Study on the relationship between traffic dominance and land use efficiency in Shanghai

Study on the relationship between traffic dominance and land use efficiency in Shanghai

KANG Chenyuan^1*, ZHOU wenqiang²

¹School of Earth Science and Engineering, Hebei University of Engineering, Handan 056038, China. ²People’s Government of Longtang Town, Minquan County, Henan Province, Minquan 476818, China.

Transportation infrastructure promotes the interconnection of production factors between regions and enhances the efficiency of land use, while land use patterns also have an impact on transportation development patterns and mixes. Although the development of different regions within megacities tends to be integrated, different transportation mixes and land inputs still bring differentiated dynamics to the development of the blocks. In order to explore the relationship between regional traffic conditions and land use efficiency, this paper investigates the traffic development conditions and land use efficiency of each district in Shanghai by using the traffic dominance degree model and the three-stage Super-SBM model, and explores the relevant role relationship between them by using the coupled coordination degree model. The results show that: (1) the central city has the highest traffic dominance, and the suburban areas, especially the distant suburban areas, have low traffic dominance; (2) the land use efficiency of the eastern area of Shanghai is significantly higher than that of the western area, and the land use efficiency of the southern area is higher than that of the northern area; (3) in terms of coupling coordination, the traffic development and land use efficiency of most districts are coordinated, but the degree of coordination varies greatly, with Huangpu District, Yangpu District, Pudong New Area, and Hongqiao District. Yangpu District, Pudong New Area and Hongkou District are at a high level of coordinated development, Chongming District and Changning District are at an uncoordinated development, and the rest of the districts are at a medium level of coordinated development.

Keywords: Traffic dominance; land use efficiency (LUE); three-stage Super-SBM model; output orientation

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How to cite this article:
KANG Chenyuan, ZHOU wenqiang. Study on the relationship between traffic dominance and land use efficiency in Shanghai. American Journal of Geographical Research and Reviews, 2022, 4:18. DOI:10.28933/ajgrr-2022-01-1805

References:

1. Hansen W G. How accessibility shapes land use[J]. Journal of the American Institute of planners, 1959, 25(2): 73-76.
2. Jin Fengjun, Wang Chengjin, Li Xiuwei,et al. China’s regional transport dominance:Density, proximity, and accessibility[J]. Journal of Geo-graphical Sciences, 2010(02):137-151.
3. Sun Hongri, Liu Yanjun, Zhou Guolei. The spati-otemporal differentiation and mechanisms of traf-fic dominance in Northeast China[J]. Journal of Geographical Sciences, 2021, 76(02):444-458.
4. Cui Xuegang, Fang Chuanglin, Wang Zhenbo, et al. Spatial relationship of high-speed transporta-tion construction and land-use efficiency and its mechanism: Case study of Shandong Peninsula urban agglomeration[J]. Journal of Geographical Sciences, 2018, 73(06):1149-1161.
5. Wu Wei, Cao Youhui, Cao Weidong,et al. The pattern of transportation superiority in Yangtze River Delta[J]. Geographical Research, 2011, 30(12):2199-2208.
6. Meng Deyou, Shen Jinghong, Lu Yuqi. Evolve-ment of Spatial Pattern of County Level Trans-portation Superiority in Henan, China[J]. Scientia Geographica Sinica, 2014, 34(03):280-287.
7. Peng Xiangming, Han Zenglin. County Transpor-tation Superiority Degree and Level of Economic Development Space Coupling Based on Quanti-tative Analysis of 44 Agricultural Counties in Liaoning Province[J]. Resource Development & Market, 2017, 33(09):1077-1083.
8. Yao M, Zhang Y. Evaluation and Optimization of Urban Land-Use Efficiency: A Case Study in Si-chuan Province of China[J]. Sustainability, 2021, 13(4): 1771.
9. He S, Yu S, Li G, et al. Exploring the influence of urban form on land-use efficiency from a spatio-temporal heterogeneity perspective: Evidence from 336 Chinese cities[J]. Land Use Policy, 2020, 95: 104576.
10. Zhao J, Zhu D, Cheng J, et al. Does regional economic integration promote urban land use ef-ficiency? Evidence from the Yangtze River Delta, China[J]. Habitat International, 2021, 116: 102404.
11. Gao X, Zhang A, Sun Z. How regional economic integration influence on urban land use efficiency? A case study of Wuhan metropolitan area, Chi-na[J]. Land Use Policy, 2020, 90: 104329.
12. Peng C, Song M, Han F. Urban economic struc-ture, technological externalities, and intensive land use in China[J]. Journal of Cleaner Produc-tion, 2017, 152: 47-62.
13. Aigner D, Lovell C A K, Schmidt P. Formulation and estimation of stochastic frontier production function models[J]. Journal of econometrics, 1977, 6(1): 21-37.
14. Meeusen W, van Den Broeck J. Efficiency esti-mation from Cobb-Douglas production functions with composed error[J]. International economic review, 1977: 435-444.
15. Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units[J]. Euro-pean journal of operational research, 1978, 2(6): 429-444.
16. Reinhard S, Lovell C A K, Thijssen G J. Envi-ronmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA[J]. European Journal of Operational Re-search, 2000, 121(2): 287-303.
17. Liu S, Xiao W, Li L, et al. Urban land use effi-ciency and improvement potential in China: A stochastic frontier analysis[J]. Land Use Policy, 2020, 99: 105046.
18. Yang K, Wen Q, Zhong T Y. Assessment of urban land use efficiency in the Yangtze River Economic Belt[J]. Resources Science, 2018, 40(10):2048-2059.
19. Fried H O, Lovell C A K, Schmidt S S, et al. Ac-counting for environmental effects and statistical noise in data envelopment analysis[J]. Journal of productivity Analysis, 2002, 17(1): 157-174.
20. Li K, Lin B. Impact of energy conservation poli-cies on the green productivity in China’s manu-facturing sector: Evidence from a three-stage DEA model[J]. Applied energy, 2016, 168: 351-363.
21. Mahmoudabadi M Z, Emrouznejad A. Compre-hensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis[J]. Interna-tional Review of Economics & Finance, 2019, 64: 359-376.
22. Acheampong R A, Silva E A. Land use–transport interaction modeling: A review of the literature and future research directions[J]. Journal of Transport and Land use, 2015, 8(3): 11-38.
23. Rokicki B, Stępniak M. Major transport infra-structure investment and regional economic de-velopment–An accessibility-based approach[J]. Journal of Transport Geography, 2018, 72: 36-49.
24. Wu C, Wei Y D, Huang X, et al. Economic transi-tion, spatial development and urban land use ef-ficiency in the Yangtze River Delta, China[J]. Habitat International, 2017, 63: 67-78.
25. Ma L, Jin F, Song Z, et al. Spatial coupling analysis of regional economic development and environmental pollution in China[J]. Journal of geographical sciences, 2013, 23(3): 525-537.