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Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid


Climate-induced extreme weather conditions make electricity infrastructure more vulnerable. They increase the risk of power-line-ignited wildfires which can, in turn, jeopardize electric power delivery. Here, leveraging machine learning, we show that lower-income communities in California not only have lower fractions of power distribution lines undergrounded, but overhead lines and poles in their neighbourhoods are also more vulnerable to wildfires. Should they bear the cost of undergrounding fire-prone lines themselves, they would have to pay a disproportionately higher cost per household. We propose a cost allocation scheme with an income threshold below which the cost is borne by utility-wide ratepayers and above which the cost is borne locally. This scheme can not only minimize the average of undergrounding costs per household as a share of income, but also homogenize such cost–income ratios across communities. Our research demonstrates the opportunity to appropriately integrate existing policies to make electricity infrastructure affordable, equitable and reliable amidst climate change.

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Fig. 1: Geospatial distributions.
Fig. 2: Correlations between distribution grid characteristics and median household income.
Fig. 3: Standardized coefficients of multivariate regressions with distribution grid characteristics as dependent variables.
Fig. 4: The variation of relative cost per household for undergrounding with the income threshold X.
Fig. 5: Relative costs per household for undergrounding versus income given different income threshold X.

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Data availability

The data utilized or generated in this study have been deposited in figshare, available at Raw data of tree canopy can be obtained from Grid data can be obtained from and ACS data can be downloaded from Solar installation data can be downloaded from

Code availability

Code scripts were developed in Python (v.3.6). The source code is available at Required Python packages and their version numbers include: numpy (v.1.19.5), scipy (v.1.1.0), Pillow (v.5.2.0), pandas (v.0.24.2), shapely (v.1.7.1), geopandas (v.0.8.2), geojson (v.2.5.0), scikit-learn (v.0.24.2), statsmodels (v.0.9.0), plotly (v.4.14.3), pyshp (v.2.1.2), torch (v.1.1.0) and torchvision (v.0.2.2).


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This work was funded in part by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Solar Energy Technologies Office Fiscal Year 2020 Funding Program (award number DE-EE0009359) to R.R. and A.M., and by a Stanford Precourt Pioneering Project award to R.R. and A.M. The views and opinions expressed by the authors do not necessarily state or reflect those of the funding sources.

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Z.W., M.W., A.M. and R.R. conceptualized the research. Z.W. developed the methodology and analysed the data. Z.W. wrote the initial paper draft. M.W. deepened the policy insight. Z.W., M.W., A.M. and R.R. edited and revised the paper. A.M. and R.R. provided funding acquisition support for the research.

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Correspondence to Arun Majumdar or Ram Rajagopal.

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Nature Energy thanks Daniel Farber, Line Roald and Qianru Zhu for their contribution to the peer review of this work.

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Wang, Z., Wara, M., Majumdar, A. et al. Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid. Nat Energy 8, 1097–1108 (2023).

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