The Policy Effectiveness and Citizen Feedback of Transferable Development Rights (TDR) Program in China: A Case Study of the Chongqing Land Ticket Model

Abstract

Over the past decade, the Chongqing land ticket model has played a pivotal role in the market-oriented reform of rural land factors and serves as a representative practice of the TDR program in China. This paper constructs a systematic evaluation framework from two perspectives—policy effectiveness and citizen feedback—to comprehensively understand the policy effect of this model. The study employs methods of policy texts bibliometrics and content analysis based on big data. The results indicate that the effectiveness of land ticket policies exhibit significant fluctuations, with peaks aligning with milestones in the model’s development. Policy measures are well-aligned with the goals set forth. However, policymakers in Chongqing have historically focused more on institutional construction within the land ticket model, only recently shifting attention to the protection of farmers’ rights and interests. This imbalance may have led to potential risks regarding the loss of farmers’ property rights. The analysis of citizen feedback from the online space further took into account the impact of policy content on its audience (farmers), revealing that shortening the compensation payment time rather than increasing the compensation amount is the most common and critical demand among farmers. This underscores the urgent need for a policy-related response from the government to meet farmer’s demands for “procedural justice”. Our conclusions address a gap in the existing literature by integrating policy text analysis with public opinion, thereby offering referential insights into understanding the evolutionary process, policy features, and implementation effects of TDR program in China.

1. Introduction

The market-oriented reform of rural land factors has been a prevailing theme in land management policy across all levels of the Chinese government in recent years. Since 1978, China has progressively transitioned from a centrally planned economy to a market-oriented economic system [1]. However, compared with the marketization of products, the reform of factor allocation in China has been relatively delayed [2], particularly in terms of land factors [3]. This is because, in China, where the population–land contradiction is pronounced, rural land not only holds economic value but also serves critical non-economic functions such as ensuring food security, safeguarding ecological diversity, enhancing the quality of the human living environment, and providing social welfare [4]. Consequently, the marketization of land factors cannot be completely detached from the macro-control of the government in China.

For a long time, the Chinese government has exercised macro-control over land resources through a dual-track urban–rural land management system [5]. By implementing zoning regulations and indicator-based controls, the state has strictly delineated the legal uses of land in rural and urban areas. Under the urban–rural zoning framework, rural construction land is primarily allocated for the building of homesteads by rural residents, with limited portions designated for village-level public infrastructure or township enterprises. Any change in the designated use of rural land without official approval is strictly prohibited [6]. However, China’s urban expansion has deeply relied on the occupation of rural land. Yet, the conversion of rural construction land into state-owned construction land has consistently been subject to strict planned management [7]. Farmers hold only the right to use homesteads, not the ownership thereof. Consequently, they are required to preserve the rural land attribute of homesteads and are prohibited from freely transacting the land on which the homesteads stand with urban residents [8]. Under land laws in China, the expropriation of homesteads by local governments represents the sole legally recognized means to convert homesteads into urban construction land [9,10]. However, local governments’ land expropriation from farmers is compulsory, and they offer compensation to farmers at a standard far lower than the market value [11], which undoubtedly harms the farmers’ property interests in the land. On the other hand, the expropriation of rural homesteads requires local governments to possess sufficient indicators1 for the conversion of rural land, which effectively subjects urban development activities to the directives of the central government [12]. Overall, the use and transfer of rural construction land, exemplified by homesteads in China, have long exhibited pronounced characteristics of a planned economy. The lack of market mechanisms has hindered both local governments and farmers from fully realizing the optimal utilization efficiency of the land that they desire.

It was not until 2008 that the reform of rural homesteads in China was formally placed on the policy agenda [13]. Starting from this year, the Chinese central government has initiated the nationwide implementation of the “Increase–Decrease Urban–Rural Linkage” Policy (hereafter referred to as “IDUL”)2. The policy was designed to promote the simultaneous reduction of rural construction land and the expansion of urban construction land in China. After reclaiming rural construction land—most of which consists of rural homesteads—local governments are permitted to increase an equivalent amount of construction land within urban areas. Notably, the state-owned construction land generated through the IDUL does not consume rural land conversion indicators, thereby providing local governments with an additional source of urban land development rights beyond the central government’s allocated indicator system [14].

In addition to promoting nationally uniform policies, the central government in the same year authorized select local governments to experiment with policy innovation based on the IDUL framework. These adaptations aimed to explore market-based mechanisms for the exchange of rural and urban construction land, serving as a pilot effort toward the broader marketization of land factor allocation. Among the pilot regions, Chongqing Municipality proposed a comprehensive plan centered on the construction of public housing, land system reform, and “hukou” (a kind of household registration) system reform3. The aim of the land system reform is to encourage a segment of farmers to relinquish their homesteads, converting their land into capital for urban immigration. This process is complemented by granting urban “hukou” to facilitate their transition from rural to urban residents. Ultimately, public housing projects will be developed in cities to accommodate the increased number of urban residents resulting from this policy [15]. As the core driving force for implementing this comprehensive plan [16], the specific operations of the land system reform in Chongqing Municipality have been named the “Chongqing land ticket model” by local officials. Under this model, farmers are able to apply for the reclamation of their homesteads, thereby converting the usage rights of homesteads into a “land ticket (a certificate equivalent to urban construction land indicator)”. Additionally, land tickets can be traded on designated platforms.

From strict planning management to the initial integration of market mechanisms in land factor allocation, the Chongqing land ticket model signifies a significant policy innovation and breakthrough within the existing land management framework in China. The academic community has consistently shown a keen interest in this model since it was put into practice. Existing research on the evaluation of the land ticket model primarily focuses on its effectiveness in economic and livelihood domains. For urban areas, the inflow of land tickets has enhanced the efficiency of urban construction land use, thereby effectively stimulating urban economic growth and generating spatial spillover effects [17,18,19]. In rural areas, the substantial property income from the transactions and the continuous operational income from reclaimed farmland have positively impacted farmers’ overall income, contributing to poverty alleviation [20,21]. Moreover, the land ticket transactions premised on the exit of homesteads have facilitated the migration of rural populations to urban areas [22]. From a macroeconomic perspective, the land ticket model has been widely acknowledged for its role in narrowing the income gap between urban and rural residents and promoting integrated urban–rural development [23,24,25].

The extant research has established a foundation for understanding the effect of the Chongqing land ticket model, but there are still deficiencies that need to be addressed. The majority of studies focus more on post-implementation evaluations while neglecting an in-depth discussion of the policy itself. This may lead to research that fails to account for the uncertainty introduced by policymakers’ subjectivity in shaping both policy content and outcomes [26]. The process of land marketization in China is characterized by path dependence, resulting in the rapid formation of a relatively complete policy framework [27], with subsequent policy improvements typically being minor adjustments to this framework. This, while acknowledging the importance of policy stability, highlights the significant impact of policy content on policy outcomes. Furthermore, in contrast to the TDR program in the United States, the land ticket model in China is not entirely subject to market mechanisms [28]. Instead, policymakers not only formulate trading rules, but also regulate the amount, time, and quantity of trading. These rule arrangements, which are rich in administrative intervention, will eventually be implemented as policy text content, thereby exerting a profound and non-negligible impact on the effect of subsequent policy implementation. Therefore, it is essential to approach the issue from the perspective of policy texts. By quantifying the variations in policy effectiveness allocated by policymakers toward multiple administrative objectives at different points in time, we can achieve a more profound characterization of both the effectiveness features and evolutionary trajectory of the Chongqing land ticket model.

On the other hand, feedback from policy recipients is seldom integrated into the evaluation of the land ticket policy, leaving a research gap that merits further attention. Under the principles of democratic governance, administration should be grounded in public opinion, and the government must respond to public demands [29]. Citizen feedback serves not only as a “reagent” that can directly reflect the effectiveness of policy implementation but also as a critical basis for policymakers to refine and update future policy content [30]. Moreover, the interests of farmers and the local government are not necessarily aligned. For local governments, land tickets, which are generated on the condition that farmers withdraw their homesteads, serve as effective instruments for accelerating dual urbanization of population and land, thereby fostering economic growth [31,32]. However, for farmers, while converting homesteads into land tickets can generate substantial economic returns, they must also carefully consider the timing of transaction applications to maximize their gains in the market. In contrast to farmers, local governments in China hold greater authority in land ticket management, leading to conflicts in practice due to differing interest preferences [33]. Some researchers have noted that farmers’ legitimate rights to participation and revenue are not fully protected in the land ticket model [34,35,36]. The resulting reasonable complaints may hinder the implementation of the policy, and these concerns should not be overlooked.

