Literature DB >> 30043345

Can China fulfill its commitment to reducing carbon dioxide emissions in the Paris Agreement? Analysis based on a back-propagation neural network.

Daoyan Guo1, Hong Chen2, Ruyin Long3.   

Abstract

Due to the increasingly severe situation regarding adaptation to climate change, global attention has focused on whether China can fulfill its commitment to the Paris Agreement as the largest producer of carbon dioxide (CO2) emissions. In this study, the CO2 emissions and CO2 intensities in China during 2030 were forecast using three scenarios, seven indicators, and a back-propagation neural network. Under the business as usual (BAU), strategic planning (SP), and low carbon (LC) scenarios, the predicted CO2 emissions in China during 2030 are 13,908.00, 11,837.60, and 9102.50 million tonnes, respectively, and the predicted CO2 intensities are 1.8652, 1.7405, and 1.5382 when considering carbon capture, utilization, and storage (CCUS). Furthermore, China cannot fulfill its commitment under the BAU scenario, whereas China will fulfill its commitment on schedule under the SP scenario. Under the LC scenario, China will fulfill its commitment ahead of schedule to reduce the CO2 intensity by 60% in 2025, and it will even reduce the CO2 intensity by 65% in 2030. In addition, if the amounts of CCUS are not considered for measuring the CO2 intensity, China can still fulfill its commitment under the LC scenario, whereas it cannot fulfill its commitment by 2030 under the SP scenario. This study evaluated the fulfillment of China's commitment in the Paris Agreement, demonstrated that CCUS plays an important role in reducing the CO2 intensity, and provided policy suggestions for the Chinese government regarding the reductions of the CO2 intensity.

Entities:  

Keywords:  Back-propagation neural network; CO2 emissions; CO2 intensity; Forecast; Paris Agreement; Scenario analysis

Mesh:

Substances:

Year:  2018        PMID: 30043345     DOI: 10.1007/s11356-018-2762-z

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


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