•Regulation adaptive strategies are proposed to evaluate the efficiencies of banks.•Shared resources between stages are considered in the operational process of banks.•Empirical study shows great ...efficiency differences between two adaptive strategies.
Banks have two primary strategies for adapting to a regulation change in the era of big data which can be characterized as natural disposability and managerial disposability. Natural disposability implies a negative strategy by which a bank attempts to decreases its vector of inputs to decrease undesirable outputs. In contrast, managerial disposability indicates a positive strategy by which a bank considers a regulation change as an opportunity and adapt the regulation change by utilizing big data technology. The operational process of a bank can be decomposed into a productivity stage and a profitability stage. Furthermore, the operation costs, a shared resource, can be used to characterize natural disposability and managerial disposability. Based on natural disposability and managerial disposability, this paper proposes two network models to estimate the efficiencies of banks. To test their practical implications, the proposed models were applied to examine the efficiencies of Chinese commercial banks in the period 2014−2018. Our key findings are as follows. (1) There exist great disparities in the inefficiencies between two adaptive strategies. The inefficiencies are primarily driven by the profitability stage under natural disposability, whereas the inefficiencies are equally attributed to both stages under managerial disposability. (2) The efficiency differences among different types of banks are insignificant under natural disposability but are significant under managerial disposability. (3) Joint-stock commercial banks are more oveall efficient than state-owned commercial banks, city commercial banks and rural commercial banks, while state-owned commercial banks show worst practice for overall efficiency and profitability stage efficiency.
•DEA is used to evaluate the energy and environmental efficiency of 30 provincial industrial sector in China.•A new DEA-based model is proposed to allocate the CO2 emissions and energy intensity ...reduction targets.•The context-dependent DEA is used to characterize the production plans.
High energy consumption by the industry of developing countries has led to the problems of increasing emission of greenhouse gases (GHG) (primarily CO2) and worsening energy shortages. To address these problems, many mitigation measures have been utilized. One major measure is to mandate fixed reductions of GHG emission and energy consumption. Therefore, it is important for each developing country to disaggregate their national reduction targets into targets for various geographical parts of the country. In this paper, we propose a DEA-based approach to allocate China’s national CO2 emissions and energy intensity reduction targets over Chinese provincial industrial sectors. We firstly evaluate the energy and environmental efficiency of Chinese industry considering energy consumption and GHG emissions. Then, considering the necessity of mitigating GHG emission and energy consumption, we develop a context-dependent DEA technique which can better characterize the changeable production with reductions of CO2 emission and energy intensity, to help allocate the national reduction targets over provincial industrial sectors. Our empirical study of 30 Chinese regions for the period 2005–2010 shows that the industry of China had poor energy and environmental efficiency. Considering three major geographical areas, eastern China’s industrial sector had the highest efficiency scores while in this aspect central and western China were similar to each other at a lower level. Our study shows that the most effective allocation of the national reduction target requires most of the 30 regional industrial to reduce CO2 emission and energy intensity, while a few can increase or maintain their 2010 levels.
Environmental problems brought by industry are attracting extensive attention so a comprehensive analysis of industrial environmental performance is increasingly important. However, the comparison of ...industrial sector efficiencies is complicated by the fact that the natural resources consumed and/or the pollutants discharged by each sector may differ. In this paper, we extend the DEA model to consider two-sided non-homogeneous problems, handling DMU sets that have non-homogeneity in both inputs and outputs. This is different from the previous researches which generally focus on regional data to avoid non-homogeneity. Today environmental reform and energy conservation in various industrial sectors are both parts of the basic state policy of China. The empirical results show that: (1) Sectors' efficiencies are still low and unbalanced. The Recycling and Disposal of Waste department achieves the best energy saving and emission reduction efficiency. (2) 38 sectors can be clustered into four groups and set new benchmark in each group. (3) The overall efficiency of 38 industrial sectors in China maintained a rising trend in five years. With this more realistic analysis of environmental efficiency, the Chinese government can make more informed decisions to realize sustainable industrial development.
•We break the homogeneous assumption of conventional DEA model.•Our model solves problems with dissimilarity in both inputs and outputs.•Our model has advantages in evaluating energy and environmental efficiency of industrial sectors.•The empirical results give a new aspect to energy conservation performance in China.
