Research on the temporal and spatial characteristics, spatial clustering and governance strategies of carbon emissions in cities of Shandong

Abstract

In September 2020, China proposed a carbon neutral target for 2060, and at the same time proposed to accelerate the implementation of the national carbon peaking task for 2030. In the context of “dual carbon,” provinces and cities urgently need to achieve low-carbon transformational development, but there are significant differences in the development level, process and trend of carbon emission reduction among regions. Therefore, it is necessary to understand the carbon emission characteristics of each city, so as to provide scientific and effective guidance for local governments to adopt differentiated low-carbon emission reduction measures. In this paper, the spatial and temporal distribution characteristics of carbon emissions and carbon intensity of 16 cities in Shandong Province were analyzed with the help of ArcGIS, and then the static and dynamic factors affecting urban carbon emissions were considered comprehensively, and the clustering analysis of 16 cities was conducted by using the systematic clustering method. The results show that, according to the characteristics of each type of cities, they can be summarized into four categories: “low economic high carbon type,” “high economic high carbon type,” “high economic low carbon type,” “low carbon.” The results show that the cities in each category do not have complete geographical continuity. Finally, after analyzing the characteristics of carbon emissions in each type of cities, this paper proposes targeted policy recommendations to promote the development of low-carbon emission reduction.

Spatial Correlations of Land Use Carbon Emissions in Shandong Peninsula Urban Agglomeration: A Perspective from City Level Using Remote Sensing Data

“…Moreover, according to Carbon Emission Accounts and Datasets, the total carbon emissions of Shandong Peninsula urban agglomeration are in the top five in China. Numerous studies have focused on carbon emissions in Shandong province, including an analysis of the spatial and temporal changes [16,28], peak projections [29,30], and emission reduction recommendations [31,32]. These studies provided the basis for the study of Shandong Peninsula urban agglomeration.…”

Commentary: Research on the spatial correlation network structure and influencing factors of carbon emissions in Chengdu-Chongqing urban agglomeration

“…At present, studies on the spatial association of carbon emissions between regions can be divided into two categories, one is to use spatial measurement (Tan et al, 2016;Marbuah and Amuakwa-Mensah, 2017;Han et al, 2018) method, such as using SDM model (Min and Tao, 2022), SBM model (Liu et al, 2021;Niu et al, 2022) The spatial dependence of carbon emissions (Xianzhao et al, 2019) and spatial spillover (Shengdong et al, 2022;Xiaoyu et al, 2022) or through spatial autocorrelation models (Zhang and Lei, 2023) spatial autocorrelation model, etc., To characterize the spatial and temporal evolution of carbon emissions (Ahui et al, 2022;Chen et al, 2023). Another approach is to use social network analysis (Tengfei et al, 2022) The other is to use social network analysis to study the structure of carbon emission linkage network, mostly based on national (Ma et al, 2021) and provincial (YANG et al, 2016) energy, industry (Shao and Wang, 2021).…”