Evaluation and drivers of global low-carbon economies based on satellite data

Jiandong Chen, Ming Gao*, Shulei Cheng, Yiyin Xu, Malin Song, Yu Liu, Wenxuan Hou, Shuhong Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Global warming is one of the largest challenges humankind is facing in this century, and how to achieve low-carbon economy has become one of the most attractive topics of global concern. However, evaluations of the low-carbon economy are insufficient due to limited methodologies and data availability. In this study, satellite data (i.e., night-time light data and net primary production) were employed to estimate the net economic output (neo), and ratio of neo to the GDP (reo), which can be used to assess the quantity and quality of worldwide low-carbon economies. Based on panel vector autoregression (pvar) analysis, we further discussed the drivers of neo and reo in global climate change mitigation towards a better low-carbon society. The results show that: (1) only France and the United Kingdom ranked within the top 10 in terms of the neo and reo in 2019, implying that they were successful in increasing both quantity and quality of low-carbon economic development; (2) the pvar analysis presented that the increase of reo granger-caused neo growth, and net primary production increment greatly helped raise the worldwide reo; (3) raising CO2 abatement policy stringency can play a major role in improving the quality of low carbon economy countries with poor quantity and quality, but it cannot significantly promote groups with high reo. Additionally, the results of this study also provided basic data, such as our calibrated global 1 × 1 km gridded night-time light data during 1992–2019 for research regarding low-carbon economy and other sustainable development issues.

Original languageEnglish
Article number153
JournalHumanities and Social Sciences Communications
Issue number1
Publication statusPublished - 28 Apr 2022


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