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Abstract: In this paper an integrated methodological approach is presented for the evaluation of the implementation of the Best Available Techniques (BAT) in facilities operated under Integrated Prevention Pollution and Control (IPPC) Directive, based on the development and application of a set of sub-indices. An aggregated form of sub-indices that could be used as a composite IPPC facility index is proposed. A sector specific index can be derived from facilities composite indices. This approach uses environmental performance data from European Polluting Emissions Register (EPER) reports, environmental permits and BAT Reference Documents, and gives a coherent and interesting picture of the degree of BAT implementation and an indication of IPPC obligations fulfilment. A specific application is demonstrated concerning the paper manufacturing sector in Greece. It is concluded for this sector that there is a measurable discrepancy from the BAT fulfilment obligations and that the most problematic sub-indices are the “Releases to Water” and “Releases to Air”. [Copyright &y& Elsevier]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

Abstract: The aim of the present work is to estimate the carbon footprint of the Greek electricity sector and the associated damages. This is being realized by quantifying the external cost related with the climate change airborne emissions (i.e. carbon dioxide) generated during all stages of the power plants life cycle in Greece. For this purpose, the paper applies the EcoSenseLE tool, together with the basic principles of the Life Cycle Assessment (LCA) approach. The examined external cost has been calculated for seven types of power plants. Results show that hydro and wind power plants have an excellent performance; close to them is the performance of PV and biomass-fired power plants. The performance of natural gas-fired power plants is good, while the performance of oil-fired power plants is, as it is expected poor. Lignite clearly has the worst performance, which affects the average external cost of the sector, since it is the dominant energy source in electricity production in Greece. Regarding the life cycle stages, for thermal systems, the climate change external cost is primarily made of CO2 emitted at the power plant, while in hydro, wind and PV systems, it is totally associated with the plant construction stage. Moreover, the paper includes also issues regarding the evolution of the examined footprint within the next years, considering the various Greek national policies and measures that have been announced and started to be realized; the cost/benefit analysis (CBA) of these policies and measures based on this footprint; and the investigation if this CBA is being affected by the CO2 abatement cost associated with the various external cost estimation approaches. [Copyright &y& Elsevier]

Abstract Natural gas as a primary fossil fuel, will grow faster than both oil and coal for the next decades and distinguishes by its lower environmental impact in comparison to other conventional fuels. Natural gas in its liquid form (Liquified Natural Gas), grows seven times faster than pipeline gas. It is, usually, transferred using liquified natural gas carriers stored in cryogenic tanks. A crucial problem in this field is the selection and the evaluation of liquified natural gas export terminals in different countries (liquified natural gas suppliers). The paper attempts to propose a decision support model specialized for this type of problem, adopting green logistics optimization tools as prescribed in circular economy philosophy. A multicriteria analysis, using Promethee II method, is carried out, evaluating all the potential export terminals and studying the case study of Greece. The approach is based on selected criteria in the fields of economic supply, low environmental impact of transportation, satisfaction of technical specifications and the current and future availability of supply. The evaluation procedure includes the ranking of different preference scenarios. The obtained results showed that there are advantageous liquified natural gas supply options for Greece, like Egypt, Algeria, Libya and Norway, with good performances in almost all the criteria. By adopting the proposed methodology, national and commercial decision makers could take advantage to assess future consortiums. Highlights • A methodology is presented to evaluate potential future LNG supply alternatives and the case study concerned Greece. • A methodology is presented to evaluate potential future LNG supply alternatives and the case study concerned Greece. • A multicriteria analysis has been adopted covering the economic, technical and environmental fields. • The selected criteria fall into the most crucial axes for the selection of LNG supplier. • The proposed model could be used by decision makers in order to assess future consortiums. [ABSTRACT FROM AUTHOR]

