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In industrialized countries, large amounts of mineral wastes are produced. They are re-used in various ways, particularly in road and earth constructions, substituting primary resources such as gravel. However, they may also contain pollutants, such as heavy metals, which may be leached to the groundwater. The toxic impacts of these emissions are so far often neglected within Life Cycle Assessments (LCA) of products or waste treatment services and thus, potentially large environmental impacts are currently missed. This study aims at closing this gap by assessing the ecotoxic impacts of heavy metal leaching from industrial mineral wastes in road and earth constructions. The flows of metals such as Sb, As, Pb, Cd, Cr, Cu, Mo, Ni, V and Zn originating from three typical constructions to the environment are quantified, their fate in the environment is assessed and potential ecotoxic effects evaluated. For our reference country, Germany, the industrial wastes that are applied as Granular Secondary Construction Material (GSCM) carry more than 45,000 t of diverse heavy metals per year. Depending on the material quality and construction type applied, up to 150 t of heavy metals may leach to the environment within the first 100 years after construction. Heavy metal retardation in subsoil can potentially reduce the fate to groundwater by up to 100%. One major challenge of integrating leaching from constructions into macro-scale LCA frameworks is the high variability in micro-scale technical and geographical factors, such as material qualities, construction types and soil types. In our work, we consider a broad range of parameter values in the modeling of leaching and fate. This allows distinguishing between the impacts of various road constructions, as well as sites with different soil properties. The findings of this study promote the quantitative consideration of environmental impacts of long-term leaching in Life Cycle Assessment, complementing site-specific risk assessment, for the design of waste management strategies, particularly in the construction sector.

In industrialized countries, large amounts of mineral wastes are produced. They are re-used in various ways, particularly in road and earth constructions, substituting primary resources such as gravel. However, they may also contain pollutants, such as heavy metals, which may be leached to the groundwater. The toxic impacts of these emissions are so far often neglected within Life Cycle Assessments (LCA) of products or waste treatment services and thus, potentially large environmental impacts are currently missed. This study aims at closing this gap by assessing the ecotoxic impacts of heavy metal leaching from industrial mineral wastes in road and earth constructions. The flows of metals such as Sb, As, Pb, Cd, Cr, Cu, Mo, Ni, V and Zn originating from three typical constructions to the environment are quantified, their fate in the environment is assessed and potential ecotoxic effects evaluated. For our reference country, Germany, the industrial wastes that are applied as Granular Secondary Construction Material (GSCM) carry more than 45,000 t of diverse heavy metals per year. Depending on the material quality and construction type applied, up to 150 t of heavy metals may leach to the environment within the first 100 years after construction. Heavy metal retardation in subsoil can potentially reduce the fate to groundwater by up to 100%. One major challenge of integrating leaching from constructions into macro-scale LCA frameworks is the high variability in micro-scale technical and geographical factors, such as material qualities, construction types and soil types. In our work, we consider a broad range of parameter values in the modeling of leaching and fate. This allows distinguishing between the impacts of various road constructions, as well as sites with different soil properties. The findings of this study promote the quantitative consideration of environmental impacts of long-term leaching in Life Cycle Assessment, complementing site-specific risk assessment, for the design of waste management strategies, particularly in the construction sector.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.

Achieving environmental sustainability has become a global concern amidst increasing climate change threat. Using quarterly frequency data for the case of Russia from 1992 to 2018, the present study explores the interaction between disaggregated energy consumption (renewable energy and non-renewable energy), trade flow and economic growth on a broader measure for environmental degradation (ecological footprint). The choice of the variables draws strength from initiative of the United Nations Sustainable Development Goals (UN-SDG, 7, 8 11 and 13) for responsible energy consumption and clean energy consumption while mitigating climate change issues. The study applied the quantile-on-quantile regression (QQR) and nonparametric causality-in-quantiles to capture these associations. The outcomes from the QQR disclosed that in the majority of the quantiles, trade openness and renewable energy use contribute to environmental sustainability, while nonrenewable energy amplifies ecological footprint. Furthermore, growth in Russia escalates its ecological footprint. Moreover, in the majority of the quantiles, all the exogenous variables can predict ecological footprint. Given the outcomes of this study, it outlines the need for a paradigm shift for alternative and clean energy consumption in Russian energy mix amidst its economic growth trajectory while accounting for green-development approaches. Pathways to fully achieve the sustainability targets are carefully outlined in the concluding section.

Achieving environmental sustainability has become a global concern amidst increasing climate change threat. Using quarterly frequency data for the case of Russia from 1992 to 2018, the present study explores the interaction between disaggregated energy consumption (renewable energy and non-renewable energy), trade flow and economic growth on a broader measure for environmental degradation (ecological footprint). The choice of the variables draws strength from initiative of the United Nations Sustainable Development Goals (UN-SDG, 7, 8 11 and 13) for responsible energy consumption and clean energy consumption while mitigating climate change issues. The study applied the quantile-on-quantile regression (QQR) and nonparametric causality-in-quantiles to capture these associations. The outcomes from the QQR disclosed that in the majority of the quantiles, trade openness and renewable energy use contribute to environmental sustainability, while nonrenewable energy amplifies ecological footprint. Furthermore, growth in Russia escalates its ecological footprint. Moreover, in the majority of the quantiles, all the exogenous variables can predict ecological footprint. Given the outcomes of this study, it outlines the need for a paradigm shift for alternative and clean energy consumption in Russian energy mix amidst its economic growth trajectory while accounting for green-development approaches. Pathways to fully achieve the sustainability targets are carefully outlined in the concluding section.

Evidence of the accumulation of carbon dioxide (CO2) and other greenhouse gases (GHGs) over the years is placing greater emphasis on the use of taxes to achieve a sustainable environment. This study evaluates the effectiveness of environmental taxes in mitigating the effect of tourism on environmental performance in EU-28 countries for the period 2002–2019. The empirical results of the panel threshold regression model suggest that the effect of tourism on environmental performance is dependent on the level of environmental taxes. Particularly, the effect of tourism on environmental performance is positive and insignificant when environmental taxes are below the threshold level of 9.43%. However, once environmental taxes cross a threshold level, tourism would improve environmental performance. This suggests a significant difference in the way tourism influences environmental performance during periods of lower and higher environmental taxes. These findings, therefore, provide insights to policymakers and other stakeholders on the use of environmental taxes as an instrument to avert environmental degradation, and thus mitigate global warming and other significant climate changes.

Evidence of the accumulation of carbon dioxide (CO2) and other greenhouse gases (GHGs) over the years is placing greater emphasis on the use of taxes to achieve a sustainable environment. This study evaluates the effectiveness of environmental taxes in mitigating the effect of tourism on environmental performance in EU-28 countries for the period 2002–2019. The empirical results of the panel threshold regression model suggest that the effect of tourism on environmental performance is dependent on the level of environmental taxes. Particularly, the effect of tourism on environmental performance is positive and insignificant when environmental taxes are below the threshold level of 9.43%. However, once environmental taxes cross a threshold level, tourism would improve environmental performance. This suggests a significant difference in the way tourism influences environmental performance during periods of lower and higher environmental taxes. These findings, therefore, provide insights to policymakers and other stakeholders on the use of environmental taxes as an instrument to avert environmental degradation, and thus mitigate global warming and other significant climate changes.