the regional average. The subsequent acceleration phase (2016–2023) saw enhanced policy support and innovation ecosystem development, propelling Zhengzhou’s NQP index to 0.828 through 9.8% annual growth—a 128.1% increase from 2015. This established a “unipolar leadership” pattern that stimulated catch-up development in secondary cities like Luoyang and Nanyang, consequently elevating the regional growth rate to 8.1%.

Spatially, the NQP distribution revealed a clearly defined core–secondary–periphery gradient structure, indicating the presence of hierarchical diffusion mechanisms. The core growth pole centered on Zhengzhou (NQP index: 0.828 in 2023) demonstrated superior performance in high-end talent concentration, technology enterprise agglomeration, and policy resource acquisition. Its leading position was further reinforced by rapid expansion in emerging industries including digital economy and biomedicine, establishing it as the primary regional innovation radiation source. The secondary coordination zone, represented by Luoyang (0.310) and Nanyang (0.234), developed differentiated innovation pathways through intelligent upgrading of equipment manufacturing and technological breakthroughs in cultural-tourism integration and agricultural product processing, respectively. Achieving annual growth rates of 7.3% and 6.8%, these cities formed complementary industrial synergies with the core region, facilitating knowledge spillovers and industrial chain integration. The peripheral development zone, encompassing cities such as Handan, Jincheng, and Bengbu, generally registered NQP levels below 0.2, indicating significant development gaps. Particularly, Hebi (0.126) and Bozhou (0.124) remained constrained by resource-dependent economic structures, exhibiting insufficient innovation investment and inefficient factor allocation that hindered their NQP development trajectories.

5.2. Spatiotemporal Evolution of LUE

Figure 4 illustrates the spatiotemporal dynamics of land use efficiency (LEU) within the CPUA during the 2010–2023 period. Temporally, the region demonstrated a consistent upward trajectory in land use efficiency, with the mean efficiency value increasing from 0.917 to 1.031, corresponding to an average annual growth rate of 1.0%. This progressive enhancement reflects the ongoing transition toward intensive and sustainable land use patterns across the urban agglomeration.

Specifically, notable inter-city disparities emerged in the efficiency patterns. Zhengzhou maintained a dominant position with the highest mean efficiency score of 1.388, a position attributable to its strategic advantages as a provincial capital, including superior resource allocation capabilities, robust economic growth dynamics, and advanced land intensification practices. Conversely, Kaifeng registered the lowest efficiency level at 0.53, constrained by structural limitations in industrial development and suboptimal land utilization intensity. While most cities demonstrated efficiency scores within the 0.5–1.2 range, several municipalities—notably Bozhou and Bengbu—achieved commendable performance through synergistic combinations of economic vitality and optimized land resource management.

Spatially, the distribution patterns exhibit distinct provincial-level characteristics. Cities within Henan Province consistently outperformed their counterparts, with Zhengzhou, Xuchang, Xinyang, and Nanyang forming a high-efficiency cluster. This spatial advantage stems from Henan’s integrated economic ecosystem, characterized by substantial scale economies, rapid urbanization, and mature industrial transformation. In contrast, Hebei-based cities including Handan, Xingtai, and Liaocheng displayed relative inefficiency, primarily impeded by structural rigidities in industrial composition and conventional land use paradigms. The mixed performance observed in Shanxi (Jincheng, Changzhi, Yuncheng) and Anhui (Bengbu, Bozhou, Huaibei) reflects the complex interplay of local socioeconomic conditions and governance frameworks.

5.3. Dynamic Evolution Characteristics of LUE

While the super-efficiency SBM model effectively measures static LUE values for cities in the CPUA, it possesses inherent limitations in capturing efficiency dynamics. To address this gap and systematically examine the evolutionary characteristics of urban land use efficiency, this study employs MaxDEA Ultra 12.0 software to calculate the Malmquist productivity indices for 2010–2023, thereby providing deeper insights into the dynamic changes in LUE across the region.

As delineated in

Table 3. Annual Malmquist index and its decomposition.

