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Article

Fiscal Ecological Cost of Land in China: Estimation and Regional Differences

School of Humanities and Law, Northeastern University, Shenyang 110169, China
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Author to whom correspondence should be addressed.
Land 2022, 11(8), 1221; https://doi.org/10.3390/land11081221
Received: 20 June 2022 / Revised: 26 July 2022 / Accepted: 30 July 2022 / Published: 2 August 2022
(This article belongs to the Topic Climate Change and Environmental Sustainability)

Abstract

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This study explains the fiscal ecological costs of land in China by dividing them into three periods: early ecological cost refers to loss of ecosystem service value after the conversion of agricultural land, mid-term ecological cost refers to land development in urban built-up areas, and later ecological cost refers to the investment cost of increasing the fiscal ecological service function of the land. Using data for 31 Chinese provinces from 2003 to 2017, we perform a “link between groups” cluster analysis with SPSS 22.0 statistical software. Squared Euclidean distance is used to analyze land in these provinces. Ecological cost in the early, middle, and late stages is clustered, and the provinces are divided into five areas according to the ecological cost of each stage in absolute terms and as a proportion of land fiscal revenue. The research shows that: (1) the fiscal ecological cost of land in China presents a spatial pattern of “higher in the east than in the west, higher in the south than in the north,” and (2) the cost is highest in the early stage, second highest in the late stage, and lowest in the middle stage. The findings yield differentiated policy recommendations for reducing the fiscal ecological cost of land in different areas.

1. Introduction

As a key factor connecting land resources and regional development [1], land finance in China (tudi caizheng) is an activity in which the local government uses the land resources within its jurisdiction to carry out and distribute fiscal revenues and expenditure. Land finance has played an important role in China’s economic and social development [2,3,4]. In recent years, the high dependence of local governments on land finance has aroused the attention of academic circles [5]. As shown in Figure 1, in 2003, China’s land fiscal revenue was 632.2 billion yuan (1 yuan is equal to about 0.15 dollars), accounting for 41.4% of local fiscal revenue. The total area of transferred land was 193,604.0 hm2 and total land transferred fees were 542.1 billion yuan. By 2017, China’s land fiscal revenue was 6842.3 billion yuan, about 10.8 times that of 2003, accounting for 47.7% of local fiscal revenues. The area of land transferred was 230,898.6 hm2, and the land transfer fees amounted to 51,984.5 billion yuan, which was about 9.6 times that of 2003.
Many scholars have studied regional differences in the positive and negative effects of land finance on socioeconomic development in China. These scholars are mainly focusing on the following aspects: First, in terms of the scale of land revenue, from 1999 to 2009, the average proportion of land taxes in fiscal revenues in various regions of China was about 18–38%, with few regional differences. Moreover, the proportion of land finance in fiscal revenues in most regions was at a moderate level [6]. Second, in terms of land finance and economic growth, the land finance index shows an increasing trend in the process of economic growth in the eastern, middle and western regions of China [7]. Third, land finance significantly increases the supply of economic public goods/services, but it has an inhibitory effect on the supply of non-economic public goods [8]. In addition, the impact of land finance on the supply of public goods differs significantly across time and space: land finance increases the supply of public goods in the eastern region but decreases it in the central and western regions [9]. Fourth, there is a two-way linkage relationship between land finance and urbanization, and land finance has a continuous positive effect on land urbanization. The promotion of urbanization reduces the urban–rural income gap in western provinces, but it is not conducive to income gap improvement in China’s eastern and central provinces [10].
These findings underscore the growing importance of land finance in China. However, these studies do not address the ecological costs of land transfers; that is, the actual monetary costs of the ecological changes caused by land transfers. In this study, we address this gap by developing a mechanism framework for understanding the fiscal ecological cost of land transfers.

2. Literature Review

In the 21st century, land finance provides funds and land for rapid urbanization, but it also generates huge environmental pressures [11,12]. In developing countries in particular, rapid urbanization not only increases the consumption of resources and energy [13] but also has a negative impact on the social and ecological environment [14]. The rapid growth of urban land cover not only leads to the loss of cultivated land, abandonment of farmland, and landscape fragmentation [15,16], but it also reduces crop yields, carbon storage, and environmental purification, and it also threatens flood control capabilities [17,18,19,20] and biodiversity [21]. In addition, the rapid advancement of urbanization leads to urban diseases, such as water pollution [22], air pollution [23], and traffic congestion [24], thus posing a threat to human health [25]. However, rapid urbanization increases the value of ecosystem waste disposal and cultural entertainment [26], and urbanization can also have a positive impact on the urban environment through green infrastructure, industrial upgrading, and environmental management [27]. In sum, the impact of rapid urbanization on the ecosystem is very complex, and coordinating the relationship between social and economic growth and environmental protection has become an urgent problem to be solved [28].
There are also significant regional differences in the relationship between urbanizaion and the ecological environment specific to China. The impact of urban expansion on the quality of the ecological environment has a spatial spillover effect [29]. Urban expansion in the eastern region has improved the quality of the ecological environment in the target province as well as neighboring provinces. In contrast, urban expansion in central China has had little impact on neighboring provinces. In the western region, urban expansion has led to a decline of the ecological quality of these provinces and an increase in the ecological quality of the neighboring provinces. There is an N-type relationship be-tween urbanization and urban ecological efficiency in China, which first rises, then de-clines and then rises again [30]. A study on the eco-efficiency of 30 provincial capital cities in China finds that almost half have high eco-efficiency, and the low-efficiency cities are mainly located in the southwestern and northwestern regions [13]. To achieve the goal of sustainable cities, the relationship between urbanization, energy, and environmental efficiency is also worthy of attention. The population peri-urbanization caused by China’s urban–rural dual household registration system inhibits the improvement of energy efficiency in the eastern and western regions, but it contributes to the improvement of energy efficiency in the central region [31]. Wang et al. studied the coupling relationship of energy and environmental efficiency with urbanization in Guangdong Province using slack data envelopment analysis; they found that only Guangzhou and Shenzhen were in the high coupling stage, while the rest of the cities remained in medium and low coupling stages [15]. Renewable energy consumption and urbanization in various regions of China are affected by other regions, but there is no causal relationship between renewable energy consumption and urbanization in most provinces and geographic regions [32].
Based on the premise of harmony between humans, nature, and society, agricultural land has economic, social, and ecological value. It also performs ecological service functions such as air purification, water conservation, and soil conservation. These positive externalities should be included in cost accounting systems for agricultural land conversion [33]. Moreover, performance evaluation systems focusing on environmental protection and farmland protection have a restrictive effect on illegal land use [34]. However, because ecological environmental risks are driven by economic interests in nature and involve complex issues such as ecological protection, we cannot quantitatively distinguish the degree of ecological environmental risk. China’s current land finance policy does not pay sufficient attention to the ecological risk of land finance, so there has been little research on the ecological costs of land finance [35]. Most scholars focus on the value of land ecosystem services and landscape design [36]. In 1997, Costanza put forward the importance of ecosystem service value assessment [37], and the Millennium Ecosystem Assessment (MA) in 2005 tabulated ecosystem service functions as supporting functions, supplying functions, regulating functions, and cultural functions, with reference to previous studies [38]. On this basis, Xie Gao-di established the ecological service value table per unit area of China’s terrestrial ecosystem to correct the ecosystem service function, and the research results have been widely used in many studies [39,40,41].
In fact, land taxes can also protect land ecology. When private property rights cannot be directly allocated, land taxation can protect biodiversity and increase ecosystem sustainability by internalizing the negative externalities generated by land transfers [42]. It can also promote sustainable land use by preventing the acquisition of all land whose marginal productivity is below the tax rate [43]. In terms of specific practice, the ancient rural land tax in Brazil, for example, has been shown to be of great benefit to the coordination of society, agriculture, and the social environment [44]. For instance, the local government of Karacalba will increase the proportion of resource and land taxes in its budget revenue [45]. Similarly, the local government in Poland promotes the use of low-carbon energy through property tax benefits [46]. The United States has also tried to integrate green buildings into its federal low-income housing production program, the largest in the world, to protect the ecological environment [47].
At present, most studies on land finance focus on its causes [48], the positive and negative impacts it has on the economy and society [49], and the transformation and optimization of land finance [50]. However, no scholars have studied the formation mechanism of the ecological cost of land finance. Nor have they explored the regional differences in this cost under the land finance model. To address this gap, this study explains the formation mechanism of the fiscal ecological cost of land in China. This cost includes ecological value loss due to the conversion of agricultural land as well as fuel consumption, exhaust gas, and dust. The urban heat island effect, fragmentation of land as a result of infrastructure construction in development zones, the construction cost of gardens and green space in urban areas, the cost of urban cleaning, and the cost of cultivated land reclamation are also included to achieve a balance of agricultural land occupation and compensation. Based on panel data from 2003 to 2017, the study estimates the fiscal ecological costs of land in 31 Chinese provinces of China. These costs are scientifically divided to reveal the reasons for the formation of regional differences. Based on these findings, different policy recommendations for reducing the fiscal ecological cost of land are put forward.

3. Theoretical Analysis

3.1. Formation Mechanism of Land Finance Ecological Cost

Since the reform of the tax-sharing system in China in 1994, the “upper collection of financial rights and decentralization of administrative powers” has led local governments to rely on land transfer fees and related taxes to develop financial resources [51]. The performance assessment system for local officials in China, which focuses on economic growth, incentivizes local governments to rely on land fiscal revenue to raise funds needed for economic construction [52]. During this transformation period, urbanization and industrialization are promoted by expanding cities and industrial parks, thereby driving the development of the local economy [53]. However, China’s unique dual land ownership system (rural land is owned by peasant collectives and urban land is owned by the state) makes local governments the only intermediary for non-agricultural utilization of agricultural land. Local governments expropriate agricultural land at low prices and sell state-owned construction land at high prices, forming differential land rents and related tax revenues [54,55]. Local governments use the public funds raised in this process (i.e., fiscal revenue from land) for infrastructure construction [56], thus forming the cyclical process of “agricultural land expropriation—infrastructure construction—state-owned land transfer—agricultural land expropriation” under the land finance model [57,58]. In this process, land-related fiscal revenues provide impetus for local economic and social construction, which brings related benefits. However, these revenues produce ecological and environmental losses. The ecological environment based on land resources not only provides human beings with a variety of raw materials for production, but it also maintains the necessary life systems for human beings. These environmental resources also maintain the biogeochemical and hydrological cycles in nature and help sustain species and genetic diversity. They also purify the environment and maintain the balance and stability of atmospheric chemistry [59]. In the process of close interaction between humans and nature, changes in land use patterns have accelerated changes in ecosystem services [60]. Thus, the fiscal ecological cost of land represents the loss of ecosystem service value. It also represents the cost of urban infrastructure construction and ecological environment maintenance in the process of converting agricultural land into urban construction land. A framework for the formation mechanism of the fiscal ecological cost of land is shown in Figure 2 [61].

