How Do National Key Development Zones Affect Land-Use Eco-Efficiency? Evidence from Counties in the Upper Reaches of the Yangtze River

Abstract

Against the backdrop of coordinated development of national land use and ecological conservation, national key development zones (NKDZs), as the core carriers of China’s main functional zone planning, provide important reference models for regional development through their pathways to improving ecological efficiency of land. A double-difference model (DID) was used to evaluate the effects of the NKDZ policy on the eco-efficiency of land use, utilizing county-level governing bodies of the upper Yangtze River region in China as a sample from 2005 to 2020. The findings indicate that (1) when the NKDZs were established, the counties’ eco-efficiency in land use in the upper reaches of the Yangtze River considerably improved by 2.6%; this conclusion holds after several robustness checks, including counterfactual tests. (2) Mechanistic analysis reveals that NKDZs promote the enhancement of eco-efficiency in land use, mainly through the technical effect, the structural effect, and the “two processes” effect of promoting the coordinated development of industrialization and urbanization. (3) Heterogeneity analysis reveals that the policy effects on land-use eco-efficiency in municipal districts and cities at the county level are much greater than those in regular counties. Concerning subdistricts, the policy significantly promotes Sichuan, Guizhou, and Chongqing, whereas the effect on Yunnan is not significant. In light of the aforementioned findings, this study makes policy recommendations in terms of technological innovation, structural optimization, and differentiated land control to offer a practical foundation and theoretical justification for the effective use of land and ecological pressure alleviation within NKDZs.

1. Introduction

For human survival and advancement, land is a crucial spatial carrier that serves multiple functions, such as economic growth, social production, and ecological and environmental protection. From farmland that ensures food security to industrial land that supports industrial development and from forests and wetlands that maintain ecological balance, the efficiency of land use directly determines the rationality of resource allocation, which in turn affects the path to achieving sustainable development goals at the national and regional levels [1,2]. This contradiction highlights the need to explore how spatial planning policies can balance land-driven growth with ecological sustainability. Globally, many developing countries are facing highly similar challenges. To rapidly catch up with economic development levels, these countries have widely adopted a “Land-driven Development” growth model—attracting investment through large-scale land concessions and by expanding the urban space to drive economic growth. While this model has accelerated urbanization and industrialization (the “two processes”) in the short term, it has also led to significant issues of over-expansion: the growth rate of urban built-up areas far exceeds population growth, and industrial land is developed in a haphazard manner, resulting in production and living spaces continuously encroaching on ecological spaces. This has created structural contradictions in land development [3]. These contradictions highlight the necessity of exploring how spatial planning policies can balance land-driven growth with ecological sustainability.

In China, as in many emerging economies, land has long been a core driver of development. However, the “land-for-growth” model has also brought ecological pressures, such as ecological space fragmentation and low land-use efficiency. To address this issue, China launched the National Main Functional Zone Planning (NMFZP) strategy in 2010, a key spatial planning policy aimed at coordinating development and protection by delineating functional zones with different development priorities. Similar to China’s “Key Development Zones” (NKDZs), other countries’ “priority development zones” (such as special economic zones in Southeast Asia or regional development hubs in Africa) are core types of regions under the NMFZP policy; they are designated as areas with strong resource carrying capacity and economic potential and are tasked with driving regional growth while maintaining ecological balance. As NKDZs expand, a key question emerges: can such zones enhance land-use ecological efficiency, i.e., balance economic output with ecological costs in land use? This question holds practical significance not only for China but also for other economies facing similar land-use conflicts.

While existing research on national key development zones and land use has laid an important foundation, significant gaps remain. Most studies focus on the national, provincial, and prefectural levels, emphasizing economic outcomes such as industrial agglomeration or GDP growth [4], with few scholars focusing on the land-use efficiency of main functional zones or a specific type of space and combining it with ecology. Furthermore, the upstream sections of the Yangtze River span China’s first and second topographical tiers, which serve as critical zones for implementing national strategic initiatives, including the Western Region Development Program, the Yangtze River Economic Zone, and the dual-city economic hub of Chengdu and Chongqing. Exploring the impacts of land exploitation and utilization in this region on socioeconomic sustainable development and ecological barrier construction is highly important.

Against this backdrop, this study focuses on the upper reaches of the Yangtze River in China using 284 county-level administrative regions within the area as a sample, with the aim of exploring the impact of NKDZs on land-use ecological efficiency and its underlying mechanisms. To this end, the article employs a DID model, treating the implementation of NKDZs as a “quasi-natural experiment”. Its core advantage lies in its ability to isolate the net policy effect: by comparing changes in land-use ecological efficiency between counties covered by NKDZs and those not covered before and after the establishment of the zones, it effectively mitigates endogeneity issues, making it more suitable than simple regression models for assessing the causal effects of spatial policies [5].

The key contributions of this article include the following: (1) Focusing on the policy effects of establishing national key development zones on land-use ecological efficiency has shifted the research focus to “ecological efficiency”, enriching related research on the main functional zone planning of the national land space. (2) Conducting more detailed observations at the county level, which is a smaller scale, makes the research conclusions more representative. (3) Comprehensively utilizing socioeconomic and geographic information data makes efficiency calculations more scientific.

2. Literature Review

The strategy of enhancing sustainable development across the economic, social, and environmental dimensions by scientifically improving land-use efficiency through spatial planning has been embraced by numerous countries [6]. Historically, China’s approach to land utilization has focused mainly on increasing the economic output, with insufficient attention given to how regional environmental conditions and resource availability constrain industrial development. As a result, regional economies have developed unevenly and inefficiently, resulting in severe ecological damage [7]. Therefore, the 2010 Plan creatively divided the national territory into distinct development classifications and introduced the notion of national key development zones. At present, relevant research mainly addresses spatial development and optimization strategies for key ecological preservation districts, ecological compensation and green development, and performance evaluations and has extensively discussed land resource utilization and ecological service efficiency. In contrast, other countries have also established similar special zones aimed at promoting the development of specific regions. For example, the United States’ “Opportunity Zones” are designed to encourage capital flows to impoverished communities [8], while India’s special economic zones are aimed at attracting foreign direct investment [9]. Although these policies have different objectives, they all seek to guide resource flows through spatial interventions to alleviate regional imbalances. China’s national key development zone policy demonstrates uniqueness in terms of scale, hierarchical structure, and the definition of core objectives, particularly in explicitly placing ecological protection and economic development on equal footing as core priorities. This model, which combines large-scale national spatial planning with mandatory ecological constraints, is rare on a global scale, making it a typical case study for examining the complex effects of spatial policies.