Nevertheless, there is currently a limited body of research evaluating the Chongqing land ticket model from the perspective of farmers. Most existing studies assess farmers’ willingness to participate in the land ticket model using game theory analysis [37,38] and questionnaire surveys [39,40]. While these approaches facilitate the identification of what constitutes a “good land ticket model” from the perspective of farmers, these studies often rely on the perspectives of hypothetical participants or farmers who have never engaged in land ticket transactions, and thus fail to directly reflect the true policy effectiveness of the land ticket model. To achieve a comprehensive understanding of the performance and limitations of the land ticket model, it is essential to incorporate feedback from actual policy recipients and employ quantitative analysis methods [41] to uncover the genuine grassroots-level effects.

In summary, this paper aims to evaluate the policy effect of the Chongqing land ticket model from two perspectives, accordingly obtaining a more comprehensive and systematic result. The first perspective is a “top-down” approach, which involves conducting a quantitative analysis of policy texts related to the land ticket. This part will depict the characteristics of the policy effectiveness, thereby providing a clearer description of the policy effect derived from the its own characteristics. As a supplement, the second, “bottom-up”, perspective entails collecting and analyzing citizen feedback data published online during the operation of the land ticket model, extracting key information to examine public opinion at the grassroots level. Finally, based on these research findings, we will summarize the issues in the institutional design of the land ticket model and analyze the underlying causes. The primary contribution of this study lies in offering a more nuanced understanding of the “bellwether” policy within the context of land factor marketization reform in China. Additionally, this study seeks to provide empirical reference to inform the broader implementation of the TDR program, the establishment of an integrated urban–rural construction land market, and the optimization of land resource allocation patterns.

2. An Overview of the Chongqing Land Ticket Model

2.1. An Adaptation of the TDR Mechanism in China

The Chongqing land ticket model essentially represents a bill-based transaction of land development rights, with an operational logic that closely parallels the widely adopted TDR programs in international contexts. The concept of TDR was initially proposed by Lloyd in 1961 [42]. Its core mechanism involves transferring the development potential (development rights) of one plot to another while maintaining the status quo established by government zoning regulations, thereby enabling higher-density development [43,44]. The advantage of this approach lies in that the recipient of the development rights can obtain additional development space permits, while also providing economic compensation to the transferor for the loss of development potential, thereby effectively resolving the “windfall–wipeout dilemma” existing under the zoning regulation [45]. The TDR theory was initially applied and tested in a landmark building protection act in New York, USA, in 1968 [46]. Over time, it evolved from individual case studies validating the theory’s effectiveness to practical programs implemented across more than 30 states in USA [47]. Currently, TDR has developed into a mature framework that engages a broader range of participants and incorporates richer incentive mechanisms [48]. It has demonstrated strong applicability in various contexts, including the preservation of farmland [49], conservation of natural habitats [50], and enhancement of urban management efficiency [51] in multiple countries.

The Chongqing land ticket model adheres to the fundamental principles of TDR by separating and allocating development rights on land plots. In this model, the development potential of rural homesteads, which has been constrained under the zoning regulation, is separated and subsequently traded to urban areas. For rural areas, farmers receive economic compensation from urban buyers for the loss of their homesteads’ development potential. For urban areas, the inflow of land tickets allows local governments to acquire additional construction land development rights [52]. However, the land ticket model developed under China’s distinctive land system exhibits notable differences compared to international TDR programs. Given the central government’s prioritization of national food security, regions transferring land development rights are not permitted to maintain their original land use practices. Instead, they are required to reclaim residential land and convert it into arable land for food cultivation [53].4 In addition, local governments exercise greater control over the land ticket model. The total number of land tickets is strictly limited to a manageable level [54], and their use must be approved by local governments, as these tickets only function as an “entry pass” for buyers to participate in urban land auctions [55]. These differences suggest that China’s land ticket trading system still bears significant marks of administrative regulation.

2.2. A Special Case in China’s TDR Programs

The IDUL promoted by the central government of China is also a cross-space transfer scheme of development rights. However, the incorporation of TDR-inspired transaction mechanisms renders the Chongqing land ticket model more market-oriented than the conventional IDUL. In the Chongqing land ticket model, compensation for rural homesteads—reflected in the land ticket price—is primarily determined by supply and demand dynamics, rather than by unilateral decisions of local governments. Moreover, the value-added proceeds from these transactions are often distributed in ways that better align with the interests of farmers. Finally, the Chongqing land ticket model enables individual farmers to submit transaction applications and broadens the transaction scope to cover the entire city. In contrast, the IDUL relies more heavily on administrative interventions by local governments, and land transfers are typically confined within the boundaries of a single county [56].

After a brief trial period of policy implementation, the Chongqing land ticket model was formally endorsed by the central government and emulated by multiple provinces in subsequent years [57]. Since 2010, over a dozen cities in China, such as Chengdu in Sichuan Province, Suqian in Jiangsu Province, and Xuancheng in Anhui Province, have successively introduced their own land ticket policies. These initiatives span the eastern, central, and western regions of China [19]. Despite no longer being the sole one, the Chongqing land ticket model continues to stand out as the most representative among the diverse TDR programs aimed at facilitating the marketization of land factors in China. Firstly, it has the longest operating history. In 2008, with the approval of the State Council (PRC), Chongqing began to explore and implement a land indicator trading mechanism, which has now been in operation for over 16 years, making it the longest-running TDR program in China. Secondly, it has achieved a high degree of marketization. Chongqing established the first rural land indicator trading platform (Chongqing Country Land Exchange, CCLE), which was directly approved by the central government and remains unique in the country. This platform ensures the trading of land tickets and facilitates transactions between farmers and individual buyers, thereby empowering them to engage directly in the market. Thirdly, Chongqing boasts the largest transaction volume. As of June 2024, nearly 2 million farmers in Chongqing had cumulatively sold 376,500 mu of land tickets, generating a transaction value of CNY 73.9 billion.5 This volume significantly outpaces that of other regions.

2.3. The Operating Procedure of the Chongqing Land Ticket Model

The production, transaction, and utilization of land tickets form the core components of the Chongqing land ticket model (Figure 1). During the production stage, farmers or rural collective economic organizations may voluntarily apply to reclaim unused homesteads (① ② ③), with the corresponding area land ticket being automatically generated and owned by the applicants upon inspection and approval by the county-level natural resources department (④ ⑤). In the transaction stage, land ticket holders can submit applications to CCLE for transactions, enabling first-trading through auctions or listings (⑥). Throughout this process, both trading prices and volumes are regulated by municipal-level natural resources department (⑦). Following the completion of first-trading, proceeds are first allocated to cover the costs of land reclamation and funds advanced by the government, with the remainder distributed between farmers and rural collective economic organizations at an 85:15 ratio (⑧). Lastly, during the utilization stage, purchasers of land tickets may use them as certificates for land indicators to apply for land expropriation from the government. After expropriation, these tickets serve as entry vouchers for participation in land auctions, allowing their nominal value to offset a portion of the land auction fees. This process completes the transformation of virtual construction land indicators (land tickets) into actual land (⑨).

3. Methods

As mentioned earlier, we aim to conduct a systematic evaluation of the Chongqing land ticket model using a multi-dimensional framework that integrates policy effectiveness and citizen feedback. To assess policy effectiveness, we evaluated its power, goals, and measures, summarizing the key features of the policy design. For the citizen feedback assessment, we employed natural language processing (NLP) techniques to extract and rank keywords from the feedback data, identifying the core demands of the policy recipients. Together, these two dimensions form a dual-evaluation framework for assessing the effect of policy, which serves as the foundation for proposing subsequent policy improvement recommendations (Figure 2).

3.1. Evaluation of Policy Effectiveness

The effectiveness of public policy refers to its impact on social life after implementation, serving as a key criterion for evaluating policy effects [58]. A policy encapsulates the vision and strategies of policymakers. At different stages of socioeconomic development, policy design may emphasize certain objectives while making trade-offs in others, leading to variations in the effectiveness of different policy goals. To accurately characterize policy effectiveness, quantitative analysis of policy texts is essential. Peng et al. proposed a multi-dimensional quantitative evaluation method for policy texts during their study of China’s technological innovation policies [59], which has been widely applied in evaluating policies related to farmland protection, intensive urban land use, and integrated urban–rural development [60,61,62]. Building on the “Policy Quantification Standard Operating Manual” developed by Peng et al. [59], our study constructs a three-dimensional assessment model focusing on the power, measures, and goals of policy to quantify the effectiveness of the Chongqing land ticket model.

