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MCCIP Science Review 2013, 34-48

The database presents the scenario results of an exploratory research, carried out at the International Institute for Applied Systems Analysis (IIASA): the Low Energy Demand (LED) study (Grubler et al. 2018). The LED scenario explored how far transformative changes that combine technological changes, end-use efficiency, and new business models for energy service provision can lead for lowering energy demand, and how these changes could drive deep decarbonisation in the long-term. The scenario development methodology included a bottom-up analysis of how currently existing, though often embryonic, social, institutional, and technological trends could become mainstream with resulting step-changes in efficiency and resulting lowered energy demand. The bottom-up demand estimations were then further explored for their supply side and emissions and climate implications with a top-down modeling framework drawing on the Shared Socioeconomic Pathways (SSP) framework (Riahi et al. 2017). The results show that global final energy demands can be drastically reduced in 2050, to around 245 EJ/yr, or 40% lower than today, whilst significantly expanding human welfare and reducing global development inequalities. According to the knowledge of the authors, LED is the lowest long-term global energy demand scenario ever published. The LED scenario meets the 1.5°C climate target in 2100 without overshoot and keeps the global mean temperature increase below 1.5°C with a probability of more than 60%, without requiring controversial negative emission technologies, such as bioenergy with carbon capture and storage (BECCS), that figure prominently in the emission scenario literature (Rogelj et al. 2015, Anderson and Peters 2016, Creutzig et al. 2016, Smith et al. 2016). Furthermore, the beneficial impacts of the LED scenario on a range of other sustainable development goals are also shown, demonstrating that efficiency of energy services provision plays a critical role in reaching low-energy futures without compromising increased living standards in the Global South, while at the same time reducing adverse social and environmental impacts of climate mitigation strategies that focus predominantly on large-scale supply-side transformations. The research is published in a peer-reviewed article in Nature Energy (Grubler et al. 2018) with ample supplementary information. Water consumption and withdrawal data are published in Parkinson et al. (2018). The data is available for download from the LED Database. The content of the LED database and any derived analysis may only be used for non-commercial scientific publications, articles, educational purposes, figures and data tables provided that the source reference pursuant to section 'Required citation' is included and all relevant publications are correctly cited. Partial reproductions of the database content may be stored in online repositories, if this is necessary to comply with a journal's data archiving and access requirements. Such reproductions must be limited to the scope of the manuscript in question, and must include a hyperlink to the source database hosted at https://db1.ene.iiasa.ac.at/LEDDB and the download date from the source database. However, any wholesale duplication, translation, reworking, processing, arrangement, transformation, or reproduction through the internet or any other channels, of the https://db1.ene.iiasa.ac.at/LEDDEB for commercial or non-commercial purposes is not permitted without the explicit written approval of IIASA.

Caught in the Act of Protest: Contextualizing Contestation (CCC)-project is set up to combine interview data of individual participants in the act of demonstrating with multilayered contextual data that allow embedding this individual participation in meaningful contextual differences. The data were collected in nine countries: Belgium, Czech Republic, Italy, Mexico, The Netherlands, Spain, Sweden, Switzerland, and the United Kingdom.

Changes in microfossils (diatoms, chrysophytes, chironomids and cladocera remains), geochemistry and deposition of atmospheric pollutants have been investigated in the sediment records of the alpine lake Gossenkollesee ( Tyrol, Austria) spanning the last two centuries. The sediment records were compared with seasonal and annual air temperature trends calculated for the elevation (2417 m a. s. l.) and the geographical position (47degrees13'46"N, 11degrees00'51"E) of the lake, and with precipitation records available since 1866 from Innsbruck. Temperature trends followed a 20 30 year oscillation between cold and warm periods. Regarding long-term changes, temperature trends showed a U-shaped trend between 1780 and 1950, followed by a steep increase since 1975.

# LQN4Energy-Replication-Package This repository contains the replication package and dataset of the paper titled "An approach using performance models for supporting energy analysis of software systems". This study has been developed by: 1. [Vincenzo Stoico](https://scholar.google.com/citations?user=E8C9Uz4AAAAJ&hl=en)(University of L'Aquila) 2. [Vittorio Cortellessa](https://scholar.google.com/citations?hl=en&user=s4JPUOEAAAAJ)(University of L'Aquila) 3. [Ivano Malavolta](https://scholar.google.com/citations?hl=en&user=ya3htIoAAAAJ)(Vrije University Amsterdam) 4. [Daniele Di Pompeo](https://scholar.google.com/citations?hl=en&user=E2dr5vIAAAAJ)(University of L'Aquila) 5. [Luigi Pomante](https://scholar.google.com/citations?hl=en&user=q2_sZiMAAAAJ)(University of L'Aquila) for further details, comments, and/or suggestions, you can write an email to the following address: <vincenzo.stoico@graduate.univaq.it> ## Repository Description This repository is made by three directories: - `code`: it contains the scripts that read the dataset and generate the results for the Digital Camera and Train Ticket Booking System. Therefore, the response time for the supplied workloads, CPU utilization, the average power, i.e., e multiplier, and the average energy consumption. - `dc_energy_estimation.py`: generates the energy estimates for Digital Camera - `dc_overall_stats.py`: calculates the performance and the energy metrics from the measurements collected for Digital Camera - `ttbs_performance_stats.py`: calculates the performance metrics from the measurements retrieved for Train Ticket Booking System - `ttbs_energy_stats.py`: calculates the energy metrics from the measurements taken for Train Ticket Booking System - `ttbs_overall_stats.py`: generates the energy estimates and the charts comparing estimates and measurements for Train Ticket Booking System. It prints the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). - `dataset`: it has two subdirectories: `dc` and `ttbs` containing the data collected during the experiments performed for Digital Camera and Train Ticket Booking System, respectively; - `model`: it includes the Layered Queuing Networks we used to retrieve CPU Utilization and the response time for both case studies; ## How do I run this? The scripts are written in python, so you must have installed the latest version of python to run them. In addition, they require `pandas`, `matplotlib`, `numpy`, `scipy`. The suite to execute the Layered Queuing Networks must be installed to retrieve performance estimatations. It is possible to find it at the following repository: [https://github.com/layeredqueuing/V5](https://github.com/layeredqueuing/V5) After installation is complete, you can execute the list of commands indicated below to obtain the results for the case studies. The commands must be executed in the described order. The results will be generated in the `results/` directory. ### Digital Camera 1. move to the `~/code` folder 2. execute `python dc_overall_stats.py`, it will take ~1m 3. move to the `~/model` directory and execute `lqns dc.lqnx > ../results/dc_estimates.csv` 4. go back to the `~/code` folder and execute `python dc_energy_estimation.py` ### Train Ticket Booking System 1. move to the `~/code` folder 2. execute `python ttbs_performance_stats.py` 3. execute `python ttbs_energy_stats.py` 3. move to the `~/model` directory and execute `lqns ttbs.lqnx > ../results/ttbs_performance_estimates.csv` 4. go back to the `~/code` folder and execute `python ttbs_overall_stats.py`