Concerning the enhancement of strategic development zones, scholars believe that government investment within these zones should be channeled through direct investment, investment subsidies, loan interest subsidies, and dedicated funding streams to facilitate various project types ranging from commercial to public welfare initiatives. These include the construction of transportation networks, industrial development and clustering, the optimization and rational layout of urban agglomerations, and the creation of necessary amenities for integrating rural migrants into cities [10]. In research on urbanization development and construction, some scholars have pointed out that the diffusion effect of “growth poles” in key development zones has not been fully realized [11]. At the regional level, western provinces show greater increases in both built-up land area and development zone expansion speed compared to other regions [12]. While the urbanization level of county towns in underdeveloped regions is increasing, the environmental sustainability of urbanization has deteriorated significantly [13], leading to landscape fragmentation and heterogeneity within key development zones [14]. Some scholars have analyzed the spatial politics of main functional zone planning in relation to urbanization issues in key development zones [15] and strategic optimization for these zones within national land space planning [16].

With respect to studies concerning environmental compensation mechanisms and sustainable growth in priority ecological conservation areas, scholars have analyzed the role of key development zones in the ecological compensation mechanism from the perspectives of market mechanisms and stakeholder interests. They argued that key development zones are both “users” and “beneficiaries” of ecosystem services and should assume the responsibilities of “payers” and “compensators” [17]. Furthermore, since key development zones and restricted development zones serve different social development functions, it is necessary to achieve a balance through appropriate ecological compensation mechanisms [18]. At the micro level, there is little difference in the willingness to pay for ecological compensation among residents in optimized and key development zones, but there is a certain difference in the level of payment [19]. The goals of key development zones include not only economic growth but also the coordinated integration of the population, ecology, and the economy, as well as green development [20]. In terms of green development evaluation, constructing a green development evaluation indicator system based on the perspective of the main functional zones, such as a differentiated indicator system with a “consistent framework but different weights” [21], is most common. A quantitative evaluation revealed that principal economic zones in the Yangtze River development belt scored substantially below both efficiency-enhanced districts and development-constrained areas in terms of sustainability indicators, with serious ecological and production problems [22].

In terms of land use and performance evaluations in national key development zones, most existing studies have focused on analyzing the main functional positioning outlined in the Plan. Relevant research indicates that national spatial planning can promote economic growth within key development zones and that there are significant differences between municipalities and counties (cities) at different administrative levels [23]. Moreover, the evaluation of ecological efficiency is a key focus of research on the main functional zones. Some scholars have incorporated the value of ecosystem services into ecological performance assessment frameworks [24]; and they have used the PSR model to conduct a longitudinal evaluation of ecological security in Yunnan Province [25]. In addition, many scholars have also evaluated provincial ecological environment effects or ecosystem services on the basis of the main functional zones, analyzing the transformation of the quality of the ecological environment between ecological economic zones, optimized development zones, and key development zones [26], as well as chronological–spatial changes in the ecological service equilibrium within key development zones [27].

With respect to land utilization and ecological effectiveness, conventional assessments of land-use efficiency mainly use single-factor evaluations, but this approach cannot capture the complex input-output relationships in land development and utilization [28]. With the increasing severity of ecological and environmental problems, the inclusion of “unwanted” outputs in land use has gradually gained attention. In 1990, Sturm and Schaltegger first conceptualized eco-efficiency as the proportional relationship between economic value added and the environmental burden. Since then, significant research efforts have focused on optimizing land use while maintaining ecological balance. The methods employed include data envelopment analysis (DEA), superefficient DEA models, and the non-expected output (SBM) model [29]. The research scales encompass the macro, meso, and micro levels, and the research domains have expanded to include sectors such as industry, agriculture, and energy [30]. Additionally, based on regional land-use or ecological efficiency measurement results, scholars have further explored their influencing factors or spatial differentiation mechanisms and proposed targeted recommendations to improve efficiency, reduce consumption, and alleviate ecological pressure [31]. Although methods for measuring land-use efficiency and ecological efficiency are relatively well established, using quasi-experimental methods to assess the impact of specific spatial planning policies as exogenous shocks on the combination of the two—land-use ecological efficiency—is still in its exploratory stage in the existing literature. This study not only focuses on measuring efficiency but also emphasizes causal assessments of policy effects, providing a new perspective on understanding how spatial planning policies influence land-use patterns and their environmental consequences at the micro level.

In summary, scholars have extensively discussed the spatial optimization, economic development, and ecological performance of national key development zones. Their research scales cover national-, provincial-, and prefecture-level cities, and their research methods include case studies, empirical testing, and GIS technology to discuss issues related to the establishment and implementation of national key development zones or national key and non-key development zones. However, existing research has not sufficiently addressed how national key development zone policies, while driving economic growth, specifically impact land-use efficiency and the dynamic balance between land use and ecological conservation. Additionally, there is a lack of detailed analysis at the county level and scientific identification of policy effects. Therefore, this paper focuses on counties and districts in the upper reaches of the Yangtze River in China and explores the impact of national key development zones on the ecological efficiency of land use based on technology, structure, and the effects of “two processes”. Furthermore, this study not only provides empirical evidence for China to optimize its national spatial development and protection framework but also offers valuable insights for other countries facing similar “growth versus protection” dilemmas. Specifically, it provides experience on how to set and enforce ecological constraints while pursuing economic growth, as well as how to assess the synergistic effects of policies.

3. Research Hypotheses and Theoretical Analysis

3.1. Institutional Context

Land is a crucial component of the regional economy and society’s ability to function sustainably. Rapid industrialization and urbanization are stages commonly experienced by many countries in their pursuit of economic growth. These processes are often accompanied by the concentration of production factors and economic activities in urban areas, driving the expansion of the urban geographical space. While great economic development has been achieved, the traditional model of overreliance on material resource consumption for growth has also shown strong unsustainability, with a series of negative impacts on land ecology, the natural environment, and people’s well-being. On the one hand, the “big pie” style of urban growth, which aims to develop through land, has led to the inefficient use of land resources. Land urbanization has greatly outpaced population urbanization, creating structural imbalances in land use and disorderly development and making reserve land resources even scarcer. On the other hand, the crude high-input, high-pollution development model has seriously endangered land ecology, degraded ecosystem functions, aggravated environmental pollution, and reduced the quality of the human environment. The emergence of these phenomena is partly due to regional development strategies that place excessive emphasis on economic growth. At the same time, administrative divisions and the similarity of development goals across regions may lead to a lack of sufficiently differentiated top-level designs in national spatial planning, thereby triggering multiple complex issues in the economic, social, ecological, and spatial spheres and posing challenges to the sustainable development of the region.

