Water scarcity, land pollution, and global warming are serious challenges and crises facing the development of sustainable or green agriculture and need to be addressed using efficient and environmentally friendly management strategies. This paper proposed an integrated framework appropriate for agricultural green total factor productivity (AGTFP) assessment coupled with microscopic and mesoscopic perspectives under water-energy-food (WEF) nexus, which generated scientific and reasonable strategies for green and low-carbon agriculture from internal core factors and peripheral environmental impacts to improve green agricultural production sustainability. Taking the Lianshui irrigation district (LID) with three sub-areas as the object, internal core factors were explored by partial least squares regression (PLSR) and the external impact path through partial least squares structural equation modeling (PLS-SEM). Results indicated that AGTFP in LID was the smallest (0.818) compared to the three sub-areas and was in a fluctuating state. Meanwhile, AGTFP which was calculated considering undesirable outputs, was closer to tangible productivity. Resource endowments and technical facilities will promote agricultural production, desirable outputs will stimulate green production, and undesirable outputs can inhibit green production. The external influence pathway was shown to be primary environment - > secondary environment - > economic aspects - > social aspects - > AGTFP. The innovative perspectives presented in this study can facilitate preferable decisions and avoid unintended consequences for human-natural systems.

The economic growth rate is intricately linked to the efficiency and effectiveness of the banking industry. A widely applicable mathematical technique for such assessments is Data Envelopment Analysis (DEA), which evaluates the relative efficiency of Decision-Making Units (DMUs) by comparing their inputs and outputs. Traditional DEA treats DMUs as black boxes, neglecting internal processes that contribute to inefficiencies in individual DMUs. Additionally, it assumes precise values for inputs and outputs that do not apply to real-world problems. This study introduces a comprehensive network series of two-stage DEA, incorporating shared inputs and intermediate measures, undesirable outputs, external inputs and outputs, initial inputs, and terminal outputs. The network two-stage DEA is extended to intuitionistic fuzzy circumstances to address uncertainty. In this extension, a non-linear intuitionistic fuzzy number, namely a parabolic intuitionistic fuzzy number, represents higher-order imprecise datasets. An illustrative example validates the proposed methodology, and comparisons with existing methods are conducted. Moreover, the methodology is applied to assess the efficiency of Indian public sector banks, demonstrating its applicability and showcasing the efficacy of the procedures and algorithms used. Decision-makers can make better choices using optimal efficiency values to gain insights into inputs, intermediate measures, and outputs.•The research study focused on a network two-stage DEA model, incorporating undesirable outputs and shared resources in the presence of uncertainty.•The methodology involves solving the network two-stage DEA model using parabolic intuitionistic fuzzy numbers.•The experimental analysis involves assessing the efficiency of Indian public sector banks.

The measurement of performance within the water industry holds significant importance for policymakers, as it can help guide decision-making for future development and management initiatives. In this study, we apply data envelopment analysis (DEA) cross-efficiency techniques to evaluate the productivity change of the Chilean water industry during the years 2010-2018. Water leakage and unplanned interruptions are included in the analysis as quality of service variables. Moreover, we use cluster analysis and regression techniques to better understand what drives productivity change of water companies. The results indicate that the Chilean water industry is characterized by considerable high levels of inefficiency and low levels of productivity change. This is due to the existence of technical regress whereas gains in efficiency were small. Concessionary water companies were found to be more productive than full private and public water companies. Best and worst performers need to make efforts to reduce production costs and improve service quality. Other factors such as customer density and ownership type statistically affect productivity.

