1. Impact of urbanization on cultivated land in China: a model-based analysis in China Xiangzheng Deng November 6-8 ， 2014 2 nd International Conference Urban Transitions and Transformations: Science, Synthesis and Policy

2. Urbanization is an inevitable development process  Urbanization is a natural process companying with the development of human society at a certain stage People inhabitance -urban area 2% -population >50% Labor shifts -Agricultural sector  Non-agricultural sector Sourced from NetEase 2012 Sources: The times of India, 2008 Global urbanization rate

3. Urbanization in China  Urban population – 1978-2013: risen from 18% to 54%  Urbanization accelerated – since middle 1990s – 3/4 of the population would live in cities by then end of 2050  Population in the urban areas – By 2020, over half of the people will move into cities, according to a planning made by NDRC – About 39 percent of the youngsters are employed in the urban, while only 27 percent of older workers work in small city or county Urbanization in China: 1949-2009 Urbanization Source: Urban China, 2010 Source: ChinaDaily, 2013

4. Views on the impacts of urbanization on agricultural land  Urbanization leading to cultivated land reduction and reduced land production directly through encroaching the land around urban core or the leapfrogged urban cluster  Urbanization playing an active role in conserving cultivated land by releasing the pressure from land occupations accompanying with the urbanization

5. Ideas on the urbanization models Impacts of different models of urbanization on the changes of cultivated land – Small town model – Lack of effective planning – Consuming large areas of cultivated land – City model – Infrastructure built – As for the scales of land consumption : urban built area > rural built area

6. Research in Progress  Failed to build a measurement indicator on urbanization models by combining the built-up area in both urban and rural area and to do an integrated research  Need to fully control some factors with no matter positive or negative effects on urbanization to get a more robust estimation  Include different scales of cities with a diverse of urbanization speed into the analyses

7. “Major” question under answer  Does urbanization of big cities in China consuming more land compared with that of small towns as well as the expansion of villages?  How has the urbanization affected the land productivity and cultivated land area for the past three decades?  Is there any kinds of urbanization model saving land?  What is the regional characteristics of land consumption of urbanization?

8. Goals of Presentation  Explored changes in China’s cultivated area and its conversion to built-up area and other uses due to urbanization, industrialization and rural settlement expansion  Analyze the impacts of the urbanization models on the changes of land productivity  Explore the various effects of urbanization models on the changes of cultivated land over time and space

9. Answer questions from two aspects  The direct impacts of urbanization on the land productivity  Impact of urbanization on cultivated land changes in China

10. Estimate the Changes in Quantity and Quality of Cultivated Land: Methodology  Identify the quantity of China’s land use change – Detection models of Land Use Change(LUC),1-km area percentage data models – Based on the prototype of the 1-km area percentage data model (1- km APDM), developed a set of programs to generate 1-km area percentage data according to map-algebra concepts – i.e., the encroachment of urban land onto cultivated land  Measure the quality of cultivated land conversions – Agro-ecological Zones (AEZ) methodology – Manipulate data with GIS technologies – Use Agro-ecological zoning model (AEZ) with “other data” to create a productivity index of cultivated land (essentially this index is a way that geographers measure the bio-productivity of land; in other words, it is a measure of the quality of land)

11. Estimate the Changes in Quantity and Quality of Cultivated Land: Database  Database: – Remote sensing data (RSD) on all of China (for land use) – Other data includes information on climate; soils; slope and elevation; etc. (e.g., from China Meteorological Bureau) – 1x1 km GRID data  Temporal scale: 1988~2000; 2000~2008

12. Decoding the information on land use changes from Landsat TM/ETM digital imagines False color composition Geometric correction 1988/1990, 1999/2000, 2005/2008 Landsat TM digital image Vector map of land use in 1988 、 2000 、 2005 、 2008 Mutual interpretation 1988/1990, 1999/2000, 2005/2008 Landsat TM registered image Land-use change map during the periods between 1988 to 2000 ； 2000-2008 1km vector map Arc/Info Overlay Land-use change map of predominant types during the period between 1988-2000 and 2000-2008 Zoning map of land-use change during the period between 1988-2000 and 2000-2008 Land use conversion maps during the period between 1988-2000 and 2000-2008

13. Characteristics and Measures on Land-use Change  The regional differentiation of land-use change rate can be represented by the dynamic degree model of land-use, i.e. where, S is the land-use change rate, S i represents the total areas of i (land-use category) at the former stage while is the weight of areas proportion of i, represents the net change of area from i to j (land- use category) at the time scale of t. The basic unit to employ the dynamic degree model is 1km GRID, and the statistical result serves as basis to draw the land-use change and land-use conversion maps classified by land-use categories.

