Measuring the eco-efficiency of the provision of drinking water by two-stage network data envelopment analysis

Mocholí-Arce, Manuel; Sala-Garrido, Ramón; Molinos-Senante, María; Maziotis, Alexandros

doi:10.1007/s10668-021-01972-2

Cited by 5 publications

(2 citation statements)

References 57 publications

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“…The articles in [58][59][60][61][62][63][64][65][66][67][68] are devoted to modeling production processes from the standpoint of eco-efficiency. They divide all the production activities of economic agents into a production stage and a stage responsible for environmental impacts (cleaning emissions, creating eco-friendly innovations, etc.).…”

Section: Fund Transfer Incomementioning

confidence: 99%

Network DEA and Its Applications (2017–2022): A Systematic Literature Review

2023

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Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.

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Section: Fund Transfer Incomementioning

confidence: 99%

Network DEA and Its Applications (2017–2022): A Systematic Literature Review

2023

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“…Xiao et al [20] used the S-U-SLM model to measure sectoral energy environmental efficiency. Mocholi-Arce et al [21] used the Double-Bootstrap method to evaluate the energy efficiency of drinking water treatment plants. Chen et al [22] employed the Windows-DEA model to calculate and analyze the transport energy efficiency in cities of China's Yangtze River Delta.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study on the Measurement and Influences of Energy Green Efficiency: Based on Panel Data from 30 Provinces in China

Lu,

Li,

Lee

2023

Sustainability

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China’s rapid economic growth has inevitably led to serious resource depletion, environmental degradation, and a decline in social welfare. As such, establishing total-factor energy green efficiency (TFEGE) and exploring its factors are of paramount importance to bolster comprehensive energy efficiency and foster sustainable development. In this research, we deployed the spatial lag model (SLM) and data envelopment analysis (DEA), using energy, capital and labor as input indicators, GDP and social dimension metrics as desirable outputs, and “three wastes” as undesirable outputs, to assess the TFEGE across 30 provinces in China from 2001 to 2020. Employing the exploratory spatial data analysis (ESDA) method, we analyzed the spatial autocorrelation of TFEGE at national and provincial levels. Simultaneously, we examined the influencing factors of TFEGE using a spatial econometric model. Our study reveals that, throughout the examined period, the TFEGE in China has generally shown a steady decline. The TFEGE dropped from 0.630 to 0.553. The TFEGE of all regions in China also showed a downward trend, but the rate of decrease varied significantly across different regions. Among them, the TFEGE of the eastern region fluctuated between 0.820 and 0.778. The TFEGE of the northeast region decreased significantly from 0.791 to 0.307. The TFEGE of the western region decreased from 0.512 to 0.486. The TFEGE of the central region decreased from 0.451 to 0.424. Beijing, Guangdong, Hainan, Qinghai, and Ningxia showed an effective TFEGE, while for other provinces, it was ineffective. The TFEGE in all four major regions failed to achieve effectiveness. Its distribution pattern was east > west > northeast > central. The TFEGE across the 30 provinces showed positive spatial autocorrelation, indicating a strong spatial clustering trend. We found that while transportation infrastructure and technological progression exert a positive impact on TFEGE, elements such as industrial structure, energy composition, and foreign direct investment negatively influence TFEGE.

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