Agriculture has the dual effect of contributing to both carbon emissions and sequestration, and thus plays a critical role in mitigating global climate change and achieving carbon neutrality. Agricultural eco-efficiency (AEE) is an important measurement through which we can assess the efforts toward reduced emissions and increased sequestration. The purpose of this study was to understand the relationship between China\'s target of carbon neutrality and AEE through an evaluative model, so as to improve AEE and ultimately achieve sustainable agricultural development. The Super-SBM model scientifically measures the AEE based on provincial panel data collected between 2000 and 2020. We selected kernel density function and spatial distribution to explore the spatial and temporal evolutionary trends, and used a Tobit model to identify the drivers of AEE. The research shows that (1) China\'s agricultural system functions as a net carbon sink, with all provinces\' agricultural carbon sequestration levels recorded as higher than their carbon emissions from 2000 to 2020. (2) Despite sequestration levels, the level of AEE in China is not high enough, and the average efficiency level from 2000 to 2020 is 0.7726, showing an overall trend where AEE decreased at first and then increased. (3) The AEE of each province is clearly polarized; there are obvious core-periphery characteristics and spatial distribution of clustered contiguous areas. Central provinces generally have lower efficiency, eastern and northeastern provinces have higher efficiency, and northeastern provinces always remain in the high-efficiency group. (4) Influencing factors show that urbanization, upgrading of industrial structure, financial support for agriculture, and mechanization have a significant positive impact on AEE. These findings have important implications for the promotion of the low-carbon green development of Chinese agriculture.

Improving the management of municipal solid waste (MSW) is fundamental to promote circular economy and sustainability. Unsorted waste involves negative environmental impacts which often are ignored in economic feasibility studies due to its difficult valuation. In this study the shadow price of unsorted waste using the directional distance function was estimated. This methodological approach also allowed us to compute eco-efficiency scores of a set of municipalities in the provision of MSW services. The empirical application focused on a sample of 119 Chilean municipalities. The results showed that the average shadow price of unsorted waste was 297.66 €/ton which means that the environmental cost of left waste as unsorted was 297.66 € per ton. A regression tree model illustrated that population density, tourism intensity and the generation of waste per capita significantly influenced the shadow price of unsorted waste. Moreover, it was illustrated that Chilean municipalities were very inefficient in the management of MSW since the average eco-efficiency score was 0.272. The findings from this study reveal that additional and alternative policies should be adopted to improve the management of MSW and increase its recycling rate.

Getting a good understanding regarding the economic and environmental performance of water utilities is of great importance to achieve the goal of an efficient and sustainable industry. In this study, we apply the range adjusted measure (RAM) data envelopment analysis (DEA) model to evaluate the integrated (production and environmental) efficiency of several water utilities located in Chile. Integrated efficiency is evaluated using the concepts of natural and managerial disposability. This approach further allows us to quantify the contribution of each input and undesirable product on efficiency scores. The results highlighted that the Chilean water industry showed high levels of production and environmental efficiency over time. Under natural disposability, water utilities could control production costs to reduce water leakage and unplanned water supply interruptions by 3.3% on average. Under managerial disposability, water utilities could further cut down undesirable outputs by 1.4% on average by adopting best managerial practices. On average, potential savings in operating costs, employment, water leakage, and unplanned water supply interruptions were higher for concessionary utilities as they showed slightly lower efficiency scores than full private utilities.

The level of development of industrial enterprises is related to a country\'s or region\'s overall competitiveness. It is critical to assess the green innovation efficiency of regional industrial enterprises scientifically and effectively in order to improve a country\'s overall green innovation capability. The green innovation system is divided into three sub-stages in this paper: technology development, economic transformation, and environmental protection. Based on the theory of innovation value chain, a three-stage super-efficiency DEA model of the cooperative game including shared inputs and undesirable outputs is constructed to calculate the overall efficiency, three sub-stages efficiency, and dynamic evolution of green innovation of industrial enterprises in China\'s provincial administrative regions and eight economic zones from 2015 to 2019 (divided by the time of ultimate output). The results indicate that (1) in terms of overall efficiency, the efficiency of green innovation is not high, and there are clear regional differences, as evidenced by the following states: the middle reaches of the Yangtze River economic zone > the eastern coastal economic zone > the southern coastal economic zone > the northern coastal economic zone > the northeastern economic zone > the northwestern economic zone > the middle reaches of the Yellow River economic zone, and the overall efficiency of the southwestern economic zone fluctuates around the average level of China; (2) from the standpoint of various stages, economic transformation stage efficiency > overall efficiency > technology development stage efficiency > environmental protection stage efficiency. The improvement of overall efficiency is largely dependent on the high efficiency of the economic transformation stage, but low efficiency in the environmental protection stage results in overall low efficiency; (3) from the perspective of the dynamic evolution trend, the overall efficiency and three sub-stages have been improved to varying degrees. However, due to the low efficiency of the environmental protection stage, there is still a long way to go to achieve the goal of innovation-driven development; (4) based on the classification analysis, it was determined that the green innovation efficiency of industrial enterprises in only a few regions belongs to the \"three high innovation type,\" which must take targeted measures to improve the inefficient innovation process links.