Abstract The present paper aims to investigate the environmental and economic impacts of co-processing petroleum fractions with bio-based feedstocks for direct refinery integration versus the conventional drop-in integration of biofuels with compatible transportation fuels. Three different pathways of market diesel (commercial diesel fuel from a typical fuel station in Greece) production with 10% v/v biocontent (biomass feed) were explored by variant biomass utilization with fossil counter parts (diesel fuel). The first case study depicts the current market diesel production process via blending petroleum fractions with FAME (Fatty Acids Methyl Esters) biodiesel. The second case study involves the production of market diesel from blending petroleum fractions with hydrotreated residual lipids (Waste Cooking Oil, WCO), while the third case study explores the alternative approach of co-processing petroleum fractions with WCO. In all cases, the market diesel production chain was based on the hydrotreating process simulated using Aspen Plus. The results have shown that the most beneficial case, by environmental and economic viewpoint, for market diesel production with 10% v/v biocontent in the Greek market is co-processing of petroleum fractions with WCO. This approach accounts for a reduced production cost (751 €/m3) and environmentally friendly profile (243 kgCO 2 -eq/m3) compared with the other two case studies. Additionally co-processing of residual biomass with petroleum fractions utilizes existing infrastructure towards the production of sustainable fuels, rendering this pathway more attractive form a CAPEX point of view. Graphical abstract Image 1 Highlights • Three production technologies of 10% bio-content diesel are evaluated. • Hybrid diesel reduces GHG emissions by ∼24% compared to current market diesel. • Renewable fuels are economically attractive for waste oil prices below 725 €/m3. • Co-processing reduces the production cost of current market diesel by 6%. [ABSTRACT FROM AUTHOR]

The recent global diffusion of circular economy regulatory policy packages (CERPPs) raises questions over their extent, composition, and potential effectiveness. While research into circular economy (CE) regulation is growing, a dearth of analyses of the optimal design of CE policy packages presents a clear gap in the literature. This paper therefore surveys current waste management policy to identify the degree to which circular economy principles are being translated into public policy globally. Examining resource use and waste management policy in 60 countries, the paper first provides a snapshot of the global spread of CE policy packages. Secondly, the assessment framework is applied to three case studies of recent CE policy packages from Finland, Greece and South Korea. These cases fall some way short of theoretical optimality, suggesting that long-term CERPP effectiveness is questionable. [ABSTRACT FROM AUTHOR]

The challenge of reducing Food Waste (FW) is a complex social, economic and environmental problem. Fortunately, the realization of FWs huge negative impact on society has triggered –in the last five years-a positive turn of focus towards the development of a common understanding of the phenomenon, the identification of its root causes and the initiation of coordinated actions to increase citizen awareness and mitigate negative consequences of FW at all levels of the food supply chain. This paper focuses on the last downstream tier of the food supply chain, i.e. the households, which are reported in literature as being responsible for eight times the energy waste of post-harvest losses on average due to the energy consumed along the supply chain and in food preparation. This study attempts to investigate the effects of shopping habits, like promotional intensive shopping and using shopping lists and eating preferences, like cooking, eating out or ordering in, on food waste generation. In doing so, the results of a survey of 500 Greek households are presented, showing that indeed, household behaviours towards shopping and eating are important food waste determinants and should not be underestimated. Furthermore, the results of this study highlight the need for more research targeted in this area, with the objective to increase consumers' awareness and problem comprehension, enhance their commitment and create the necessary environment for triggering meaningful behavioral changes capable of creating long-term and sustainable results. [ABSTRACT FROM AUTHOR]

This paper investigates numerically the deep renovation of a multi-family building in Greece to reduce dramatically its energy demand and also to incorporate renewable energy sources, rendering it a positive one; thus in position on an annual basis to offer net electricity to the grid. The examined building has 8 apartments of 75 m2 floor area each and is located in Moschato, a suburb of Athens in Greece. The goal of the present investigation is to determine the energy savings, but also to calculate the financial and environmental benefits through a life cycle analysis. The energy simulation of the building is conducted on annual basis by using a novel and detailed dynamic software tool (INTEMA.building), which is developed in the Dymola environment using the Modelica modeling language. This tool makes possible the detailed simulation of both passive and active systems in the building environment. Furthermore, it includes the control of the energy systems and can provide accurate enough results, encompassing detailed numerical models for the systems investigated, accounting for an adjustable time step of the dynamic analysis. According to the calculations, the proposed retrofitting scenario can achieve a reduction of the heating loads by 93% and of cooling loads by 78% respectively. The electrical demand for domestic hot water can be decreased by about 79%, while the electricity demand for appliances and lighting by about 60%. In terms of specific thermal needs, the specific heating demand can be reduced from 151.5 kWh/m2 down to 10.7 kWh/m2, while the cooling specific demand from 112.6 kWh/m2 to 24.4 kWh/m2. Moreover, it is calculated that the reduction in the primary energy demand after the renovation can be up to 88%, with the building providing around 5.3 MWh of net electricity to the grid on a yearly basis through a net-metering connection. Finally, the life cycle cost analysis indicated 622 k€ savings and specific CO 2 avoidance per renovated floor area in the range of 2.64 tons CO 2 /m2. • A new tool for dynamic simulations of buildings and energy systems is presented. • A holistic renovation scenario of a multi-family Greek building is investigated. • The primary energy reduction after the renovation was found at 88%. • The renovation payback period is 3.8 years and the specific lifecycle gains 1037€/m2. • The specific CO 2 avoidance was calculated at 2.64 tons CO 2 /m2. [ABSTRACT FROM AUTHOR]

