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In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country's higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model. [ABSTRACT FROM AUTHOR]

Light pollution from the use of artificial lighting poses significant impacts on human health, traffic safety, ecological environment, astronomy, and energy use. The advancement of characteristics of light pollution assessment technology has played a significant role in shaping prevention and control policies, thereby enabling measures, such as environmental standards and legislation and product procurement guidelines, but considerable variation in the definition, control strategies, and regulatory frameworks remains. Therefore, there is a need to review the characteristics of light pollution, including the assessment technique, policy, and legislation. Through the literature review, it can be found that technical standards are required to prevent light pollution. For example, light pollution is decreased by 6% in France through the legislation of artificial light. Key approaches are suggested to control global light pollution, including implementing ambient brightness zoning, regulating lighting product usage, and establishing dark sky reserves. Technology and policy should be integrated. The precise data coming from satellite imagery, drones, and balloons could provide guidance when making the policies. [ABSTRACT FROM AUTHOR]

The circular economy is a decisive strategy for reconciling economic development and the environment. In France, the CE was introduced into the law in 2015 with the objective of closing the loop. The legislation also delegates energy policy towards the French regions by granting them the jurisdiction to directly plan the energy–climate issues on their territory and to develop local energy resources. Thereby, the SUD PACA region has redefined its objectives and targeted carbon neutrality and the transition to a CE by 2050. To study this transition, we developed a TIMESPACA optimization model. The results show that following a CE perspective to develop a local energy system could contribute to reducing CO2 emissions by 50% in final energy consumption and reaching almost free electricity production. To obtain greater reductions, the development of the regional energy systems should follow a careful policy design favoring the transition to low energy-consuming behavior and the strategical allocation of resources across the different sectors. Biomethane should be allocated to the buildings and industrial sector, while hydrogen should be deployed for buses and freight transport vehicles. [ABSTRACT FROM AUTHOR]