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Aligning renewable energy expansion with climate-driven range shifts

Nature Climate Change volume 14, pages 242–246 (2024)Cite this article

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Abstract

Fossil fuel dependence can be reduced, in part, by renewable energy expansion. Increasingly, renewable energy siting seeks to avoid significant impacts on biodiversity but rarely considers how species ranges will shift under climate change. Here we undertake a systematic literature review on the topic and overlay future renewable energy siting maps with the ranges of two threatened species under future climate scenarios to highlight this potential conflict.

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Fig. 1: Examples of species with documented impacts from RE expansion.
Fig. 2: Present and future species distribution models of Joshua tree and kit fox highlighting the areas of overlap with RE.

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Data availability

Datasets are available in the DRYAD repository, accessible at https://doi.org/10.5061/dryad.bnzs7h4j0 (ref. 57). Private access link to download the data files: https://datadryad.org/stash/share/G6ZVrB6TIqhDxNj1_N7IWob-2Opt269EwgnsQKgMMmg.

Code availability

Code for dispersal simulations and species distribution model analysis used in this study are adopted from https://github.com/fmachados/grinnell (ref. 50) and https://github.com/marlonecobos/kuenm (ref. 54).

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Acknowledgements

Funding for U.A., A.B.S. and R.R.H. was provided by the Alfred P. Sloan Foundation’s Energy and Environment Program G-2022-17177. Funding for R.R.H. was also provided by the Agricultural Experiment Station Hatch projects CA-R-A-6689-H and CA-D-LAW-2352-H, the Energy and Efficiency Institute, the Institute of the Environment and the Department of Land, Air & Water Resources at the University of California Davis (UCD). A.B.S. was partially supported by the Alan Graham Fund in Global Change. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. We also thank the Global Ecology and Sustainability Lab (UCD) for their valuable comments that improved the manuscript. Icons for the taxonomic groups in Figs. 1 and 2 were retrieved from Noun Project (creator credits: E. Boatman, G. Lonescu, Aleks, J. Meysmans, Corpus Delicti, N. Smith, Vectors Market, M. Livolsi, G. Chicco, B. Agustín Amenábar Larraín and E. Harrison). Photos were retrieved from iNaturalist (creator credits: Chilipossum, Nmoorhatch, Opisska, Douggoldman, Jbartelett79, Johnkrampl, Milliebasden, Codrin_bucur, Ognevit and Euqirneto) and USGS (photographer credit: P. Leitner).

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Authors and Affiliations

  1. Wild Energy Center, University of California, Davis, CA, USA

    Uzma Ashraf & Rebecca R. Hernandez

  2. Department of Land, Air and Water Resources, University of California, Davis, CA, USA

    Uzma Ashraf & Rebecca R. Hernandez

  3. U.S. Geological Survey, Northeast Climate Adaptation Science Center, Amherst, MA, USA

    Toni Lyn Morelli

  4. Center for Conservation & Sustainable Development, Missouri Botanical Garden, Saint Louis, MO, USA

    Adam B. Smith

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Contributions

All authors conceived the idea for this manuscript. U.A. collected the data and conducted the analysis. U.A. and R.R.H. developed the figures and manuscript text draft. R.R.H., T.L.M. and A.B.S. edited the manuscript text and figures.

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Correspondence to Rebecca R. Hernandez.

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Nature Climate Change thanks Henriette Jager and Andrea Santangeli for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Analysis of renewable energy siting and biodiversity-related academic articles.

Cumulative number (n = 157) of renewable energy siting- and biodiversity-related academic articles published over time, categorized by tier (a, b, c) and meeting criteria for inclusion in the systematic literature review. Articles that met criteria were allocated to a three-tiered, non-exclusive classification of increasing depth if it documented the: (a) concept of biodiversity, for example, the inclusion of wildlife and other taxa, protected areas for conservation and similar overlapping topics (n = 146, 93%), (b) role of climate change on biodiversity and/or the taxonomic group(s) and/or the species of interest (n = 12, 18.4%) and (c) role of climate change as a driver of range shifts for biodiversity and/or the taxonomic group(s) and/or the species of interest (n = 2, 1.9%).

Extended Data Fig. 2 Frequency of specific keywords within the corpus.

The x-axis represents the key terms used in the search, including the six key terms per category were: (1) Biodiversity - “biodiversity,” “climate change,” “protected area,” “endangered,” “species,” and “wildlife,”; (2) Methods - “multiple criteria decision analysis,” “criteria,” “analytic hierarchy process,” “overlay analysis”, “suitability prediction,” and “maxent;” (3) Renewable Energy Siting - “energy,” “solar,” “wind,” “site,” “plan,” and “planning.” The y-axis shows the mean number of appearances of these keywords in all the articles (error bars represent 95% confidence intervals).

Extended Data Fig. 3 Alignment of renewable energy expansion with climate-driven range shifts workflow.

An example workflow showing major action steps (a) to align renewable energy expansion with climate-driven range shifts. First, research activities (for example, systematic literature review, interviews) are conducted to inform and identify an appropriate list of species that are threatened by climate change and require mitigation action. Diverse research activities (for example, systematic literature review, interviews) that capture the full knowledge system of actors and entities for a specific context and/or geography (for example, wind development in Texas) will reduce the chances of omitting a species of interest. Next, individual or batch species distribution modelling (SDM) is performed for each species and overlaid with spatially explicit models of renewable energy (RE) scenarios. Subsequent analyses are conducted to identify “Optimal RE Siting Pathways” (that is, spatial datasets) and ultimately, a set of decision outcomes that minimize conflicts with species impacted by climate change (“Decision Outcomes”). We provide a more detailed example of “Core Alignment Analyses” in (b).

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Supplementary Fig. 1 and Text 1 and 2.

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Ashraf, U., Morelli, T.L., Smith, A.B. et al. Aligning renewable energy expansion with climate-driven range shifts. Nat. Clim. Chang. 14, 242–246 (2024). https://doi.org/10.1038/s41558-024-01941-3

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