Merge pull request #265 from carbonplan/katamartin/bump-deps

Bump deps

Author: katamartin

articles/carbon-removal-mechanisms/index.md

@@ -129,7 +129,7 @@ As these examples help illustrate, differentiating carbon removal from avoided e
 
 Let’s work through examples involving biological systems. These can be more complex than the engineered systems described above because CO₂ in biological systems is constantly in flux and often harder to measure. As a result, humans have less direct control over outcomes, and need to consider a wider range of risks. The intuition we’ve developed above can help reduce those challenges.
 
-Consider a tree and its role in the carbon cycle. The tree uses photosynthesis to convert light energy into chemical energy, which it uses to convert CO₂ into compounds that make up the plant’s tissue. (For the purposes of the carbon cycle, scientists call a plant’s tissue its biomass. About 50% of the above-ground biomass of a tree is carbon from the atmosphere.<Cite id='martin.2018/>)
+Consider a tree and its role in the carbon cycle. The tree uses photosynthesis to convert light energy into chemical energy, which it uses to convert CO₂ into compounds that make up the plant’s tissue. (For the purposes of the carbon cycle, scientists call a plant’s tissue its biomass. About 50% of the above-ground biomass of a tree is carbon from the atmosphere.<Cite id='martin.2018'/>)
 
 Viewed in isolation, a tree performs carbon removal, at least for the period of its life when it is growing and therefore sequestering net CO₂ from the atmosphere into its tissues. How much? The rate depends on factors like the age and species of the tree and properties of the surrounding ecosystem, with carbon removal generally accelerating early in the growth of a tree and then eventually reaching saturation — the point at which CO₂ emissions from the decay of organic material like leaves balances the CO₂ removal from photosynthesis, and no further carbon removal occurs. During the growth and maturation of a tree — or of a forest with many trees — a cumulative quantity of carbon fluxes from the atmosphere into the biosphere.
 
@@ -297,7 +297,7 @@ Danny, Jeremy, and Toly developed the concepts and typology. All authors develop
 
 Please cite as:
 
-T Rinberg, D Cullenward, J Hamman, J Freeman (2020) “Carbon removal mechanisms” CarbonPlan <span style={{overflowWrap: 'break-word'}}>https://carbonplan.org/research/carbon-removal-mechanisms</span>
+T Rinberg, D Cullenward, J Hamman, J Freeman (2020) “Carbon removal mechanisms” CarbonPlan <span style={{overflowWrap: 'break-word'}}>[https://carbonplan.org/research/carbon-removal-mechanisms](https://carbonplan.org/research/carbon-removal-mechanisms)</span>
 
 </Endnote>
 

articles/cdr-scale-barriers/index.md

@@ -183,7 +183,7 @@ All authors helped design the project and [interview questions](https://files.ca
 
 Please cite as:
 
-N Merchant, F Chay, D Cullenward, J Freeman (2022) “Barriers to scaling the long-duration carbon dioxide removal industry” CarbonPlan <span style={{overflowWrap: 'break-word'}}>https://carbonplan.org/research/cdr-scale-barriers</span>
+N Merchant, F Chay, D Cullenward, J Freeman (2022) “Barriers to scaling the long-duration carbon dioxide removal industry” CarbonPlan <span style={{overflowWrap: 'break-word'}}>[https://carbonplan.org/research/cdr-scale-barriers](https://carbonplan.org/research/cdr-scale-barriers)</span>
 
 The full report is available as a PDF in two versions: [dark mode](https://files.carbonplan.org/CDR-Scale-Barriers.pdf) and [light mode](https://files.carbonplan.org/CDR-Scale-Barriers-Light.pdf).
 

articles/cdr-verification-explainer/index.md

@@ -83,7 +83,7 @@ Throughout the uncertainty mapping, we distinguish between CDR pathways, CDR app
 
