A target-oriented DEA model for regional construction productive efficiency improvement in China

Luo, Ming; Fan, Hongqin; Liu, Guiwen

doi:10.1016/j.aei.2020.101208

Cited by 11 publications

(4 citation statements)

References 48 publications

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“…Multi-task transfer learning is suitable when some attributes differ between the source and target domains. Liu and Fan [12] transferred a CNN model for forecasting accident frequency trained on data from Beijing to Shanghai. The bottom network layers that extract spatial features were frozen.…”

Section: Transfer Learningmentioning

confidence: 99%

Leveraging Machine Learning for Road Accident Analysis

Guntaka,

Yallavula,

Gade

et al. 2023

ijmst

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Road accidents result in high human and economic costs globally. This paper examines how advanced machine learning techniques can support enhanced analysis of road accident data to uncover patterns and insights to guide traffic safety interventions. Novel machine learning methods proposed include hybrid neural network architectures optimized using nature-inspired algorithms and interpretable rule-based tree ensemble techniques. Our investigation commences with the training and evaluation of each model on a diverse dataset comprising various road-related features. The performance metrics, including accuracy predictive capabilities. The results reveal nuanced strengths and weaknesses in each approach.

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Section: Transfer Learningmentioning

confidence: 99%

Leveraging Machine Learning for Road Accident Analysis

Guntaka,

Yallavula,

Gade

et al. 2023

ijmst

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“…Cai, H. et al (2019) measured the efficiency of the photovoltaic (PV) industry in China [13]. Luo M. studied the TFP in the Chinese construction industry, and drew a lot of exciting conclusions [14,15]. Huijun Li and Bo Li (2019) indicated that environmental regulations can effectively promote industrial green transformation by improving the green total factor productivity (GTFP) [16].…”

Section: Literature Reviewmentioning

confidence: 99%

The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry

Ding

Zhang

2021

Sustainability

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Total factor productivity (TFP) is critical to the sustainable development of the rural distribution industry. Improvements in productivity of the rural distribution industry can promote the high-quality development of the Chinese distribution industry. Studying the characteristics and influencing factors of total factor productivity in regard to the rural distribution industry in China is significant for promoting the transformation and development of the rural distribution industry. This paper uses the DEA–Malmquist Index to measure the total factor productivity (TFP) of the Chinese rural distribution industry and its decomposition index, and uses a panel data model to empirically study its influencing factors. The results show that, from 2008 to 2018, the TFP of the Chinese rural distribution industry showed a trend of rising first and then fluctuating and declining, with an average annual growth rate of 2.93%; the fluctuation direction of the TFP of the rural distribution industry in the eastern and western regions of China is basically the same, which has had a reverse change relationship with the central and northeast regions for many years. The industrial structure, urbanization rate, rural informatization rate, and conditions of the transportation facilities have significant impacts on the TFP of the rural distribution industry, among which the informatization rate has the greatest positive impact.

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“…The results indicate that developed eastern region can focus on promoting innovative building technologies to increase productivity, developing midland region may pay more attention to the construction skill training to improve the professional level of labour force and underdeveloped western region can ensure the steady development of the construction industry by implementing strategic planning and raising workers' wages. However, Luo et al. (2021) develop a target-oriented DFM model to identify the optimal pathways for improving the regional performance of construction industry and then form the policy and strategic planning of different stages of construction development.…”

Section: Literature Reviewmentioning

confidence: 99%

Productivity assessment of the real estate industry in China: a DEA-Malmquist index

Liu

Yang

et al. 2021

ECAM

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PurposeThe high-quality development of the real estate industry is crucial to the transformation of China's economy. However, few studies apply the productivity to explore the development path of the real estate industry in China. To fill this gap, this study mainly investigates the total factor productivity (TFP) of the real estate industry of 30 sample provinces in mainland China from 2007 to 2016.Design/methodology/approachThe Malmquist index is applied to estimate the productivity (i.e. TFP) of the real estate industry, based on the data envelopment analysis (DEA). Then, the truncated tobit regression analysis explores the external influencing factors on the TFP of the real estate industry.FindingsThrough empirical analysis, it is found that the high-quality development of the real estate industry depends on the technological innovation by the real estate enterprises and the targeted policies by the provincial government. Moreover, the development of the real estate industry has a positive correlation with the growth of China's economy but a negative correlation with the development of other industries.Practical implicationsTFP mainly reveals the development status of the provincial real estate industry and identifies the driving force for exploring the high-quality development mode of the real estate sector. Furthermore, the fluctuation rule of TFP can be applied to predict the development trend of the real estate industry in the future.Originality/valueAs an application, this study measures the TFP of the Chinese real estate industry in different provinces and periods. The results have meaningful policy implications for policymakers regulating the real estate industry.

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A target-oriented DEA model for regional construction productive efficiency improvement in China

Cited by 11 publications

References 48 publications

Leveraging Machine Learning for Road Accident Analysis

Leveraging Machine Learning for Road Accident Analysis

The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry

Productivity assessment of the real estate industry in China: a DEA-Malmquist index

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