Zhang He, Peng Jingyi, Wang Rui, Zhang Mengxiao, Gao Chang, Yu Yang
Tianjin University, Tianjin, China.
Tsinghua Tongheng Urban Planning & Design Institute, Beijing, China.
Heliyon. 2023 Jun 1;9(6):e16693. doi: 10.1016/j.heliyon.2023.e16693. eCollection 2023 Jun.
Chinese cities contributes a large amount of CO emissions. Reducing CO emissions through urban governance is an important issue. Despite the increasing attention paid on the CO emission prediction, few studies consider the collective and complex influence of governance element system. To predict and regulate CO emissions through comprehensive urban governance elements, this paper use the random forest model through the data from 1903 Chinese county-level cities in 2010, 2012 and 2015, and establish a CO forecasting platform based on the effects of urban governance elements. The results are as follows: (1) The municipal utility facilities element, the economic development & industrial structure element, and the city size &structure and road traffic facilities elements are crucial for residential, industrial and transportation CO emissions, respectively; (2) Governance elements have nonlinear relationship with CO emissions and most of the relations present obvious threshold effects; (3) Random forest can be used to predict CO emissions more accurately than can other predictive models. These findings can be used to conducts the CO scenario simulation and help government formulate active governance measurements.
中国城市排放了大量的一氧化碳。通过城市治理减少一氧化碳排放是一个重要问题。尽管对一氧化碳排放预测的关注日益增加，但很少有研究考虑治理要素系统的综合和复杂影响。为了通过全面的城市治理要素预测和调控一氧化碳排放，本文利用随机森林模型，通过2010年、2012年和2015年中国1903个县级市的数据，建立了一个基于城市治理要素影响的一氧化碳预测平台。结果如下：（1）市政公用设施要素、经济发展与产业结构要素、城市规模与结构及道路交通设施要素分别对居民、工业和交通一氧化碳排放至关重要；（2）治理要素与一氧化碳排放呈非线性关系，且大多数关系呈现明显的阈值效应；（3）随机森林用于预测一氧化碳排放比其他预测模型更准确。这些发现可用于进行一氧化碳情景模拟，并帮助政府制定积极的治理措施。
