Common weights in dynamic network DEA with goal programming approach for performance assessment of insurance companies in Iran

Impact of oil price volatility and economic policy uncertainty on business investment - Insights from the energy sector

This research employs a worldwide sample of 4017 energy sector companies from 1996 to 2022 to examine the effects of economic policy uncertainty (EPU) and oil price uncertainty (OPU) on corporate investment in oil/energy sector and this study analyze how market instability and international economic disasters shape the connection between OPU and business assets. GLM regression with firms-years fixed effects and firm-based clustering indicate that both OPU and EPU had a detrimental influence on corporate investment in energy sector. Generalized linear models provide a universal method for addressing various response modeling issues. It is also revealed that oil-producing nations experience OPU and EPU's negative effects more severely than oil-consuming nations. This paper also demonstrates that the link between corporate investment, OPU and EPU is influenced by nations that produce oil, market volatility, and global financial crises. Strong evidence was found supporting the notion that OPU and EPU had a statistically significant detrimental impact on business assets. The findings of the paper are consistent under a variety of robustness tests and show that the association between OPU and EPU and business assets still holds. The results have significant bearing on the asset strategies that company managers and governments should adopt in light of the volatility of oil prices and EPU and this study provide valuable insights for policymakers who are focused on achieving energy transition, enhancing energy security, and meeting environmental goals such as reducing greenhouse gas emissions.

An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU

One method that has been proposed for the measurement of sustainability is Data Envelopment Analysis (DEA). Despite its advantages, the method has limitations: First, the efficiency of Decision-Making Units is calculated with weights that are favorable to themselves, which might be unrealistic, and second, it cannot account for different perceptions of sustainability; since there is not an established and unified definition, each analyst can use different data and variations that produce different results. The purpose of the current paper is twofold: (a) to propose an alternative, multi-dimensional DEA model that handles weight flexibility using a different metric (an alternative optimization criterion) and (b) the inclusion of a computational stage that attempts to incorporate different perceptions in the measurement of sustainability and integrates machine learning to explore country sustainability composite indices under different perceptions and assumptions. This approach offers insights in areas such as feature selection and increases the trust in the results by exploiting an inclusive approach to the calculations. The method is used to calculate the sustainability of the 28 EU countries.

A review of the literature on DEA models under common set of weights

Purpose

Data envelopment analysis (DEA) is a mathematical method for the evaluation of the relative efficiency of a set of alternatives, which produces multiple outputs by consuming multiple inputs. Each unit is evaluated on the basis of the weighted output over the weighted input ratio with a free selection of weights and is allowed to select its own weighting scheme for both inputs and outputs so that the individual evaluation is optimized. However, several situations can be found in which the variability between weighting profiles is unsuitable. In those cases, it seems more appropriate to consider a common vector of weights. The purpose of this paper is to include a systematic revision of the existing literature regarding the procedures to determine a common set of weights (CSW) in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure. The discussion and findings of this paper provide insights into future research on the topic.

Design/methodology/approach

This paper includes a systematic revision of the existing literature about the procedures to determine a CSW in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure.

Findings

The discussion and findings of the literature review might insights into future research on the topic.

Originality/value

This papers revise the state of the art on the topic of models with CSW in DEA methodology and propose a systematic classification of the contributions with respect to several criteria. The paper would be useful for both theoretical and practical future research on the topic.

Energy and environmental efficiency of OECD countries in the context of the circular economy: Common weight analysis for malmquist productivity index

Circular economy (CE) aims at sustainable development (SD) by focusing more on renewable sources of energy and precise management of waste to i) guarantee the secure access to resources, ii) combat climate change and global warming. Environmental issues arising from energy use and lack of policies to monitor them challenge sustainable development. Circular economy emphasizes the economic development with the least amount of undesirable environmental impacts. To evaluate the environmental performance of decision-making units (DMUs) with data envelopment analysis (DEA), this paper develops a common set of weights (CSW) model using the ideal point method. Therefore, energy and environmental efficiency of the organization for economic cooperation and development (OECD) countries is analyzed using the Malmquist productivity index (MPI) during 2012-2015. Although Switzerland has the highest energy and environmental efficiency during 2012-2014, findings indicate that Ireland and the USA have continuously improved their energy and environmental efficiency.