An Integrated Measurement of the Efficiency of China’s Industrial Circular Economy and Associated Influencing Factors

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.

The Efficiency of Circular Economies: A Comparison of Visegrád Group Countries

Efficiency of circular economies is one of the most important areas of the improvement of economic growth in a circular way, that is, improving worldwide GDP. The issue of circular economies, namely their efficiency, is a current topic of evidence of many literary sources in the literature. This issue is solved in the conditions of the Czech Republic, Poland, Hungary and Slovakia. The goal of the study is to compare the circular efficiency within the Visegrád Group and efficiency of Visegrád Group countries to the European Union 28 average. Data envelopment analysis slack-based models are implemented to evaluate the output efficiencies of the selected subjects. Truncated regression is used to measure the impact of selected indicators on circular efficiency. The Visegrád Group countries are not among the most advanced in terms of recycling and the use of the circular economy, which was confirmed by this research. However, developments suggest significant improvements. The significance of this research lies in several benefits. One of the benefits is the perception of regional differences and the setting of EU cluster policies at the regional level. The idea of changing inputs is very significant since the outputs are oriented to the recycling rates of materials and waste. This research has shown that a higher level of GDP does not necessarily mean a higher level of efficiency of the circular economy.