Eco-efficiency Evaluation Research Articles

How Eco-Efficiency Is the Forestry Ecological Restoration Program? The Case of the Sloping Land Conversion Program in the Loess Plateau, China

The Forestry Ecological Restoration Program (FERP) aims to restore the world’s degraded forest landscapes to restore biodiversity and mitigate climate change. Scientific evaluation of eco-efficiency of forestry restoration programs (EEoFERPs) is the basis for developing and implementing inclusive and sustainable development policy measures. We take the world’s largest FERP—China’s Sloping Land Conversion Program (SLCP)—as an example. Using 314 county-level panel data in the Loess Plateau, the core area for the implementation of the SLCP, during 2002–2015, this study aims to evaluate the eco-efficiency of the Sloping Land Conversion Program (EEoSLCP) based on a DEA model and to measure the eco-efficiency dynamic changes through a Malmquist index model. The results show that: (1) The overall EEoSLCP of the Loess Plateau is at a low level, with an average efficiency of only 0.357 from 2002 to 2015. EEoSLCP is on an overall upward trend, mainly influenced by technical efficiency. (2) There are significant spatial differences in EEoSLCP among counties on the Loess Plateau, with an overall decreasing distribution trend from south to north and from southeast to northwest. (3) The Malmquist index of the EEoSLCP grew at an average annual rate of 17.7%, with technical efficiency changes being the most important factor driving its growth. Our results suggest that in the future, when implementing or designing FERPs, it is necessary not only to select the appropriate restoration plan precisely while respecting the laws of nature, but also to improve the management and technical level of FERPs accordingly.

Eco-Efficiency Assessment of Beijing-Tianjin-Hebei Urban Agglomeration Based on Emergy Analysis and Two-Layer System Dynamics

In the process of the economic development of the Beijing-Tianjin-Hebei urban agglomeration, ecological and environmental issues are still an important factor restricting high-quality development. The study of eco-efficiency is of great significance for coordinating the relationship between economy, resources and environment. This paper used a combinated method of two-layer system dynamics and emergy analysis to construct an emergy–system dynamics coupling model for eco-efficiency evaluation from the subsystems of resource flow, energy flow, currency flow and population flow of urban system, which is used to simulate and analyze the eco-efficiency of the Beijing-Tianjin-Hebei urban agglomeration. The results show that the overall eco-efficiency of the Beijing-Tianjin-Hebei urban agglomeration is not high, with an average value of 0.3786, and there is a trend of the value rising first and then falling from 2000 to 2035. The index values of emergy waste rate, contaminant emergy ratio, emergy output rate and environmental load rate after the decomposition of the eco-efficiency show that the high environmental pressure, the low re-use rate of pollutants and the low production efficiency of the system are important reasons for the low eco-efficiency in regional economic development. Finally, through scenario simulation, we propose that optimizing the economic structure, adjusting the population size and rationally arranging the fixed assets investment are conducive to improving the eco-efficiency of the Beijing-Tianjin-Hebei urban agglomeration.

Influencing factors on ecological efficiency: Based on 11 cities in Zhejiang Province, China.

Ecological efficiency (eco-efficiency) is the effectiveness of ecological resources in meeting human needs and is a good representation of the quality of a region's development. The traditional concept of improving eco-efficiency refers to maximizing economic benefits by minimizing resource costs and environmental loads. This article argues that the goal of eco-efficiency evaluation is not only to maximize economic benefits but also to achieve high-quality and coordinated development in many aspects so that more people can enjoy the fruits of development. Therefore, in the evaluation system of eco-efficiency, the input indexes take into account the consumption of human, energy resources, and the environmental load caused by them in a region. The output indexes take into account the four dimensions of "economy, innovation, social harmony, and openness." This study first establishes the nonexpected output superefficiency slacks-based measure model under the assumption of variable returns to scale to measure eco-efficiency in 11 cities of Zhejiang Province, China. Second, the spatial and temporal trends of eco-efficiency are studied with the help of the Malmquist index model. Moreover, regression analysis was conducted using the panel Tobit method to discuss the influencing factors of eco-efficiency. Several key results were obtained in this study: (1) The overall eco-efficiency in Zhejiang Province is rising steadily, but there are serious regional imbalances. (2) The improvement of eco-efficiency mainly relied on the scale efficiency from 2008 to 2013, but on pure technical efficiency from 2013 to 2018. (3) The share of tertiary industry, the number of scientific researchers, and the foreign trade dependence positively affect the improvement of eco-efficiency, but highway transportation mileage has a negative impact on the improvement of eco-efficiency. Integr Environ Assess Manag 2023;19:139-151. © 2022 SETAC.

