China is the largest carbon dioxide emitter in the world, and reducing China’s transportation carbon emissions is of great significance for the world. Using the Chinese provincial data from 2005–2015, this article analyzes the convergence characteristics of per capita transportation carbon emissions in China. It employs the log t regression test method and the club clustering algorithm developed by Phillips and Sul (2007) to separate the provinces and municipalities in China into three convergence clubs with different transportation carbon emission levels and one divergent group. Among them, the divergent group consisted of Beijing and Liaoning; the high carbon emission club consisted of Shanghai and Inner Mongolia; the low carbon emission club consisted of Jiangxi, Henan, Shandong, Hebei, and Sichuan; the medium carbon emission club consisted of the remaining 21 provinces and municipalities. On this basis, this article adopts the Ordered Logit model to explore factors influencing the formation of the convergence clubs. The regression results showed that the per capita transportation carbon emissions in the provinces with a high energy intensity of the transportation sector, a high urbanization level, or a high fixed assets investment intensity of the transportation sector tended to converge into the high carbon emission club.
This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.
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