The Scale and Revenue of the Land-Use Balance Quota in Zhejiang Province: Based on the Inverted U-Shaped Curve

Abstract

The project-based construction land-use policy of ‘increasing versus decreasing balance’ (IVDB) is pivotal to easing the contradiction between urban and rural land in China. Understanding the relationship between the scale and revenue of the balanced quota is crucial for increasing the efficiency of quota-allocated, and further improving, IVDB performance. However, existing studies have rarely revealed the impact of the balanced quota’s scale on its revenue, supported through empirical evidence. In this study, we analyzed the relationship between the scale and revenue of the balanced quota and used the quadratic econometric model to explore the inverted U-shaped impact of the scale of the balanced quota on the revenue of the 1907 IVDB projects in Zhejiang province. The results show that: (1) the relationship between the quota’s scale and the revenue shows an inverted ‘U’ type in Zhejiang. On the premise of considering three control variable groups, the optimally balanced quota of Zhejiang province is 7.19 ha. (2) There is spatial heterogeneity in the optimal scale of the balanced quota in Zhejiang and the appreciated scale of the quota in northeast and southwest Zhejiang is 9.50 ha and 6.03 ha, respectively. Then we discussed problems associated with the scale and revenue of the project-based balanced quota under the implementation of the IVDB policy. The study enriches the performance analysis of IVDB policy from the point of view of economic perspective and tries to provide a scientific basis for the appropriate size quota for local government. Finally, comprehensive consideration of inputs to allocate the balanced quota, optimizing the rural resettlements spatial planning, and strengthening central-government supervision is put forward.

1. Introduction

Farmland and homesteads are associated with human settlement in rural areas, which provide space for production and for living [1]. However, with increasing industrialization, urbanization and population concentration towards the ever-growing cities, these two types of land use have been facing dilemmas [2,3,4,5,6].On the one hand, noteworthy farmland has been abandoned in many developed countries and some developing countries since the 1950s [7]. On the other hand, as massive numbers of rural migrant workers have flooded into urban areas to earn a living, many rural settlements remain unoccupied seasonally or permanently [8,9,10,11]. Traditionally, many countries around the world have long employed rural land consolidation and land reclamation to solve the above problems and revitalize rural development [12,13,14,15]. In particular, under its strict cultivated land protection and spatial planning system, China has implemented the increasing versus decreasing balance (IVDB) urban-rural construction land-use policy, which is similar to that of transferable development rights (TDR) in the United States [16].

Generally, the IVDB policy aims to balance the increases in urban construction land with a reduction in rural construction land [17] and this rural construction land will be reclaimed as cultivated land to ensure the dynamic balance of total arable land [18,19]. Since its initiation in 2000, and subsequently formally proposed in official documents in 2004, the IVDB policy has been implemented throughout China. The former Ministry of Land and Resources (MLR) 1 issued the policy document for IVDB in 2005, Proposals for regulating the pilot of increasing versus decreasing balance of urban-rural built land. The policy stipulates clearly that a balanced quota, which is used to control the scale of rural demolition and urban construction, is assigned by the central government in the form of projects and strictly restricted to the scope of counties. Under the guidance of the IVDB policy, the first round of experiments for IVDB was launched in 2006 and 183 projects with 4923 ha of balanced quotas were allocated to Tianjin, Shandong, Hubei, Sichuan and Jiangsu provinces. By 2019, 31 provinces in mainland China have adopted the IVDB policy, with approval of 681,670 ha of balanced quota to implement the pilot projects [20]. In practice, multiple implementations have been created to achieve a spatial equilibrium between urban and rural construction land, including the transfer of the farmland development rights program and flat-for-flat compensation formula in Zhejiang province [21,22] and the land coupon programs in Chongqing [23,24].

Meanwhile, the IVDB policy has kept pace with China’s land management tools. For the past few years, the central government incorporated the IVDB policy into the country’s poverty alleviation support system [25]. Specifically, poverty-stricken areas and counties were given the right to determine the quantity of the balance quota for as long as they needed to, and the quota could be transferred at the provincial or even national level according to relevant regulations. The CNY 1896 billion cross-provincial quota-transfer funds were channeled to poverty-stricken areas from 2018 to 2020 [26]. During the five-year transition period (2020–2025) of effectively combining achievements in poverty alleviation with rural vitalization, the above provisions continue to be implemented according to the ‘Measures for transferring inter-provincial quota linked to the increasing versus decreasing balance of urban-rural built land during the transition period’ in 2021. In the context of factors’ marketization, the latest evolution of the IVDB policy is that the power to assign the balanced quota is devolved from the central government to the provincial government, as stipulated in the Notice of the Ministry of Natural Resources on the management of the 2020 Land Use Plan. As we all know, the revenue of the balanced quota is the key economic motivation for implementation of the IVDB policy and homesteads are referred to as ‘sleeping’ land assets figuratively. Therefore, under the background of project-based IVDB policy, what is the relationship between the scale and revenue of the balanced quota? Particularly, as the local government’s demand for urban construction land is always enormous, the balanced quota’s scale has carried the potential risk of being much too much when the IVDB policy is oriented by the provincial government. It is interesting as to whether there is an optimal scale of the balanced quota to obtain the maximum benefits with the implementation of the project-based IVDB policy. Even furthermore, are there differences in the optimal scale of the balance quota in different regions? The answers to the above questions could improve the efficiency of quota-allocation and provide a scientific basis for local governments to measure the appropriately balanced quota of each IVDB project, which will eventually enhance the overall welfare of the IVDB policy implementation.

