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Demand prediction and regulation zoning of urban-industrial land: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China

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Abstract

As a main type of urban construction land, urban-industrial land is used to provide the judging criteria for construction land scale in the planning period when urban population, industrial development, investment scale, and other conditions are uncertain in China; however, research on expected indicator such as urban-industrial land in overall land use plan mainly focuses on qualitative analysis; quantitative analysis research has not yet been carried out. Using MATLAB R2016a software modeling tools to establish GM (1, 1) model and RBF neural network model, respectively, this paper predicted the demand of urban-industrial land in Beijing-Tianjin-Hebei Urban Agglomeration. Comparing the predicated results with the actual value of urban-industrial land in Beijing, Tianjin, and 11 prefecture-level cities in Hebei Province, we determined the reasonable prediction model for urban-industrial land after testing the accuracy of the two prediction models. The results showed that the RBF neural network model was the more reasonable prediction model for urban-industrial land. Using the predicted results of the RBF neural network model, combining expected indicators of overall land use plan (2006–2020) in Beijing and Tianjin, as well as 11 prefecture-level cities in Hebei Province in the planning target year, determined remaining usable time of urban-industrial land. Finally, combined with the actual scale of urban-industrial land in 2015 and the predicated scale of urban-industrial land in 2020, the remaining usable time of each city’s urban-industrial land was calculated in terms of the average annual growth rate of urban-industrial land from 2009 to 2015. According to the comparative relationship between the remaining usable time and the remaining time of the overall land use plan (5 years), urban-industrial lands were divided into three kinds of regulation zones: reasonable reduction zone, optimized adjustment zone, and core development zone. The policy implications for urban-industrial land in each regulation zone were also provided. This paper can provide reference for regulation zoning of urban-industrial land in developing countries and regions.

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References

  • António, X., Maria, B., Rui, F., & Maria, S. (2018). A regional composite indicator for analysing agricultural sustainability in Portugal: A goal programming approach. Ecological Indicators, 89, 84–100.

    Article  Google Scholar 

  • Aurbacher, J., & Dabbert, S. (2011). Generating crop sequences in land-use models using maximum entropy and Markov chains. Agricultural Systems, 104(6), 470–479.

    Article  Google Scholar 

  • Bartoli, A., Cavicchioli, D., Kremmydas, D., Rozakis, S., & Olper, A. (2016). The impact of different energy policy options on feedstock price and land demand for maize silage: The case of biogas in Lombardy. Energy Policy, 96, 351–363.

    Article  Google Scholar 

  • Beijing Bureau of Statistics. (2017). Beijing statistical yearbooks (2010~2016). Beijing: China Statistical Press.

    Google Scholar 

  • Chen, L., & Pai, T. (2015). Comparisons of GM (1, 1) and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan. Atmospheric Pollution Research, 6(4), 572–580.

    Article  CAS  Google Scholar 

  • Chen, Z., Tang, J., Wan, J., & Chen, Y. (2017). Promotion incentives for local officials and the expansion of urban construction land in China: Using the Yangtze River Delta as a case study. Land Use Policy, 63, 214–225.

    Article  Google Scholar 

  • Fu, H., Manogaran, G., Wu, K., Cao, M., Jiang, S., & Yang, A. (2019). Intelligent decision-making of online shopping behavior based on internet of things. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.03.010.

  • Ghritlahre, H., & Prasad, R. (2018). Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. Journal of Environmental Management, 223, 566–575.

    Article  Google Scholar 

  • Hao, S., Xie, T., Wu, W., Gao, X., Deng, L., & Li, Q. (2014). Construction land demand forecast in Chengdu city based on a RBF neural network. Resources Science, 36(6), 1220–1228.

    Google Scholar 

  • He, B. J., Zhao, D. X., Zhu, J., Darko, A., & Gou, Z. H. (2018). Promoting and implementing urban sustainability in China: An integration of sustainable initiatives at different urban scales. Habitat International, 82, 83–93.

    Article  Google Scholar 

  • He, B.-J., Zhu, J., Zhao, D.-X., Gou, Z.-H., Qi, J.-D., & Wang, J. (2019). Co-benefits approach: Opportunities for implementing sponge city and urban heat island mitigation. Land use policy, 86, 147–157.

    Article  Google Scholar 

  • Hebei Bureau of Statistics. (2017). Hebei statistical yearbooks (2010~2016). Shijiazhuang: China Statistical Press.

