Evaluating the Impact of Rural Construction Land Marketization on Rural Industrial Integration

Abstract

Industrial prosperity is the prerequisite and foundation of rural revitalization, while rural collective operating construction land (COCL), as an important resource for rural industrial development, has significant theoretical and practical guiding significance regarding whether its market-oriented reform can promote rural industrial integration (RII). This study innovatively combines the synthetic control method (SCM) and mediation effect model to assess the impact of rural collective operating construction land entering the market (COCLEM) on RII, using panel data from 86 counties in Hunan Province (2011–2022), and its underlying mechanisms. This study finds that COCLEM significantly enhances RII, and this conclusion remains robust after a series of tests. A mechanism analysis indicates that COCLEM primarily promotes RII through population agglomeration, fiscal support, and technological upgrading. Furthermore, this study reveals that COCLEM effectively facilitates the extension of the agricultural industry chain and promotes the development of agricultural services. Nevertheless, the improvement of rural infrastructure still requires policy guidance and sustained investment to provide more robust support for RII. Therefore, policymakers should actively formulate a COCLEM policy framework, enhance rural public services, and increase infrastructure investment to continuously promote regional population agglomeration. Local governments should be responsible for ensuring policy implementation and financial support. Additionally, emphasis should be placed on the role of COCLEM in advancing agricultural technology to support RII. Compared with existing research, this study provides new empirical methods and theoretical insights for detailed research on the development of RII.

1. Introduction

Rural industrial integration (RII) is an important way to promote the high-quality development of the rural economy. China’s No. 1 Central Document for 2024 advocates for transforming agriculture into a modernized key industry and promoting the integrated development of primary, secondary, and tertiary industries in rural areas. RII refers to the interaction and integration of various industries in rural areas or different sectors within the same industry. It involves the combination of labor, capital, and technology across agriculture, industry, and services to facilitate the diversification and modernization of the rural economy [1]. With rural industrial development, industry boundaries are gradually diminishing and becoming increasingly blurred. The trend of industrial integration reflects not only an inherent demand of rural industrial development but also a crucial pathway for achieving rural industrial revitalization. The advancement of RII facilitates the transformation of the agricultural industry and plays a significant role in fostering new economic growth points in rural areas [2] while enhancing farmers’ income [3].

How can RII be effectively promoted? Generally, RII must overcome constraints related to land, labor, and capital while promoting development through optimized resource allocation and technological innovation [4]. Among these factors, land plays a crucial role in industrial development [5]. The activation of land resources is also essential for achieving industrial revitalization [6] and has become an important means of achieving RII. Since the launch of the pilot reform of “three pieces of land” in 2015, the rural collective operating construction land entering the market (COCLEM) has emerged and has initially formed a framework with clear transaction guidelines and a complete institutional structure [7]. COCLEM refers to the inclusion of rural collective operational construction land (COCL) in the land market, allowing it to be traded in compliance with statutory regulations, which promotes the rational allocation of land resources and improves the efficiency of land use through the market-oriented operation of land, and it helps overcome the constraints of the existing land management system. COCL, as a crucial land policy instrument, enhances rural land use efficiency through market transactions, thereby fostering the integrated development of RII [8]. However, market-driven RII may also lead to unintended consequences, such as rising land prices, displacement of agricultural land, and increased agricultural development costs. Additionally, enterprise entry may cause pollution spillovers, restrict agricultural capital deepening, attract foreign capital to rural areas, and drive labor migration. The imbalance in rural industrial development may exert pressure on agricultural management entities and then hinder the development of rural industry [9]. Can COCLEM facilitate RII? Through what mechanisms does it exert its influence? How can COCLEM be leveraged to enhance rural industrial integration? Responding to the above questions is of great practical significance for grasping the universal law of rural industrial revitalization, developing a theoretical framework to analyze the impact of COCLEM on RII, and improving COCLEM to promote the integration of rural industries.

Recent academic research on RII has expanded, with scholars conducting in-depth investigations from multiple perspectives. Scholars have examined the fundamental concepts of RII [10], practical challenges and corresponding strategies [11], measurement methodologies [12], and influencing factors [13]. Some scholars have quantified the level of rural industrial integration and conducted extensive theoretical and empirical analyses of its economic and social impacts, including income disparities [14]. The influencing factors of RII development have been explored from various perspectives, primarily focusing on financial services [13] and technological advancements [15]. The acceleration of rural industrial development hinges on the deepening reform of key production factors, including labor, capital, and land [5]. Overall, the existing literature primarily examines rural financial support, services, and the digital economy in relation to RII development, and there is not much literature examining the impact of land factors on the development of RII.

Land plays a crucial role as both a resource and spatial foundation for urban and rural economic development [16]. In developing countries, restrictions on rural land transactions are seen as an important obstacle to rural economic development [17]. From the perspective of land allocation, the market mechanism has played a crucial role in the evolution of China’s rural land system reform [18]; the existing literature focuses on the reform of agricultural land transfer from the perspective of the reform of agricultural land transfer and has empirically examined the role of large-scale land operations, formed through agricultural land transfers, in promoting RII at both macro and micro levels [19].

Unlike previous studies focusing on agricultural land transfers, some scholars have examined the economic impact of the COCLEM reform. The existing literature indicates that the COCLEM reform is a distinctive change within China’s land system. While scholars do not directly examine its economic impact, they primarily focus on aspects such as land property rights [20], land market dynamics [21], and the right to land development [22]. These studies provide a foundational basis for the present research and perspectives on land transfer, laying a certain foundation for this study. Current research on COCLEM primarily explores its effects on economic development [23], land finance [24], poverty reduction [25,26], urban–rural integration [27,28,29], and farm household income. However, studies on its impact on RII remain limited, with only one empirical study utilizing the DID (difference-in-difference) method to investigate this aspect [30].

