- Email: [email protected]
Intergenerational Efficiency of Farmland Conversion and Farmland Resource Loss in China
CHINA POPULATION, RESOURCES AND ENVIRONMENT Volume 17, Issue 3, May 2007 Online English edition of the Chinese language journal Cite this article as: Chn Popu Res Envi, 2007, 17(3): 28–34.
SHORT COMMUNICATION
Intergenerational Efficiency of Farmland Conversion and Farmland Resource Loss in China Tan Rong*, Qu Futian China Center for Land Policy Research, Nanjing Agricultural University, Nanjing 210095, China
Abstract: One of the requirements of sustainable utilization of resources is the efficiency of intergenerational allocation, which is important to the long-term utilization of resources. Based on the principle of efficient intergenerational allocation of resource in resource economics, this paper builds an ex post model to measure the efficiency of intergenerational allocation of farmland resource, and points out that with the continuous testing and modifying in practice according to the model results, the allocation of farmland resource in the future can continuously converge at the efficient allocation. This paper employs this model to test the allocation of the farmland resource from 1989 to 2003, and the result shows that if the test sample is divided into 1989 to 1996 period and 1997 to 2003 period, which due to the stricter farmland resource protection policy issued after 1996, the inefficient farmland conversion of the three main regions in China in the period of 1989 to 1996 was 6.58%, 6.84%, 7.85%, respectively. Keywords: intergenerational allocation; farmland conversion; excessive loss
1
Introduction
Scarcity and constrain on renewable natural resources, especially the exhausted resources, lead to the importance of the intergenerational allocation efficiency. Farmland conversion from rural to urban sector is thus an issue of exhausted resource allocation because of the difficulty in reclamation from the urban to the rural after the farmland has been converted. In China most of farmland conversion occurs around the urban area, where the land is more fertile than in the remote area. Although the economic growth needs farmland conversion, only efficient use can meet the harmony between natural resource protection and economic growth. Current studies have paid attention to the incompleteness of market mechanism and the irrational intervention of the government in the market system (Tan, Qu, 2005; Qu, Feng, Zhu, et al, 2004), which causes farmland over-conversion, but there is seldom study focusing on the efficiency of intergenerational allocation of farmland conversion in China. For the moment we do not go further to the topic of equality between generations. Instead, we would like to examine only the efficiency according to the neoclassic rule in resource allocation. Also it should be mentioned
that efficiency not only includes intergenerational allocation efficiency, there are many other aspects related to this issue, but in this paper we only focus on whether current farmland conversion meets the efficiency standard in China aiming to avoid the complexity and difficulties on explanation. This paper also only testifies the ex post data in order to escape the inaccuracy of the forecast of future situations. Sometimes the ex post method is a suitable way for policy makers to access the performance of one policy and make improvement on it according to what have been learned.
2
Model
The principle for efficient allocation of intergenerational allocation is well discussed, and the basic marginal rule can be found in any neoclassic environmental textbooks (Perman, Yue, McGilvray, Commo, 2003; Zhang, 1998; Qu, 2001), which is to ensure the marginal present revenue among different generations are equal. International studies on this topic will have certain assumptions on the revenue and discount rate in the future, and then maximize the total revenue among different generations to obtain the optimal utilization of the resources among different phases (Winjum,
Received date: 2006-11-18 * Corresponding author. E-mail: [email protected] Copyright © 2007, Chinese Society for Sustainable Development and Research Center for Sustainable Development of Shandong Province. Electronic version published by Elsevier Limited. All rights reserved.
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
Brown, Schlamandinger, 1998; Tassone, Wesseler, Nesci, 2002). This inevitably results in the subjective error for the assumptions. The studies on this topic in China mostly remain at the theoretical analysis, e.g. illustrating either the importance of the intergenerational efficiency (Wei, 2002), or importance of how to find a reasonable discount rate in resource utilization (Chen, 2003). Based on the theoretical principle for efficient allocation between generations, this paper builds up a C-D production model to calculate the marginal revenue of urban construction land and the farmland. The C-D production forms are shown as equations (1) and (2).
NRAMi NRUi
F
G
A u K AMi u LAMi u Land AMi D
E
B u KUi u LUi u LandUi
H
J
(1) (2)
Where, NRAMi is the net revenue of agricultural sector. NRUi is the net revenue of urban sector. K is the capital investment. L is the labor input. Land is the land resource input. The subscript Ui denotes the urban sector in i phase, and the subscript AMi is the rural sector in i phase. If the coefficients of the equations (1) and (2) are estimated, the marginal revenue of farmland conversion can be calculated through the deduction of marginal revenue of urban sector and the marginal revenue of rural sector, where the latter includes the marginal non-market value of the agricultural land resource. The calculation formula is shown as (3). MRi
w(TRi ) w( Land)
w( NRUi ) w( NRAMi ) w(RANMi ) w(LandUi ) w( LandAMi ) w(LandAMi )
(3)
Where, MRi is the marginal revenue of farmland conversion in i phase. TRi denotes the total revenue of farmland revenue in i phase. NRUi is the total revenue of urban construction land in i phase. NRAmi is the total revenue of farmland revenue in i phase. RANMi is the non-market value of farmland resource in i phase. LandUi is the amount of urban construction land in i phase, and LandAmi is the amount of farmland in i phase. After the above calculation, the inefficient farmland loss according to the efficiency principle can be calculated. The relation between farmland conversion quantities with the marginal revenue should be estimated first. Assume the demand and supply curves of farmland conversion are the forms like equations (4) and (5). (4) QD a u P b , b >0
Q
S
c u P
d
, d >0
(5)
Then, according to the basic pricing formulas in microeconomics, which are P=MC/(1+(1/Ed))and P=MR/(1+ (1/Es)), the equations (4) and (5) can be changed into equa-
tions (6) and (7). logLANDcovi C1 C2 u logMRUi
(6)
logLANDcovi C3 C4 u log(MRAMi MRANMi)
(7)
Where, LANDcovi is the amount of farmland conversion. MR denotes the marginal revenue. The subscripts are the same meaning as above. After the estimation of C1C2C3C4, and make equal of the marginal revenue in different phases, then the ratio of farmland conversion in each phase is obtained, which meets the requirement of efficiency standard. If comparing the calculated efficient results with those in reality, then the inefficient farmland conversion loss can be calculated. So far, the model for accessing the efficiency allocation of farmland conversion between different generations is built, while the measurement of non-market value of farmland resource is still missing, which will be discussed in the next section. The fourth part of this paper will be the employment of the model on provincial panel data in China from 1989 to 2003.