City	Technical Efficiency Change	Technological Change	Pure Technical Efficiency Change	Scale Efficiency Change	Total Factor Productivity
Zhengzhou	1.016	1.061	1.012	1.004	1.072
Kaifeng	0.932	1.078	1.020	0.956	1.093
Luoyang	1.009	1.045	0.993	1.015	1.033
Pingdingshan	1.012	1.060	1.019	1.028	1.055
Xinxiang	1.148	1.286	1.149	1.019	1.072
Jiaozuo	1.058	1.261	1.088	1.002	1.268
Xuchang	0.997	1.067	0.998	1.001	1.064
Luohe	0.921	1.103	1.033	0.932	1.048
Anyang	0.963	1.350	0.943	1.033	1.097
Hebi	0.956	1.278	1.073	1.059	1.352
Puyang	1.150	1.030	1.037	1.253	1.088
Shangqiu	0.984	1.104	1.014	1.006	1.140
Zhoukou	0.988	1.121	0.979	1.010	1.111
Zhumadian	1.045	1.056	1.011	1.042	1.092
Xinyang	1.032	1.122	1.263	1.080	1.168
Nanyang	1.000	1.114	1.002	0.998	1.116
Sanmenxia	1.047	1.129	1.029	1.010	1.182
Jincheng	1.121	1.168	1.012	1.105	1.222
Changzhi	1.058	1.240	1.038	1.020	1.387
Yuncheng	1.007	1.175	1.012	1.002	1.216
Handan	0.993	1.090	0.992	1.003	1.083
Xingtai	0.907	1.124	0.989	0.872	1.169
Liaocheng	0.988	1.001	1.007	0.992	1.033
Heze	1.008	1.165	1.007	1.001	1.171
Bengbu	1.016	1.280	1.047	1.012	1.296
Bozhou	1.009	1.138	1.047	0.991	1.142
Huaibei	1.097	1.071	1.085	1.004	1.076
Suzhou	1.000	1.144	1.005	1.000	1.144
Fuyang	1.030	1.240	1.030	1.007	1.259
Mean	1.017	1.141	1.032	1.016	1.146

, 65.52% of cities exhibited growth in comprehensive technical efficiency during the study period, with an average annual growth rate of 1.7%. Puyang, Xinxiang, and Jincheng demonstrated the most rapid growth at 15.0%, 14.8%, and 12.1%, respectively. Technological progress emerged as the primary driver of efficiency improvement, registering an impressive annual growth rate of 14.3%. Notably, all 29 cities achieved total factor productivity (TFP) indices exceeding 1, confirming an overall dynamic growth trajectory in land use efficiency across the urban agglomeration. The scale efficiency analysis reveals that 79.3% of cities maintained scale efficiency indices above 1, indicating successful transition from input surplus conditions and demonstrating positive scale effects that significantly contributed to comprehensive technical efficiency growth. Conversely, the remaining 21.7% of cities exhibited scale efficiency indices below 1, suggesting suboptimal input scales that necessitate fundamental shifts from extensive utilization patterns toward intensive land use through optimized input scaling. Pure technical efficiency showed an annual growth rate of 3.2%, though six cities—Luoyang, Xuchang, Anyang, Zhoukou, Handan, and Xingtai—displayed declining trends. This pattern indicates inadequate utilization of existing technological capacities in these municipalities, resulting in substantial input–output redundancies that require enhanced economic output generation and reduced undesirable outputs.

Based on the decomposed Malmquist indices presented in

Table 4. Malmquist index and decomposition by year.

Year	Technical Efficiency Change	Technological Change	Pure Technical Efficiency Change	Scale Efficiency Change	Total Factor Productivity
2010~2011	0.996	1.181	1.057	0.970	1.171
2011~2012	1.032	1.137	1.082	0.970	1.116
2012~2013	1.001	1.101	0.943	1.082	1.101
2013~2014	0.985	1.105	1.047	0.966	1.082
2014~2015	1.023	0.986	1.031	1.001	1.009
2015~2016	1.030	1.294	1.060	1.029	1.178
2016~2017	0.951	1.118	0.933	1.088	1.059
2017~2018	0.949	1.387	1.012	0.943	1.376
2018~2019	1.124	1.123	1.042	1.128	1.150
2019~2020	1.003	0.833	1.024	1.007	1.012
2020~2021	1.097	1.389	1.049	1.087	1.484
2021~2022	1.141	1.119	1.185	0.984	1.254
2022~2023	0.890	1.092	0.962	0.957	1.146
Mean	1.041	1.143	1.033	1.016	1.164

, the TFP for land use in the CPUA consistently exceeded 1 throughout 2010–2023, achieving an impressive annual growth rate of 16.4%. Technological progress, with its 14.3% annual growth rate, constituted the dominant driving force, thus validating research hypothesis H2. The complementary 4.1% growth in comprehensive technical efficiency reveals a distinctive “technology-led, efficiency-coordinated” development pattern, while simultaneously indicating significant potential for further optimization in resource allocation and management efficiency—a finding consistent with Song et al.’s [50] research.

