Measuring the efficiency of the urban integrated water service by parallel network DEA: The case of Italy

R&D investment and financing efficiency in Chinese environmental protection enterprises: perspectives of COVID-19 and supply chain financial regulation

“…Efficiency is the extent to which the output tends to the frontier under given input conditions. This study adopts the BCC model (Mozaffari et al 2020) to calculate financing efficiency from the perspective of input and output under the condition that the scale of returns of EPEs are variable, by referring to the data envelopment analysis method used by international scholars to measure the efficiency of financial companies (Kok and Munir 2015), the water supply industry (Storto 2020), and in management (Amara, Rhaiem, and Halilem 2020). Since the BCC model requires positive input and output indicators to measure efficiency (but the growth rates of main business income and the return on assets selected in this study are negative), Formula (3) is used for normalisation:…”

Technical and Scale Efficiency of the Brazilian Municipalities’ Water and Sanitation Services: A Two-Stage Data Envelopment Analysis

“…In Brazil, with only 4% of WSSs being privately operated, the influence of private entities is rather irrelevant, despite contradicting results with respect to the efficiency of privately managed utilities when compared to that of publicly managed ones: Carvalho and Sampaio [8] revealed that private WSS operators were more efficient than their public counterparts, but Seroa da Motta and Moreira [9], da Silva e Souza et al [10], and Barbosa et al [11] found no evidence of such a reality. In fact, Cetrulo et al [12] and Marques and Simões [13] had already found evidence of private WSS operators outperforming public WSS operators in Portugal, while lo Storto [14] and Maziotis et al [15] proved that the juridical nature of ownership does not have a statistically significant impact on WSS efficiency in Italy and Chile, respectively. Furthermore, as the fifth largest country in the world, it is important to understand the impact of regional contrasts on the efficiency of WSSs.…”

“…In fact, in recent decades, benchmarking studies in WSSs have been a popular trend in the literature, with most of them using Data Envelopment Analysis (DEA) as an efficiency measurement tool [22]. Such works include, e.g., De Witte and Marques [22] in designing performance incentives in the drinking water sector internationally, Carvalho et al [18] in identifying the most efficient clusters of Brazilian water companies, Pinto et al [23] in assessing the influence of the operational environment on the performance of Portuguese water utilities, Molinos-Senante and Maziotis [24] in understanding the influence of exogenous and quality of service variables on the performance of water companies in England and Wales, Walker et al [25] in studying the economic and environmental efficiency of water companies in the United Kingdom and the Republic of Ireland, Cetrulo et al [26] in analysing the performance of Brazilian water utilities, Henriques et al [20] in benchmarking the quality of service of wastewater operators in Portugal, lo Storto [14] in measuring the efficiency of urban integrated water services in Italy, Maziotis et al [15] in understanding the impact of external costs of unplanned supply interruptions on the efficiency of Chilean water companies, Molinos-Senante et al [27] in evaluating trends in the performance of Chilean water companies, Mocholi-Arce et al [28] in assessing the performance of English and Welsh water companies, Sala-Garrido et al [29] in proposing a composite indicator to assess the quality of service of Chilean water companies, and Salazar-Adams [30] in estimating the efficiency of post-reform water utilities in Mexico. A useful bibliometric analysis on the last twenty years of water utility benchmarking can be found in the work of Goh and See [31].…”

Applying the Super-EBM model and spatial Durbin model to examining total-factor ecological efficiency from a multi-dimensional perspective: evidence from China

“…Second, to ensure that the measurement results are scientific and accurate,some scholars are trying to find a continuous improvement and appropriate EE evaluation method.At this stage, the mainstream evaluation methods of EE mainly include data envelopment analysis (DEA) (Storto 2016;Wang et al2019;Mavi et al2019;Storto 2020),stochastic frontier analysis (Moutinho et al 2020),emergy analysis (Li et al2011;Merlinab and Boileaua 2017),material flow analysis (Wang et al2016;,ecological footprint (Yang andYang 2019),and lifecycle assessment (Onat et al 2019;Alizadeh et al 2020).In these evaluation methods,DEA as a non-parametric calculation method that can simultaneously consider multiple outputs and multiple inputs,has been widely accepted by academia.The advantage of the DEA method is that it can avoid the influence of subjective factors on the weight during the measurement, so that the evaluation result of EE is more accurate.Because EE evaluation must consider not only desirable output but also undesirable output (e.g., environmental pollutants). Tone (2001) proposed slack-based measure model (SBM) model,and the model can simultaneously consider undesired output and slack variables,and can efficiently avoid redundancy and shortage problems.But the SBM model still exists the problem of potentially loses proportionality with the original inputs or outputs, thence Tone and Tsutsui (2010) proposed the epsilon-based measure model (EBM) and overcome those defects by a hybrid distance function.…”