• Energy Research
• Frontiers in Environmental Science close

Sunscreens are of emerging concern regarding environmental effect. After leaving the skin either through bathing or washing, the ingredients contained in the product formulation can be released into rivers, lakes, seashores, and/or sewage treatment plants. Nanomaterials used as UV-filters are of particular concern in this context as they may have a negative effect on these systems. To assess the risks posed, the exposure and hazard of nanoparticulate UV-filters must be considered through the entire lifecycle of the sunscreen product. This includes not only usage, but also manufacturing and disposal at the end of life of the product, as some nanomaterials may be released into the environment at each stage. This includes also developing relevant approaches that take into account realistic scenarios of environmental release and fate. Nanoparticulate UV-filters typically consist of a mineral nanoparticle core (TiO2 or ZnO) coated with surface layers aimed at optimizing the dispersion in the formulation and at supressing any photo-sensibility. This coating plays a key role in the associated risk since it affects the nanoparticle surface properties, which control both fate and hazard. At present, knowledge gaps remain regarding the safety of nanomaterials used in sunscreen, as very few studies have focused on real sunscreen filters and formulations throughout their lifecycle so far. A literature review is proposed here from the design of nanoparticulate UV-filters and formulations, to the release, fate, and effect in the different compartments encountered along the product lifecycle. The resulting state of the art highlights knowledge gaps and will likely help regulators, manufacturers, and consumers choose appropriate guidance. By considering each development stage of the sunscreen, from the choice of the UV-filter(s) and its (their) integration into a cosmetic formulation to the knowledge of the risk involved in this choice all along the product lifecycle, an eco-design approach can be achieved where release or toxicity are reduced. Sustainability can thus be accounted for, during the design process, by making the appropriate choices (in advance) that help minimize or prevent the environmental impact of the sunscreen.

Sunscreens are of emerging concern regarding environmental effect. After leaving the skin either through bathing or washing, the ingredients contained in the product formulation can be released into rivers, lakes, seashores, and/or sewage treatment plants. Nanomaterials used as UV-filters are of particular concern in this context as they may have a negative effect on these systems. To assess the risks posed, the exposure and hazard of nanoparticulate UV-filters must be considered through the entire lifecycle of the sunscreen product. This includes not only usage, but also manufacturing and disposal at the end of life of the product, as some nanomaterials may be released into the environment at each stage. This includes also developing relevant approaches that take into account realistic scenarios of environmental release and fate. Nanoparticulate UV-filters typically consist of a mineral nanoparticle core (TiO2 or ZnO) coated with surface layers aimed at optimizing the dispersion in the formulation and at supressing any photo-sensibility. This coating plays a key role in the associated risk since it affects the nanoparticle surface properties, which control both fate and hazard. At present, knowledge gaps remain regarding the safety of nanomaterials used in sunscreen, as very few studies have focused on real sunscreen filters and formulations throughout their lifecycle so far. A literature review is proposed here from the design of nanoparticulate UV-filters and formulations, to the release, fate, and effect in the different compartments encountered along the product lifecycle. The resulting state of the art highlights knowledge gaps and will likely help regulators, manufacturers, and consumers choose appropriate guidance. By considering each development stage of the sunscreen, from the choice of the UV-filter(s) and its (their) integration into a cosmetic formulation to the knowledge of the risk involved in this choice all along the product lifecycle, an eco-design approach can be achieved where release or toxicity are reduced. Sustainability can thus be accounted for, during the design process, by making the appropriate choices (in advance) that help minimize or prevent the environmental impact of the sunscreen.

Some of the globe’s most economically advanced nations make up the G7 (Canada, Japan, France, Germany, Italy, United States and United Kingdom). Nevertheless, in tandem with such strong economic growth, the environmental conditions in these nations have deteriorated, raising serious issues among stakeholders. Therefore, we examine the effect of eco-innovation and trade openness on CO2 emissions in G7 nations. We also take into account the role of renewable energy, economic growth and nonrenewable energy use using a dataset covering the period from 1990–2019. We employed recent econometric techniques such as slope heterogeneity (SH) and cross-sectional dependence (CSD), Westerlund cointegration, fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), panel quantile regression and panel causality tests to assess these associations. The outcomes of the CSD and SH tests disclosed that using a first-generation unit root test will produce biase outcomes. Furthermore, the outcomes of the Westerlund cointegration disclosed support long-run association between CO2 and its drivers. In addition, the results of the long-run estimators (FMOLS and DOLS) unveiled that nonrenewable energy and trade openness contribute to the damage to the environment while economic expansion, renewable energy and eco-innovation enhance the quality of the environment. Furthermore, the outcomes of GDP, REC and ECO curb CO2 while NREC energy and TO surge CO2. Finally, the outcomes of the panel causality test unveiled that CO2 emissions can be predicted by all the exogenous variables.