Empirical studies have examined the macro-level impact of COCLEM on land finance using correlation analysis [24]. Research also indicates that reforms in rural construction land transfer positively contribute to poverty alleviation [26]. Based on the reform of Chongqing’s land ticket system by utilizing the synthetic control method (SCM) and DID method, scholars have also tested the positive impact of the reform of the rural construction land transfer system on the reduction of poverty [25] by utilizing the county data from Deqing in Zhejiang Province; scholars have used the SCM and DID methods to test the positive impact of COCLEM on urban–rural integration development [27] by utilizing the county data from Deqing in Zhejiang Province; used SCM and DID to test the positive impact of COCLEM on urban–rural integration development [29]; and empirically found COCLEM not only through improving land allocation efficiency [31] but also through reducing land price under the perfect market [32]. Although SCM is advantageous in land policy research, its application in RII studies remains scarce, highlighting the need for further investigation. Prior studies have predominantly used traditional econometric methods such as OLS and DID. However, SCM remains underutilized in policy evaluation, despite its effectiveness when pre-intervention outcomes and covariates are well balanced [33]. Therefore, this study adopts SCM to construct a counterfactual framework to more accurately assess the impact of COCLEM on RII and combines DID to conduct dynamic analysis to improve the robustness of the research conclusions. Because COCLEM centralizes rural land resources, it facilitates enterprise settlement, making its impact on RII a significant research concern; however, few empirical studies have utilized DID and SCM to assess the impact of COCLEM on the integration of rural industries. In addition, the impact of COCLEM on RII and its mechanism have not been precisely and comprehensively elaborated in the literature. This study aims to assess the effects of COCLEM on RII.

In recent years, the market-oriented reform of COCL in China has been accelerating, drawing considerable attention to its impact on RII. However, the transfer of COCL still faces numerous challenges, such as inefficient land use [34] and insufficient industrial chain integration [35]. These issues hinder the sustainable development of the rural economy, making it crucial to examine the impact of COCLEM on RII. Although existing studies have explored the effects of COCLEM on agricultural productivity, most of them focus on urban COCLEM, lacking a systematic analysis of COCLEM. This study constructs a COCLEM model and utilizes empirical data from Liuyang City to systematically assess the impact of COCLEM on RII.

This study investigates key scientific questions concerning COCLEM’s impact on RII: (1) COCLEM’s influence on RII and the underlying mechanisms driving this effect, which is crucial for assessing its impact in Liuyang City and for providing insights into how similar processes might operate in other regions or countries seeking to optimize land transfer systems and integrate rural industries; (2) the ways in which COCLEM affects various dimensions of RII, including population scale, government fiscal support, and agricultural technological upgrading, examining the pathways through which COCLEM influences these key aspects of RII, shedding light on its role in shaping rural economic structures, and offering policy insights for regions facing similar challenges; (3) the broader effects of COCLEM on economic, social, and technological integration within rural areas, which aims to deepen the understanding of RII’s comprehensive impact and provide a framework for regions and countries striving for sustainable RII and socio-economic development; and (4) the optimization of COCLEM from a policy perspective to further promote RII, with findings offering targeted recommendations aimed at refining land transfer mechanisms and improving RII, focusing on enhancing the efficiency and sustainability of agricultural development, insights that can be adapted to support the implementation of similar policies in regions facing comparable challenges in balancing land use, rural economic growth, and technological upgrading.

The main contributions of this study are as follows:

(1)

Theoretical contribution: This study examines the impact of COCLEM on RII, enriching the theoretical framework of land transfer and RII. It elucidates the overall logic of COCLEM and RII, as well as the specific impacts on different aspects of RII.

(2)

Methodological innovation: This study combines SCM with DID, which enhances the robustness of causal identification and research findings.

(3)

Mechanism exploration: This study conducts a detailed analysis of how COCLEM influences RII through factors such as population agglomeration, fiscal support, and technological upgrading, providing insights for future research.

(4)

Policy implications: The findings provide empirical evidence for policymakers to design more effective COCLEM and RII policies, contributing to the optimization of COCLEM in promoting RII.

The remainder of this paper is organized as follows: In Section 2, we outline the analysis framework. In Section 3, we introduce the description of the study area, data, and variables. The results are presented in Section 4. Section 5 presents the discussion. Section 6 presents the conclusion and policy implications.

2. Analysis Framework

RII is an important measure for realizing the revitalization of rural industries and promoting the modernization of agriculture and rural areas, as well as a prerequisite for realizing the common prosperity of farmers and rural areas [36]. However, China’s RII is still in its early stages, and the shortage of construction land for rural industrial development has led to bottlenecks in actor participation and constraints on the capacity of emerging business entities, thereby hindering the realization of RII objectives. COCLEM, as a key institutional initiative for unlocking the potential of rural land, seeks to overcome the constraints imposed by the existing land management system. By mitigating land shortages in rural industrial development, COCLEM promotes and facilitates RII (Figure 1).

COCLEM has been shown to increase a region’s demographic appeal, strengthen local financial capacity, and modernize agricultural production methods [37]; strengthen local financial strength [38]; and promote the modernization of agricultural production methods [39]. However, there is limited research on the specific mechanisms by which COCLEM influences RII. From an institutional economics perspective, COCLEM optimizes resource allocation, facilitates factor mobility, and fosters coordinated regional economic development; meanwhile, regional economics theories emphasize the centrality of the agglomeration effect of production factors in regional economic development, especially the important roles of population, capital, and technology in the upgrading of industrial structure. This study aims to bridge this research gap by systematically analyzing the intrinsic mechanisms through which COCLEM influences RII through the construction of theoretical mechanisms and empirical analysis.

2.1. Population Scale Effect

COCLEM aims to optimize rural land allocation and enhance its market value, which not only increases the property income of village collectives and farmers but also enhances the attractiveness of rural areas to the labor force and promotes regional population agglomeration and scale expansion [40]. Existing studies indicate that population growth drives demand for public services, leading local governments to invest in rural infrastructure, thereby improving residents’ quality of life and attracting laborers to remain or return to their hometowns [41] and thus promoting the agglomeration and development of industries and services in rural areas. In this context, it facilitates the introduction of advanced technology, management experience, and market concepts by enterprises, which will stimulate the vitality of rural industrial development and form a complementary and synergistic industrial chain, thus laying the foundation for RII, but also making it easier to realize the popularization of technical training and knowledge among farmers so as to improve their skills and productivity and to provide human resources support for RII [42].