Temporal analysis reveals substantial fluctuations in TFP growth. The modest 0.9% increase during 2014–2015 likely reflects broader economic slowdown and industrial restructuring delays [51], while the remarkable 48.4% surge in 2020–2021 can be attributed to concentrated implementation of innovation policies and post-pandemic productivity recovery. Furthermore, scale efficiency indices demonstrated declining trends across six specific time nodes (2010, 2011, 2013, 2017, 2021, and 2022), indicating gradually weakening alignment between input scale and output effectiveness. This scale inefficiency consequently constrained comprehensive technical efficiency improvement, particularly evident in cities like Handan and Xingtai where land expansion-dependent development models remain prevalent.

5.4. Impact Mechanism Analysis

5.4.1. Benchmark Regression Analysis

This study employs a fixed-effects model to examine the impact of NQP on LUE, with results presented in

Table 5. Benchmark regression results of the impact of NQP on LUE.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
NQP	0.760 **	0.770 ***	0.873 ***	0.860 ***	1.009 ***	1.178 ***
	(2.5672)	(2.6247)	(2.9195)	(2.8978)	(3.5459)	(2.9987)
X1	0.434 ***	0.420 ***	0.508 ***	0.505 ***	0.507 ***	0.420 ***
	(4.1815)	(3.3085)	(3.6949)	(3.6174)	(3.6396)	(2.7507)
X2	−15.440	−15.510	−15.089	−15.014	−11.920	−13.551
	(−1.4006)	(−1.4137)	(−1.3961)	(−1.3885)	(−1.0761)	(−1.1649)
X3		−0.554	−3.415	−3.405	−1.949	−1.632
		(−0.1669)	(−0.9841)	(−0.9805)	(−0.5367)	(−0.4404)
X4			1.422 *	1.405 *	1.135	1.085
			(1.8319)	(1.7896)	(1.4437)	(1.3891)
X5				0.023	0.025	0.003
				(0.3759)	(0.4162)	(0.0390)
X6					0.377	0.481 *
					(1.5882)	(1.7019)
X7						−0.008
						(−0.8615)
Constant	−6.216 ***	−5.962 ***	−7.551 ***	−7.632 ***	−10.137 ***	−9.117 ***
	(−3.5792)	(−2.7354)	(−3.1718)	(−3.2520)	(−3.3908)	(−3.0725)
YearFE	Yes	Yes	Yes	Yes	Yes	Yes
CityFE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	406	406	406	406	406	406
Adjusted R2	0.687	0.686	0.688	0.687	0.688	0.688

Note: The values outside parentheses represent estimated coefficients, while values inside parentheses denote cluster-robust t-statistics from regression analysis. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. “Yes” signifies the inclusion of fixed effects. Control variables are defined as follows: X₁: GDP, X₂: Science and technology investment level, X₃: Education investment level, X₄: Government intervention (measured by local fiscal expenditure as a percentage of regional GDP), X₅: Total population, X₆: Cultivated land resource area, X₇: Proportion of primary industry value added. The same conventions apply to subsequent tables.

. All specifications include year fixed and city fixed effects, while control variables are sequentially added to test the robustness of the findings. The baseline specification [Column (1)] incorporating only GDP and technology investment shows a statistically significant coefficient of 0.760 for NQP (p < 0.05), providing preliminary evidence of its positive effect. As additional controls for technology investment, government intervention, population size, and cultivated land area are incrementally included [Columns (2)–(5)], the NQP coefficients increase to 0.770, 0.873, 0.860, and 1.009, respectively, all significant at the 1% level. The fully specified model [Column (6)] yields a NQP coefficient of 1.178, maintaining 1% statistical significance. These results demonstrate that NQP development significantly enhances land use efficiency after controlling for economic scale, technological inputs, and policy interventions. The consistent positive relationship across different model specifications confirms the robust association between the core explanatory variable and the outcome measure.

