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Smart charging is a means of monitoring and actively controlling EV chargers to optimize the distribution and consumption of energy with a focus on peak-load avoidance. This paper describes the most important requirements that have influenced the design and implementation of the "Smart Charging System" (SCS). It presents the architecture and main functional building blocks of the SCS, which have been realized in an iterative development process as an extension component of the already existing open-source solution "Open e-Mobility". We also provide details on the functionality of the core smart charging algorithm within SCS and show how various data sources can be utilized by the system to increase the safety and efficiency of EV charging processes. Furthermore, we describe our iterative approach to developing the system, introduce the real-world testbed at SAP Labs France in Mougins/France, and share evaluation results and experiences gathered over a three-year period. [ABSTRACT FROM AUTHOR]

This paper develops a pseudo-panel approach to examine household electricity demand behavior through the household life cycle and its response to income variations to help strengthen the energy policy-making process. Our empirical methodology is based on three rich independent microdata surveys (the National Housing Surveys), which are representative of the French housing sector. The resulted sample covers the 2006–2016 period. Using within estimations, this paper finds striking evidence that the income elasticity of French residential electricity demand is 0.22, averaged over our four cohorts of generations. In light of other works, our estimate stands in the lower range. The empirical results also show that residential electricity consumption follows an inverted U-shaped distribution as a function of the age of the household's head. Most notably, it appears that households at the mid-point of their life cycle are relatively the largest consumers of electricity. This outcome has important implications for policy-making. Any public policy aimed at reducing household energy consumption should consider this differentiation in consumption according to the position of households over the life cycle, and therefore target as priority households at the highest level of consumption. [ABSTRACT FROM AUTHOR]

A large fraction of heat production from power sources is wasted in the environment, despite its numerous potential energy uses, for example, district heating, seawater desalination, or hydrogen production. In this paper, we use the MIXOPTIM methodology to evaluate performance indicators of mixes of electric power sources with and without cogeneration of heat to assess the benefits it entails. It appears from the study that the cogeneration option is highly interesting for mixes with a large nuclear share. The case of France is analyzed in detail, with an evaluation of the overall potential of cogeneration in terms of economic competitiveness as well as in terms of greenhouse gas emission reduction. [ABSTRACT FROM AUTHOR]

In order to more accurately predict the global solar energy consumption, a new grey prediction model FDGM(1,1, t α ) is proposed in this paper. The grey wolf optimizer (GWO) is used to optimize the fractional-order r and the time power α in the model. The proposed FDGM(1,1, t α ) model, two statistical models and other five existing grey models are used to simulate and predict the solar energy consumption in the four economies (France, South Korea, OECD and Asia Pacific region) from 2010 to 2029. The simulation results show that our proposed FDGM(1,1, t α ) has higher accuracy than the other seven models. The prediction results based on FDGM(1,1, t α ) show that in 2029, the solar energy consumption in South Korea, the OECD and the Asia Pacific region will reach 33,935.32 Ten-trillion J, 1,222,123.45 Ten-trillion J and 2,297,274.45 Ten-trillion J, respectively, but that in France will slowly increase in the next few years and will gradually decrease after reaching a peak of 14,727.34 Ten-trillion J in 2026. The better forecasting solar energy consumption by using the proposed model can provide useful information for the policy-makers to formulate and improve energy policies and measures. [ABSTRACT FROM AUTHOR]

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]

• Overheating regulations and calculation methods in 26 European countries were compared. • Most of the existing calculation methods are outdated and do not fit climate-proof buildings. • France requires a mixed-mode operation of naturally ventilated households. • The UK developed a heatwave-based calculation approach. • Comfort-based, multi-zonal, and time-integrated calculation approaches are needed. With the ongoing significance of overheating calculations in the residential building sector, building codes such as the European Energy Performance of Building Directive (EPBD) are essential for harmonizing the indicators and performance thresholds. This paper investigates Europe's overheating calculation methods, indicators, and thresholds and evaluates their ability to address climate change and heat events. e study aims to identify the suitability of existing overheating calculation methods and propose recommendations for the EPBD. The study results provide a cross-sectional overview of twenty-six European countries. The most influential overheating calculation criteria are listed the best approaches are ranked. The paper provides a thorough comparative assessment and recommendations to align current calculations with climate-sensitive metrics. The results suggest a framework and key performance indicators that are comfort-based, multi-zonal, and time-integrated to calculate overheating and modify the EU's next building energy efficiency regulations. The results can help policymakers and building professionals to develop the next overheating calculation framework and approach for the future development of climate-proof and resilient residential buildings. [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]