Some studies have been conducted to analyze the determinants of a balanced quota and associated recommendations are provided under specific contexts in China. Peng and Huang (2021) suggest that the balanced quota be incorporated into municipal or county spatial planning indicators for unified management and use [27]. Cai and Liu (2021) insist that the scale and transferring scope of the balanced quota should be determined and adjusted by the market [28]. Combined with a case study of IVDB policy implementation, Zheng (2020) recommends that collective organizations and farmers participate in determining the quantity of quotas to consolidate the achievements of poverty alleviation [29]. In a case study of Huantai county in Shandong province, Long, et al. (2012) argue that the IVDB policy implementation with a top-down decision-making mechanism should incorporate elements of bottom-up planning [17]. In fact, the deep involvement of local villagers is a common feature of most successful IVDB cases [30]. Additionally, focusing on the revenue of the balanced quota, scholars analyze cases of measurement and distribution [31,32] and the direct or indirect effects on economic growth. However, few studies have revealed the appropriate scale of the balanced quota in the process of the project based IVDB implementation. There is also a lack of research on the relationship between the quota’s scale and revenue supported by empirical data. The existing scattered case analyses are not enough to show the overall situation of the IVDB policy implementation within a region, which means that it is difficult to effectively guide the further improvement of the IVDB policy. As such, knowledge of the relationship between the balanced quota’s scale and revenue based on the IVDB projects is still rare. What is more, the knowledge gap, if filled, could provide the support of economic theory and method for the decision-making of local government, which has been leading the IVDB implementation since 2020. Meanwhile, the moderate scale quota plays a crucial role in improving the performance of IVDB policies.

By 2019, 31 provinces in China (excepting Taiwan, Hong Kong and Macao) have adopted the IVDB policy [20]. Among them, Zhejiang is the first province to explore the transferring quota in an urban setting, through construction land replacement, rural land consolidation and reclamation and so on 2. After being integrated in the IVDB policy, the province has taken the lead in carrying out a county-level comprehensive land consolidation project 3. In addition, Zhejiang province, with rapid economic development, is faced with many problems such as limited land space, insufficient reserve cultivated land resources, high population density and a large gap in the urban construction land index. There is no doubt that the IVDB policy has become a long-term mechanism to relieve land shortage in Zhejiang. Therefore, we took Zhejiang as a representative case area to explore the relationship between the scale and the revenue of the project-based balanced quota.

The rest of this article is organized as follows. Section 2 analyzes the theory of the relationship between the balanced quota’s scale and revenue. Then we put forward the research hypothesis. Section 3 explains the methodology and data sources used and provides insight into the descriptive statistics. Section 4 provides the empirical results, followed by discussion and key policy implications in Section 5. The final section summarizes the main findings and points out deficiencies.

2. Theoretical Analysis and Hypotheses

According to the Economies of Scale Theory, as the output of the enterprise increases, the marginal cost gradually decreases, which can realize the benefit of scale. However, if the scale continues to expand, the cost will increase due to factors such as uneconomical management. Then the Theory of Moderate Scale is derived. Further, the moderate scale operation refers to the practice of moderately expanding the scale of production and operation units under the existing conditions, so that the allocation of various production factors tends to be reasonable and the best operating benefits can be achieved. In the field of land management, whether it is agricultural land or urban land, there is an inverted ‘U’ curve relationship between input and output. For example, the scale of farmland and agricultural efficiency and/or farmers’ income shows an inverted ‘U’ curve relationship [33,34] and there are moderate scale boundaries in land transfer and land trusteeship [35].Similar research studies the optimal scale of towns and urban construction land, etc. Based on the above theories and research results, we try to make general logical inferences concerning the implementation of the project-based IVDB policy; as the scale of the balanced quota increases, the capital, labor and other factors of production input in each process, such as demolition, resettlement and new construction, will gradually approach the optimal combination ratio, which shows an increasing trend of marginal revenue; when the whole inputs reach the best combination, the marginal earnings of the IVDB project achieves the peak and the optimal scale of the balanced quota is realized. With the expansion of the quota’s scale, the ratio of production factors gradually deviates from the optimal combination ratio, showing a trend of diminishing marginal income. In general, there may exist an inverted ‘U’ curve relationship between the scale and revenue of the balanced quota (Figure 1).

