Confidence intervals in greenhouse gas inventories

• Grant number: 21/14296-5
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: January 01, 2023
• End date: November 30, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Paulo César Goulart de Miranda
• Grantee: Paulo César Goulart de Miranda
• Company: Deep Brasil Informação e Tecnologia S/A
• CNAE: Desenvolvimento e licenciamento de programas de computador não-customizáveis
• City: São José dos Campos
• Associated researchers: Fernanda Massaro Leonardis ; Flávia Bittencourt Moré ; Vítor Fonseca Loures
• Associated scholarship(s): 23/04133-7 - Elaboration of research routines and filters to create a database of emission factors uncertainty of CO2 and CO2e (CO2 equivalent) for corporate inventories., BP.TT; 22/15063-7 - CONFIDENCE INTERVALS IN GREENHOUSE GAS INVENTORIES, BP.TT; 22/15659-7 - CONFIDENCE INTERVALS IN GREENHOUSE GAS INVENTORIES, BP.TT

Abstract

The Deep company has as its main product a platform that collects and processes accounting and operational data through a tool adaptable to Integrated Business Management Systems (ERP). It provides real-time reports of social and environmental indicators related to the activities of the client company. The project presented in this document aims to develop a technical feasibility study of a new module to be added to the existing tool. The module intends to improve the calculation of indicators of Greenhouse Gases (GHG) emissions. Nowadays, these calculations are based on average values and, the module will include the standard deviations and other measures of variability. Thus, the innovation proposed by the module under development will estimate the confidence intervals and maximum peak emission values, which are relevant parameters to process the targets and carbon offsets. The evaluation of the technical feasibility of the project will consider three main factors: the availability of data on emission variability, the expertise in modeling carbon emissions and capture processes and, the development of algorithms to aggregate data reported in the literature. Therefore, the operational viability of the module will consider Deep institutional capacity and its team. Herewith, it will be analyzed whether the factors responsible for the successful product's implementation in its current format (without the variability module) remain present. Among these factors are: the ability to integrate with management modules (ERP systems), mastery of the principles of GHG inventory accounting, knowledge about carbon offset negotiation initiatives, and support from experts in Data Science and Statistics. This study intends to validate the economic expectations of Deep. At first, the new module will be offered to customers as a free bonus for a trial period, thenceforth, this new module should generate extra revenue from each customer. The studies should indicate whether there will be a separate charge or charged together with the original product. (AU)