Coupling Material Flow Analysis and Network DEA for the evaluation of eco-efficiency and circularity on dairy farms

Society is facing different challenges such as resource scarcity, climate change, and food security. Thus, proposing methodologies to assess the sustainability of promoted circular systems is essential to balance economic benefits and the preservation of nature. The eco-efficiency concept seeks to achieve sustainable management and production, creating more value and reducing resource consumption and environmental impacts. This paper proposes a new method for eco-efficiency and circularity assessment through a combined input-output oriented approach of Material Flow Analysis (MFA) and network Data Envelopment Analysis (DEA). Overall, the definition of boundaries and the quantification of inputs and outputs of the system are carried out, and then, the DEA assessment establishes eco-efficiency levels and targets for inefficient systems. The composite method was applied to evaluate the eco-efficiency and circularity of 50 dairy farms located in Galicia, Spain. To do this, a two-stage network structure is considered, evaluating the stages of feed crop (first stage) and milk farming (second stage), where cattle manure is valorised as organic fertilizer to feed crop farming, closing the loop. The results indicate that the first stage presents the lowest efficiency indexes. In this sense, the main reductions should be focused on pesticides and fertilizers as improvement actions to be implemented. Moreover, on average, efficient dairy farms present a higher level of circularity than inefficient ones. In this context, this method seeks to be useful to inform decision makers about their eco-efficiency performance to achieve sustainable and efficient circular systems.

A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency

Improving technological innovation efficiency and eco-efficiency is the key to achieving a high-quality, sustainable mining industry in China. In order to explore the internal process of technological innovation to promote economic and environmental benefits and to clarify the optimization path to improve the overall efficiency of the mining industry. A two-stage DEA model is used to construct indicators to measure Chinese 30 provinces mining technological innovation efficiency (MTIE), mining eco-efficiency (MEE), and mining comprehensive efficiency (MCE). Because of the lag in innovation, the study period was divided into nine periods from 2008 to 2018 into three-year time slices, and the dynamic evolution of time and space was analyzed. It was found that: (i) MCE and MTIE maintained the same changing trends, with the MCE being generally higher in areas with higher levels of mining economic development; (ii) the MTIE and MEE in the western area were higher than in the other regions, of which Qinghai was the only province that had efficiencies of more than 1 in both stages; (iii) most provinces belonged to type II regions with high MEE-low MTIE or type III regions with low MEE-low MTIE. The scientific achievements output in the first stage was generally insufficient, and the inputs and outputs in the second stage were redundant. There are three paths to optimize efficiency: unilateral optimization, gradual optimization, and jumping optimization. Finally, based on the above findings, policy recommendations are given from three aspects; reducing excess capacity, increasing investment, and building a platform, all of which can provide optimized plans and references for realizing the high-quality development of the Chinese mining industry.

Dual-level eco-efficiency analysis in sustainable management: An empirical study on textile manufacturers and supply chains in China

