Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China

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1 July, 2018 Journal of Resources and Ecology Vol. 9 No.4 J. Resour. Ecol (4) DOI: /j.issn x Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China MOU Chufu 1,2, WANG Limao 1,2*, QU Qiushi 1,2, FANG Yebing 1,2,3, ZHANG Hong 4 1. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing , China; 2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing , China; 3. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, Anhui , China; 4. College of Geospatial Information, The PLA Information Engineering University, Zhengzhou , China Abstract: This paper analyzes panel data from to identify the factors driving the expansion of construction land in Ningbo city; it uses panel data, regional-level, and year-by-year regression models. The results indicate the following: (1) For each 1% increase in the size of the economy, urban population, and industrial structure adjustment coefficient, the amount of construction land increased by 0.35%, 0.52% and 1%, respectively. (2) The factors driving the expansion of urban construction land differed across regions. In more highly developed areas such as Yuyao, Cixi, Fenghua and the downtown area, population growth was the most obvious driving factor with coefficients of 4.880, 1.383, and 0.583, respectively, in those areas. Here, the impact of industrial structure adjustment was lower than that of population growth (with coefficients of 1.235, 0.307, and 0.242), while economic development was an increasingly insignificant factor (with coefficients of 0.302, 0.071, and 0.297). On the other hand, economic development was the most important factor for the expansion of construction land in relatively less developed areas such as Xiangshan and Ninghai counties with coefficients of and 0.195, respectively. Here, population growth (with coefficients of and 0.132) and industrial structure adjustment (with coefficients of and 0.067) were comparatively weaker driving factors. (3) The results of the year-by-year regression indicate the increased impact of economic development as a driving factor (from in 2005 to in 2012). The influence of the population growth factor slowly declined (from in 2005 to in 2012) and from 2009 on was less influential than the economic development factor. The industrial structure coefficient remained negative and its influence diminished from year to year (from in 2004 to in 2012). Key words: expansion of construction land; driving factors; panel data model; Ningbo City 1 Introduction As the scale of China s economy continues to expand and the population grows rapidly, the number and scale of Chinese cities are also expanding. The processes, mechanisms and effects of urban expansion are hot research topics in the fields of geography, economics, ecology and environmental science (Vu M H et al., 2017; Wei Y D et al., 2017; Hassan M. M et al., 2016). The spatial and temporal expansion of cities is a complex socio-economic phenomenon that has become one of the most significant characteristics of China's urbanization process. It is of great importance for urban management and scientific urban planning in China to summarize the temporal and spatial characteristics of urban expansion and to reveal the patterns and the evolutionary path urbanization is taking. Domestic research on urban land expansion includes feature descriptions, spatial differences, expansion dynamics and expansion model research, among which the study of the forces driving the expansion of urban land has been a Received: Accepted: Foundation: National Key Research & Development Plan (2016YFA ); National Natural Science Foundation of China ( ). *Corresponding author: WANG Limao, lmwang@igsnrr.ac.cn Citation: MOU Chufu, WANG Limao, QU Qiushi, et al Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China. Journal of Resources and Ecology, 9(4):

2 366 Journal of Resources and Ecology Vol. 9 No. 4, 2018 hot topic in recent years. Many scholars have conducted qualitative or quantitative research from different perspectives on the mechanisms that are driving the expansion of construction land (Liu T et al., 2010). Generally, the driving factors can be summarized as natural environmental factors (Gaglio M et al., 2017; Liu R et al., 2009), industrial structure adjustments (Zhao K et al., 2011; Sun C et al., 2016; Zhang Z L et al., 2009), population growth (Liu R et al., 2009;He D et al., 2005), infrastructure construction (Fan X et al., 2016; Li J et al., 2015; Zhang Q et al., 2016), economic growth (Verburg P H et al., 1999; Ma Q et al., 2016), investment increases (Zhang Q et al., 2016) and several other factors. As for research objectives, due to the difficulty acquiring usable data, few studies have been carried out about panel data simulation on expansion force of construction land in single cities. Most research in this area uses one of the following methods: spatial analysis models to analyze and simulate urban expansion with remote sensing images (Zhang Y H et al., 2013; Li B, 2012; Hu D Y et al., 2006); simple regression models to analyze single objects (Verburg P H et al., 1999; Shu B R et al., 2013); or time series regressions of multiple objects with multi-year data (He D et al., 2005; Zhou Y et al., 2016). Based on a summary of the results and shortcomings of previous research, this paper carries out a quantitative study of factors driving the expansion of urban construction in Ningbo from 2003 to 2012; the study uses a combination of panel data from Ningbo city and construction land data obtained from field research. The research aims to explore how regional development affects the scale of urban construction land use. The conclusion provides insights into how the expansion of construction land can be controlled. 2 Research models and indicators 2.1 Indicator selection and data sources Ningbo, the second largest city in Zhejiang Province, is a coastal city in eastern central China located at the interchange of the Yangtze River and the coastal shipping channel. Historically it was one of the ports of the ancient Silk Road, and today is an important node city of the Yangtze River economic zone and a key coastal city in China s Belt and Road initiative. As a result of an administrative reorganization in 2016, Ningbo now has jurisdiction over six districts (Haishu, Jiangbei, Beilun, Yinzhou, Zhenhai, and Fenghua), two counties (Xiangshan and Ninghai) and two county-level cities (Yuyao and Cixi). In view of the continuity of research data and the actual situation of Ningbo s development, this study combines Haishu, Jiangdong, Jiangbei, Zhenhai, Beilun and Yinzhou Districts and refers to them as the Central Area. The Central Area, Fenghua City, Yuyao City, Cixi City, Xiangshan County and Ninghai County together form the study area for this research (Fig. 1). The effect of natural factors on land use is stable during a discrete period of time so that natural factors are unlikely to be the main factors driving changes in construction land use during a short time period. Social factors will play the leading role. As a result, we selected economic development, urban population increase and industrial structure adjustment as the three main factors driving construction land change (Table 1). The socioeconomic development data used in the study are taken from editions of the Ningbo Statistical Yearbook, and the land use data is taken from the Ningbo Land Bureau. 2.2 Spatial correlation test There is a close relationship between the districts and counties in Ningbo. The less the distance between the samples, the closer the connection (Chen Q, 2014). Therefore, it is necessary to use a spatial correlation test to determine whether there is a spatial dependency in the expansion of the land area of Ningbo s districts and counties before determining whether or not to use a space measurement model. 1) Constructing a spatial adjacency matrix The foundation of the spatial correlation test is the construction of a spatial adjacency matrix. The influence of the expansion of land used for urban construction is generally Fig.1 Location of the study area Table 1 Definition of variables Variables Construction land (CL) Population (POP) Economic development (ED) Industrial structure adjustment (ISA) Description Area of construction land Non-agricultural population Per capita GDP Tertiary industrial output value divided by secondary industrial output value

3 MOU Chufu, et al.: Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China 367 greater among neighboring counties than counties that are not, especially in the context of regional economic integration. Since the distribution of the counties is quite even and the adjacency relationships are very clear, we decided to use a traditional binary adjacent matrix. The matrix W is set as follows: if region i is adjacent to region j, then W ij is equal to 1 or equal to 0; the elements on the main diagonal are zero. Matrix W is normalized by a line, which means each element in the matrix is divided by the sum of elements of their rows to ensure that the sum of each row is 1. 2) Estimation of the spatial Moran Index Spatial autocorrelation refers to the potential interdependence between observational data for some variables in the same distribution area. The statistical tool for traditional spatial autocorrelation is the Moran Index. The key difference between spatial autocorrelation statistics and traditional statistical data is that spatial autocorrelation statistics include spatial weight matrices. The Moran Index is defined as: n n n n 2 Moran ' I wij ( Xi X)( X j X) / S wij (1) i j i i j i n where n is the number of sample areas, S ( Xi X), n i 1 X i is the property value of area i, X is the average of all attribute values, and w ij is spatial weight matrix (Wang X Y et al., 2013). The Moran Index is between 1 and 1. If the value of the Moran Index approaches 1, it means that the observation variables show highly positive dependence in space. The closer the value of the Moran Index is