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Multimodal transport is an effective method to reduce the energy consumption and carbon dioxide (CO 2) emissions in transportation sector. Evaluating the low-carbon performance is a prerequisite for developing multimodal transport. This paper establishes the new cross-efficiency (CE) network data envelopment analysis (DEA) approach, and applies it to evaluate low-carbon efficiency of railway-waterway intermodal transportation (RIT) in China from 2017 to 2022. The proposed approach takes advantage of network DEA and considers the internal structure in the RIT system as well as the interconnections between various transportation modes. This article comprehensively analyzes the evaluation results in terms of the overall situation, geographic distribution characteristics, internal stages and links, external influencing factors, and efficiency prediction, and the results find that: (1) the low-carbon efficiency of China's overall RIT needs further improvement. (2) The geographical distribution shows a trend that the efficiency in the Northern Region is higher than that in the Southern Region. (3) The level of urban industry, external transportation conditions, and foreign trade development will all affect the performance of RIT. Finally, according to the empirical results, management strategies are proposed. The main contributions include: proposing a network-structured CE-DEA method focusing on internal states; The advantages of CE network DEA are utilized to study CO 2 emissions of multimodal transport; The low-carbon efficiency of RIT in China is comprehensively analyzed. This study enriches the research on DEA methods, and the results can provide a basis for decision-making by the government and transport enterprises, help promote the development of low-carbon transport in China, and provide a reference for the optimization of multimodal transport in other countries. • Constructed a cross-efficiency DEA approach. • Analyzed the low-carbon efficiency of multimodal transport. • Discussed the distribution and influencing factors of efficiency. [ABSTRACT FROM AUTHOR]

• Identifying a common best practice frontier most similar to management goals. • The possible additional benefits generated from the global reallocation. • The stepwise reallocation paths for DMUs with limited reallocation potentials. • The case study based on 18 ports in Korea. Common benchmarking can identify a common best practice frontier as the benchmark to assess and improve the performance of Decision-Making Units (DMUs) experiencing similar circumstances. At present, further exploration in common benchmarking is still needed. For instance, the similarity between management goals and targets belonging to a common best practice frontier. To fill this gap, we propose the following innovative work: (1) A novel Data Envelopment Analysis (DEA) model is proposed to identify a common best practice frontier on which targets closest to management goals can be found for all DMUs experiencing similar circumstances. These targets need to be achieved through a global reallocation of inputs/outputs among DMUs; (2) Considering the targets closest to management goals are too ambitious for some DMUs, an iterative algorithm is given based on another new DEA model for providing the stepwise reallocation paths. The stepwise reallocation paths can distribute the total reallocation efforts into several intermediate steps; (3) The effectiveness of the proposed model and algorithm is illustrated by applying them to a case of 18 ports in Korea. For this purpose, we use the input–output data set of these ports in 2010 and assume actual management goals for every port. The results of the case show some conclusions. First, the target of each port located on the common best practice facet is closest to its management goal. Second, global redistribution can make all ports obtain the best input–output levels. Third, ports with limited reallocation potential can monotonically and gradually achieve their distant targets through our stepwise schemes. [ABSTRACT FROM AUTHOR]

As haze intensifies in China, controlling haze emission has become the country's top priority for environmental protection. Because haze moves across different regions, it is necessary to develop a data envelopment analysis (DEA) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co‐opetition matrices of Chinese provinces, then builds the co‐opetition DEA model to evaluate the haze emission efficiency of them, and finally uses the haze data of 2015 as an example to assess the applicability of the model. The results of the study include the following: First, compared with the traditional CCR (A. Charnes & W. W. Cooper & E. Rhodes) model, this study constructs the co‐opetition DEA cross‐efficiency model that integrates haze's feature of cross‐border moving; thus, it is more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained from the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision‐making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, according to the evaluation based on the co‐opetition DEA method, varies greatly: Those with high efficiency are mostly inland provinces with slow economic growth and adverse climatic conditions, whereas many of the provinces with low efficiency are located in the relatively prosperous East China. The specific co‐opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants around the world and can contribute empirical support to the haze reducing efforts of the government with its empirical results. [ABSTRACT FROM AUTHOR]

