Super Slacks-based Measure Model Research Articles

A Spatiotemporal Evolution and Pathway Analysis of Rural Development Efficiency: A Case Study of the Yangtze River Delta

Sustainable rural development is crucial for urban–rural integration and achieving shared prosperity. This study assesses rural development efficiency (RDE) at the county level in the Yangtze River Delta (YRD) region from 2012 to 2021 using the super-slacks-based measure model (SBM). By employing the Theil index and spatial Markov chains, this study explores the spatiotemporal evolution of RDE, categorizes rural development types, and proposes differentiated developmental pathways. The findings reveal (1) an initial upward trend in overall RDE in the YRD followed by stabilization, with regional disparities narrowing yet overall efficiency levels remaining relatively low; (2) a spatial distribution pattern of RDE characterized by high efficiency in the southeast and low efficiency in the northwest, forming a “core–periphery” structure, with pure technical efficiency identified as a significant bottleneck; (3) stability and “club convergence” in RDE, with geographic spatial patterns significantly influencing the transition process and a notable spatial spillover effect; (4) the classification of rural development types into six categories based on an “input–output–efficiency” perspective, each with specific developmental pathways. This study concludes that optimizing resource allocation efficiency and defining development pathways tailored to local conditions are essential for driving sustainable rural development in the YRD.

Spatiotemporal patterns, effects, and the interactive driving mechanisms of urban sustainability based on the eco-efficiency framework: Evidence from Chinese prefecture-level cities

Eco-efficiency is a vital tool for evaluating and advancing regional sustainable development. This study presents an urban eco-efficiency (UEE) framework that deserves widespread application. By using the Super Slacks-Based Measure model, the research calculates the UEE of 284 prefecture-level cities in China from 2005 to 2022. The study employs the Standard Deviational Ellipse to illustrate the spatiotemporal pattern of UEE in China and examines its effects using non-parametric kernel density estimation, the coefficient of variation, and both global and local Moran's I. The integration of UEE with urban development patterns, which is not immediately clear, is achieved through the entropy-based TOPSIS model. Additionally, the Malmquist index, the Two-way Fixed Effects (TWFE) model, and the Panel Vector Autoregression (PVAR) model reveal the dynamic changes and interactive driving mechanisms of UEE in China. The results indicate that: (1) UEE in China displays a consistent “core-periphery” pattern, exhibiting a notable hierarchical impact from regional, scale, and administrative perspectives. The complexity of UEE's spatiotemporal variations has increased due to the impact of COVID-19. (2) The UEE of Chinese cities shows a significant ‘club’ effect, with efficiency thresholds for high-high and low-low aggregations set at 0.8. (3) Only 25% of Chinese cities have achieved optimal UEE levels. Notably, cities in the west contribute most to national UEE growth, while midland cities contribute the least. The decreasing trend in UEE growth underscores the urgent need for a shift in China's urban development model. (4) UEE in China benefits from a resilient self-reinforcing mechanism, with urban development and economic growth significantly aiding UEE growth. Despite a non-significant negative impact from government intervention, the inhibitory effect of an intensive industrial structure on UEE turns into a promotional effect, especially following COVID-19.

Analysis of the spatial correlation pattern of logistics carbon emission efficiency and its influencing factors: the case of China.

As logistics carbon emission efficiency is an essential industry linking regions, investigating this issue from a spatial correlation perspective is practically significant. Utilizing data from 282 prefecture-level cities spanning 2006 to 2019, we used a super slacks-based measure model, a modified gravity model, motif analysis, the Infomap algorithm, and an exponential random graph model to analyze the spatial correlation patterns and influencing factors of logistics carbon emission efficiency. The following conclusions were drawn. (1) The spatial correlation of logistics carbon emission efficiency during the study period exhibited a core-edge pattern, with the central region emerging as a high-correlation hub. (2) The scale of the spatial association network community of carbon emission efficiency in the logistics industry changed constantly, and the stability of the network community structure gradually increased. From a microstructural perspective, the dispersed-mode structure was a pivotal element in the formation of the spatial correlation network of logistics carbon emission efficiency. (3) Node interaction tendencies were a critical force driving network formation. Financial investment, government concern, international openness, population density, and innovation ability were conducive to the formation of spatial correlations of logistics carbon emission efficiency.

Investigating farmer’s identity and efficiency of tourism-oriented farm diversification

Whether farm management in conducting tourism activity becomes more efficient or not is an important theoretical and empirical question for the promotion of tourism in agriculture. Thus, this study theoretically and empirically evaluated the efficiency of educational dairy farms that provide educational tourism by data envelopment analysis. The financial data were collected by the author’s survey of these farms located around the Tokyo Metropolitan area. Based on the theoretical framework that stipulates that the efficiency of farm activity is determined by a farmer’s identity, a bilateral slacks-based measure (SBM) model and Super SBM model were applied to empirically evaluate efficiency. The results revealed that those farmers who engage in processing milk products and direct selling have higher efficiency than those who do not. This is because having an enlarged identity that provides a wider perspective on farm activity enables these farmers to create demand and reduce marginal cost. This wider perspective was nurtured through the network of educational tourism activity. Thus, educational tourism activity by dairy farmers can nurture a new business opportunity and lead to efficient farm resource allocation. Identity can be a crucial factor in building rural entrepreneurship in tourism.