3.2. Loss of Ecosystem Service Value after Agricultural Land Conversion

In the early stage of the land finance model, cultivated land with ecosystem service functions was expropriated as state-owned land and converted into construction land for urban and industrial parks [62]. Agricultural land provides important outputs for ecosystem services, including air conditioning, environmental purification, water conservation, soil conservation, and nutrient cycling. The long-term low-density and low-intensity land use characteristics of agricultural land are conducive to the protection of biodiversity and cultural service functions [63,64]. These ecosystem services are manifestations of the positive externalities of agricultural land, which are provided by agricultural land for free. This land also yields agricultural products, but these positive externalities will disappear and lead to environmental damage after agricultural land conversion. Thus, the problem of the disappearance of ecological functions of agricultural land in the land finance model is becoming more prominent. To address this problem, we should price the ecological cost scientifically and reasonably and provide ecological compensation accordingly.

3.3. Ecological Loss during Land Development in Urban Built-Up Areas

In the middle stage of the land finance model, the local government develops the ex-propriated farmland and completes the infrastructure construction of water supply routes, roads, electricity, communications, and gas to realize land urbanization. The conversion of agricultural land has promoted the development of other industries and greatly promoted the growth of GDP. However, the process of economic development is also accompanied by negative externalities, such as air pollution, water pollution, solid waste pollution, and land fragmentation [65]. For example, urban development is accompanied by an increase in artificial, impervious pavement, which promotes the development of traffic and commerce but also produces a heat island effect and urban waterlogging [66]. In the process of economic development, additional resources such as coal, oil, diesel, and steel will inevitably be consumed. In order to ensure the efficient use of land resources, resource consumption in the process of economic development must be considered when calculating the transfer of land resources from agricultural departments to other departments to obtain benefits. The fragmentation of the ecological landscape caused by urbanization not only reduces the ability of the ecological environment to provide important ecosystem services [67], but it also reduces the utilization rate of infrastructure, resulting in idle costs or repeated infrastructure construction costs.

3.4. Cost of Increasing the Ecological Service Function of Land Finance

In the later stage of the land finance model, local governments still need to pay eco-logical costs. On the one hand, in order to achieve the “balance of occupation and compensation” of cultivated land, the newly developed farmland produces reclamation costs. Although the economic and social value of newly developed cultivated land is supplemented, the ecological services value of climate regulation, water conservation, and other services declines. On the other hand, in order to protect the ecological environment of cities and industrial parks, local governments need to construct and maintain urban green spaces such as vegetation, grasslands, and water bodies [68]. They also need to promote clean cities, all of which require investment in human, material, and financial resources.

4. Data Source and Calculation Method

4.1. Data Source

Due to differences in land management systems and lack of statistical data, “China” in this paper does not include Hong Kong, Macao, and Taiwan.
Data on land transferred area, land transferred fee, amount of transferred land, and land expropriation area come from the “China Land and Resources Statistical Yearbook” (2004–2018). The net profit per unit area of rice, corn, and wheat is the average from 2003 to 2017, which comes from “The Compilation of Cost and Income Data of National Agri-cultural Products” (2003–2017). The proportion of the sown area of rice, wheat, and corn in each province to the total sown area of the three crops was unchanged in 2016, according to the “China Food Yearbook” (2017). The composition of the cultivated land area in each province is the value in 2017, which comes from the “China Rural Statistical Year-book” in 2021. The data for new urban road area, length of drainage pipeline, length of water supply pipeline, length of gas pipeline, length of heating pipeline, and the maxi-mum monthly average temperature of each province were obtained from the “China Statistical Yearbook (2002–2018).” The annual maximum temperature data for each province are taken from the “China Environmental Statistics Yearbook (2018).” Data on newly added urban green space area and newly added urban road cleaning area come from the “China Urban Construction Statistical Yearbook (2022–2017).” The total amount of major air pollutants emitted by the combustion of 1 m3 diesel comes from the “Chinese Environmental Impact Assessment Training Textbook.”

4.2. Calculation Methods

4.2.1. Loss of Ecosystem Service Value after Land Conversion

The ecosystem service value equivalent factor is the relative rate of contribution to the ecosystem potential service value. Xie Gao-di et al. believed that the ecosystem service value of a standard equivalent factor can be replaced by the net profit from the grain pro-duction of the agricultural ecosystem per unit area [69]. The equivalent value for China can be obtained by calculating rice, wheat, and corn. Considering the fluctuations in planting area and grain price caused by natural and market factors, the average net profit of grain in China from 2003 to 2017 was taken as the standard equivalent factor value in this study. The standard equivalent factor is calculated as follows:
D i = P i 1 × S i 1 + P i 2 × S i 2 + P i 3 × S i 3
In the above formula, D refers to the ecosystem service value (yuan/hm2) of a standard equivalent factor. P1, P2, and P3 refer to the net profit per unit area (yuan/hm2) of rice, wheat, and corn, respectively. S1, S2, and S3 refer to the ratio of the sown area of rice, wheat, and corn to the total sown area of the three crops, respectively; i is for province. The average net profit per unit area of the three crops from 2003 to 2017 is 0.52 yuan/kg for rice, 0.28 yuan/kg for wheat, and 0.23 yuan/kg for corn.
E S V i = D i × Z S M i p × V i p + D i × Z S M i d × V i d × 50 a
In the above formula, ESV is the total value of cultivated land ecosystem services (in billion yuan); ZSMp and ZSMd are the paddy land and dry land expropriated area, respectively. Vp and Vd represent the ecosystem service value of paddy land and dry land, respectively; i is for province. The total ecological value of paddy land ecosystems is equivalent to 3.89 equivalent factors, and that of dry land ecosystems is equivalent to 4.01 equivalent factors. The composition of cultivated land area in each province takes the value for 2017, and the transfer year is 50 years, as is standard.

4.2.2. Ecological Loss during Land Development in Urban Built-Up Area

(1) Energy consumption for infrastructure construction in the development zone. The cost of resource consumption reduction (energy consumption, exhaust dust emission, and residual soil and stone) was calculated based on the urban road area and the length of drainage, water supply, and gas and heating pipelines in China from 2002 to 2017. The engineering machinery used in infrastructure construction mainly consumes diesel oil. The following items are used to calculate the energy consumption calculation of five types of infrastructure construction (water supply, roads, electricity, communication, and gas): diesel fuel consumption for topsoil stripping and transportation on urban roads; consumption of diesel oil for the excavation and shipment of roadbed concrete, asphalt, and gravel; diesel consumption for water supply, drainage, and gas and heating pipeline ditching and filling; diesel fuel consumption for transport of residual soil; and volume and total price of diesel fuel consumption. At the same time, the waste dust emission scale is calculated according to diesel consumption. The calculation process and formula are as follows:
C E R i = U R A i × S S T i + C A T i × 0.4   L / m 3 × 10 4
F C R i = U R A i × S S T i + C A T i 25.0   m 3 / Train × 25.0   km × 2 × 0.5   L / km × 10 4
In Formulas (3) and (4), CER is the total fuel consumption of earthwork excavated from urban roads. URA is new urban road area. SST is surface soil stripping thickness of plough layer (value: 0.25 m). CAT is subgrade concrete thickness and pavement asphalt thickness (0.4 m). The average transport distance is 25.0 km, and the average round trip is 50 km. FCR is the total transportation fuel consumption of stripped topsoil, subgrade concrete, and asphalt gravel. i is the province.
F C P i = L D P i + L S P i + L G P i + L H P i × 2 × D P G + P D × T W P × 0.4   L / m 3 × 10 4
F C T i = L D P i + L S P i + L G P i + L H P i × P D × T W P 25.0   m 3 / Train × 25   km × 2 × 0.5   L / km × 10 4
In Formulas (5) and (6), FCP is the fuel consumption of soil excavation and covering of pipeline. LDP is the length of drainage pipeline. LSP is the length of water supply pipeline. LGP is the length of gas pipeline. LHP is the length of heating pipeline. DPG is the distance between the top of the pipeline and ground (value: 1.5 m). PD is the pipeline diameter (1 m). TWP is the trench width of pipeline laying (value: 1.5 m). FCT is the fuel consumption of residual soil transportation caused by the space occupied by pipelines.
D A i = T D C i × D P i
T D C i = C E R i + F C R i + F C P i + F C T i
T E E i = T D C i × 8.57   kg   N O 2 + 10.0   kg   S O 2 + 1.8   kg   D D / m 3 × 10 2 .  
In Formulas (7)–(9), DA is the amount of diesel consumed. TDC is the total amount of diesel consumed. DP is the price of diesel oil, as of March 16, 2022. TEE is the total amount of exhaust dust emissions. DD is dust.
(2) Heat island effect of hardened roads in built-up areas. The heat island effect in this land finance model refers to fiscal funds put into urban road construction, which change the nature of the underlying surface by covering it with asphalt. Because of its good thermal conductivity and fast heating transfer rates, it absorbs a lot of radiation from the sun in the daytime. After sunset, it continuously emits heat to the air, heating the surrounding atmosphere [70]. It is assumed that the near-surface air temperature is mainly determined by road surface temperature and is not affected by wind speed and other conditions; the relationship between road surface temperature and near-surface air temperature is calculated as in [71]:
A T H i   A T L i = k i × R T H i   R T L i
In Formula (10), ATH and ATL are the daily maximum and minimum atmospheric temperatures. RTH and RTL are the maximum and minimum temperature of road surface. k is the correlation coefficient between atmospheric temperature and road temperature. i is the province.
A T a i = A T H i   A T L i × U R A i U B A i × R A H R A L R A ¯  
In Formula (11), ATai is the influence of ground temperature on atmospheric temperature. URA is new urban road area, and UBA is new urban built-up area. RA is the heat absorption rate of pavement, and R A ¯ is the average heat absorption rate of pavement. The temperature difference across the 31 provinces is twice the difference between the annual maximum temperature and the average monthly maximum temperature in 2017. The maximum and minimum heat absorption rates of pavement is 0.9 and 0.65, respectively, and the average heat absorption rate of pavement was 0.8 [72].
(3) The fragmentation of the land ecological landscape in the built-up area. The agricultural landscape has the characteristics of contiguous homogeneity and single cover. After infrastructure construction in the urban built-up area is completed, the local government transfers it to various land units in the form of land parcels (plots), and the ecological landscape changes from a single agricultural landscape to a variety of landscapes, such as industrial, commercial, and residential land. The degree of fragmentation of the ecological landscape caused by the land finance model is measured by three indicators: degree of fragmentation of transferred land, average area of transferred land, and degree of urban agglomeration of transferred land. The calculation process is as follows:
D F L i = 100 × N T L i L T A i  
A A L i = L T A i N T L i ,    
                  D A L i = 100 × U B A i L T A i × 100 %
In Formulas (12)–(14), DFL is the degree of fragmentation of transferred land. NTL is the number of transferred lands. LTA refers to the land transferred area. AAL is the average area of transferred land. DAL is the degree of urban agglomeration of transferred land. i is the province.