3.1.1. Standards of Measurement

The policy power (P) refers to the administrative impact of the policy text. The power is determined by the hierarchical level of the administrative agency that promulgates the policy, with higher-level agencies generally conferring greater policy power [63]. In this study, the policy power is categorized into five levels, scored from 1 to 5 points based on the hierarchical level of the issuing administrative agency.

Policy goals (G) are used to evaluate the clarity and specificity of a policy text’s governance orientation. Clearer and more quantifiable policy goals are generally easier to achieve [64]. However, the management objectives are typically not singular but may encompass multiple governance dimensions. Therefore, it is necessary to categorize the policy goals involved in the Chongqing land ticket model for better evaluation. This study adopts the “production–transaction–utilization” life cycle of land ticket as a framework and further refines the policy goals into nine items.6 After matching the specific policy provisions in the policy text coding table, each item is scored at five levels (5 points to 1 point) based on the comprehensiveness and precision of quantification.

Policy measures (M) are defined as the tools and instruments used to achieve policy goals. For policies to be effective, policy measures must align closely with policy goals and lead to actionable programs that are as detailed as possible. Moreover, given the multiplicity of policy goals, policy measures are often presented as a set, requiring the classification of policy tools within the policy text to establish reasonable scoring criteria. In this paper, we refer to the “Mandates, Inducements, Capacity Building, System Changing” quadratic criterion proposed by McDonell and Elmore in 1987 [65] and the “Regulation, Economy, Information” trichotomy criterion proposed by Bemelmans-Videc et al. in 2011 [66]. The type of policy measures in Chongqing land ticket model is divided into “Command, Incentive, Guidance”. Subsequently, five levels (5 points to 1 point) were assigned according to the completeness of the content and the ability of a certain type of policy measures.

In summary, the three-dimensional effectiveness evaluation model and its measurement standards for Chongqing land ticket policies used in this study are illustrated in Figure 3 and

Table 1. Measurement standards for various dimensions of policy effectiveness in Chongqing land ticket model.

Score	Scoring Criteria	Score	Scoring Criteria
Power dimension (P)		G7—Revenue distribution
5	P1- National People’s Congress (PRC)	5	Clearly define the ownership of the proceeds from the transaction and propose a specific plan for the distribution of revenue, etc.
4	P2- State Council (PRC)	4	Clarify revenue ownership and outline a basic distribution framework; some roles or proportions are mentioned, though lacking full institutional detail.
3	P3- Chongqing Municipal People’s Government	3	Policy goals are clear but lack detailed explanations or quantitative standards.
2	P4- Chongqing Bureau of Land and Housing Administration	2	Touch on benefit sharing in general terms, without indicating who holds ownership or how revenue is to be divided.
1	P5- Chongqing Country Land Exchange (CCLE)	1	Only expressing policy expectations or requirements of principle.
Goal dimension (G)		G8—Use and circulation of land tickets
G1—Application for reclamation		5	Specify the conditions and area for land tickets, clearly define the conditions for the circulation of land tickets and the attribution of benefits, etc.
5	Clearly define the prerequisites for reclamation application, provide a detailed guide to the process, and specify the areas and types of land that can be reclaimed to generate land tickets, etc.	4	Define basic conditions and partial spatial scope for land ticket use; offer preliminary guidance on circulation and benefit attribution, though not fully elaborated.
4	Clearly state goals and main conditions for reclamation; outline basic steps and partially specify land types or areas. Some guiding requirements details are present but not comprehensive.	3	Policy goals are clear but lack detailed explanations or quantitative standards.
3	Policy goals are clear but lack detailed explanations or quantitative standards.	2	Mention land ticket circulation or use without clarifying applicable areas, conditions, or benefit arrangements.
2	Mention general goals and land types but lack procedures or clear criteria. Implementation depends heavily on interpretation.	1	Only expressing policy expectations or requirements of principle.
1	Only expressing policy expectations or requirements of principle.	G9—Organizational support
G2—Reclamation procedure		5	Clearly define the responsibilities of each department and provide specific work guidelines or pilot project plans, etc.
5	Clarify the cost of reclamation programs, develop a procedural plan for generating construction land indicators, etc.	4	Identify key responsible departments and outline general duties or arrangements; some work guidelines are included but lack full procedural clarity.
4	State the estimated costs and outline steps for generating land indicators; provide partial financial or procedural clarity. Some elements remain vague or incomplete.	3	Policy goals are clear but lack detailed explanations or quantitative standards.
3	Policy goals are clear but lack detailed explanations or quantitative standards.	2	Briefly reference departmental roles without specifying tasks, coordination mechanisms, or implementation steps.
2	Mention costs or land indicators vaguely without specific plans or standards.	1	Only expressing policy expectations or requirements of principle.
1	Only expressing policy expectations or requirements of principle.	Measure dimension (M)
G3—Reclamation check and acceptance		M1—Command type
5	Quantitative standards and implementation procedures for the completion and acceptance of reclamation projects are established, as well as the text materials required, etc.	5	Clearly define the regulatory requirements for indicators such as the total amount of transactions, spatial scale, and reclamation quality, or the process of reclamation application and land ticket transactions, or clearly define the punitive measures for violating the above requirements.
4	Set basic acceptance criteria and describe parts of the procedure or required materials. Quantitative indicators may be partial or general.	4	Set preliminary thresholds or assessment criteria for transaction scale, quality, or process control; mention regulatory or punitive intent, but details are partially specified.
3	Policy goals are clear but lack detailed explanations or quantitative standards.	3	The above is included, but lacks detailed quantification, evaluation criteria, or penalties.
2	Mention acceptance in principle but lack concrete criteria, procedures, or document requirements.	2	Address regulatory issues in general terms, with limited reference to benchmarks, evaluation systems, or enforcement tools.
1	Only expressing policy expectations or requirements of principle	1	The above is included, but remains at the level of calling, advocacy, and publicity.
G4—Application for transaction		M2—Incentive type
5	Regulating the conditions for applying for land transfer and specifying the materials to be submitted when applying for land ticket trading, etc.	5	Clearly define financial, tax, information, talent, and organizational and technology support plans and provide a reduction or exemption from fees and charges. Clearly define a revenue distribution plan or price protection mechanism.
4	Clearly describe application conditions and list some required documents, but details may be incomplete or lack procedural clarity.	4	Outline support measures in key areas like finance, taxation, or talent; include partial provisions on revenue sharing or fee reduction, though lacking full operational clarity.
3	Policy goals are clear but lack detailed explanations or quantitative standards.	3	The above is included, but lacking a detailed description of the supporting content, distribution plan, or protection mechanism.
2	Briefly mention the trading application, with vague or no mention of supporting materials or requirements.	2	Mentions support or incentive themes without clarifying implementation pathways, responsible bodies, or safeguard arrangements.
1	Only expressing policy expectations or requirements of principle	1	The above is included, but remains at the level of calling, advocacy, and publicity.
G5—Rules of transaction		M3—Guidance type
5	Clearly define the detailed rules for the types, varieties, methods, and processes of land ticket transactions, etc.	5	Actively encourage farmers to participate in the production of land tickets and protect their rights and interests. Clearly require public disclosure or follow-up supervision of information such as reclamation plans and transaction results, clearly provide details of pilot projects, and provide a list of rights and responsibilities of relevant departments.
4	Specify main transaction types and outline general methods or procedures; some rules are clear, but others remain ambiguous or incomplete.	4	Emphasize farmer participation and outline basic rights protections; includes partial disclosure requirements or details, though some aspects lack binding force.
3	Policy goals are clear but lack detailed explanations or quantitative standards.	3	The above is included, but there is a lack of strict regulations on the principles of voluntariness and public oversight. Only a list of pilot projects is given, and only a list of functional departments responsible for policy implementation is given.
2	Vaguely refer to land ticket transactions without specifying types, methods, or procedures.	2	Refers to participation or transparency in broad terms, without specifying rules, enforcement mechanisms, or the scope of departmental responsibilities.
1	Only expressing policy expectations or requirements of principle	1	The above is included, but remains at the level of calling, advocacy, and publicity.
G6—Settlement of compensation payments
5	Establish a minimum protection price for land ticket transactions, clearly specify the process and time requirements for the allocation of payments, and establish a mechanism to monitor and publish the results of the allocation, etc.
4	Proposes baseline pricing or outlines general payment allocation steps; includes partial timelines or oversight measures, though some aspects lack precision.
3	Policy goals are clear but lack detailed explanations or quantitative standards.
2	Refers to payment distribution or pricing without offering specific mechanisms, timelines, or enforcement procedures.
1	Only expressing policy expectations or requirements of principle

.

