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Recycling of non‐renewable resources serves both in reducing the consumption of virgin supplies and the discharge of associated residuals back into the natural environment. On the other hand, recycling has been criticized because of its environmental impacts. The aim of the present paper is to identify and quantify the environmental effect of recycling of a glass bottle.

For this purpose, the life cycle assessment polygon framework is being used. This framework has been developed for evaluating the results of a life cycle inventory analysis using critical volume aggregation and polygon‐based interpretation.

Recycling strategies can, in most cases, reduce the total environmental burden of the glass container examined. However, this reduction may considerably vary in relation to each “ecological parameter” (consumption of energy, consumption of water, air emissions, waterborne waste and solid waste), depending mainly on the “recycling mix” (the percentage of recycled material used in production and the percentage of product waste that goes for recycling).

The extent to which these findings could be generalized to other materials and products could be confirmed by more applications of the framework.

This paper may help in developing recycling strategies.

A conceptual framework for the environmental evaluation of recycling, considering, not only general recycling targets, but also the particular interests or conditions that may exist, is introduced.

The purpose of the paper is to quantify the external cost of airborne pollutants (i.e. sulphur and nitrogen oxides, carbon dioxide, and particulate matters) generated during electricity production in the thermal power plants in Greece.

This paper applies the EcoSenseLE online tool to examine the external cost of air pollution generated in all thermal power stations of 50 MW or higher capacity in Greece. The external cost has been calculated for five damage categories and by fuel types. Data for 2004 has been used and projections up to 2030 have been made.

This research finds that the external cost of the natural gas power plants is €13.87 per MWh which is the lowest for the system while the cost is estimated at €43.89 per MWh for lignite plants. The external cost of air pollution from the power sector in 2004 has been estimated at €1.89 billion and is expected to increase to €2.48 billion in 2025. The marginal external cost at the plant level varies from €8.76 to 93.15 per MWh. This variation is due to the quality of fuel, and the technology of plants.

The general limitation of the external cost methodology applies to this work as it uses the standard method developed for the ExternE project. Similarly, the data limitations as well as assumptions related to the costs and exclusions/omissions of cost elements affect the results.

The estimation of external costs is important for decision makers in the electricity sector to develop strategies for emission reduction and to develop environmental and energy policies.

The contribution of the paper lies in its use of recent data for the estimation and projection of the external costs until 2030. This allows an understanding of the changes in the external costs from power generation in the country.

The purpose of this paper is to assess the performance of Greek fossil fuel‐fired power stations employing a data envelopment analysis (DEA) model combined with bootstrapping.

DEA is used to derive aggregate performance indicators using data on inputs and desirable and undesirable outputs for a sample of fossil fuel‐fired power stations. The statistical significance of the derived aggregate performance indicators is assessed via the bootstrapping approach.

The results suggest that the power stations in the sample are considerably more inefficient than revealed by the initial point estimates of inefficiency. Moreover, the non‐lignite‐fired stations of the sample are on an average more efficient than the lignite‐fired stations.

DEA represents a useful framework for exploring the current state to derive aggregate performance indicators of power stations, and moreover, the statistical properties of these metrics can be assessed via the bootstrapping approach.

The bootstrapping approach in DEA shows its superiority over DEA models that do not address the uncertainty surrounding point estimates. The DEA bootstrapping model used in this study to model environmental performance in the power station electricity production setting provides bias correction and confidence intervals for the point estimates and it is therefore more preferable.

The derivation of aggregate performance indicators of Greek fossil fuel‐fired power stations is an important addition to the existing literature on energy economics. The paper is also innovated in providing the statistical properties of the derived performance metrics.