2.2. Fiscal Support Effect

Acting as intermediaries for higher authorities, local governments employ performance-driven governance to implement COCLEM. This often involves increasing fiscal input and formulating incentive policies tailored to local conditions to ensure that policy objectives are met [43]. Increased fiscal spending improves rural infrastructure and expands public services, thereby improving farmers’ quality of life and enhancing investment attractiveness for attracting external investment in rural industrial projects [44]. Additionally, policy measures such as tax exemptions, financial subsidies, technical support, and market promotion reduce enterprise investment costs and attract foreign capital, and local governments can reduce the investment costs of enterprises and attract foreign capital, thus enhancing the competitiveness and attractiveness of rural industries [45] and providing important support for RII.

2.3. Technological Upgrade Effect

COCLEM boosts rural land value and increases property income for farmers and collective economic organizations, enabling them to invest more in advanced agricultural technologies [46]. Agricultural technology advancements improve product quality and increase added value, which promotes the extension of the agricultural industry chain and enables farmers to produce agricultural products of higher quality and greater market competitiveness [47], thus enhancing market demand for agricultural products and creating conditions for the integration and development of agriculture with the processing industry and the service industry [48]. In addition, the upgrading of agricultural technology can also encourage farmers to master modern agricultural technology and management methods and realize the quality improvement and professional development of rural labor, which not only attracts more young laborers to participate in the process of modern agricultural development, but also enables farmers to engage in more specialized and efficient agricultural and related industrial development activities, improves vocational skills and production efficiency, and lays the foundation for RII.

Based on the above analysis, the following hypotheses are proposed.

H1. 

Can COCLEM potentially enhance RII?

H2. 

COCLEM influences RII through population scale, fiscal support, and technological upgrade effects.

3. Study Area, Data Sources, and Variables

3.1. Study Area

Liuyang City (27°42′–28°12′ N, 112°48′–113°36′ E), situated in Hunan Province, encompasses an area of 4997 square kilometers (green area Figure 2). The region comprises four streets, 27 towns, and one township. In 2023, the per capita regional GDP reached CNY 121,400, with a total resident population of 1,477,000.

Liuyang City was selected as the study area for two primary reasons. First, as one of the 33 pilot regions for COCLEM since 2015, it exhibits a more complex geographic environment than other pilot areas. Second, unlike Liuyang, the remaining 85 counties and districts in Hunan Province have not yet implemented COCLEM, enabling a quasi-experimental research design where these regions serve as control units. Selecting Hunan as the research target provides valuable insights for other domestic and international regions with underdeveloped economies and complex terrains that seek to promote RII through land policy reforms.

3.2. Description of Variables

3.2.1. Dependent Variable

DLRII: RII represents a dynamic agricultural evolution process characterized by deep integration, mutual penetration, and restructuring with secondary and tertiary industries. Its primary objectives include enhancing agricultural production efficiency, fostering sustainable economic growth, and facilitating the optimization and upgrading of agricultural structures to maximize agricultural functionality [49]. The measurement of RII encompasses three key dimensions: agricultural industry chain extension, expansion of the agricultural service sector, and enhancement of rural infrastructure (

Table 1. Comprehensive evaluation indicator system of RII.

Indicators	Connotations	Measurement Methods	References
Extension of the Agricultural Industry Chain	Reflects the level of agricultural product output	Total grain production (tons)	Zeng et al., 2022 [19]
	Reflects the modernization level of agricultural operations and the degree of industrial upgrading	Added value of the primary industry (CNY 10,000)	Li et al., 2024 [50]
Expansion of Agricultural Services	Reflects the integration of agriculture with the service industry	Total output value of agriculture, forestry, animal husbandry, and fishery (CNY 10,000)	Fofana et al., 2020 [51]
Support Capacity of Rural Infrastructure	Reflects the efficiency of industrial information flow in rural areas	Number of fixed-line telephone users (households) (when measuring the development level of rural infrastructure, the number of fixed telephone users was once an important indicator of communication development. However, with the rapid proliferation of mobile communication technology, the representativeness of this indicator has declined. Moreover, because this study is based on county-level data spanning a ten-year period, some infrastructure variables exhibit significant data availability issues. Therefore, using the number of fixed telephone users as a proxy variable is reasonable to some extent, despite its inherent limitations. Future studies should refine measurement indicators to enhance accuracy.)	Gao et al., 2025 [52]

).

3.2.2. Explanatory Variable

According to Zhang et al. [53], the interaction term $COCLE{M}_i\times yea{r}_t$ indicates the policy effect of COCLEM. $reform$ indicates whether COCLEM has been implemented or not, assigning a value of 1 to $i$ for implementation and 0 otherwise. $COCLEM$ represents the year of implementation. Given that the actual implementation year of COCLEM in Liuyang City, Hunan Province, is 2016, the years 2011 to 2015 are assigned a value of 1 to $t$, while the years 2016 to 2022 are assigned a value of 0.

3.2.3. Control Variable

Drawing on existing research, the following factors that may affect the level of RII development are selected as predictor variables (in the model, we assessed the issue of multicollinearity using the VIF test (

Table A1. Variable correlation tests.

