Application of the data envelopment analysis technique to measure the environmental efficiency of the 27 countries of the European Union during the period 2012-2020.

In the last decade, we have experienced a changing geopolitical context that has caused changes in the energy context. In addition, human activity contributes to global warming or sea level rising, i.e., climate change. A set of action policies have been implemented to continue fighting against this environmental situation (such as the Paris Agreement, the COP27, or the European Green Deal for 2030); therefore, it is necessary to determine whether we are on the right track. It is compulsory to develop predictive models that accurately analyze the current status and the already path undertaken. To this end, this article analyzes the environmental efficiency of the 27 countries of the European Union (excluding the UK) using the so-called data envelopment analysis (DEA). In particular, economic (GDP and GDP per capita), environmental (CO2 and CH4 emissions), electricity production data, the volume of vehicles, and the industrial production rate of the different countries were collected to calculate environmental efficiency. Once these data were collected, the environmental efficiency was calculated using two methods based on the DEA. The results show that out of the 27 countries, only 12 have a relatively high environmental efficiency, although it could be improved, implementing a set of corrections. However, other countries have a low eco-efficiency performance and they must improve in the coming years. We can highlight that rich countries are closer to achieving high environmental efficiency than less developed countries. Graphical Abstract: Political map of the European Union indicating the average eco-efficiency with colors of the 27 countries of the DEA method. Supplementary Information: The online version contains supplementary material available at 10.1007/s10098-023-02553-9.

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach.

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.

Will the Balearics and the Canary Islands meet the European Union targets for municipal waste? A comparative study from 2000 to 2035.

Recycling and selective collection rates in touristic islands are low, and they have to be increased to move them towards a more circular economy. This paper attempts to determine whether it will be possible to achieve the European Union's (EU) legally binding targets regarding the different fractions of municipal solid waste (MSW) in the two Spanish regions with the highest per capita MSW generation rates. After reviewing the MSW generation literature in touristic islands, the driving forces affecting MSW generation in the Balearics and Canary archipelagos from 2000 to 2018 are identified. Their evolution until 2035 is forecasted, using a methodology which combines econometric models, System Dynamics and Scenario Analysis. Results reveal that the biggest MSW generator in both archipelagos is the touristic population, followed by the resident population in the Balearics, and by the per capita income in the Canaries. Then, by using simulations to create different scenarios, the annual ratios to be reached by 2035 are estimated for each MSW fraction in both archipelagos. The current low recycling rates (15.81% in the Balearics and 11.18% in the Canaries) would have to be increased by an annual average of 2.89% and 3.16% respectively, to achieve the compulsory 65% target established by the MSW EU Directive for 2035. Therefore, both territories are currently far from the right pathway to meet the EU goals. However, the European Green Deal has to promote circular economy projects that help achieve each type of waste targets.

Modelling the driving forces of the municipal solid waste generation in touristic islands. A case study of the Balearic Islands (2000-2030).

The improvement of municipal solid waste (MSW) management in touristic islands has not been sufficiently studied, and by using the Spanish Balearics as a case study, a hybrid methodology has been developed which: (i) identifies the three most influential variables (driving forces) of the MSW generation system through an econometric model based on official historical data for a given period (2000-2014), (ii) develops a System Dynamics (SD) model of the evolution of MSW generation in this archipelago based on the same data and time period, and (iii) forecasts the performance of the MSW management system for a future period (2015-2030). Six different scenarios are considered with varying assumptions, objectives and management policies, while applying Scenario Analysis to the SD model developed. The results show that by maintaining the current policies ("business as usual" BAU scenario) it will be impossible to meet the goals set by the Regional and the Spanish National Plans based on the European MSW Directive. However, by implementing the improvements proposed in the simulated alternative scenarios, the model predicts that by 2030, generation of MSW will have increased by 15% compared to 2014, mainly due to the effect of the Tourist Population which is 37.5% higher than the Resident Population. In addition, for the most optimistic scenario, the amount of MSW sent to landfills would decrease by 40%, and selective collection would increase by 30%, compared to 2014.

Comparison of the carbon footprint of different patient diets in a Spanish hospital.

OBJECTIVES: Mitigating climate change requires management strategies to reduce greenhouse gas emissions in any sector, including the health system. Carbon footprint calculations should play a key role in quantifying and communicating these emissions. Food is among the categories with low accuracy because the carbon footprint for food is still under development. We aimed to quantify the carbon footprint of different diets. METHODS: Average carbon footprint for a normal diet was based on detailed composition data in Juan Ramón Jiménez Hospital (Huelva, Spain). In addition, the carbon footprints of 17 other therapeutic diets were estimated using a streamlined variation of each diet published by Benidorm Clinical Hospital (Spain). RESULTS: The carbon footprint was calculated for 18 hospital diets for a variety of patients. The reference menu corresponds to the normal diet provided to patients who do not have special dietary requirements. This menu has a low carbon footprint of 5.083 CO2 eq/day. CONCLUSIONS: Hospital diets contribute to the carbon footprint of a hospital. The type of diet has a significant impact on the greenhouse gas emissions. A Mediterranean diet is associated with lower environmental impact than diets with more meat, in particular red meat.