Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China

Qingchun Guan (), Tianya Meng, Chengyang Guan, Junwen Chen, Hui Li and Xu Zhou
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Qingchun Guan: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Tianya Meng: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Chengyang Guan: College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Junwen Chen: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Hui Li: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Xu Zhou: College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

Land, 2024, vol. 13, issue 11, 1-20

Abstract: Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R 2 of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

Keywords: carbon emissions; coastal cities; nighttime lighting data; multi-scale analysis; decoupling effect (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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