Variable	VIF	1/VIF
COCLEM	1.08	0.9259
Household savings deposit balance	6.44	0.1554
Labor productivity	5.03	0.1986
Number of beds in hospitals	4.93	0.2030
Government intervention	3.34	0.2993
The degree of urbanization	2.72	0.3673
Population density	1.91	0.5245
Labor input	1.87	0.5346
Human resources	1.31	0.7658

), and the results indicated that the collinearity among variables was within an acceptable range. Furthermore, considering that the policies and land marketization process in Liuyang City may generate certain spillover effects on surrounding counties, future studies may integrate spatial econometric methods to systematically assess spillover effects and refine control group selection, thereby improving the robustness of research findings.): (1) the degree of urbanization is expressed as the level of urbanization in each county [54]; (2) government intervention is expressed as the ratio of fiscal expenditure to GDP [55]; (3) population density is expressed as the ratio of the total resident population of the county to the total area of the county’s administrative region in that year [56]; (4) labor input is expressed as the proportion of employees to the total population [57]; (5) human resources are expressed as the ratio of the number of students enrolled in secondary schools to the total population [35]; (6) labor productivity is expressed as the proportion of GDP to the total population [58]; (7) household savings balance is expressed as the logarithm of the year-end balance of financial institution deposits [59]; and (8) the number of hospital beds is measured as the logarithm of beds available in medical and health centers [60].

3.3. SCM Model

This study employs SCM to assess the impact of COCLEM on RII, addressing potential endogeneity issues inherent in DID estimation. DID may yield biased estimates, potentially overstating COCLEM’s effects, while propensity score matching (PSM) struggles to capture region-specific variations and may introduce matching errors due to data heterogeneity. Considering these methodological constraints and the uniqueness of the treatment group—Liuyang City—ensuring robustness and selecting an appropriate control group is crucial [61].

First, the treatment and control groups are defined: Liuyang City, which adopted COCLEM in 2016, serves as the treatment group, while non-COCLEM counties in Hunan Province form the control group. Next, a synthetic control group is created by assigning optimal weights to control counties, ensuring a close match with Liuyang City’s pre-intervention characteristics. Least squares and other optimization techniques are employed to minimize pre-2016 differences between groups. The policy effect is then estimated by comparing post-intervention RII outcomes between Liuyang City and its synthetic counterpart. A panel dataset covering 86 counties from 2011 to 2022 ensures a comprehensive analysis. Finally, robustness checks, including placebo and sensitivity analyses, validate that the observed effects stem from COCLEM rather than external factors. This step-by-step approach enhances the accuracy and objectivity of the assessment.

This study applies SCM to evaluate the impact of COCLEM on land transfer and RII in Liuyang City, Hunan Province. The key hyperparameters are set as follows: The control group comprises 86 counties in Hunan Province that did not adopt COCLEM. Counties with substantial pre-intervention differences from Liuyang City were excluded to enhance comparability between treatment and control groups. Second, the time frame is defined as 2011–2016 for the pre-intervention period and 2017–2022 for the post-intervention period, facilitating a comprehensive analysis of the policy’s long-term effects. For weighting constraints, optimal weights were assigned to control group units using an optimization algorithm to match Liuyang City’s pre-intervention characteristics. Sensitivity analysis was conducted to ensure the robustness of the results. A weighted distance metric was applied to quantify treatment–control group similarity, enhancing the precision of the synthetic control group and policy effect estimation. These settings ensured the robustness and accuracy of the SCM, providing a reliable foundation for evaluating the impact of COCLEM.

The following model is constructed to test the impact of RII on DLRII:

$$Y_{it}=D_{it}{Y}_{it,j}+(1-D_{it})Y_{it,N}+{\epsilon }_{it}$$

(1)

In Equation (1), assume that $J+1$ counties are selected, with each city being denoted by the subscript [$i =1,\dots ,J+1$ ], where $J$ counties serve as the control group that are not affected by the policy. Let $T$ represent the number of observation periods, with each period being denoted by the subscript $t$ ($t=1,\dots ,T$), where $T_0$ indicates the year before COCLEM, satisfying $1\le T_0\le T$. $Y_{it,N}$ and $Y_{it,j}$ represent RII of county $i$ in period $t$ when it is not affected by the policy. $D_{it}$ is a dummy variable indicating whether county $i$ is affected by the policy in period $t$; if city $i$ is affected by the policy, the variable equals 1; otherwise, it equals 0. ${\epsilon }_{it}$ represents the random disturbance term.

A linear combination of the other $J$ control group counties not affected by COCLEM is constructed by assigning a vector of weights that are non-negative and sum up to one. Using the similarity of the variables before COCLEM as a criterion, an optimal weight vector is estimated for the control group counties, which is then weighted to construct a “synthetic control group”. Finally, impacts were assessed by comparing the differences between the treatment group and the “synthetic control group” before and after the impact of COCLEM. The calculation process and results were processed by StataMP 18 software.

3.4. DID Model

To examine the mechanisms through which population scale, government support, and technological upgrading affect DLRII, a mediation model is employed for empirical analysis. The procedure consists of the following steps: (1) treatment and control group selection: define the treatment group (population exposed to the policy intervention) and the control group (unaffected population); (2) data collection and processing: gather data from both before and after the intervention, ensuring that the treatment and control groups are comparable before the intervention; and (3) establishment of the DID model: utilize a regression analysis model to estimate DID effect. The regression model is as follows:

$$ri{i}_{at}={\gamma }_0+{\gamma }_{1}reform\cdot year+{\eta }_t+{\gamma }_{2}contro{l}_{at}+{\sigma }_{at}$$

$$M_{at}={\kappa }_0+{\kappa }_{1}reform\cdot year+{\eta }_t+{\kappa }_{2}contro{l}_{at}+{\sigma }_{at}$$

$$ri{i}_{at}={\nu }_0+{\nu }_{1}reform\cdot year+{\nu }_2{M}_{at}+{\eta }_t+{\nu }_{3}contro{l}_{at}+{\sigma }_{at}$$

$ri{i}_{at}$ refers to the DLRII of county $a$ in year $t$. The core explanatory variable, $reform$, is a dummy variable that reflects whether county $a$ implemented COCLEM in year $t$. If the policy was implemented, the value is set to 1; otherwise, it is set to 0. ${\eta }_t$ represents the year fixed effects. $contro{l}_{at}$ denotes a series of control variables that affect the DLRII of the county, with specific definitions and measurement methods being provided in Section 3.2.3. ${\sigma }_{at}$ represents the random disturbance term. $M_{at}$ indicates the mechanism variables, including population scale effect, fiscal support effect, and technological upgrade effect, respectively. $\gamma$, $\kappa$, and $\nu$ represent the parameters to be estimated.