3.1.2. Data Sources and Text Encoding

In this section, we take the policy texts related to land ticket issued by various departments at all levels from 2008 to 2020 as the object of quantification7. Through fuzzy searches on the websites of the Chongqing Municipal People’s Government, the Chongqing Bureau of Planning and Natural Resources,8 CCLE, and other administrative departments using the keyword “land ticket”, we obtained policy texts related to the production, transaction, and utilization of land tickets. After screening, we selected 39 of them as research samples.

Specifically, a policy document eligible for inclusion in the research sample must satisfy all of the following conditions:

• The policy document must be officially released by a government department or its subordinate institution and possess legal normative authority;
• The term “land ticket” must be explicitly included in either the title or main text of the policy document;
• The policy document must apply to the entire scope of Chongqing city, rather than being restricted to a specific district or region;
• The policy document must provide detailed content regarding at least one aspect of the production, transaction, or utilization of land tickets, rather than merely referencing the term “land ticket”;
• The content of the policy document must not duplicate that of previously issued policies;
• Policy documents jointly issued by multiple departments may be considered as research samples, provided that at least one of the issuing entities is among the aforementioned departments (e.g., the Chongqing Bureau of Planning and Natural Resources);
• Attachments to policy documents are excluded from consideration as research samples.

To facilitate the extraction of key information from the policy texts, we identified and coded the content of the policy texts, then read and collated the textual analysis units, resulting in a total of 134 codes that serve as the data basis for evaluating policy effectiveness.

Table 2. Code of content analysis unit of Chongqing land ticket policies (2008–2020) (partial list).

No.	Year	Department	Document Title	Document Number	Format	Code	Content Analysis Unit
1	2008	Chongqing Municipal People’s Government	Provisional administrative measure of Chongqing Country Land Exchange	No.127 Document of Chongqing Municipal People’s Government [2008]	Measure	1
						1-1	Article 4. Trading species
						1-2	Article 5. Monitoring agency
						……	……
……						1-18	Article 31. Protection of interests allocation
5	2010	Chongqing Bureau of Land and Housing Administration	Supplementary opinions on improving the distribution of land ticket payments	No.220 Document of Chongqing Bureau of Land and Housing Administration [2010]	Opinions	5
						5-1	Section 1. Use of payments of rural collective construction land reclamation
……						5-2	Section 2. Use of payments that higher than the cost of reclaiming rural collective construction land
39	2020	Chongqing Municipal People’s Government	Execution plan for reforming the property rights system for natural resource assets in Chongqing Municipality	No.56 Document of Chongqing Municipal People’s Government General Office [2020]	Plan	39
						39-1	Section 7. Promoting the restoration of natural ecological space systems and reasonable compensation

partly lists the identification details of the policy texts, including the publication time and issuing authority.

3.1.3. Data Analysis

Following the measurement method provided by Peng et al. [59], Formulas (1) and (2) were used to calculate the policy effectiveness of each policy text and the overall policy effectiveness score for each year, respectively.

$$PG{M}_i=\left({\sum }_{j=1}^n\left(g_{ij}+m_{ij}\right)\right)\times p_i$$

(1)

$${PGM}_t={\sum }_{i=1}^{N}PG{M}_i$$

(2)

$$t=\left[\mathrm{2008,2020}\right]$$

(3)

where t denotes the year, N denotes the number of policies enacted in year t, i denotes the ith policy enacted in year t, n denotes the total number of policy clauses in the ith policy, j denotes the jth policy clause in the ith policy, p_i denotes the policy power score of the ith policy, g_ij and m_ij denote the policy goals and policy measures scores of the jth policy clause in the ith policy, respectively, PGM_i denotes the policy effectiveness of the ith policy, and PGM_t denotes the overall policy effectiveness score of the Chongqing land ticket model in year t.

3.2. Evaluation of Citizen Feedback

Citizen feedback data are a direct expression of public demands [67], and can be used as a lens for observing the effects of policies after they have been implemented. With the development of information technology, the domain of political interaction has expanded into virtual space, and the Internet has become a critical medium for communication between the government and the public [68]. In recent years, the Chinese government has increasingly integrated public opinions gathered from the Internet into its decision-making processes, treating this as an essential strategy to promote governance innovation [69]. The year 2008 is widely recognized as the starting point of online political consultation in China. Through the rapid development of e-government platforms, channels for public opinion have been significantly broadened, overcoming the hierarchical limitations inherent in traditional information transmission mechanisms such as mail and petitions [70]. Unlike blogs or portal websites established by media companies, various online political consultation platforms are official systems developed by central or local governments, specifically designed to collect public feedback on policy content and social conditions. These platforms exhibit clearer demand orientation and less entertainment compared to others. Due to lower costs and more responsive, online political consultations have gradually emerged as an important avenue for citizens to protect their rights and interests [71].

3.2.1. Data Sources

To evaluate the policy effect of the Chongqing land ticket model, citizen feedback text data were obtained from the “I want to consult” section of the Chongqing Online Political Consultation Platform (http://cqwz.cqnews.net/, accessed on 10 July 2024). Established in 2010 by the Chongqing Municipal People’s Government as an e-government initiative, this platform allows citizens to provide feedback on policy suggestions and complaints. The platform operates on the principle of authenticated user engagement, ensuring that feedback is directed to the relevant departments or individuals for consideration. Responses received are subsequently disseminated on the platform, fostering a transparent and inclusive dialogue. Given that the geographical scope of the land ticket model is confined to the boundaries of Chongqing city, it aligns seamlessly with the target audience of this platform. A comprehensive search of the platform for keywords such as “land ticket” was conducted, and the results were filtered to eliminate duplicates and missing entries. This process yielded a total of 1744 pieces of content, amounting to 334,115 words, collected between 2011 and 2021.9

3.2.2. Research Tools

Compared questionnaires or small-scale interviews, the online data offers the advantage of more comprehensively capturing feedback from policy audiences. However, a challenge that arises is that the vast volume of feedback data makes it infeasible to analyze each text content individually [72]. To address this, we introduce natural language processing (NLP) tools to transform the collected unstructured text into structured and quantifiable content for analysis. NLP, as a result of interdisciplinary research in computer science, linguistics, and artificial intelligence, serves as a major technical means widely applied in text and language analysis [73]. In the field of Chinese corpora data research, some researchers have utilized NLP for analyzing energy policies and regional economic policies [74,75]. Additionally, this technology has been employed to analyze Chinese Internet big data, describing public sentiment regarding fertility policies and land expropriation policies [76,77]. These studies rely on the decomposition of text units and employ algorithms such as TF-IDF (Term Frequency—Inverse Document Frequency) or LDA (Latent Dirichlet Allocation) to retrieve key information, thereby effectively converting large-scale text data into visual representations, providing valuable insights for our subsequent research. Among these methods, TF-IDF is an unsupervised algorithm based on statistical features that not only reports the importance of words within a corpus but also minimizes interference from high-frequency, low-significance words during the extraction [78], aligning well with the requirements of extracting critical citizen feedback information in our study.

3.2.3. Data Analysis

Owing to the relatively complex structure of Chinese corpora and the absence of spaces as delimiters for word separation, we employ the “Jieba” tool to preprocess the text data. Jieba is a third-party Python library that demonstrates excellent performance in Chinese word segmentation tasks. Additionally, Jieba supports the integration of custom dictionaries, which enhances the precision of word segmentation outcomes. The procedures are outlined below:

• Employ the Jieba word segmentation function (in Python v3.9) to accurately tokenize Chinese policy consulting texts, breaking sentences into discrete words;
• Take the text content of citizen feedback as the original corpus and define the encoding format as UTF-8.
• To prevent the interference of semantically insignificant words during the keyword extraction process, a customized dictionary was developed based on the HIT stopword dictionary (

  Table 3. Newly added stopword list during keyword extraction process.