3
Marginal non-market value of farmland
Many categories of value belong to farmland resource besides the function supporting the agricultural production, which are excluded from the traditional market system. For example, farmland resource has the functions of water regulation, gas regulation, erosion control, biological control, and even amusement of the landscape. So far, a study of Costanza (1997) is concerned and cited frequently on the value of the world’s ecosystem services and natural capital (Costanza, d’Arge R, de Groot R, et al, 1997). It can be seen as a completed and detailed research on ecosystem services and natural capital of natural resource based on former studies. This paper will also cite some results of the Costanza (1997)’s study combined with the real conditions in China to estimate the marginal ecological revenue of the China’s farmland resource. Some results of the Costanza (1997) on the ecological value of the farmland resource according to 1994 constant price are given as follows: the ecological value of the farmland resource in forestry was 969 US$ha-1yr-1 on average, 2007 US$ha-1yr-1 in tropic area, 302 US$ha-1yr-1 in temperate area; the ecological value of the farmland resource in grassland was 232 US$ha-1yr-1; the ecological value of the farmland resource in water was 8498 US$ha-1yr-1; the ecological value of the farmland resource in cropland (including the cultivated land and garden plot) was 92 US$ha-1yr-1. The method to calculate the ecological value of farmland resource in China is shown in the following steps: First, find the acreage of different categories of farmland resources in different provinces. The grassland and water data can be
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
the relation between the gross value and the quantity of the farmland is a simple equation, or linear equation, which means the gross value equals to the quantity of the farmland times the value of the unit quantity. The differential coefficient of the gross value (marginal revenue) is exactly the value of per unit quantity (a constant), so the average value can be seen as the marginal revenue. It can also be explained intuitionally that the loss of the ecological value of the last unit of the converted farmland is exactly the average eco logical value of this unit.
gained directly in the government report; the forestry should be subdivided according to the different climate zones, and the cropland is the sum of cultivated land and garden plot. Second, with the data in Table 1, calculate the average ecological value of different provinces in different years weighted by the acreage of farmland resource. Last, eliminate the inflation and change it into the 2003 constant price. The result is shown in Table 1. The ecological value of farmland resource in Table 2 is an average value, and it is not equal to the marginal value in theory. But the ecological value is a non-market value, and
Table 1 Ecological value of farmland resource in different provinces in China Unit: 104yuan RMB ha-1 yr-1 (2003 constant price) City
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Beijing
0.44
0.46
0.52
0.57
0.68
0.86
1.01
1.38
1.18
1.21
1.22
1.26
1.30
1.28
1.28
Tianjin
1.75
1.80
1.99
2.22
2.72
3.36
3.85
4.21
4.33
4.31
4.26
4.24
4.29
4.28
4.32
Hebei
0.46
0.48
0.51
0.56
0.66
0.82
0.93
1.06
1.03
1.02
1.00
0.99
1.00
0.99
1.01
Shanxi
0.33
0.33
0.36
0.38
0.44
0.55
0.64
0.71
0.72
0.71
0.70
0.73
0.73
0.72
0.73
Inner Mongolia 0.22
0.26
0.27
0.30
0.34
0.42
0.50
0.54
0.56
0.56
0.56
0.56
0.57
0.57
0.58
Liaoning
0.53
0.57
0.66
0.71
0.81
0.97
1.13
1.24
1.21
1.18
1.16
1.16
1.16
1.15
1.17
Jilin
0.43
0.45
0.47
0.51
0.58
0.70
0.81
0.89
0.90
0.89
0.88
0.86
0.87
0.87
0.88
Heilongjiang
0.47
0.40
0.44
0.50
0.59
0.73
0.87
0.95
0.97
0.97
0.94
0.93
0.93
0.93
0.93
Shanghai
1.29
1.37
1.52
1.70
2.07
2.61
3.10
5.95
3.48
3.48
3.53
3.62
3.62
3.64
3.64
Jiangsu
1.67
1.61
1.84
1.80
2.33
2.86
3.44
4.18
3.82
3.80
3.75
3.75
3.78
3.75
3.79
Zhejiang
0.52
0.53
0.54
0.59
0.81
1.38
1.13
1.24
1.20
1.18
1.17
1.18
1.18
1.16
1.19
Anhui
0.98
1.02
1.06
1.12
1.27
1.73
1.72
1.85
1.83
1.80
1.76
1.77
1.78
1.76
1.79
Fujian
0.45
0.45
0.45
0.48
0.54
1.20
0.79
0.88
0.85
0.85
0.84
0.86
0.85
0.85
0.85
Jiangxi
0.61
0.62
0.64
0.69
0.77
1.42
1.09
1.24
1.16
1.15
1.13
1.13
1.13
1.13
1.14
Shandong
0.89
0.95
0.99
1.03
1.16
1.42
1.67
1.88
1.88
1.87
1.85
1.86
1.89
1.88
1.90
Henan
0.71
0.71
0.71
0.75
0.86
0.98
1.14
1.30
1.30
1.27
1.23
1.22
1.23
1.23
1.25
Hubei
0.81
0.85
0.92
1.19
1.10
1.76
1.85
2.02
2.09
2.05
2.01
1.99
2.00
1.99
2.03
Hunan
0.61
0.61
0.64
0.73
0.87
1.64
1.38
1.46
1.52
1.53
1.53
1.56
1.54
1.53
1.57
Guangdong
0.54
0.52
0.54
0.59
0.74
2.01
1.02
1.13
1.11
1.09
1.07
1.09
1.08
1.07
1.07
Guangxi
0.33
0.33
0.34
0.36
0.43
1.69
0.71
0.77
0.76
0.73
0.72
0.72
0.72
0.71
0.72
Hainan
0.30
0.29
0.31
0.34
0.46
1.48
0.78
1.10
0.82
0.80
0.78
0.79
0.78
0.77
0.78
Sichuan
0.36
0.37
0.38
0.41
0.48
0.89
0.64
0.69
0.74
0.74
0.73
0.73
0.74
0.74
0.75
Guizhou
0.23
0.23
0.24
0.26
0.31
0.70
0.45
0.50
0.51
0.51
0.51
0.50
0.51
0.51
0.51
Yunnan
0.26
0.26
0.27
0.30
0.38
1.79
0.56
0.62
0.63
0.64
0.64
0.63
0.62
0.62
0.63
Tibet
0.32
0.33
0.57
0.63
0.71
1.02
1.09
1.17
1.23
1.24
1.24
1.24
1.24
1.24
1.25
Shaanxi
0.27
0.27
0.28
0.31
0.34
0.44
0.52
0.58
0.6
0.59
0.58
0.57
0.58
0.57
0.58
Gansu
0.27
0.29
0.31
0.35
0.40
0.50
0.60
0.59
0.68
0.67
0.66
0.65
0.68
0.68
0.69
Qinghai
0.30
0.40
0.49
0.52
0.69
0.88
1.11
1.31
1.3
1.31
1.3
1.29
1.33
1.36
1.39
Ningxia
0.23
0.25
0.29
0.32
0.42
0.53
0.62
0.66
0.69
0.69
0.68
0.68
0.69
0.68
0.69
Xinjiang
0.34
0.41
0.44
0.50
0.64
0.81
1.08
1.21
1.24
1.25
1.21
1.21
1.25
1.25
1.25
Source: Calculated by the authors.