•The article performs the productivity change of the high-tech industry, rather than the efficiency evaluation.•This article divides all provinces into three main regions and conducts an analysis of ...productivity change in high-tech industries in each region.•This work specifically examines the productivity changes by dividing the innovation process into the technology development stage and economic transformation stage.•This work also adopts the Hicks-Moorsteen index method to measure and decompose the productivity change of China's high-tech industries.
The high-tech industry is a high-end component of the modern industrial system, and the key to high-quality development is to improve total factor productivity (TFP). This article divides the innovation process of the high-tech industry into two sub-stages: technology development and economic conversion. Based on the panel data of high-tech sectors, Malmquist and Hicks-Moorsteen indices are applied to measure the productivity changes. Furthermore, the index decomposition dissects the technological change rate and technical efficiency change, thereby identifying the main factors affecting the productivity change. The results show that the TFP of high-tech industries in China and various regions is upward. From the perspective of each region, the main growth driver in the national and Eastern regions is technological progress, while that in the Central and Western regions is mainly technical efficiency changes. In addition, in terms of each sub-stage, the main growth driver in the technology development stage is technical efficiency change, while in the economic conversion stage and the overall system, technological progress is the primary growth driver.
The rapid growth of the economy in China has caused many problems for the country, particularly the energy shortage and environmental pollution. Thus, establishing a sustainable development of ...society has attracted considerable attention in recent years. In this paper, we evaluate the sustainability efficiency in terms of energy usage and environmental impact by using a new equilibrium efficient frontier data envelopment analysis (EEFDEA) approach. For the first time, DEA is used in the context of required fixed reductions in both total energy consumption and total environmental pollution, which is a realistic context that introduces a “zero-sum” aspect to the problem. As part of the analysis, the generalized equilibrium efficient frontier (EEF) is constructed based on minimum satisfaction degree maximization of all units, considering both minimum and maximum adjustment strategy, while most existing studies only minimized the weighted sum reduction. Finally, the sustainability efficiency of transportation sectors of China's 30 main regions is analyzed by using our proposed model. The results show that more effective measures should be taken for sustainable development.
•We measure sustainability efficiency in term of energy usage and environmental impact by using a new generalized equilibrium efficient frontier DEA approach.•The equilibrium efficient frontier is based on minimum satisfaction degree maximization, considering both minimum and maximum adjustment strategy.•The proposed approaches are applied in transportation sectors of 30 provincial-level regions in mainland China.
•We allocate a fixed cost to decision making units with two-stage network structures.•All units and sub-units can be simultaneously efficient with common weights.•We take the size of operation units ...into account to obtain the final allocation plan.•Our proposed approach can always guarantee a unique fixed cost allocation plan.•We apply the proposed approach to a numerical example and an empirical study.
A prominent issue in many organizations involves the fair allocation of a total fixed cost among a group of entities. This paper extends the traditional fixed cost allocation problem to situations where the decision making units (DMUs) have a two-stage network structure. To this end, this paper first uses the data envelopment analysis (DEA) methodology to determine the relative efficiency while taking the internal structure and possible allocated costs into account. It shows that each DMU can separately maximize its relative efficiency to one through determining a series of allocations and selecting a set of relative weights. Next, we demonstrate that there exists an efficient allocation set based on a set of common weights, using which all DMUs and their two sub-stages can be simultaneously efficient. However, there are alternative allocation plans in the efficient allocation set. According to this non-uniqueness problem, we further optimize the allocation plans by taking the size of operation units into account, such that the allocation result is proportional to current input usages and output productions from a size point of view. In addition, we suggest a min–max model and a feasible computation algorithm for it to generate the final allocation plan in a way that minimizes the deviation between the efficient allocations and size allocations. More importantly, by repeatedly minimizing the maximum deviation, our proposed method can guarantee a unique allocation plan for all DMUs and sub-stages. Finally, both a numerical example modified from previous literature and an empirical application of bank activities are used to demonstrate the efficacy and usefulness of the proposed approach.
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•We investigate the potential for energy saving and carbon emission reduction.•Dynamic data, technology heterogeneity, and closest target have been considered.•The potential and route ...can be achieved in step by step with least effort.•Our proposed model is applied to China’s regional industrial sectors.
Rapid economic growth of China’s industry has brought many problems. Among them, the problems of energy shortage and environmental pollution have become increasingly serious. The quick development of the big data has brought new challenges and opportunities for environmental management. In this paper, we propose a new data envelopment analysis (DEA) model to analyze the energy and environmental efficiency of industrial sectors from China’s 30 provincial-level regions in order to determine the potential and route for energy saving (ES) and carbon emission reduction (CER). The new DEA model not only considers the dynamic data, but also involves the technology heterogeneity and closest targets, which could achieve the potential or provide the route for ES and CER step by step with least effort. The new approach is illustrated by using the regional industrial dataset of China and some implications for ES and CER are proposed.