 ### 01 — Uncertainty impact
 
-The first dimension we considered was the extent to which the uncertainty associated with a given component could impact the final estimate of carbon removal or storage duration. We classified the potential uncertainty impact of each component as having a negligible (<1%), low (1-5%), medium (5-20%), high (20-50%), or very high (>50%) potential impact. In some cases, project-level choices made within a single CDR pathway could notably change uncertainty estimates — for example, choosing among several potential biomass feedstocks or storage strategies might substantially increase or decrease uncertainty. In such cases, we assigned a range of potential impacts.
+The first dimension we considered was the extent to which the uncertainty associated with a given component could impact the final estimate of carbon removal or storage duration. We classified the potential uncertainty impact of each component as having a negligible ({'<'}1%), low (1-5%), medium (5-20%), high (20-50%), or very high ({'>'}50%) potential impact. In some cases, project-level choices made within a single CDR pathway could notably change uncertainty estimates — for example, choosing among several potential biomass feedstocks or storage strategies might substantially increase or decrease uncertainty. In such cases, we assigned a range of potential impacts.
 
 Note that this metric represents a combination of both how wide a component’s uncertainty bounds are (assuming the best practice quantification approach is followed) as well as how important the component is to the overall calculation of carbon removal or storage durability. For example, a highly uncertain component could have a low uncertainty impact if it plays a minor role in determining net carbon removal. Conversely, if the component plays a major role, it could have a relatively high uncertainty impact even if it can be estimated with only modest uncertainty.
 
@@ -191,7 +191,7 @@ This work was informed by the input and feedback from more than [30 external par
 
 Please cite as:
 
-F Chay, J Klitzke, Z Hausfather, K Martin, J Freeman, D Cullenward (2022) “Verification Confidence Levels for carbon dioxide removal” CarbonPlan https://carbonplan.org/research/cdr-verification-explainer
+F Chay, J Klitzke, Z Hausfather, K Martin, J Freeman, D Cullenward (2022) “Verification Confidence Levels for carbon dioxide removal” CarbonPlan [https://carbonplan.org/research/cdr-verification-explainer](https://carbonplan.org/research/cdr-verification-explainer)
 
 </Endnote>
 

articles/cdr-verification-explainer/methods.md

@@ -59,7 +59,7 @@ Throughout the uncertainty mapping, we distinguish between CDR pathways, CDR app
 
 ### 01 — Uncertainty impact
 
-The first dimension we considered was the extent to which the uncertainty associated with a given component could impact the final estimate of carbon removal or storage duration. We classified the potential uncertainty impact of each component as having a negligible (<1%), low (1-5%), medium (5-20%), high (20-50%), or very high (>50%) potential impact. In some cases, project-level choices made within a single CDR pathway could notably change uncertainty estimates — for example, choosing among several potential biomass feedstocks or storage strategies might substantially increase or decrease uncertainty. In such cases, we assigned a range of potential impacts.
+The first dimension we considered was the extent to which the uncertainty associated with a given component could impact the final estimate of carbon removal or storage duration. We classified the potential uncertainty impact of each component as having a negligible ({'<'}1%), low (1-5%), medium (5-20%), high (20-50%), or very high ({'>'}50%) potential impact. In some cases, project-level choices made within a single CDR pathway could notably change uncertainty estimates — for example, choosing among several potential biomass feedstocks or storage strategies might substantially increase or decrease uncertainty. In such cases, we assigned a range of potential impacts.
 
 Note that this metric represents a combination of both how wide a component’s uncertainty bounds are (assuming the best practice quantification approach is followed) as well as how important the component is to the overall calculation of carbon removal or storage durability. For example, a highly uncertain component could have a low uncertainty impact if it plays a minor role in determining net carbon removal. Conversely, if the component plays a major role, it could have a relatively high uncertainty impact even if it can be estimated with only modest uncertainty.
 

articles/cmip6-downscaling-explainer/index.md

@@ -242,7 +242,7 @@ Looking ahead, we are eager for others to explore these downscaling implementati
 
 Oriana wrote the article, with support from Jeremy, Kata, Joe, and Sadie. Oriana, Cindy (now at Meta), Max, and Joe implemented the downscaling methods. Raphael executed the dataset production. Kata and Jeremy created the map tool and article figures. John Abatzoglou (UC Merced) and Ethan Gutmann (NCAR) advised on the implementation of MACA and En-GARD, respectively.
 
