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[Display omitted] • We evaluated urban land-use intensity from input, density and output. • The spillover effects of urban land-use intensity on CO 2 emissions were explored. • A machine learning method (BPNN) was proposed to predict CO 2 emissions. Currently, China will promote county towns' urbanization, and few studies have analyzed spatial effects of urban land-use intensity on CO 2 emissions and predicted CO 2 emissions at the county level accurately. Here, taking data from 2010 to 2015 at the county level of Zhejiang Province as an example, we analyzed the spatial effect of urban land-use intensity from three aspects of input, density and output on CO 2 emissions by the spatial Durbin model (SDM). And then a machine learning method of Back Propagation Neural Network (BPNN) was proposed to predict CO 2 emissions for 2035 nonlinearly under the different promotions of urban land-use intensity. The main result and conclusion showed that: (1) The spillover effects of urban land-use density were negatively related to CO 2 emissions, and urban land-use output intensity showed positive spillover effects on CO 2 emissions; (2) The prediction of BPNN showed that the improvement of urban land-use intensity would not be effective in CO 2 emissions reductions for southwestern counties with both low levels of urbanization and urban land-use intensity; (3) It was effective in CO 2 emissions reduction by slowing down the growth rate of urban land-use capital input intensity, especially for the northeast region which was developed by the port economy. Our study encouraged a regional differentiated urban land-use intensity improvement strategy for Zhejiang to achieve low carbon development. [ABSTRACT FROM AUTHOR]