The mining industry needs community and regional governmental support to maintain its current activities and, more importantly, to develop future projects. The failure to manage environmental risks in an acceptable manner during mine operation, closing and post-closing periods is a critical factor. The mining industry must regain its reputation that has been lost over decades and centuries of environmental degradation. Several environmental management tools (e.g., life cycle assessment, multi-criteria decision analysis, etc.) are widely applied during mine development, operation and closure periods. Nevertheless, due to uncertainties associated with the post-closure phase and the end of economic activity (implying no more revenues for stakeholders in the form of workers’ salaries and municipality taxes), it is crucial to adopt sound management practices during this period to achieve sustainability in the mining sector.As operational methodologies that can be used as a reference are lacking, the management of environmental risks during and after underground coal mine closures is, in many cases, limited or is developed without specific guidance.This statement is supported by the fact that the European Commission, through the Research Fund for Coal and Steel, encouraged research, pilot and demonstration projects and accompanying measures within the coal sector via the coal programme priority of recent years (2012–2014), namely, the “Management of environmental risks during or after mine closure”.The aim of this paper is to provide mine operators with an organized informational framework that could be applied during future underground coal mine closures independent of the major environmental problems faced and directly connected to the types and characteristics of coal and the exploitation methods used. The investigation was conducted using a literature review and interviews with experts from European universities, research institutions and coal mining companies from Poland and Spain

Based on extensive experience in shield support design and the results of underground measurements and observations, the major factors influencing the stability of the roof in retreat longwall panels were determined. A practical method for assessing the risk of roof falls is presented. It is a combination of empirical methods and expert techniques that enable investigators to determine the probability and potential consequences of roof falls. This report includes an example of applying the method to one longwall panel. The method is a practical decision tool to support the mining management when planning the roof fall preventive measures.

This paper presents a one-year forecast of European thermal coal spot prices by means of time series analysis, using data from IHS McCloskey NW Europe Steam Coal marker (MCIS). The main purpose was to achieve a good fit for the data using a quick and feasible method and to establish the transformations that better suit this marker, together with an affordable way for its validation.Time series models were selected because the data showed an autocorrelation systematic pattern and also because the number of variables that influence European coal prices is very large, so forecasting coal prices as a dependent variable makes necessary to previously forecast the explanatory variables.A second-order Autoregressive process AR(2) was selected based on the autocorrelation and the partial autocorrelation function.In order to determine if the results obtained are a good fit for the data, the possible drivers that move the European thermal coal spot prices were taken into account, establishing a hypothesis in which they were divided into four categories: (1) energy side drivers, that directly relates coal prices with other energy commodities like oil and natural gas; (2) demand side drivers, that relates coal prices both with the Western World economy and with emerging economies like China, in connection with the demand for electricity in these economies; (3) commodity currency drivers, that have an influence for holders of different commodity currencies in countries that export or import coal; and (4) supply side drivers, involving the production costs, transportation, etc.Finally, in order to analyse the time series model performance a Generalized Regression Neural Network (GRNN) was used and its performance compared against the whole AR(2) process. Empirical results obtained confirmed that there is no statistically significant difference between both methods. The GRNN analysis also allowed pointing out the main drivers that move the European Thermal Coal Spot prices: crude oil, USD/CNY change and supply side drivers