This repository provides the detailed data for the figures in the manuscript, "Cost Increase in the Electricity Supply to Achieve Carbon Neutrality in China".

The article was submitted to Nature Communications for peer review on 22 August 2021 and accepted on 22 April 2022. The details of the main article are as follows.

DOI: 10.1038/s41467-022-30747-0

Online link: https://www.nature.com/articles/s41467-022-30747-0

Citation: Zhuo, Z., Du, E., Zhang, N. et al. Cost increase in the electricity supply to achieve carbon neutrality in China. Nat Commun 13, 3172 (2022).

If you want to use the date provided in the repository, please cite the above article. It will be better if you can also cite this dataset.

This record contains the underlying data/supplementary materials/appendix for the publication "Socially responsible firms" published in Journal of Financial Economics in 2016.

The repository primarily contains the data files related to the publication entitled "Synergizing Human Health and Climate Benefits in China’s Environmental Protection Tax". Certain datasets are directly accessible via the links provided in the Data and Code Availability section of the article and are therefore not duplicated within this repository. For any additional data not included, please feel free to contact us, and we will provide it upon receiving a reasonable request.

This repository includes relevent economic data for the Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems, developed by the research team of Dr. Fangxing Fran Li at The University of Tennessee and his PhD student Qiwei Zhang during 2021-2022. This data set will be of interest to you if you conduct electricity economic simulation studies, such as economic dispatch and LMP calculation. We provide a comprehensive economic data set for WECC and NPCC systems based on real-world grid operational data. Users can access the developed data set for generator aggregation, fuel cost, generator capacity, and line rating. This dataset can be found at Github as well. See https://github.com/enliten/ENLITEN-Grid-Econ-Data/.

In this thesis, I evaluated the impact of rare diseases from a health, social, and economic perspective. This study included patients with rare diseases in Hong Kong, recruited from hospitals from the Hospital Authority, and from patient groups. Patient's family members, and other health and social care professionals were also recruited. For the evaluation of the clinical and economic implication of genomic technologies in the healthcare setting, critically ill patients with suspected monogenic disorder were recruited from the clinical setting, and were offered rapid whole-exome sequencing (rWES). DNA sample collection, library preparation, variant analysis, and data interpretation were performed. Patient's healthcare record (electronic patient record) was also reviewed. There were patient's demographic data, sequencing data and healthcare utilisation data, all of which were strictly confidential. Participants did not provide consent for the data to be shared. In addition, to evaluate the health, social, and economic consequences of rare diseases in Hong Kong, participants were recruited to complete the Client Service Receipt Inventory for the RAre disease population (CSRI-Ra), which is a tool to collect comprehensive socio-economic data in both the healthcare and social care setting. Data are sensitive and included but not limited to data on patient's demographics, HKID, rare diseases, income, social security support, employment, healthcare utilisation record, medication record, resource utilisation, informal carer support, health status, quality of life, etc. Participants were reminded that the data will be kept strictly confidential and will not be shared.

Amazon dataset was retrieved from data.world, an open-access repository. Created by @revanthkrishnaa.The DataSet contains historical sales data for 45 Amazon stores located in different regions.Dataset DescriptionThe DataSet contains historical sales data for 45 Amazon stores located in different regions. Each store contains a number of departments, and have to predict the department-wide sales for each store.In addition, Amazon runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the Dataset is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data.This file contains anonymized information about the 45 stores, indicating the type and size of store.This is the historical training data, which covers to 2019-02-05 to 2021-11-01. Within this file you following are the different fields:Store - the store numberDept - the department numberDate - the weekWeekly_Sales - sales for the given department in the given store IsHoliday - whether the week is a special holiday week Temperature - average temperature in the region Fuel_Price - cost of fuel in the regionMarkDown1-5 - anonymized data related to promotional markdowns that Amazon is running. MarkDown data is only available after Nov 2020 and is not available for all stores all the time. Any missing value is marked with an NA.CPI - the consumer price indexUnemployment - the unemployment rateFor convenience, the four holidays fall within the following weeks in the dataset (not all holidays are in the data):Super Bowl: 12-Feb-19, 11-Feb-20, 10-Feb-21, 8-Feb-18Labor Day: 10-Sep-19, 9-Sep-20, 7-Sep-21, 6-Sep-18Thanksgiving: 26-Nov-19, 25-Nov-20, 23-Nov-21, 29-Nov-18Christmas: 31-Dec-19, 30-Dec-20, 28-Dec-21, 27-Dec-18Show less

a. The maximum legal age for service members is between 17 and 62 years with few exceptions, so only those who were 17–70 years of age were included. Outliers were dropped.b. A total of 4,642,870 individuals provided only 1 specimen.Summary statistics for individuals’ ages at the time of serum collection and the timing of collections for specimens in the DoD Serum Repository.

All evaluation metrics are presented in percentage (%).

Data extraction form.

Statistical values and key attributes for residential building energy datasets.