Agricultural production is a sector with high socio-economic significance and key implications on employment and nutritional security. However, the impacts of agrifood production and consumption patterns on the environment are considerable, mainly due to the demand of large inputs of resources. This paper presents a case study of olive oil production in Greece, an important agri-product especially for countries in the Mediterranean basin. Life Cycle Assessment has been used to quantify the environmental performance of olive oil production. Fourteen sub-systems of the overall olive oil production are investigated. All key parameters that are associated with the life cycle of olive oil production are studied and environmental “hotspots” are diagnosed. Cultivation of olive trees and production of olive oil are the sub-systems that are responsible for the majority of the environmental impacts and thus any effort to minimize the overall life cycle impact from olive oil production should include them. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of the present paper was to examine if Environmental Management Accounting, and especially its tool Environmental Cost Accounting, can support both the augmentation of environmental nonprofit/nongovernmental sector’s accountability, and the provision of meaningful results for policy decision making. Based on a case study at the Hunting Federation of Macedonia and Thrace, a nonprofit forestry organization responsible for the management and protection of wildlife resources in Greece, implications about the challenges of applying Environmental Management Accounting in the sector were examined. It was concluded that the existing Environmental Management Accounting tools can partly address the several dimensions of their operation and additional accounting regimes may be needed. Nevertheless, these tools can prove to be a useful starting point. [Copyright &y& Elsevier]

Abstract: In recent years a growing number of companies around the world have started reporting on issues other that those in the financial domain, in order to discharge their accountability efforts. While such non-financial accounting and reporting footprints, in terms of relevant policies and practices, are evident among all regions and such activities are becoming a universal trend, the level of uptake and diffusion varies from country to country. This paper contributes to the country-level analyses of non-financial reporting by assessing the quality and inclusiveness of triple-bottom-line reports published by companies operating in Greece. To accomplish this, a numerical scoring system was devised, based on the de facto global standard on corporate non-financial reporting, the Global Reporting Initiative guidelines. Overall findings reveal major gaps in the disclosing practices of Greek organizations and denote that there is much room for improvement in order to meet international standards and promote effective engagement with their stakeholders. [Copyright &y& Elsevier]

Abstract: Regional Higher Education Institutions (HEIs) may contribute significantly to the promotion of sustainability through several means connected with their functioning. Nevertheless, several challenges can present themselves during the planning and implementation of such initiatives. The aim of this paper is to underline these limitations through the relevant literature and highlight their main consequences on the achievement of sustainability. An empirical study, investigating the perception of students regarding the constraints to achieving sustainability in a regional university of Greece, was conducted and is discussed in relation to the university''s specific characteristics – its fragmented nature with campuses situated on five Aegean islands, its public status as well as its role in creating and dispersing knowledge in less developed areas of the country. [Copyright &y& Elsevier]

Abstract: This paper sets out to investigate the main determinants of industrial decision-making process with respect to energy efficiency investments. Based on a survey among Greek industries, in the empirical analysis probit models are employed. The results support that an energy saving campaign should regard industries as subgroups with different needs and different managerial aspects. The importance of qualified employees is also established from this survey. An energy conservation framework would be more effective if the diffusion of information with regard to industrial energy saving investments is based on investment in human capital. [Copyright &y& Elsevier]