The relevance, relative importance and colinearity of input parameters to the results of building energy demand forecasts were investigated. Two calendar years of historical data including weather variables and days of the week were used. The study also aimed to assess the performance of multiple-linear-regression, support-vector-machine and artificial-neural-network models for predicting the daily heating, ventilation and air-conditioning energy consumption of a commercial building in France. Mean absolute error, root-mean-square error and coefficient of variation of root-mean-square error were selected as the performance criteria. The results showed that the best performance was achieved through the artificial-neural-network model according to all performance measures. Besides, the other two models were not able to meet the prediction requirements for energy consumption in a building since their coefficient-of-variation-of-root-mean-square-error values were not below 30%. The results also indicated that there was multiple colinearity between the number of degree days and outdoor temperature. Furthermore, the most significant parameter for daily energy consumption was found to be the number of degree days, followed by global radiation, sunshine rate and the day of the week. [ABSTRACT FROM AUTHOR]

To ensure regular and high yields, current agriculture is based on intensive use of pesticides and fertilizers, which are detrimental to the environment and human health. Moreover, as pest resistance to pesticides increases, and more and more pesticides are taken off the market, national and European policies are becoming powerful drivers to deliver pesticide-free farming systems. Whereas numerous studies have compared organic versus conventional systems, our study assessed, for the first time, the performances of a pesticide-free arable cropping system (No-Pesticide), using synthetic fertilizers, specifically designed to produce high yields and meet environmental goals. This system was compared with an input-based cropping system designed with the same environmental targets (PHEP: productive with high environmental performances) in an 11-year field trial in France (Paris Basin). Banning pesticides did not result in a significant average yield gap (in GJ.ha−1.year−1 or in kg N.ha−1.year−1) calculated over the crop sequence. Yet, some crops' yields significantly decreased, due either to pest damages, or to limited nitrogen nutrition. In the No-Pesticide system, the mycotoxin content of cereal grains was lower than the regulatory threshold, and the average wheat protein content was higher than the required standard for baking. Indirect energy consumption, total greenhouse gas emissions, number of technical operations, nitrogen fertilizer amounts, and treatment frequency indexes were significantly lower compared to the PHEP system. Conversely, results showed significantly higher direct energy consumption, direct greenhouse gas emissions, and number of work hours for weed control. We identify highly effective agricultural strategies to avoid pesticide use (e.g., widely diverse and long crop sequence; introduction of hemp) and pinpoint several technical lock-ins hampering steady production in pesticide-free systems. We argue that more experiments should be undertaken to deliver technical knowledge for managing major or orphan species within pesticide-free systems, and to provide supplementary results, including economic and social performances. [ABSTRACT FROM AUTHOR]

Meeting energy needs and ensuring ecological sustainability are important tasks for policymakers. In particular, changing the energy mix used in daily electricity generation can have a major effect on ecological conditions, and this is a previously neglected research topic. Therefore, this paper aims to investigate the influence of daily electricity generation from nuclear power and renewables on the achievement of carbon neutrality targets in the four largest nuclear power countries (i.e., the U.S., China, France, and Russia). The study applies the wavelet-transform coherence (WTC), quantile-on-quantile regression (QQR), and Granger causality in quantiles (GCQ) approaches for the period from January 2, 2019 to December 29, 2022. The outcomes present that electricity generation from nuclear energy reduces carbon (CO 2) emissions only in Russia, while hydropower generation does not contribute to CO 2 emission reduction in any country. Solar electricity generation decreases CO 2 emissions in different quantiles in the U.S. and Russia, while electricity generation from wind power reduces CO 2 emissions in the U.S. and France. The empirical outcomes also reveal that expanding solar, wind, and nuclear power in the energy mix can enhance the potential of the U.S., France, and Russia to achieve carbon neutrality and secure a sustainable future. However, China is using nuclear and renewables inefficiently regarding carbon neutrality. Therefore, the empirical evidence for China is highly controversial compared to former studies. [Display omitted] • The study examines renewable and nuclear electricity effect on carbon neutrality. • The study focuses on top four major nuclear power countries. • The study applies nonlinear approaches by using high-frequency (daily) data. • Renewable and nuclear electricity have time, frequency, & quantile-varying effect. • The best electricity source change according to each country. [ABSTRACT FROM AUTHOR]