China first proposed the idea of main functional zones in 2002 to address the numerous issues that have emerged in the development and use of the national land space. The seventeenth CPC National Congress report proposed “perfecting regional policies and adjusting regional layouts in accordance with the requirements of the main functional zones” in 2007. In the Twelfth Five-Year Plan, which was formally announced by the State Council in December 2010, the implementation of the overall strategy of regional development and the main functional area strategy is emphasized. This plan elevated main functional area planning to the highest level of the national strategy. A strategic plan to implement the major functional area system—later refined to a primary functional area system—was formed during the Third Plenary Session of the 18th CPC Central Committee in 2013. Relying on the planning of main functional zones, national key development zones are important carriers for supporting national economic development and population agglomeration. Each area possesses a central metropolis with a certain radiation-driven capability, considerable growth potential, a well-established urban structure, and a high capacity for scientific and technical innovation. In addition to implementing the overall regional development strategy and fostering regional economic growth, the creation of national key development zones is needed to lessen the strain on the environment, population, and resources of both highly and poorly developed regions. Therefore, against the backdrop of rapid economic development and land resource utilization, can certain types of development zones achieve synergy between economic development and ecological protection? What are their internal development mechanisms? This is the research focus of this paper.

3.2. Research Hypotheses

The national key development zones are urbanized areas for “dual” development, and the Plan makes clear that the direction of their development focuses on two main aspects. The first is optimizing their structure and improving their efficiency. In addition to supporting the country’s economic growth, national key development zones are essential for promoting coordinated regional development and carrying out overall regional development plans, as mentioned in the above section. Under this functional position, the national key development zones, on the basis of a sound urban-scale structure, focus on building a modern industrial system; promoting new industrialization; and constructing a sound, efficient, regionally integrated, and urban-rural coordinated infrastructure network, thereby reducing ecological strain, increasing the standard of development, and expanding the economic benefits per unit of land area. The second is to preserve the environment and reduce consumption. In addition to emphasizing a particular function, fostering the development of primary functional areas should address the relationships between several main functions. The Plan points out that although the main function of key development zones is to promote population and economic agglomeration and to provide industrial, consumer, scientific, technological, and service products, the relationship with ecological environmental protection must also be properly handled. The basic agricultural land within key development zones must be protected to avoid interference with ecological spaces, such as forestland, grassland, water, and wetland, and to provide a certain amount of agricultural and ecological products on that basis. Accordingly, the following assumptions are made in this paper (Figure 1):

H1. 

The establishment of national key development zones has improved the eco-efficiency of land use.

The national key development zones’ policies can continuously optimize the internal land-use structure and the industrial structure and improve the eco-efficiency of land use by combining innovative factors. This is based on the history of the establishment of national key development zones and their functional positioning. The following is the specific performance metric:

First, we address the technological effect. The degree of technical innovation and technological innovation talent is an indicator of the technological impact of the creation of national key development zones. On the one hand, the theory of endogenous growth states that the ability to continuously innovate technology is the driving force behind long-term economic growth [32]. It is also a significant way to lower environmental costs and energy consumption, which will improve the eco-efficiency of land use. The Plan places a strong emphasis on fostering new industrialization in national key development zones, boosting the ability to innovate on one’s own, and utilizing cutting-edge technology to revolutionize established industries. Consequently, in line with the idea that population and economic development are synchronized, the Plan suggests that key development zones should concentrate the economy and a certain scale of the population and that the population of restricted and prohibited development areas should be concentrated in key development zones. On the other hand, national key development zones will actively work to improve the environment for technological innovation, encourage businesses to conduct innovative research and development, and realize the transformation of old and new kinetic energy in land development and utilization. Areas with a high degree of economic growth and a favorable job climate are suitable for key development zones, talent introduction, and policy incentives. High-quality talent can be concentrated to accelerate the accumulation of human capital, thus forming a “reservoir” of technological innovation talent; enhancing intraregional sharing, matching, and learning effects; and effectively reducing the cost of talent training [33]. As a result, the technological innovation capacity of key development zones will continue to increase, driven by human capital. The region’s land input-output efficiency will be somewhat enhanced, and the environmental effect of the conventional economic growth model will be lessened by the superposition of technological innovation, talent, and their levels.

Second, we address structural effects. The structural effect of the establishment of national key development zones is manifested in the industrial structure and the land-use structure. The Plan states that national key development zones must first form a modern industrial system of division of labor and coordination and enhance industrial agglomeration capacity. The construction of national key development zones may have a “compensatory effect”, compensating for the shrinking production possibilities of traditional enterprises due to insufficient funds in the process of transformation and upgrading [34] and promoting the expansion of the production scale and the upgrading of the industrial structure of enterprises. Moreover, the process of input factor reallocation can adjust the industrial structure through comparative advantage, cost pressure, barriers to entry, etc., and encourage rationalization of the industrial structure [35]. An essential component of improving the land-use structure is the modernization and transformation of the industrial structure, which helps to realize efficient land use while reducing ecological costs. Second, the construction of national key development zones promotes the cross-regional flow of production factors, promotes regional synergies, and strengthens the links between regions. Through the marginal impact of diffusion, production patterns in various locations exacerbate competitiveness. Because of this, businesses are more inclined to keep improving their industrial buildings, which maximizes the land-use structure. The structure of land use determines its function. Local governments will enact increasingly complex and unique land-use rules based on the endowment of natural resources and the relative competitive advantages of different places to further increase the eco-efficiency of land use.