4.2.3. The Cost of Increasing the Ecological Service Function of Land Finance

In the later stage of the land finance model, activities to increase ecological service functions, such as newly developed land, urban cleaning, and urban green space construction require money to be spent. In order to implement the policy of agricultural land occupation and compensation balance, 31 provinces have formulated corresponding standards for the reclamation fees of general agricultural land occupied due to construction within the province. This study structures these fees in accordance with the minimum standards for cultivated land reclamation fees in each region before 2017. For regions without specific standards, it is calculated according to the standard of 10 yuan/m2. The cost of lawn and tree planting is mainly calculated for the construction of garden green space. The price of lawn is 30.0 yuan/m2. The distance for trees is 6.0 m, and the price is 80.0 yuan per tree. The cost of urban cleaning is calculated as 12.0 yuan/m2 for labor and 4.0 yuan/m2 for machinery. The calculation process of the cost of increasing ecological service function in the land finance model is as follows:
F L R i = A L E i × L R P i × 10 4 × 10 9
  U G E i = I U G i × 10 4 × 30   yuan / m 2 + 80   yuan / 6   m × 6   m × 10 9
C C E i = M C A i × 4   yuan / m 2 + A C A i × 12   yuan / m 2 × 10 4 × 10 9 / a × 2 a  
In Formulas (15)–(17), FLR is the fees of land reclamation. ALE is the area of land expropriated. LRP is the land reclamation fee per square meter. UGE is the new urban green space construction expense. IUG is the increment of urban green space area. CCE is the city cleaning expenses. IUC is the increment of urban cleaning area. MCA is the mechanical cleaning area. ACA refers to artificial cleaned area. i is for province.

5. Results

5.1. Land Fiscal Ecological Cost at Each Stage

5.1.1. Loss of Ecosystem Service Value after Agricultural Land Conversion

According to Formulas (1) and (2), the ecological cost in the early stage of land finance model and its proportion in total fiscal funds for land in the 31 provinces in China from 2003 to 2017 are calculated, as shown in Table 1.
Table 1 shows that under the influence of climate, hydrology, topography, and other factors in different regions, the main grain crops in North China, Northeast China, and Northwest China are wheat and corn, which yield low net profits. In contrast, rice, with high net profits, is planted in East China, South China, and Southwest China. The ecosystem service value of the standard equivalent factors over the 31 provinces of China was higher in the south and lower in the north. It was also higher in the east and lower in the west. More specifically, eastern provinces have the highest ecological cost in the early stage of the land finance model, and the fiscal ecological costs of land in the central provinces are next. The ecological cost of land finance is lowest in the western provinces. However, due to the influence of the level of economic development, the proportions of early ecological costs in the land finance funds show different rules. The ratio of early ecological costs is the highest in Ningxia, Xinjiang, Tibet, and Qinghai. Provinces in north, central and northeast China are next. In Beijing, Tianjin, and Shanghai, the proportion of early ecological costs to fiscal land revenue is the lowest. Therefore, the local government should avoid ignoring the ecological cost of agricultural land in the process of land expropriation and the occupation of farmland in the process of urbanization.

5.1.2. Ecological Loss during Land Development in Urban Built-Up Areas

According to Formulas (3)–(14), the energy consumption and emission scale of infrastructure construction, impact of road temperature on air temperature, and fragmentation degree of the ecological landscape in newly built-up areas in 31 provinces across China from 2003 to 2017 are calculated, as shown in Table 2.
Table 2 shows that among the 31 provinces, diesel consumption in the eastern provinces, such as Shandong, Jiangsu, Guangdong, and Zhejiang, is the largest, followed by the central regions such as Hebei, Henan, Hubei, and Hunan. Less developed regions such as Gansu, Qinghai, Ningxia, Tibet, and Hainan have the lowest diesel consumption and cost, which matches the urbanization construction speed of these regions over the past 15 years. Beijing and Shanghai have low diesel fuel consumption and capital over the past 20 years due to their strong infrastructure and high urbanization level. However, the proportion of diesel fuel consumption funds to land finance funds shows the opposite tendency. Tibet, Ningxia, Qinghai, Xinjiang, and other places have a small scale of land finance, so the proportion is relatively high. Shanxi, Heilongjiang, Sichuan, Henan, and other east and central provinces accounted for the second. Due to the large scale of land finance, Beijing, Shanghai, Zhejiang, Fujian, and Jiangsu have the lowest proportion of diesel consumption funds.
The change in the nature of the underlying urban surface is one of the reasons for the heat island effect. The influence of road surface temperature on atmospheric temperature is also closely related to the temperature difference between day and night, but the latter also depends on natural geographical conditions and can be ignored here. In addition, different types of land use, such as public service facilities, residential, commercial, and green space, also have an impact on the urban thermal environment [73]. Pavement temperature can be reduced by increasing pavement albedo and permeability, and green infrastructure such as ecological sidewalks or green roofs can be built to alleviate the urban heat island effect.
In terms of the degree of fragmentation of the ecological landscape in newly built-up areas, among the 31 provinces, Yunnan, Sichuan, Hunan, Guangxi, Tibet, Hubei, and other mountainous, plateau, and hilly areas have complex topography, and the land transferred here has the highest fragmentation degree [74]. In the middle and lower reaches of the Yangtze River, the middle and lower reaches of the Yellow River, the Northeast Plain, the Pearl River Delta, and other plains areas, the fragmentation of the transferred land is relatively low and is closely related to the level of social and economic development. The higher the economic development, the higher the fragmentation of transferred land. The fragmentation of the transferred land has a significant negative correlation with the area of transferred land. Generally, the larger the average area of the land to be transferred, the lower the fragmentation degree. In terms of the urban agglomeration degree of land, the higher the level of economic development, the higher the degree of agglomeration, indicating that these areas allocate a high proportion of land resources to tertiary industries such as commerce and real estate [75].

5.1.3. Cost of Increasing the Ecological Service Function of Land Finance

According to Formulas (15)–(17), the cost of increasing land fiscal ecological service function in the 31 provinces from 2003 to 2017 is calculated, as shown in Table 3.
Table 3 shows that the total cost of increasing the ecological service function of the land finance model is the largest in Guangdong, Zhejiang, Shandong, Jiangsu, and other eastern provinces, which is positively correlated with the land transfer area and infrastructure construction in these regions. At the same time, this value is also affected by the manner of cleaning (artificial cleaning or mechanical cleaning) used by the cities in each region. The larger the area of mechanized cleaning is, the lower the cost is, such as in Beijing, Shanghai, and Tianjin. However, the ratio of ecological cost to land finance capital in the later stage of land finance in the 31 provinces shows the opposite tendency: The more developed the economy is, the lower the proportion of ecological cost to land finance capital in the later stage of land finance.

5.1.4. Fiscal Ecological Cost of land in 31 Provinces

During the 15 years from 2003 to 2017, the 31 provinces witnessed rapid urbanization and improved infrastructure: a process in which land finance played an important role. However, it has also brought negative effects, such as reduced value for agricultural land ecosystem services, energy consumption, exhaust gas and dust emissions, urban heat island effects, and ecological landscape fragmentation. The construction of a good urban living environment for cleaning, green space construction, and the implementation of the “agricultural land occupation and compensation balance policy” requires corresponding investment. According to the formation mechanism of the fiscal ecological cost of land, the scale of various ecological costs in the early, middle, and late stages of the land finance model for the 31 provinces and their proportions in fiscal land funds are estimated, as shown in Table 4.
As can be seen in Table 4, Jiangsu, Zhejiang, Shandong, and Guangdong, four coastal provinces, have the most fiscal ecological land cost. In Sichuan, Hubei, Anhui, Liaoning, Jiangxi, Hunan, Henan, and other regions of that scale, the fiscal ecological land cost is second. Ningxia, Gansu, Hai-nan, Qinghai, Tibet, and other less developed western provinces have the smallest fiscal ecological land cost. This is related to the speed of local economic development and the scale of new infrastructure. However, the fiscal ecological land cost in Beijing, Shanghai, and Tianjin is relatively small. This is due to the highly developed infrastructure and high level of urbanization. The scale of fiscal ecological land cost in China presents a spatial pattern of “higher in the east than in the west, higher in the south than in the north”.

5.2. Total Fiscal Ecological Cost of Land in China

From 2003 to 2017, China transferred a total of 376,374.0 hm2 of state-owned construction land, with a total land finance fund of 39,815.6 billion yuan. Based on the formation mechanism of the fiscal ecological cost of land, the comprehensive scale of fiscal ecological land cost in China from 2003 to 2017 is estimated, as shown in Table 5.
It can be seen from Table 5 that the quantifiable land fiscal ecological cost in China from 2003 to 2017 is 2645.1 billion yuan, and the ecological cost of each additional 1 billion yuan of land finance revenue is 664,000 yuan, accounting for 6.64% of fiscal land revenue. The ecological cost accounts for 3.91%, 0.22%, and 2.51% in the early, middle, and late periods of the land finance model operation. At the same time, 5.8 t of waste gas and dust and 341,000 m3 of residual soil and stone were generated for every 1 billion yuan of land finance funding. On average, 56.7 plots of land were transferred, and 76 hm2 of land was used for urban expansion. During the past 15 years, the fragmentation of transferred land in China under the land finance model was 61.4 plots/km2, and the degree of urban agglomeration of transferred land was 82.3%. The climate of the 31 provinces varies greatly, and urban road construction has different degrees of influence on air temperature.

5.3. Cluster Partition and Robustness Tests of the Fiscal Ecological Cost of Land

5.3.1. Cluster Partition of Land Fiscal Ecological Cost

Jiangsu, Zhejiang, Shandong, and Guangdong, all located in the eastern region, have the highest level of economic development in the country. Therefore, revealing the law of regional differences in the scale of fiscal ecological land costs and classifying regions with approximately the same situation into the same category can provide a reference for the construction of policies to reduce the scale of fiscal ecological land costs and eliminate disadvantages in the process of urbanization. In order to study the regional differences of fiscal ecological land cost scale in the 31 provinces, the method of “linking between groups” in cluster partition using SPSS 22.0 statistical software was adopted, and the number of clusters was set to four to six for clustering, with square Euclidean distance as the metric standard. Table 6 shows the clustering of the ecological cost of land finance in the early, middle, and late stages for the 31 provinces in Table 5.
In order to make the zoning results more accurate, the three zoning results in Table 5 and Table 6 are compared and combined, and the 31 provinces are divided into five areas based on urbanization development status, physical–geographical conditions, and land finance situation. The scale of the ecological land finance cost and its proportion in land finance funds in Beijing, Shanghai, and Tianjin is very low, while the level of economic development and urbanization is high, so they are classified as Area I (Developed–Low Ecological Cost Area). Jiangsu, Zhejiang, Shandong, and Guangdong are Area II (Highest Ecological Cost Area). Sichuan, Hubei, Anhui, Liaoning, Jiangxi, Hunan, Henan, Fujian, and Hebei are Area III (Higher Ecological Cost Area). Chongqing, Guangxi, Inner Mongolia, Yunnan, Jilin, Guizhou, Heilongjiang, Shaanxi, Shanxi, and Hainan are Area IV (Medium Ecological Cost Area). Finally, in Xinjiang, Ningxia, Gansu, Qinghai, and Tibet, the scale of ecological land finance cost is very low. However, the proportion of land finance funds is high, so this region is Area V (Less Developed–Low Ecological Cost Area). The clustering results of the fiscal ecological land cost scale are shown in Figure 3.

