How urban land-use intensity affected CO2 emissions at the county level: Influence and prediction

Abstract

Currently, China will promote county towns’ urbanization, and few studies have analyzed spatial effects of urban land-use intensity on CO2 emissions and predicted CO2 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 CO2 emissions by the spatial Durbin model (SDM). And then a machine learning method of Back Propagation Neural Network (BPNN) was proposed to predict CO2 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 CO2 emissions, and urban land-use output intensity showed positive spillover effects on CO2 emissions; (2) The prediction of BPNN showed that the improvement of urban land-use intensity would not be effective in CO2 emissions reductions for southwestern counties with both low levels of urbanization and urban land-use intensity; (3) It was effective in CO2 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.

Keywords

• Urban land-use intensity
• CO2 emissions
• Spatial effect
• Machine learning
• Prediction