14. Changes in cultivated land Panel a: 1988 to 2000Panel b: 2000 to 2008 Conversions of Cultivated Land in China during 1988 to 2000 (Panel a) and 2000 to 2008 (Panel b)  In 1988-2000, 2.7 million hectares of new cultivated land was created. China’s farmers were cultivating 1.9% more land in 2000 than they were in 1988.  In 2000-2008, the cultivated land area of China actually lose considerable quantities of land, by 0.58 million hectares.

15. Changes in cultivated land Panel a Panel b Land conversions from Cultivated Land to other uses, 1988-2000 (Panel a) and 2000- 2008 (Panel b)  It should be noted that only in the case of Beijing, Shanghai and Zhejiang did the conversions exceed 5% in 1986-2000.  Apparently, the provinces that experienced the most conversions are Shanghai and Shandong in 2000- 2008.

16. Changes in cultivated land Panel cPanel d Land conversions from other uses to cultivated Land, 1988-2000 (Panel c) and 2000-2008 (Panel d)  During 1988-2000, In northeast China, there were large tracts of forests that were converted to cultivated land; Some areas in Sichuan also were converted from forests to cultivated  During 2000-2008, there are less tracts of land that were converted to cultivated land

17. Changes in potential agricultural productivity and production due to land conversions Province Total production potential in 1988 IncreaseDecrease Net change Percentage change Province Total production potential in 1988 IncreaseDecrease Net change Percentage change Beijing412023837-814-19.75Hubei1490007212320-1599-1.07 Tianjin622013204-191-3.06Hunan1410003331160-827-0.59 Hebei726003961950-1554-2.14Guangdong906002673460-3193-3.52 Shanxi34600268237310.09Guangxi11300013408524880.43 Inner Mongolia 361004940163033109.17Hainan16100191352-161 Liaoning3400014705059652.84Chongqing5630087396-309-0.55 Jilin31400197044115294.87Sichuan1760004171390-973-0.55 Heilongjiang533006210524568610.67Guizhou63300613995140.81 Shanghai917001010-1010-11.01Yunnan679008961090-194-0.29 Jiangsu1140002405000-4760-4.18Tibet194004-3-0.16 Zhejiang691003133040-2727-3.95Shaanxi40800434379550.13 Anhui1370004712110-1639-1.2Gansu320005531743791.18 Fujian48100543772-229-0.48Qinghai27805.924742.66 Jiangxi1060005371030-493-0.47Ningxia85401200108109212.79 Shandong976001621430-1268-1.3Xinjiang28700275088318676.51 Henan111000150013401600.14Taiwan135001376-63-0.46 Total19657702897134826-5855-0.3 Change of total production potential associated with changes in cultivated land by provinces for 1988-2000, measured in billion Kcal and percentage change (%).  The average potential agricultural productivity fell by 2.2% during 1988-2000, and the total production potential fell by 5.9 trillion Kcal, or by only 0.3%

18. Changes in potential agricultural productivity and production due to land conversions Province Total production potential in 2000 IncreaseDecrease Net change Percentage change Province Total production potential in 2000 IncreaseDecrease Net change Percentage change Beijing33063.4280.8-277.3-8.39Hubei14740131.92371.1-2339.3-1.59 Tianjin60291.9139.7-137.8-2.29Hunan14017321215.1-1213-0.87 Hebei71046122.6914.9-792.3-1.12Guangdong8740737.52164.7-2127.2-2.43 Shanxi346311.4798.4-797-2.3Guangxi11348831.5787.1-755.7-0.67 Inner Mongolia 39410460.6118.8341.70.87Hainan1593923131.1-108.1-0.68 Liaoning3496540.6221.4-180.9-0.52Chongqing5599123.72111.1-2087.3-3.73 Jilin3292971.9155.8-83.9-0.25Sichuan17502768.91553-1484.1-0.85 Heilongjiang589861380.2802.8577.40.98Guizhou6381427.61103.9-1076.3-1.69 Shanghai816001993.9-1993.9-24.43Yunnan6770694.4981.6-887.2-1.31 Jiangsu109240288319.2-8291.2-7.59Tibet193701.3-1.3-0.07 Zhejiang6637315.71673.6-1657.9-2.5Shaanxi40855170.1550.1-380.1-0.93 Anhui135361535.93594.5-3058.6-2.26Gansu32379316.6408.1-91.5-0.28 Fujian47871181220.3-1202.3-2.51Qinghai28544.621.6-17-0.59 Jiangxi105507633.21060.9-427.6-0.41Ningxia9632249.4215.234.20.35 Shandong96332166.42967-2800.6-2.91Xinjiang305671781.646.11735.65.68 Henan11116099.31243.1-1143.8-1.03Taiwan134376.9181.8-174.9-1.3 Total1959913644939348-32899-1.68 Change of total production potential associated with changes in cultivated land by provinces for 2000-2008, measured in billion Kcal and percentage change (%)  The average potential agricultural productivity fell by 1.3% during 2000-2008, and the total production potential fell by 32.9 trillion Kcal, or by around 1.7%