5.3.2. ANOVA of Cluster Partition of Fiscal Ecological Land Cost

According to the results of the cluster partition, we selected the amount of fiscal ecological land cost and its proportion in land fiscal revenue for the 31 provinces from 2003 to 2017 as the dependent variable to conduct a one-way analysis of variance (ANOVA) test with the area as the independent variable, as shown in Table 7.

5.4. Regional Difference and Reasons for Fiscal Ecological Cost of Land

According to the clustering results, the total scale of fiscal ecological land costs and the cost of land finance per 1 billion yuan from 2003 to 2017 were calculated for each area, as shown in Table 8 and Table 9.
Area I (Developed–Low Ecological Cost Area): During the 15 years from 2003 to 2017, a total of 143,884.7 hm2 of land was transferred in Area I, and the land fiscal revenue was 4609.9 billion yuan. The quantifiable ecological cost in Area I under the land finance model is 128.7 billion yuan, and the ecological cost of each increase of 1 billion yuan in fiscal land revenue is 27.9 million yuan, accounting for 2.79%. The proportions of ecological cost in the early, middle, and late periods were 1.27%, 0.13%, and 1.39%, respectively. Each 1 billion yuan of fiscal land revenue produces 3.5 t of waste gas and dust, 241,000 m3 of residual soil and stone, 9.1 plots of land transferred on average, and 32.2 hm2 of urban expansion. The fragmentation degree of the transferred land is 29.0 plots/km2, the average area of transferred land is 3.5 hm2/plot, and the urban agglomeration degree of transferred land is 103.2%. As the national political, economic, and cultural center, this area leads the country in its level of urbanization, socioeconomic development, and education and cultural infrastructure. This infrastructure is constantly updated, and the level of urban greening is continuously improving to meet the needs of local development [76]. The population density is high, per capita land area is small, and state-owned land is fragmented. The tertiary industry (real estate and other service industries) is developed [77], land transfer prices are high, and the proportion of fiscal ecological land cost to fiscal land funds is extremely low.
Area II (Highest Ecological Cost Area): This area is located on or near the eastern coast, and it witnessed rapid urbanization from 2003 to 2017. A total of 11,199,361.2 hm2 of state-owned construction land was transferred, and the land finance fund was 15,601.2 billion yuan. The total fiscal ecological land cost is 910.6 billion yuan; 58.4 million yuan per billion yuan of land finance, accounting for 5.84% of land finance. The early, middle, and late stages accounted for 3.64%, 0.21%, and 1.99%, respectively. Each increase of 1 billion yuan of land finance funds will produce 5.5 t of waste gas dust, 322,000 m3 of residual soil and stone, 37.9 plots of land transferred, and 69.5 hm2 of urban expansion. The fragmentation degree of transferred land was 49.3 plots/km2, the average area of transferred land was 2.0 hm2/plot, and the urban agglomeration degree of transferred land was 90.5%. Area II has a superior geographical location, rapid economic development, and high openness to foreign trade, which is strongly supported by national policies. Flat terrain is conducive to urban infrastructure construction. The population growth rate is fast, the fragmentation degree of land transfer is high, and the area of land transfer is small. The urban greening level is high, and its later ecological cost is large.
Area III (Higher Ecological Cost Area): This area includes part of the central and eastern regions. During the 15 years from 2003 to 2017, the land transferred area was 1,382,043.8 hm2, the land finance fund was 12,992.6 billion yuan, and the land fiscal ecological cost was 984.5 billion yuan, accounting for 7.58% of the land finance. The proportion of ecological costs to land fiscal scale in the early, middle, and late periods were 4.64%, 0.22%, and 2.71%, respectively. Each increase of 1 billion yuan of land finance funds will produce 5.8 t of waste gas dust, 337,000 m3 of residual soil and stone, 73.1 plots of land transfer, and 75.9 hm2 of urban expansion. The fragmentation degree of the transferred land in this region is 68.7 plots/km2, the average area of transferred land is 1.5 hm2, and the urban agglomeration degree of transferred land is 71.4%. This area mostly consists of China’s major agricultural provinces, and cultivated land is the main land use type in this area [78]. The fiscal ecological land cost in the early stage is higher than in the other areas. Compared with Areas I and II, the urban agglomeration degree of the transferred land is lower, and secondary industry and towns develop faster. The population density is large, the fragmentation of state-owned land transfer is relatively high, and the transferred land area is small.
Area IV (Medium Ecological Cost Area): This area is mainly distributed in the central, western, and northeast regions. From 2003 to 2017, a total of 725,370.9 hm2 of state-owned construction land was transferred, with total land finance funds of 5939.9 billion yuan and total fiscal ecological land costs of 514.6 billion yuan, accounting for 8.66% of the total land finance funds. Its land finance accounts for 4.41%, 0.28%, and 3.97% in the early, middle, and late periods, respectively. Each increase of 1 billion yuan of land finance funds will produce 7.4 t of waste gas dust, 418,000 m2 of residual soil and stone, 91.7 plots of transferred land, and 109.0 hm2 of urban expansion. The fragmentation degree of transferred land in this region is 75.1 plots/km2, the average area of transferred land is 1.3 hm2, and the urban agglomeration degree of transferred land is 89.3%. This area is mostly located on the second ladder of China, with large terrain fluctuations, including the Inner Mongolia Plateau and Loess Plateau in the north and the Yunnan–Guizhou Plateau in the south [79], and high fragmentation of transferred land and a small settlement area. The level of economic development is not as good as that in the central and eastern regions. Jilin and Heilongjiang have suffered serious population loss, and the development of urbanization has been hindered there.
Area V (Less Developed–Low Ecological Cost Area): This area is located in northwest China and the Qinghai–Tibet Plateau. From 2003 to 2017, a total of 225,713.4 hm2 of state-owned construction land was transferred, with total land finance funding of 672.0 billion yuan. In the past 15 years, the total fiscal ecological cost of land was 106.8 billion yuan, accounting for 15.90% of the land finance funds: 9.76%, 0.56%, and 5.58% in the early, middle, and late periods in the land financial model, respectively. Each increase of 1 billion yuan of land finance funds will produce 14.9 t of waste gas and dust, 845,000 m3 of residual soil and stone, 190.9 plots of land transfers, and 235.6 hm2 of urban expansion. The fragmentation of transferred land is 56.8 plots/km2, the average area of transferred land is 1.8 hm2, and the urban agglomeration degree of transferred land is 70.1%. This area accounts for 42.8% of China’s land area. It is characterized by a small population, low level of urbanization, relatively backward economic development, weak infrastructure, and a low level of industrial structure. The scale of land finance in this area is small, but the fiscal ecological land cost accounts for a large proportion of land finance revenue, which is much higher than the average level in China, due to the overall low economic development of this region.

6. Discussion

6.1. Innovations and Recommendations

At present, scholars are paying considerable attention to the social and economic impact of land finance, but only a few scholars are paying attention to the negative contribution of land finance to the amount of urban public green space and to the environmental effects, such as carbon emissions, in the process of land urbanization [80,81]. Moreover, the fiscal ecological land costs of this process are not considered. This study uses a variety of methods and formulates several indicators to quantify the ecological effects of the land finance model. Data from 31 provinces in China spanning a 15-year period are collected, sorted, and analyzed. Ecological land finance costs are calculated and compared, with the aim of contributing to the study of ecological issues related to land finance and the formulation of local government policies. According to the situation of each area, this study puts forward differentiated policy recommendations to reduce fiscal ecological land costs, as follows.
The core of reducing fiscal ecological land costs is to reduce the dependence of local governments on land finance. Specifically, we recommend the development of financial resources for local governments, such as levying property tax, incorporating ecological environmental performance into local government performance evaluation systems [82], and introducing a for-profit third-party organization to monitor the environmental performance of local governments [83].
Area I (Developed–Low Ecological Cost Area): The urbanization rate in this area has reached more than 80%, and the scale of land finance is large, but the ecological cost is small. The area of cultivated land in this area is small, as is the scale of ecological cost. The focus of reducing the fiscal ecological land cost can be placed in the middle and later stages. First, clean energy such as solar energy, wind energy, and biomass energy should be used to reduce exhaust gas and dust emissions in the infrastructure construction process of the development zone. Environmentally friendly materials with low albedo and good permeability should be developed and applied to pave roads to reduce the heat island effect. Second, urban land should be used efficiently and intensively in combination with national land space planning. Finally, mechanical cleaning methods, tree species with high survival rates, and mechanical maintenance methods for urban green space can be adopted to reduce later costs, and the experience of Hong Kong and Singapore can be used for reference in urban green space construction [70].
Area II (Highest Ecological Cost Area): The scale of land finance and land fiscal ecological costs are the highest in this area. We should refer to Area I to reduce the fiscal ecological land costs in the middle and later periods. In addition, we could strip and reuse the cultivated land resources occupied by the construction in the area and regularly assess rewards and punishments. This area should implement the transfer of cultivated land within the province, open the channel of land transfer, and increase the flexibility of land transfer to reduce early fiscal ecological land costs. The urban thermal environment can be improved through optimization of building layout, thus alleviating the heat island effect [84].
Area III (Higher Ecological Cost Area): The area is located in central and eastern China and is in a period of rapid urbanization. As China’s main grain-producing areas, these provinces need to ensure that the quantity and quality of cultivated land do not decrease to safeguard national food security. The ecological service function of existing cultivated land can be increased through land consolidation and the transformation of low- and medium-yield fields. In the process of urbanization construction, we should draw lessons from the experience of Areas I and II to carry out urbanization construction in an intensive but cost-efficient way and avoid the old road of “development first and renovation later” [85].
Area IV (Medium Ecological Cost Area): The fiscal ecological costs of land in this area is lower than that in Areas I, II, and III. Southwest China and the Loess Plateau region have complex and difficult surface morphology; land transfer should be allocated around existing urban areas as much as possible, and old city reconstruction and redevelopment should be carried out to improve the degree of urban agglomeration. Inner Mongolia, Jilin, and Heilongjiang are suffering from serious population loss and have rich reserves of cultivable land resources. The abandoned and idle cultivated land in “hollow villages” should be reconstructed in a centralized manner, and large-scale and digital agriculture should be carried out, drawing on the experience of America and Europe [86].
Area V (Less Developed–Low Ecological Cost Area): The level of urbanization in this area is relatively backward, and the scale of land fiscal revenue is small, but ecological costs account for a relatively high proportion of it. The speed of land urbanization is much faster than the speed of population urbanization and industrial urbanization, and the development of urbanization is abnormal. It is necessary to avoid rapid urbanization development at the cost of wasting land and inefficient use of land [87]. Some advanced enterprises and industries should be introduced to create more sources of tax revenue for local governments.