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
4
Estimation and results
4.1 Data The land resource data used to estimate the model comes from the statistic data of the land administration of China (19891995) and land yearbook of China (19941997) compiled by the former National Land Management Bureau, and the annals on the land resource of China (19992003) compiled by the Ministry of Land and Resources of China. The data of the other variables come from the Statistic Yearbook of China (19902004). In order to get the comparable data of the agricultural and non-agricultural sectors, indexes of each variable are chosen that can be found in the subdivided data in each industry. The gross revenue of the agricultural sector NRAMi is the primary industry (agriculture) data of GDP, and the other part of the GDP is the gross revenue of the non-agricultural sector NRUi. The capital investment K is the sum of new basic construction investment, new renewal and reconstruction investment, and the fixed capital investment by urban and rural collective communities in each year. The total investment includes four aspects: the basic construction, renewal and reconstruction, real estate development, and the other fixed capital investment. So the data used in this paper underestimates the investment of the production. But for the aim of the model is to calculate the MR (marginal revenue) and MC (marginal cost) of the land resource, so the estimation will not influenced by the error of the investment, for the error is considered in the constant term. The labor data is the data of the employed person in each production sector. The land resource input data in the agricultural sector LandAMi includes the acreage of forestry, grassland, cultivated land, garden plot, and water area. The land input data in the non-agricultural sector LandUi is the acreage of the construction land. The conversion data of farmland resource QD and QS are the data of the cultivated land occupied by the construction land each year. Since farmland resource not only includes the cultivated land, so this index underestimates the quantity of farmland conversion. The underestimation of the farmland conversion data leads to the underestimation of the slope of the MC and overestimation of the slope of MR curves, so it overestimate the expense loss and excessive loss I, which causes the underestimation of the excessive loss II. Since the limitation of the data and the cultivated land to be occupied is the main proportion of the total conversion data, this index is acceptable. All the price data are changed into the 2003 constant price. The provincial panel data of 30 provinces, municipalities, and autonomic regions from 1989 to 2003 are used to estimate the model, which excludes the data of Hong Kong, Macao, and Taiwan. Chongqing’s data is also excluded for its incompleteness.
4.2 Estimation method and result Due to the different situations between different provinces in China, and the lopsided policy on farmland conversion in each region, this study divided the whole country into three regions according to the national standard, which are as follows: (a) Eastern China: municipalities of Beijing, Tianjin, and Shanghai, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi and Hainan provinces. (b) Central China: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan provinces, the autonomous region of Inner Mongolia (c) Western China: Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, and Qinghai provinces, autonomous regions of Tibet, Ningxia, and Xinjiang. First, estimate the equation (1) and (2), then calculate the marginal revenue of the two sectors with the equation (3) and (4). To estimate the (1) and (2), first change the equation into a logarithmic style, then employ the general least squares method with the first order component of autocorrelation, AR (1), which indicates an autoregressive component. In the estimation of the equation (1), a cross-section weight by the dummy variables of each province is used to estimate the coefficients of AR (1) and Landagr because of the contribution of farmland resource in different provinces are different, and then different coefficients for each province can be obtained. In the estimation of the equation (2), only the AR (1) is estimated with the cross-section weight, for the construction land is not influenced by the terrain and climate. In addition, White Heteroskedasticity-Consistent Standard Errors and Covariance method is employed in the estimation to reduce the Heteroskedasticity. The result is shown in Table 2. The regression result is shown in Table 2. Each coefficient is significant at 1% level, and each statistic index passes the test. The marginal revenue of each period can be calculated with the original data with the coefficients in Table 2 and the data in Table 1. The result is shown in Table 3. The marginal revenue of farmland conversion between 1989 and 1996 was lower than that between 1997 and 2003 in the whole three regions, which has verified that the inefficiency of inter-generational allocation occurred in that period. The over-conversion of the farmland in the beginning of the 1990s caused the total intergenerational allocation inefficiency in the sample range of this study. In other words, if reducing the conversion amount before 1996, and (or) leaving them to the 19972003 period, farmland conversion would be at least a Pareto improvement, even if the allocation in the later period didn’t meet the efficient one, which still needed to be tested several years later. If we only focus on 19892003 period, the over conversion of the farmland in 19891996 can be calculated by the steps in the above model to estimate the equation (6) and (7)
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
2003. The over conversion proportions were 6.58%, 6.84% and 7.85% respectively, which showed that the central government farmland conversion decision in that period was not reasonable. Actually, from the beginning of the 1990s the large increase of the built-up area all over the country swallowed a large amount of farmland resource, while the utilization was inefficient. The central government began to relocate and reclaim the built-up area in the suburban area and forbid illegal or inefficient built-up projects after 1996, which has done some positive effect to prevent the farmland from over-use.
with the same data but in different sample range. The result is shown in Table 4. Each coefficient is significant at 1% level, and each statistic index passes the test. Based on the estimation of the equation (6) and (7), the efficient intergenerational allocation ratio between the two samples can be obtained. Then with the real conversion amount from 1989 to 2003, the over-conversion amount between 1989 and 1996 can be calculated. The result is shown in Table 5. From Table 5 we can see that the inefficiency of the intergenerational allocation existed in all the three regions from 1989 to 1996 if set sample range is set from 1989 to
Table 2 Result of the regression of equation (1) and (2)
Region
Eastern China (t test)
Central China (t test)
Western China (t test)
Period
19891996 19972003 19891996 19972003 19891996 19972003
Equation (1) A
Į
ȕ
2.382
0.003
0.028
(68.65)
(20.89)
(28.94)
3.892
0.004
0.042
(45.27)
(17.89)
(25.56)
2.087
0.002
0.022
(60.23)
(21.34)
(22.85)
3.012
0.003
0.031
(55.45)
(20.28)
(21.89)
1.968
0.004
0.019
(58.56)
(19.24)
(25.54)
2.789
0.004
0.033
(45.67)
(18.62)
(28.53)
Equation (2) Ȗ
R2
DW
Omit
0.999
2.231
Omit
0.998
2.182
Omit
0.999
2.151
Omit
0.999
2.193
Omit
0.999
2.255
Omit
0.997
2.221
B
Ȥ
į
İ
0.387
0.042
0.114
0.022
(4.93)
(7.30)
(9.01)
(5.28)
0.512
0.053
0.194
0.034
(5.28)
(6.76)
(8.89)
(6.45)
0.312
0.038
0.108
0.019
(5.12)
(8.30)
(8.35)
(4.78)
0.478
0.041
0.134
0.033
(6.89)
(7.92)
(9.67)
(5.56)
0.313
0.032
0.098
0.016
(4.93)
(6.30)
(7.25)
(6.12)
0.402
0.038
0.145
0.029
(5.67)
(6.89)
(8.34)
(7.56)
R2
DW
0.999
1.887
0.997
1.821
0.998
1.781
0.998
1.883
0.992
1.872
0.999
1.923
Source: Calculated by the authors.
