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Analysis of Spatial Pattern Evolution and Influencing Factors of Regional Land Use Efficiency in China Based on ESDA-GWR

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Analysis of Spatial Pattern Evolution and Influencing Factors of Regional Land Use Efficiency in China Based on ESDA-GWR www.nature.com/scientificreports OPEN Analysis of Spatial Pattern Evolution and Infuencing Factors of Regional Land Use Efciency in Received: 22 May 2017 Accepted: 19 November 2018 China Based on ESDA-GWR Published: xx xx xxxx Xiaoshu Cao1, Yongwei Liu1, Tao Li2 & Wang Liao1 In order to give an in-depth understanding of the contradictions arising from the land resource supply and demand, this study selected 30 provinces (some are autonomous regions or municipalities) in China to be the research unit, used the carbon emission as an undesirable output, and adopted the Super- SBM DEA model and ESDA-GWR method to research the evolution characteristics and infuencing factors of land use efciency in China in 2003–2013. The results indicated that: (1) The land use efciency in China overall was moderately inefective and the overall utilization level was low; (2) The Global Spatial Autocorrelation was instable and had maintained a high level; (3) The “hot spots” mainly being distributed in the southeast coastal regions and “cold spots” being found in the central and western regions, so that as time goes on, the pattern of “high in the east and low in the west” has been gradually formed and stabilized. (4) The GWR model analysis showed that the natural factors such as NDVI, DMSP/OLS and DEM have a signifcant impact on land use efciency, thereby providing an important contribution to this study. For the eastern coastal areas, the emphasis should be improving their OT, PF and PGDP, for the western region, should focus on improving its comprehensive economic development level to improve the DMSP/OLS, while strengthening the ecological environment to improve the level of NDVI. How to economically and intensively utilize the fnite land resource has been the core issue in China’s economic and social development. In 2014, China approved the construction land of 403,800 hectares and approved the farmland occupation about 160,800 hectares, resulting in a sharp decline in China’s arable land stock. As the social economy of China develops, the contradiction arising from the land supply and demand has gradually been intensifed, posing challenges for the sustainable economic and social development. Many scholars have studied the agrarian problems in China1–4. Terefore, implementing a very stringent strategy for economical and intensive land use –in other words, to increase the land use efciency – is important for resolving the contradic- tion arising from the land resource supply and demand. Clearly, it is of great practical signifcance to explore the spatio-temporal variation characteristics of land use efciency and its factors. Land use efciency refers to the increase in the output of a unit land area with respect to regional social and economic activities. It is not only related to the efcient use of land resources, but also is the essential foundation for the sustainable development of urban regional systems. For some time, the land use efciency has been an important topic for scholars in China and elsewhere. Te early researches on land use efciency mainly focused on the urban land use, the theoretical modeling of urbanization, and the urban management5–8. Te current researches primarily focused on the functions and operational mechanisms of the land market, the land property rights and the allocation efciency, the land use efciency evaluation and application, etc.9–16. According to the research results of efciency evaluation of city land utilization in the country, urban agglomerations and diferent cities, scholars have conducted extensive and deep researches on urban land use efciency in China. Tis research mainly studies the basic theory of urban land use efciency, the evaluation index systems, the model construction and evaluation methods, the comprehensive utilization efectiveness, and the ways for improving the urban land use efciency17–21. 1School of Geography Science and Planning, Sun Yat-sen University, Guangzhou, 510275, China. 2Institute of Transport Geography and Spatial Planning, Shaanxi Normal University, Xi’an, 710119, China. Correspondence and requests for materials should be addressed to X.C. (email: [email protected]) or Y.L. (email: [email protected]) SCIENTIFIC REPORTS | (2019) 9:520 | DOI:10.1038/s41598-018-36368-2 1 www.nature.com/scientificreports/ Year 2003 2007 2010 2013 Minimum Value 0.234 0.243 0.241 0.227 Maximum Value 1.157 1.261 1.195 1.228 Average Value 0.613 0.616 0.635 0.623 Yunnan, Shanghai, Shanghai, Beijing, Fujian, Shanghai, Beijing, Tianjin, Fujian, Guangdong, Shanghai, Tianjin, Fujian, Guangdong, Yunnan, Yunnan, Anhui, Fujian, Optimal (≥1) Beijing, Zhejiang, Beijing, Guangdong, Anhui, Anhui, Tianjin, Zhejiang, Guangdong, Zhejiang, Tianjin, Liaoning, Yunnan, Zhejiang, Liaoning Liaoning Liaoning Anhui Highly Inefective Guizhou, Gansu, Ningxia, Gansu Ningxia, Gansu Ningxia, Gansu [0, 0.25) Ningxia Qinghai, Shanxi, Inner