•Novel algorithms are proposed to accelerate the computation process in the big data environment.•An easy algorithm is developed to divide the large scale DMUs into small scale and identify all ...strongly efficient DMUs.•We only need to select two reference points as the sample in the situation of just one input and one output.•A variant of the algorithm is then presented to handle cases with multiple inputs or multiple outputs.
Data envelopment analysis (DEA) is a self-evaluation method which assesses the relative efficiency of a particular decision making unit (DMU) within a group of DMUs. It has been widely applied in real-world scenarios, and traditional DEA models with a limited number of variables and linear constraints can be computed easily. However, DEA using big data involves huge numbers of DMUs, which may increase the computational load to beyond what is practical with traditional DEA methods. In this paper, we propose novel algorithms to accelerate the computation process in the big data environment. Specifically, we firstly use an algorithm to divide the large scale DMUs into small scale and identify all strongly efficient DMUs. If the strongly efficient DMU set is not too large, we can use the efficient DMUs as a sample set to evaluate the efficiency of inefficient DMUs. Otherwise, we can identify two reference points as the sample in the situation of just one input and one output. Furthermore, a variant of the algorithm is presented to handle cases with multiple inputs or multiple outputs, in which some of the strongly efficient DMUs are reselected as a reduced-size sample set to precisely measure the efficiency of inefficient DMUs. Last, we test the proposed methods on simulated data in various scenarios.
Accelerating the development of renewable energy is seen as an effective way for achieving the goals of carbon peak and carbon neutrality. The polices of Renewable Electricity Standard (RES) and ...Renewable Energy Certificates (REC) play increasing and important roles in developing renewable energy. In this paper, we develop an analytical model to analyze the impacts of the interaction of RES and REC polices on the renewable energy investment levels of an electricity generation firm and the carbon emissions. Our analysis reveals several interesting insights. First, we find that the green tags price under REC policy has a non-monotonic effect on the renewable energy investment, which highly depends on the quota (i.e., the required percentage of renewable electricity consumption on total electricity consumption) under the RES policy. Specifically, when the quota in RES policy is set too high, an increase in the green tags price will increase renewable energy investment; otherwise it will reduce the electricity generation firm's incentive to invest in renewable energy. Second, we show that the green tags price also has a non-monotonic effect on the carbon emissions. Specifically, when the quota in RES policy is set small enough, an increase in the green tags price will decrease the carbon emission. However, when the quota in RES policy is high enough, an increase in the green tags price will increase the carbon emission.
•Impacts of green tags price on renewable energy investment are analyzed.•Impacts of green tags price on carbon emissions are analyzed.•Green tags price has a non-monotonic effect on the renewable energy investment.•Green tags price has a non-monotonic effect on the carbon emissions.
•We measure the efficiency of two-stage system with shared and reused resource.•We introduce additive and non-cooperative efficiency measures to this system.•A heuristic algorithm is suggested to ...transform nonlinear model into a linear one.•We prove the heuristic algorithm to be a good and effective approach.•The new approaches are applied to industrial production processes of China.
Data envelopment analysis (DEA) is an approach for measuring the performance of a set of homogeneous decision making units (DMUs). Recently, DEA has been extended to processes with two stages. Two-stage processes usually have undesirable intermediate outputs, which are normally considered be unrecoverable final outputs. In many real situations like industrial production however, many first-stage waste products can be immediately used or processed in the second stage to produce new resources which can be fed back immediately to the first stage. The objective of this paper is to provide an approach for analyzing the reuse of undesirable intermediate outputs in a two-stage production process with a shared resource. Shared resources are input resources that not only are used by both the first and second stages but also have the property that the proportion used by each stage cannot be conveniently split up and allocated to the operations of the two stages. Additive efficiency measures and non-cooperative efficiency measures are proposed to illustrate the overall efficiency of each DMU and respective efficiency of each sub-DMU. In the non-cooperative framework, a heuristic algorithm is suggested to transform the nonlinear model into a parametric linear one. A real case of industrial production processes of 30 provincial level regions in mainland China in 2010 was analyzed to verify the applicability of the proposed approaches.
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