Table 3 Net marginal revenue of farmland conversion in different areas in China in two different periods Unit: yuan ha-1 yr-1(2003 constant price) Region Eastern China
19891996
19972003
Region
41 646
54 313
Central China
19891996
19972003
Region
19891996
19972003
12 335
Western China
8 033
10 083
Sichuan
4 077
7 164
3 983
4 171
Guizhou
14 478
18 329
Jilin
5 995
6 679
Yunnan
10 043
13 660
21 359
Heilongjiang
8 818
13 535
Tibet
11 654
14 180
Beijing
69 584
75 065
Shanxi
Tianjin
38 637
40 283
Inner Mongolia
Hebei
13 348
18 073
Liaoning
18 146
8 979
8 998
11 034
Shanghai
119 611
146 062
Anhui
5 752
6 423
Shaanxi
9 640
12 932
Jiangsu
25 456
29 276
Jiangxi
9 950
11 995
Gansu
5 661
8 718
Zhejiang
59 659
61 174
Henan
9 235
13 047
Qinghai
6 624
7 804
Fujian
32 998
48 383
Hubei
14 501
18 478
Ningxia
7 664
8 713
Hunan
15 073
Xinjiang
1 883
3 146
Shandong
16 127
20 478
Guangdong
43 059
48 278
Guangxi
10 127
14 378
Hainan
12 710
12 265
Source: Calculated by the authors
13 478
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
Table 4 Result of the regression of equation (6) and (7)
Region
Eastern China (t test)
Central China (t test)
Western China (t test)
Period
Equation (6) C1
19891996
omit
19972003
omit
19891996 19972003 19891996 1997~2003
omit
C2 0.471 (5.14) 0.812 (4.28) 0.413 (6.34)
omit
0.678 (5.23)
omit
0.327 (4.64)
omit
0.562 (6.89)
Equation (7)
R2
DW
0.984
2.034
0.991
2.156
0.982
2.052
0.987
2.146
0.991
2.037
0.995
2.198
C3
C4
omit
0.112 (2.75)
omit
0.233 (4.13)
omit
0.097 (2.86)
omit
0.198 (3.46)
omit
0.078 (4.22)
omit
0.146 (4.98)
R2
DW
0.987
2.097
0.982
2.121
0.992
2.086
0.998
2.034
0.983
2.043
0.987
2.056
Source: Calculated by the authors.
Table 5 Inter-generational allocation loss of farmland conversion between 1989 and 1996 (sample as19892003) Region
Ratio(%)
Amount(ha)
Region
Ratio(%)
Amount(ha)
Region
Ratio(%)
Amount(ha)
Eastern China
6.58
31 142
Central China
6.84
21 402
Western China
7.85
12 590
Beijing
5.19
920
Shanxi
6.14
1 426
Sichuan
9.05
5 004
Tianjin
6.04
486
Inner Mongolia
8.36
1 748
Guizhou
8.29
1 058
Hebei
4.93
2 389
Jilin
6.94
723
Yunnan
5.81
1 398
Liaoning
6.98
3 161
Heilongjiang
7.07
2 927
Tibet
7.75
423
Shanghai
7.07
2 838
Anhui
6.51
4 511
Shaanxi
7.49
1 903
Jiangsu
6.33
5 353
Jiangxi
4.04
402
Gansu
8.41
979
Zhejiang
4.84
2 470
Henan
6.85
4 162
Qinghai
3.19
56
Fujian
2.87
711
Hubei
8.42
4 415
Ningxia
8.28
374
Shandong
7.92
4 562
Hunan
6.63
1 619
Xinjiang
9.11
1 770
Guangdong
5.05
3 165
Guangxi
7.41
1 669
Hainan
8.84
894
Source: Calculated by the authors.
It should be mentioned that the potential application value of the above model is that farmland conversion between 1989 and 1996 is inefficient according to the marginal revenue between 1997 and 2003, which means the farmland protection policy after 1996 in China did improve the efficient generational allocation of farmland conversion. But the result does not offer the evidence that we should convert more farmland after 1996, because we do not know whether the marginal revenue is as equal as the later period, say 20042010. We cannot draw the conclusion until we reach 2010 and test the reality data again. So at this moment we’d better keep the same rate of farmland conversion like 19972003, and check it after 2010. If the marginal revenue of 19972003 was higher than that of 20042010, then we can convert more farmland for the equal revenue principle
requirement, otherwise we should control more strictly on the farmland conversion than in 19972003. Under this rule, farmland conversion will converge gradually at the efficient standard.
5
Conclusions
This paper sets up a model to measure the intergenerational efficiency of farmland conversion in China between 19891996 and 19972003. The finding of this paper is that if the sample range was set from 1989 to 2003, there would exist an over conversion of farmland resource on intergenerational allocation before 1996. The over-conversion ratios of the eastern, central and western regions are 6.58%, 6.84% and 7.85% to each total conversion amount respectively.
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
Although farmland conversion after 1996 was not verified to meet the efficient one, which depends on the future conversion revenue, to control the conversion speed and constrain the conversion quantity is an appropriate way to ensure the intergenerational allocation efficiency for the utilization of farmland resource nowadays.