6.2. Policy Implications

We put forward the concept of fiscal ecological cost of land and explain the formation mechanism of this cost, which is of great significance for reducing dependence on land finance and ensuring the healthy development of urbanization. Through the above discussion, we clearly recognize the important position of fiscal ecological costs of land in land fiscal revenue and the reasons for the formation of regional differences in the fiscal ecological cost of land. Based on the above findings, we put forward policy recommendations including balanced farmland occupation and replenishment, reclamation and reuse of farmland, use of clean energy, research and development of environmentally protective materials for road surfaces, construction of green land, and urban cleaning. Therefore, this study has theoretical and practical value for the rich research content of land finance and the transformation and optimization of land finance.

6.3. Limitations and Prospects

The following three points are worth exploring in the study of land fiscal ecological cost. (1) First, the data in this study are all national data; a follow-up study could further narrow the scope to provinces or cities; more in-depth results may be found. (2) Due to the availability of data and verified research methods, noise pollution in the process of urban development and the construction cost of urban water surfaces and other infrastructure in the later stage of the land finance model could not be effectively explored [88]. (3) Because of space limitations, we did not study the spatial–temporal pattern and driving mechanism of fiscal ecological land cost, which needs to be further discussed in subsequent studies. In addition, governments at all levels should try to reduce dependence on land finance and the scale of fiscal ecological land costs in the future.

7. Conclusions

By sorting out the process of land finance and developing a model, this study puts forward a definition of land fiscal ecological cost, explains the formation mechanism for fiscal ecological land cost, and scientifically estimates the scale of fiscal ecological cost of land across 31 provinces in China from 2003 to 2017. The results show that the fiscal eco-logical cost of land in China presents a spatial pattern of “higher in the east than in the west, higher in the south than in the north.” Over the different stages of the land finance model, the quantified ecological cost of available funds is highest in the early stage, second in the later stage, and lowest in the middle stage. At the same time, it is necessary to also employ energy conservation and emissions reductions to reduce costs that cannot be quantified with capital, such as waste gas dust, residual soil and stone, heat island effects, and land fragmentation generated in the middle stage of the land finance model.
This study uses SPSS 22.0 statistical software to cluster and partition the fiscal eco-logical costs of land in 31 provinces and divides them into five regions. By comparison, it is found that the cost of each additional 1 billion yuan of land finance funds goes in the order Area I > Area II > Area III > Area IV > Area V, indicating that the ecological cost of land finance funds per unit cost is positively correlated with the degree of economic development. The degree of fragmentation of transferred land was in the order Area IV > Area III > Area V > Area II > Area I, and the average area of transferred land was in the order Area I > Area II > Area V > Area III > Area IV, indicating that the more complex the terrain, the higher the degree of fragmentation of transferred land and the smaller the land area to be transferred. The degree of fragmentation of transferred land is also related to industrial structure. The land fragmentation degree of the area dominated by primary industry is significantly higher than that of the region dominated by secondary and tertiary industries. Affected by the level of economic development and topography, the urban agglomeration degree of transferred land shows the rule of Area I > Area II > Area IV > Area III > Area V.
Finally, according to the scale of fiscal ecological cost of land in the five areas, policy recommendations are put forward as follows. Focusing on reducing dependence on land finance, cultivated land protection, cultivated land reclamation, and large-scale operations should be carried out in the early stage. In the medium term, we should use clean energy and develop environmentally friendly pavement materials. In the later stage, suit-able vegetation and cleaning methods should be selected.

Author Contributions

Conceptualization, Y.W. and X.Y.; methodology, Y.W. and X.Y.; data curation, X.Y.; writing—original draft preparation, X.Y.; writing—review and editing, X.Y.; visualization, X.Y.; supervision, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the 2020 Central University Basic Scientific Research Funding Program (grant no. N2014005) and the National Natural Science Foundation of China 2020 (grant no. 42071248).

Data Availability Statement

Except for the noted quotation, the data are from the authors’ own research.

