Geografie 2026, 131, 97-124
https://doi.org/10.37040/geografie.2026.008
Nonlinear and spatiotemporal dynamics of land finance and urban expansion in China: A GeoXAI-enhanced analysis
Yichuan Tian
Soochow University, School of Politics & Public Administration, Department of Political Science, Suzhou, China
Received June 2025
Accepted May 2026
References
1. BAI, X., SHI, P. (2025): China’s urbanization at a turning point – challenges and opportunities. Science, 388, 6747, eadw3443.
<https://doi.org/10.1126/science.adw3443>
2. CAO, J., TAO, T. (2023). Using machine-learning models to understand nonlinear relationships between land use and travel. Transportation Research Part D: Transport and Environment, 123, 103930.
<https://doi.org/10.1016/j.trd.2023.103930>
3. , V., HASSIJA, V., SULTHANA, A.R., GHOSH, D., DHINGRA, D., SIKDAR, B. (2023): A Review of Trustworthy and Explainable Artificial Intelligence (XAI). IEEE Access, 11, 78994−79015.
<https://doi.org/10.1109/ACCESS.2023.3294569>
4. , M., ZENG, L., DENG, Y., CHEN, S., GU, X. (2024): The Impact of Land Marketization on Urban Resilience: Empirical Evidence from Chinese Cities. Land, 13, 9. 1−24.
<https://doi.org/10.3390/land13091385>
5. CHEN, T., GUESTRIN, C. (2016): XGBoost: A Scalable Tree Boosting System. Paper presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA.
<https://doi.org/10.1145/2939672.2939785>
6. , V. (2023): A Critical Review of Urban Sprawl Studies. Journal of Geovisualization and Spatial Analysis, 7, 2, 28.
<https://doi.org/10.1007/s41651-023-00158-w>
7. COHEN, J., COHEN, P., WEST, S.G., AIKEN, L.S. (2013): Applied multiple regression/correlation analysis for the behavioral sciences. London: Routledge.
<https://doi.org/10.4324/9780203774441>
8. , R., DAVE, D., NAIK, H., SINGHAL, S., OMER, R., PATEL, P., QIAN, B., WEN, Z., SHAH, T., MORGAN, G., RANJAN, R. (2023): Explainable AI (XAI): Core Ideas, Techniques, and Solutions. ACM Computing Surveys, 55, 9, Article 194.
<https://doi.org/10.1145/3561048>
9. , M.V., HILBER, C.A.L., SCHÖNI, O. (2018): Institutional settings and urban sprawl: Evidence from Europe. Journal of Housing Economics, 42, 4−18.
<https://doi.org/10.1016/j.jhe.2017.12.002>
10. , X., QIU, S., SUN, Y. (2020): Land finance dependence and urban land marketization in China: The perspective of strategic choice of local governments on land transfer. Land Use Policy, 99, 105023.
<https://doi.org/10.1016/j.landusepol.2020.105023>
11. , E., HU, T., LI, Z. (2025): Revealing key factors of heat-related illnesses using geospatial explainable AI model: A case study in Texas, USA. Sustainable Cities and Society, 122, 106243.
<https://doi.org/10.1016/j.scs.2025.106243>
12. , F.L., JALALUDIN, B.B. (2009): Impact of Urban Sprawl on Overweight, Obesity, and Physical Activity in Sydney, Australia. Journal of Urban Health, 86, 1, 19−30.
<https://doi.org/10.1007/s11524-008-9332-5>
13. , J., SHEN, Y., WU, J., ZHANG, R. (2022): Land finance in China: Analysis and review. China Economic Review, 76, 101868.
<https://doi.org/10.1016/j.chieco.2022.101868>
14. HARRIS, C., PYMAR, R., ROWAT, C. (2021): Joint shapley values: a measure of joint feature importance. arXiv preprint arXiv:2107.11357
15. , T., TIBSHIRANI, R. (1986): Generalized additive models. Statistical Science, 1, 3, 297−310.
<https://doi.org/10.1214/ss/1177013604>
16. , A.A., RAVULA, P., NEDUMARAN, S., LAGERKVIST, C.-J. (2022): Perceptions of the impacts of urban sprawl among urban and peri-urban dwellers of Hyderabad, India: a Latent class clustering analysis. Environment, Development and Sustainability, 24, 11, 12787−12812.
<https://doi.org/10.1007/s10668-021-01964-2>
17. , X., GAO, J. (2025): Toward Common Prosperity: Measuring decrease in inequality in China prefecture-level cities. Structural Change and Economic Dynamics, 72, 29−46.
<https://doi.org/10.1016/j.strueco.2024.10.006>
18. , Y.N., CONNOR, D.S., STUHLMACHER, M., PENG, J., TURNER II, B.L. (2024): More urbanization, more polarization: evidence from two decades of urban expansion in China. NPI Urban Sustainability, 4, 1, 33.
<https://doi.org/10.1038/s42949-024-00170-z>
19. JAIN, M. (2023): Assessment of spatial disparities in India: A contribution to advancing urban research methods in rapid growth contexts. In Habilitation Treatise: Social Science Open Access Repository, 38.