The transportation industry is the main source of carbon dioxide (CO 2) emissions, and the development of multimodal transport is conducive to reducing transportation carbon emissions. In order to evaluate the low carbon efficiency of multimodal transport, this article proposes a cross efficiency data envelopment analysis (DEA) method for complex network structures from the perspective of fairness, and takes China's rail-water intermodal transport as an example for research. The proposed method includes two secondary goal models that reduce the unreasonable differences between self-evaluation and peer-evaluation and treat all stages within the network system equally. Then, the improved entropy weight method is used to aggregate the cross efficiency. In addition, a numerical example is given to verify the effectiveness of the proposed method. The results of the case study show that the efficiency of Tangshan Port and Rizhao Port is relatively high, and others should learn from the successful experiences and develop improvement strategies based on their own characteristics. The conclusions can provide reference for transportation enterprise management and policy formulation, and the proposed method can be widely applied to the performance analysis of enterprise network, industrial chain, and supply chain. [Display omitted] • A novel cross efficiency DEA model is proposed. • The proposed approach includes two secondary goal models. • The low carbon efficiency of multimodal transport is evaluated. [ABSTRACT FROM AUTHOR]

Currently, the hazy weather in China is increasingly serious. It is urgent for China to reduce haze emissions in environmental governance. A feasible way is to control haze emissions by optimizing the input sources. This paper proposed an innovative method in which the haze emission is controlled by readjusting input indicators. The output efficiency of input indicators in 29 provinces in China is calculated through 7 input indicators (namely, SO emissions, NO emissions, soot emissions, coal consumption, car ownership, capital, and labor force) as well as GDP (desirable output) and PM emissions (undesirable output). The results showed that the input indicators are excessive in redundancy on condition that PM emissions and GDP are equal. The input indicators are high in redundancy rate except labor force. The redundancy rates of soot emissions, SO emissions and coal consumption are relatively high and, respectively, are 78, 67.18, and 61.14%. Moreover, all the provinces are redundant in inputs except Beijing, Tianjin, and Shanghai which are optimal in input-output efficiency. The redundancy of middle and western provinces, such as Ningxia, Guizhou, and Shanxi, is relatively large. The ideas and methods proposed in this paper can provide a reference for the future researches that aim to reduce the input indicators of undesirable output, and the empirical results can provide empirical support for the PM abatement in China. [ABSTRACT FROM AUTHOR]

• A novel method that achieves optimal allocation efficiency is proposed. • Our novel method can lead to less economic losses of cities in China. • The deprivation coefficient per capita was used for the fairness of the method. • A case study on 27 polluted cities with high homogeneity in China is conducted. Rational allocation of pollutant emission rights is one of the ways to promote the sustainable economy and environment of cities. Based on the overall goal of emission reduction in the 13th Five-Year Plan proposed by China, the Yangtze River Delta city agglomeration with high homogeneity is selected as the research object, and three allocation models of SO 2 emission rights are established using data envelopment analysis (DEA), shadow price and information entropy. The fairness of the three allocation schemes is evaluated by the per capita relative deprivation coefficient (RDC). The study shows that: (1) the three-objective information entropy allocation model takes into account the principles of maximum output-input efficiency, protection of regional GDP and scheme fairness, and the relative deprivation coefficients (RDCs) are all less than 0.5, which can be used as the best scheme for SO 2 emission rights allocation in the Yangtze River Delta city agglomeration. (2) Cities in Jiangsu province need to reduce SO 2 emissions. (3) Taking city agglomerations with high homogeneity as the research object has more practical significance, which can provide policy advice for sustainable development of similar city agglomerations. [ABSTRACT FROM AUTHOR]

