Developing disaster mutual assistance decision criteria for electricity industry
Bibliographic record
Abstract
Purpose Disaster mutual assistance (DMA) or mutual aid is a reciprocal arrangement between organizations that permits and prearranges one company to access resources from another company to recover from disaster impacts faster. As a practical tool to access response resources quickly, DMA can be an important element of an effective emergency management process, but the decision to provide (or not to provide) DMA is challenging and involves a number of factors. The purpose of this paper is to present the results of a study conducted to identify DMA decision criteria and their weights based on electricity companies operating in North America. Design/methodology/approach The authors employed a combination of Delphi and analytical hierarchy process (AHP) methods. Delphi method identified the decision criteria that should be considered before electricity utilities enact their DMA agreements. A standard AHP calculated the weights of identified DMA criteria. Findings In total, 11 criteria were identified and classified into three main groups: responding criteria, requesting criteria and disaster criteria. Of the 11, “Emergency Conditions” within the responding criteria group, “Extent of Damage” of the requesting criteria group, and “Size of Disaster”, associated with the disaster criteria group, had the highest weight. Three other factors (“Work Safety Practice”, “Natural Hazards” and “Availability of Resources”) carried a noticeable weight difference, while the remaining factors were weighted relatively lower. Practical implications At present, a decision to provide mutual assistance is highly subjective, based on “gut feel”, and dependent on interpersonal relationships between the requestor and the provider. However, mobilizing and dispatching electricity industry crews is a risky and costly operation for both requesting and responding companies and requires careful assessment for which a cost-benefit threshold has not been developed. This cost-benefit perspective is often frowned upon owing to the intended altruistic nature of DMA agreements and its influence on decision makers. The developed criteria in this study are intended to assist electricity companies in making a more informed and quantifiable decision when deliberating a request for mutual assistance. These criteria may also be used by assistance-requesting companies to better identify electricity companies that are more likely to provide assistance to them. Originality/value This study contributes to the literature by examining the current state of DMA in electricity utilities, identifying decision criteria and weighing such criteria to enable electricity companies in making more objective decisions, thereby, increasing the overall effectiveness of their disaster management process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".