to 0, the more randomly distributed and independent in geographical space the observations are. Taking construction land as the lagging variable, we examine the spatial correlation of each district and county from 2003 to 2012 and obtain the global Moran Index of the construction land area (Table 2). Both the positive and negative values are found in each year s Moran Index and all of the values are close to zero. This indicates that the construction areas in districts and counties in Ningbo City do not show a significant spatial correlation in this case. Table 2 Global Moran Index of Construction Land Area in the Counties Year Moran Index Z-score P-value Year Moran Index Z-score P-value Note: Z-score and P-value are statistical concepts. Since P-values for all years were not statistically significant, it is highly likely that this data is not spatially correlated. 2.3 Measurement model setting Since we have confirmed that the spatial effect is not obvious, we can now take the construction land area in 2003 and 2012 as the dependent variable, take the screened independent variables as the influence variables, and use a panel data regression model to estimate and analyze the factors influencing construction land use changes. We assume that changes in construction land are determined by a series of such as economic, social, policy and location variables. Based on this assumption, we can set a panel model of construction land change based on the production model of Cobb-Douglas. The model is as follows: lncl it = α + β 1 lned it + β 2 lnpop it + β 3 ISA it + η i + ε it (2) In Formula 2, CL it is the construction land area of area i in year t; α is the constant term; β i is the regression coefficient; η i is the individual effect to control for the unique nature of districts and counties; and ε it is the disturbance term. The ED it, POP it and ISA it, respectively, represent economic growth, population changes and industrial structure adjustment. The impact of economic growth and population change on construction land has been confirmed to be exponential by previous researches, so the logarithms of CL it ED it and POP it are taken to accommodate the metering model. In addition, in order to eliminate the impact of the price factor on the analysis, we use the following method to convert per capita GDP at current prices to actual per capita GDP: Actual per capita GDP = GDP per capita (current price) * 100/consumer price index (3) where the consumer price index (CPI) reflects the changes in the price level of households purchasing consumer goods and services. 3 Empirical analysis of driving factors of construction land expansion Based on the descriptive analysis of the characteristics driving the expansion of Ningbo s construction land area, a panel data regression model was used to analyze factors affecting the expansion of construction land, such as economic growth, population growth and industrial structure adjustment. We used the results of the regression model to analyze how the three factors performed in regional and time series and obtained the economic meaning of the measurement result. 3.1 Characterization of Ningbo s urban land expansion Because it is one of the key cities at the intersection of the Yangtze River economic zone and the coastal economic zone, construction land area in Ningbo is undergoing a period of rapid growth. From a construction land growth trend chart of the six regions of Ningbo City in 2003 to 2012 (Fig. 2), we can determine that the growth of the total amount of construction land area in the six research areas is relatively

4 368 Journal of Resources and Ecology Vol. 9 No. 4, 2018 constant. The Central Area and Cixi City have a higher growth rate than the other research areas. The total amount of their construction land is already very large, however, they still have reached average annual growth rates of 6.6% and 6.0%, respectively. Yuyao City, Fenghua City, Xiangshan County and Ninghai County have lower average annual growth rates of 4.9%, 4.8%, 4.5% and 3.3%, respectively. The land use expansion contribution rate and expansion intensity can also be used to illustrate land expansion characteristics (Shu B R et al., 2013). The expansion contribution rate refers to the proportion that the construction land increment of each district made up in the increment of the whole city that year. We can determine from Fig. 3 that, from 2003 to 2012, the contribution rate of construction land of the Central Area decreased from 70% to approximately 30%, while the proportions of other districts and counties fluctuated but generally increased. The gap between the Central Area and other areas gradually narrowed. This is a universal phenomenon in most rapidly developing cities in recent years land available for construction in the central area reaches the development limit, while the surrounding areas still have land available and begin periods of construction land use expansion. The expansion intensity (R) represents the range of land area change per unit of