Schematic diagram of the research framework: A new network DEA method. [Display omitted] • A new network DEA allocation model considering cross-border transmission of SO 2. • The WRF-CMAQ is combined with the DEA allocation model for the first time. • The principles of optimal efficiency, GDP protection and fairness is considered. • The per capita relative deprivation coefficient was used to evaluate the fairness. • The new network DEA model is a powerful tool for SO 2 emission rights allocation. Currently SO 2 emission rights are allocated without considering the cross-border transmission characteristics of pollutants, and the allocation scheme cannot reflect the fact that air pollutant overflows in space. In this paper, the WRF (Weather Research and Forecasting Model)-CMAQ (Community Multiscale Air Quality) is combined with the data envelopment analysis (DEA) allocation model for the first time, and a two-stage (Stage-1: SO 2 formation, Stage-2: SO 2 treatment) network DEA allocation model is proposed with the SO 2 spatial transmission taken as the intermediate output or input index between decision-making units. Based on the principles of efficiency, economic protection and fairness, the model is applied to the study of SO 2 emission rights allocation in the Yangtze River Delta city agglomeration with high homogeneity in China. The results show that: (1) When the weight coefficient (α 1) in stage-1 is 0.7, 0.8 or 0.9, the per capita relative deprivation coefficient (PRDC) of each decision-making unit (DMU) is less than 0.5 in the allocation scheme, indicating that our scheme can be used as a reference to allocate emission rights in the Yangtze River Delta city agglomeration. (2) With great robustness and universality, our allocation model allows decision-makers to adjust the GDP constraint coefficient and the weight coefficients in the two stages according to their preferences, so as to obtain the optimal allocation results. The model proposed in this paper can help decision-makers to formulate effective policies to reduce pollutant emission, and promote energy conservation & emission reduction and coordinate environmental governance in regions of city agglomerations. [ABSTRACT FROM AUTHOR]

It is an urgent task for China to control haze emission in environmental treatment. Emission right trading is a feasible way to achieve the reduction of pollutant emission on the premise that the total pollutant emission amount is under control, and the allocation of initial emission rights based on the target total amount is the key to emission right trading. In this paper, via the input-oriented ZSG-DEA model, the inter provincial allocative efficiency of PM 2.5 emission rights is investigated under the condition that the target total amount is fixed. The results showed that (1) after initial emission rights were allocated in accordance with the ZSG-DEA model, PM 2.5 emission amounts of all provinces would be in a new common DEA frontier so as to realize the overall Pareto optimality with a set total amount; (2) two factors, namely land areas and atmospheric environmental capacities of all provinces, were considered in the actual allocation to avoid the homogenization of all evaluated units found in the previous evaluation literature on allocative efficiency, thereby making the evaluation results more in line with the actual situations in all provinces. Such an investigation approach can provide guidance on the allocation of initial emission rights in emission right trading; and the research results can offer empirical support for haze-reducing work load conducted by central and local governments of China. [ABSTRACT FROM AUTHOR]

Accurate evaluation of emission governance efficiency can build fundament to develop haze control strategy towards sustainable development. By features of the haze, we view the haze formation stage as the first sub-process and the haze control stage as the second sub-process. This paper proposes an additive aggregation network data envelopment analysis (DEA) model with undesirable intermediate measures and undesirable outputs, which have not been thoroughly studied in previous literature. We found the newly developed network DEA model was nonlinear and cannot be converted into a linear program, and then developed an improved second-order cone programming approach to solve this problem. After analyzing the data of haze control in China, we drew the following conclusions: Firstly, different weights of preference for two sub-process can lead to the variation in the overall efficiency. Under different weights of preference, although the efficiency of the haze formation has a very small change in some provinces, the efficiency of the haze control has a large change. Secondly, decision makers can achieve the adjust goal of reducing haze by adjusting their preferences on the information of the haze formation and haze control stages, which are helpful for policy making in haze control strategy and sustainable development. [ABSTRACT FROM AUTHOR]

