Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors

James R. Oakleaf, Christina M. Kennedy, Sharon Baruch-Mordo, James S. Gerber, Paul C. West, Justin A Johnson, Joseph Kiesecker

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

Mapping suitable land for development is essential to land use planning efforts that aim to model, anticipate, and manage trade-offs between economic development and the environment. Previous land suitability assessments have generally focused on a few development sectors or lack consistent methodologies, thereby limiting our ability to plan for cumulative development pressures across geographic regions. Here, we generated 1-km spatially-explicit global land suitability maps, referred to as “development potential indices” (DPIs), for 13 sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). To do so, we applied spatial multi-criteria decision analysis techniques that accounted for both resource potential and development feasibility. For each DPI, we examined both uncertainty and sensitivity, and spatially validated the map using locations of planned development. We illustrate how these DPIs can be used to elucidate potential individual sector expansion and cumulative development patterns.

Original languageEnglish (US)
Article number101
JournalScientific Data
Volume6
Issue number1
DOIs
StatePublished - Jun 27 2019

Bibliographical note

Funding Information:
This work was supported by The Nature Conservancy, Anne Ray Charitable Trust, MacArthur Foundation, The Robertson Foundation, donation from Roy Vagelos, and the 3M Foundation. We thank Colorado State University Geospatial Centroid for their digitizing and tabular data development. We also thank all data providers who contributed open source data used in our analysis, thereby providing a better understanding of the world we live in. Special thanks to Phil Connors who mentored us through the constant effort of improving this analysis and led by example on how to eventually finish a lengthy project.

Publisher Copyright:
© 2019, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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