Specifically, when the project-based balanced quota is within the moderate scale (0 to M* in Figure 1), the quota will promote revenue mainly through an incremental and cost-saving mechanism. In terms of the incremental mechanism, the larger the balanced quota, the larger the scale of demolition in rural areas and new construction in urban areas. After deducting part of the resettlement land of farmers in the demolition areas, the scale of surplus land that can be transferred to urban construction is relatively large. Under the premise of a certain unit price of the quota, the larger the scale of the saving, the higher the total income. Taking five IVDB projects in Dongbao District of Hubei province as an example, under the control of the balanced quota, the scale of the demolition areas is 100.71, 64.20, 30, 20, and 18.91 ha, respectively, and the area of residential land for resettlement farmers is 21.91, 12.72, 5.36, 4.03, and 3.00 ha, respectively. Therefore, the new construction land quota that can be transferred to cities and towns is 80.06, 51.55, 24.64, 16.39, and 17.19 ha, respectively [32] 4. Through the analysis of this case, we can intuitively find that the scale of the balanced quota is directly proportional to the revenue of the savings indicator that can bring economic benefits. In terms of the cost-saving mechanism, the input costs involved in the implementation of the IVDB policy mainly include the demolition and compensation of farmers’ homesteads, land reclamation, infrastructure construction and resettlement housing construction. On the one hand, a certain scale of demolition and reclamation is convenient for mechanized operations and can surely reduce labor costs 5. On the other hand, the average total cost of infrastructure construction such as for water and electricity will decrease along with the increase in supply.

When the quota exceeds the moderate scale (the right side of M* in Figure 1), it will inhibit the return of the quota. This phenomenon is mainly caused by the law of increasing marginal costs and diminishing marginal returns. Firstly, transaction costs can be more expensive if the scale of the balanced quota is too large. The large scale means that the number of farmers involved is huge and the government needs to spend too much time and funds on mobilizing demolition, determining compensation and resettlement methods and coordinating disputes over ownerships. All these lead to higher transaction costs for demolition and resettlement. Secondly, the unit return will be lower. On the premise that the urban construction land will not expand indefinitely, the larger the balanced quota, the more the new urban construction scale can be transferred, which is likely to cause a buyer’s market because of the oversupplying. An extreme example is that, according to China’s rate of urbanization (the population urbanization rate will be 75% to 80%), there will be nearly 200 million farmers moving to cities or towns in the next two decades; at the same time, about 2 million ha of newly added urban construction land is needed, on the condition of an urban construction land planning standard of 100 square meters per person. However, the current rural homestead area is as high as 13 million ha. So, under the circumstance that the scale of rural homesteads is huge while the demand for newly constructed urban land is limited, the benefit of the saving quota formed by the reclamation of homesteads can only be determined by the lowest price of many quota-sellers. The price will be close to the cost of demolition. Additionally, the inputs will be inefficiently allocated. There is a certain investment combination ratio between land input and other production inputs such as capital and labor. If the balance quota exceeds an appropriate scale, it will not be able to effectively cooperate with other factors to form economies of scale. This will cause low efficiency and even inefficient allocation of production factors.

Based on the above analysis, the core hypothesis of this study is put forward as follows: there may exist an inverted U-shaped curve relationship between the scale and the revenue of the project-based balanced quota, and there is an appropriate quota scale that maximizes the revenue.

3. Materials and Methods

3.1. Econometric Model

The discussion above provides the theoretical analysis for the scale and revenue of the balanced quota in the IVDB policy implement. Based on this, referring to [33,35], we establish the following quadratic econometric model, with the project as the research unit in this study:

$$R_i={\alpha }_0+{\alpha }_{1}Are{a}_i+{\alpha }_{2}Are{a}_i^2+{{\displaystyle \sum }}_{j=1}^2{\beta }_{j}Co{n}_{ij}+{{\displaystyle \sum }}_{j=1}^6{\gamma }_{j}Ec{o}_{ij}+Re{g}_k+{\epsilon }_i$$

(1)

where $R_i$ is the revenue of the $i$ project; $Are{a}_i$, $Are{a}_i^2$ respectively are the scale and squared scale of the balanced quota in project $i$; $Co{n}_{ij}$, $Ec{o}_{ij}$ and $Re{g}_k$ are three types of control variables, with $Co{n}_{ij}$ used to control the characteristics of the balanced quota, $Ec{o}_{ij}$ and $Re{g}_k$ respectively, control the socioeconomic and regional characteristics of the county where the project is located; $\alpha$, $\beta$ and $\gamma$ represent the parameters to be estimated of explanatory variables; ${\epsilon }_i$ represents the random error term.

3.2. Variables and Definition

(1)

Explained variable. In order to measure the revenue of the balanced quota as comprehensively as possible, the total income of the quota (R) is selected as the explained variable. R refers to the benefits obtained by transferring the balanced quota which is approved for urban construction, after deducting the scale for resettlement in each project area.

