School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China.
Zhenjiang Institute for Innovation and Development, Nanjing Normal University, Zhenjiang 212016, China.
Int J Environ Res Public Health. 2022 Jun 10;19(12):7165. doi: 10.3390/ijerph19127165.
Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing's industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.
基于人口、富裕和技术的随机影响回归模型（STIRPAT），考虑了总人口、工业产值、劳动生产率、工业化率、能源强度、研发（R&D）强度和能源结构等因素对南京工业碳排放的影响。其中，总人口、工业产值、劳动生产率和工业能源结构对南京工业碳排放的增加起到了促进作用，影响程度依次减弱。这四个因素每变化 1%，碳排放分别增加 0.52%、0.49%、0.17%和 0.12%。工业化率、R&D 强度和能源强度抑制了工业碳排放的增加，抑制作用依次减弱。这三个因素每变化 1%，就分别抑制了南京工业碳排放的增加 0.03%、0.07%和 0.02%。然后，以 2006 年至 2020 年南京工业碳排放的相关数据为样本，采用单变量、一阶方程的灰色滚动预测模型（GRPM(1,1)）对疫情影响下 2021 年至 2030 年南京工业碳排放进行预测，并建立高碳、基准和低碳三种发展情景，得出高碳发展情景下南京 2030 年工业碳排放将达到 2.2995 亿吨，基准发展情景下为 2.2692 亿吨，低碳发展情景下为 2.2091 亿吨。这不仅可以为未来控制工业碳排放提供数据参考，也为城市规划决策者提供政策建议和发展路径。最后，希望这为南京等具有类似发展的其他城市提供参考。
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