  Category	Newly Added Stop Words
  Numerals and time words	0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 2007, 2008, 2009, 2011, 2013, 2014, 2015, 2016, 2017, 2018, 2020, 2021, Year, Month, Day.
  County and district names	Chongqing, Municipality, Wanzhou, Qianjiang, Fuling, Yuzhong, Dadukou, Jiangbei, Shapingba, Jiulongpo, Nan’an, Beibei, Yubei, Banan, Changshou, Jiangjin, Hechuan, Yongchuan, Nanchuan, Qijiang, Dazu, Bishan, Tongliang, Tongsan, Rongchang, Kaizhou, Liangping, Wulong, Chengkou, Fengdu, Dianjiang, Zhong, Yunyang, Fengjie, Wushan, Wuxi, Shizhu, Xiushan, Youyang, Pengshui, County, District, Autonomous Region.
  Frequently-occurring words with no textual meaning	For this reason, Over the years, Attach importance to, Earnestly request, The masses, Pay attention to, Understand, Due to, Implement, Further, Then, Carefully, All along, Handle, We, I, Concerning, Matter, Thing, Attach importance to, Explanation, Situation, At present, Coordinate, Reply, Response, Hope for, Still, Problem, Unit, Gratitude, think, Long overdue, However, Hello, Indicator, Homestead, Handle, Reflect, Want to say, You, Hello, Leader, Thank you, Hope, Suggest, Want.

  ).10 This dictionary was incorporated into the keyword extraction avoidance rules to ensure that the resulting outcomes are highly pertinent to the research focus (land ticket).

• By employing the “jieba.analyse.extract” function with the parameters “topK = 20” and “withWeight = True”, we aimed to compute the TF-IDF values for all word segmentation results across the entire corpus and extract the top 20 keywords ranked in descending order of their importance.

The value of TF-IDF can be calculated as follows:

$${tf}_{ij}={\displaystyle \frac{n_{ij}}{\sum _k{n}_{kj}}}$$

(4)

$${idf}_i=\log {\displaystyle \frac{\left|D\right|}{\left|j:t_i\in d_j\right|}}$$

(5)

$${TF-IDF}_{ij}={tf}_{ij}{\times idf}_i$$

(6)

where n_ij denotes the frequency of word i in text j, Σ_kn_kj denotes the total number of words in all texts, D denotes the total number of texts in the corpus, and {j:t_i∈d_j} denotes the number of texts in the corpora that contain word i. The value of TF-IDF_ij in Formula (5) is the product of the value of tf_ij in Formula (3) and the value of idf_i in Formula (4), and the higher the value of TF-IDF for the keyword, the higher importance of the term in the whole text.

• Organize the categories according to the meaning of the keywords.
• To achieve keyword visualization and further enhance the intuitive understanding of the text’s theme, a word cloud was generated by using the “wordcloud“ library based on the extracted keywords. Ultimately, the TF-IDF values of the top 50 keywords were divided into five grade intervals by the 20% percentile method, and keywords of different weights were marked in sequence from red to green.

4. Results

4.1. Policy Effectiveness Score and Characteristics of the Chongqing Land Ticket Policies

During the policy effectiveness quantification stage of the Chongqing land ticket model, we assembled a six-member evaluation team comprising two land policy research experts (university professors), two land policy researchers (doctoral students), and two technical staff members from the government’s land planning and management department. All team members were thoroughly familiar with the quantification standards for policy effectiveness and meticulously reviewed the policy content across 134 analysis units. Each member independently completed the scoring process without influencing one another. Given that the raters’ scores are based on their subjective interpretation of the policy text content, we employed Cronbach’s Alpha coefficient to assess the reliability of the scoring results for each dimension of policy effectiveness. Upon testing, the inter-rater consistency was found to be satisfactory across all dimensions. Specifically, the reliability coefficients were as follows: Power dimension (P) = 1.0, Goal dimension (G) = 0.8649, and Measure dimension (M) = 0.835. Based on the scoring criteria presented in Table 1, the resulting scores are summarized in

Table 4. Effectiveness score of Chongqing land ticket policies from 2008 to 2020.

	Year
Goals	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Total
1. Application for reclamation	67.00	0	0	86.17	17.00	0	16.67	22.50	0	0	16.67	0	0	226.00
2. Reclamation procedure	23.50	13.67	17.33	36.00	25.00	0	0	0	0	13.67		0	0	129.17
3. Reclamation check and acceptance	0	14.67	0	11.33	0	0	15.67	0	0	0	0	0	0	41.67
4. Application for transaction	68.50	0	0	0	0	0	0	23.00	17.33	0	0	0	0	108.83
5. Rules of transaction	153.00	30.50	16.00	39.83	10.50	66.33	0	290.50	99.00	14.33	0	16.33	22.00	758.33
6. Settlement of compensation payments	0	0		107.17	17.50	0	0	42.00	50.17	65.67	16.67	0	0	299.17
7. Revenue distribution	17.00	0	18.33	11.67	0	0	0	28.50	0	155.33	18.00	36.00	0	284.83
8. Use and circulation of land ticket	0	0	0	11.00	0	0	0	124.50	64.67	0	0	0	0	200.17
9. Organizational support	0	0	23.00	33.17	0	51.00	48.67	39.00	17.00	73.33	0	0	0	285.17
Total	329.00	58.83	74.67	336.33	70.00	117.33	81.00	570.00	248.17	322.33	51.33	52.33	22.00	——

.

Based on the quantification results, it is evident that the three-dimensional effectiveness of the Chongqing land ticket policies demonstrates the following characteristics:

• Table 5. Department and format of Chongqing land ticket policies.

  Department	Format Year	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Total
  State Council (PRC)			1												1
  	Opinions		1												1
  Chongqing Municipal People’s Government		1	1	2	1	3	2	2	4	2				1	19
  	Measures	1													1
  	Plans		1							1					2
  	Circulars				1	2			1	1				1	6
  	Opinions			2		1	2	2	2						9
  	Government decrees								1						1
  Chongqing Bureau of Planning and Natural Resources													2		2
  	Circulars												2		2
  Chongqing Bureau of Land and Housing Administration			1	1	6		1	1		2	2	2			16
  	Measures				1										1
  	Plans						1								1
  	Provisions				1										1
  	Rules									1					1
  	Circulars		1		3					1	2	1			8
  	Detailed rules				1			1							2
  	Opinions			1								1			2
  CCLE											1				1
  	Measures										1				1
  Total		1	3	3	7	3	3	3	4	4	3	2	2	1	39

  indicates that the policy power (P) scores exhibit relative consistency across the years under consideration. Among the 39 policy texts analyzed, all were issued by the Chongqing Municipal People’s Government and the Chongqing Bureau of Land and Housing Administration (currently the Chongqing Bureau of Planning and Natural Resources), except for two policies respectively issued by the State Council (PRC) and the CCLE (Table 5). The policy texts issued by the Chongqing Municipal People’s Government, which is responsible for steering the overall direction of the land ticket model, predominantly take the form of opinions and notices. In contrast, the policy texts issued by the Chongqing Bureau of Land and Housing Administration, which oversees the specific implementation of the land ticket model, include notices as well as methods, detailed rules, regulations, and guidelines. Moreover, the two sets of policies differ in their focus. The former offers policy support and guidance for the land ticket model from a macro perspective, addressing rural financial service reform, urban–rural household registration integration, and the supervision of trading venues. In contrast, the latter imposes institutional constraints and provides detailed operational guidance at a micro level, focusing on aspects directly related to the production, transaction, and utilization of land tickets, including transaction procedures, fund management rules, and standardized reclamation processes.

2.

The scores for policy goals (G) and measures (M) in each year are comparable, exhibiting similar changing trends, which can be attributed to the clarity of the content and the comprehensiveness of the support measures. In addition to the two comprehensive policy documents,11 the majority of the remaining texts adhere to the principle of “one document, one matter, one solution”. Specific management measures corresponding to the depth of the content are typically proposed for issues in the production, transaction, and utilization of land ticket. This feature is particularly evident in policies related to reclamation work and the management of land ticket funds.

3.