Eco-efficiency analysis at multi-levels can address the disconnection between its macro-level necessity and micro-level contribution. This cross-level analysis helps policymaking on systematically improving the sustainability of industry. Therefore, in this study, the dual-level eco-efficiency evaluation combing with interactive econometric analysis was applied for contributing a more holistic view of sustainable management in the heavy pollution industry. The empirical study was based on 12 international clothing brands and retailers' textile supply chains and their 202 major upstream manufacturers in China from 2015 to 2019. The dual-level evaluation uncovered a significant improvement in eco-efficiency both at the textile manufacturer and supply chain levels during the five years. The manufacturers' average eco-efficiency increased by 54%. The dyeing and finishing companies' efficiency of emissions to air, energy use, GHG emissions, and water use averagely increased by 165%, 39%, 28%, and 19%, respectively; discharges to water did not change significantly. Meanwhile, the interactive econometric analysis revealed the effects of green management certification, technological innovation, and the number of buyers/suppliers on dual-level eco-efficiency. The influencing mechanism of the same factor on eco-efficiency was demonstrated heterogeneous at manufacturer and supply chain levels. Adopting green management certification was an efficient facilitator to promote textile manufacturers' eco-efficiency, but not sufficient to advance supply chain's ecological performance until 2018. Conversely, inducing technological innovation can promote industry sustainability benefitting from knowledge search and absorption at the supply chain level. Depending on the multi-level lens, this study underlines that the same factor may have heterogeneous impacts at different levels and provides a drawable approach that can support decision-making on improving overall eco-efficiency for stakeholders such as companies, brands and retailers, and the government.

Evaluation of the Ecological Benefits of Recycling Multiple Metals from Lithium Battery Saggars Based on Emergy Analysis

With the rapid development of China’s new energy industry, the use of lithium-ion batteries has increased sharply, and the demand for battery cathode metals such as nickel, cobalt, and manganese has also increased rapidly. Scrapped ceramic saggars that are used to produce the cathode materials of lithium-ion batteries contain large amounts of nickel, cobalt, and manganese compounds; thus, recycling these saggars has high economic value and ecological significance. In this paper, the emergy method is used to analyze the ecological benefits of the typical Ni–Co-containing saggar recycling process in China. This paper constructs an ecoefficiency evaluation index for industrial systems based on emergy analysis to analyze the recycling of nickel and cobalt saggars. The ecological benefits are analyzed, and the following conclusions are drawn. (1) The Ni–Co-containing saggar recycling production line has good economic and ecological benefits. (2) The process has room for improvement in the energy use efficiency and clean energy use of the crystallization process and the efficiency of chemical use in the cascade separation and purification process. This study also establishes a set of emergy analysis methods and indicator system for the evaluation of the ecological benefit of the recycling industry, which can provide a reference for the evaluation of the eco-economic benefit of similar recycling industry processes.

A New Method for the Dynamic Comprehensive Evaluation of Regional Eco-efficiency: Evidence from Jiangsu Province

Regional eco-efficiency is an important indicator for observing the harmonious coexistence of man and nature, which also provides a quantitative idea for the observation of the effect of China’s Ecological Civilization Construction. Among the various studies of regional eco-efficiency evaluation, problems of interacting indicators and dynamic comparisons are still waiting to be resolved. Based on this situation, this paper proposes a new evaluation approach, integrating bi-capacity, VIKOR method and time series entropy technology, to explore the dynamic comprehensive evaluation of regional eco-efficiency. Eco-efficiency performances of 13 cities in Jiangsu Province during 2014 to 2018 are also be calculated by this new approach in case study.

Eco-efficiency evaluation model: a case study of the Yangtze River Economic Belt.

After the concept of ecological efficiency (eco-efficiency) was put forward and constantly supplemented, it generally refers to the maximization of economic benefits with minimum energy consumption and environmental damage. In a new eco-efficiency model proposed by this paper, the input indexes take into account the consumption of capital, human, resources and energy, and the environmental load caused by them. The output indexes take into account GDP, income, and tax revenue. An optimal weighted cross-evaluation efficiency (OWCE) model based on data standardization is proposed, by improving the traditional data envelopment models of CCR and BCC. The OWCE model not only objectively weights but also unifies the comparison scale, and facilitates the establishment of the super-efficiency decomposition model, which is conducive to further exploring the reasons for the difference of eco-efficiency in various regions. Empirically, the eco-efficiencies of 11 provinces (municipalities) along the Yangtze River Economic Belt (YREB) were analyzed based on the data from 2008 to 2019. The results show that there has been a serious imbalance in the 11 provinces, showing a trend of high in the east and low in the west, although the eco-efficiency has been improving continuously in the past 10years. Shanghai, Zhejiang, and Jiangsu, which are located in the traditional Yangtze River lower delta region, are the top in terms of eco-efficiency, among which Shanghai ranks the first place with absolute advantage, and also is far ahead in sub-efficiencies of basic input, energy consumption, capital and human input, and environmental cost. Geographical location, especially whether it is close to the ocean, and the length of river flow have a certain positive impact on eco-efficiency. Through in-depth analysis, high-energy consumption, high pollution, and low economic output are the main reasons for low eco-efficiency in the middle and upper reaches of the Yangtze River.