Third, we address the “dualization” effect. The “dualization” effect of national key development zones is specifically manifested in the two major aspects of industrialization and urbanization. The planning and construction of industrial parks and development zones should adhere to the circular economy idea and aggressively increase the degree of clean production. On the one hand, the Plan places a strong emphasis on advancing the modern industrialization process in key areas for national growth. New industrialization is embodied in the high-end quality of products, the intensification of production processes, and the maximization of production efficiency, which can greatly improve the eco-efficiency of land use. Businesses will fully utilize their subjective initiative to lower production costs through the normalization of the green production mode under the market competition mechanism of survival of the fittest [36]. The development of eco-industrial parks can achieve industrial agglomeration, encourage intensive and cost-effective land use, and lessen environmental pollution and ecological damage. On the other hand, the Plan proposes accelerating the process of urbanization in national key development zones, increasing the comprehensive strength of cities, and promoting the formation of city clusters with a division of labor, complementary advantages, intensification, and efficiency. In light of recent urbanization, the scale effect of factor agglomeration and positive externalities such as sharing and spillover have come to the forefront [37]. The process of urbanization encourages the influx of populations, land, capital, technological, and other factors to the central areas of towns and cities. The optimization of land-use efficiency and environmental governance is triggered by the geographical unit’s encouragement of factor reallocation and structural adjustment on the basis of the carrying capacity of the environment and land. However, the process of “dualization” growth unavoidably results in environmental damage and the waste of land resources, and local governments are strongly motivated to grow their local economies. The former paradigm of economic growth, which is based on high energy consumption, high pollution, and low efficiency, is difficult to alter quickly. Additionally, the “big cake” style of urbanization will result in several lingering issues. Consequently, the effect of “dualization” development on the efficiency of land ecological use should be examined in light of current circumstances. Thus, the following hypothesis is proposed in this paper:

H2. 

Through structural, technical, and “dualization” consequences, the creation of national key development zones may affect the ecological efficiency of land use.

4. Research Design, Variables, and Data

4.1. Model Setup

In addition to affecting land-use eco-efficiency both before and after policy implementation, the creation of national key development zones will result in variations in land-use eco-efficiency between the policy’s implementation areas and other places. Owing to these two differences, this paper regards the establishment of national key development zones as a “quasi-natural experiment” and uses a double-difference model to examine the ‘net effect’ of this policy on the eco-efficiency of land use. Since 2011 was designated the policy’s starting year and the National Main Functional Areas Plan was promulgated at the end of 2010, 110 county-level administrative units in the upper reaches of the Yangtze River were chosen as the experimental group, and the remaining 174 were chosen as the control group (see Figure 2 for details). The double-difference model was used to analyze the differences in the eco-efficiency of land use of the two samples. The following baseline regression model was developed, as shown in Equation (1):

$$Ef{f}_{it}={\alpha }_0+{\beta }_{1}DI{D}_{it}+\gamma X_{it}+{\mu }_i+{\mu }_t+{\epsilon }_{it}$$

(1)

$$DI{D}_{it}=Trea{t}_i\times Pos{t}_t$$

(2)

In this study, years are indicated by _t, and county administrative units are indicated by _i. ${Eff}_{it}$ is defined as the explanatory variable, that is, the land-use eco-efficiency of the ith county administrative unit in year t. ${DID}_{it}$ is the core explanatory variable, representing the interaction term between the group dummy variable and the time dummy variable after the NKDZ policy was proposed. It is the dummy variable indicating whether the ith county-level administrative unit was designated as a key development zone in 2011; ${Treat}_i$ is the group dummy variable, where ${Treat}_i=1$ if the county-level administrative unit is designated as a national key development zone, and ${Treat}_i=0$ otherwise; ${Post}_t$ is the time dummy variable; it is set to 1 in the year the NKDZ policy is implemented and thereafter, and 0 otherwise. ${\beta }_1$, the estimated coefficient, represents the policy effect of NKDZs. When ${\beta }_1$ is significantly positive, the establishment of NKDZs contributes to enhancing the eco-efficiency of land use, and vice versa. $X_{it}$ represents a set of control variables. ${\epsilon }_{it}$ is the randomized perturbation term. ${\mu }_i$ is the area fixed effect, and ${\mu }_t$ is the time fixed effect.

4.2. Variable Measurement

4.2.1. The Explained Variable

Land-use eco-efficiency, which is the economic benefit per unit area of land divided by the ecological cost to realize that advantage, is the term used to describe the economic benefit generated per unit area of land. This cost-benefit analysis relies on inputs from a variety of resource factors, including but not limited to capital, labor, and energy. Drawing on the relevant study by Tone et al. [38], the KLEM model was used to construct a system of indicators for measuring land-use eco-efficiency, including inputs, outputs, and undesired outputs (

Table 1. Land-use eco-efficiency measurements.

Type of Indicator	Level 1 Indicators	Secondary Indicators	Data Acquisition
Input indicators	Capital input (K)	Total fixed asset investment	Statistical Yearbook
	Labor input (L)	Total population at year-end	Statistical Yearbook
	Energy input (E)	Energy consumption index	Nighttime Lighting Data
	Natural factor input (M)	Construction land area and arable land area	Land-use Remote Sensing Data
Expected output	Economic benefit	GDP per unit of land	Statistical Yearbook
Non-expected output	Pollution output	Annual average concentration of PM2.5	Remote Sensing Data

). The DEA model has been identified as the most prevalent model in the extant research on efficiency measurement, encompassing both the radial model (CCR and BBC) and the non-radial model (SBM). However, these models are inadequate for precise efficiency estimation. The EBM model, a hybrid distance function that incorporates both radial and non-radial components, has been shown to circumvent the limitations of traditional models. Specifically, it addresses the understatement of inefficiency caused by the radial models which assume proportional adjustments of all inputs/outputs, and avoids the overestimation of inefficiency due to the non-radial models which ignore the proportional relationships between variables, thereby achieving more accurate efficiency measurement results. Due to space limitations, the formulae can be found in extant studies [39].

Among these, total fixed asset investment across society serves as a quantitative indicator of capital investment, reflecting the level of capital support in land development; the total population at year-end represents the quantity of labor input, forming the human resource foundation for land development; the energy consumption index measures the intensity of energy input in land use; the area of construction land reflects the spatial scale of land development, while the area of arable land indicates agricultural ecological functions, together constituting the natural resource input foundation; GDP per unit of land can measure the economic value of a unit of land and is the core indicator of the expected economic benefits; the annual average concentration of PM2.5 can reflect the air pollution generated by land use and is a non-expected output indicator for measuring the ecological and environmental costs.

4.2.2. Core Explanatory Variable

National key development zones. In this paper, dummy variables are used to represent the NKDZ policy variables. The variable takes a value of 0 for all control and pre-2011 treatment group counties and a value of 1 for post-2011 treatment group counties.

