Local carbon emission zone construction in the highly urbanized regions: Application of residential and transport CO$_2$ emissions in Shanghai, China

Abstract

Optimizing urban morphology can help city managers deploy internal carbon reduction strategies. However, due to the numerous factors that influence urban carbon emissions and the complex paths of their effects, existing research findings are diverse and difficult to compare. This study constructed the local carbon emission zone (LCEZ) which links the highly urbanization morphology profiles and CO$_2$ emission intensity to favor summarizing the form characteristics for the low CO$_2$ emission control in new communities. The approach involved selecting six indicators from the perspectives of urban external morphology, internal characteristics, and development intensity, and using geographically weighted regression (GWR) to identify three key factors. The selected factors were divided into low(1) medium(2) and high(3) categories and were combined with each other to construct the LCEZ. The nonparametric test and GeoDetector model were used to test the difference and similarity of LCEZs, respectively. The results showed that 1) The impacts of functional mixing entropy (FME), building height (BH) and road density (RD) on residential and transport CO2$_2$ emissions (RTCE) were relatively large and positive; 2) The low- and medium-FME areas were the major types of LCEZ; 3) The area proportions of the low FME LCEZs in different population areas of the RTCE cold spot regions were the highest (81.84%–88.25%); 4) It is recommended that the morphological feature values of compact-low rise-low network (1-1-1), mid compact-low rise-mid network (1-1-2), compact-mid rise-low network (1-2-1) and mid compact-mid rise-mid network (1-2-2) were taken as the low CO$_2$ emission control of highly urbanized regions.

Publication
In Building and Environment

Figure 1. The flowchart of the LCEZ construction. Step 1: Luojia1-01 night light image data is the proxy variable of the RTCE map. GWR is geographically weighted regression. Step 2: L is low. M is medium. H is high. the solid line is the three key factors. The dotted line means that n key factors can be selected in the future. The point is POI. the column is BF. the line is RD. A&B represents Dense&sparse forests. C&D represents Farmland&grassland&wetland. G represents the water body.

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