The peaks of policy effectiveness correspond to the key operational nodes of the land ticket model. The peaks of policy effectiveness are concentrated in the years of 2008, 2011, 2015, 2016, and 2017 (Figure 4), with scores of 329, 336.33, 570, 248.17, and 322.33 respectively. Among them, 2008 and 2015, 2016 can be regarded as the “institutional years” of this model. The promulgation of the Provisional administrative measure of Chongqing Country Land Exchange in 2008 marked the initial establishment of the land ticket model. This document, centered on the platform for land ticket transactions by CCLE, stipulated its institutional functions and management processes, and initially clarified the basic contents such as the objects and rules of land ticket transactions, establishing the basic framework of the model. In 2015, the guiding document for the land ticket model, the Measures of Chongqing Land Ticket Management, was issued. The numerous transaction rules, organizational support, and indicators transferring details during the previous trial period were reorganized, and most of these regulations have not been modified since then. On the other hand, 2011 can be regarded as the “trading years” of the Chongqing land ticket model, being the years with the largest number (52,900 mu) of reclamation program and land ticket transactions since the implementation of the model. The local government updated and improved the policies in a timely manner according to the development needs of the trading market. The fluctuations in policy effectiveness observed in 2016 and 2017 were primarily driven by external policy shocks. Specifically, 2016 marked the first year of China’s 13th Five-Year Plan, during which poverty alleviation was elevated to a national strategic priority, referred to as the “Battle against Poverty.” In response, the central government mandated local governments to adopt measures such as “relocation for poverty alleviation”12 to provide economic support and improved living conditions for farmers in impoverished areas. Under the pressure of centrally assigned poverty alleviation targets, policymakers in Chongqing re-emphasized the land ticket model, recognizing its potential to convert farmers’ land assets into economic income. Consequently, in 2016 and 2017, Chongqing initiated improvements to certain aspects of the land ticket model (e.g., granting priority participation rights to poor farmers), thereby tilting the policy design toward safeguarding farmers’ rights and interests. Notably, these refinements significantly boosted farmers’ enthusiasm for applying for homestead reclamation, culminating in the second-largest transaction surge in the history of the land ticket model in 2017 (48,032 mu).

4.

The annual fluctuations in policy effectiveness are notably significant. Following the peak of policy effectiveness, there is often a subsequent trough. In the year succeeding the five years with the highest policy effectiveness, the average difference in policy effectiveness compared to the previous year was −211 points. This occurs because after a large number of policy documents are issued within a single year, it requires time for grassroots levels to comprehend the implementation goals and execute the established plans. Furthermore, the policy goals associated with the Chongqing land ticket model are relatively limited and concentrated, making it challenging for subsequent policies to establish new regulations for the same policy goals or modify the preceding policies.

5.

The focus of policy effectiveness allocation has undergone a notable shift. The Chongqing land ticket model, which relies on institutional safeguards at one end and is closely tied to public welfare at the other, has placed significant emphasis on grassroots-level land reclamation and land ticket trading rules, as well as fund settlement and income distribution for farmers. Furthermore, from a temporal perspective, 2017 marks a pivotal turning point in the orientation of the policy effectiveness (Figure 5). Around this year, the emphasis of policy implementation transitioned from accelerating institutional construction to enhancing the income rights and interests of farmers. This shift occurred because, following the overheating of the trading market, farmers’ awareness of property rights gradually increased. At this time, concentrated feedback regarding issues such as non-standardized reclamation procedures and prolonged compensation payment cycles prompted policymakers to implement moderate reforms in the land ticket model, thereby addressing relevant complains.

The Chongqing land ticket model has, over the course of more than a decade, rapidly progressed through the stages of pilot construction, system refinement, and exemplary case development by leveraging a steady stream of innovative policies. Beyond unlocking idle rural land resources and fostering regional economic growth, this model has also integrated the governance goals of multiple sectors—including agriculture, social security, and finance—into a unified policy framework via land ticket transactions. Consequently, it has transcended being a unidirectional policy confined to a single domain. In the context of Chongqing’s local new urbanization initiatives and comprehensive urban–rural reform efforts, the land ticket mechanism has been effectively employed as an appropriate policy instrument, seamlessly integrating into a series of significant socioeconomic reforms during the same period.

The analysis of the policy text reveals that, in its early stages, the Chongqing land ticket policy prioritized institutional construction while somewhat overlooking the protection of farmers’ rights and interests. The subsequent sections of this article will shift the evaluation perspective to examine the effects of the policy on farmers from their standpoint. Specifically, the key information and distribution patterns of citizen feedback collected from online space during the implementation of the Chongqing land ticket model will serve as a foundation for exploring recommendations to refine the policy in its next phase.

4.2. Key Information of Citizen Feedback in the Chongqing Land Ticket Model

After statistical analysis, “compensation payment” emerged as the most significant keyword in political consultation texts, with a corresponding TF-IDF value of 0.05616 (

Table 6. Topics and keywords of citizen feedback in Chongqing land ticket model.

Category	Amount	TF-IDF	Keywords	Frequency	TF-IDF
Economic compensation	1454	0.16415	compensation payment	332	0.05616
			compensation	489	0.04738
			subsidies	179	0.01796
			funds	207	0.01467
			payments	110	0.01450
			allowances	137	0.01348
Policy consultation	1231	0.10167	policy	723	0.05163
			consultation	248	0.02673
			application	260	0.02331
Reclamation procedure	1002	0.12113	measurement	336	0.03631
			acceptance check	186	0.02575
			notifications	230	0.02194
			signature	143	0.01882
			surveying	127	0.01832
Transformation of identity	670	0.08247	pension insurance	205	0.03014
			“hukou”	227	0.02161
			social insurance	157	0.01665
			poor household	81	0.01407
Complaints and inquiries	363	0.04463	village cadres	194	0.02689
			village committee	169	0.01774

). According to the TF-IDF algorithm, the importance of a word in a specific text is directly proportional to its term frequency and inversely proportional to its document frequency across the entire corpora. Compared to methods that calculate word importance based solely on frequency, the TF-IDF algorithm assigns higher weights to words with stronger predictive power for the text’s theme. This explains why, despite higher frequencies for terms like “policy” (n = 723) and “measurement” (n = 336), the more thematically relevant term “compensation payment” (n = 332) receives a higher weight after inverse document frequency normalization. Additionally, semantically related terms such as “compensation” and “subsidies” form the “economic compensation” category (Figure 6), which achieves the highest TF-IDF value among all categories at 0.16415.

However, does this imply that citizen feedback within the land ticket model is primarily focused on “result justice” as represented by the amount of monetary compensation? The answer is no. While the aforementioned method can identify text priorities through keyword extraction, it cannot fully discern the specific meanings conveyed by these keywords and therefore fails to accurately extract information with practical significance. For instance, relying barely on the keyword “compensation” makes it impossible to determine whether the public opinion solicitation pertains to “the inquiring about compensation standards,” “the quantity of compensation distribution,” or “timing of compensation distribution.”

Therefore, we meticulously reviewed all feedback information item by item. Through analyzing the textual content and specific context of each piece of feedback, we categorized the feedback related to “compensation payment” into two primary types (Figure 7). The first type pertains to the reclaiming of economic compensation. Specifically, this reflects farmers’ demands for overdue land ticket payments after completing transactions, with the hope that local government will promptly distribute the delayed compensation. For instance, one farmer posted online: “… Our family actively responded to the government’s policy on relocating dilapidated houses. Like others in our community, we borrowed money to move into a new house and agreed to let the government demolish our old house (referring to the homestead). We expected to quickly receive compensation from the government to repay our debts. However, nearly a year has passed since the land-ticket transaction was completed, and we still haven’t received any compensation. … Now I’m facing a severe debt crisis. I hope the relevant departments will no longer delay issuing my compensation.” This type of feedback typically arises when local governments fail to adhere to their promised timelines for compensation allocation. Since farmers lack the ability to ensure compliance with prearranged plans, they resort to expressing frustration, describing their difficulties, and seeking attention from department leaders via online platforms.

The second type involves disputes over compensation amounts. Specifically, this reflects farmers’ dissatisfaction with the compensation provided for reclaimed homesteads (as land ticket prices). A farmer once questioned the land ticket model online: “The government consistently compensates us based on the lowest standard (referring to the minimum protection price for transactions), yet the value of houses fluctuates over time. I don’t believe my house (referring to the homestead) is worth only this much (land-ticket price).” According to regulations, farmers cannot negotiate directly with buyers but must entrust their land tickets to a trading platform for auction, receiving compensation at a uniform price. Consequently, some farmers fail to achieve their desired returns, leading to complaints.

By comparing the total number and annual distribution, it is evident that the two types of citizen feedback under the “compensation payment” category exhibit significant quantitative differences. First, in terms of the total number, among the 1744 feedback information texts, 750 were related to the first type of economic compensation demands. This not only far surpassed the number of the second type of economic compensation demands during the same period but also constituted the largest proportion (43%) in the entire feedback information text database (Figure 8). Second, the number of feedback items related to “reclaiming of economic compensation” underwent a pronounced fluctuation. Beginning in 2013, this type of demand rapidly increased and peaked in 2015 and 2016. From 2017 onward, the number of such demands gradually declined but continues to occasionally appear on political consultation platforms to this day. In contrast, the annual changes in the second type of economic compensation demands were much more stable, and relative to the number of other types of feedback information, it can be concluded that this has never been a primary concern for farmers.

