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

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 (CO) emissions. In this study, the CO emissions and CO 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 CO emissions in China during 2030 are 13,908.00, 11,837.60, and 9102.50 million tonnes, respectively, and the predicted CO 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 CO intensity by 60% in 2025, and it will even reduce the CO intensity by 65% in 2030. In addition, if the amounts of CCUS are not considered for measuring the CO 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 CO intensity, and provided policy suggestions for the Chinese government regarding the reductions of the CO intensity.

Simulation analysis of carbon peak path in China from a multi-scenario perspective: evidence from random forest and back propagation neural network models

“…Sun and Liu ( 2016 ) employed least squares support vector machine (LSSVM) to predict the trend of carbon emissions in the three major industries and residential consumption sectors in China. Combining the Back Propagation neural network and scenario analysis, Guo et al ( 2018 ) forecasted China’s respective CO2 emissions and intensity in 2030 under business-as-usual (BAU), strategic planning (SP), and low-carbon (LC) scenarios. BP is currently the most widely used artificial neural network (Sun and Xu 2016 ; Wang et al 2016 ).…”

Impacts of regional development on emissions in China’s transport sector

“…In order to curb the trend of global warming, the seventh World Government Summit, held in Dubai in February 2019, called for a more sustainable and inclusive "Globalization 4.0" to address the risks and challenges ahead. As the world's largest carbon emitter, China's rapid growth in carbon emissions over the past 40 years has attracted global attention (Guo et al, 2018). From the General Debate of the UN General Assembly at its seventyfive session in September 2020 to the Climate Ambition Summit in December 2020, China has repeatedly stated its goal of reaching a peak in carbon emissions by 2030, carbon neutral by 2060 .…”

Impacts of Regional Development on Emissions in China's Transport Sector

“…In order to curb the trend of global warming, the seventh World Government Summit, held in Dubai in February 2019, called for a more sustainable and inclusive "Globalization 4.0" to address the risks and challenges ahead. As the world's largest carbon emitter, China's rapid growth in carbon emissions over the past 40 years has attracted global attention (Guo et al, 2018). From the General Debate of the UN General Assembly at its seventyfive session in September 2020 to the Climate Ambition Summit in December 2020, China has repeatedly stated its goal of reaching a peak in carbon emissions by 2030,35 carbon neutral by 2060 .…”