Dynamic simulation research on urban green transformation under the target of carbon emission reduction: the example of Shanghai

Hua Shang and Hailei Yin ()
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Hua Shang: Dalian University of Technology
Hailei Yin: Dalian University of Technology

Palgrave Communications, 2023, vol. 10, issue 1, 1-16

Abstract: Abstract This paper aimed to predict the trend of carbon emissions during the green transformation process in Shanghai, with a focus on the city’s urban system structure. Green development has become an inevitable trend in urban progress, as traditional urban development has led to severe environmental problems caused by the emissions of a large amount of carbon dioxide. This study was motivated by the need for cities to actively pursue green transformation and achieve carbon peaking targets. Through a literature analysis, it was found that urban green transformation is influenced by various factors such as economy, energy, population, technology, and policy. Furthermore, carbon dioxide emissions primarily arise from fossil fuels and are regulated by carbon emission trading (CET) policies. With this knowledge, the urban system was divided, and the flow of carbon was analyzed. Using the general methodology of the IPCC, the carbon production resulting from energy consumption in Shanghai from 2014 to 2019 is calculated to construct an urban system dynamic (SD) model, which is used to predict the carbon emissions expected during the green transformation from 2020 to 2025. The key findings of the study are as follows: (1) The dynamic model of the urban green transformation system proved to be effective in predicting carbon emissions. (2) Based on the current status of green transformation in Shanghai, the city is capable of achieving its expected carbon emission peaking target by 2025. (3) The progress and timing of green transformation and carbon peaking in Shanghai vary across different scenarios, highlighting the importance of collective adjustments to identify the most appropriate path for urban green transformation. These findings provide valuable insights for cities seeking to adopt green development measures, facilitating the acceleration of their green transformation efforts and early attainment of carbon peaking targets.

Date: 2023
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DOI: 10.1057/s41599-023-02283-9

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