5.4.2. Heterogeneity Regression Analysis

Given the established positive relationship between NQP and LUE, we further investigate potential heterogeneous effects across cities with different economic scales, cultivated land endowments, and development levels. Using mean values of GDP, cultivated land area, and per capita GDP as grouping thresholds, we conduct subgroup analyses on 29 sample cities (

Table 6. Heterogeneity regression results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Large Cities	Small Cities	Abundant Cultivated Land	Scarce Cultivated Land	High Development Level	Low Development Level
NQP	2.304 ***	−3.541	−0.635	1.079 *	1.135 *	−3.491
	(3.2652)	(−1.4757)	(−0.4343)	(1.9073)	(1.7494)	(−1.6379)
Constant	−32.747	−1.169	−4.644	−10.559 **	−10.012 *	−6.629
	(−1.3681)	(−0.6285)	(−0.9824)	(−2.4868)	(−1.9603)	(−1.3248)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
YearFE	Yes	Yes	Yes	Yes	Yes	Yes
CityFE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	196	210	207	199	210	196
Adjusted R2	0.725	0.730	0.616	0.767	0.806	0.798

Note: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

). The results reveal significant urban characteristics in the NQP-LUE relationship.

For city scale heterogeneity, large cities exhibit a NQP coefficient of 2.304 (p < 0.01), indicating that urban agglomeration enables NQP to enhance intensive land use through technological spillovers, industrial upgrading, and factor marketization. In contrast, small cities show an insignificant negative coefficient (−3.541), suggesting limited technological absorption capacity and underdeveloped innovation ecosystems constrain NQP’s positive effects.

Regarding cultivated land resource heterogeneity, cities with scarce cultivated land demonstrate a significant positive NQP coefficient of 1.079 (p < 0.1). The underlying mechanism involves land constraints forcing urban spatial transformation toward non-agricultural industries that better align with NQP elements like digital technologies and high-end human capital. Conversely, cities abundant in cultivated land show an insignificant negative coefficient (−0.635), reflecting inadequate synergy between NQP and LUE due to traditional agricultural dependence, single land use structures, and slow technological penetration in agriculture.

Concerning economic development heterogeneity, high-development cities exhibit a positive NQP coefficient of 1.135 (p < 0.1), driven by substantial innovation investment and effective industry–university–research collaboration that facilitate technological transformation and land use structure upgrading. Low-development cities show an insignificant negative coefficient (−3.491), indicating insufficient R&D funding and brain drain hinder the establishment of effective NQP-LUE coordination.

5.4.3. Robustness Test

To test the reliability and stability of the benchmark regression results, this study used the strategies of lagging the explanatory variable by one, two, and three periods to conduct robustness tests.

Table 7. Robustness test results.

Variables	(1)	(2)	(3)
Lagged NQPt−1	0.880 **
	(2.4298)
Lagged NQPt−2		0.782 *
		(1.8131)
Lagged NQPt−3			0.192
			(0.3334)
Constant	−13.078 ***	−12.992 ***	−14.180 ***
	(−3.9303)	(−3.1306)	(−2.7632)
Control variables	Yes	Yes	Yes
YearFE	Yes	Yes	Yes
CityFE	Yes	Yes	Yes
Observations	377	348	319
Adjusted R2	0.691	0.678	0.672

Note: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

presents the results of these specifications, all of which maintain the same set of control variables and include both year fixed and city fixed effects consistent with our baseline methodology. The empirical evidence demonstrates a persistent yet temporally diminishing effect of NQP on LUE. Specifically, the one-period lag specification [Column (1)] yields a statistically significant coefficient of 0.880 (p < 0.05), indicating strong short-term persistence. The two-period lag model [Column (2)] produces a coefficient of 0.782 (p < 0.10), suggesting a moderately sustained medium-term effect. However, the three-period lag specification [Column (3)] shows an insignificant coefficient of 0.192, revealing the temporal boundary of NQP’s influence on LUE.