time: R = (U b U a ) / (U a T) 100% (4) where U b is the land area at the end of the period, U a is the land area at the beginning of the period, and T is the time interval. As can be seen from Fig. 4, in most years the expansion intensities were quite stable and remained below 8%, except for the period of explosive land use expansion in Pre-regression test and regression model selection 1) Stationarity test Econometric theory shows that many economic variables in panel data are non-stationary. The results obtained by using non-stationary variables for regression analysis are quite likely to be spurious. To avoid spurious regressions, it is necessary to carry out a unit root test on the panel data. Considering the data structure of this study (equilibrium panel, n = 6, t = 10), the LLC test was used. The adjusted t* is , which is significantly less than the P value (0.0187). Therefore, we rejected the null hypothesis that the panel data contains a unit root, which means the panel is a smooth process. 2) The Hausman test and other pre-regression tests Before the panel regression, the Hausman test was used to select the fixed effect model or the random effect model. Other tests included the panel correlation test (to confirm the presence of intergroup correlation), the heteroscedasticity test (to confirm whether there was heteroscedasticity) and the autocorrelation test (to confirm the presence of time series characteristics). The test results are summarized in Table 3. Fig.2 Construction land growth trends of six main administrative districts in Ningbo Fig.4 Land expansion intensity of the six main administrative districts in Ningbo Table 3 Test results before regression Fig.3 Contribution of construction land expansion of the six main administrative districts in Ningbo Hausman test Panel correlation test Heteroscedasticity test Autocorrelation test Chi Prob > chi Original hypothesis Reject Reject Accept Reject Result Fixed effect Does not exist Exists Does not exist

5 MOU Chufu, et al.: Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China 369 The Hausman test results show that a fixed effect model applies to this study. At the same time, there is no panel correlation or intra-sequence correlation while the heteroscedasticity exists. The sequence number is greater than the number of cross sections. Taking all these factors into account, we determine that the FGLS (Feasible generalized least squares) model should be used in the regression. The regression results are summarized in Table 4. The results show that the coefficients of economic growth, population increase and industrial structure change are all significant at the 1% level, which indicates that the coefficient obtained under the model has strong interpretation. 3.3 Results of the panel regression of driving factors We can draw the following conclusions from the regression results in Table 4. 1) Economic growth contributes significantly to the growth of construction land Specifically, for every 1% increase in the size of the economy, the amount of construction land in the region will increase by approximately 0.35%. This is because, on the one hand, the growth of regional GDP is always accompanied by an increase in business efficiency, and this inevitably leads to enterprises expanding in scale, which increases demand for industrial land, thus driving regional construction land to expand. On the other hand, as their incomes increase, residents consume less agricultural products as a proportion of total consumption and more non-agricultural products. Changes in the structure of demand lead to changes in industrial structure and, thus, a large number of production factors enter the secondary and tertiary industries, driving the expansion of construction land. 2) The total population growth leads to an increase in urban construction land Specifically, for every 1% increase in the urban population, built-up area increases by 0.52%. This is consistent with the mainstream viewpoint that the urban population has a positive effect that drives urban construction land use. In fact, the growth of the urban population brings more than increased demand for housing. Demand for services like education, medical care and employment also increase along with population growth. Therefore, the solution to traffic congestion, housing tensions and other congestion problems in large cities are policies promoting an approach to urbanization that supports balanced development in both large cities and small cities. From the point of view of land use intensity, the population pressure in large cities can be eased Table 4 FGLS regression simulation results Variables Coefficient Standard deviation T statistic P value lned lnpop ISA cons through the development of small towns. This can help to control the over-expansion of built-up areas in large urban areas, and prevent the "city disease" that may occur in the process of developing large cities. 