  • Hermanns, T., Katharina, H., Hannes, J. K., Katharina, S., Li, Q., & Faust, H. (2017). Sustainability impact assessment of peatland-use scenarios: Confronting land use supply with demand. Ecosystem Services, 26, 365–376.

    Article  Google Scholar 

  • Kelly, O., Carlos, A., Gustavo, E., Alexandre, S., Nero, L., Getulio, F., José, R., & Alexandre, R. (2018). Markov chains and cellular automata to predict environments subject to desertification. Journal of Environmental Management, 225, 160–167.

    Google Scholar 

  • Kun, Y., Meie, P., Yi, L., Kexin, C., Yisong, Z., & Xiaolu, Z. (2018). A time-series analysis of urbanization-induced impervious surface area extent in the Dianchi lake watershed from 1988–2017. International Journal of Remote Sensing, 1–20.

  • Li, Y., Chen, X., Tang, B., & Wong, S. (2018). From project to policy: Adaptive reuse and urban industrial land restructuring in Guangzhou city. China. Cities, 2018, 68–76. https://doi.org/10.1016/j.cities.2018.05.006.

    Article  Google Scholar 

  • Li, C., Gao, X., He, B.-J., Wu, J., & Wu, K. (2019a). Coupling coordination relationships between urban-industrial land use efficiency and accessibility of highway networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China. Sustainability, 11, 1446.

    Article  Google Scholar 

  • Li, C., Wu, K., & Gao, X. (2019b). Manufacturing industry agglomeration and spatial clustering: Evidence from Hebei Province, China. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-019-00328-1.

  • Lin, J. (2009). Urban rural construction land growth in China. Beijing: The Commercial Press.

    Google Scholar 

  • Liu, S., Dang, Y., & Fang, Z. (2004). Grey systems theory and applications. Beijing: Science Press.

    Google Scholar 

  • Maria, S., Claire, H., Alice, B., Paul, J. B., James, C., Paul, G., David, H., Jerry, K., & Kevin, A. (2016). A nexus perspective on competing land demands: Wider lessons from a UK policy case study. Environmental Science & Policy, 59, 74–84.

    Article  Google Scholar 

  • Ministry of Housing and Urban-Rural Development of China. (2011). Code for classification of urban land use and planning standards of development land (GB 50137–2011). Beijing: China Architecture & Building Press.

    Google Scholar 

  • Monteiro, D. S. E., Dourado, M. R., & Dias, C. C. (2018). Bee-inspired RBF network for volume estimation of individual trees. Computers and Electronics in Agriculture, 152, 401–408.

    Article  Google Scholar 

  • Morteza, T., Abbas, R., Farshad, S., & Abdeshahi, A. (2018). Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models. Journal of Cleaner Production, 172, 3028–3041.

    Article  Google Scholar 

  • National Bureau of Statistics of China (NBSC). (2017). China Statistical Yearbook for Regional Economy (2010~2016). Beijing: Architecture & Building Press.

    Google Scholar 

  • Panigrahi, J. K., & Mohanty, P. K. (2012). Effectiveness of the Indian coastal regulation zones provisions for coastal zone management and its evaluation using SWOT analysis. Ocean & Coastal Management, 65, 34–50.

    Article  Google Scholar 

  • Pignatti, E., Leng, S., Carlone, D. L., & Breault, D. T. (2016). Regulation of zonation and homeostasis in the adrenal cortex. Molecular and Cellular Endocrinology, 441, 146–155.

    Article  Google Scholar 

  • Pilehforooshha, P., Karimi, M., & Taleai, M. (2014). A GIS-based agricultural land-use allocation model coupling increase and decrease in land demand. Agricultural Systems, 130, 116–125.

    Article  Google Scholar 

  • Qiu, R., Xu, W., & Zhang, J. (2015). The transformation of urban industrial land use: A quantitative method. Journal of Urban Management, 4(1), 40–52.

    Article  Google Scholar 

  • Reshmidevi, T. V., Eldho, T. I., & Jana, R. (2009). A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds. Agricultural Systems, 101(1–2), 101–109.

    Article  Google Scholar 

  • Shu, H., & Xiong, P. P. (2019). Reallocation planning of urban industrial land for structure optimization and emission reduction: A practical analysis of urban agglomeration in China’s Yangtze River delta. Land Use Policy, 81, 604–623.