To resolve the environmental problems of China\'s aquaculture industry, we must examine the current situation and comprehensively consider aquaculture growth, resource conservation and environmental protection. Using the unit investigation and evaluation method to evaluate the nonpoint source pollution of each province, this paper calculates eco-efficiency to evaluate the coordination of environment and aquaculture growth based on the slacks-based measure directional distance function dealing with undesirable outputs. The results reveal that the eco-efficiency of aquaculture in China from 2003 to 2018 is 0.70 and obviously lower than the industry\'s economic efficiency, indicating aquaculture development has not been coordinated with resources and the environment. Environmental pollution brings great loss to the economic efficiency of aquaculture. Specifically, eastern China, with the highest aquaculture output, shows the best degree of coordination, followed by western China. Six provinces or province-level municipalities, including Fujian, Shanghai, Beijing, Hainan and Tianjin, are growing soundly and rapidly, while central China exhibits the most obvious imbalance among the environment, resources and aquaculture development.

Sustainable development has gained significant attention in the literature due to the increased global awareness of environmental sustainability during the last decade. Sustainable development has three aspects, including economic, social, and environmental. The challenge of sustainable development is to establish a balance between these three aspects. Assessing the efficiency of a company contributes comprehensive information to improve its overall performance. Despite numerous studies in this field, the literature lacks studies that simultaneously consider all three aspects of sustainable development, especially the social aspect. The main objective of this paper is to calculate the technical, social, and environmental efficiency scores. We also introduce a new efficiency called sustainable efficiency that merges all three sustainable development aspects in one efficiency score. This study applies two existing data envelopment analysis (DEA) models to evaluate technical, social, environmental, and sustainable efficiencies. These models, namely the three-step method and the modified three-step method, are computationally intensive. Also, this paper introduces two new DEA models, namely the common weight goal programming DEA and the common weight DEA, to assess the efficiencies with much fewer computations. Each model produces results that are different from one another. Therefore, the TOPSIS approach is applied to provide an overall result by integrating the results obtained from the four presented models. For this purpose, the implementation of four TOPSIS models is required. To illustrate the capability and validity of the developed models in efficiency calculation, a case of Iranian airlines is presented. The selected airlines are evaluated in different aspects, and final results are obtained by applying TOPSIS. The findings show that using TOPSIS to combine the results of several DEA models leads to a fully ranking of airlines in four aspects of technical, social, environmental, and sustainable efficiencies. Also, it is recommended to managers to probe pairwise comparison between different efficiencies of airlines in order to find and improve the weak ones.

With increasing global awareness of sustainable development and environmental protection, the importance of green supplier selection has become widely recognized. Recently, more and more literature focus on the green supplier selection issue and measurement of supplier performance; the measurement indicators mainly defined two sets of criteria, including management criteria and green criteria. A novel methodology based on data envelopment analysis (DEA) has been developed, which is effective for solving multi-criteria decision problems in supplier selection and evaluating the performance of a set of homogeneous decision-making units (DMUs). To solve the green supplier selection problem, previous studies apply the DEA method by dividing management criteria as inputs and green criteria as outputs. This paper aims to propose a DEA-type green supplier selection methodology by considering the management criteria and green criteria. Unlike the previous method, we define a reasonable and effective division of measurement indicators, where management criteria consist of input variables, desirable and undesirable output variables, while green criteria consist of desirable and undesirable output variables. We also provide an improved model with multi-criteria and multi-objective programming by considering DEA efficiency decomposition, which evaluates management, green, and overall efficiency of candidate suppliers simultaneously. The multi-criteria decision model will help companies to select the best green supplier. To demonstrate the applicability and effectiveness of our proposed model, we present a numerical example at last.