[5] Qu, F. T., Feng, S. Y., Zhu, P. X., et al, 2004. Institutional arrangements, price system and farmland conversion. China Economics Quarterly, 1, 229248 [6] Tan, R., Qu, F. T., 2005. How to harmonize the contradiction between utilization of natural resources and the sustainable development of economy. Journal of Natural Resources, 6, 197205
References
[7] Tassone, V. C., Wessele, J., Nesci, F. S., 2002. Diverging incentives for afforestation from carbon sequestration: an eco-
[1] Chen, A. N., 2003. Approach to issues of discount rate in the utilization of natural resources. Resources Science, 3, 1316
nomic analysis of the EU afforestation program in the south of Italy. Forest Policy and Economics, 1, 567578
[2] Costanza, R., d’Arge R., R. de Groot, et al, 1997. The value of
[8] Wei, X. P., 2002. Study on problems of intergenerational alloca-
the world’s ecosystem services and natural capital. Nature,
tion of mineral resource. Journal of China University of Min-
387, 253260
ing & Technology (Social Sciences), 2, 7579
[3] Perman, R., Yue, M., McGilvray, J., Common, M., 2003. Natu-
[9] Winjum, J. K., Brown, S., Schlamandinger, B., 1998. Forest
ral Resource and Environmental Economics. The 3rd edition,
harvest and wood products: sources and sinks of atmospheric
Addison-Wesley Publishers, UK, 359361 [4] Qu, F. T., 2002. Resource Economics. China Agricultural Press, Beijing, 6068
carbon dioxide. Forest Science, 44, 272284 [10] Zhang, F., 1998. Environment and Natural Resources Economics. Shanghai People Press, Shanghai, 3742
SHORT COMMUNICATION
Intergenerational Efficiency of Farmland Conversion and Farmland Resource Loss in China Tan Rong*, Qu Futian China Center for Land Policy Research, Nanjing Agricultural University, Nanjing 210095, China
Abstract: One of the requirements of sustainable utilization of resources is the efficiency of intergenerational allocation, which is important to the long-term utilization of resources. Based on the principle of efficient intergenerational allocation of resource in resource economics, this paper builds an ex post model to measure the efficiency of intergenerational allocation of farmland resource, and points out that with the continuous testing and modifying in practice according to the model results, the allocation of farmland resource in the future can continuously converge at the efficient allocation. This paper employs this model to test the allocation of the farmland resource from 1989 to 2003, and the result shows that if the test sample is divided into 1989 to 1996 period and 1997 to 2003 period, which due to the stricter farmland resource protection policy issued after 1996, the inefficient farmland conversion of the three main regions in China in the period of 1989 to 1996 was 6.58%, 6.84%, 7.85%, respectively. Keywords: intergenerational allocation; farmland conversion; excessive loss
1
Introduction
Scarcity and constrain on renewable natural resources, especially the exhausted resources, lead to the importance of the intergenerational allocation efficiency. Farmland conversion from rural to urban sector is thus an issue of exhausted resource allocation because of the difficulty in reclamation from the urban to the rural after the farmland has been converted. In China most of farmland conversion occurs around the urban area, where the land is more fertile than in the remote area. Although the economic growth needs farmland conversion, only efficient use can meet the harmony between natural resource protection and economic growth. Current studies have paid attention to the incompleteness of market mechanism and the irrational intervention of the government in the market system (Tan, Qu, 2005; Qu, Feng, Zhu, et al, 2004), which causes farmland over-conversion, but there is seldom study focusing on the efficiency of intergenerational allocation of farmland conversion in China. For the moment we do not go further to the topic of equality between generations. Instead, we would like to examine only the efficiency according to the neoclassic rule in resource allocation. Also it should be mentioned
that efficiency not only includes intergenerational allocation efficiency, there are many other aspects related to this issue, but in this paper we only focus on whether current farmland conversion meets the efficiency standard in China aiming to avoid the complexity and difficulties on explanation. This paper also only testifies the ex post data in order to escape the inaccuracy of the forecast of future situations. Sometimes the ex post method is a suitable way for policy makers to access the performance of one policy and make improvement on it according to what have been learned.
2
Model
The principle for efficient allocation of intergenerational allocation is well discussed, and the basic marginal rule can be found in any neoclassic environmental textbooks (Perman, Yue, McGilvray, Commo, 2003; Zhang, 1998; Qu, 2001), which is to ensure the marginal present revenue among different generations are equal. International studies on this topic will have certain assumptions on the revenue and discount rate in the future, and then maximize the total revenue among different generations to obtain the optimal utilization of the resources among different phases (Winjum,
Received date: 2006-11-18 * Corresponding author. E-mail: [email protected] Copyright © 2007, Chinese Society for Sustainable Development and Research Center for Sustainable Development of Shandong Province. Electronic version published by Elsevier Limited. All rights reserved.
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
Brown, Schlamandinger, 1998; Tassone, Wesseler, Nesci, 2002). This inevitably results in the subjective error for the assumptions. The studies on this topic in China mostly remain at the theoretical analysis, e.g. illustrating either the importance of the intergenerational efficiency (Wei, 2002), or importance of how to find a reasonable discount rate in resource utilization (Chen, 2003). Based on the theoretical principle for efficient allocation between generations, this paper builds up a C-D production model to calculate the marginal revenue of urban construction land and the farmland. The C-D production forms are shown as equations (1) and (2).
NRAMi NRUi
F
G
A u K AMi u LAMi u Land AMi D
E
B u KUi u LUi u LandUi
H
J
(1) (2)
Where, NRAMi is the net revenue of agricultural sector. NRUi is the net revenue of urban sector. K is the capital investment. L is the labor input. Land is the land resource input. The subscript Ui denotes the urban sector in i phase, and the subscript AMi is the rural sector in i phase. If the coefficients of the equations (1) and (2) are estimated, the marginal revenue of farmland conversion can be calculated through the deduction of marginal revenue of urban sector and the marginal revenue of rural sector, where the latter includes the marginal non-market value of the agricultural land resource. The calculation formula is shown as (3). MRi
w(TRi ) w( Land)
w( NRUi ) w( NRAMi ) w(RANMi ) w(LandUi ) w( LandAMi ) w(LandAMi )
(3)
Where, MRi is the marginal revenue of farmland conversion in i phase. TRi denotes the total revenue of farmland revenue in i phase. NRUi is the total revenue of urban construction land in i phase. NRAmi is the total revenue of farmland revenue in i phase. RANMi is the non-market value of farmland resource in i phase. LandUi is the amount of urban construction land in i phase, and LandAmi is the amount of farmland in i phase. After the above calculation, the inefficient farmland loss according to the efficiency principle can be calculated. The relation between farmland conversion quantities with the marginal revenue should be estimated first. Assume the demand and supply curves of farmland conversion are the forms like equations (4) and (5). (4) QD a u P b , b >0
Q
S
c u P
d
, d >0
(5)
Then, according to the basic pricing formulas in microeconomics, which are P=MC/(1+(1/Ed))and P=MR/(1+ (1/Es)), the equations (4) and (5) can be changed into equa-
tions (6) and (7). logLANDcovi C1 C2 u logMRUi
(6)
logLANDcovi C3 C4 u log(MRAMi MRANMi)
(7)
Where, LANDcovi is the amount of farmland conversion. MR denotes the marginal revenue. The subscripts are the same meaning as above. After the estimation of C1C2C3C4, and make equal of the marginal revenue in different phases, then the ratio of farmland conversion in each phase is obtained, which meets the requirement of efficiency standard. If comparing the calculated efficient results with those in reality, then the inefficient farmland conversion loss can be calculated. So far, the model for accessing the efficiency allocation of farmland conversion between different generations is built, while the measurement of non-market value of farmland resource is still missing, which will be discussed in the next section. The fourth part of this paper will be the employment of the model on provincial panel data in China from 1989 to 2003.
