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Ensemble intelligence algorithms and soil environmental quality to model economic quantity of land resource allocation and spatial inequality

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  • Gao, Feng
  • Yi, Shiyi
  • Li, Xiaonuo
  • Chen, Weiping

Abstract

With the increasing concern on soil pollution in context of land market reform, it’s an emerging topic to discuss whether soil pollution can cause land economic value substantially depreciated at geospatial scale. This study proposed a data science approach by synthesizing machine learning and deep learning algorithms to establish the land economic equivalent model (LEEM) with best prediction ability. The results indicated that Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) performed well in estimating residential/commercial and industrial land value, respectively. Spatial variation of soil environment quality indeed largely affected economic productivity of residential/commercial land with contribution of 13.82%. For industrial land, population density was strongly related to the land price (11.77%), of which importance score was much higher than other 10 variables except distance to center business district (CBD, 28.75%) and transaction time (28.61%). Another important finding was that soil protection measures could generate extra benefits equivalent with a mean of 22.46% and 28.92% for residential/commercial and industrial land value in current status, respectively, and high-value areas could also be predicted for effective decisions on land allocation and trading. In terms of sustainable land management, the regression tree-based LEEM is an effective tool in assisting decision makers with urban land reclamation, planning and pricing.

Suggested Citation

  • Gao, Feng & Yi, Shiyi & Li, Xiaonuo & Chen, Weiping, 2024. "Ensemble intelligence algorithms and soil environmental quality to model economic quantity of land resource allocation and spatial inequality," Land Use Policy, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:lauspo:v:141:y:2024:i:c:s0264837724001005
    DOI: 10.1016/j.landusepol.2024.107147
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    1. Friso de Vor & Henri de Groot, 2011. "The Impact of Industrial Sites on Residential Property Values: A Hedonic Pricing Analysis from the Netherlands," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 609-623.
    2. Zabel, Jeffrey E. & Guignet, Dennis, 2012. "A hedonic analysis of the impact of LUST sites on house prices," Resource and Energy Economics, Elsevier, vol. 34(4), pages 549-564.
    3. Ma, Jun & Cheng, Jack C.P. & Jiang, Feifeng & Chen, Weiwei & Zhang, Jingcheng, 2020. "Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques," Land Use Policy, Elsevier, vol. 94(C).
    4. Kim, GwanSeon & Schieffer, Jack & Mark, Tyler, 2020. "Do superfund sites affect local property values? Evidence from a spatial hedonic approach," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 15-28.
    5. Wang, Songtao & Yang, Zan & Liu, Hongyu, 2011. "Impact of urban economic openness on real estate prices: Evidence from thirty-five cities in China," China Economic Review, Elsevier, vol. 22(1), pages 42-54, March.
    6. Carranza, Juan Pablo & Piumetto, Mario Andrés & Lucca, Carlos María & Da Silva, Everton, 2022. "Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values," Land Use Policy, Elsevier, vol. 119(C).
    7. Mastromonaco, Ralph & Maniloff, Peter, 2018. "An examination of geographic heterogeneity in price effects of superfund site remediation," Economics Letters, Elsevier, vol. 171(C), pages 23-28.
    8. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.
    9. Twumasi, Clement & Twumasi, Juliet, 2022. "Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1258-1277.
    10. van Duijn, Mark & Rouwendal, Jan & Boersema, Richard, 2016. "Redevelopment of industrial heritage: Insights into external effects on house prices," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 91-107.
    11. John Braden & Xia Feng & DooHwan Won, 2011. "Waste Sites and Property Values: A Meta-Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 50(2), pages 175-201, October.
    12. Joshua Muldavin, 2000. "The Paradoxes of Environmental Policy and Resource Management in Reform-Era China," Economic Geography, Taylor & Francis Journals, vol. 76(3), pages 244-271, July.
    13. Cheng, Jing, 2022. "Analysis of the factors influencing industrial land leasing in Beijing of China based on the district-level data," Land Use Policy, Elsevier, vol. 122(C).
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