Evaluation and Dynamic Evolution of Eco-Efficiency of Resource-Based Cities—A Case Study of Typical Resource-Based Cities in China

Evaluation and dynamic evolution of eco-efficiency of resource-based cities is of great significance for ecological restoration, industrial transformation and sustainable development of resource-based cities. Based on the data of 40 resource-based cities from 2012 to 2017, we first study eco-efficiency of resource-based cities from both static and dynamic aspects by super-efficiency DEA and the Malmquist index. Additionally, then, we analyze its dynamic evolution trend by Kernel density estimation. The main results are as follows: First, the eco-efficiency shows a steady growth trend; growing cities are the highest, followed by regenerative cities and mature cities, declining cities are the lowest. Second, technological progress has the greatest contribution to growth of eco-efficiency, and scale efficiency restricts its growth. Third, efficiency distribution of resource-based cities with different life cycles show different characteristics of evolution, and the regional gap is narrowing gradually. Finally, management implications are put forward based on the empirical conclusions.

Eco-efficiency evaluation in wastewater treatment plants considering greenhouse gas emissions through the data envelopment analysis-tolerance model

The eco-efficiency evaluation in wastewater treatment plants (WWTPs) is used to know and improve the environmental and economic efficiency of these processes, systems, products, and services. The eco-efficiency evaluations in WWTP contemplate the inputs to be minimized, the desirable results to be maximized, and the undesired results to be minimized. Data envelopment analysis (DEA) is a widely used method to evaluate the eco-efficiency of WWTPs; integrating several approaches in a single index, traditional DEA models do not take into account the uncertainty in the data. This study evaluates the eco-efficiency of a sample of Catalan WWTPs, considering the uncertainty of the data (DEA tolerance model), and it is for the first time that together with CO2, other greenhouse gas (GHG) such as CH4 and N2O are considered as part of the process outputs. GHG emissions were quantified using methods reported in the literature. Seven hundred twenty-nine eco-efficiency scores were estimated for each WWTP instead of a single score like conventional DEA models, analyzing optimistic and pessimistic scenarios. The WWTPs were classified according to the estimated eco-efficiency scores, accounting for the uncertainty in each of the scenarios, and demonstrating the changes in the performance of the WWTPs in the different scenarios. Only two WWTPs were eco-efficient in all the scenarios evaluated. This approach provides essential information to improve efficiency and innovation in the wastewater sector.

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

Ecoeficiencia de los modelos de producción agrícola de maíz duro y su influencia al cambio climático en Shushufindi Ecuador

La ecoeficiencia se define como la cantidad o valor económico de un producto por su influencia ambiental e identifica en términos socioeconómicos y ambientales la sostenibilidad de un sistema. El objetivo de esta investigación fue evaluar la ecoeficiencia de tres modelos de producción agrícola de maíz duro presentes en el cantón Shushufindi, Ecuador. Los modelos identificados en el estudio fueron el modelo convencional (MC), semi-convencional (MS) y tradicional (MT). La influencia ambiental se determinó mediante dos indicadores de impacto contemplados en el Análisis de Ciclo de Vida, como son las emisiones de GEIs, según las directrices del IPCC (2006a) y la huella hídrica (HH), a través de los componentes dados por Hoekstra y col., (2011). Para la evaluación de la ecoeficiencia se consideraron los lineamientos de Ribal y col. (2009), aplicando un modelo de optimización por programación no lineal (GLP). Las emisiones de GEIs del MC fueron de 2926,92 kgCO2eq ha