Some of the globe’s most economically advanced nations make up the G7 (Canada, Japan, France, Germany, Italy, United States and United Kingdom). Nevertheless, in tandem with such strong economic growth, the environmental conditions in these nations have deteriorated, raising serious issues among stakeholders. Therefore, we examine the effect of eco-innovation and trade openness on CO2 emissions in G7 nations. We also take into account the role of renewable energy, economic growth and nonrenewable energy use using a dataset covering the period from 1990–2019. We employed recent econometric techniques such as slope heterogeneity (SH) and cross-sectional dependence (CSD), Westerlund cointegration, fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), panel quantile regression and panel causality tests to assess these associations. The outcomes of the CSD and SH tests disclosed that using a first-generation unit root test will produce biase outcomes. Furthermore, the outcomes of the Westerlund cointegration disclosed support long-run association between CO2 and its drivers. In addition, the results of the long-run estimators (FMOLS and DOLS) unveiled that nonrenewable energy and trade openness contribute to the damage to the environment while economic expansion, renewable energy and eco-innovation enhance the quality of the environment. Furthermore, the outcomes of GDP, REC and ECO curb CO2 while NREC energy and TO surge CO2. Finally, the outcomes of the panel causality test unveiled that CO2 emissions can be predicted by all the exogenous variables.

The emission peak and carbon neutrality targets pose a great challenge to carbon emission reduction in the coal industry, and the coal industry will face an all-around deep adjustment. The forecast of coal price is crucial for reducing carbon emissions in the coal industry in an orderly manner under the premise of ensuring national energy security. The volatility and instability of coal prices are a result of multiple influencing factors, making it very difficult to make accurate predictions of coal price changes. We propose in this paper an innovative hybrid forecasting method (CEEMDAN-GWO-CatBoost) for forecasting coal price indexes by combining machine learning models, feature selections, data decomposition, and model interpretation. By combining high forecasting accuracy with good interpretability, this method fills a gap in the field of coal price forecasting. Initially, we examine the factors that influence coal prices from five angles: Supply, demand, macroeconomic factors, freight costs, and substitutes; and we employ Spearman correlation analysis to reduce the complexity of the attribute set and devise a coal price forecasting index system. Secondly, the CEEMDAN method is used to decompose the raw coal price index data into seven intrinsic modal functions and one residual term in order to weaken the volatility of the data caused by complex factors. Next, the CatBoost model hyperparameters are optimized using the Grey Wolf Optimizer algorithm, while the coal price data is fed into the combined forecasting model. Lastly, the SHAP interpretation method is introduced for studying the important indicators affecting coal prices. The experimental results show that the combined CEEMDAN-GWO-CatBoost forecasting model proposed in this paper has significantly better forecasting performance than other comparative models, and the SHAP method employed in this study identifies the macroeconomic environment, freight costs, and coal import volume as significant factors affecting coal prices. As part of the contribution of this paper, specific recommendations are made to the government regarding the formulation of a regulatory policy for the coal industry in the context of carbon neutrality based on the findings of this research.

The emission peak and carbon neutrality targets pose a great challenge to carbon emission reduction in the coal industry, and the coal industry will face an all-around deep adjustment. The forecast of coal price is crucial for reducing carbon emissions in the coal industry in an orderly manner under the premise of ensuring national energy security. The volatility and instability of coal prices are a result of multiple influencing factors, making it very difficult to make accurate predictions of coal price changes. We propose in this paper an innovative hybrid forecasting method (CEEMDAN-GWO-CatBoost) for forecasting coal price indexes by combining machine learning models, feature selections, data decomposition, and model interpretation. By combining high forecasting accuracy with good interpretability, this method fills a gap in the field of coal price forecasting. Initially, we examine the factors that influence coal prices from five angles: Supply, demand, macroeconomic factors, freight costs, and substitutes; and we employ Spearman correlation analysis to reduce the complexity of the attribute set and devise a coal price forecasting index system. Secondly, the CEEMDAN method is used to decompose the raw coal price index data into seven intrinsic modal functions and one residual term in order to weaken the volatility of the data caused by complex factors. Next, the CatBoost model hyperparameters are optimized using the Grey Wolf Optimizer algorithm, while the coal price data is fed into the combined forecasting model. Lastly, the SHAP interpretation method is introduced for studying the important indicators affecting coal prices. The experimental results show that the combined CEEMDAN-GWO-CatBoost forecasting model proposed in this paper has significantly better forecasting performance than other comparative models, and the SHAP method employed in this study identifies the macroeconomic environment, freight costs, and coal import volume as significant factors affecting coal prices. As part of the contribution of this paper, specific recommendations are made to the government regarding the formulation of a regulatory policy for the coal industry in the context of carbon neutrality based on the findings of this research.