Acknowledgments

The authors acknowledge all colleagues and friends who voluntarily reviewed the translation of the survey and study manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhu, X.; Wei, Y.; Lai, Y.; Li, Y.; Zhong, S.; Dai, C. Empirical Analysis of the Driving Factors of China’s “Land Finance” Mechanism Using Soft Budget Constraint Theory and the PLS-SEM Model. Sustainability 2019, 11, 742. [Google Scholar] [CrossRef]
  2. Mo, J. Land Financing and Economic Growth: Evidence from Chinese Counties. China Econ. Rev. 2018, 50, 218–239. [Google Scholar] [CrossRef]
  3. Kan, Z. The Analysis of Domestic Land Finance Research Situation from the Perspective of Knowledge Map. China Land Sci. 2018, 32, 86–92. [Google Scholar] [CrossRef]
  4. Wu, F. Land Financialisation and the Financing of Urban Development in China. Land Use Policy 2022, 112, 104412. [Google Scholar] [CrossRef]
  5. Wang, W.; Ye, F. The Political Economy of Land Finance in China. Public Budg. Financ. 2016, 36, 91–110. [Google Scholar] [CrossRef]
  6. Gu, N.; Wang, X.; Chen, X. Causes of Regional Differences of Land Finance: An Emporical Analysis Based on Provincial Panel Data. Ind. Econ. Rev. 2011, 2, 103–112. [Google Scholar]
  7. Zou, X.-Q. An Analysis on the Correlation between Land Finance and Economic Development: Based on Land Kuznets Curve Hypothesis and Panel Data Evidence. China Land Sci. 2013, 27, 14–19. [Google Scholar] [CrossRef]
  8. Lu, J.; Li, B.; Li, H. The Influence of Land Finance and Public Service Supply on Peri-Urbanization: Evidence from the Counties in China. Habitat Int. 2019, 92, 102039. [Google Scholar] [CrossRef]
  9. Wei, Y.-F. Land Financial Dependence, Urban Public Service Supply and Population Urbanization—A Panel Date Analysis Based on 35 Large and Medium-Sized Cities. Econ. Rev. J. 2021, 7, 118–128. [Google Scholar]
  10. Guo, J. Research on the Interactive Relationships between Land Finance and Land Urbanization: An Empirical Analysis Based on VAR Model. J. China Agric. Univ. 2018, 23, 206–214. [Google Scholar]
  11. Zhao, Z.; Bai, Y.; Wang, G.; Chen, J.; Yu, J.; Liu, W. Land Eco-Efficiency for New-Type Urbanization in the Beijing-Tianjin-Hebei Region. Technol. Forecast. Soc. Chang. 2018, 137, 19–26. [Google Scholar] [CrossRef]
  12. Yan, B.; Wu, L.; Wang, X.H.; Wu, J. How Can Environmental Intervention Work during Rapid Urbanization? Examining the Moderating Effect of Environmental Performance-Based Accountability in China. Environ. Impact Assess. Rev. 2021, 86, 106476. [Google Scholar] [CrossRef]
  13. Yin, K.; Wang, R.; An, Q.; Yao, L.; Liang, J. Using Eco-Efficiency as an Indicator for Sustainable Urban Development: A Case Study of Chinese Provincial Capital Cities. Ecol. Indic. 2014, 36, 665–671. [Google Scholar] [CrossRef]
  14. Shao, Z.; Bakker, M.; Spit, T.; Janssen-Jansen, L.; Qun, W. Containing Urban Expansion in China: The Case of Nanjing. J. Environ. Plan. Manag. 2020, 63, 189–209. [Google Scholar] [CrossRef]
  15. Wang, J.; Wang, S.; Li, S.; Feng, K. Coupling Analysis of Urbanization and Energy-Environment Efficiency: Evidence from Guangdong Province. Appl. Energy 2019, 254, 113650. [Google Scholar] [CrossRef]
  16. Hou, D.; Meng, F.; Prishchepov, A.V. How Is Urbanization Shaping Agricultural Land-Use? Unraveling the Nexus between Farmland Abandonment and Urbanization in China. Landsc. Urban Plan. 2021, 214, 104170. [Google Scholar] [CrossRef]
  17. Avashia, V.; Garg, A. Implications of Land Use Transitions and Climate Change on Local Flooding in Urban Areas: An Assessment of 42 Indian Cities. Land Use Policy 2020, 95, 104571. [Google Scholar] [CrossRef]
  18. Wu, Y.; Tao, Y.; Yang, G.; Ou, W.; Pueppke, S.; Sun, X.; Chen, G.; Tao, Q. Impact of Land Use Change on Multiple Ecosystem Services in the Rapidly Urbanizing Kunshan City of China: Past Trajectories and Future Projections. Land Use Policy 2019, 85, 419–427. [Google Scholar] [CrossRef]
  19. Dumortier, J.; Elobeid, A. Effects of a Carbon Tax in the United States on Agricultural Markets and Carbon Emissions from Land-Use Change. Land Use Policy 2021, 103, 105320. [Google Scholar] [CrossRef]
  20. Deslatte, A. Managerial Friction and Land-Use Policy Punctuations in the Fragmented Metropolis. Policy Stud. J. 2018, 48, 700–726. [Google Scholar] [CrossRef]
  21. Mcdonald, R.I.; Kareiva, P.; Forman, R.T.T. The Implications of Current and Future Urbanization for Global Protected Areas and Biodiversity Conservation. Biol. Conserv. 2008, 141, 1695–1703. [Google Scholar] [CrossRef]
  22. Salerno, F.; Viviano, G.; Tartari, G. Urbanization and Climate Change Impacts on Surface Water Quality: Enhancing the Resilience by Reducing Impervious Surfaces. Water Res. 2018, 144, 491–502. [Google Scholar] [CrossRef]
  23. Liu, Z.; Ding, M.; He, C.; Li, J.; Wu, J. The Impairment of Environmental Sustainability Due to Rapid Urbanization in the Dryland Region of Northern China. Landsc. Urban Plan. 2019, 187, 165–180. [Google Scholar] [CrossRef]
  24. Kashem, S.B.; Irawan, A.; Wilson, B. Evaluating the Dynamic Impacts of Urban Form on Transportation and Environmental Outcomes in US Cities. Int. J. Environ. Sci. Technol. 2014, 11, 2233–2244. [Google Scholar] [CrossRef]
  25. Wang, Z. Evolving Landscape-Urbanization Relationships in Contemporary China. Landsc. Urban Plan. 2018, 171, 30–41. [Google Scholar] [CrossRef]
  26. Ye, Y.; Bryan, B.A.; Zhang, J.; Connor, J.D.; Chen, L.; Qin, Z.; He, M. Changes in Land-Use and Ecosystem Services in the Guangzhou-Foshan Metropolitan Area, China from 1990 to 2010: Implications for Sustainability under Rapid Urbanization. Ecol. Indic. 2018, 93, 930–941. [Google Scholar] [CrossRef]
  27. Song, X.; Chang, K.T.; Yang, L.; Scheffran, J. Change in Environmental Benefits of Urban Land Use and Its Drivers in Chinese Cities, 2000–2010. Int. J. Environ. Res. Public Health 2016, 13, 535. [Google Scholar] [CrossRef]
  28. Fang, W.; An, H.; Li, H.; Gao, X.; Sun, X.; Zhong, W. Accessing on the Sustainability of Urban Ecological-Economic Systems by Means of a Coupled Emergy and System Dynamics Model: A Case Study of Beijing. Energy Policy 2017, 100, 326–337. [Google Scholar] [CrossRef]
  29. Chen, D.; Lu, X.; Hu, W.; Zhang, C.; Lin, Y. How Urban Sprawl Influences Eco-Environmental Quality: Empirical Research in China by Using the Spatial Durbin Model. Ecol. Indic. 2021, 131, 108113. [Google Scholar] [CrossRef]
  30. Bai, Y.; Deng, X.; Jiang, S.; Zhang, Q.; Wang, Z. Exploring the Relationship between Urbanization and Urban Eco-Efficiency: Evidence from Prefecture-Level Cities in China. J. Clean. Prod. 2018, 195, 1487–1496. [Google Scholar] [CrossRef]
  31. Han, J.; Miao, J.; Shi, Y.; Miao, Z. Can the Semi-Urbanization of Population Promote or Inhibit the Improvement of Energy Efficiency in China? Sustain. Prod. Consum. 2021, 26, 921–932. [Google Scholar] [CrossRef]
  32. Bao, C.; Xu, M. Cause and Effect of Renewable Energy Consumption on Urbanization and Economic Growth in China’s Provinces and Regions. J. Clean. Prod. 2019, 231, 483–493. [Google Scholar] [CrossRef]
  33. Chengshun, S. Compensation Standard for Cultivated Land Protection from the Perspective of Cultivated Land Comprehensive Value in Wuhan Metropolitan Area. Res. Soil Water Conserv. 2017, 24, 330–335. [Google Scholar] [CrossRef]
  34. Tang, P.; Feng, Y.; Li, M.; Zhang, Y. Can the Performance Evaluation Change from Central Government Suppress Illegal Land Use in Local Governments? A New Interpretation of Chinese Decentralisation. Land Use Policy 2021, 108, 105578. [Google Scholar] [CrossRef]
  35. Duo, C. Analysis on the Change Pattern and Causes of “Turning Counties (Cities) into District” in China since the Reform and Opening-Up. Urban Dev. Stud. 2018, 25, 33–40. [Google Scholar]
  36. Colding, J. ‘Ecological Land-Use Complementation’ for Building Resilience in Urban Ecosystems. Landsc. Urban Plan. 2007, 81, 46–55. [Google Scholar] [CrossRef]
  37. Costanza, R.; De Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; Neill, R.V.O.; Paruelo, J.; Raskin, R.G.; et al. The Value of the World’s Ecosystem Services and Natural Capital. Ecol. Econ. 1998, 25, 3–15. [Google Scholar] [CrossRef]
  38. Carlos, C.; Simon, H.; Mc Michael, A. Millennium Ecosystem Assessment (MA), Ecosystems and Human Well-Being; World Resources Institute/Island Press: Washington, DC, USA, 2005. [Google Scholar]
  39. Costanza, R.; De Groot, R.; Sutton, P.; Van Der Ploeg, S.; Anderson, S.J.; Kubiszewski, I.; Farber, S.; Turner, R.K. Changes in the Global Value of Ecosystem Services. Glob. Environ. Chang. 2014, 26, 152–158. [Google Scholar] [CrossRef]
  40. Xie, G.D.; Lu, C.X.; Leng, Y.F.; Zheng, D.U.; Li, S.C. Ecological Assets Valuation of the Tibetan Plateau. J. Nat. Resour. 2003, 18, 189–196. [Google Scholar]
  41. Wang, X.; Pan, T.; Pan, R.; Chi, W.; Ma, C.; Ning, L.; Wang, X. Impact of Land Transition on Landscape and Ecosystem Service Value in Northeast Region of China from 2000–2020. Land 2022, 11, 696. [Google Scholar] [CrossRef]
  42. Lafuite, A.S.; Denise, G.; Loreau, M. Sustainable Land-Use Management Under Biodiversity Lag Effects. Ecol. Econ. 2018, 154, 272–281. [Google Scholar] [CrossRef]
  43. Kalkuhl, M.; Edenhofer, O. Ramsey Meets Thünen: The Impact of Land Taxes on Economic Development and Land Conservation. Int. Tax Public Financ. 2016, 24, 350–380. [Google Scholar] [CrossRef]
  44. Nicolaus, A.; Barretto, A.; Sparovek, G.; William, G.; Ferreira, L.; Mar, C.F.; Souza, D.; Appy, B.; Mario, C.; De Guedes, G.; et al. Taxation Aiming Environmental Protection: The Case of Brazilian Rural Land Tax. Land Use Policy 2022, 119, 106164. [Google Scholar] [CrossRef]
  45. Musagaliev, A. The role of resource taxes in shaping revenues of the republic of Karakalpakstan. Int. J. Res. Soc. Sci. 2018, 8, 794–801. [Google Scholar]
  46. Kałazny, A. Taxation of Assets Used to Generate Energy—In the Context of the Transformation of the Polish Energy Sector from Coal Energy to Low-Emission Energy. Energies 2021, 14, 4587. [Google Scholar] [CrossRef]
  47. Yeganeh, A.; Mccoy, A.P.; Reichard, G.; Schenk, T.; Yeganeh, A. Green Building and Policy Innovation in the US Low-Income Housing Tax Credit Programme Green Building and Policy Innovation in the US Low-Income Housing Tax Credit Programme. Build. Res. Inf. 2020, 49, 543–560. [Google Scholar] [CrossRef]
  48. Xu, N. What Gave Rise to China’ s Land Finance? Land Use Policy 2019, 87, 104015. [Google Scholar] [CrossRef]
  49. Guo, S.; Shi, Y. Infrastructure Investment in China: A Model of Local Government Choice under Land Financing. J. Asian Econ. 2018, 56, 24–35. [Google Scholar] [CrossRef]