3
Marginal non-market value of farmland
Many categories of value belong to farmland resource besides the function supporting the agricultural production, which are excluded from the traditional market system. For example, farmland resource has the functions of water regulation, gas regulation, erosion control, biological control, and even amusement of the landscape. So far, a study of Costanza (1997) is concerned and cited frequently on the value of the world’s ecosystem services and natural capital (Costanza, d’Arge R, de Groot R, et al, 1997). It can be seen as a completed and detailed research on ecosystem services and natural capital of natural resource based on former studies. This paper will also cite some results of the Costanza (1997)’s study combined with the real conditions in China to estimate the marginal ecological revenue of the China’s farmland resource. Some results of the Costanza (1997) on the ecological value of the farmland resource according to 1994 constant price are given as follows: the ecological value of the farmland resource in forestry was 969 US$ha-1yr-1 on average, 2007 US$ha-1yr-1 in tropic area, 302 US$ha-1yr-1 in temperate area; the ecological value of the farmland resource in grassland was 232 US$ha-1yr-1; the ecological value of the farmland resource in water was 8498 US$ha-1yr-1; the ecological value of the farmland resource in cropland (including the cultivated land and garden plot) was 92 US$ha-1yr-1. The method to calculate the ecological value of farmland resource in China is shown in the following steps: First, find the acreage of different categories of farmland resources in different provinces. The grassland and water data can be
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
the relation between the gross value and the quantity of the farmland is a simple equation, or linear equation, which means the gross value equals to the quantity of the farmland times the value of the unit quantity. The differential coefficient of the gross value (marginal revenue) is exactly the value of per unit quantity (a constant), so the average value can be seen as the marginal revenue. It can also be explained intuitionally that the loss of the ecological value of the last unit of the converted farmland is exactly the average eco logical value of this unit.
gained directly in the government report; the forestry should be subdivided according to the different climate zones, and the cropland is the sum of cultivated land and garden plot. Second, with the data in Table 1, calculate the average ecological value of different provinces in different years weighted by the acreage of farmland resource. Last, eliminate the inflation and change it into the 2003 constant price. The result is shown in Table 1. The ecological value of farmland resource in Table 2 is an average value, and it is not equal to the marginal value in theory. But the ecological value is a non-market value, and
Table 1 Ecological value of farmland resource in different provinces in China Unit: 104yuan RMB ha-1 yr-1 (2003 constant price) City
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Beijing
0.44
0.46
0.52
0.57
0.68
0.86
1.01
1.38
1.18
1.21
1.22
1.26
1.30
1.28
1.28
Tianjin
1.75
1.80
1.99
2.22
2.72
3.36
3.85
4.21
4.33
4.31
4.26
4.24
4.29
4.28
4.32
Hebei
0.46
0.48
0.51
0.56
0.66
0.82
0.93
1.06
1.03
1.02
1.00
0.99
1.00
0.99
1.01
Shanxi
0.33
0.33
0.36
0.38
0.44
0.55
0.64
0.71
0.72
0.71
0.70
0.73
0.73
0.72
0.73
Inner Mongolia 0.22
0.26
0.27
0.30
0.34
0.42
0.50
0.54
0.56
0.56
0.56
0.56
0.57
0.57
0.58
Liaoning
0.53
0.57
0.66
0.71
0.81
0.97
1.13
1.24
1.21
1.18
1.16
1.16
1.16
1.15
1.17
Jilin
0.43
0.45
0.47
0.51
0.58
0.70
0.81
0.89
0.90
0.89
0.88
0.86
0.87
0.87
0.88
Heilongjiang
0.47
0.40
0.44
0.50
0.59
0.73
0.87
0.95
0.97
0.97
0.94
0.93
0.93
0.93
0.93
Shanghai
1.29
1.37
1.52
1.70
2.07
2.61
3.10
5.95
3.48
3.48
3.53
3.62
3.62
3.64
3.64
Jiangsu
1.67
1.61
1.84
1.80
2.33
2.86
3.44
4.18
3.82
3.80
3.75
3.75
3.78
3.75
3.79
Zhejiang
0.52
0.53
0.54
0.59
0.81
1.38
1.13
1.24
1.20
1.18
1.17
1.18
1.18
1.16
1.19
Anhui
0.98
1.02
1.06
1.12
1.27
1.73
1.72
1.85
1.83
1.80
1.76
1.77
1.78
1.76
1.79
Fujian
0.45
0.45
0.45
0.48
0.54
1.20
0.79
0.88
0.85
0.85
0.84
0.86
0.85
0.85
0.85
Jiangxi
0.61
0.62
0.64
0.69
0.77
1.42
1.09
1.24
1.16
1.15
1.13
1.13
1.13
1.13
1.14
Shandong
0.89
0.95
0.99
1.03
1.16
1.42
1.67
1.88
1.88
1.87
1.85
1.86
1.89
1.88
1.90
Henan
0.71
0.71
0.71
0.75
0.86
0.98
1.14
1.30
1.30
1.27
1.23
1.22
1.23
1.23
1.25
Hubei
0.81
0.85
0.92
1.19
1.10
1.76
1.85
2.02
2.09
2.05
2.01
1.99
2.00
1.99
2.03
Hunan
0.61
0.61
0.64
0.73
0.87
1.64
1.38
1.46
1.52
1.53
1.53
1.56
1.54
1.53
1.57
Guangdong
0.54
0.52
0.54
0.59
0.74
2.01
1.02
1.13
1.11
1.09
1.07
1.09
1.08
1.07
1.07
Guangxi
0.33
0.33
0.34
0.36
0.43
1.69
0.71
0.77
0.76
0.73
0.72
0.72
0.72
0.71
0.72
Hainan
0.30
0.29
0.31
0.34
0.46
1.48
0.78
1.10
0.82
0.80
0.78
0.79
0.78
0.77
0.78
Sichuan
0.36
0.37
0.38
0.41
0.48
0.89
0.64
0.69
0.74
0.74
0.73
0.73
0.74
0.74
0.75
Guizhou
0.23
0.23
0.24
0.26
0.31
0.70
0.45
0.50
0.51
0.51
0.51
0.50
0.51
0.51
0.51
Yunnan
0.26
0.26
0.27
0.30
0.38
1.79
0.56
0.62
0.63
0.64
0.64
0.63
0.62
0.62
0.63
Tibet
0.32
0.33
0.57
0.63
0.71
1.02
1.09
1.17
1.23
1.24
1.24
1.24
1.24
1.24
1.25
Shaanxi
0.27
0.27
0.28
0.31
0.34
0.44
0.52
0.58
0.6
0.59
0.58
0.57
0.58
0.57
0.58
Gansu
0.27
0.29
0.31
0.35
0.40
0.50
0.60
0.59
0.68
0.67
0.66
0.65
0.68
0.68
0.69
Qinghai
0.30
0.40
0.49
0.52
0.69
0.88
1.11
1.31
1.3
1.31
1.3
1.29
1.33
1.36
1.39
Ningxia
0.23
0.25
0.29
0.32
0.42
0.53
0.62
0.66
0.69
0.69
0.68
0.68
0.69
0.68
0.69
Xinjiang
0.34
0.41
0.44
0.50
0.64
0.81
1.08
1.21
1.24
1.25
1.21
1.21
1.25
1.25
1.25
Source: Calculated by the authors.