Shipping management has an essential influence on the development of shipping industry. Management efficiency is one of the important indexes to measure the quality of shipping management. This paper aims to assess the management efficiency from a new perspective of congestion index to point out the causes affecting efficiency and provide suggestions to the shipping company. Based on a two-stage data envelopment analysis, we establish a novel congestion management system to comprehensively examine the organization's congestion from three aspects, including the macro-level, the micro-level, and resource allocation. Accordingly, we apply three congestion models, namely stage congestion, slack-based congestion, and resource reallocation congestion, respectively, to measure Hapag-Lloyd's congestion index from 2017 to 2020. We find that: 1) Hapag-Lloyd's capacity is higher than its market share; 2) major events (e.g., merger and acquisition, COVID-19) have significant impacts on the congestion index of the company; 3) excessive redistributable resource input invalidate the congestion index. [Display omitted] • The management efficiency of shipping company was assessed by a two-stage data envelopment analysis. • A novel congestion management system was established to comprehensively examine the organization's congestion. • It is found that COVID-19 have significant impacts on the congestion index of the company. [ABSTRACT FROM AUTHOR]

The output loss between cooperation and non-cooperation strategies for areas 1–4 (2012–2016). • Environmental control parameters are set to manipulate multiple situations. • Non-equal proportional change of indices and preference coefficient for ports are proposed. • A distance formula of expected output for PM emission reallocation is provided. • New DEA models are applied to 11 Chinese main ports. The emission of particulate matter (PM) and other atmospheric pollutants in port operations and shipping have caused a huge negative impact on the environment. Consequently, how to evaluate the environmental efficiency of ports and put forward corresponding countermeasures on this basis is becoming a more important issue than ever before from the perspectives of the government, academia, and society. We construct three data envelopment analysis (DEA) models to evaluate the environmental efficiency of ports under the circumstances of environmental control, non-environmental control and PM emission through inter-ports cooperation. The innovation of the DEA models constructed in this paper lies in: (1) Setting environmental control parameters to uniformly manipulate the situations of environmental control and non-environmental control, etc.; (2) Allowing non-equal proportional change of input index, expected output and non-expected output index; (3) Setting preference coefficients for ports to admit favorable decisions; (4) Providing a distance formula of expected output for PM emission reallocation. Further, data from 11 major ports in China are collected to compare the expected output under different PM emission standards assuming the situation of environmental control and non-environmental control, port cooperation, and non-cooperative sewage discharge. The empirical results show that: (1) Ports in the eastern China (Shanghai, Ningbo, and Nanjing) have higher port efficiency; (2) Port cooperation can improve the overall expected output but it will lose its edge with the improvement of PM emission standards. (3) Ports follow the same trend of output loss regardless of favorable decisions. In the end, the author makes a summary, puts forward relevant policy suggestions and makes the recommendation for future research. [ABSTRACT FROM AUTHOR]

Under the premise that the total emissions of PM (particulate matter) of air pollution is established, the allocation of PM emission rights in various provinces of China is the feasible way to governance the ash haze. In this paper, a data envelopment analysis (DEA) model that takes into account the objectives of efficiency and equity is constructed. On the basis of evaluating the PM emission performance of various provinces in China, the corresponding allocation steps are put forward. Then, the results of the DEA model and information entropy are compared. The research shows that: (1) under the same constraint conditions, the emission right of PM is allocated with DEA model, which leads to lower cost, less fluctuation of distribution amount and higher overall national performance. (2) the population Gini coefficient method is used to evaluate and it is found that the Gini coefficient value is smaller according to the distribution results of DEA model, indicating that the PM emission right amount is more fairly distributed among provinces. The functions of this paper are as follows: (1) a new double-objective DEA model is constructed, which is suitable for multi-agent performance evaluation with multiple objectives; (2) a scheme to allocate PM among provinces is proposed, which can provide reference for the practice of haze reduction in China [ABSTRACT FROM AUTHOR]