3) Changes in industrial structure lead to changes in urban construction land use, but the functions of different industries are different An increase in the industrial structure coefficient will lead to a reduction of urban construction land. In particular, if the industrial structure coefficient increases by 1%, the scale of construction land in the area where the increase occurs drops by approximately 0.35%. In other words, the increase in the output value of a secondary industry or the decrease in the output value of a tertiary industry will lead to an increase in urban construction land. This shows that industrialization plays a very important role in the development of urban construction land. It also shows that there is still a great deal of pressure to protect urban construction land. In the industrial structure of Ningbo, the proportion of the secondary industries is relatively high, and the level of intensity of land use needs to be improved. Ningbo needs to curb the expansion of construction land through new-type industrialization and structural optimization. Additionally, tertiary industries characteristically have low demand for land. They require less land than secondary industries to produce the same output. This shows that we should vigorously develop tertiary industries in the future and give full play to their huge role in promoting employment and the prosperity of the market, both of which improve the level of intensive use of construction land. 3.4 Results of the regional-level regression The results of the land expansion analysis at the regional level show that most of the factors influencing expansion in the six regional models are significant and the significance levels are quite high. Their adjusted R 2 are all above 0.9, and the P values of each model are significant at the 1% level, which means the fitting of the model is ideal. At the regional level, economic development, population growth and industrial structure adjustment all have very strong effects on the growth of construction land. 1) Population growth (lnpop) has a very significant positive impact on land use expansion (average value 2.471) in the Central Area, Yuyao City, Cixi City and Fenghua City. 2) Except for Yuyao, the impact of economic development (lned) on the land expansion is mostly positive (average value 0.203). That is, regardless of other factors, for every 1% increase in economic growth, construction land will expand by 0.2%. However, the elasticity coefficient of growth is significantly smaller than for the coefficient of population growth (average value 2.471). This is because the four regions have passed the early stage of industrialization, and labor and resource intensive industries are now taking the lead. The influx of large numbers of migrant workers makes the land use expansion caused by demand

6 370 Journal of Resources and Ecology Vol. 9 No. 4, 2018 Table 5 Regression coefficients and the main statistics of the six main administrative districts in Ningbo Variables Central Area Yuyao Cixi Fenghua Xiangshan Ninghai cons lned lnpop ISA Adjusted R F-statistic for housing more urgent than that caused by demand for industrial land. 3) Industrial structure adjustment (ISA) has had a positive effect on urban expansion in the four regions that are more developed. These four regions began industrializing earlier. In recent years, an economic transformation initiative to shift labor-intensive production away from the urban areas has gotten underway. Traditional industries are gradually being replaced with high-end service businesses. However, enterprises that have moved out of the central area still need to utilize construction land in the suburbs. Therefore, given this logic, the industrial structure adjustment has a positive effect on urban land expansion in these areas (average value 0.234), especially in Yuyao (coefficient 1.235), where cultural and creative industries are well developed. 4) Xiangshan County and Ninghai County are different than the other four regions. Economic growth (average value 0.304) is the main driving factor behind the expansion of construction land in these two research areas. The forces of population growth and industrial structure adjustment are relatively weak (average values and 0.015, respectively). From local field research, we see that these two districts are ecological development areas in the urban functional zoning plan for Ningbo. They are more focused on environmentally friendly economic development and ecological conservation. The construction of eco-economic zones occupied most of the construction land. Moreover, the lack of labor-intensive and resource-intensive industries has led to an outflow of local residents and demand for residential land continues to weaken. Therefore, the contribution of population factors to land use expansion in these two districts is low or even negative, a situation very different than that found in the other four areas. To summarize, economic growth has a positive influence on the expansion of construction land in each region, and the average regression coefficient for the six regions is The influence in Xiangshan County and Ninghai County is more obvious (average value 0.304) and the role of population growth and industrial restructuring is more pronounced in economically developed areas than in ecological conservation areas. 