We compare the performance of four types of support schemes aimed at improving residential energy efficiency in France: the income tax credit, a grant scheme, the reduction of the value-added tax, and the White Certificates. We use the TREMI dataset which covers close to 14,000 households that conducted conservation works. To address self-selection bias, we use a double-robust inverse probability weighting estimator. We assess the effect of the adoption of each scheme on the funding acquired, the private and total investment, and the reduction of the household energy expenses. For each scheme, we use the estimates to deduct its cost-effectiveness, the involved redistribution, and its ability to trigger additional investment in energy conservation works. We find funding from the schemes to reduce energy expenses most cost-effectively via the White Certificates. Redistribution is neutral for each of the four schemes: higher and lower income households equally benefit from them. [ABSTRACT FROM AUTHOR]

Earth–air heat exchanger (EAHE) systems are used to pre-heat or pre-cool air before entering into a building using shallow geothermal energy. Assessment of EAHE systems is important to quantify the profitability of these systems. For this purpose, an EAHE system built at ICUBE at the University of Strasbourg in the northeast of France was studied using energy and exergy analyses for a typical heating period (between 25 February and 3 March). Energy analysis was used to determine the heat gained by the air in the system during the studied period and to determine the Coefficient Of Performance (COP) of the system. Additionally, exergy analysis, which considered temperature, pressure, humidity, and the variation in the control volume boundary temperature, was realized to determine inefficiencies in the system by determining the exergy destroyed in each component of the system and evaluating its exergetic efficiency. Results showed that the heat energy gained using the system was around 63 kWh and that the exergetic efficiency of the system was about 57% on average. The comparison of exergetic efficiency between the EAHE components showed that the fan has the lowest performance and should be improved to achieve better overall performance. [ABSTRACT FROM AUTHOR]

Previously documented studies in the literature on how tourism leads to economic growth in the form of tourism-led growth hypotheses (TLGH) has been investigated. This study presents a new perspective on the growth of tourism by considering its impact on conventional energy consumption, real income level, and emission via the channel of globalization. Sequences of econometric tests were conducted to validate the hypothesized claims between tourism development and growth impact on conventional energy consumption and pollution proxy by ecological footprints, globalization GDP per capita, biocapacity, and tourists for the case of France. Empirical evidence from the Granger causality test presents a uni-directional causality from ecological footprints to GDP per capita and from biocapacity to ecological footprints. The correlation matrix shows interrelation amongst series with biocapacity significantly correlating with ecological footprints with tourist’s arrival having a positive correlation with ecological footprints and a negative one with biocapacity. GPD per capita was found to positively affect the ecological footprints and have a negative correlation with biocapacity and a significant relationship with tourists' arrivals. Additionally, globalization exerts a positive impact on ecological footprints, and its effect on biocapacity was found to be negative although globalization's effect on tourists’ arrivals and per capita GDP is significant. The ARDL estimation indicated biocapacity as a neutral agent for ecological footprints, tourist arrivals having a negative impact on ecological footprints, and globalization significantly affecting ecological footprints. From these findings, it is evident that tourism growth has a significant impact on energy consumption and pollution. Policy recommendations were also provided in this study accordingly. [ABSTRACT FROM AUTHOR]

Recent economics literature emphasises the inextricable link between economic prosperity and efficient energy supply and consumption, arguing vehemently that much of today's prosperity is predicated on secure and reliable energy use and that modern industry would grind to a halt without the necessary energy infrastructure. France is an example of an advanced country with enormous energy infrastructure. The country's economic growth has been rather sluggish in recent years, despite its robust and efficient energy production and consumption. The current study analyses empirically whether France's current sustainable electricity use defies the theoretical and empirical literature. To achieve this objective, the standard tools of growth empirics were used over the period 1961–2015. The Zivot–Andrews trended and de-trended structural break tests, as well as Autoregressive Distributed lag (ARDL) bounds testing were employed to analyse the data. The findings of this study demonstrated that France's economic growth is stimulated by electricity usage, financial development, capital, import, and export. The study sheds light on the feedback impact between economic growth and power usage as well as capital and economic growth. With these findings, we argue that it is not electricity that has ceased to serve a functional purpose in France, rather there is a need for entrepreneurial innovative capacity to create factors that require an infinite horizon for the continued use of electricity, which seems to be impeding the country's economic growth potential, among other things. [ABSTRACT FROM AUTHOR]