6. Discussion

Building on the empirical results presented in Section 5, this section first analyzes the spatiotemporal evolution characteristics of new quality productivity (NQP) and land use efficiency (LUE), then elucidates the mechanisms and transmission pathways through which NQP influences LUE, and finally provides an in-depth interpretation of the heterogeneity in these effects within the frameworks of economic geography and regional development theory.

6.1. Analysis of NQP Spatiotemporal Evolution Characteristics

This study reveals a distinct “stepwise rise” in new quality productivity (NQP) within the Central Plains Urban Agglomeration (CPUA) from 2010 to 2023, with the composite index increasing from 0.280 to 0.828. Spatially, a “core–secondary–periphery” hierarchical structure has emerged. Zhengzhou, as the core growth pole, plays an innovative leading role, while peripheral cities such as Handan and Hebi remain constrained by resource-dependent economic structures, with NQP indices consistently below 0.2. This pattern aligns with findings on productivity spatial differentiation in China’s major urban agglomerations [52,53] and underscores the role of agglomeration in driving technological advancement [54]. The developmental challenges in peripheral cities also provide empirical support for the mechanism whereby institutional barriers in land governance constrain productivity growth [55,56].

This phased evolution and spatial polarization find parallels in other transitioning economies. For instance, in central and eastern European countries like Romania, integration into the EU single market has also led to a pronounced concentration of innovative activities in capital regions (e.g., Bucharest–Ilfov), while peripheral areas struggle with slower adaptation and path dependency [57,58]. Despite differing institutional contexts, the spatial clustering of new productivity factors during rapid structural adjustment appears to be a common phenomenon. This highlights a universal policy challenge: mitigating regional disparities requires targeted interventions to foster knowledge spillovers, a lesson relevant to both China’s regional coordination strategies and the EU’s cohesion policy.

6.2. Analysis of LUE Evolution and Driving Mechanisms

The average land use efficiency (LEU) in the CPUA increased from 0.917 to 1.031 during the study period, with notable interprovincial disparities. However, this aggregate improvement masked significant interprovincial disparities. A distinct efficiency gradient was observed, with cities within Henan Province consistently outperforming their counterparts in Hebei, Shanxi, and Anhui, reflecting the compounded advantages of economic scale, policy coordination, and infrastructure development in core regions—a pattern consistent with established studies [59,60] on the spatial stratification of development efficiency across China’s major urban clusters, highlighting the role of provincial administrative boundaries in shaping resource allocation and economic outcomes.

The decomposition of the Malmquist index reveals that the 16.4% average annual growth in total factor productivity (TFP) was predominantly driven by technological progress. This finding not only aligns with the conclusions of Wang et al. [61] and Liu et al. [62] regarding the primacy of technological advancement in boosting land efficiency but also validates our Hypothesis H2, confirming it as a key transmission pathway through which NQP enhances LUE. However, the study also identifies that 21.7% of cities (e.g., Handan, Xingtai) exhibited scale efficiency indices below 1, indicating suboptimal alignment between input scale and output effectiveness. This finding echoes Tian et al.’s [63] observation that excessive reliance on land expansion leads to weakened matching between factor input and output. Such cities, constrained by extensive development models, have yet to achieve optimal input–output scaling, necessitating structural reforms in land use planning and factor allocation.

6.3. Impact of NQP on LUE and Heterogeneity Analysis

The benchmark regression results confirm a significant positive impact of NQP on LUE, supporting Hypothesis H1. This aligns with the transmission logic proposed by Liu et al. [23], wherein land resource allocation enhances productivity through technological innovation incentives. An economic geography perspective further illuminates the underlying dynamics. Large cities benefit from cumulative causation where innovation-driven sectors cluster, reinforcing land use intensification. In contrast, peripheral or resource-dependent regions exhibit spatial lock-in due to path dependency and weak absorptive capacity, which constrains NQP’s transformative effect on LUE. This pattern resonates with established findings on regional divergence in innovation-led development [52,53].