By dissecting the type of citizen feedback information, especially through quantitative analysis of two specific types of “economic compensation”, we have noticed an interesting fact: in the Chongqing land ticket model, farmers may be more concerned about the “procedural justice”, represented by “receiving economic compensation on time as agreed”, rather than the “result justice”, represented by “how much economic compensation they can receive”. This is a relatively counter-intuitive finding. In contrast to the land expropriation disputes in China, where farmers often choose to resist due to the insufficient compensation from the government [79], such a phenomenon has not occurred in the Chongqing land ticket model. Even the low-intensity complaint type of resistance is relatively rare.

Another issue we are concerned about is whether the keywords we extracted mostly convey the genuine opinions of farmers. In this regard, we aimed to indirectly assess the reliability of farmers’ demands for “procedural justice” in the Chongqing land ticket model by analyzing the government’s delays in distributing economic compensation. We collected and analyzed 359 pieces of information on land ticket fund published in the “Transaction Announcements” and “Direct Disbursement of Funds” sections on the website of the CCLE (www.ccle.cn, accessed on 2 August 2024) from 2014 to 2020. The results indicate that during this period, the average time taken for payment disbursement across all districts and counties exceeded the stipulated duration of 63 days.14 Notably, the deviations were particularly pronounced in 2015, 2016, 2018, and 2019. These findings confirm that farmers have consistently experienced delays in receiving their land ticket payments over an extended period (Figure 9). Consequently, this has provided evidence for the public to be more concerned about whether procedural justice in the Chongqing land ticket model can be implemented.

In conclusion, the analysis of citizen feedback information reveals that the persistent inadequacies in the Chongqing land ticket policy, particularly in settlement of compensation payments and revenue distribution, have to some extent compromised the ability to ensure the realization of farmers’ benefits. Consequently, keywords such as “economic compensation” dominate the feedback information in terms of frequency and significance. Among these, the issue of “failure to receive land ticket payments on time” has been a central concern for farmers over an extended period and remains unresolved. This suggests that in the next phase, the refinement of the Chongqing land ticket policy should prioritize addressing the “procedural justice” demands emphasized by policy audiences. Key tasks should focus on optimizing the revenue-sharing ratio and standardizing the disbursement procedures for transaction funds. Through continuous updates to policy frameworks and the implementation of complementary administrative measures, the policy can effectively respond to citizen feedback.

5. Discussion

5.1. Why Is Policy Effectiveness More Focused on Institutional Construction?

• The pioneering nature of the Chongqing land ticket model necessitates a focus on policy effectiveness. In the early stages of policy formulation, it is predictable that decisions will prioritize rapid enhancement of core content construction. As an early implementation of TDR program in China, the Chongqing land ticket model lacks mature references for guidance. Local governments must explore a viable path within the existing land institution framework, ensuring compliance with higher-level laws while facilitating market-based transactions of rural construction land. From 2008 onward, it took seven years of piloting before the various components of the policy were officially established. During this period, the regulation permitting rural construction land to enter the market represented an innovative measure grounded in China’s land management realities, requiring extensive supporting policies to ensure the model’s operational success.
• Accelerating the institutional construction of land ticket model can enhance the political capital for local government officials’ promotion. As previously discussed, the land ticket model was a pilot program initiated by Chongqing Municipality under authorization from the central government. Since innovative achievements have been incorporated into the evaluation criteria for officials, the outcomes of pilot programs, as a form of policy innovation performance, have become a stepping stone for local government officials (policy makers) in their pursuit of career advancement [80]. For Chongqing’s local government officials, the land ticket model, being the first authorized, provided an excellent opportunity to demonstrate their policy design and implementation capabilities to the central government. However, as the central government extended its authorization to other regions, the uniqueness of Chongqing diminished. Distinctive localized land ticket models gradually formed a “pilot competition,” where those achieving remarkable results in pilot programs and developing localized experiences that attract greater attention and recognition from the central government would stand a better chance of promotion [81], while others might miss opportunities or even face elimination. Therefore, it is understandable that Chongqing’s policymakers adopted the strategy of accelerating the institutional construction of the land ticket model to transform it into a “star pilot program”.
• The rapid and comprehensive establishment of an institution related to land tickets can effectively address the government’s urgent need for land space. Land tickets provide at least an additional 10% of new construction land, significantly mitigating the challenge of insufficient development space. Although regulations stipulate that the total volume of land ticket transactions should not exceed 10% of the state-issued annual plan for new construction land, this limit has been frequently surpassed in practice. For instance, in 2020, the former Ministry of Land and Resources approved 190,000 mu of new construction land for Chongqing, while land ticket transactions amounted to approximately 27,000 mu, accounting for 14.21%. The supplementary construction land facilitated by land ticket has expanded opportunities for local industrial and real estate development in Chongqing and enhanced the government’s leverage in attracting investment. This clearly explains why the government is motivated to expedite the improvement of institutional frameworks to ensure the smooth functioning of land ticket transactions.
• The trading of land ticket can generate substantial off-budget fiscal revenue for the government. The rule that a “land ticket must be converted into land use rights through auctions” allows the government to capture the residual value of land development rights that have not been fully commercialized. Since land ticket trading represents a cross-regional model, the land value increment generated during this process is ultimately reflected in the price of the converted construction land indicators. This price consists of two components: the nominal value of the land ticket and the premium from the auction of the land transfer. When construction land indicators are converted into commercial or residential land, the latter’s proportion significantly increases, meaning that most of the land value increment accrues to local governments. In contrast, farmers receive only a small fraction of the revenue from land ticket trading. While the price of land ticket barely covers the costs associated with converting them into industrial land, developers must still pay substantial additional fees to the government when they chase for commercial or residential land. This implies that the remaining portion of the market price for land development rights—payable by developers to farmers—is effectively transferred to the government in the form of land transfer fees. Consequently, the significant profits the government derives from the specific rules of the land ticket model, underpinned by its monopoly in the primary land market, serve as a key driver for accelerating the refinement of the policy’s core content.

5.2. Why Is Citizen Feedback More Concerned with Procedural Justice?

• Certain procedures of the Chongqing land ticket model have infringed upon the legitimate rights and interests of farmers. This primarily results from the excessive administrative intervention by the government in land ticket transactions. According to the regulations, both the total volume of land ticket transactions and the transaction timing are determined by government departments. After voluntarily reclaiming their homesteads, farmers must patiently await their turn within designated transaction batches. Furthermore, farmers are unable to negotiate directly with buyers but must entrust the government to conduct transactions on their behalf, ultimately accepting the transaction outcomes passively. Although land ticket transactions incorporate market mechanisms such as auctions, the government’s excessive control over these transactions has undermined farmers’ rights to participation and transactional equality. This prevents them from fully realizing the value of their land rights and fails to ensure that transaction proceeds will not be intercepted or misappropriated by the government.
• Farmers urgently require the income from land ticket transactions as start-up capital for adapting to urban life. In China, the identities of rural and urban residents are differentiated by the “hukou” system. The right to use homesteads free of charge is one of the few privileges afforded to individuals with a rural “hukou” [82]. When farmers apply to participate in land ticket transactions, they are relinquishing this benefit and forfeiting their right to a rural residence. Consequently, these individuals must decide whether to relocate to urban areas. However, under the “hukou” system, farmers are unable to access the same identity-based privileges as urban residents. To ensure their children receive equitable access to quality compulsory education, as well as better medical and pension benefits, they must incur additional costs [83]. This also increases the likelihood that farmers migrating to cities will encounter significant hardships. They cannot return to rural areas, as their homesteads have been reclaimed, leaving them without a fallback option should they leave the city. Moreover, if they remain in urban areas, they face elevated living costs due to systemic identity-based discrimination. Although the local government in Chongqing pledged to grant urban “hukou” to farmers relocating to the city as part of its household registration reform, the policy was not implemented instantaneously and required a substantial amount of time for full execution. The funds from land ticket transactions serve as a crucial safeguard during their transition period of shifting between rural and urban identities. Consequently, delays in compensation disbursement can lead to significant dissatisfaction among them.
• Farmers exhibit relatively low sensitivity to the price of land ticket. On the one hand, their expectations regarding the transaction value of homesteads are comparatively modest. Land tickets arise from homesteads reclamation by farmers; however, the transfer of rural homesteads in China is restricted within collective economic organizations, and urban–rural transaction channels remain unopened. Consequently, there is no unified urban–rural market to reveal the market value of rural homesteads. Farmers’ perception of homestead value primarily stems from prices in the rural transaction market, which are significantly lower than concurrent urban housing prices. On the other hand, farmers lack more advantageous options for converting land assets into cash. Prior to the land ticket model, under the dual urban–rural system in China, the sole method for transforming rural collective construction land into urban state-owned construction land was through land expropriation. However, land expropriation compensation in China is not based on real-time land market prices but rather determined by government-set standards. If the per-unit-area transaction price of land ticket exceeds the expropriation compensation standard, farmers have no rational grounds to decline participation in land ticket transactions. Over the past decade, the Chongqing Municipal Bureau of Land and Housing Administration has adjusted the transaction guidance price of land ticket upward three times to safeguard the minimum income of participating farmers. In 2019, the minimum protection price per mu of land ticket was raised to CNY 178,000. After deducting reclamation costs of CNY 37,000 and distributing 85% to farmers, a remaining balance of CNY 119,850 was allocated. This amount significantly surpasses the concurrent land expropriation compensation standard. Given the absence of alternative channels for converting land assets into cash, farmers tend to accept this outcome relatively willingly.