3.5. Data Sources and Descriptive Statistics

The dataset comprises panel data from 86 counties in Hunan Province (2011–2022). Liuyang City, the only treatment group, piloted COCLEM in September 2016, making it the sole county implementing the policy within the study period. Data primarily originate from the Hunan Statistical Yearbook, supplemented by sources such as the National Bureau of Statistics and county government websites to ensure consistency. Interpolation techniques were applied to address missing values in the dataset.

The description of the variables is shown in

Table 2. Summary statistics of variables in the study area.

Variable	Observations	Mean	Std. Dev.	Min	Max
RII	1032	12.31	0.73	13.62	12.31
The degree of urbanization	1032	42.57	10.00	78.37	42.57
Government intervention	1032	0.24	0.12	0.74	0.24
Population density	1032	5.67	0.53	6.74	5.67
Labor input	1032	0.24	0.27	1.36	0.24
Human resources	1032	0.05	0.02	0.30	0.05
Labor productivity	1032	10.33	0.55	11.95	10.33
Household savings deposit balance	1032	13.94	0.76	16.09	13.94
Number of beds in hospitals	1032	7.81	0.63	9.36	7.81

.

4. Results

4.1. SCM Results

4.1.1. SCM Assessment Results

Through SCM calculation and analysis, the optimal weights for synthetic Liuyang City are determined. The city consists of two counties: Ningxiang City with a weight of 0.876 and Shaoshan City with a weight of 0.124. Both counties have weights exceeding 0.1, which indicates that the weighted average data effectively simulate the trajectory of DLRII prior to COCLEM.

Table 3. Fitting and comparison of predictive variables.

Variable	Actual Liuyang City	Synthetic Liuyang City
RII	13.2135	13.2130
The degree of urbanization	52.5247	46.8950
Government intervention	0.0640	0.0775
Population density	5.6536	6.1614
Labor input	0.5502	0.5256
Human resources	0.0440	0.0487
Labor productivity	11.1529	11.1115
Household savings deposit balance	14.7071	14.3445
Number of beds in hospitals	8.7802	8.1530

presents the fitting and comparison results of the predictor variables. The data show a high degree of agreement between the variables before the implementation of COCLEM, demonstrating the simulation’s high accuracy.

Although spillover effects from Liuyang to neighboring counties could concern causal inference, SCM mitigates this by constructing a weighted synthetic control unit based on economic, industrial, and social similarities rather than purely geographical proximity [62]. This approach reduces the direct influence of spatial spillover effects on the control group. To further verify the robustness of our findings, we conduct several validity tests, including an assessment of SCM validity (Section 4.1.2), treatment group variation (Section 4.1.3), and a mixed placebo test (Section 4.1.4). These tests evaluate the potential influence of spillover effects from neighboring regions, reinforcing the reliability of this study’s estimation results.

Figure 3 compares the DLRII of actual and synthetic Liuyang City. The solid line represents the real Liuyang City, while the dotted line represents the synthetic Liuyang City. From 2011 to 2016, the average DLRII difference between actual and synthetic Liuyang was only 1.03%, with minimal fluctuations. However, the difference increased significantly from 2017 to 2020, with the average difference rising to 17.72%, particularly after 2020, when the gap remained above 22%. The 13.9% increase in RII suggests that COCLEM has positively impacted RII, highlighting the role of COCLEM in optimizing rural industrial structures. This growth reflects the effectiveness of policies in enhancing the integration of rural industrial chains and stimulating rural economic vitality, suggesting that COCLEM contributes to RII. Overall, the DLRII in the real and synthetic Liuyang City before COCLEM were nearly identical, effectively simulating the actual development trajectory prior to COCLEM. After COCLEM, the DLRII in real Liuyang City increases significantly, and the gap with synthetic Liuyang City widens rapidly.

Since 2020, the RII gap has continued to widen, a trend influenced by multiple external factors. For example, the COVID-19 pandemic disrupted rural industrial chains, and government support policies for rural economic recovery may have affected the outcomes of COCLEM. To mitigate potential confounding factors, this study employs a spatiotemporal placebo analysis in Section 4.1.4 to ensure the robustness of the results and provide a more accurate assessment of COCLEM’s actual impact on RII.

4.1.2. Validity Test

To verify the robustness of the assessment results, a randomized control unit permutation test is employed. In this process, a region was randomly selected from the control group, and assuming it implemented COCLEM at the end of 2016, the effects of COCLEM in other Hunan Province counties were reassessed using the aforementioned method. A significant difference between the assumed “COCLEM effect” in other counties and the actual effect in Liuyang City would confirm the objectivity and rationality of the evaluation.

Because all samples are within Hunan Province, the variables produce relatively small differences. Therefore, 78 samples with errors exceeding 1.25 times those of Liuyang City were screened. Figure 4 visualizes the results of the permutation test, showing predicted trend changes in Liuyang City and other counties under COCLEM by randomly selecting control units. The solid line represents Liuyang City, while the dashed line represents other counties. As seen in the figure, the DLRII in Liuyang City has increased rapidly since 2016, with a growth rate that is significantly higher than that of other counties. This significant difference highlights the effectiveness of COCLEM and the substantial disparity in DLRII between Liuyang City and other counties. Therefore, the evaluation results are valid, and the implementation of COCLEM has a significant positive impact on DLRII.