(2)

Explanatory variable. The quotas related to the scale of the project based IVDB include the following three main types: the scale of the project area, the balanced quota and the saving quota. Generally, the scale of the project area is approximately twice the size of the balanced quota, and the scale of the saving quota is the amount transferred to the urban construction on the condition of subtracting the rural resettlement space from the balanced quota. Considering that the balanced quota is closely related to other scales, this study chose the balanced quota as the core explanatory variable (Area).

(3)

Control variables. In order to avoid the problem of reducing the reliability of the model due to missing variables, we draw on the related research methods of [34], and select three control variable groups to control other factors that may affect the revenue of the balanced quota. The first group is the utilization characteristics of the balanced quota in each project after they are transferred to cities and towns (Con). Since the amount and specific uses of the balanced quota transferred to urban areas can affect the explained variable, the proportion of urban construction scale in the balanced quota and the proportion of commercial and residential construction land in the urban construction area are selected as control variables. The second group is the characteristics of social and economic conditions at the county level in which the project is located (Eco). Considering that the unit price of the quota will change due to the level of socio-economic development in different regions, the total population, GDP growth rate, urbanization rate, proportion of the service industry, fiscal revenue versus expenditure ratio and per capita disposable income ratio of urban versus rural residents are included in the control variables. The third group is the regional characteristic control variables (Reg). Our study selects municipal administrative units as dummy variables to further control external environmental factors, such as natural environment, resource endowment and other social or economic conditions in different project areas. Except for the municipal-level dummy variable, all other variable definitions and descriptive statistics are shown in

Table 1. Variable definitions and descriptive statistics.

Variable		Name	Definition (Unit)	Mean	Standard Deviation
Dependent variable		Revenue of the balanced quota	Revenue from the transfer of balanced quota to urban use (CNY10 thousand)	415.46	1481.01
Independent variable		Scale of the balanced quota	The scale of the project-based balanced quota approved by the superior government (ha)	162.44	159.05
Control variables	Project features	Proportion of new construction in urban	Actual land supply scale for new construction in urban/the approved balanced quota (%)	0.66	0.42
		Proportion of commercial and residential land	Sum of commercial and residential land for new construction/actual land scale for new construction in urban (%)	0.25	0.38
	Socio-economic characteristics	Total population	Total resident population in 2019 (10 thousand people)	77.67	41.75
		GDP growth rate	(GDP in 2019-GDP in 2018)/GDP in 2018 (%)	0.08	0.05
		Urbanization rate	Urban population/total population in 2020 (%)	0.66	0.09
		Proportion of the service production	GDP of tertiary industry/GDP in 2019 (%)	0.47	0.07
		Fiscal revenue vs. expenditure ratio	Total fiscal revenue/general public budget expenditure in 2019	1.11	0.39
		Per capita disposable income of urban vs. rural	Per capita disposable income in urban/per capita disposable income in rural in 2019	1.77	0.18

.

3.3. Data Resource and Description

Data in this paper include two main categories: the project data of IVDB and the socioeconomic data. The former was obtained from the ‘Online supervision system for increasing versus decreasing balance of urban-rural built land’ by the MNR. The data includes the approval, establishment, implementation process and acceptance inspection of each project. The statistical indicators include the scale of the balanced quota and reclamation, the newly added scale of rural and urban construction land, the total project investment and the revenue of the quota, number of farmers and per capita annual income before and/or after the implementation of the project, etc. Due to the projects that have not yet been completed, the inspection cannot obtain key information such as the revenue of the balanced quota, so our research object deals with 1907 projects of the IVDB in Zhejiang province, which had completed the acceptance inspection by the end of 2018 and could clearly locate the county administrative units of every project 6, so that 1097 projects are distributed in 62 counties of 11 cities in Zhejiang (Figure 2).

As far as a single project is concerned, the largest scale of the balanced quota was approved during the second batch of IVDB projects in Jiande county, Hangzhou city in 2012, reaching 67.30 ha; while the smallest project was the civil aviation navigation station program in Nanxun District, Huzhou City in 2006, with a scale of only 0.01 ha. In 2012, the comprehensive improvement project of rural land in Beitangtou & Highland in Qinmin Village of Haining prefecture obtained 3revenue of CNY 302.22 million and the approved balanced quota was 23.65 ha. The comparison of the scale of the balanced quota with benefits for 11 cities in Zhejiang province is plotted in Figure 3. The above intuitive statistics show that the scale of the quota and the revenue are not completely positively correlated and the relationship between the two needs to be further demonstrated.

In addition, the total population, GDP, output value of tertiary industry, total fiscal revenue and general public budget expenditure per capita disposable income of urban and rural residents and other social-economic data of the 62 counties were extracted from the statistical yearbooks and statistical bulletin. The urbanization rate was calculated according to China’s Seventh Census. Meanwhile, in order to maintain consistency with the IVDB projects statistical time node as far as possible, most social and economic data are from the year of 2019. The mean and standard deviation of each index are shown in Table 1.