4.2.3. Control Variables

Referring to previous studies, control variables were selected from five dimensions: economic development [40], government intervention [41], financial conditions [42], human capital, and information infrastructure. Economic development (eco) uses the logarithmic value of per capita GDP to represent the total regional economy and residents’ income level. The government’s intervention level (gov) is measured by the ratio of fiscal expenditure to GDP; a higher ratio indicates a stronger influence of the government on resource allocation and economic regulation. Financial conditions (fin) are measured by the ratio of financial institutions’ loan balances at year-end to GDP, reflecting the extent to which financial resources support the real economy in the region. The human capital level (hum) is measured by the proportion of primary and secondary school students in the total population, reflecting the quality of the labor force and the potential for human capital accumulation. Information infrastructure level (inf): The logarithmic value of the number of fixed telephones per 10,000 people at the end of the year is used as a measure to reflect the prevalence of information transmission carriers in the region.

4.3. Research Overview

The upper reaches of the Yangtze River are a key region for ecological security and economic development in the Yangtze River basin. Geographically, it covers multiple provinces and municipalities including Sichuan, Chongqing, Guizhou, and Yunnan, with a total length of 4511 km and a controlled watershed area of 1 million square kilometers (Figure 3). The region is primarily characterized by mountainous terrain, plateaus, and basins, with a rich ecosystem and over 70% of its land consisting of farmland and forestland, making it an important agricultural base and ecological barrier. As an overlapping zone of strategic initiatives such as the Western Development Strategy and the Yangtze River Economic Belt, the region has seen a gradual increase in its land-use area. However, development in areas such as the Jinsha River basin and the Three Gorges Reservoir region directly impacts the safety of the middle and lower reaches of the Yangtze River. The dual attributes of “development needs and ecological sensitivity” make research on land-use ecological efficiency particularly significant for the region’s sustainable development.

4.4. Data Sources

The research sample of this paper is panel data from 284 counties in the upper Yangtze River region from 2005 to 2020. The processing group of national key development zone sample data is manually arranged and condensed in accordance with the State Council’s 2010 Main Functional Areas Planning policy. Societal and financial data were taken from the China Urban Statistical Yearbook, the China County Statistical Yearbook, the EPS database, and other relevant publications. (EPS (Economy Prediction System) is a data platform developed by Beijing Fucaster Information Technology Co., Ltd., covering diverse data such as the global economy, the Eurasian economy, and China’s macroeconomy.) In instances of missing data, they are addressed through consultation of the statistical yearbooks of provinces, cities, and counties, in addition to the statistical bulletin of national economic and social development. The interpolation method is employed to enhance and supplement any remaining missing data. The areas of built-up land and cultivated land were obtained by using the global 300 m resolution land-use dataset released by the European Space Agency (ESA). These raster data were processed via the ArcGIS 10.8 software to obtain the land cover of the county unit from 2005 to 2020. The Center for Resource and Environmental Science and Data of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 4 December 2024) provided the DMSP/OLS global nighttime lighting data, from which the energy consumption data were processed and obtained. The relevance of these data has been demonstrated by significant linear correlations between energy consumption and nighttime lighting data, as shown in the literature [43]. The Chinese Academy of Sciences and the Center for Resource and Environmental Science and Data provided this information. The PM2.5 data are derived from the raster dataset obtained by the Atmospheric Composition Observatory (ACO) via MISR and MODIS aerosol remote sensing data inversion provided by NASA. To ensure the scientific rigor and accuracy of the research, the per capita GDP and the number of fixed telephones per 10,000 people at the end of the year were log-transformed to reduce data distortion. For a comprehensive overview of the variables and their respective descriptive statistics, please refer to

Table 2. Descriptive statistics.

Variables	N	Mean	SD	Min	Max
Eff	4544	0.307	0.264	0.047	1.000
edl	4544	9.898	0.866	7.451	12.350
gov	4544	0.317	0.320	0.027	3.941
fin	4544	0.700	0.569	0.033	7.518
hum	4544	0.119	0.050	0.001	0.332
inf	4544	1.209	1.201	−3.066	5.069

.

5. Empirical Results and Analysis

5.1. Results of Measuring Land-Use Eco-Efficiency

To visually present the basic characteristics of land-use ecological efficiency in counties along the upper reaches of the Yangtze River, this paper first calculates the land-use ecological efficiency of sample counties from 2005 to 2020. According to Figure 4, before the implementation of the national key development zone policy, the changes in land-use ecological efficiency between counties with key development zones and those without were similar. After the policy was implemented, the efficiency values of both groups showed a downward trend, but the decline in the experimental group was significantly smaller than that in the control group. This phenomenon provides a real-world context worthy of further exploration for subsequent benchmark regression analysis—under the overall trend of declining efficiency, the experimental group’s efficiency declined at a slower rate, suggesting that the national key development zone policy may have played a certain buffering or mitigating role in slowing the decline in land-use ecological efficiency, which warrants rigorous validation through modeling.

5.2. Baseline Regression Results

Table 3. Benchmark regression results.

Explanatory Variable	(1)	(2)	(3)	(4)	(5)	(6)
DID	0.039 ***	0.028 ***	0.026 ***	0.026 ***	0.025 ***	0.026 ***
	(9.760)	(6.816)	(5.749)	(5.806)	(5.426)	(5.534)
edl		−0.073 ***	−0.077 ***	−0.078 ***	−0.081 ***	−0.084 ***
		(−8.701)	(−8.602)	(−8.793)	(−9.008)	(−9.254)
gov			−0.016	−0.014	−0.015	−0.016
			(−0.982)	(−0.865)	(−0.946)	(−1.020)
fin				−0.016 ***	−0.017 ***	−0.018 ***
				(−4.009)	(−4.226)	(−4.255)
hum					0.173 **	0.172 **
					(2.320)	(2.324)
inf						−0.006 *
						(−1.874)
Constant term (math.)	0.298 ***	1.028 ***	1.068 ***	1.093 ***	1.096 ***	1.133 ***
	(234.774)	(12.229)	(11.725)	(12.029)	(12.075)	(12.348)
Regional fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
R2	0.937	0.939	0.939	0.939	0.939	0.939
N	4544	4544	4544	4544	4544	4544

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the outcomes of the benchmark regression. Without control variables included, the regression results are displayed in the first column. The estimated coefficient of the key explanatory variable is approximately 0.039, and it passes the significance test at the 1% level. This finding indicates that the establishment of national key development zones can significantly increase the eco-efficiency of land use in the upper reaches of the Yangtze River. Following the gradual incorporation of the five control variables (the economic development level, government intervention, financial status, human capital, and information infrastructure), a decline in the regression coefficients of the core explanatory variable was observed. However, all of these coefficients were found to be significant at the 1% confidence level, thereby substantiating the hypothesis that the establishment of national key development zones can indeed promote eco-efficiency in land use. The regression results of Column (6) demonstrate that, when controlling for the area, time, and other influential factors, the implementation of this policy in national key development zones enhances land-use eco-efficiency by an average of 2.6% in comparison with the control group. Moreover, the baseline regression results substantiate Hypothesis 1.