  50. Cheng, J.; Zhao, J.; Zhu, D.; Jiang, X.; Zhang, H.; Zhang, Y. Land Marketization and Urban Innovation Capability: Evidence from China. Habitat Int. 2022, 122, 102540. [Google Scholar] [CrossRef]
  51. Wu, Q.; Li, Y.; Yan, S. The Incentives of China’s Urban Land Finance. Land Use Policy 2015, 42, 432–442. [Google Scholar] [CrossRef]
  52. Huang, D.; Chan, R.C.K. On ‘Land Finance’ in Urban China: Theory and Practice. Habitat Int. 2018, 75, 96–104. [Google Scholar] [CrossRef]
  53. He, C. Land Use Change and Economic Growth in Urban China: A Structural Equation Analysis. Urban Stud. 2014, 51, 2880–2989. [Google Scholar] [CrossRef]
  54. Ong, L.H. “Land Grabbing” in an Autocracy and a Multi-Party Democracy: China and India Compared. J. Contemp. Asia 2019, 10, 1569253. [Google Scholar] [CrossRef]
  55. Guo, S.; Liu, L.; Zhao, Y. The Business Cycle Implications of Land Fi Nancing in China. Econ. Model. 2015, 46, 225–237. [Google Scholar] [CrossRef]
  56. Murakami, J. The Government Land Sales Programme and Developers’ Willingness to Pay for Accessibility in Singapore, 1990–2015. Land Use Policy 2018, 75, 292–302. [Google Scholar] [CrossRef]
  57. Liu, Y. Financing China’s Suburbanization: Capital Accumulation through Suburban Land Development in Hangzhou. Int. J. Urban Reg. Res. 2017, 40, 1112–1133. [Google Scholar] [CrossRef]
  58. Lin, G.C.S. China’s Landed Urbanization: Neoliberalizing Politics, Land Commodification, and Municipal Finance in the Growth of Metropolises. Environ. Plan. A Econ. Space 2014, 46, 1814–1835. [Google Scholar] [CrossRef]
  59. Sutton, P.C.; Anderson, S.J.; Costanza, R.; Kubiszewski, I. The Ecological Economics of Land Degradation: Impacts on Ecosystem Service Values. Ecol. Econ. 2016, 129, 182–192. [Google Scholar] [CrossRef]
  60. Tian, Y.; Xu, D.; Song, J.; Guo, J.; You, X.; Jiang, Y. Impacts of Land Use Changes on Ecosystem Services at Different Elevations in an Ecological Function Area, Northern China. Ecol. Indic. 2022, 140, 109003. [Google Scholar] [CrossRef]
  61. Wang, Y. The Formation Mechanism and Scale Estimation of the Ecological Cost of the Land Finance Mode: Take Liaoning Province as an Example. J. Nanjing Agric. Univ. 2021, 21, 138–151. [Google Scholar]
  62. Bren, C.; Reitsma, F.; Baiocchi, G.; Barthel, S.; Güneralp, B.; Erb, K. Future Urban Land Expansion and Implications for Global Croplands. Proc. Natl. Acad. Sci. USA 2017, 114, 8939–8944. [Google Scholar] [CrossRef] [PubMed]
  63. Gatzweiler, F.W. Biodiversity and Cultural Ecosystem Services. Encycl. Biodivers. 2013, 1, 332–340. [Google Scholar] [CrossRef]
  64. Assandri, G.; Bogliani, G.; Pedrini, P.; Brambilla, M. Beautiful Agricultural Landscapes Promote Cultural Ecosystem Services and Biodiversity Conservation. Agric. Ecosyst. Environ. 2018, 256, 200–210. [Google Scholar] [CrossRef]
  65. Choy, L.H.T.; Lai, Y.; Lok, W. Economic Performance of Industrial Development on Collective Land in the Urbanization Process in China: Empirical Evidence from Shenzhen. Habitat Int. 2013, 40, 184–193. [Google Scholar] [CrossRef]
  66. Karsznia, I.; Julita, Ł. Land Use Institutions and Social-Ecological Systems: A Spatial Analysis of Local Landscape Changes in Poland. Land Use Policy 2022, 114, 105937. [Google Scholar] [CrossRef]
  67. Bryan-Brown, D.N.; Connolly, R.M.; Richards, D.R.; Adame, F.; Friess, D.A.; Brown, C.J. Global Trends in Mangrove Forest Fragmentation. Sci. Rep. 2020, 10, 7117. [Google Scholar] [CrossRef]
  68. Chen, A.; Yao, X.A.; Sun, R.; Chen, L. Effect of Urban Green Patterns on Surface Urban Cool Islands and Its Seasonal Variations. Urban For. Urban Green. 2014, 13, 646–654. [Google Scholar] [CrossRef]
  69. Xie, G. Improvement of the Evaluation Method for Ecosystem Service Value Based on Per Unit Area. J. Nat. Resour. 2015, 30, 1243–1254. [Google Scholar]
  70. Masoudi, M.; Yok, P.; Chin, S. Multi-City Comparison of the Relationships between Spatial Pattern and Cooling Effect of Urban Green Spaces in Four Major Asian Cities. Ecol. Indic. 2019, 98, 200–213. [Google Scholar] [CrossRef]
  71. Pomerantz, M.; Pon, B.; Akbari, H.; Chang, S.C. The Effect of Pavements’ Temperatures on Air Temperatures in Large Cities; Lawrence Berkeley National Laboratory: Berkeley, CA, USA, 2000. [Google Scholar]
  72. Pomerant, Z.M.; Akbari, H.; Chen, A. Paving Materials for Heat Island Mitigation; Lawrence Berkeley National Laboratory: Berkeley, CA, USA, 1997. [Google Scholar]
  73. Chen, Y.; Yang, J.; Yang, R.; Xiao, X.; Cecilia, J. Contribution of Urban Functional Zones to the Spatial Distribution of Urban Thermal Environment. Build. Environ. 2022, 216, 109000. [Google Scholar] [CrossRef]
  74. Hu, Z.; Wang, S.; Bai, X.; Yang, Y.; Tian, S.; Li, C.; Deng, Y. Changes in Ecosystem Service Values in Karst Areas of China. Agric. Ecosyst. Environ. 2020, 301, 107026. [Google Scholar] [CrossRef]
  75. Fan, X.; Qiu, S.; Sun, Y. Land Finance Dependence and Urban Land Marketization in China: The Perspective of Strategic Choice of Local Governments on Land Transfer. Land Use Policy 2020, 99, 105023. [Google Scholar] [CrossRef]
  76. Shao, S.; Tian, Z.; Fan, M. Do the Rich Have Stronger Willingness to Pay for Environmental Protection? New Evidence from a Survey in China. World Dev. 2018, 105, 83–94. [Google Scholar] [CrossRef]
  77. Taylor, P.; Wei, Y.; Lam, P.T.I.; Chiang, Y.H.; Leung, B.Y.P. The Effects of Monetary Policy on Real Estate Investment in China: A Regional Perspective. Int. J. Strateg. Prop. Manag. 2014, 18, 368–379. [Google Scholar] [CrossRef]
  78. Guo, P.; Zhang, F.; Wang, H. The Response of Ecosystem Service Value to Land Use Change in the Middle and Lower Yellow River: A Case Study of the Henan Section. Ecol. Indic. 2022, 140, 109019. [Google Scholar] [CrossRef]
  79. Li, C.; Wu, Y.; Gao, B.; Zheng, K.; Wu, Y.; Li, C. Multi-Scenario Simulation of Ecosystem Service Value for Optimization of Land Use in the Sichuan-Yunnan Ecological Barrier, China. Ecol. Indic. 2021, 132, 108328. [Google Scholar] [CrossRef]
  80. Long, X.; Ji, X.; Ulgiati, S. Is Urbanization Eco-Friendly? An Energy and Land Use Cross-Country Analysis. Energy Policy 2017, 100, 387–396. [Google Scholar] [CrossRef]
  81. Zhang, W.; Xu, H. Effects of Land Urbanization and Land Finance on Carbon Emissions: A Panel Data Analysis for Chinese Provinces. Land Use Policy 2017, 63, 493–500. [Google Scholar] [CrossRef]
  82. Pang, R.; Zheng, D.; Shi, M.; Zhang, X. Pollute First, Control Later? Exploring the Economic Threshold of Effective Environmental Regulation in China’s Context. J. Environ. Manag. 2019, 248, 109275. [Google Scholar] [CrossRef]
  83. Niu, X.; Wang, X.; Gao, J.; Wang, X. Has Third-Party Monitoring Improved Environmental Data Quality? An Analysis of Air Pollution Data in China. J. Environ. Manag. 2020, 253, 109698. [Google Scholar] [CrossRef]
  84. Yang, J.; Yang, Y.; Sun, D.; Jin, C.; Xiao, X. Influence of Urban Morphological Characteristics on Thermal Environment. Sustain. Cities Soc. 2021, 72, 103045. [Google Scholar] [CrossRef]
  85. Broto, V.C. Energy Landscapes and Urban Trajectories towards Sustainability. Energy Policy 2017, 108, 755–764. [Google Scholar] [CrossRef]
  86. Jiang, S.; Zhou, J.; Qiu, S. Technological Forecasting & Social Change Digital Agriculture and Urbanization: Mechanism and Empirical Research. Technol. Forecast. Soc. Chang. 2022, 180, 121724. [Google Scholar] [CrossRef]
  87. Ji, L.; Zhang, W. Fiscal Incentives and Sustainable Urbanization: Evidence from China. Sustainability 2020, 12, 103. [Google Scholar] [CrossRef]
  88. Li, P.; Wang, Z. Environmental Co-Benefits of Urban Greening for Mitigating Heat and Carbon Emissions. J. Environ. Manag. 2021, 293, 112963. [Google Scholar] [CrossRef]
Figure 1. Changes in land finance in China from 2003 to 2017.
Figure 1. Changes in land finance in China from 2003 to 2017.
Land 11 01221 g001
Figure 2. The formation mechanism framework of land fiscal ecological cost.
Figure 2. The formation mechanism framework of land fiscal ecological cost.
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Figure 3. Clustering results of land fiscal ecological cost scale in 31 provinces.
Figure 3. Clustering results of land fiscal ecological cost scale in 31 provinces.
Land 11 01221 g003
Table 1. Ecological cost in the early stage of the land finance model and its proportion in fiscal land funds for the 31 provinces in China from 2003 to 2017.
Table 1. Ecological cost in the early stage of the land finance model and its proportion in fiscal land funds for the 31 provinces in China from 2003 to 2017.
ProvincesNet Profit Per Unit Area
(yuan/kg)
D
(yuan/hm2)
LTA
(hm2)
Earlier Costs
WheatCornRiceAmount
(Billion Yuan)
Proportion
(%)
Beijing1411.11376.23394.21388.233,277.39.30.50
Tianjin1411.71213.53824.11410.063,483.117.91.76
Hebei1521.11182.13376.41354.5183,573.149.83.64
Shanxi976.41212.02657.51143.562,727.814.43.37
Inner Mongolia864.21419.03852.81395.8131,006.236.76.18
Liaoning1261.21405.03898.61901.1191,500.972.74.09
Jilin867.71659.04141.12095.565,718.127.55.86
Heilongjiang949.71232.03497.92083.169,338.228.86.30
Shanghai1097.31509.84230.63385.347,124.331.31.81
Jiangsu1380.21209.34116.82639.0401,155.3208.53.95
Zhejiang1021.31011.53498.23122.3227,126.8139.03.64
Anhui1424.91065.83174.82078.0183,121.375.14.26
Fujian857.3877.03005.32865.8111,594.262.54.33
Jiangxi549.4964.32936.82910.2123,226.970.16.79
Shandong1641.31481.94226.51608.0390,632.1125.94.00
Henan1652.41268.93798.01666.5162,479.254.13.78
Hubei973.41098.83900.02593.5159,322.981.65.80
Hunan755.31243.03229.33062.7112,821.067.76.48
Guangdong849.21030.92805.62649.7180,447.094.02.80
Guangxi423.8974.42759.82331.586,236.139.85.64
Hainan0.0692.12172.32172.319,595.88.43.00
Chongqing860.91227.13642.32571.483,650.142.63.38
Sichuan983.11166.43768.42278.5154,404.369.74.05
Guizhou548.31021.33055.31780.362,901.422.33.93
Yunnan545.71015.23064.01702.878,094.426.54.13
Tibet1810.61232.72877.61771.44540.21.611.10
Shaanxi1011.41043.03433.01153.066,102.815.22.82
Gansu836.01183.83298.5788.858,488.19.24.35
Qinghai1087.71697.3441.21231.315,724.43.96.80
Ningxia873.31697.24275.51876.941,816.015.713.35
Xinjiang1541.71643.14414.91669.9105,144.735.213.00
Table 2. The ecological cost in the middle stage of the land finance model and its proportion in fiscal land funds for 31 provinces in China from 2003 to 2017.
Table 2. The ecological cost in the middle stage of the land finance model and its proportion in fiscal land funds for 31 provinces in China from 2003 to 2017.