4. Results

4.1. Comparison of Returns under Different Quota Scales

In order to clarify the scale of the balanced quota and the characteristics of revenue in different projects, all objects are divided into below 3.33 ha (50 mu), 3.33–6.66 ha (50–100 mu), 6.66–13.33 ha (100–200 mu), 13.33–33.33 ha (200–500 mu), and more than 33.33 ha (500 mu) for group descriptive statistics (

Table 2. Comparison of the scale and revenue of the balanced quota in different projects.

Balanced Quota Scale/ha	Number of Project	Quota Revenue/10 Thousand
		Mean	Standard Deviation
[0, 3.33]	555	61.556	209.317
(3.33, 6.66]	356	265.860	600.845
(6.66, 13.33]	430	618.132	1510.287
(13.33, 33.33]	482	731.467	2371.445
(33.33, 67.33]	84	536.909	1552.598

). From the perspective of scale characteristics, the scale of the balanced quota generally does not exceed 33.33 ha, and the number of projects with 3.33, 6.66, and 13.33 ha as grouping intervals is relatively evenly distributed. From the perspective of revenue characteristics, when the scale of the balanced quota is within 33.33 ha, the revenue increases with the expansion of the quota scale while, when the scale exceeds 33.33 ha, the revenue shows a declining situation. In general, the change trend and fluctuation characteristics of the quota scale and revenue provide evidence for the existence of the optimal balanced quota scale. On this basis, the following will use the econometric model to further explore the quantitative relationship between the scale of the quota and the revenue.

4.2. The Influence of Turnover Index Scale on Index Return

Before estimating the nonlinear model, the variables such as the balanced quota scale, income and population, with large standard deviations, were logarithmically processed, for the purpose of eliminating heteroscedasticity 7. At the same time, all independent variables were multicollinearity tested by variance inflation factor method 8. The results show that the VIF values of all variables are less than 10, which means that there is no collinearity problem. Then, based on OLS, Stata 16.0 software is used to estimate the impact of the scale of the balanced quota on revenue. In the process of estimating, considering that the sample data has mixed cross-sectional characteristics, the robust standard error regression method is used. The results are shown in

Table 3. Estimated results of the impact of the scale of project-based balanced quota on revenue.

Variable	Model 1		Model 2
	Coefficient	Robust Standard Error	Coefficient	Robust Standard Error
Balanced quota scale	1.197 ***	0.187	0.556 ***	0.153
Balanced quota scale squared	−0.119 ***	0.025	−0.0594 ***	0.021
Proportion of new construction in urban			−0.633 ***	0.161
Proportion of commercial and residential land			0.177	0.158
Total population			−0.864 ***	0.218
Urbanization rate			−12.29 ***	0.865
GDP growth rate			−10.97 ***	1.541
Proportion of the service production			−3.257 ***	1.223
Fiscal revenue vs. expenditure ratio			5.472 ***	0.406
Per capita disposable income of urban vs. rural			0.122	0.562
Constant term	−1.064 ***	0.311	7.430 ***	1.492
municipal-level dummy variable	controlled		controlled
sample size	1907		1907
R2	0.022		0.269

Note: *** denotes a significance level of 10%.

, where model 1 is the estimated result without adding control variables and model 2 is the result of adding other control variables.

According to Table 3, no matter whether control variables are added or not, when the revenue of the balanced quota is taken as the explained variable, the regression coefficients of the first and second terms of the quota scale are positive and negative, respectively, and both are significant at the level of 1%. This measurement result verifies the theoretical hypothesis of this paper, that is, there is an inverted U-shaped relationship between the scale of the balanced quota and the revenue in the project based IVDB implementation. Specifically, the inverted u-shaped relationship means that the revenue gradually increases and then decreases with the expansion of the quota scale. The gradually expanding scale makes all kinds of inputs close to the optimal combination ratio step by step, along with the increase in the revenue. When the scale and other factors of production such as labor and capital reach the optimal ratio, the maximum revenue is achieved, and the quota scale reaches the optimal size. If the scale exceeds the optimal size, the whole input will be faced with deviation from the optimal production state and this will lead to a decrease in revenue eventually. Moreover, the optimal scale of the balanced quota can be calculated from the regression coefficients of the first and second terms of the independent variable in the model estimation results. According to model 2 of Table 3, the logarithm of the appropriate scale of the balanced quota is 0.31 ha in Zhejiang province and the corresponding moderate scale is 7.19 ha. Combined with the 1907 IVDB projects completing inspection in Zhejiang, 22.44% of the approved balanced quota exceeds the appropriate scale, which means that the quota is inefficiently allocated.