The improvement in land-use eco-efficiency is not positively impacted by economic development, according to a regression analysis of the control variables. This may be attributed to the fact that large-scale land development and utilization in areas of high economic development occurred previously, resulting in significant environmental degradation and elevated ecological pressure. Government intervention has been demonstrated to exert a substantial negative influence on land-use eco-efficiency. This is because excessive government intervention can result in inadequate market economic vitality, with local governments often resorting to the introduction of short-term, high-return, high-pollution projects in pursuit of economic performance. This impedes the enhancement of land-use eco-efficiency. Financial status has a substantially negative regression coefficient. This may be due to the prominent problems of financial inhibition and financing discrimination in the financial sector, which inhibit its contribution to land-use eco-efficiency [44]. The regression coefficient of information infrastructure is significantly negative due to the fact that the current construction of information infrastructure suffers from uneven coverage and a fragmented layout. Moreover, there is a lack of information exchange and sharing between provincial, municipal, and county departments horizontally and vertically in the key development region, which inhibits the synergistic development pace of information infrastructure and land-use eco-efficiency to a certain extent.

5.3. Parallel Trend Test

The impact of the establishment of NKDZs on land-use eco-efficiency was examined via a double-difference model. This model is predicated on the idea that before the policy was put into effect, there was no discernible trend difference in land-use eco-efficiency between the experimental and control groups. The parallel trend test in this research is carried out via the event study method. The configuration of the model is delineated in Equation (3):

$$Ef{f}_{it}={\beta }_0+{\beta }_1{\displaystyle \sum _{t=-4}^{{t=4}^{+}}DI{D}_{it}}+\gamma X_{it}+{\mu }_i+{\mu }_t+{\epsilon }_{it}$$

(3)

where ${DID}_{it}{=Treat}_i\times {Post}_i$ represents the interaction term between the treatment group dummy variable ${Treat}_i$ and the year dummy variable ${Post}_i$. The period before the policy was put into effect is indicated by a negative value of _t, while the period following the policy’s implementation is indicated by a zero or a positive value. In addition, four years prior to and following the implementation of the policy were employed for testing because of the study’s lengthy sample period. The base period was selected as the five years prior to the policy’s implementation and beyond, and the four years after the policy’s implementation were combined into the fourth year.

The results of the parallel trend test are displayed in Figure 5. It is clear that none of the estimates of ${\beta }_1$ are significant when t is smaller than zero. This suggests that, prior to the introduction of the NKDZ policy, there was no discernible difference between the land-use eco-efficiencies of the treatment and control groups. The assessment of β₁ became important once the NKDZ program was put into effect. This result suggests that the eco-efficiency of land use in the counties changed significantly as a result of the implementation of the NKDZ policy. Therefore, the assumption of the parallel trend test was satisfied. Notably, the regression coefficients in the final period of the parallel trend test are positive but not significant, which may be attributable to the decrease in the policy effect of the NKDZs on the eco-efficiency of land use.

5.4. Placebo Test

The core logic of the placebo test is to verify the reliability of the true policy effect by constructing a “false policy shock”. If the true estimate is caused by confounding factors such as omitted variables, then similar results may also appear under the false policy; conversely, it indicates that the true effect is robust. This paper subsequently employs a random sampling placebo test to mitigate possible estimation bias arising from unaccounted-for key factors. The specific method is as follows: A total of 110 county-level administrative units were randomly selected from the whole research sample as the “pseudo-experimental group”. It was assumed that these county-level administrative units were the areas covered by the national key development zone policy. (These counties were artificially assigned a virtual “policy coverage” status, rather than undergoing actual policy adjustments.) And the rest of the county-level administrative units were used as the control group. The kernel density and p-value associated with the placebo coefficient were generated from 500 estimation replications and are depicted in Figure 6. It is evident that after random assignment, the estimated coefficients for the treatment group clustered near zero, differing significantly from the actual estimated coefficient of 0.026, and most p-values significantly exceeded 0.1. Thus, the comparison between the true estimated coefficient and the placebo outcomes indicates that no significant bias resulted from the omission of critical explanatory variables in the estimation.

5.5. Other Robustness Tests

On the basis of the above analysis, other robustness tests are further conducted in this paper. Specifically, first, the dependent variable is modified. Land-use eco-efficiency, as measured through an output-oriented EBM model with constant returns to scale, is employed as a proxy variable. Column (1) in

Table 4. Robustness test regression estimation results.

Variable	(1)	(2)	(3)	(4)
	Replace the Explained Variable	Exclude Other Policies	Remove Some Samples	Increase Control Variables
DID	0.003 ***	0.024 ***	0.019 ***	0.023 ***
	(2.647)	(5.120)	(3.190)	(4.922)
Constant terms	0.004	1.135 ***	1.358 ***	1.122 ***
	(0.191)	(12.374)	(12.057)	(12.265)
Control variables	Yes	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes
R2	0.982	0.939	0.923	0.939
N	4544	4544	3424	4544

Note: *** p < 0.01.

reports the estimation results. Second, interference from other policies is excluded at the same time. Following the designation of national key development zones, concurrent policies such as the Guidelines on Accelerating Ecological Civilization Construction issued by the Central Committee of the Communist Party of China and the State Council in 2015 and the Yangtze River Economic Belt Development Strategy implemented since 2016 may have affected the land-use eco-efficiency of county-level administrative units. These parallel initiatives could contaminate the evaluation of policy effects specifically attributable to the national key development zones. Therefore, to rule out the potential impacts of these policy implementations, we re-estimate the baseline regression by including interaction terms between the treatment group and year dummies for 2015 and 2016. The re-estimated results are presented in Column (2) of Table 4. Third, some samples are excluded. The establishment of national key development zones may be influenced by multiple factors, including spatial development patterns, natural geographical locations, and resource endowments. Districts, ordinary counties, and county-level cities present varying characteristics in terms of development levels, location conditions, and ecological foundations, with significant differences among them. Therefore, samples of districts and county-level cities were excluded, and only ordinary county samples were retained for estimation. The re-estimated results are presented in Column (3) of Table 4. Fourth, the control variables are increased. The economic development levels of counties in national key development zones are uneven, and their marketization levels and market potential also vary. Therefore, the retail sales of per capita social consumer goods are used to represent the marketization conditions of each county, and they are included as a control variable for re-estimation. Column (4) in Table 4 reports the estimation results.