4.1.3. Treatment Group Variation

The placebo test was performed by excluding Liuyang City from the dataset, selecting a random unincorporated control area, and synthesizing the expected status using the remaining data. The results were analyzed by comparing the actual and synthetic conditions of the area. If the two are similar, it may indicate flaws in the assessment method. Conversely, significant differences confirm the validity of the test. Abadie et al. emphasized that selecting an appropriate control group and matching variables is crucial, as excessive differences can undermine the effectiveness of SCM. The synthetic weights of both Ningxiang City and Shaoshan City exceeded 0.1. However, Ningxiang City exhibited significant discrepancies compared with other control areas, leading to the failure of a valid synthetic control. Thus, a placebo test was conducted for Shaoshan City to verify the reliability of the findings.

Figure 5 presents the placebo test results for Shaoshan City. Before 2016, the DLRII in synthetic Shaoshan City closely matched that of actual Shaoshan City, indicating a good fit. After 2016, the DLRII in actual Shaoshan City dropped significantly below that of synthetic Shaoshan City, suggesting that COCLEM was ineffective in promoting DLRII in the region and may have had a negative impact. Figure 6 illustrates the distribution of prediction errors between Shaoshan City and other regions. As in the previous test, regions with prediction errors exceeding 1.25 times the standard value are excluded. The results indicate no significant differences from other regions.

4.1.4. Mixed Placebo Test

Validity tests and treatment group variations confirmed the robustness of the conclusions. However, COCLEM may be influenced by economic, political, and cultural factors, and its implementation timing is subject to evaluation and decision-making, introducing potential subjectivity. To eliminate potential subjectivity in DLRII and verify the independence of COCLEM’s effects from its implementation timing, this study employs a mixed placebo test along the dual dimensions of “region + time.” The empirical results are presented in Figure 7.

Figure 7 presents the results of the “region + time” mixed placebo test. The solid line represents the experimental group implementing COCLEM, while the dotted line represents the synthetic experimental group under hypothetical conditions. The vertical dotted line in 2013 marks the year preceding virtual COCLEM implementation. From 2014 to 2015, the DLRII trends in both the experimental and synthetic groups were similar, suggesting that their trajectories before virtual COCLEM implementation were nearly identical. This supports the hypothesis that changes before and after COCLEM implementation are independent of its timing. The pre-COCLEM period had an insignificant effect on the experimental group, further validating the robustness of COCLEM’s impact on DLRII. The test results strengthen the reliability and credibility of the research findings.

4.2. DID Results: Mechanism Testing

Although SCM effectively verifies the causal effect of COCLEM from a dynamic perspective, it has limitations in exploring specific impact mechanisms. Moreover, few studies have employed SCM for mechanism testing. COCLEM may exert different effects on RII before and after its implementation within the same region. Additionally, disparities may exist between regions where COCLEM has been implemented and those where it has not, aligning with the principles of “quasi-natural” experiments. To validate and complement the SCM conclusions while identifying the specific mechanisms through which COCLEM influences DLRII, we employed the DID method for analysis.

Table 4. Intermediary test.

	Benchmark Regression	Population Scale Effect
	RII	Population Scale Effect	RII
COCLEM	0.062 *** (5.97)	7.743 *** (6.90)	0.044 *** (2.85)
Population scale effect			0.003 *** (2.77)
Constants	9.876 *** (13.91)	−22.971 (−0.42)	9.468 *** (13.90)
Control variable	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
R-square	0.6427	0.5891	0.6494
Observations	1032	1032	1032

Notice: *** p < 0.01. The values in parentheses refer to t-statistic values.

,

Table 5. Intermediary test.

	Benchmark Regression	Fiscal Support Effect
	RII	Financial Support Effect	RII
COCLEM	0.062 *** (5.97)	0.206 *** (10.83)	0.040 *** (3.46)
Fiscal support effect			0.126 *** (3.12)
Constants	9.876 *** (13.91)	3.806 *** (3.79)	8.644 *** (10.43)
Control variable	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
R-square	0.6427	0.9505	0.6262
Observations	1032	1032	1032

Notice: *** p < 0.01. The values in parentheses refer to t-statistic values.

and

Table 6. Intermediary test.

	Benchmark Regression	Technological Upgrade Effect
	RII	Technological Upgrade Effect	RII
COCLEM	0.062 *** (5.97)	13.447 *** (7.77)	0.028 *** (3.71)
Technological upgrade effect			0.003 *** (2.77)
Constants	9.876 *** (13.91)	−283.665 *** (−3.32)	9.806 *** (16.19)
Control variable	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
R-square	0.6427	0.5773	0.6072
Observations	1032	1032	1032

Notice: *** p < 0.01. The values in parentheses refer to t-statistic values.

present the results of the mechanism test examining the impact of COCLEM on DLRII. The benchmark regression results show that, controlling for other factors, the DLRII increased by 13.9% following COCLEM implementation, confirming the robustness of SCM findings.

4.2.1. Population Scale Effect

COCLEM fosters industrial development and job creation in rural areas, attracting more residents and thereby expanding the rural population, which subsequently promotes DLRII. Following Li’s study [63], we use the “end-of-year household population” to measure changes in rural population size under the COCLEM framework. Table 4 indicates that an increase in population size positively impacts RII. Thus, rural population growth serves as a key mechanism through which COCLEM influences DLRII (Table 4).

4.2.2. Fiscal Support Effect

COCLEM enhances land resource allocation and generates new revenue streams for local governments, thereby incentivizing policy implementation, increasing public service investment, and fostering diversified rural industrial development. Following Chen’s study [64], we employ the logarithmic value of general budget expenditure to represent financial support. Empirical results in Table 5 indicate that enhanced government support significantly improves DLRII. Thus, increased government financial support serves as a key mechanism through which COCLEM influences DLRII (Table 5).

4.2.3. Technological Upgrade Effect

COCLEM unlocks land resource potential, enhances total machinery power, and supports large-scale farmland formation. This not only improves agricultural production efficiency and quality but also facilitates structural adjustments in the agricultural sector. Based on Gao’s research [65], the total amount of agricultural machinery serves as an indicator of agricultural technological advancement, with the “total power of agricultural machinery” measuring technological upgrades. Empirical results in Table 6 suggest that advancements in agricultural technology contribute to DLRII growth. Thus, advancements in agricultural technology serve as a critical mechanism through which COCLEM influences DLRII (Table 6).