Among the first group of control variables, the proportion of new construction in urban areas has a significant negative impact on the revenue of the balanced quota. Although this seems unexpected, it is actually reasonable. In this respect, our explanation is that, although the larger the scale urban new construction means a higher the demand for quota and makes it easier to increase the economic benefits theoretically, in reality the benefits are also limited by such factors as an underdeveloped economy and weak financial strength in some projects, resulting in the low transaction unit price of the balanced quota and low revenue. In the second group of control variables, fiscal revenue vs. expenditure ratio is positively correlated with the revenue of the balanced quota. The main reason for this is that a higher ratio represents a bigger surplus, meaning local governments have the financial capacity to pay for the balanced quota. Urbanization rate, GDP growth rate and proportion of the service production are negatively correlated with the revenue. A possible explanation is that the former three variables are key standards to measure the level of economic development, and the higher the level of economic development, the better the rural economic situation. Therefore, the time and cost of the demolition, transaction, resettlement and other aspects will be longer and more expensive. Further, the 1907 projects in this paper are counted according to the acceptance inspection data, which may lead to fewer projects not only being approved but also accepted in developed areas. All this can cause negative correlation. In addition, per capita disposable income, urban vs. rural, does not pass the significance level.

4.3. Spatial Heterogeneity Analysis

Due to the differences in resource endowment, economic development and implementation cycle of IVDB projects in different regions, the optimal quota scale for maximizing revenue contains distinctions. Zhejiang province is divided into the northeast and southwest region according to the urban spatial pattern of ‘one bay, two cores, four poles and multiple clusters’ in Zhejiang 9. OLS estimation is performed on the two sub-samples, respectively, in this study. The regression results of spatial heterogeneity are shown in

Table 4. Estimated results of the spatial heterogeneity of the scale and revenue in Zhejiang IVDB projects.

Variable	Northeast Zhejiang		Southwest Zhejiang
	Coefficient	Robust Standard Error	Coefficient	Robust Standard Error
Balanced quota scale	1.091 ***	0.207	0.973 ***	0.202
Balanced quota scale squared	−0.110 ***	0.028	−0.108 ***	0.028
Proportion of new construction in urban	−1.338 ***	0.205	−0.118	0.183
Proportion of commercial and residential land	0.0106	0.210	0.515 **	0.254
Total population	−0.624 **	0.271	−1.361 ***	0.301
Urbanization rate	−15.01 ***	1.263	−3.015 ***	1.115
GDP growth rate	−10.00 ***	2.051	−8.999 ***	1.645
Proportion of the service production	−6.758 ***	1.217	−2.676 *	1.476
Fiscal revenue vs. expenditure ratio	5.344 ***	0.539	3.567 ***	0.569
Per capita disposable income of urban vs. rural	−5.175 ***	0.821	2.719 ***	1.025
Constant term	19.11 ***	1.857	0.241	2.412
sample size	1370		537
R2	0.188		0.224

Note: *, ** and *** denote a significance level of 1%, 5% and 10%, respectively.

.

In the two sub-sample models of Table 4, the coefficient of the first term of the balanced quota scale variable is significantly positive and the secondary term is significantly negative, indicating that the scale has a significant inverted ‘U’ impact on the revenue of the balanced quota in different regions, which is consistent with the baseline regression results of Table 3. Furthermore, combined with the regression coefficient of core explanatory variable, the optimal scale of the balanced quota in northeast and southwest Zhejiang is 9.50 ha and 6.03 ha, respectively 10. The former scale is larger than that of the latter, which is closely related to the geomorphological factors of Hang-Jia-Hu Plain and Ning-Shao Plain distributed in the Hangzhou Bay area. In addition, there are two pivotal problems that need to be specially explained in the regression results to distinguish spatial heterogeneity. Firstly, the slope of the inverted ‘U’-shaped curve in the northeastern Zhejiang is greater than that of southwestern Zhejiang, indicating that the marginal return of the balanced quota in northeastern Zhejiang is higher. This result is in line with the general rule that the more developed the region is, the higher the unit price of the quota. Secondly, the control variable of proportion of commercial and residential land has a significant positive correlation with revenue in southwest Zhejiang. This is consistent with the normal expectation that the transfer income of urban construction and the revenue of the balanced quota is positively correlated.

4.4. Robustness Test

(1)

Change the selected model. Usually, different models may obtain different regression results. Considering the numerical feature that the dependent variable (R) is not less than 0, we choose the Tobit model, which can handle the tail-broken data to re-regression. The estimate results are shown in

Table 5. Robustness test of model estimation results.

Variable	Model 3		Model 4
	Coefficient	Robust Standard Error	Coefficient	Robust Standard Error
Balanced quota scale	3.692 ***	1.053	0.030 *	0.017
Balanced quota scale squared	−0.428 ***	0.121	−0.005 **	0.003
Constant term	22.04 ***	7.750	12.48 ***	0.103
control variables	control		control
sample size	1907		1907
R2			0.015
LR	702.87
Prob > chi2	0.0000

Note: *, ** and *** denote a significance level of 1%, 5% and 10%, respectively.

for model 3. It can be found that, regardless of the direction or significance, the influence of the scale of the balanced quota and its square term on the revenue is consistent with the results in Table 3, indicating that the inverted U-shaped relationship between the scale of the quota and the revenue is robust.