5.3. Policy Improvement Suggestions

• Strengthen the protection of farmers’ rights and interests in the early stage of policy design. The initial design of a policy is a critical juncture that determines its long-term evolution. Once an initial policy is implemented, it will form a specific pattern of interest distribution and action rules. The vested interest groups within it will tend to maintain the existing policy content to preserve their advantageous position. In the land ticket model, it can be observed that the absence of the rights and interests of farmers, a vulnerable group, has been systematized and legalized, and even gradually internalized by them as a common belief, making it difficult to reverse through subsequent remedial policies. Therefore, it is imperative to address this potential risk at the source of policy design. (1) During the early stages of policy formulation, individual farmers should be granted full participation rights. Representatives of farmers could be included in the team of policy designers, ensuring that issues genuinely concerning farmers are integrated into the policy framework. (2) The valuable experience from pilot program should be fully leveraged. For highly controversial or broadly influential policy updates, small-scale pilots should precede district-wide implementation, with decisions based on feedback and evaluation outcomes. (3) Given the loss of homesteads during land ticket transactions and the challenges farmers face upon urban migration, a “compensation-first, reclamation-second” fund allocation mechanism could be established, supported by financial contributions from land reserve institutions and urban investment entities. (4) The bargaining right over land ticket should gradually shift to farmers, enabling them to negotiate directly with potential buyers for more transparent pricing and expedited revenue disbursement. (5) Stricter punitive measures must be implemented to supervise and prevent delays in farmers’ legitimate earnings, particularly addressing issues such as the withholding or misappropriation of land ticket funds by village cadres.
• The government should minimize excessive intervention in land ticket trading. In this model, local governments have implemented a structural substitution of administrative power for market mechanisms through full-chain control over “indicator generation–pricing–trading–revenue distribution.” In contrast to the role of the government in the USA TDR programs, local governments in China should reassume their roles as platform builders and transaction regulators. (1) In the USA TDR programs, the transactions of land development rights typically involve third-party intermediaries (such as appraisal agencies, legal services, and non-profit organizations), enabling land right holders to access transaction plans that align with market value assessments. Local governments in China should progressively introduce independent third-party transaction service institutions into land ticket trading while gradually restricting the functions and authority of trading platforms under their jurisdiction, thereby minimizing administrative intervention in land ticket trading. (2) Drawing on the operational methods of the USA TDR programs, local governments should gradually phase out price controls and total volume controls on land ticket. Pricing and trading volumes for land ticket should be entirely determined by market forces. (3) Likewise, the government should refocus its policy priorities on protecting areas designated for transferring land development rights. Compared to the stringent regulatory measures applied to development density in the USA TDR programs, the Chongqing land ticket model appears relatively lenient in managing farmland post-reclamation. A potential solution could involve integrating reclaimed farmland through leasing rather than expropriation, facilitating scale-agriculture-operation with enhanced productivity and stronger protection.
• In policy updates, it is essential to respond to public demands. The emergence of online political consultation platforms has enhanced the capacity of policy audiences to provide upward feedback. However, the government’s resolve within the land ticket model to translate scattered public demands into concrete policy outputs remains insufficient. Currently, the government primarily addresses citizen feedback through online channels. While this approach can swiftly alleviate some complaints, it fails to systematically adjust key aspects such as program design, distribution schemes, and rights protection in the land ticket model. Consequently, it does not effectively reduce the generation of negative feedback. (1) Local governments can introduce appealing and comparable policies in relevant management domains, explore new market-oriented mechanisms for land factors, and thereby empower farmers with the right to “vote with their feet”. This approach can compel existing policies to address their deficiencies and deliver tangible benefits to farmers through inter-policy competition. (2) The local government should regularly analyze online public opinion, systematically incorporate reasonable, widespread, and actionable demands into new policy content, and ensure continuous policy updates to align with public expectations and foster timely governmental responses.

6. Conclusions

This paper comprehensively evaluates the policy effects of the Chongqing land ticket model from the perspectives of policy effectiveness and citizen feedback. First, using a three-dimensional policy effectiveness evaluation framework, a quantitative analysis of 39 related policy documents issued between 2008 and 2020 demonstrates that policymakers, including the Chongqing Municipal People’s Government and the former Chongqing Municipal Bureau of Land and Housing Administration, have consistently invested significant effort in maintaining the land ticket model. They have implemented appropriate measures to achieve established policy goals and promptly updated policy content in response to institutional design requirements during the model’s operation. However, it is noteworthy that prior to the shift in policy effectiveness allocation focus in 2017, more emphasis was placed on institutional construction than on profit distribution. Prior to this, the policy effectiveness of this model had predominantly centered on institutional construction, particularly in the formulation and refinement of trading rules. Subsequently, this paper employs natural language processing tools to analyze 1744 pieces of online feedback related to the land ticket model on the Chongqing Online political consultation platform. The findings indicate that economic compensation has been a primary concern for farmers, with the reclaiming of land ticket payments being their most critical and widespread demand. This analysis not only offers an additional perspective on the policy evaluation, but also reveals that farmers in the Chongqing land ticket model are more concerned with the realization of “procedural justice” than “result justice”, thereby contributing to a deeper understanding of the TDR program in China. Finally, the article examines the differences in policy effectiveness allocation across various policy goals within the Chongqing land ticket model, as well as the reasons why farmers prioritize procedural justice. It recommends that local governments update the innovation of land ticket policies by focusing on forward protection of farmers’ rights, reducing administrative intervention in the transaction market, and establishing policy-oriented mechanisms to address citizen feedback.

Given the accessibility of the data, this paper still has some limitations that should be addressed in future research: (1) Non-public policy texts within the government are not accessible. These internal documents, such as directives, memos, and decision reports, are crucial components of policy effectiveness but remain inaccessible to external researchers. Future studies should incorporate in-depth interviews with policymakers and implementers to better understand the role these internal documents play in shaping policy outcomes. (2) It is necessary to further examine the causal relationship between policy effectiveness and citizen feedback. This study evaluates the policy effects of the Chongqing land ticket model from these two relatively independent perspectives. The quantitative assessment reveals a strong correlation between these two variables in terms of quantity and temporal distribution. For instance, the number of farmers’ reclaiming demands for economic compensation aligns closely with the peaks of policy effectiveness during specific years (2015, 2016, and 2017). Since 2017, when the focus of policy effectiveness shifted toward profit distribution, the frequency of such demands has declined significantly. This suggests that the allocation of policy effectiveness may have influenced changes in farmers’ demands, presenting an important area for further investigation. Future research should aim to verify this causal relationship and uncover the underlying mechanisms, which may necessitate additional data support (e.g., linking feedback information to specific districts or counties to control the fixed effect of regional policy differences). (3) Citizen feedback in online-space is influenced by technological development levels. The penetration rate of the Internet and smart devices in rural Chongqing is relatively low, which limits the collection of comprehensive citizen feedback from online sources. To enhance the reliability of findings, future research should supplement online data with interview and questionnaire data from the policy audience. Quantitative analysis can then be used to cross-compare these data sets, ensuring a more robust and reliable understanding of citizen feedback.