4.3. Further Discussion

This study evaluates the impact of COCLEM by developing a measurement framework for DLRII and employing the entropy method to compute a comprehensive index for each county. Given the multidimensional nature of RII, this study further decomposes it into three key dimensions: agricultural industry chain extension, agricultural service industry expansion, and rural infrastructure support. The impact of COCLEM on each dimension is examined separately to elucidate its role in fostering RII.

4.3.1. Impact of COCLEM on the Extension of the Agricultural Industry Chain

The extension of the agricultural industry chain constitutes a fundamental component of RII, primarily involving the integration of agriculture with secondary and tertiary industries, the enhancement of agricultural value-added, and the development of a competitive modern agricultural system. In the traditional agricultural model, production is primarily concentrated on raw agricultural products, with limited processing, distribution, and branding. COCLEM facilitates the extension and optimization of the agricultural value chain. Regression results indicate that COCLEM significantly promotes the extension of the agricultural industry chain at the 1% significance level (Figure 8a and

Table 7. Intermediary test.

Variable	Extension of the Agricultural Industry Chain	Expansion of Agricultural Services	Support Capacity of Rural Infrastructure
COCLEM	0.0976 *** (34.94)	544700.3 *** (30.29)	0.0952 (1.45)
Constants	−0.6539 *** (−3.61)	−3110169 *** (−5.04)	6.9738 *** (0.17)
Control variables	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
R-square	0.6541	0.7165	0.5894
Observations	1032	1032	1032

Notice: *** p < 0.01. The values in parentheses refer to t-statistic values.

).

4.3.2. Impact of COCLEM on the Expansion of the Agricultural Services Sector

The expansion of the agricultural services sector reflects the modernization of agricultural production and represents a key dimension of RII. Agricultural services encompass various functions, including the supply of production materials, agricultural product processing and marketing, financial and insurance services, and agricultural technology promotion. Their development directly influences the vitality and sustainability of RII. COCLEM enhances the agricultural service system, increases the specialization of agricultural production and operations, and fosters rural employment and income growth. Regression analysis reveals that COCLEM significantly promotes agricultural service development at the 1% significance level (Figure 8b and Table 7).

4.3.3. Impact of COCLEM on Rural Infrastructure Support

Rural infrastructure is a vital external factor for RII, with telecommunication infrastructure playing a key role in agricultural information exchange, market connectivity, and industrial integration. While COCLEM may yield short-term economic benefits, regression results suggest that its effect on rural infrastructure support is not statistically significant. This may be attributed to the long-term nature of infrastructure investments and the gradual replacement of landline telephony by mobile internet technology, resulting in COCLEM’s minimal impact on this variable (although results suggest a negligible impact of COCLEM on rural infrastructure, this finding may be affected by data limitations and indicator selection. Certain aspects of infrastructure development may not be adequately captured by current statistical indicators. Future research should incorporate a more comprehensive set of indicators to improve the robustness of conclusions and provide a more holistic assessment of the long-term policy effects.). However, infrastructure development still requires policy support and long-term investment to strengthen RII (Figure 8c and Table 7).

5. Discussion

5.1. Research Key Points and Discussion

Using panel data from 86 counties in Hunan Province (2011–2022), this study systematically assesses the impact of COCLEM on DLRII and investigates its underlying mechanisms. The results demonstrate that COCLEM significantly enhances DLRII, a finding that remains robust across multiple reliability tests. Previous studies by Wang et al. [66], Ma et al. [34], and Yang et al. [6] highlight the critical role of land transfer in RII, consistent with the findings of Zhang et al. [27]. Investigating land transfer, a key element of rural economic development, offers insights into the positive effects of COCLEM on RII [7]. By focusing on COCLEM, this study establishes a foundation for its further development and integration into rural revitalization strategies. While Xu et al., Zou et al., Wang et al., and Li et al. [17,18,19,20] have demonstrated that COCLEM promotes rural economic development, empirical studies directly linking COCLEM to RII remain limited, particularly when the experimental group is regionally constrained and methods such as DID or PSM introduce potential biases. This study addresses this gap by employing SCM, providing a more comprehensive understanding of COCLEM’s impact on RII.

A comparative analysis of DLRII across Hunan Province’s counties yields several key findings. The data suggest that COCLEM strengthens agricultural–industrial synergy through optimized land resource allocation, significantly enhancing DLRII in Liuyang City. This market-driven land transfer mechanism revitalizes the rural economy and promotes the coordinated development of agriculture, industry, and services. Moreover, the study reveals a cumulative effect of COCLEM’s benefits: initially modest, its impact on RII strengthens as market mechanisms mature and the industrial chain expands (Figure 9 and Figure 10). Research by Mi et al. [23], Wei et al. [24], Liu et al. [25], and Chari et al. [26] similarly demonstrates that COCLEM enhances rural infrastructure and fosters urban–rural integration [21,22]. However, Chen et al. [67] present contrasting findings, arguing that land use transformation can impede urban–rural integration due to constrained development opportunities and limited resources in rural areas. This divergence highlights the necessity of expediting land use transformation to stimulate rural economic growth, a key focus of this study.

This study primarily examines COCLEM as a mechanism of rural land transfer, emphasizing its pivotal role in strengthening RII and fostering economic development. As a core component of the three major land reforms, COCLEM provides critical insights into broader rural land reform frameworks. A mediated effects model further clarifies the mechanisms through which COCLEM influences RII. First, COCLEM enhances the market value of rural land, increasing its appeal to labor and thereby facilitating population return and agglomeration. This process not only secures a stable labor supply but also drives local market demand and fosters rural industrial diversification. This finding aligns with Zeng et al. [68], which underscores the critical role of population agglomeration in industrial development. Second, COCLEM brings additional fiscal revenues to the local government, which gives the government more sufficient funds for infrastructure construction, public service upgrading, and industrial policy support; creates a better business environment for rural enterprises and foreign investors; and further promotes COCLEM. This is consistent with Nadolnyak et al.’s [69] findings on financial support and rural economic growth. Third, COCLEM-induced capital promotes agricultural modernization, encourages the adoption of advanced agricultural technologies, and facilitates industrial upgrading. The integration of agriculture with industry and services improves agricultural efficiency and fosters the high-quality development of RII, as evidenced by Gao et al. [70] in their research on rural industrial upgrading.