Data driven eco-efficiency evaluation and optimization in industrial production

To improve the ecological efficiency (eco-efficiency) of industrial production and promote its sustainable development, we present a data-driven method for evaluating and optimizing the eco-efficiency of industrial production system. The data of industrial production system are collected and processed in a unified dimension from the perspective of emergy, then the eco-efficiency evaluation model of industrial production system is constructed to realize the quantitative evaluation, the correlation between various factors and eco-efficiency is analysed, and a date driven eco-efficiency optimization decision is built to improve eco-efficiency and production benefits in industrial production. In the application to an example in a manufacturing enterprise, the method identifies opportunity for reducing resource consumption by 8.3% and waste discharge by 6.7%, enhancing the eco-efficiency by 13.8% and production benefit by 8.1%. This paper provides theoretical and methodological support for the evaluation and optimization of eco-efficiency of industrial production system, and also provides theoretical basis for the sustainable development of industrial production.

A two-stage eco-efficiency evaluation of China’s industrial sectors: A dynamic network data envelopment analysis (DNDEA) approach

Past studies used the network data envelopment analysis approach from a two-stage perspective (the production stage and waste/pollutant treatment stage) to evaluate industrial eco-efficiency and assess internal ineffective sources. However, these studies did not consider the dynamic features, i.e., capital investments in the current period are used to establish fixed assets for two stages, which can also be used in the later period, thus affecting its two-stage eco-efficiency. A dynamic network data envelopment analysis approach is adopted to fill this research gap and evaluate Chinese industrial two-stage eco-efficiency during 2010–2015, reflecting the practical production context and achieving comparable efficiencies among different periods. The major results of our study are: Firstly, not considering the dynamic features leads to an underestimation of the two-stage eco-efficiency. Secondly, the production efficiency was higher than the treatment efficiency, and the two-stage eco-efficiency was between the two during the study period. Furthermore, eastern China performed best with respect to the two-stage eco-efficiency, including production and treatment efficiencies, followed by the central and the western areas. Finally, a fifth of the 30 regions had a relatively low value of the input and output efficiencies of both production and treatment stages based on the clustering analysis.

Eco-Efficiency of Milk Production in Poland Using the Life Cycle Assessment Methodologies

Purpose: The main objective of the study was to perform an environmental and economic evaluation of milk production in the main production types pursued by Polish farms. The second objective was to analyze the eco-efficiency of milk production. Design/Methodology/Approach: The study was conducted in a group of 24 farms, among which 15 farms focused on milk production and 9 farms conducted mixed livestock production (milk production and pig fattening). The data for the study concerned the period 2017-2018. Cattle was raised in a closed breeding cycle. Fat stocks was supplied by calves born on the farms. Life cycle assessment (LCA) at the stage from cradle-to-farm gate and LCC were used for environmental impact assessment. The functional unit was 1 kg raw milk corrected for fat and protein (FPCM). The studied environmental profile was applied to five categories of impacts: climate change (GWP100), acidification (AP), eutrophication (EP), abiotic resource depletion potential for fossil fuels (ADP fuel), abiotic resource depletion potential for minerals (ADP min) and photochemical ozone creation potential (POCP). Findings: A farming type specializing in milk production showed a more favourable environmental profile compared to the mixed livestock type. The group of processes responsible for generating direct emissions in cattle raising (enteric fermentation and manure management) had the greatest impact on GWP100 and AP. Imported feed and home grown feed contributed much to ADP fuel, ADP min and EP. The higher eco-efficiency of milk production was recorded for the milk farming type. In the type of mixed livestock farming, both the reduction of total environmental impact and costs should be the primary factors in improving the eco-efficiency of milk production. Practical Implications: Attention should be paid to the practical importance of eco-efficiency analysis, which, so far, has been an insufficiently used measurement tool for achieving targets in sustainable milk production. Originality/value: In the article we propose the evaluation of eco-efficiency of milk production by considering both environmental and economic impacts from the life cycle perspective.