3.5 Results of the time series regression In addition to the regional regression, we also used a time series to analyze the direction of actions and the amounts of changes of the variables in the expansion of construction land. The regression results of the model show that the overall R 2 is not as high as those in the regional level regression, but is still acceptable at the statistical level. The t value of each regression coefficient is significant and the model is well fitted. Table 6 and Fig. 5 describe how each variable changes from year to year. 1) Economic development was significant at the 5% significance level in all but a few years (Table 4). The regression coefficient increased from in 2005 to in 2012, which indicates that the impact of the economic development factor on construction land expansion is increasing. 2) The force of population growth on land use expansion slowed down after After 2008, it began to lag behind the role of economic growth as a driving force. This may have been due to gradual cooling of the commercial and residential land boom. New arrivals to the area need to consume the housing stock that was built before their arrival, and this slows increases in the expansion of land for housing. Thus, the impact of the population on urban construction land expansion has declined. 3) The regression coefficient of industrial structure adjustment during the study period was negative, which means that its increase had a negative effect on the growth of construction land. This coefficient increased year by year from in 2004 to in This shows that the negative effect grew weaker and weaker. In conclusion, the results of the year-by-year regression show that economic development and population increases had positive effects on the expansion of construction land. The averages of these two regression coefficients during the research years were and 0.515, respectively. The effect of industrial structure adjustment was negative. In particular, the effect of the economic development factor grew over time; the effect of the population growth factor slowly declined and lagged behind the effect of economic development in the later years of the study. The role of the industrial structure coefficient remained negative. 4 Conclusions and discussion 4.1 Discussion The study of the forces driving the expansion of land use has always been a difficult, hot button issue in urban geography. This study uses econometric analysis of urban construction land expansion in typical areas of Ningbo City during to prove that Ningbo s construction land expansion in was the inevitable result of the combined effects of population growth, economic growth, industrial structure adjustment and other factors. On the one

7 MOU Chufu, et al.: Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China 371 Table 6 Year-by-year regression results of urban construction land expansion of Ningbo from 2003 to 2012 Variable Coefficient Variable Coefficient Variable Coefficient Variable Coefficient cons(2003) lned(2003) lnpop(2003) ISA(2003) ( 0.160) (0.280) (0.270) ( 0.650) cons(2004) lned(2004) lnpop(2004) ISA(2004) (16.697) (1.813) (0.763) (2.397) cons(2005) lned(2005) lnpop(2005) ISA(2005) (17.341) (1.778) (0.715) (1.440) cons(2006) lned(2006) lnpop(2006) ISA(2006) (0.980) ( 0.380) (2.730) ( 2.960) cons(2007) lned(2007) lnpop(2007) ISA(2007) (6.328) (0.637) (0.268) (0.563) cons(2008) lned(2008) lnpop(2008) ISA(2008) (6.620) (0.660) (0.282) (0.592) cons(2009) lned(2009) lnpop(2009) ISA(2009) (5.126) (0.524) (0.242) (0.340) cons(2010) lned(2010) lnpop(2010) ISA(2010) (3.063) (0.302) (0.132) (0.220) cons(2011) lned(2011) lnpop(2011) ISA(2011) (4.901) (0.480) (0.210) (0.317) cons(2012) lned(2012) lnpop(2012) ISA(2012) (5.656) (0.548) (0.249) (0.378) Note: No heteroscedasticity is found after residual plot and BP tests, and the value in parentheses is the t value. Fig.5 Trends of the regression coefficient of each variable hand, changes in quantity, structure and the forces driving construction land use in Ningbo not only reflect the acceleration of the urbanization process, but also reflect Ningbo s efforts to design a reasonable layout for industrial land, protect ecological land such as forest and water areas, upgrade industrial structure, and other aspects. On the other hand, such changes also expose certain problems such as the low level of land intensive utilization and the uneven distribution of population in Ningbo. Over the long-term, the existence of these problems will affect the sustainable development of the urban and rural social economy. How to make intensive use of land and enhance the intensity of land use are problems that the Ningbo government needs to study further. Based on these conclusions, this paper offers several suggestions for the overall utilization of urban and rural construction land in Ningbo. 