19. Changes in potential agricultural productivity and production due to land conversions Panel a: 1988 to 2000 Panel b: 2000 to 2008 Changes in total production potential (measured in million kcal) associated with changes in cultivated area in China during 1988 to 2000 (Panel a) and during 2000 to 2008 (Panel b).  During 1988-2000, the quantity of cultivated land rose by 1.9%. The average potential productivity of land fell by only 2.2%  During this period, the quantity of cultivated land deceased 0.58 million hectares and the average potential productivity of land fell by 1.7%

20. Summary  Indeed, net cultivated land actually increased during the study period, 1986 to 2000. Our decomposition of cultivated land changes show that nearly half of lost cultivated land was due to cultivated land being converted to grassland (30%) and forest (17%). Of the remaining, nearly 40% was due to the shift to built-up area.  There also was a considerable amount of newly cultivated land created, some shifting into cultivation from grassland and other from forestry areas  Although newly cultivated area rose, average potential agricultural productivity actually fell

21. Answer questions from two aspects  The direct impacts of urbanization on the land productivity  Impact of urbanization on cultivated land changes in China

22. Three kinds of models of urbanization  Indicator measured by remote sense digital images – Exploring the sizes of “villages”, “towns”, “cities” by a sampled survey – Raw data, Landsat TM/ETM, CBERS – Four time period: late 1980s, mid-1990s, late 1990s, mid-2010s (Liu et al, 2002; Liu et al, 2009 )  Aggregated based on neighborhood of the residential polygons with the reference for the year 2005 – 18 counties/cities within nine provinces sampled and re-visited which are located in the eastern, central and western regions of China – The threshold to identifying the three levels of residential areas Villages, equal or less then one square kilometer Towns, one to five square kilometers Cities, bigger then five kilometers

23. Three kinds of urbanization models City Model Urbanization of Beijing Village Model Town Model Urbanization of Fangshan Urbanization of Yancun Beijing Municipal Institute of City Planning & Design, 2007

24. Controlling and influencing factors  Geophysical variables – slope, percent of plain area, elevation, distances of counties’ (cities’) seats to the capital cities and nearest port cities, and so on. DEM data and topographic map Thematic maps on residence and road network – precipitation and average temperature data China Meteorological Administration during 1950-2000  Economic variables – economic data and population of counties (cities) National and provincial bureaus of statistics, various years – FDI – development zones  Policy variables Household registration policy County updated to city

25. Descriptive statistics of the main variables VariablesUnit 199620002008 mean std. deviation mean std. deviation mean std. deviation cultivated land areahectare692776609168250679526606268643 cultivated land area (1989)hectare66630612126663061212-- cultivated land area (1995)hectare--69277660916927766091 built-up area ： village-model land ratiopercentage67.16%22.01%65.71%21.78%60.54%22.36% town-model land ratiopercentage12.00%11.57%12.66%11.87%15.96%12.97% city-model land ratiopercentage20.84% 21.63%22.21%23.50% policy factors ： non-agricultural population registered (t-1) percentage23.76%20.38%25.19%20.43%45.51%34.83% County upgraded to city (yes =1)0.240.430.260.440.290.45 foreign direct investment per capitayuan per capita221223143911389452005019 development zone (exist =1)0.360.480.370.480.410.5

26. Descriptive statistics of the main variables (continued) variableunit 199620002005 mean std. deviation mean std. deviation mean std. deviation Economic factors: GDP(t-1)million yuan*4331.9896036567.6816370946718823 Agriculture GDP (t-1) million yuan825.86591.79986.72696.271338.68962.11 Industry GDP (t-1) million yuan2004.274980.473043.478167.194324.1111028 Service Industry GDP (t-1) million yuan1501.854647.552537.498225.313804.2113123 population (t-1) Person631933607333653616628337731665821621 Environmental factors: slopeDegree222222 distance to the nearest portKilometer467342467342467342 distance to the capital cityKilometer164961649616496 DEMMeter233255233255233255 plain area proportionpercentage0.530.380.530.380.530.38 annual precipitationmm101651010165101016510 average temperature ℃ 136 6 6 Observations870 879