Guizhou, Qinghai, Shanxi, Mongolia, Xinjiang, Xinjiang, Shaanxi, Inner Guizhou, Shanxi, Qinghai, Guizhou, Xinjiang, Qinghai, Moderately Sichuan, Shaanxi, Mongolia, Sichuan, Xinjiang, Sichuan, Henan, Shanxi, Guangxi, Henan, Inner Inefective Jilin, Henan, Hebei, Chongqing, Henan, Guangxi, Inner Mongolia, Mongolia, Shaanxi, Sichuan, [0.25, 0.5) Guangxi, Hubei, Guangxi, Hebei, Jilin, Shaanxi, Hebei, Hubei, Jilin, Hebei, Hubei, Jilin, Hunan, Heilongjiang, Jiangxi, Hunan, Hubei, Jiangxi, Hunan, Chongqing Hainan, Jiangxi Chongqing Heilongjiang Slightly Inefective Hunan, Shandong, Heilongjiang, Jiangxi, Heilongjiang, Chongqing, Shandong, Hainan [0.5, 0.75) Hainan Shandong, Hainan Shandong Close to Efective Jiangsu Jiangsu Jiangsu Jiangsu [0.75, 1) Table 1. Statistics on Land Use Efciency in China. Te research on land use efciency tends to be gradually perfected in respects of methods, models and index- ing systems. Among them, DEA has been widely used in empirical research, but most of the studies were based on the traditional DEA. In addition, the perspectives of the spatial correlation and the heterogeneity were seldom involved. Te existing research methods gradually use the qualitative analysis instead of the quantitative analysis, such as the regression statistical analysis, the data envelopment analysis and the spatial analysis method, and the existing research method has fully integrated the mainstream technology methods and spatial analysis trends. However, most researchers mainly use the traditional DEA method to evaluate the land use efciency, which gen- erally is based on the economic and social development of the region. A research which only studies the infuen- tial factor namely the natural environment on the land use efciency in a particular area is far not enough. Based on this, the outstanding feature of this study is using the Super-SBM DEA method, which has more advantages than the traditional method and can evaluate the land use efciency more deeply. Furthermore, it can take into account the importance of both the social and natural factors. Here, in this paper, it intends to understand the evolution of land use efciency and infuence factors in China. Te goal of this study is to analyze the evolution of land use efciency by using the Super-SBM DEA model and the ESDA-GWR method and is to list the infuential factors for land use efciency based on GWR. Upon the study of the undesirable output of carbon dioxide, the infuence of natural and socioeconomic factors also is analyzed. Te development of economy and society in China has been permeated with the contradictions when comes to the protection of resources and the environment, especially the land resource issue remains as a cardinal issue. Te CO2 emission, as one of the outputs of pollutions, produces a negative impact, but making the land use efciency model can more truly refect the actual situation of China. Results Evolution of Land Use Efciency Based on Super-SBM DEA and ESDA. In overall, it stays at a low level, but the regional diferences are obvious. Te results of the Super-SBM DEA model calcu- lations for efciency are shown in Table 1. Among them, the average score in 2013 was 0.623, which was only 50.76% of the optimal level (Shanghai had the highest score of 1.228). Nine provinces (30%) reached optimal levels of efciency, while the remaining 21 provinces (70%) were sub-optimal, meaning that the overall land use efciency was at a low level. In terms of the regional diferences, the non-equilibrium of spatial diferences was consistent with the level of economic development, which showed the spatial pattern characteristic of being high in the east and low in the middle and west of the country. Trough the analysis, we found that the highest, lowest, and average values for each target year had an overall increasing trend. Tis paper divided the efciency levels into fve categories, highly inefective, moderately inef- fective, slightly inefective, close to efective, and optimal. Te results showed that the most common category was moderately inefective, followed by optimal, highly inefective, slightly inefective, and close to efective-with a smaller distribution. Te range of distributions in each area was large with clear polarization in efciency. At the same time, regions of each type gradually tended to be stable, forming a clear distribution pattern of land use efciency in China. Global Spatial Autocorrelation was Not Stable and Maintained a High Level. Tis study used GeoDa 1.6.7 sofware to calculate the global spatial autocorrelation index, with results shown in Table 2. Te results showed that there was signifcant positive spatial autocorrelation for each year. In 2013, for example, the seven provinces that had optimal efciency were mainly distributed throughout the eastern region, making up 77.78% of that region. In the central region, fve provinces were moderately inefective, which accounted for 83.33% of that region. In the west, nine provinces were highly or moderately inefective. On the whole, the highest SCIENTIFIC REPORTS | (2019) 9:520 | DOI:10.1038/s41598-018-36368-2 2 www.nature.com/scientificreports/ Year 2003 2007 2010 2013 Moran’s I Value 0.302 0.214 0.353 0.326 P value 0.006 0.004 0.005 0.003 Table 2.
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