Prediction of plant carbon sink potential in Beijing-Tianjin-Hebei region of China

Yuansheng Huang, Zhaobei Li () and Mengshu Shi
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Yuansheng Huang: North China Electric Power University
Zhaobei Li: North China Electric Power University
Mengshu Shi: North China Electric Power University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 2, No 30, 3529-3556

Abstract: Abstract Carbon sink is the process of absorbing CO2 from the atmosphere, through forests, wetlands, grasslands, and oceans. Through this process, greenhouse gases can be absorbed from the atmosphere. In order to analyze the contribution of carbon sinks to carbon neutrality, three parts of works were carried out on carbon sinks in Beijing-Tianjin-Hebei. Firstly, the regional carbon sinks were calculated based on actual data. Secondly, the macro-factors were selected related to carbon sinks, on which the econometric analysis was carried out. Finally, a carbon sink prediction model was constructed based on partial least squares regression. After that, three carbon sink development scenarios with different intensities were set up. Under these three scenarios, the carbon sink potential of the Beijing-Tianjin-Hebei region was forecasted from 2020 to 2030. The results show that under the strong carbon sink scenario, the carbon sink in Beijing-Tianjin-Hebei can neutralize 388.6901 million tons/a of carbon emissions by 2030, whose contribution rate to China's carbon emission reduction can reach 10.48%. Compared with the baseline scenario and weak carbon sink scenario, the strong carbon sink scenario can achieve greater carbon sink potential and contribute more to China's carbon neutrality. As a conclusion, the target of carbon neutrality of China can be easier to achieve under the strong carbon sink scenario.

Keywords: Carbon sink; Carbon neutrality; Beijing-Tianjin-Hebei region; Econometrics; Potential prediction; Partial least squares regression (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-022-02846-x

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