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
4
Estimation and results
4.1 Data The land resource data used to estimate the model comes from the statistic data of the land administration of China (19891995) and land yearbook of China (19941997) compiled by the former National Land Management Bureau, and the annals on the land resource of China (19992003) compiled by the Ministry of Land and Resources of China. The data of the other variables come from the Statistic Yearbook of China (19902004). In order to get the comparable data of the agricultural and non-agricultural sectors, indexes of each variable are chosen that can be found in the subdivided data in each industry. The gross revenue of the agricultural sector NRAMi is the primary industry (agriculture) data of GDP, and the other part of the GDP is the gross revenue of the non-agricultural sector NRUi. The capital investment K is the sum of new basic construction investment, new renewal and reconstruction investment, and the fixed capital investment by urban and rural collective communities in each year. The total investment includes four aspects: the basic construction, renewal and reconstruction, real estate development, and the other fixed capital investment. So the data used in this paper underestimates the investment of the production. But for the aim of the model is to calculate the MR (marginal revenue) and MC (marginal cost) of the land resource, so the estimation will not influenced by the error of the investment, for the error is considered in the constant term. The labor data is the data of the employed person in each production sector. The land resource input data in the agricultural sector LandAMi includes the acreage of forestry, grassland, cultivated land, garden plot, and water area. The land input data in the non-agricultural sector LandUi is the acreage of the construction land. The conversion data of farmland resource QD and QS are the data of the cultivated land occupied by the construction land each year. Since farmland resource not only includes the cultivated land, so this index underestimates the quantity of farmland conversion. The underestimation of the farmland conversion data leads to the underestimation of the slope of the MC and overestimation of the slope of MR curves, so it overestimate the expense loss and excessive loss I, which causes the underestimation of the excessive loss II. Since the limitation of the data and the cultivated land to be occupied is the main proportion of the total conversion data, this index is acceptable. All the price data are changed into the 2003 constant price. The provincial panel data of 30 provinces, municipalities, and autonomic regions from 1989 to 2003 are used to estimate the model, which excludes the data of Hong Kong, Macao, and Taiwan. Chongqing’s data is also excluded for its incompleteness.
4.2 Estimation method and result Due to the different situations between different provinces in China, and the lopsided policy on farmland conversion in each region, this study divided the whole country into three regions according to the national standard, which are as follows: (a) Eastern China: municipalities of Beijing, Tianjin, and Shanghai, Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi and Hainan provinces. (b) Central China: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan provinces, the autonomous region of Inner Mongolia (c) Western China: Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, and Qinghai provinces, autonomous regions of Tibet, Ningxia, and Xinjiang. First, estimate the equation (1) and (2), then calculate the marginal revenue of the two sectors with the equation (3) and (4). To estimate the (1) and (2), first change the equation into a logarithmic style, then employ the general least squares method with the first order component of autocorrelation, AR (1), which indicates an autoregressive component. In the estimation of the equation (1), a cross-section weight by the dummy variables of each province is used to estimate the coefficients of AR (1) and Landagr because of the contribution of farmland resource in different provinces are different, and then different coefficients for each province can be obtained. In the estimation of the equation (2), only the AR (1) is estimated with the cross-section weight, for the construction land is not influenced by the terrain and climate. In addition, White Heteroskedasticity-Consistent Standard Errors and Covariance method is employed in the estimation to reduce the Heteroskedasticity. The result is shown in Table 2. The regression result is shown in Table 2. Each coefficient is significant at 1% level, and each statistic index passes the test. The marginal revenue of each period can be calculated with the original data with the coefficients in Table 2 and the data in Table 1. The result is shown in Table 3. The marginal revenue of farmland conversion between 1989 and 1996 was lower than that between 1997 and 2003 in the whole three regions, which has verified that the inefficiency of inter-generational allocation occurred in that period. The over-conversion of the farmland in the beginning of the 1990s caused the total intergenerational allocation inefficiency in the sample range of this study. In other words, if reducing the conversion amount before 1996, and (or) leaving them to the 19972003 period, farmland conversion would be at least a Pareto improvement, even if the allocation in the later period didn’t meet the efficient one, which still needed to be tested several years later. If we only focus on 19892003 period, the over conversion of the farmland in 19891996 can be calculated by the steps in the above model to estimate the equation (6) and (7)
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
2003. The over conversion proportions were 6.58%, 6.84% and 7.85% respectively, which showed that the central government farmland conversion decision in that period was not reasonable. Actually, from the beginning of the 1990s the large increase of the built-up area all over the country swallowed a large amount of farmland resource, while the utilization was inefficient. The central government began to relocate and reclaim the built-up area in the suburban area and forbid illegal or inefficient built-up projects after 1996, which has done some positive effect to prevent the farmland from over-use.
with the same data but in different sample range. The result is shown in Table 4. Each coefficient is significant at 1% level, and each statistic index passes the test. Based on the estimation of the equation (6) and (7), the efficient intergenerational allocation ratio between the two samples can be obtained. Then with the real conversion amount from 1989 to 2003, the over-conversion amount between 1989 and 1996 can be calculated. The result is shown in Table 5. From Table 5 we can see that the inefficiency of the intergenerational allocation existed in all the three regions from 1989 to 1996 if set sample range is set from 1989 to
Table 2 Result of the regression of equation (1) and (2)
Region
Eastern China (t test)
Central China (t test)
Western China (t test)
Period
19891996 19972003 19891996 19972003 19891996 19972003
Equation (1) A
Į
ȕ
2.382
0.003
0.028
(68.65)
(20.89)
(28.94)
3.892
0.004
0.042
(45.27)
(17.89)
(25.56)
2.087
0.002
0.022
(60.23)
(21.34)
(22.85)
3.012
0.003
0.031
(55.45)
(20.28)
(21.89)
1.968
0.004
0.019
(58.56)
(19.24)
(25.54)
2.789
0.004
0.033
(45.67)
(18.62)
(28.53)
Equation (2) Ȗ
R2
DW
Omit
0.999
2.231
Omit
0.998
2.182
Omit
0.999
2.151
Omit
0.999
2.193
Omit
0.999
2.255
Omit
0.997
2.221
B
Ȥ
į
İ
0.387
0.042
0.114
0.022
(4.93)
(7.30)
(9.01)
(5.28)
0.512
0.053
0.194
0.034
(5.28)
(6.76)
(8.89)
(6.45)
0.312
0.038
0.108
0.019
(5.12)
(8.30)
(8.35)
(4.78)
0.478
0.041
0.134
0.033
(6.89)
(7.92)
(9.67)
(5.56)
0.313
0.032
0.098
0.016
(4.93)
(6.30)
(7.25)
(6.12)
0.402
0.038
0.145
0.029
(5.67)
(6.89)
(8.34)
(7.56)
R2
DW
0.999
1.887
0.997
1.821
0.998
1.781
0.998
1.883
0.992
1.872
0.999
1.923
Source: Calculated by the authors.
