Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO₂ emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO₂ emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO₂ emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO₂ emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO₂ emissions, Brazil would be the best option.

Concept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies. However, these strategies often rely on complete classifier retraining without considering the properties of the drift, which can prove inadequate in many scenarios. Limited attention has been given to understanding the nature of drift and its characterization, which are crucial for designing effective reaction strategies. Drift descriptors provide a means to explain how new concepts replace existing ones, offering valuable insights into the nature of drift. In this context, this work examines the impact of four descriptors—severity, recurrence, frequency, and speed—on concept drift through extensive theoretical and experimental analysis. Experiments were conducted on five datasets with 32 descriptor variations, eight drift detectors, and a non-detection context, resulting in 1440 combinations. The findings reveal three key conclusions: (i) reaction strategies must be tailored to different types of drift; (ii) severity, recurrence, and frequency descriptors have the highest impact, whereas speed has minimal influence; and (iii) there is a need to incorporate mechanisms for describing concept drift into the strategies designed to address it.