1) Make intensive use of construction land, especially the urban construction land outside of the Central Area. It is important to revitalize the stock and control the increment, in order to improve the efficiency of urban and rural construction land use. 2) Actualize dynamic monitoring of land use status. Changes in use status from cultivated land to construction land should be the subject of yearly or quarterly dynamic remote sensing monitoring. Taking strict control of construction land development and reducing the amount of agricultural land taken over for construction are fundamental measures needed to protect land resources. 3) Strengthen the macro control role of the land use planning function in land management, and pay attention to coordinating land use planning and overall city planning. Land use planning and city planning have common planning objects and planning goals with respect to land use. The two kinds of planning must be coordinated and linked together. The key goal of coordination is to plan the intensive and reasonable use of regional urban land. There are still shortcomings in the research framework and methods: 1) The spatial correlation test results of this study show no significant spatial correlation between the samples. This may have a relationship to the number of samples, but it is

8 372 Journal of Resources and Ecology Vol. 9 No. 4, 2018 also possible that relevant land policies have limited the spatial spillover of construction land from one area to another. This will be the focus of follow-up studies. 2) The urban population is characterized by the statistic non-farm population data. However, because of China s household registration system, the current urban population can t fully represent the actual urban population. A follow-up study will consider the impact of permanent residents and floating population on construction land. 4.2 Conclusions The main conclusions of this study are as follows: 1) Panel regression shows that economic growth, population growth and the development of secondary industries significantly promote the growth of urban construction land use, while the development of tertiary industries reduces the scale of construction land use. In particular, for every 1% increase in economic growth, the amount of construction land use increases by approximately 0.35%. For every 1% increase in the urban population, construction land area increases by 0.52%. For every 1% increase in the industrial structure adjustment coefficient, construction land use is reduced by approximately 1%. 2) The results of the regional level regression show that economic growth, population growth and industrial structure adjustment are the factors driving the expansion of construction land use in most research areas. In particular, economic growth has a positive impact on the expansion of construction land in all the districts studied (average regression coefficient is 0.119) with the exception of Yuyao. The impact is more significant in the southern areas, such as Xiangshan County and Ninghai County (average regression coefficient is 0.304). The role of population growth and industrial structure adjustment is more obvious in economically developed areas (average regression coefficients are and 0.362) like the Central Area, Yuyao City, Cixi City and Fenghua City. 3) The results of the year to year regression show that economic growth and population growth have a definite positive effect on the expansion of construction land use. In particular, the driving effect of the economic growth factor is increasing while the driving effect of the population growth factor is slowly declining and has lagged behind the effect of economic growth in recent years. The influence of industrial structure adjustment remains negative but the negative correlation is weakening year by year. References Chen Q, Advanced Econometrics and Stata Applications (Second Edition). Higher Education Press. Fan X, Zheng D, Shi M, How Does Land Development Promote China s Urban Economic Growth? The Mediating Effect of Public Infrastructure. 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9 MOU Chufu, et al.: Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China 373 牟初夫 1,2, 王礼茂 1,2, 屈秋实 1,2, 方叶兵 1,2,3, 张宏 4 1. 中国科学院地理科学与资源研究所, 北京 ; 2. 中国科学院大学资源与环境学院, 北京 ; 3. 安徽师范大学国土资源与旅游学院, 安徽芜湖 ; 4. 解放军信息工程大学地理空间信息学院, 郑州 摘要 : 本文基于 2003~2012 年宁波市下辖区县的面板数据, 结合面板计量模型与分区域回归 逐年回归方法, 分析了宁波市城市建设用地扩张的主要驱动因素, 得到以下主要结论 :(1) 经济总量 人口和产业结构调整系数每增长 1%, 建设用地规模会增加 0.35% 0.52% 和 1% (2) 不同区域城市建设用地扩张的主导驱动因素有所差异 发展程度相对较高的中心区 余姚 慈溪和奉化, 人口增长因素用地扩张的作用最为明显 ( 系数分别为 4.880, 1.383, 和 0.583), 产业结构调整因素的作用次于人口增长 ( 系数分别为 1.235, 0.307, 和 0.242), 而经济发展因素则显得越来越不显著 ; 而发展程度相对较低的象山县和宁海县引起城市扩张的主要驱动因子是经济发展因素 ( 系数分别为 和 0.195), 人口和产业结构调整的驱动力相对较弱 (3) 逐年回归的结果表明, 经济发展因素的驱动作用随时间推移越来越大 ( 系数从 2005 年的 到 2012 年的 1.459); 人口增长因素的驱动作用则缓慢下降 ( 系数从 2005 年的 到 2012 年的 0.044), 在 2008 年后已小于经济发展因素的贡献力度 ; 产业结构系数与建设用地增长暂时为负相关, 且负相关性越来越弱 ( 从 2004 年的 到 2012 年的 0.589) 关键词 : 建设用地扩张 ; 驱动因素 ; 面板数据 ; 宁波