Development of a risk assessment software for cumulative effect

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2025-01-03 DOI:10.1016/j.mex.2025.103155
Effah Kwabena Antwi , Gifty Osei , Wiafe Owusu-Banahene , John Boakye-Danquah , Prisca-Maria Okafor , Kobina Korankye , Akua Nyamekye Darko , Priscilla Toloo Yohuno (Apronti)
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引用次数: 0

Abstract

Regional Risk Assessment is essential for evaluating the environmental impacts of large-scale resource development projects. However, existing Regional Asessement (RA) frameworks often lack generalizability which hinders result standardization. To address these challenges, the Risk Assessment Framework for Cumulative Effects (RAFCE) was developed to provide a standardized approach for impact identification, prioritization, and mitigation during RA. Despite these strengths, the RAFCE's reliance on spreadsheet-based manual data entry and calculations, coupled with the absence of collaborative features, increases the risks of human error and inflates operational costs including time taken to complete an RA.
This paper proposes a software implementation of RAFCE to enhance efficiency and accuracy in the RA process. This is a novel approach that provides a platform unique of its kind for systematically evaluating the cumulative effects of resource exploration by multiple stakeholders. The development process involved three main steps:
  • Developing a NoSQL Database to efficiently store and retrieves RA data,
  • Implementing an API and Backend with Java Spring Boot automates critical functionalities and
  • Building a React-based Frontend Development: that provides a user-friendly interface, that simplifies data entry and software interaction.
By automating calculations and improving the user interface, the proposed software mitigates the risks associated with manual processes and significantly reduces the cost and time required for the RA process, thereby enhancing its reliability and efficiency.

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MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
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