JOURNAL ARTICLE

Expanding the paradigm of evaluating community benefits in investments in grid resilience utilising a balanced scorecard approach.

  • Published In: Journal of Business Continuity & Emergency Planning, 2025, v. 19, n. 3. P. 241 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Schlegelmilch, Jeff; Hansen, Sean; Hristova, Ilina; Krasniqi, Qëndresa; Potter, Alexandra; Ratner, Jacqueline; Samur, Antonia 3 of 3

Abstract

This article focuses on developing and testing a balanced scorecard approach to evaluate community benefits from investments in electric grid resilience. The National Center for Disaster Preparedness at Columbia University, in partnership with the utility ComEd, created a multi-domain scorecard incorporating economic, public health, social vulnerability, and critical infrastructure variables to quantify resilience and vulnerability at the community level. The scorecard aims to support more informed, equitable investment decisions by integrating publicly available data with community-specific factors, though it is limited by data granularity, update frequency, and geographic applicability primarily within the US. Case studies applying the scorecard to diverse census tracts demonstrated its potential to identify varying vulnerabilities and inform tailored resilience strategies, while emphasizing the need to complement quantitative analysis with community engagement.

Additional Information

  • Source:Journal of Business Continuity & Emergency Planning. 2025/03, Vol. 19, Issue 3, p241
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:1749-9216
  • DOI:10.69554/pyok2320
  • Accession Number:183384435
  • Copyright Statement:Copyright of Journal of Business Continuity & Emergency Planning is the property of Henry Stewart Publications LLP and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.