<p>This research brief introduces a novel quantity to estimate the population burden of environmental emergency orders: <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11113_2025_9942_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{\text{E}\text{O}}_{pd,j}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mspace width="0.222222em" /> <msub> <mtext>EO</mtext> <mrow> <mi>p</mi> <mi>d</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation>, the person-days, <i>pd</i>, in geographic region, <i>j</i>, under an emergency order (EO). Using the case of evacuations, we pair spatial data on every mandatory evacuation for wildfires and floods in British Columbia from 2017 to 2023 with local data on population demographics from the 2016 Canadian census. Empirically, we describe the <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11113_2025_9942_Article_IEq2.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{\text{E}\text{O}}_{pd,j}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mspace width="0.222222em" /> <msub> <mtext>EO</mtext> <mrow> <mi>p</mi> <mi>d</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation> over years and by hazard at the provincial level, and then estimate community-level <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11113_2025_9942_Article_IEq3.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{\text{E}\text{O}}_{pd,j}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mspace width="0.222222em" /> <msub> <mtext>EO</mtext> <mrow> <mi>p</mi> <mi>d</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation> and bivariate correlations with sociodemographics across census subdivisions (municipalities and equivalents). During this period, the provincial-level <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11113_2025_9942_Article_IEq4.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{\text{E}\text{O}}_{pd,j}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mspace width="0.222222em" /> <msub> <mtext>EO</mtext> <mrow> <mi>p</mi> <mi>d</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation> was 2.71&#xa0;million person-days under mandatory evacuation order. Evacuation mandates affected 34% of subdivisions, and among affected communities, the average <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11113_2025_9942_Article_IEq5.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{\text{E}\text{O}}_{pd,j}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mspace width="0.222222em" /> <msub> <mtext>EO</mtext> <mrow> <mi>p</mi> <mi>d</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation> was 12,085 person-days. We find that per-capita <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11113_2025_9942_Article_IEq6.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{\text{E}\text{O}}_{pd,j}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mspace width="0.222222em" /> <msub> <mtext>EO</mtext> <mrow> <mi>p</mi> <mi>d</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation> correlated positively with the share of Indigenous people, low-income residents, and adults with high-school-or-less education. Person-time measures of emergency orders provide insight into the population burden of environmental hazards across contexts, can easily be extended to other cases, such as air quality alerts or heat warnings, and may help demographers study direct environmental effects or effect moderation across places.</p>

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Person-Days Under Emergency Order: A Research Brief on Wildfire and Flood Evacuation Mandates in British Columbia

  • Ethan J. Raker,
  • Xueqing Zhang

摘要

This research brief introduces a novel quantity to estimate the population burden of environmental emergency orders: \(\:{\text{E}\text{O}}_{pd,j}\) EO p d , j , the person-days, pd, in geographic region, j, under an emergency order (EO). Using the case of evacuations, we pair spatial data on every mandatory evacuation for wildfires and floods in British Columbia from 2017 to 2023 with local data on population demographics from the 2016 Canadian census. Empirically, we describe the \(\:{\text{E}\text{O}}_{pd,j}\) EO p d , j over years and by hazard at the provincial level, and then estimate community-level \(\:{\text{E}\text{O}}_{pd,j}\) EO p d , j and bivariate correlations with sociodemographics across census subdivisions (municipalities and equivalents). During this period, the provincial-level \(\:{\text{E}\text{O}}_{pd,j}\) EO p d , j was 2.71 million person-days under mandatory evacuation order. Evacuation mandates affected 34% of subdivisions, and among affected communities, the average \(\:{\text{E}\text{O}}_{pd,j}\) EO p d , j was 12,085 person-days. We find that per-capita \(\:{\text{E}\text{O}}_{pd,j}\) EO p d , j correlated positively with the share of Indigenous people, low-income residents, and adults with high-school-or-less education. Person-time measures of emergency orders provide insight into the population burden of environmental hazards across contexts, can easily be extended to other cases, such as air quality alerts or heat warnings, and may help demographers study direct environmental effects or effect moderation across places.