Solar irradiation is a major contributor to renewable energy sources and its utilization is vital for designing a sustainable and green energy network. Greece is a country with high solar potential and thus the exploitation of solar irradiation in this country is an urgent need in order to reduce fossil fuel utilization. Moreover, the increase in solar energy exploitation will lead to the mitigation of environmental policy goals for cleaner energy production processes. To date, Greece exploits solar irradiation mainly with flat plate collectors for low-temperature heating applications and with photovoltaics, while there are no installations including concentrating solar systems although they are attractive technologies. The objective of the present review paper is to demonstrate and to discuss the present steps in the domain of solar concentrating systems in Greece, as well as to suggest future steps aiming to increase the solar energy contribution in this country. More specifically, this work includes studies about parabolic trough collectors, linear Fresnel reflectors, compound parabolic concentrators, solar dishes, solar tower and concentrating thermal photovoltaics. The existing situation regarding solar energy utilization in Greece is collaborated and predictions about future perspectives are provided. It is concluded that although much research interest has been devoted to installations in Greece, no real solar concentration systems are currently in operation. Moreover, the existing studies indicate that solar concentrating systems lead to sustainable and viable investments with acceptable payback period time in the range of 5–7 years. As a result, it is suggested that this is the time to proceed in the next step of the development of real solar concentrating installation in Greece. • All the solar concentrating systems for the Greek climate are included in this review. • Applications for power, refrigeration, heating and polygeneration are presented. • Up today, many theoretical studies have been conducted for Greek solar systems. • In the future, there is a need for real applications and experimental studies. • The examined cases are found to be viable for the climate conditions of Greece. [ABSTRACT FROM AUTHOR]

Solar-driven technologies for electricity production are very promising technologies for developing sustainable and viable energy units. The objective of this work is the energy and financial investigation of a solar-driven thermoelectric generator system. This system operates without concentrator; it absorbs the incident solar irradiation and it converts it partially to electricity. In this paper, a small module is investigated in details and the analysis is extended for a solar field of 100 m2. A numerical model is developed in Engineering Equation Solver for simulating the solar thermoelectric generator behavior and it is validated using experimental data from the literature. The maximum instantaneous efficiency is found close to 4.5% in the reference operating conditions, while the mean yearly efficiency for the climate conditions of Athens (Greece) is found at 3.2%. The yearly electricity yield is found to be 5414 kWh for a solar potential per area of 1695 kWh/m2. The parametric financial analysis proved that generally, the investment is viable and for a reasonable specific capital system cost of 1000 €/kW the payback period is about 4.5 years and the levelized cost of electricity is around 0.044 €/kWh. • A solar-driven thermoelectric generator is studied in energy and financial terms. • The system is investigated on a yearly basis for the location of Athens in Greece. • The yearly electrical efficiency is found at 3.2% and the electricity production 5414 kWh. • In the main scenario, the levelized cost of electricity is found at 0.0617 €/kWh. • The payback period is found to be about 4.5 years which indicates a viable investment. [ABSTRACT FROM AUTHOR]

Water chemical variables such as total nitrogen (TN) and total phosphorus (TP) are soluble and are optically inactive. Remote sensing (RS) technique is able to monitor the optical activity of water but it has limited capability to monitor the TN and TP concentrations. In order to determine the TN and TP concentrations of Geshlagh Reservoir (GR), RS was used. The other water quality variables that were utilized included chlorophyll-a, Secchi disk depth (SDD) and total suspended solids (TSS). These variables are optically active and have a high correlation coefficient with TN and TP concentrations. The five images of Landsat 8 Operational Land Imager (OLI) taken at different times were used. Artificial neural network (ANN) model and linear regression (LR) model were utilized to determine the relationship of OLI data and TP and TN concentrations in GR. Results showed that the TN and TP concentrations have a high correlation with chlorophyll-a concentration. The spectral reflectance of bands ratio (band3/band2) and the single band of bands 3 and 4 were obtained as the most suitable bands to determine the chlorophyll-a concentration in GR. Compared with LR model, the accuracy of the ANN model was higher in the testing phase of TP prediction with R = 0.81 and TN prediction with R = 0.93. It can be concluded that the Landsat 8 OLI data has a high potential to provide the data of solubility variables of the water quality for the management of reservoirs. The contribution of this paper is that the developed ANN model will improve the prediction accuracy of inactive water quality variables based on the RS data. Image 1 • Saptiotemporal patterns of TP-TN concentrations were mapped in GR. • Changes in TP-TN concentrations affected by different factors were elucidated. • Landsat 8 OLI-based, the ANN and LR estimation models of TP-TN concentrations were developed. • The ANN model results were more accurate compared with the those of LR model. [ABSTRACT FROM AUTHOR]