27. Equations  Cultivated land area = f (ratio of urbanization models, social and economic variables, geophysical variables, other control factors, random error term)  Build-up areas of urbanization models = f (social and economic variables, geophysical variables, other control factors, random error term)

28. Estimation results, for the eastern region, the decision factors of urbanization models and cultivated land, 1995-2000, (Pooled OLS) Explanatory variables "Small-town" model proportion "City" model proportion Cultivated land area(3SLS) Explained variables in 1989 1.018 (199.39)*** "Small-town" model proportion 0.0840.117 (77.85)***(4.25)*** "City" model proportion 0.8530.036 (64.85)***(1.80)* Policy instrumental variables Non-agri population registered (t-1) -0.0250.087 (2.36)**(5.96)*** County updating to city (yes=1) 0.0040.013 -1.22(3.02)*** Foreign direct investment per capita 0.001-0.003 -0.82(1.30) Development zone (exist=1) -0.010.011 (2.83)***(2.50)**

29. Explanatory variables Town-area land expansion City-area land expansion cultivated land area Socio-economic factors Agriculture GDP(t-1) -0.0060.0020.021 (1.98)**(0.42)(3.25)*** Industry GDP(t-1) 0.0020.005-0.009 -0.65(1.45)(1.66)* Service Industry GDP(t-1) 0.0030.014-0.025 -0.87(3.00)***(3.66)*** Population(t-1) -0.003-0.022-0.006 (-0.71)(4.85)***(0.80) Physiographic factor Slope -0.002 -0.001 (2.15)**(1.88)*(0.30) Distance to the nearest port 0.001-0.0040.007 (-0.87)(2.70)***(2.83)*** Distance to the capital city -0.000-0.0070.02 (0.02)(2.96)***(5.48)*** DEM -0.0000.002-0.000 (0.33)(2.66)***(0.06) Plain area proportion -0.001-0.012-0.043 (0.22)(1.53)(3.44)*** Average precipitation -0.007-0.0570.041 (1.17)(7.97)***(3.89)*** Average temperature 0.0000.005-0.004 (1.06)(9.34)***(4.58)*** R20.780.910.99 Observations1738

30. Estimation results for the eastern region, the decision factors of urbanization model and cultivated land, 2000-2008, (Pooled OLS) Explanatory variables "Small-town" model proportion "City" model proportion Cultivated land area(3SLS) Explained variables in 1989 1.023 (128.84)*** "Small-town" model proportion 0.826-0.146 (54.23)***(2.92)*** "City" model proportion 0.793-0.067 (64.47)***(2.01)* Policy instrumental variables Non-agri population registered (t-1) 0.0210.037 (2.10)*(3.51)*** County updating to city (yes=1) -0.005-0.001 (-0.95)(-0.13) Foreign direct investment per capita 0.003-0.008 (1.14)(-2.88)*** Development zone (exist=1) 0.000-0.005 (0.03)(-0.95)

31. Explanatory variables Town-area land expansion City-area land expansion cultivated land area Socio-economic factors Agriculture GDP(t-1) 0.015-0.0180.014 (3.59)***(-4.24)***(1.38) Industry GDP(t-1) 0.0040.012-0.003 (1.04)(2.75)***(0.34) Service Industry GDP(t-1) -0.0150.022-0.037 (-2.69)***(3.82)***(3.25)*** Population(t-1) -0.012-0.0070.038 (-2.07)*(-1.25)(3.06)*** Physiographic factor Slope 0.004-0.0010.005 (3.03)***(-0.46)(1.38) Distance to the nearest port -0.011-0.0010.002 (-3.49)***(-0.36)(0.39) Distance to the capital city -0.0000.004-0.003 (-0.13)(1.28)(-0.41) DEM -0.001-0.000-0.008 (0.89)(-0.30)(-3.42)*** Plain area proportion 0.0170.0010.009 (1.77)(0.13)(0.43) Average precipitation 0.008-0.012-0.091 (1.01)(-1.47)***(-5.42)*** Average temperature -0.0000.0040.000 (-0.60)(5.37)***(0.06) R20.560.850.97 Observations1738