20. , L.R., BRETT, J.M. (1984): Mediators, moderators, and tests for mediation. Journal of applied psychology, 69, 2, 307.
<https://doi.org/10.1037/0021-9010.69.2.307>
21. , G., LI, F. (2019): Urban sprawl in China: Differences and socioeconomic drivers. Science of the Total Environment, 673, 367−377.
<https://doi.org/10.1016/j.scitotenv.2019.04.080>
22. , M., CAO, Y., DAI, J., SONG, J., LIANG, M. (2025): A Comprehensive Review of Urban Expansion and Its Driving Factors. Land, 14, 8, 1534.
<https://doi.org/10.3390/land14081534>
23. , Z. (2024): GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models. Annals of the American Association of Geographers, 114, 7, 1365−1385.
<https://doi.org/10.1080/24694452.2024.2350982>
24. , Z., ZOU, F., TAN, Y., ZHU, J. (2021): Does Financial Excess Support Land Urbanization – An Empirical Study of Cities in China. Land, 10, 6. 635, 1−23.
<https://doi.org/10.3390/land10060635>
25. , T., CAO, G., YAN, Y., WANG, R.Y. (2016): Urban land marketization in China: Central policy, local initiative, and market mechanism. Land Use Policy, 57, 265−276.
<https://doi.org/10.1016/j.landusepol.2016.06.001>
26. , Y., FAN, P., YUE, W., SONG, Y. (2018a): Impacts of land finance on urban sprawl in China: The case of Chongqing. Land Use Policy, 72, 420−432.
<https://doi.org/10.1016/j.landusepol.2018.01.004>
27. , Z., LIU, S., QI, W., JIN, H. (2018b): Urban sprawl among Chinese cities of different population sizes. Habitat International, 79, 89−98.
<https://doi.org/10.1016/j.habitatint.2018.08.001>
28. LUNDBERG, S.M., LEE, S.-I. (2017): A unified approach to interpreting model predictions. Paper presented at the Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA.
29. , S., HUANG, Y., SUN, T. (2025): Urban sprawl, public transportation efficiency and carbon emissions. Journal of Cleaner Production, 489, 144652.
<https://doi.org/10.1016/j.jclepro.2025.144652>
30. , R., FRAGKIAS, M., GÜNERALP, B., MAHENDRA, A., REBA, M., WENTZ, E.A., SETO, K.C. (2022): Npj Urban land expansion: the role of population and economic growth for 300+ cities. Urban Sustainability, 2, 1, 5.
<https://doi.org/10.1038/s42949-022-00048-y>
31. , R.I., MANSUR, A.V., ASCENSÃO, F., COLBERT, M.L., CROSSMAN, K., ELMQVIST, T., GONZALEZ, A., GÜNERALP, B., HAASE, D., HAMANN, M., HILLEL, O., HUANG, K., KAHNT, B., MADDOX, D., PACHECO, A., PEREIRA, H.M., SETO, K.C., SIMKIN, R.D., WALSH, B., WERNER, A.S., ZITER, C. (2020): Research gaps in knowledge of the impact of urban growth on biodiversity. Nature Sustainability, 3, 1, 16−24.
<https://doi.org/10.1038/s41893-019-0436-6>
32. , F., MARQUES, G. (2024): An explainable machine learning approach for automated medical decision support of heart disease. Data & Knowledge Engineering, 153, 102339.
<https://doi.org/10.1016/j.datak.2024.102339>
33. MINISTRY OF HOUSING AND URBAN-RURAL DEVELOPMENT OF CHINA (2005): China Urban Construction Statistical Yearbook (2005). China Statistics Press, Beijing.
34. MINISTRY OF HOUSING AND URBAN-RURAL DEVELOPMENT OF CHINA (2020): China Urban Construction Statistical Yearbook (2020). China Statistics Press, Beijing.
35. , W.E. (1999): An Essay on Fiscal Federalism. Journal of Economic Literature, 37, 3, 1120−1149.
<https://doi.org/10.1257/jel.37.3.1120>
36. OSMAN, T., DIVIGALPITIYA, P., ARIMA, T. (2016). Driving factors of urban sprawl in Giza Governorate of Greater Cairo Metropolitan Region using AHP method. Land Use Policy, 58, 21−31.
<https://doi.org/10.1016/j.landusepol.2016.07.013>
37. PETERSON, G.E. (2009): Unlocking land values to finance urban infrastructure, 7, World Bank Publications, Washington.
<https://doi.org/10.1596/978-0-8213-7709-3>
38. , P., BOULESTEIX, A.-L., BISCHL, B. (2019): Tunability: importance of hyperparameters of machine learning algorithms. The Journal of Machine Learning Research, 20, 1, 1934−1965.
39. , T. (2025): Local government land monopoly in China: the influence of residential land price on supply. China Economic Journal, 18, 3, 263−292.
<https://doi.org/10.1080/17538963.2024.2399463>
40. QIU, Y., NIU, J., ZHANG, C., CHEN, L., SU, B., ZHOU, S. (2025). Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect. Water Research, 274, 123129.