The accelerating depletion of fossil fuel reserves and the growing awareness about climate issues have put forth a plethora of interesting approaches that attempt to tackle the crucial problem of energy saving, specifically in buildings, known as a major energy consumer. Existing approaches tackle the problem of energy efficiency in buildings by proposing model-based approaches such as knowledge models (thermal, CO 2 , cost, etc.) and regressive models. However, different factors make the building of knowledge models particularly challenging, including the very complicated interaction between several heterogeneous phenomena that can impact the use of energy in buildings like the buildings envelope characteristics, their positions, the weather conditions, but also the occupant's behavior is a critical issue in the process. More Recently, techniques from machine learning (ML) to support energy saving in buildings gained increased interest. They learn from collected historical data a model that forecasts the future energy behavior of the building. Although occupant's behavior to save energy in the building is far from trivial, has received less attention from these studies. This paper takes on this challenge and proposes an energy management system based on historical data thanks to case-based reasoning approach. We guide the occupant by proposing an action plan (opening/closing of doors/windows, etc.) to help him in the process of improving his indoor comfort (thermal, air quality, luminosity, etc.) without using more energy if not using less. To encourage the occupant to trust the inference mechanism learnt and cooperate with the energy management system, this approach generates explanations arguing the proposed action plan. We assess the performance of our energy management technique on real-word data collected from a research building at the University of Grenoble, France. • Model-based systems are difficult to develop. • Case-based reasoning models building behavior without the need for a building model. • Explanation fosters cooperation between the user and the energy management system. [ABSTRACT FROM AUTHOR]

This study aims to examine the relationship between military expenditure and environmental sustainability in developed Mediterranean countries: Greece, France, Italy, and Spain. Sustainable economic growth is strictly related to energy consumption which leads to producing a higher level of carbon emissions. Besides, there may be a nexus between military expenditures and environmental pollution. This study focuses on developed Mediterranean countries since carbon emissions and greenhouse gas emissions are relatively high in these countries. Furthermore, France and Italy are the top countries in terms of total military spending. We investigate the relationship between military expenditure and carbon emissions using the Global Vector Autoregression model proposed by Pesaran et al. (J Bus Econ Stat 22 129:162, Pesaran et al., J Bus Econ Stat 22:129–162, 2004) and Dees et al. (J Appl Econ 22(1):38, Dees et al., J Appl Econ 22:1–38, 2007) between 1965 and 2019. The empirical findings indicated that the relationship between carbon emission and military expenditure should be taken into account from a global perspective for environmental sustainability, and an increase in the global military expenditure seems to be very harmful to the global environment. It can be concluded that country-based prevention cannot provide the desired solution in combating environmental pollution. [ABSTRACT FROM AUTHOR]

The article analyzes mechanical wind-powered water supply systems situated in southern France and northern Germany in the time period between 1880 and 1950, when wind energy was not yet framed as a renewable, but as a rural, decentralized, and manageable solution to generate energy. Using a local approach, I highlight wind energy use in rural areas of Western societies as part of the non-synchronical process of technological modernization that unfolded at the same time as electrification, industrialization, and the implementation of large technological systems. I develop my argument in two steps. First, I provide the micro-historical case studies to examine socio-technological contexts that motivated people to advocate for the use of wind energy in times of electrification and industrialization. Second, I show the connection to general and specific contemporary discourses about wind energy and energy futures in order to provide evidence that the wind projects were part of rural modernization processes. [ABSTRACT FROM AUTHOR]

• A retrospective material and energy flow analysis of the European flat glass industry over 75 years. • A long-term study of the use of raw materials and energy for architectural glass manufacturing. • A comparative analysis of the intensity and the total emissions of CO 2 resulting from the manufacturing process. • CO 2 emissions fell significantly until the 1980s, but have then levelled off and even increased. • Discussion on measures and research needs to foster decarbonisation through reuse and recycling of glazing. In our built environment, flat glass is an indispensable material. Its production requires the mining of raw materials and the use of fossil fuels and results in the emission of pollutants including CO 2. In Western European countries, these emissions have been the target of a series of energy efficiency measures and emission reduction policies since 1945. Beginning with that year, this article studies the material history of the flat glass industry in Belgium, France and the EU and analyses the evolution of the flows of materials, energy and CO 2 related to the manufacturing process. Based on a retrospective material and energy flow analysis (MEFA), the objective is to assess the environmental footprint of the architectural flat glass sector and to provide an understanding of the socioecological patterns that have shaped its metabolism. Based on an extensive data collection, this paper is the first publication of long-term time series on the manufacturing of architectural glass. The findings reveal two major concerns: on the one hand, energy efficiency measures have weakened ever since the 1990s, despite the tightening of the European environmental policies; on the other hand, the demand for architectural glass in the EU remains particularly high, resulting in a production rate that uses considerable energy and exploits a large amount of raw materials. The relative decoupling of the energy and CO 2 intensities seems to have reached a threshold and has not resulted in an absolute decoupling of environmental burdens from industrial growth. We discuss measures and research needs to support a decarbonisation of the flat glass sector. [ABSTRACT FROM AUTHOR]

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption. [ABSTRACT FROM AUTHOR]