Columns (1) through (4) present positive regression coefficients that are significant at the 1% level. Consequently, the positive impact of establishing national key development zones on land-use eco-efficiency, as demonstrated in baseline regressions, persists across multiple robustness checks, indicating robustness.

5.6. Mechanistic Analysis

The baseline results demonstrate that the national key development zone policy enhances land-use eco-efficiency in county-level areas across the upper Yangtze River region. Through what specific mechanisms does this policy achieve such outcomes? The theoretical analysis in the preceding text indicates that the implementation of policies in national key development zones can increase the ecological efficiency of land use through technological and structural effects. Mechanism variables were embedded in the benchmark regression to test for mechanism effects. The specific model settings are as shown in Equation (4):

$$Meditatio{n}_{it}={\alpha }_1+{\beta }_{2}DI{D}_{it}+\gamma C_{it}+{\mu }_i+{\mu }_t+{\epsilon }_{it}$$

(4)

where ${Meditation}_{it}$ represents the mechanism variable designation, with all other variables maintaining the notation defined in Equation (1). The mechanism affects the model analysis process as follows: first, Equation (1) is estimated to obtain coefficient ${\beta }_1$ for the treatment–time interaction term $DID$; then, Equation (4) is estimated to derive ${\beta }_2$, which captures the policy’s causal effect on the mechanism variable. A statistically significant ${\beta }_1$ indicates that the national key development zone policy enhances county-level land-use eco-efficiency through this channel.

First, we address the technological effect. This study uses invention patents per 10,000 people (inn) to proxy for technological innovation, capturing county-level technological capacity. The results in Column (1) of

Table 5. Influence mechanism analysis.

Variable	Technical Effect	Structural Effect	Industrialization	Urbanization
	(1)	(2)	(3)	(4)
	inn	ind	com	urb
DID	0.006 ***	0.239 ***	0.003 ***	0.022 ***
	(17.279)	(−4.414)	(11.598)	(20.556)
Constant terms	0.236 ***	4.941 ***	−0.024 ***	0.225 ***
	(4.100)	(6.604)	(−9.208)	(10.965)
Control variables	Yes	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes
R2	0.711	0.755	0.901	0.985
N	4544	4544	3680	4544

Note: *** p < 0.01.

show a statistically significant positive effect of national key development zones on technological innovation. This finding indicates that establishing such zones enhances the degree of technical advancement in policy-implemented regions. The coefficient of the core explanatory variable $\mathrm{D}\mathrm{I}\mathrm{D}$ is 0.006 and passes the 1%-level significance test. This confirms that national key development zone policies enhance county-level land-use eco-efficiency through the technological innovation channel, thereby validating the technology effect mechanism.

Second, we address the structural effects. The structural effect refers to the transition process where county-level dominant industries shift toward secondary and tertiary sectors. This paper employs the tertiary-to-secondary value-added ratio (ind) to measure industrial upgrading, reflecting advanced restructuring of the output composition. Column (2) in Table 5 reveals a strong, statistically significant impact of national key development zones on industrial upgrading, indicating that the establishment of national key development zones helps the industrial structure of policy-implementing regions to become more advanced. The regression coefficient of the core explanatory variable $\mathrm{D}\mathrm{I}\mathrm{D}$ was 0.239 and passed the significance test at the level of 1%. This confirms that national key development zone policies enhance county-level land-use eco-efficiency through the structural effect channel, thereby validating the structural mechanism.

Third, we address the “two processes” effect. This study proxies industrialization (com) with the number of industrial enterprises and urbanization (urb) with the ratio of the construction land area to the total administrative area. These metrics reflect the development of county-level industrialization–urbanization. The results in Columns (3) and (4) of Table 5 demonstrate significantly positive effects of national key development zones on industrialization and urbanization. This indicates that establishing such zones effectively enhances industrial and urban development levels in policy-targeted regions. The coefficients for the $\mathrm{D}\mathrm{I}\mathrm{D}$ interaction term are 0.003 and 0.022, and both passed the 1%-level significance test. This confirms that national key development zone policies improve county-level land-use eco-efficiency by advancing industrialization and urbanization, empirically validating the “dual-process” effect.

In summary, the mechanism analysis verifies Hypothesis 2: “The establishment of National Key Development Zones affects land-use eco-efficiency through three mechanisms: the technology effect, the structural effect and the effects of “two processes”.

5.7. Heterogeneity Analysis

(1)

Distinguishing between different county types: Significant disparities in socioeconomic development status, industrial structure, and market potential exist across municipal districts, counties, and county-level cities within the upper Yangtze River region. Such heterogeneity may cause differential impacts of national key development zone policies on land-use eco-efficiency enhancement. In general, the economic development level, industrial structure, and degree of land development of municipal districts are greater than those of county-level cities and counties, and they are also more proactive and efficient in implementing ecological and environmental protection policies. This paper divides the samples into municipal districts, county-level areas, and county-level cities for testing, and the regression results are shown in

Table 6. Heterogeneity analysis: differences based on different county types.

Variable	(1)	(2)	(3)
	Municipal Districts	County Area	County-Level Cities
DID	0.102 ***	0.008	0.062 ***
	(5.439)	(1.047)	(3.365)
Constant terms	0.401 ***	1.529 ***	−0.282
	(3.450)	(12.790)	(−1.197)
Control variables	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes
R2	0.984	0.926	0.824
N	1120	3008	416

Note: *** p < 0.01.

. The regression coefficients for urban districts and county-level cities are significantly positive, while those for ordinary counties are positive but not significant. The coefficients for urban districts are significantly higher than those for ordinary counties and county-level cities, indicating that the policy of national key development zones has a more pronounced effect on improving the ecological efficiency of land use in urban districts. This is due to the economic benefits brought by their superior administrative locations, as municipal districts can attract high-quality technical talent to enhance technological innovation capabilities, with obvious agglomeration effects in terms of the economy, population, and industry. Moreover, environmental regulations within municipal districts are stricter, allowing for improved land-use efficiency while reducing ecological pressure.

(2)

Distinguishing between different locations: Studies have shown that the efficiency of environmental governance varies greatly among provinces (autonomous regions and municipalities) in China, and the changes between provinces (autonomous regions and municipalities) also differ [45]. On the basis of provincial administrative divisions, we partition the upper Yangtze River region into four units, namely Yunnan Province, Guizhou Province, Sichuan Province, and Chongqing Municipality, for regression analysis.

