![]() The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe. Computers & Industrial Engineering, 101, 528–543. Big data applications in operations/supply-chain management: A literature review. Identification properties of recent production function estimators. Finally, using expenditures related to digital transformation, we find that digital transformation imposes a cost burden and validate that using annual report information to measure corporate digitalization has veracity.Īckerberg, D. Mechanistic studies show that the direct effect of digital transformation increases firms’ electricity consumption and electricity intensity, thus increasing carbon emissions and carbon intensity the indirect effect reduces carbon emissions and carbon intensity by increasing firms’ productivity, alleviating their financing constraints, saving energy consumption, and reducing energy intensity. Digitalization has a greater impact on firms in high-carbon emitting industries and on non-state-owned enterprises. After replacing emissions and digitization measures, excluding confounding policy and potential omitted variables, the conclusions still hold. To address the endogeneity, we employ propensity score matching method and “Broadband China” as a quasi-natural experiment. In this paper, we explore the impact of digital transformation on corporate carbon performance using data from the pollution emissions and taxation survey data of Chinese listed companies from 2009–2015 and find that digital transformation can significantly reduce carbon emissions by 8% and carbon intensity by 10%. Note: Documents for previous caNanoLab releases are available from the caNanoLab Presentations, Demos and Other Materials Archive and the Docs Archive Page - calab.The relationship between digital transformation and carbon emissions at the firm level remains unclear. To access online help in the application, click the Help button. Note: Materials for previous caNanoLab releases are available from the caNanoLab Presentations, Demos and Other Materials archive. The caNanoLab Data Submission Video and accompanying transcript is available to assist people in submitting data in caNanoLab. ![]() Application Support: Demos and Other Materials.caNanoLab User Forum - Forum for recommending caNanoLab enhancements.Installation and Downloads caNanoLab 3.1.8 Artifacts If you are interested in submitting your data into caNanoLab, contact data curation steps are explained in the Curation of Biomedical Data into ISA-TAB-Nano and caNanoLab Standard Operating Procedure. Data curators assist in extracting data from publications and nanotechnology experiments, annotating extracted data, and providing overall data quality control. Data curation activities on caNanoLab are currently performed by the NCI Alliance for Nanotechnology in Cancer Data Coordinators and the NCI data curator. Data Curationĭata curation is a key component for ensuring that data is appropriately annotated to facilitate data sharing in a semantically interoperable fashion. Publications and reports from nanotechnology studies in biomedicineĬaNanoLab was developed as a collaboration between the NCI Center for Biomedical Informatics and Information Technology (CBIIT), the NCI Nanotechnology Characterization Laboratory (NCL), and the NCI Office of Cancer Nanotechnology Research (OCNR).In Vitro characterizations such as cytotoxicity, blood contact properties, oxidative stress, and immune cell functions.Physico-chemical characterizations including size, molecular weight, shape, physical state, surface chemistry, purity, solubility, and relaxivity. ![]() ![]()
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