Our subgroup analysis elucidates how these macro-level patterns manifest across three contextual dimensions:

• City Scale: Large-scale cities (e.g., Zhengzhou, Luoyang) exhibit a strong positive NQP–LUE relationship (coefficient = 2.304, p < 0.01), underscoring the role of agglomeration economies in amplifying innovation-led land efficiency [64,65]. For instance, Zhengzhou (2023 NQP index = 0.828) saw the proportion of high-tech enterprises among industrial enterprises above designated size rise from 12.3% to 41.7% between 2010 and 2023, with industrial land intensity (industrial added value per unit land) increasing from 320 million yuan/km² to 870 million yuan/km²—a 171.9% improvement in land output efficiency. In contrast, small cities and low-development cities show an insignificant or even negative relationship, constrained by more fundamental structural barriers. Beyond a simple lack of technological absorption capacity, these regions face institutional and policy mismatches where NQP-focused strategies are often ill-suited to local contexts, path dependency in traditional economic structures that hinder factor reallocation, and weak synergistic linkages within local innovation ecosystems [60,62]. Consequently, NQP inputs may not translate into effective land use optimization, highlighting that the mere presence of advanced factors is insufficient without supportive institutional environments and adaptive local governance.
• Cultivated Land Endowment: Cities with scarce cultivated land (e.g., Sanmenxia) demonstrate a positive NQP–LUE relationship (coefficient = 1.079, p < 0.1), supporting the view that resource constraints can stimulate optimal factor allocation [66]. This dynamic mirrors land-constrained economies such as Japan [67] and the Netherlands [68], where high land pressure has long been a catalyst for technological advancement and ultra-intensive land management practices. In contrast, cities abundant in cultivated land (e.g., Bozhou, Suzhou) show insignificant effects, likely hindered by path dependency in traditional agricultural models.
• Economic Development Level: High-development cities (e.g., Zhengzhou, Xuchang) effectively leverage innovation investment and industry–university–research collaboration to translate NQP into technological advancements, thereby enhancing LUE (coefficient = 1.135, p < 0.1). This reflects the “innovation factor dependence” characteristic of NQP cultivation, wherein technological progress serves as the operable conduit. This aligns with the experience of advanced economies, where well-funded national innovation systems and strong industry–academia linkages are fundamental to sustaining productivity growth [69]. Low-development cities (e.g., Hebi, Jincheng), however, lack such synergistic mechanisms due to R&D funding shortages and high-end talent loss [70,71], highlighting the need for differentiated policy support.

7. Conclusions, Policy Implications and Research Limitations and Future Research Directions

7.1. Conclusions

This study systematically investigates the spatiotemporal evolution and mechanistic relationship between new quality productivity (NQP) and land use efficiency (LEU) within the Central Plains Urban Agglomeration (CPUA) from 2010 to 2023. The principal conclusions are as follows:

(1)

Spatiotemporal evolution of NQP. The comprehensive NQP index of the CPUA demonstrated a remarkable “stepwise rise,” increasing from 0.280 to 0.828. This evolution was characterized by two distinct phases: an initial accumulation period (2010–2015) and an accelerated promotion period (2016–2023). Spatially, a clear “core–secondary–periphery” gradient structure was identified. Zhengzhou emerged as the unequivocal core growth pole, leveraging its agglomeration advantages in talent, technology, and policy. Secondary cities like Luoyang and Nanyang developed synergistic niches through industrial intelligent upgrading and characteristic sector breakthroughs. In contrast, peripheral cities such as Handan and Hebi, constrained by resource-dependent economic structures and inefficient factor allocation, lagged significantly with NQP indices below 0.2.

(2)

Dynamics of LUE and its drivers. The average LUE in the CPUA improved from 0.917 to 1.031, yet with pronounced spatial disparities. Zhengzhou, benefiting from its provincial capital status and intensive land use practices, led with an efficiency score of 1.388, while Kaifeng trailed at 0.53 due to a weak industrial base and extensive land use patterns. Cities within Henan Province consistently outperformed those in adjacent provinces, underscoring the role of economic scale and advanced industrial structure. Malmquist index decomposition revealed that total factor productivity grew at an impressive average annual rate of 16.4%, primarily driven by technological progress. However, scale inefficiency persisted in 21.7% of cities (e.g., Handan, Xingtai), where an over-reliance on land expansion diluted input–output matching.

