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This study investigates the capacity utilization (CU) of Chinese manufacturing industries, using a CU indicator based on data envelopment analysis and directional distance functions (DDFs). The inputs are separated into variable inputs and a quasi-fixed input to measure the gap of DDFs, which indicated either under-utilization of inputs or overcapacity. Moreover, we define an indicator for CU change over time and introduced the corresponding decomposition. We note that, during the study time period (2007–2010), the CU of Chinese manufacturing industries improved, which implies that Chinese manufacturing industries expanded their production and got closer to their capacity during the examined period. The driving force of this improvement is technical changes. The higher average CU values of light manufacturing industries than that of the heavy industries and the extremely high CU values of two light industries reveal a severe overcapacity problem in the light industries. We also provided the methods and conduct analysis on determining optimal variable inputs and the type of the overcapacity on specific DMUs. The bootstrap regression procedures are employed to test the influence of environmental variables on CU values. Finally, we provide policy implications and suggestions for policymakers who oversee the development of Chinese manufacturing industries.

As significant strategic players in China's economy, high-tech industries need to evaluate and analyze the technological innovation activities from a system point of view to understand and improve their technological innovation efficiency and, thereby, promote their development. Different high-tech industries have different characteristics and thus benefit from different industrial development policies. However, few studies to date have discussed this issue from a systematic perspective. In this study, technological innovation activities are divided into a research and development (R&D) stage and a commercialization stage. A high-tech industrial evaluation framework of technological innovation efficiency based on two-stage network data envelopment analysis (DEA) is constructed with shared inputs, additional intermediate inputs, and free intermediate outputs. Our empirical results indicate that the overall efficiency of most industries is relatively low and the differences between the five high-tech industries (i.e., sub-sectors) we examined are large. The Spearman correlation shows that overall efficiency and R&D efficiency are more correlated than overall efficiency and commercialization efficiency. Additionally, R&D has better average efficiency. The sub-sector with the highest average efficiency is computers and office equipment, and the one with the lowest average efficiency is medicines. These findings indicate the inadequacy but potential for breakthroughs in the evolution of high-tech industries in China. The analysis proves that it is necessary to create different industrial policies to encourage effective progress in certain high-tech industries, and some guidelines for doing so are provided.

Although a large amount of past research has studied the measurement of institutional research and development (R&D) efficiency, there has been limited dynamic empirical investigation of this topic. This study differs from previous studies in its consideration of R&D capital stock, which is defined as a carry-over activity between two consecutive periods. To further test high-tech institutes’ R&D performance in the pre-reform and post-reform periods, we evaluate a panel of 29 high-tech institutes of the Chinese Academy of Sciences (CAS) for 2011–2017 with a dynamic slacks-based measure. (a) The empirical value of the average overall efficiency is 0.5559, and the clustering analysis shows that institutes operating above the average overall efficiency level follow an increasing and then decreasing trend, while those operating below the average overall efficiency level follow a decreasing and then increasing trend. (b) The efficiency of low-performing institutes has increased from 0.2259 in the pre-reform period to 0.2727 in the post-reform period, which does not reflect the efficiency improvements of high-performing institutes. (c) High-tech institutes selected as pilot institutes have an average efficiency level of 0.6524, which is higher than that of non-pilot institutes (0.4765), indicating the benchmarking effect of pilots. (d) The types of pilot institutes can be ordered as follows: feature institutes > centres of excellence > innovative academies. (e) The total-factor input efficiency level has decreased in the post-reform period. Only the total-factor efficiency of high-quality papers has increased in the post-reform period (by 0.0094).

With growing investments in the state key laboratories (SKLs) of China, it is essential to assess the efficiency of the administrative departments regarding managing the SKLs of China. However, few studies have been conducted about the evaluation of the performance of Chinese administrative departments in managing SKLs. To fill this gap, this paper investigates the performance within 22 SKL administrative departments in China, based on a data envelopment analysis cross-efficiency prospect aggregation approach incorporating the risk preference of decision maker. The empirical results show the major findings: (a) serious imbalance exists regarding the investment of 22 SKL administrative departments; (b) there are great differences among the performance of 22 SKL administrative departments; (c) some SKL administrative departments face an insufficient situation regarding the outputs; (d) different risk attitudes of decision maker have the significant impact on the evaluation results of the 22 SKL administrative departments. Based on these findings, we provide several policy suggestions for the development of SKLs in China.

With the growing expenditure on the RD (ii) there is a decline in the average overall efficiencies after 2012; (iii) there are great differences regarding the performances of RD (iv) high attention to the RD and (v) the IEDSs with the relatively high profitability or high government support in terms of R&D activities have relatively poor performance. Based on these findings, several policy suggestions are proposed for the R&D activities of Chinese IEDSs.

This paper aims to measure the evolution of destocking performance of the Chinese Real Estate Industry based on a DEA (Data Envelopment Analysis)-Malmquist approach, which is seldom used in this industry in existing literature. In 2016, the Chinese government introduced a unified national destocking policy to reduce real estate inventory to save the downturn in the real estate market, but the effect was less than expected and led to soaring house prices in first-tier cities. By analysing the destocking performance over the period from 2005 to 2015, we find the following: (1) It is impossible to use a unified policy to effectively address the national destocking issue because of the difference of DMUs' destocking efficiency, input redundancy and total factor productivity score changes. (2) With the current destocking performance and investment status, the government is still ignoring the existing commercial real estate problems. (3) The redundancy of firm assets and staff indicates that zombie firms may exist and risk future unemployment in the real estate industry. (4) Despite the recently repeated government interventions in this industry, destocking performance remains falling since 2008, and problems in other regions is more severe than in central cities. (5) The financial crisis triggered by the US subprime mortgage crisis has had a great impact on China's real estate industry. The destocking performance dropped sharply in 2008, forcing the Chinese government to introduce policies to stimulate the real estate market. Policy recommendations are also put forward based on the findings.