(2)

Replace the explained variable. The revenue of the balanced quota is replaced by the profit of the quota and the impact of the index scale and net income is estimated as robustness test. The profit of the quota is obtained by subtracting the actual total investment 11 from the revenue and the OLS regression is performed after the negative number is turned to the positive and the logarithm is processed. The results are shown in Table 5 for model 4. Similarly, the primary item of the balanced quota scale is negative and the secondary item is positive, with the significance tests of 10% and 5%, respectively, being passed to verify the existence of an appropriate scale of the project-based balanced quota.

5. Discussion and Policy Implication

5.1. Problems Associated with the Scale and Revenue of the Balanced Quota

In the context of spatial planning with tight constraints, as the incremental quota becomes less and less, the balanced quota will be more pivotal for local governments in acquiring urban construction land. At the same time, the revenue of the balanced quota provides indispensable capital for the revitalization of rural regions. The spatial relocation under the IVDB implication conforms to the development of urban-rural coordination [20]. Therefore, though the IVDB implicating process may impose on the welfare of vulnerable groups such as peasants [17], the quality of reclaimed farmland land may be poorer than the occupied land, and the hidden debt of local government may be at greater risk, we can still safely deduce that the IVDB policy will be implemented persistently in China over years and decades. Therefore, there are two pivotal issues to be further concerned combined with our theoretical analysis and empirical results on the relationship between balanced quota’s scale and revenue.

The first issue is how to determine the optimal scale of the balanced quota under the project-based IVDB policy? As the 1907 projects of Zhejiang province show, there is an inverted ‘U’ curve relationship between the scale of the balanced quota and its revenue. In other words, the quota’s scale follows the rule that ‘the more is not the better’. Three perspectives need to be considered to achieve the optimal scale to maximize returns. One is the factor of natural perspective. The implementation of IVDB policy is strongly dependent on the regional location, topographic features, soil quality, irrigation conditions and other natural factors. From the perspectives of ecological benefit, requisition-compensation, balance of cultivated land and farmers’ use of cultivated land, Yang, et al. (2015) analyzed the rationality of the project of IVDB in the China Mountain Area [36]. If local governments pursue land indicators excessively and promote rural demolition arbitrarily, without considering the objective limitations of natural conditions, the IVDB policy will be out of control and the overall interests will be damaged. The other is the production perspective. As the theory of Economies of Moderate Scale says, as a kind of production factors, land should be considered in relation to labor, capital and other inputs. Only when the proportion of all kinds of production factors are close to the optimal combination can the revenue of the balanced quota be maximized. Then the local governments’ financial burden will be reduced and the economic incentive mechanism of the IVDB policy will be brought into full play. In addition, the market perspective is an indispensable consideration. According to some Chinese cases, building high-rise buildings can save 90% of the balanced quota to transfer to urban-supporting construction, while building ‘small villas’ can only transfer 60% of the total quota. In the process of the IVDB’s implementation, the prefectural government also tends to save more transferable quota. Contrary to most humans’ intuitive prediction, our measurement results of both the whole region and the spatial heterogeneity of Zhejiang province show that, the larger the scale of urban construction is, the lesser the quota’s income. Essentially, this is a reflection of market behavior. When the quota supply exceeds the demand, the earnings will naturally decline. Therefore, rather than trying to reduce the living space of farmers to obtain a bigger urban construction quota, it is better to find ways to turn to a seller’s market for the balanced quota.

The second issue is to what extent the provincial government can orient the project based IVDB policy. In China’s vertical land management system, there exist three levels of government: central, provincial and prefectural [37,38]. In fact, in 2004 has clarified the division of land management powers between central and local government. That is, the power and responsibility to regulate the total amount of newly added land for construction belongs to the central government, while the power to revitalize existing land for construction belong to local government. The responsibility for protecting a rational use of land rests with the local government at all levels, with the provincial government bearing the primary responsibility. As the implication of the IVDB policy almost has no effect on incremental construction land, relevant power for the policy rests with provincial and prefectural government according to the above document. However, considering the possibility of overuse of land resources by local government, the central government has been controlling the balanced quota before 2020. Under the new system of spatial planning, provincial government has the right to arrange the implication of the IVDB policy, in line with the tight constraints of resource utilization. The provincial government’s role is shifting from that of a hub [37] to a decider, and neither the top-down [39,40,41] nor bottom-up [42,43,44] theories of the process of IVDB can explain well the administrative discretion of the role. Particularly, the provincial government and prefecture may have the possibility of engaging in collusive behaviors because of common economic interests [45]. Experience and lessons from “centralization-decentralization-recentralization” [46] may be conductive to understanding the function of the provincial government in the IVDB policy implementation.