ProvincesEnergy Consumption of Infrastructure
Construction
Heat Island EffectEcological Landscape FragmentationProportion
(%)
TDC
(10,000 L)
DA
(Billion Yuan)
TEE
(t)
RE
(10,000 m3)
AT ai
(℃)
DFL
(plot/km2)
AAL
(hm2/plot)
DAL
(%)
Beijing26,9782.15495.436,768.61.0434.92.9120.80.11
Tianjin31,8792.56493.741,897.11.0227.33.799.80.24
Hebei44,8523.59136.352,665.11.4443.62.357.00.25
Shanxi24,0711.94903.227,747.81.8646.22.281.40.44
Inner Mongolia27,9052.15684.230,465.31.8646.92.146.10.36
Liaoning48,4093.79861.061,943.51.1732.13.151.40.21
Jilin25,7212.05239.331,584.80.8977.31.397.70.42
Heilongjiang22,1741.74516.926,704.61.1853.91.975.00.37
Shanghai20,9461.64266.732,224.80.0027.13.795.30.09
Jiangsu122,3699.424,926.5146,715.41.2840.32.562.00.18
Zhejiang73,2415.614,919.191,293.31.0964.71.570.00.15
Anhui47,9743.79772.355,568.41.4743.82.358.10.21
Fujian28,9382.25894.731,813.20.9440.82.490.90.15
Jiangxi29,3162.35971.633,027.00.8241.62.477.30.22
Shandong125,2689.725,517.2146,897.81.1537.42.778.30.31
Henan42,9173.38742.248,669.90.9040.72.588.40.23
Hubei39,2653.07998.248,670.70.9791.21.157.90.22
Hunan33,2402.66770.937,960.61.30149.00.772.00.25
Guangdong97,5867.519,878.3117,307.50.7775.81.3205.80.22
Guangxi23,3161.84749.425,301.41.17141.60.789.60.26
Hainan67130.51367.57801.41.1927.63.690.40.19
Chongqing29,0222.35911.733,584.71.0038.32.6117.80.18
Sichuan54,9404.311,191.367,556.80.85162.90.6105.60.25
Guizhou14,7931.23013.417,507.40.6073.01.4105.90.20
Yunnan17,8821.43642.620,742.60.67168.00.6100.90.22
Xizang26290.2535.63537.40.5891.51.1166.61.50
Shaanxi24,1751.84924.527,042.61.1845.02.2122.50.34
Gansu96770.71971.210,062.81.5546.42.267.30.35
Qinghai46430.4945.75603.01.8351.81.964.20.62
Ningxia10,1460.82066.711,751.71.4630.73.369.40.66
Xinjiang21,9221.74465.625,859.60.3472.31.468.70.62
Table 3. The cost of increasing the fiscal ecological service function of land in 31 provinces from 2003 to 2017.
Table 3. The cost of increasing the fiscal ecological service function of land in 31 provinces from 2003 to 2017.
ProvincesFLR
(Billion Yuan)
UGE
(Billion Yuan)
CCE
(Billion Yuan)
Late Cost
(Billion Yuan)
Proportion
(%)
Beijing7.713.20.621.51.15
Tianjin5.98.70.815.41.51
Hebei16.317.71.935.92.62
Shanxi10.710.20.921.85.10
Inner Mongolia14.810.92.428.24.76
Liaoning15.716.51.833.91.91
Jilin8.98.50.918.43.91
Heilongjiang2.09.11.912.92.83
Shanghai19.46.31.527.21.57
Jiangsu33.037.14.274.31.41
Zhejiang49.022.64.275.91.99
Anhui11.218.62.232.01.81
Fujian22.214.71.538.32.65
Jiangxi19.315.92.337.53.63
Shandong27.443.44.475.12.39
Henan23.020.82.145.93.20
Hubei23.512.62.138.22.72
Hunan23.213.03.039.23.75
Guangdong18.453.513.785.62.55
Guangxi17.910.22.630.74.35
Hainan2.82.41.66.82.43
Chongqing14.015.12.031.12.47
Sichuan23.525.03.451.83.01
Guizhou12.48.71.422.53.97
Yunnan26.39.92.038.15.95
Tibet1.21.60.12.920.02
Shaanxi12.211.81.425.34.69
Gansu5.36.60.712.65.94
Qinghai0.51.40.32.33.98
Ningxia1.64.71.17.46.33
Xinjiang0.710.31.312.34.53
Table 4. The fiscal ecological cost of land and proportion in land fiscal funds in 31 provinces.
Table 4. The fiscal ecological cost of land and proportion in land fiscal funds in 31 provinces.
ProvincesEarly Cost
(Billion Yuan)
Middle Cost
(Billion Yuan)
Late Cost
(Billion Yuan)
Land Fiscal Ecological Cost
(Billion Yuan)
Proportion
(%)
Jiangsu208.59.474.3292.25.54
Zhejiang139.05.675.9220.55.77
Shandong125.99.775.1210.76.70
Guangdong94.07.585.6187.25.58
Sichuan69.74.351.8125.87.30
Hubei81.63.038.2122.88.73
Anhui75.13.732.0110.86.28
Liaoning72.73.733.9110.36.20
Jiangxi70.12.337.5109.910.65
Hunan67.72.639.2109.410.48
Henan54.13.345.9103.37.21
Fujian62.52.238.3103.17.14
Hebei49.83.535.989.16.51
Chongqing42.62.331.175.96.03
Guangxi39.81.830.772.310.24
Inner Mongolia36.72.128.267.011.30
Yunnan26.51.438.166.010.30
Shanghai31.31.627.260.13.47
Xinjiang35.21.712.349.118.15
Jilin27.52.018.447.810.20
Guizhou22.31.222.546.08.10
Heilongjiang28.81.712.943.49.50
Shaanxi15.21.825.342.47.85
Shanxi14.41.921.838.08.91
Tianjin17.92.515.435.83.52
Beijing9.32.121.532.81.76
Ningxia15.70.87.423.920.34
Gansu9.20.712.622.610.64
Hainan8.40.56.815.75.62
Qinghai3.90.42.36.511.40
Tibet1.60.22.94.732.61
Table 5. Comprehensive scale of fiscal ecological costs of land in China from 2003 to 2017.
Table 5. Comprehensive scale of fiscal ecological costs of land in China from 2003 to 2017.
ProjectAmountCost/Billion Yuan Land Fiscal Fund
Land finance Land transferred area3,676,374.0 hm292.3 hm2/billion yuan
Land fiscal fund39,815.6 billion yuan——
Early costLoss of cultivated land ecosystem service value1556.9 billion yuan39.1 million yuan/
billion yuan
Middle costEnergy costs87.3 billion yuan2.2 million yuan/
billion yuan
Exhaust dust emission230,772.7 t5.8 t/billion yuan
Residual soil and stone13.6 billion m3340,000 m3/billion yuan
Heat island effect————
Number of land transfers2,256,407 plots56.7 plots/billion yuan
Fragmentation degree of transferred land61.4 plots/km2——
New urban built-up area30,253.4 km276.0 hm2/billion yuan
Average area of transferred land1.6 hm2/plot——
Degree of urban agglomeration of transferred land82.3%——
Late costFees of land reclamation469.8 billion yuan11.8 million yuan/
billion yuan
New urban green space construction expenses460.8 billion yuan11.6 million yuan/
billion yuan
City cleaning expenses70.3 billion yuan1.8 million yuan/
billion yuan
The total costQuantifiable cost2645.1 billion yuan66.4 million yuan/
billion yuan
Table 6. Clustering results of land fiscal ecological cost of 31 provinces in 4, 5, and 6 class.
Table 6. Clustering results of land fiscal ecological cost of 31 provinces in 4, 5, and 6 class.
ProvincesClass IVProvincesClass VProvincesClass VI
JiangsuIJiangsuIJiangsuI
ZhejiangIIZhejiangIIZhejiangII
ShandongIIShandongIIShandongII
GuangdongIIGuangdongIIGuangdongII
SichuanIIISichuanIIISichuanIII
HubeiIIIHubeiIIIHubeiIII
AnhuiIIIAnhuiIIIAnhuiIII
LiaoningIIILiaoningIIILiaoningIII
JiangxiIIIJiangxiIIIJiangxiIII
HunanIIIHunanIIIHunanIII
HenanIIIHenanIIIHenanIII
FujianIIIFujianIIIFujianIII
HebeiIIIHebeiIVHebeiIV
ChongqingIIIChongqingIVChongqingIV
GuangxiIIIGuangxiIVGuangxiIV
Inner MongoliaIIIInner MongoliaIVInner MongoliaIV
YunnanIIIYunnanIVYunnanIV
ShanghaiIIIShanghaiIVShanghaiIV
XinjiangIVXinjiangVXinjiangV
JilinIVJilinVJilinV
GuizhouIVGuizhouVGuizhouV
HeilongjiangIVHeilongjiangVHeilongjiangV
ShaanxiIVShaanxiVShaanxiV
ShanxiIVShanxiVShanxiV
TianjinIVTianjinVTianjinV
BeijingIVBeijingVBeijingV
NingxiaIVNingxiaVNingxiaVI
GansuIVGansuVGansuVI
HainanIVHainanVHainanVI
QinghaiIVQinghaiVQinghaiVI
TibetIVTibetVTibetVI
Table 7. One-way ANOVA of land fiscal ecological cost and its proportion to land finance revenue in 31 provinces.
Table 7. One-way ANOVA of land fiscal ecological cost and its proportion to land finance revenue in 31 provinces.
Sum of SquaresdfMean SquareFSig.
Fiscal
ecological cost of land
(billion yuan)
Between Groups123,552.198430,888.05067.3120.000
Within Groups11,930.91326458.881
Total135,483.11130
Proportion
(%)
Between Groups624.7814156.19510.8690.000
Within Groups373.6382614.371
Total998.41930
Sig. > a = 0.05 indicates that the fiscal ecological land cost and its proportion to land fiscal revenue is significantly different in the different areas. That is, the partition result in this study is reasonable.
Table 8. Total scale of land fiscal ecological cost in each area from 2003 to 2017.
Table 8. Total scale of land fiscal ecological cost in each area from 2003 to 2017.
ProjectArea IArea ⅡArea ⅢArea ⅣArea Ⅴ
Land financeLand transferred area
(hm2)
14,388.5119,936.1138,204.472,537.122,571.3
Land fiscal fund
(billion yuan)
4609.915,601.212,992.65939.9672.0
Early costLoss of cultivated land ecosystem service value
(billion yuan)
58.5567.5603.3262.165.6
Middle costEnergy costs
(billion yuan)
6.232.228.616.63.8
Exhaust dust emission
(t)
16,255.985,24175,338.343,952.79984.9
Residual soil and stone
(10,000 m3)
110,890.5502,214.1437,875.1248,482.656,814.3
Heat island effect——————————
Number of land transfers
(plot)
41,733591,558949,870544,979128,267
Fragmentation degree of transferred land
(plot/km2)
29.049.368.775.156.8
New urban built-up area
(km2)
1484.810,847.89861.86475.81583.2
Average area of transferred land (hm2/plot)3.52.01.51.31.8
Degree of urban agglomeration of transferred land (%)103.290.571.489.370.2
Late costLand reclamation fees
(billion yuan)
32.9127.8177.7122.09.4
New urban green space construction expenses
(billion yuan)
28.2156.6154.796.724.6
City cleaning expenses
(billion yuan)
2.826.520.317.23.5
The total amountQuantifiable cost
(billion yuan)
128.7910.6984.5514.6106.8
Table 9. Cost of land finance funds per 1 billion yuan in each area from 2003 to 2017.
Table 9. Cost of land finance funds per 1 billion yuan in each area from 2003 to 2017.
ProjectArea IArea ⅡArea ⅢArea ⅣArea Ⅴ
Land financeLand transferred area
(hm2/billion yuan)
31.276.9106.4122.1335.9
Land fiscal fund
(million yuan/billion yuan)
——————————
Early costLoss of cultivated land ecosystem service value
(million yuan/billion yuan)
12.736.446.444.197.6
Middle costEnergy costs
(million yuan/billion yuan)
1.32.12.22.85.6
Exhaust dust emission
(t/billion yuan)
3.55.55.87.414.9
Residual soil and stone
(10,000 m3/billion yuan)
24.132.233.741.884.5
Heat island effect——————————
Number of land transfers
(plot/billion yuan)
9.137.973.191.7190.9
Fragmentation degree of transferred land
(plot/km2)
——————————
New urban built-up area
(hm2/billion yuan)
32.269.575.9109.0235.6
Average area of transferred land (hm2/plot)——————————
Degree of urban agglomeration of transferred land (%)——————————
Late costLand reclamation fees
(million yuan/billion yuan)
7.18.213.720.514.0
New urban green space construction expenses
(million yuan/billion yuan)
6.110.011.916.336.6
City cleaning expenses
(million yuan/billion yuan)
0.61.71.62.95.2
The total amountQuantifiable cost
(million yuan/billion yuan)
27.958.475.886.6159.0
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Wang, Y.; Yang, X. Fiscal Ecological Cost of Land in China: Estimation and Regional Differences. Land 2022, 11, 1221. https://doi.org/10.3390/land11081221

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Wang Y, Yang X. Fiscal Ecological Cost of Land in China: Estimation and Regional Differences. Land. 2022; 11(8):1221. https://doi.org/10.3390/land11081221

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Wang, Yubo, and Xizhu Yang. 2022. "Fiscal Ecological Cost of Land in China: Estimation and Regional Differences" Land 11, no. 8: 1221. https://doi.org/10.3390/land11081221

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