    Article  Google Scholar 

  • Tianjin Bureau of Statistics. (2017). Tianjin statistical yearbooks (2010~2016). Tianjin: China Statistical Press.

    Google Scholar 

  • Xiao, D. (2012). Research on factors of urban and industrial-mining land growth and its spatial variation based on spatial quantitative model: An empirical analysis on prefectural-level units in the southeastern part of Hu’s Line of China. Beijing: Peking University.

    Google Scholar 

  • Xie, B., Chen, Y., Bai, Z., & Pei, T. (2014). A quantitative study on the interaction between urban industrial land use changes and economic development in Gansu Province. Journal of Arid Land Resources and Environment, 28(10), 7–13.

    Google Scholar 

  • Yang, J., Guo, A., Li, Y., Zhang, Y., & Li, X. (2018a). Simulation of landscape spatial layout evolution in rural-urban fringe areas: A case study of Ganjingzi District. GIScience & Remote Sensing, 56(3), 388–405.

    Article  Google Scholar 

  • Yang, J., Liu, W., Li, Y., Li, X., & Ge, Q. (2018b). Simulating intraurban land use dynamics under multiple scenarios based on fuzzy cellular automata: A case study of Jinzhou district, Dalian. Complexity, 2018, 1–17.

    Google Scholar 

  • Yang, K., Yu, Z., Luo, Y., Yang, Y., Zhao, L., & Zhou, X. (2018c). Spatial and temporal variations in the relationship between lake water surface temperatures and water quality - A case study of Dianchi lake. Science of the Total Environment, 624, 859–871.

    Article  CAS  Google Scholar 

  • Yang, K., Yu, Z., Luo, Y., Zhou, X., & Shang, C. (2019). Spatial-temporal variation of lake surface water temperature and its driving factors in Yunnan-Guizhou Plateau. Water Resources Research. https://doi.org/10.1029/2019WR025316.

  • Yin, K., Xu, Y., Li, X., & Jin, X. (2018). Sectoral relationship analysis on China’s marine-land economy based on a novel grey periodic relational model. Journal of Cleaner Production, 197, 815–826.

    Article  Google Scholar 

  • Yong, L., Xingguang, C., Bo-Sin, T., & Wai, W. S. (2018). From project to policy: Adaptive reuse and urban industrial land restructuring in Guangzhou city, China. Cities, 82, 68–76.

    Article  Google Scholar 

  • Zhai, T., Guo, J., Ou, M., & Kong, W. (2015). Study on allocation of total construction land in Jiangsu province based on the Gini coefficient. China Population. Resources and Environment, 25(4), 84–91.

    Google Scholar 

  • Zhang, D., Zang, G., Li, J., Ma, K., & Liu, H. (2018). Prediction of soybean price in China using QR-RBF neural network model. Computers and Electronics in Agriculture, 154, 10–17.

    Article  Google Scholar 

  • Zhao, R. (2012). The expansion and the driving mechanism of the urban-industrial land in Hebei province. Beijing: Peking University.

    Google Scholar 

Download references

Funding

This study has been supported by Xi’an University of Architectural Science and Technology Talents Science and Technology Foundation (RC1813), National Natural Science Foundation of China (41371226), Beijing Municipal Science and Technology Project (Z161100001116016), State Scholarship Fund of China (201908610060), Shaanxi Soft Science Research Program (2019KRM103) and Special Research Project of Education Department of Shaanxi-Study of the mechanism of collective construction land entering the land market from the perspective of urban and rural integration.

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Authors and Affiliations

  1. School of Public Administration, Xi’an University of Architecture and Technology, Xi’an, 710055, China

    Chenxi Li

  2. School of Public Administration, Hebei University of Economics and Business, Shijiazhuang, 050061, China

    Xing Gao

  3. School of Land Science and Technology, China University of Geosciences (Beijing), Beijing, 100083, China

    Jingyao Wu & Kening Wu

  4. Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Land and Resources, Beijing, 100035, China

    Kening Wu

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  1. Chenxi Li
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  2. Xing Gao
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Correspondence to Chenxi Li.

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Li, C., Gao, X., Wu, J. et al. Demand prediction and regulation zoning of urban-industrial land: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China. Environ Monit Assess 191, 412 (2019). https://doi.org/10.1007/s10661-019-7547-4

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