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Developing China's high-tech industry is important to make the country innovation-oriented. This requires optimising the allocation of innovation resources and improving innovation efficiency. However, few studies have investigated this topic and the realisation path for the high-tech industry. This study develops an input-oriented inverse data envelopment analysis (DEA) model with frontier changes to analyse the optimisation of resource allocation in China's high-tech industry during 2019–2025. With this method, decision makers can scientifically analyse the specific amount of resource investment. We also construct an analysis framework from short- and long-term perspectives. The results show that the excessive input of research and development (R&D) personnel and unbalanced allocation of capital resources are the main barriers to the development of high-tech industries in the short term, and in the mid- and long terms, the demands for investment in talent and capital will continue to increase. Improvement directions for promoting the development of China's high-tech industry are discussed. Finally, we present valuable information for policymaking to promote progress in high-tech industries in different regions. [ABSTRACT FROM AUTHOR]

As an important resource allocation and production analysis method, the existing inverse data envelopment analysis (Inverse DEA) methods generally ignore the production trade-offs in the production process. However, in some managerial practice, decision makers (DMs) generally have value judgements concerning the importance of inputs and outputs. These value judgements reflect the production trade-offs of DMs for different inputs and outputs and influence the production process. Therefore, this study investigates the inverse DEA method with production trade-offs. By analyzing the effect of production trade-offs on efficient frontier, this study reveals that the existing methods for identifying the limitations of changed range of inputs and outputs in inverse DEA model under variable returns to scale (VRS) are invalid when considering the production trade-offs, and based on this, the new inverse DEA methods with production trade-offs for resource allocation and production analysis are developed. Moreover, the limitations of changed range of inputs and outputs of the proposed methods are identified, thus avoiding the problem of infeasible solutions. In addition, the application scenarios and practical values of the proposed methods are discussed. Finally, two examples are provided to illustrate the rationality and effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

Decision-maker (DM) can assign the weights of the distance measures in the relative closeness of technique for order preference by similarity to ideal solution (TOPSIS) for achieving his/her desirable ranking of alternatives. This phenomenon is called strategically setting distance measure weights in this study, which may affect the fairness of the results. In order to prevent this phenomenon, the idea of data envelopment analysis (DEA) is introduced to determine the objective weights of distance measures, and an improved TOPSIS is proposed in this study. The proposed method not only determines weights of the distance measures from an objective perspective and achieves the fully ranking of alternatives, but also provides the directions for improvement. Moreover, in comparison with the other methods, the advantages of the proposed method are analyzed, and the meanings and properties of the new relative closeness are discussed and proved. Finally, an example of evaluating the innovation development of high-tech industries in central and western regions of China is investigated to illustrate the effectiveness and the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

The existing studies on environmental efficiency evaluation generally have the problem of efficiency overestimation. To solve this problem, a new data envelopment analysis (DEA) cross‐efficiency approach with undesirable outputs is developed to evaluate environmental efficiency from the perspectives of both self‐evaluation and peer evaluation. Then, three new evaluation strategies, namely, economic development strategy, environmental protection strategy, and win–win strategy, are proposed to reflect the needs of decision makers under different policy objectives. The proposed cross‐efficiency approach with different evaluation strategies not only realizes the cross evaluation of environmental efficiency, but also guarantees the relative uniqueness of the optimal solution on the basis of the preferences of decision makers. Combining the metafrontier DEA approach and DEA window analysis, a new cross‐efficiency analytical framework is constructed to gradually analyse the influences of policy objectives, technology heterogeneity, and dynamic correlation on the environmental efficiency. Subsequently, the environmental efficiency of China's economic development during 2006–2015 is in‐depth analysed on the basis of the proposed analytical framework, and some interesting conclusions, and some useful suggestions are obtained. [ABSTRACT FROM AUTHOR]

• The objective constraint is introduced to develop a new inverse data envelopment analysis method. • Four objectives are defined to describe the safety requirements of China's road transportation. • Technological heterogeneity is considered to evaluate road transportation's safety efficiency. • The specific realization paths including the adjustment of production elements are derived. • Some useful implications are summarized to promote the realization of China's safety objectives. The number of fatalities in road traffic accidents shows a gradual upward trend in recent years, and this has brought great pressure on the realization of China's safety objectives. Meanwhile, how to make a scientific and feasible path for achieving the safety objective also poses a great challenge to the existing methods. Therefore, the paper introduces the objective constraint to develop a new inverse data envelopment analysis (DEA) model with undesirable outputs for ensuring the realization of safety objective under the current technical level. Subsequently, two safety objectives and two additional objectives are defined for China's road transportation according to the actual requirements of decision makers, and then their realization paths are determined by using the new inverse DEA model, respectively. By analyzing the connections and differences of different objectives and their realization paths, some useful implications are summarized to promote the realization of the safety objective of China's road transportation: First, identifying the requirements of decision makers is critical for making the realization path of safety objective; second, reducing desirable outputs, increasing inputs, and improving safety efficiency are the efficient ways to achieve safety objective; third, the influence of technology heterogeneity should be properly considered in the process of making the realization path of safety objective. In general, the paper provides a powerful decision support for achieving the safety objective of China's road transportation. [ABSTRACT FROM AUTHOR]