(3)

The NQP-LUE nexus and its heterogeneity. Empirical analysis confirms that NQP exerts a statistically significant positive impact on LUE. However, this effect is marked by substantial heterogeneity: The impact is strongest in large-scale cities (coefficient = 2.304, p < 0.01), where technological agglomeration and industrial upgrading drive intensive land use. In contrast, small cities, hampered by limited technological absorption capacity, show insignificant or even negative effects. Arable land scarcity acts as a catalyst: cities with less cultivated land show a positive NQP effect (coefficient = 1.079, p < 0.1), while those abundant in arable land are constrained by path dependency in traditional agricultural models, rendering the NQP effect insignificant. The NQP-LUE synergy is effectively harnessed in high-development cities (coefficient = 1.135, p < 0.1) through robust innovation investment and industry–university–research collaboration. Conversely, low-development cities fail to establish this linkage due to R&D funding shortages and talent bottlenecks.

7.2. Policy Implications

Grounded in the above findings, we propose a tiered and targeted policy framework to harness NQP for enhancing LUE across the heterogeneous urban landscape of the CPUA.

(1)

Differentiated Strategies Based on Urban Scale and Capacity. Core leading cities (e.g., Zhengzhou, Nanyang): Continue to bolster NQP investment to solidify their role as innovation radars. Policy should focus on creating high-tech industrial clusters and fostering frontier technology R&D to set benchmarks in intensive land use. Secondary transitional cities (e.g., Luoyang, Zhoukou): Prioritize the application and diffusion of existing advanced technologies. Implement “Smart Land Management Systems” to optimize resource allocation precision and facilitate industrial intelligent upgrading. Lagging small-scale cities (e.g., Hebi): Adopt a dual strategy. First, enhance physical infrastructure and public services to improve basic factor agglomeration capacity. Second, actively foster regional partnerships to integrate into the regional industrial division of labor, leveraging spillover effects from core cities.

(2)

Place-Based Interventions Tailored to Arable Land Endowments. Cities with scarce arable land (e.g., Sanmenxia): Establish incentive mechanisms for cultivated land protection and efficient use. Promote multifunctional land use models, such as ecological and urban agriculture, to maximize the value per unit of land. Strictly control construction land expansion and vigorously promote the secondary development of stock land. Cities rich in arable land (e.g., Anyang, Kaifeng): Drive the deep integration of NQP with traditional agriculture. Increase investment in agricultural technology innovation to develop smart and precision agriculture. Scientifically plan urban-rural land use structures to balance the dual imperatives of cultivated land preservation and sustainable urban development.

(3)

Pathways Aligned with Regional Economic Development Levels. High-economic-development cities (e.g., Zhengzhou, Luoyang): Focus on building a modern industrial system with NQP at its core. Strengthen the synergy between urban planning and land use planning to reserve strategic space for innovative and future industries. Low-economic-development Cities: Address foundational bottlenecks. Increase investment in education and vocational training to enhance human capital. Optimize the business environment to attract external investment and technology through targeted policy preferences, thereby cultivating a local ecology conducive to NQP development and land efficiency improvement.

7.3. Research Limitations and Future Research Directions

While this study systematically analyzes the correlation mechanism between NQP and LUE in the CPUA, several limitations should be acknowledged. First, the use of linear interpolation for missing data, though statistically reasonable to a certain extent, may not fully capture nonlinear data generation processes, potentially affecting estimation accuracy. Second, the research unit focuses on the prefecture-level city scale, making it difficult to capture the micro-mechanism of the coupling relationship between urban-rural factor flow, county industrial differences, and NQP and land efficiency. Third, while the fixed-effects model employed in this study controls for time-invariant city characteristics and common temporal trends, it does not directly capture the potential spatial spillover effects of NQP. Therefore, future research can be deepened from the following aspects: (1) Indicator system refinement: Incorporate emerging dimensions such as digital economy scale, green innovation efficiency, and institutional quality to enhance the conceptual and empirical measurement of NQP. (2) Multi-scale analysis: Conduct county-level or firm-level studies using micro-survey data to uncover the interactive effects of factor mismatch, industrial restructuring, and land governance on NQP and LUE. (3) Spatial spillover effects: Given the strong economic and innovation linkages within urban agglomerations, future research could incorporate spatial econometric models (e.g., Spatial Durbin Model) to disentangle direct and indirect (spillover) effects of NQP on LUE.