Spatial conflict measurement in resource-based cities and spatial responses

Abstract

Scientifically diagnose the spatial conflict of resource-based cities and clarify the complex relationship between spatial conflict and land use are important for urban development transformation and rational use of regional resources. Based on the characteristics of resource-based cities, this study constructed a spatial conflict measurement model considering spatial pressure, spatial exposure, and spatial risk response dimensions with the perspective of land use and ecosystem service value, and explored the relationship between spatial conflict and land use using a geographically weighted regression model. The results indicate that: 1) From 2009 to 2020, the spatial conflict index of Xintai City remained stable with a change of 0.0018 only. The change trends of different conflict levels varied. The spatial pattern was zonal, and the conflict level gradually decreased from the center to the peripheral areas. 2) From 2009 to 2020, the average land use dynamic degree of Xintai City was 23.14%, the spatial layout was significantly different, and the land use changes were mainly cultivated land conversion to woodland, land reclamation, expansion of construction land, and afforestation. 3) Based on the analysis of the geographically weighted regression model, the dynamic degree of land use was significantly correlated with the change of spatial conflict index (P < 0.01, R 2 =0.8517), and the average coefficient value was 0.1381, dominated by positive correlation. The research provides a scientific basis for the rational use of regional land, governance of spatial conflicts, and optimization of spatial structure in the city.

Forest Soil Carbon Efflux Evaluation Across China: A New Estimate With Machine Learning

“…Forest types in this study were classified into six categories, including deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), broadleaf and needleleaf mixed forest (MF) and bamboo forest (BF). Especially for BF, the often ignored type (Chen et al, 2008;Zhan et al, 2012;Zheng et al, 2010Zheng et al, , 2020 was supplemented in this data set.…”

“…That led to regional estimates of soil respiration that were usually based on the integration of data from different kinds of literature rather than uniform field observations (Jian et al., 2021a). Annual Rs data sets in China’s forest ecosystems have been developed in recent 10 years (e.g., Chen et al., 2008 ( N = 62), Zheng et al., 2010 ( N = 50), Zhan et al., 2012 ( N = 120), Song et al., 2014 ( N = 139)). In addition to the fact that the relatively small sample size, different soil respiration monitoring methods (e.g., various infrared gas analyzers (IRGA), gas chromatography, and alkali absorption method) may also lead to uncertainty in soil respiration estimates (Jian et al., 2020).…”