In addition, this study further analyzes the constituent variables of RII by combining COCLEM to reveal whether COCLEM has an impact on different aspects of RII, thus expanding the development of the field. This study finds that COCLEM promotes the extension of the agricultural industry chain and the expansion of the agricultural service industry, providing a new impetus for RII, but COCLEM does not have a significant effect on the promotion of rural infrastructure, so policy guidance and long-term investment are needed to provide more solid support for RII development. The study goes beyond the limitations of the empirical analysis of DID and the study of RII as a whole, deepens the understanding of whether COCLEM can affect different aspects of RII, and provides new perspectives and theoretical insights for detailed research on the development of RII.

The novelty of this study stems from its application of SCM and DID methodologies, mitigating potential DID biases arising from small experimental samples or unsuitable control groups. By integrating SCM, which generates a synthetic control group from unaffected counties, this study strengthens the robustness of its findings. This methodological framework reinforces the validity of the findings and provides a more robust foundation for refining COCLEM policies to advance RII and overall economic development.

5.2. Research Innovations and Limitations

However, this study has certain limitations. Although COCLEM significantly enhances RII, its practical implementation presents challenges. First, rising land prices may negatively impact small and medium-sized farmers by increasing production costs, which could hinder the expansion of the agricultural industry chain. Second, while local government revenues have increased, optimizing fund allocation for RII development remains an area for further study. Additionally, the effectiveness of technological upgrades relies on farmers’ acceptance and an improved technology dissemination system, necessitating coordination among the government, enterprises, and research institutions. While this study examines COCLEM in Hunan Province, further research is needed to assess its impact on RII in other regions (e.g., plateau and plain areas), as geographic differences may shape the relationship between COCLEM and RII.

Potential biases in the analysis may influence the results. The limited number of experimental groups may constrain the generalizability of the findings. To address these limitations, future research could leverage advances in temporal modeling, such as integrating SCM with later temporal attention [71], which could provide a more nuanced understanding of the dynamic temporal effects of COCLEM on RII over time. Furthermore, applying spatial modeling techniques, such as combining k-means clustering with artificial neural networks (ANNs) [72], could enhance the analysis of COCLEM’s spatial heterogeneity across regions, providing deeper insights into geographic and contextual influences. Future studies could incorporate qualitative methods, such as interviews and field observations, to explore the social and cultural determinants of farmers’ participation in RII. Lastly, although this study primarily focuses on COCLEM, a comprehensive analysis of homesteads, agricultural land, and rural infrastructure would provide deeper insights into their interactions and impact on RII.

6. Conclusions and Policy Implications

This study finds that, with fiscal support from local governments, the implementation of COCLEM significantly enhances the efficiency of rural infrastructure development, promotes the prosperity of rural industries and services, and ultimately drives RII. For instance, following COCLEM, Liuyang City developed an industrial park in XihuTan Village through land equity investment, attracting over 100 enterprises. The collective’s dividends reached over CNY 8 million, and villagers’ income significantly increased, with annual earnings rising from less than CNY 30,000 to CNY 200,000. This transformation demonstrates the powerful impetus of COCLEM in promoting rural economic development. Moreover, the infrastructure upgrade implemented by the Liuyang municipal government in 2024 improved transportation conditions, shortening commute times to the industrial park and attracting more industrial projects. The enhanced infrastructure also supports the development of the Nongken Cultural Tourism Zone in Beisheng Town, which is expected to serve a population of 200,000, thereby boosting cultural tourism consumption and local employment, further promoting regional population agglomeration. These outcomes indicate that the COCLEM policy not only holds significant theoretical potential for wider application but also demonstrates considerable economic and social benefits in practice, offering a strong foundation for advancing RII. Based on these findings, the following policy recommendations are proposed:

First, there should be an active construction of a COCLEM system, improving rural public service levels and increasing investment in infrastructure construction, while continuously promoting regional population agglomeration. From a practical perspective, this study suggests developing a robust policy framework for COCLEM to enhance rural public services, boost infrastructure investment, and foster regional population concentration. The findings indicate that COCLEM substantially increases land’s economic potential, attracts labor, and broadens the population base—critical drivers of RII development. These findings underscore the need for government investment in rural infrastructure to enhance public services, improve employment conditions, and attract skilled labor, while also incentivizing the return of migrant workers. Simultaneously, policies should encourage rural labor to participate in diverse industries, foster the integration of agriculture with modern services and industries, and enhance DLRII.

Second, the accountability of local leaders should be reinforced to ensure sustained fiscal support for COCLEM. This study highlights that COCLEM’s success is largely dependent on local government financial support, which is crucial for advancing RII. Revenue from COCLEM can enable local governments to finance infrastructure and industrial development, thereby significantly enhancing RII. The study recommends that local governments optimize financial support mechanisms, lower entry barriers for businesses through subsidies and tax incentives, and attract foreign capital, technology, and management expertise to advance RII.

Third, emphasis should be placed on COCLEM’s role in advancing agricultural technology to drive rural industrial prosperity. The study underscores that COCLEM enhances land resource efficiency while also driving technological innovation in agriculture through deeper integration with industry and services. Given these findings, local governments should enhance collaborations with universities and research institutions to facilitate cross-sector technological innovation. Furthermore, the study suggests investing in training programs to enhance rural laborers’ skills, particularly in sectors where agriculture intersects with industry and modern services. Raising awareness of COCLEM can enhance farmers’ adoption of new technologies, facilitating the seamless transition of innovations from policy to practice.