<https://doi.org/10.1016/j.watres.2025.123129>
41. , L., LEICHTLE, T., HUANG, X., WURM, M., TAUBENBÖCK, H. (2025): The decreasing housing utilization efficiency in China’s cities. Nature Cities, 2, 1, 70−80.
<https://doi.org/10.1038/s44284-024-00177-8>
42. SMOLKA, M.O. (2013): Implementing value capture in Latin America: Policies and tools for urban development. Lincoln Institute of Land Policy, Cambridge.
43. SRIVASTAVA, N., GOHIL, B.N., RAY, S. (2025): Unraveling Road Accident Risk Prediction Models With eXplainable AI. IEEE Transactions on Computational Social Systems, 1−13.
<https://doi.org/10.1109/TCSS.2025.3639249>
44. , C., CHEN, Z., CHENG, Y., CHEN, S., LI, W., DING, Z. (2025a): Decoding Multi-Scale Environmental Configurations for Older Adults’ Walkability with Explainable Machine Learning. Sustainability, 17, 18, 8499.
<https://doi.org/10.3390/su17188499>
45. , C., CHENG, Y., CHEN, S., LI, W., NIE, K., DING, Z. (2025b): Perception of Child-Friendly Streets and Spatial Planning Responses in High-Density Cities Amidst Supply – Demand Disparities. Buildings, 15, 21, 3908.
<https://doi.org/10.3390/buildings15213908>
46. , H.S., RAMACHANDRA, T.V., JAGADISH, K.S. (2004): Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5, 1, 29−39.
<https://doi.org/10.1016/j.jag.2003.08.002>
47. , X., ZHOU, F. (2013): Land finance and the tax-sharing system: An empirical explanation. Social Sciences in China, 4, 208, 40−59.
48. , L. (2015): Land use dynamics driven by rural industrialization and land finance in the peri-urban areas of China: “The examples of Jiangyin and Shunde”. Land Use Policy, 45, 117−127.
<https://doi.org/10.1016/j.landusepol.2015.01.006>
49. TIKOUDIS, I., DIMITROPOULOS, A., OUESLATI, W. (2018): Causes and consequences of urban sprawl. In Rethinking urban sprawl: Moving towards sustainable cities, 113−142.
50. , D., CHU, J., HAN, Q., LIU, X. (2022): How Land Finance Drives Urban Expansion under Fiscal Pressure: Evidence from Chinese Cities. Land, 11, 2.
<https://doi.org/10.3390/land11020253>
51. , D., CHU, J., MACLACHLAN, I., QIU, J., SHI, T. (2023): Modelling the Impacts of land finance on urban expansion: Evidence from Chinese cities. Applied Geography, 153, 102896.
<https://doi.org/10.1016/j.apgeog.2023.102896>
52. UN (2014): World urbanization prospects. New York: United Nations.
53. , Z., CAO, C., CHEN, J., WANG, H. (2020): Does Land Finance Contraction Accelerate Urban Shrinkage? A Study Based on 84 Key Cities in China. Journal of Urban Planning and Development, 146, 4, 04020038.
<https://doi.org/10.1061/(ASCE)UP.1943-5444.0000613>
54. , Y., LI, C., SHI, K., LIU, S., CHANG, Z. (2022): Exploring the effect of urban sprawl on carbon dioxide emissions: An urban sprawl model analysis from remotely sensed nighttime light data. Environmental Impact Assessment Review, 93, 106731.
<https://doi.org/10.1016/j.eiar.2021.106731>
55. , J., SIEBER, R. (2023): The challenges of integrating explainable artificial intelligence into GeoAI. Transactions in GIS, 27, 3, 626−645.
<https://doi.org/10.1111/tgis.13045>
56. , W., DENG, M., TANG, J., LUO, L. (2023): Geographically weighted regression with the integration of machine learning for spatial prediction. Journal of Geographical Systems, 25, 2, 213−236.
<https://doi.org/10.1007/s10109-022-00387-5>
57. , B., ZHOU, X. (2024): Land finance and urban Sprawl: Evidence from prefecture-level cities in China. Habitat International, 148, 103074.
<https://doi.org/10.1016/j.habitatint.2024.103074>
58. , X.Q. (2016): The trends, promises and challenges of urbanisation in the world. Habitat International, 54, 241−252.
<https://doi.org/10.1016/j.habitatint.2015.11.018>
59. , Y., LIU, Y., LI, Y., CHU, J., YANG, Q. (2025): Nonlinear and spatial non-stationary effects of land finance on urban expansion at the county level in China: Insights from explainable spatial machine learning. Cities, 160, 105850.
<https://doi.org/10.1016/j.cities.2025.105850>
60. , Y., WANG, J., LIU, Y., YUE, W. (2023): Quantifying multiple effects of land finance on urban sprawl: Empirical study on 284 prefectural-level cities in China. Environmental Impact Assessment Review, 101, 107156.
<https://doi.org/10.1016/j.eiar.2023.107156>