5.2. Policy Implication

For the purpose of coordinating the relationship between the scale and revenue of the balanced quota and improving the performance of the IVDB policy, this paper offers the following three suggestions.

Firstly, the scale of the balanced quota should be determined through comprehensive consideration of inputs. In the process of implementing the project based IVDB policy, in addition to the quota quantity, such factors as the coordination and governance capacity of local government, fund raising for demolition, reclamation and resettlement, and the unavoidable transaction costs can affect the returns of the quota. Only when the whole inputs are coupled and coordinated can the benefits be maximized. Therefore, when the provincial government approves the balanced quota or the county government applies for it to the superior government, the first step is to assess how well each input matches up. Then the optimal balanced quota of every project can be determined comprehensively. The optimal scale determined based on these comprehensive factors can realize the maximization of the revenue, theoretically. Based on the revenue of the balanced quota, the incremental benefits returning to the demolished areas will also be increased accordingly, and the economic situation of farmers will be significantly improved.

Secondly, the spatial planning of rural resettlements needs to be optimized. Rural spatial plans and project design are highly significant for IVDB policy implementation. In the whole process of planning and designing, it is pivotal to listen to the farmers’ thinking and respect their willingness. In particular, when the rural resettlement program is launched, planners should consider suitable distance for cultivation, space for storing goods such as farm tools, stable cost of living, etc. In this case, farmers’ enthusiasm for cooperation can be motivated greatly in the implementation of demolition and reclamation, which will improve the efficiency of the balanced quota’s producing and trading eventually. Additionally, the quota for the integration of tertiary industries in rural areas should be reserved if it is necessary and the countryside has a sound industrial base. Then farmers can obtain sustainable income in this way. The above measures are necessary supplements to determine the optimal scale of the balanced quota, so are in line with the inherent requirements of rural revitalization. In particular, the layout of rural residential areas based on spatial planning is conducive to improving the living environment of farmers and creating an ecologically livable production and living space.

Thirdly, central-government supervision should be strengthened in the process of the provincial-oriented IVDB implementation. While the provincial government leads the IVDB policy, there is also a risk of over-expanding the scale of the balanced quota, driven by government performance assessment and land finance. The ‘merging villages and living together’ policy in Shandong province is typical evidence. Then the central government, the paramount decision-maker at the top of governmental hierarchy in China, scan supervise and regulate the implement of IVDB directly and design policies to make provincial government more accountable [37]. To some extent, the strong supervision of central government provides the most solid backing to protect the interests of farmers. Any local government behavior at the expense of farmers’ interests will be sanctioned severely by central government.

6. Conclusions

Under the background of IVDB implementation power moving down to the provincial government, this paper verified the inverted U-shaped impact of the scale of the balanced quota on the revenue at both theoretical and empirical levels and obtains the appropriate quota scale corresponding to the maximization of the earnings. Based on 1907 IVDB projects in Zhejiang province, the conclusions are as follows. First, with the quantity increase of the balanced quota, the revenue of the quota climbs and then declines. In other words, the relationship between the quota’s scale and the revenue shows an inverted ‘U’ type. On the premise of controlling the characteristics of the projects and the socio-economic development of the county in which the IVDB project is located, the optimal balanced quota of Zhejiang province is 7.19 ha. When the quota exceeds this critical point, the revenue will decrease constantly. Second, there is spatial heterogeneity in the optimal scale of the balanced quota in Zhejiang. Specifically, the optimal scale of the quota in northeast and southwest Zhejiang is 9.50 ha and 6.03 ha, respectively, and the marginal return of the quota in the former is higher than the latter, which is consistent with the general rule that the more developed the region is, the higher the unit price of the quota. In the context of increasing the efficiency of quota-allocation and further improving the IVDB performance, we suggest that: (1) the scale of the balanced quota should be determined through comprehensive consideration of inputs, (2) the spatial planning of rural resettlements need to be optimized, and (3) central-government supervision should be strengthened in the process of the provincial-oriented IVDB implementation.

However, despite our study being carefully conducted, there are still several crucial limitations. On the one hand, considering that the prefectural government plays a pivotal role in adopting the IVDB and the early characteristics of the policy, we use a county-level project as the basic research unit along with the advantages of data acquisition. With the evolution and development of the IVDB policy, the balanced quota will be transferred across prefectures or even across provinces around China with high probability. These phenomena mean that the project based IVDB beyond the county is much different from the within, which may lead to quite different relationship between the scale and the revenue of the balanced quota. In short, more attention should be paid to the cross-regional implement of the IVDB to explore the optimal scale of the balanced quota. On the other hand, due to the inseparability of the capital invested in demolition and reclamation of the IVDB project, the appropriate scale oriented to the maximization of revenue mainly adopts the gross profit index. The net profit after deducting the actual invested capital amount is not analyzed as a dependent variable, which is involved only in the robustness test. Future research needs to find a more scientific way to deal with profits.