Online Optimization of Fluid Catalytic Cracking Process via a Hybrid Model Based on Simplified Structure-Oriented Lumping and Case-Based Reasoning

Study on the Drivers of Inclusive Green Growth in China Based on the Digital Economy Represented by the Internet of Things (IoT)

With the vigorous development of digital economy based on digital technologies such as Internet of things (IoT), big data, and artificial intelligence, new vitality has been injected into China's economic model. Inclusive green growth (IGG) supports the transformation of society towards a better quality of life and well-being, as well as environmental protection. Therefore, it is crucial to identify the main drivers of IGG. However, IGG is subject to a variety of interpretations and lacks definitional clarity. To brigade this gap, this study primarily evaluates the performance of IGG and explores the key drivers on IGG in China. Specifically, the data envelopment analysis (DEA) model is employed to calculate IGG for 281 cities in China during 2005-2020. Subsequently, we take advantage of a nest of machine learning (ML) algorithm to demonstrate the vital drivers of urban IGG, which avoids the defects of endogenous linear hypothesis of traditional econometric methods. The results indicate that digitization represented by the IoT and other digital technology is the core drivers of the urban IGG in the overall sample, accounting for about 50% among all of drivers. This finding provides new evidence supporting the "high-quality development" strategy in China, as well as shedding light on grasping the principal fulcrum to achieve the transformation towards IGG in developing economies similar to China.

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach

In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inclusive growth efficiency (IGE). Furthermore, a nest of machine learning (ML) algorithms are introduced to explore the drivers of urban IGE, which overcomes the defects of endogeneity and multicollinearity of traditional econometric methods. We find for the overall sample that entrepreneurship and innovation contribute the most to IGE, accounting for about 35%, respectively, and they are the most critical drivers, while the heterogeneity test results reveal that the contribution of influencing factors has changed for different regions such as the eastern region, the central region, and the western region. Based on the experimental results of the ML model, we provide some policy suggestions for China and similar developing countries and emerging economies to promote IG.