Mehdi Toloo

This paper addresses a critical gap in circular economy (CE) research by introducing a novel methodological framework that integrates Network Data Envelopment Analysis (NDEA) with a chance-constrained programming. The proposed approach captures the interrelated dynamics of economic production and waste treatment subsystems, while accounting for stochastic variables and data uncertainties to provide robust CE efficiency estimates. Using data from 26 European (EU) countries from 2013 to 2020, our results reveal that achieving CE efficiency requires a balanced focus on economic production and waste management. Although strong economic output can support circularity, waste treatment efficiency often plays a decisive role in determining overall CE performance. Moreover, we find that economic size does not necessarily translate into circular efficiency, whilst large economies may face challenges with effective waste management and resource recovery despite their economic status. The proposed approach offers policymakers and practitioners a robust empirical framework to guide CE improvements, particularly in regions where environmental practices lag behind economic achievements. Stronger incentives and regulatory measures are recommended to enhance circular activities within the EU and foster greater circular efficiency across countries.

In data envelopment analysis literature, the estimation of economies of scope involves comparing two frontiers: one for diversified firms producing two outputs together (the diversified frontier) and the other involving additive firms (the additive frontier). A firm’s scope economies can be assessed by comparing the costs of its outputs against these two frontiers. If the cost for the additive frontier exceeds or falls short of that for the diversified frontier, one can determine the presence or absence of scope economies for the diversified firm. However, constructing the additive frontier requires the assumption that the cost of joint production by a diversified firm equals the cost of separate production by specialized firms. In the absence of such an additive cost structure in empirical studies, scope economies evaluation becomes problematic. To address this, we propose an alternative measurement scheme. Using the free disposability postulate of production theory, our scheme creates two sets of virtual specialized firms from the observed diversified firms, then updates them within their respective specialized firm sets. This approach enables an evaluation of a firm's scope economies by comparing the cost of separate production against updated specialized technologies with the joint production cost against diversified technology. Finally, the scheme also allows an examination of whether a merger between any two specialized firms would be profitable.

Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations’ mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II). The Bayesian maximum entropy (BME) method generates potential stations as the input of a framework based on the transinformation entropy (TE) method to maximize the coverage and minimize the probability of selecting stations. Also, the fuzzy degree of membership and the nonlinear interval number programming (NINP) approaches are used to survey the uncertainty of the joint information. To obtain the best Pareto optimal solution of the AQMN characterization, a robust ranking technique, called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach, is utilized to select the most appropriate AQMN properties. This methodology is applied to Los Angeles, Long Beach, and Anaheim in California, USA. Results suggest using 4, 4, and 5 stations to monitor CO, NO₂, and ozone, respectively; however, implementing this recommendation reduces coverage by 3.75, 3.75, and 3 times for CO, NO₂, and ozone, respectively. On the positive side, this substantially decreases TE for CO, NO₂, and ozone concentrations by 8.25, 5.86, and 4.75 times, respectively.