Cover: Participatory Modeling of Climate Change Impacts on Public Health in Long Beach, California

Participatory Modeling of Climate Change Impacts on Public Health in Long Beach, California

Discussion from a Workshop Hosted by the RAND Frederick S. Pardee Center for Longer Range Global Policy and the Future Human Condition

Published Mar 7, 2018

by Laura Schmitt Olabisi, Gulrez Shah Azhar, Michele Abbott, Robert J. Lempert

Download Free Electronic Document

FormatFile SizeNotes
PDF file 0.1 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

Research Question

  1. Could causal loop diagramming, an exercise that supports the conceptualization of policy problems spanning multiple sectors and engagement in long-range strategic thinking, be used to explore the systemic connections between climate change and public health issues?

Participatory modeling aims to incorporate stakeholders into the process of developing models for the purpose of eliciting information, appropriately reflecting stakeholder interests and concerns, and improving stakeholder understanding, and acceptance of the analysis. Participatory modeling, using causal loop diagramming (CLD), was used to explore the impact of climate change on public health in Long Beach, California. CLD, commonly used in participatory modeling, provided useful information to serve as the basis for a quantitative system dynamics model to protect the citizens of Long Beach, and potentially other cities or regions affected by climate change.

Diverse stakeholders constructed CLDs depicting the impacts of climate change on public health in Long Beach. This exercise aimed to (1) identify public health issues that might be caused or exacerbated by climate change; (2) examine the systemic connections between climate change and other drivers of public health/illness and mortality; and (3) identify feedback loops to gain an understanding of how climate change could impact public health over coming decades.

Six groups of five stakeholders were tasked with depicting the impacts of climate change on public health. Each group designated a key health outcome of concern on a citywide scale, including critical drivers of the outcome at higher and lower scales if necessary (for example, state laws, or household-level decisions that affect health outcomes in the aggregate). Social, environmental, political, and economic variables were all considered. After the small group diagramming exercise, groups presented diagram results to other participants, and the discussion around the diagrams was recorded.

Key Finding

Participatory Modeling, Using Causal Loop Diagramming (CLD), Provides a Suitable Tool for Understanding Interlocking Causes and Results of Climate Change on a Vulnerable Location

  • Overall, participatory modeling using CLD clarified the systemic nature of the public health impacts of climate change in Long Beach, California, by revealing potential policy tradeoffs, as well as underlying socioeconomic drivers of vulnerability to extreme heat.


  • Future exercises could work to include those most vulnerable to climate change impacts (for example, the poor, elderly, and those with chronic health conditions), who were not represented at this workshop.
  • As time did not allow for quantitative simulation of the conceptual models developed by the stakeholders, future development of this work could include a quantitative model based on the CLDs, and data collection around some of the key variables in the model.

Research conducted by

This project is a RAND Venture. Funding was provided by gifts from RAND supporters and income from operations. These proceedings were hosted by the Pardee Center for Longer Range Global Policy and the Future Human Condition.

This report is part of the RAND conference proceeding series. RAND conference proceedings present a collection of papers delivered at a conference or a summary of the conference.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.