32. The estimation result of urbanization models  In 1995-2000, the household registration policy has significant different impacts on different urbanization models ， the influence coefficient is -0.025 ； However, the household registration policy has significant positive influence on “City” model ， the coefficient is 0.087  In 1995-2000, the implementation of county to city (or district) has a negative effect on “Small-town” model, but has a significant positive effect on “City” model, the influence coefficient of estimation is 0.013, and statistical tests of coefficient are significant at the level of 1%. However, in 2000-2008, the estimation results show that the development of county to city (district) has an effect on “Small-town” model and “City” model

33. The estimation results of urbanization models  Further, In 1995-2000, the effects of the foreign direct investment on both “Small-town” model and “City” model are not significant, but in 2000-2008, the effect was significant in “City” model, the influence coefficient is 0.008  The regional development policy has a significant negative effect on “Small town” model, but has a positive effect on “City” model, this is because the development zones are generally set up around the town and consequently promote its expansion

34. The estimation results of the cultivated land model  In 1995-2000, the cultivated land which was used in the small town model and city model is more economical than in village model, and the less percent of occupied the cultivated land is 0.12% and 0.04%, respectively  In 2000-2008, the cultivated land which was more used in the small town model and city model than in village model, and the urbanization level increases every one percent will lead to the cultivated land which was occupied by construction land increase more than 0.15% and 0.07%.

35. The estimation results of the cultivated land model  Natural factors are also the important factors to explain regional differences of cultivated land. In the seven natural factors which are considered, there are five variables are reached 1% significant level, they are the nearest distance to the province capital, the nearest distance to the port, the ratio of plain area, precipitation and average temperature  In 2000-2008, when the agricultural GDP growth by 1%, the cultivated land will increase (or save) about 0.02%. This is because agriculture development needs a large number of cultivated land, the more agriculture develop, the more cultivated land used for farming  In 2000-2008, as industrial GDP or service industry GDP growth by 1%, the cultivated land will reduce about 0.003% and 0.037%, respectively

36. Decomposition analysis of cultivated land change;(1996-2000) Variable Estimated parameter [1] Variation （ % ） [2] Influence [3]=[1]×[2] Rate of contribution （ % ） [4]=[3]/ （ -1.48 ） *100 Urbanization (construction land area ratio) “Village”(Rural residential) Town 0.1170.660.08-5 City 0.0360.790.03-2 Agriculture GDP* （ t-1 ） 0.02118.080.37-25 Industry GDP* （ t-1 ） -0.00940.45-0.3524 Service industry GDP* （ t-1 ） -0.02550.07-1.2786 Population * （ t-1 ） -0.0063.12-0.021 Other variables -0.3221 Change of cultivated land area （ % ） -1.48100 Note: Italicized data represents that the coefficient based on the decomposition analysis is not significant

37. Decomposition analysis of cultivated land change;(2000-2008) Variable Estimated parameter [1] Variation （ % ） [2] Influence [3]=[1]×[2] Rate of contribution （ % ） [4]=[3]/ （ -1.48 ） *100 Urbanization (construction land area ratio) “Village”(Rural residential) Town -0.1463.33-0.48623.2 City -0.0671.87-0.1256.0 Agriculture GDP* （ t-1 ） 0.01435.670.499-23.8 Industry GDP* （ t-1 ） -0.00342.08-0.1266.0 Service industry GDP* （ t-1 ） -0.03749.92-1.84788 Population * （ t-1 ） 0.03811.940.454-21.6 Other variables 1.15077.7 Change of cultivated land area （ % ） -2.1100 Note: Italicized data represents that the coefficient based on the decomposition analysis is not significant

38. Summary  Assuming that other factors remain constant, in 1995- 2000 of eastern region, the urbanization alleviates the loss of cultivated land by 7%, compared with the expansion of villages or the development of small towns  In the period of 2000-2008, the rapid urbanization resulted in the cultivated land loss by 29.2%. The policies designed to protect cultivated land by encouraging people move to small towns may actually accelerate the occupation of cultivated land

39. Concluding remarks  We saw net cultivated land actually increased during the study period 1986 to 2000. Although newly cultivated area rose, average potential agricultural productivity actually fell.  Despite this, when examined in the aggregate for the entire period, the effect on total agricultural potential output was negligible.  Economic growth is the major determinant of any changes in cultivated land use  social, economic, and geophysical factors, such as industrial structure, population growth…played an important role in influencing urbanization  Although urbanization has an effect on the changes of cultivated land, its effect is marginal

40. Thank you for your attention Xiangzheng Deng November 6-8 ， 2014 2 nd International Conference Urban Transitions and Transformations: Science, Synthesis and Policy