Spatial-Temporal Changes and Driving Factors of Land-Use Eco-Efficiency Incorporating Ecosystem Services in China

With rapid urbanization in China, the dramatic land-use changes are one of the most prominent features that have substantially affected the land ecosystems, thus seriously threatening sustainable development. However, current studies have focused more on evaluating the economic efficiency of land-use, while the loss and degradation of ecosystem services are barely considered. To address these issues, this study first proposed a land use-based input–output index system, incorporating the impact on ecosystem services value (ESV), and then by taking 30 provinces in China as a case study. We further employed the super-efficiency slacks-based model (Super-SBM) and the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model to explore the spatial–temporal changes and driving factors of the evaluated land-use eco-efficiency. We found that the evaluated ESV was 28.09 trillion yuan (at the price of 2000) in 2015, and that the total ESV experienced an inverted U-shaped trend during 2000–2015.The average land-use eco-efficiency exhibited a downward trend from 0.87 in 2000 to 0.68 in 2015 with distinct regional differences by taking into account the ESV. Our results revealed that northeastern region had the highest efficiency, followed by the eastern, western, and central region of China. Finally, we identified a U-shaped relationship between the eco-efficiency and land urbanization, and found that technological innovation made great contributions to the improvement of the eco-efficiency. These findings highlight the importance of the ESV in the evaluation of land-use eco-efficiency. Future land development and management should pay additional attention to the land ecosystems, especially the continuous supply of human well-being related ecosystem services.

Evaluation of the eco-efficiency of waste treatment facilities in Korea

As waste treatment facilities become old, their operation and maintenance costs could increase, which translates to higher treatment costs and environmental burden. This study applies the concept of eco-efficiency to evaluate waste treatment facilities considering the various conditions of waste treatment operations based on the case of Korea. In Korea, waste treatment methods were largely divided into five categories, and facilities for each treatment method were chosen based on their size, location, and waste properties for data collection. The method for evaluating the facilities’ eco-efficiencies were developed using the concepts of environmental and economic performances. The results of the eco-efficiency evaluation showed that landfills were most environmentally efficient, followed by the organic waste biogasification facilities, food waste recycling facilities, incineration facilities, and, lastly, combustible waste-to-fuel facilities. The results of the eco-efficiency evaluation were further reviewed in terms of three factors which assess the status of existing facilities as well as to identify the environmental and economic indicators which significantly affect the eco-efficiency of waste treatment facilities by treatment method and propose strategic policies for promoting better waste treatment operations.

Evaluation and improvement of agricultural eco-efficiency in China

Evaluation and improvement of agricultural eco-efficiency in China

Evaluating the Eco-Efficiency of Wastewater Treatment Plants: Comparison of Optimistic and Pessimistic Approaches

The assessment of wastewater treatment plant (WWTP) performance has gained the interest of water utilities and water regulators. Eco-efficiency has been identified as a powerful indicator, as it integrates economic and environmental variables into a single index. Most previous studies have employed traditional data envelopment analysis (DEA) for the evaluation of WWTP eco-efficiency. However, DEA allows the selection of input and output weights for individual WWTPs for the calculation of eco-efficiency scores. To overcome this limitation, we employed the double-frontier and common set of weights methods to evaluate the eco-efficiency of a sample of 30 WWTPs in Spain. The WWTPs were ranked based on eco-efficiency scores derived under several scenarios including best- and worst-case scenarios; this approach to performance assessment is reliable and robust. Twenty-six of the 30 WWTPs were not classified as eco-efficient, even under the most favorable scenario, indicating that these facilities have substantial room for the reduction of costs and greenhouse gas emissions. The ranking of WWTPs varied according to the scenario used for evaluation, which has notable consequences when eco-efficiency scores are used for regulatory purposes. The findings of this study are relevant for water regulators and water utilities, as they demonstrate the importance of weight allocation for eco-efficiency score estimation.