Table 3 Net marginal revenue of farmland conversion in different areas in China in two different periods Unit: yuan ha-1 yr-1(2003 constant price) Region Eastern China
19891996
19972003
Region
41 646
54 313
Central China
19891996
19972003
Region
19891996
19972003
12 335
Western China
8 033
10 083
Sichuan
4 077
7 164
3 983
4 171
Guizhou
14 478
18 329
Jilin
5 995
6 679
Yunnan
10 043
13 660
21 359
Heilongjiang
8 818
13 535
Tibet
11 654
14 180
Beijing
69 584
75 065
Shanxi
Tianjin
38 637
40 283
Inner Mongolia
Hebei
13 348
18 073
Liaoning
18 146
8 979
8 998
11 034
Shanghai
119 611
146 062
Anhui
5 752
6 423
Shaanxi
9 640
12 932
Jiangsu
25 456
29 276
Jiangxi
9 950
11 995
Gansu
5 661
8 718
Zhejiang
59 659
61 174
Henan
9 235
13 047
Qinghai
6 624
7 804
Fujian
32 998
48 383
Hubei
14 501
18 478
Ningxia
7 664
8 713
Hunan
15 073
Xinjiang
1 883
3 146
Shandong
16 127
20 478
Guangdong
43 059
48 278
Guangxi
10 127
14 378
Hainan
12 710
12 265
Source: Calculated by the authors
13 478
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
Table 4 Result of the regression of equation (6) and (7)
Region
Eastern China (t test)
Central China (t test)
Western China (t test)
Period
Equation (6) C1
19891996
omit
19972003
omit
19891996 19972003 19891996 1997~2003
omit
C2 0.471 (5.14) 0.812 (4.28) 0.413 (6.34)
omit
0.678 (5.23)
omit
0.327 (4.64)
omit
0.562 (6.89)
Equation (7)
R2
DW
0.984
2.034
0.991
2.156
0.982
2.052
0.987
2.146
0.991
2.037
0.995
2.198
C3
C4
omit
0.112 (2.75)
omit
0.233 (4.13)
omit
0.097 (2.86)
omit
0.198 (3.46)
omit
0.078 (4.22)
omit
0.146 (4.98)
R2
DW
0.987
2.097
0.982
2.121
0.992
2.086
0.998
2.034
0.983
2.043
0.987
2.056
Source: Calculated by the authors.
Table 5 Inter-generational allocation loss of farmland conversion between 1989 and 1996 (sample as19892003) Region
Ratio(%)
Amount(ha)
Region
Ratio(%)
Amount(ha)
Region
Ratio(%)
Amount(ha)
Eastern China
6.58
31 142
Central China
6.84
21 402
Western China
7.85
12 590
Beijing
5.19
920
Shanxi
6.14
1 426
Sichuan
9.05
5 004
Tianjin
6.04
486
Inner Mongolia
8.36
1 748
Guizhou
8.29
1 058
Hebei
4.93
2 389
Jilin
6.94
723
Yunnan
5.81
1 398
Liaoning
6.98
3 161
Heilongjiang
7.07
2 927
Tibet
7.75
423
Shanghai
7.07
2 838
Anhui
6.51
4 511
Shaanxi
7.49
1 903
Jiangsu
6.33
5 353
Jiangxi
4.04
402
Gansu
8.41
979
Zhejiang
4.84
2 470
Henan
6.85
4 162
Qinghai
3.19
56
Fujian
2.87
711
Hubei
8.42
4 415
Ningxia
8.28
374
Shandong
7.92
4 562
Hunan
6.63
1 619
Xinjiang
9.11
1 770
Guangdong
5.05
3 165
Guangxi
7.41
1 669
Hainan
8.84
894
Source: Calculated by the authors.
It should be mentioned that the potential application value of the above model is that farmland conversion between 1989 and 1996 is inefficient according to the marginal revenue between 1997 and 2003, which means the farmland protection policy after 1996 in China did improve the efficient generational allocation of farmland conversion. But the result does not offer the evidence that we should convert more farmland after 1996, because we do not know whether the marginal revenue is as equal as the later period, say 20042010. We cannot draw the conclusion until we reach 2010 and test the reality data again. So at this moment we’d better keep the same rate of farmland conversion like 19972003, and check it after 2010. If the marginal revenue of 19972003 was higher than that of 20042010, then we can convert more farmland for the equal revenue principle
requirement, otherwise we should control more strictly on the farmland conversion than in 19972003. Under this rule, farmland conversion will converge gradually at the efficient standard.
5
Conclusions
This paper sets up a model to measure the intergenerational efficiency of farmland conversion in China between 19891996 and 19972003. The finding of this paper is that if the sample range was set from 1989 to 2003, there would exist an over conversion of farmland resource on intergenerational allocation before 1996. The over-conversion ratios of the eastern, central and western regions are 6.58%, 6.84% and 7.85% to each total conversion amount respectively.
Tan Rong, et al / China Population, Resources and Environment, 2007, 17(3): 28–34
Although farmland conversion after 1996 was not verified to meet the efficient one, which depends on the future conversion revenue, to control the conversion speed and constrain the conversion quantity is an appropriate way to ensure the intergenerational allocation efficiency for the utilization of farmland resource nowadays.
[5] Qu, F. T., Feng, S. Y., Zhu, P. X., et al, 2004. Institutional arrangements, price system and farmland conversion. China Economics Quarterly, 1, 229248 [6] Tan, R., Qu, F. T., 2005. How to harmonize the contradiction between utilization of natural resources and the sustainable development of economy. Journal of Natural Resources, 6, 197205
References
[7] Tassone, V. C., Wessele, J., Nesci, F. S., 2002. Diverging incentives for afforestation from carbon sequestration: an eco-
[1] Chen, A. N., 2003. Approach to issues of discount rate in the utilization of natural resources. Resources Science, 3, 1316
nomic analysis of the EU afforestation program in the south of Italy. Forest Policy and Economics, 1, 567578
[2] Costanza, R., d’Arge R., R. de Groot, et al, 1997. The value of
[8] Wei, X. P., 2002. Study on problems of intergenerational alloca-
the world’s ecosystem services and natural capital. Nature,
tion of mineral resource. Journal of China University of Min-
387, 253260
ing & Technology (Social Sciences), 2, 7579
[3] Perman, R., Yue, M., McGilvray, J., Common, M., 2003. Natu-
[9] Winjum, J. K., Brown, S., Schlamandinger, B., 1998. Forest
ral Resource and Environmental Economics. The 3rd edition,
harvest and wood products: sources and sinks of atmospheric
Addison-Wesley Publishers, UK, 359361 [4] Qu, F. T., 2002. Resource Economics. China Agricultural Press, Beijing, 6068
carbon dioxide. Forest Science, 44, 272284 [10] Zhang, F., 1998. Environment and Natural Resources Economics. Shanghai People Press, Shanghai, 3742