The Selection of Strategic Alliance in IC Packaging and Testing Industry with DEA Resampling Comparative Evaluation

Merger and acquisition (M&A) cases and the speed of horizontal integration have increased rapidly in the integrated circuit (IC) packaging and testing industry. Therefore, cooperation with suitable strategic alliance partner is a vital success factor for enterprises. This study proposes a “comparative evaluation” model that searches out appropriate strategic alliances on the basis of the resampling model acquired via data envelopment analysis (DEA). The realistic public data of 20 companies was collected from 2015 to 2019 in the IC packaging and testing industry. The super slacks-based measure model was used to evaluate the performance before alliance in the period from 2015 to 2019. The resampling past–present–future model was used to forecast the performance in 2020–2024. Afterward, a future strategic alliance for comparative evaluation of efficiency was established. The results of the alliance were divided into the groups “ineffective” and “effective”. The results show that 11 companies in the “effective” group achieved both improvements and 8 companies in the “ineffective” group achieved only unilateral improvements. The comparison model describes the efficiency of both sides simultaneously, not only from the perspective of the target but also from the perspective of the partner company. The evaluation model proposed in the study enables enterprises to find suitable alliance partners.

A modified slacks-based measure of efficiency in data envelopment analysis

The slacks-based measure (SBM) model can divide the set of observations into two mutually exclusive and collectively exhaustive sets: efficient and inefficient. However, it fails to provide more details about efficient DMUs, which reveals the lack of discrimination power in the SBM model. With the aim of addressing this issue, the super SBM (SupSBM) model has been suggested which can rank the SBM-efficient DMUs without providing any useful information about SBM-inefficient DMUs. As a result, in order to fully rank both efficient and inefficient DMUs, one needs to run both SBM and SupSBM models which leads to a significant increase in the number of required computations. This paper tackles this problem and modifies the SBM model which measures SBM-efficiency score for inefficient DMUs and SupSBM-efficiency score for strong efficient DMUs, simultaneously. Finally, a simulation study is presented to illustrate the superiority of our proposed model over the existing models with various problem sizes.

A modified slacks-based super-efficiency measure in the presence of negative data

As a non-radial super-efficiency model, Super slacks-based measure (SBM) can rank efficient decision making units (DMUs) while it cannot deal with negative data. This paper proposes an improved Super SBM model and the corresponding improved SBM model under the condition of variable returns to scale, both of which are feasible and allow input-output variables to take negative values. Based on them, a two-stage approach is provided, which has the following advantages in the presence of negative data: compared with radial super-efficiency models capable of dealing with negative data, it can judge the efficiency of DMUs just by the resulting super-efficiency score; it yields a strongly Pareto efficient projection for each DMU; for inefficient DMUs, it provides better target; for efficient DMUs with the super-efficiency score greater than one, it reduces or expands at least one of outputs or inputs to reach the super-efficiency frontier; it is monotonous, units-invariant and translation invariant for both inputs and outputs. The proposed method successfully overcomes the drawbacks of the current super-efficiency models capable of handling negative data and extends Super SBM to the situation where negative data exist.

Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis

In this paper, we develop an integrated model for slacks-based measure (SBM) simultaneously of both the efficiency and the super-efficiency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the efficient DMUs by the SBM model of Tone (2001) [20] before applying the super SBM model of Tone (2002) [21] for the DMUs to achieve their super-efficiency scores, our integration can obtain the efficiency scores of the inefficient DMUs and the super-efficiency scores of the efficient DMUs by solving simultaneously these two models by an one-stage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time effectiveness of our proposed model as compared with the traditional solution approaches.

An extension on super slacks-based measure DEA approach

In order to break the tie of efficient decision-making units, super-efficiency data envelopment analysis is proposed to fully discriminate them. Recently, a slacks-based version of the super slacks-based measure (S-SBM) is developed and a novel two-stage approach is proposed to calculate both super-efficiency score by the S-SBM model and efficiency score by the slacks-based measure model. In this paper, we extend the approach to consider continuity of efficiency scores. We illustrate the discontinuity of efficiency measure, and define a continuous slacks-based measure which is proved continuous and directly calculated. An interesting efficiency zone category is also provided. In addition, this paper investigates the relationship among the super-efficiency measures of the proposed approach and some existing approaches under variable returns to scale.

Developing an Output-Oriented Super Slacks-Based Measure Model with an Application to Third-Party Reverse Logistics Providers

Outsourcing is an increasingly significant topic pursued via corporations seeking enhanced efficiency. Third-party reverse logistics involves the employ of external firms to carry out some or all of the firm's logistics activities. Output-oriented super slacks-based measure (SBM) model is one of the models in data envelopment analysis (DEA). In many real-world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a chance-constrained output-oriented super SBM model is developed and also its deterministic equivalent, which is a nonlinear program, is derived. Furthermore, it is shown that the deterministic equivalent of the stochastic output-oriented super SBM model can be converted into a quadratic program. In addition, sensitivity analysis of the stochastic output-oriented super SBM model is discussed with respect to changes on parameters. Finally, a numerical example demonstrates the application of the proposed model. Copyright © 2011 John Wiley & Sons, Ltd.