Table 7. Heterogeneity analysis: regional differences based on different locations.

Variable	(1)	(2)	(3)	(4)
	Yunnan	Guizhou	Sichuan	Chongqing
DID	−0.004	0.032 **	0.043 ***	0.033 ***
	(−0.719)	(2.237)	(5.729)	(2.744)
Constant terms	0.707 ***	1.478 ***	0.503 ***	0.333 ***
	(2.815)	(6.700)	(2.91)	(3.130)
Control variables	Yes	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes	Yes
Time fixed effect	Yes	Yes	Yes	Yes
R2	0.867	0.861	0.958	0.977
N	1296	848	1792	608

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

presents the estimation outcomes. The coefficients observed for Guizhou, Sichuan, and Chongqing are 0.032, 0.043, and 0.033, respectively, each demonstrating statistical significance at the 1% level. These findings indicate that the national key development zones significantly enhance land-use eco-efficiency in these three provincial units. In contrast, Yunnan has an insignificant negative coefficient. Potential contributing factors include Sichuan’s and Chongqing’s relatively high economic development levels, the in-depth advancement of the Chengdu–Chongqing economic circle, significant population agglomeration effects, and superior technological innovation capacity, collectively propelling land-use eco-efficiency improvements.

6. Conclusions, Recommendations, and Future Explorations

6.1. Research Conclusions

As an important part of the national main functional zone plan, national key development zones should focus on reducing energy and resource consumption and protecting the ecological environment while achieving economic growth and improving efficiency. Therefore, the national key development zone policy is viewed in this study as a type of natural experiment. Panel data from 284 counties in the upper Yangtze River region from 2005 to 2020 were used to test the impacts empirically via a DID model, and impact mechanisms of the implementation of the national key development zone policy on land-use ecological efficiency were analyzed. The study revealed that, first, compared with counties not covered by the policy, the national key development zone policy significantly improved the ecological efficiency of land use in the policy implementation areas. Numerous robustness tests, such as parallel trend and placebo testing, supported this finding. Second, the impact mechanism test revealed that NKDZs can increase land-use ecological efficiency in counties through technological and structural effects and the effects of “two processes”. Specifically, the development zone has promoted an increase in the number of invention patents, optimized the proportion of the tertiary and secondary industries, and driven the clustering of industrial enterprises and the rational expansion of urban construction land. These factors have collectively improved the ecological efficiency of land use. Third, heterogeneity research demonstrated that the creation of NKDZs improved land-use ecological efficiency in municipal districts, county-level cities, and the provinces of Sichuan, Guizhou, and Chongqing but has no significant impact on ordinary counties or the province of Yunnan. On the basis of the above conclusions, the construction of national key development zones in the future should be promoted as follows:

6.2. Research Recommendations

(1)

Promotion and application of key national development zones in a sustainable manner. On the one hand, land resources within national key development zones should be utilized in accordance with the basic principles of intensive, efficient, and sustainable use to increase the land-use efficiency per unit area. The best possible distribution of land resources should be encouraged in important development counties and reasonably demarcating production spaces, living spaces, and ecological spaces, and they should be transitioning from a model where “demand determines supply” to one where “supply guides demand”. During the urbanization process, permanent boundaries for urban development and construction should be established to firmly prevent uncontrolled and blind urban expansion and protect ecological spaces and basic farmland within key development zones. On the other hand, national key development zones should strengthen land consolidation and strictly control the flow of land resources. With reserve land resources becoming increasingly scarce, it is important to harness the potential of different kinds of existing land and govern the land-use requirements of different stakeholders. Efforts should be stepped up to investigate and consolidate idle and inefficient land in key development zones and implement policies linking land-use increases and decreases among administrative regions.

(2)

Acceleration of technological innovation and industrial restructuring and upgrading to strengthen internal motivation for the scientific and efficient use of land. On the one hand, regions should actively promote the integration and exchange of innovative elements such as high-quality talent, advanced technology, and capital to facilitate the aggregation of innovative resources. Universities, research institutes, and other institutions should be encouraged to cultivate specialized talent around technological innovation, increase incentives and subsidies for technological innovation, strengthen talent recruitment, and make use of contemporary IT tools such as big data and the internet to foster new productive forces. This will enhance the ecological efficiency of land use while promoting environmental improvement. On the other hand, a positive feedback mechanism between industry and land use should be established. Through industrial upgrading and economic aggregation, specific pathways for efficient land use in key development counties can be clarified. Relying on existing characteristic industries and fully leveraging local comparative advantages can guide industrial development toward scale and intensification. Prioritizing the cultivation of non-polluting industries can improve market exit mechanisms for high-polluting enterprises and create a more favorable policy environment for industrial transfer.

(3)

Adoption of differentiated land-use control policies tailored to local conditions and specific cities. In light of China’s transition from fast growth to high-quality development, regional spatial planning should prioritize the development of new urbanization and industrialization. County-level socioeconomic performance indicators should incorporate factors such as changes in natural resource levels and the ecological environment. Municipal districts should leverage their existing well-developed industrial systems, fully utilize their innovation cluster advantages, further stimulate the competitive effects of various market and resource entities, enhance the study and creation of cutting-edge environmental protection technologies, and guide the conversion of environmental protection technology achievements. Ordinary counties should establish a solid foundation for green industries and green ecology, promote the integration of their own advantageous resources, ensure that resources and environmental carrying capacity complement socioeconomic development levels, avoid environmental pollution issues caused by blind investment attraction, and enhance the ecological and social benefits of land use.

6.3. Future Research Directions

Although this study provides empirical evidence of the role of national key development zones in improving the ecological efficiency of land use, it does not include an analysis of long-term dynamic changes after policy implementation, making it impossible to verify the sustainability or attenuation characteristics of the effects. Furthermore, the scope of this study is limited to the upper reaches of the Yangtze River in China, and the universality of the conclusions has not been tested in different institutional, cultural, or geographical contexts. Future research can be advanced in the following directions: (1) Combine subjective and objective measurement methods to optimize the calculation of land-use ecological efficiency, while refining spatial transmission pathways through case studies. (2) Extend the observation period to track the long-term dynamic effects of policy implementation and verify the sustainability or phased changes in these effects. (3) Expand the scope of research to other river basins in China and development zones in other countries to test the universality of the conclusions under different institutional and cultural contexts.