-O Chegwidden, R Hagen, K Martin, M Jones, A Banihirwe, C Chiao, S Frank, J Freeman, J Hamman (2022) “Open data and tools for multiple methods of global climate downscaling" CarbonPlan <span style={{overflowWrap: 'break-word'}}>https://carbonplan.org/research/cmip6-downscaling-explainer</span>
+O Chegwidden, R Hagen, K Martin, M Jones, A Banihirwe, C Chiao, S Frank, J Freeman, J Hamman (2022) “Open data and tools for multiple methods of global climate downscaling" CarbonPlan <span style={{overflowWrap: 'break-word'}}>[https://carbonplan.org/research/cmip6-downscaling-explainer](https://carbonplan.org/research/cmip6-downscaling-explainer)</span>
 
 </Endnote>
 

articles/dac-calculator-explainer/index.md

@@ -199,7 +199,7 @@ Noah and Jennifer developed the underlying model with their collaborators Michae
 
 Please cite as:
 
-N McQueen, J Wilcox, J Hamman, J Freeman (2021) “The cost of direct air capture” CarbonPlan <span style={{overflowWrap: 'break-word'}}>https://carbonplan.org/research/dac-calculator-explainer</span>
+N McQueen, J Wilcox, J Hamman, J Freeman (2021) “The cost of direct air capture” CarbonPlan <span style={{overflowWrap: 'break-word'}}>[https://carbonplan.org/research/dac-calculator-explainer](https://carbonplan.org/research/dac-calculator-explainer)</span>
 
 </Endnote>
 

articles/fire-forests-inventories/index.md

@@ -192,7 +192,7 @@ Oriana developed the analysis with input from Sadie, Grayson, and Danny. All aut
 
 Please cite as:
 
-O Chegwidden, G Badgley, S Frank, D Cullenward (2022) “Fire, forests, and greenhouse gas inventories in California” CarbonPlan <span style={{overflowWrap: 'break-word'}}>https://carbonplan.org/research/fire-forests-inventories</span>
+O Chegwidden, G Badgley, S Frank, D Cullenward (2022) “Fire, forests, and greenhouse gas inventories in California” CarbonPlan <span style={{overflowWrap: 'break-word'}}>[https://carbonplan.org/research/fire-forests-inventories](https://carbonplan.org/research/fire-forests-inventories)</span>
 
 </Endnote>
 

articles/forest-offsets-explainer/components/triangle.js

@@ -2,14 +2,18 @@ import { Box } from 'theme-ui'
 
 const Triangle = () => {
   return (
-    <Box
-      as='svg'
-      width='10'
-      height='10'
-      viewBox='0 0 10 10'
-      sx={{ fill: 'primary' }}
-    >
-      <polygon points='4.5,0 0,10 9,10' />
+    <Box as='span' style={{ whiteSpace: 'nowrap' }}>
+      (
+      <Box
+        as='svg'
+        width='10'
+        height='10'
+        viewBox='0 0 10 10'
+        sx={{ fill: 'primary' }}
+      >
+        <polygon points='4.5,0 0,10 9,10' />
+      </Box>
+      )
     </Box>
   )
 }

articles/forest-offsets-explainer/faq.md

@@ -20,38 +20,13 @@ components:
 
 # FAQ
 
-<Box sx={{ color: 'secondary', fontSize: [2, 2, 2, 3] }}>
-  We are publishing responses to frequently asked technical questions about our
-  recent article{' '}
-  <Link
-    sx={{ color: 'secondary', '&:hover': { color: 'primary' } }}
-    href='/research/forest-offsets-explainer'
-  >
-    Systematic over-crediting of forest offsets
-  </Link>
-  . Most of these questions can be answered by reading our <Link
-    href='https://www.biorxiv.org/content/10.1101/2021.04.28.441870v1.article-info'
-    sx={{ color: 'secondary', '&:hover': { color: 'primary' } }}
-  >
-    preprint
-  </Link>, including the extended methods. To make this information more accessible,
-  we developed this FAQ.
-  <br />
-  <br />
-  Prepared by{' '}
-  {authors.map((d, i) => {
-    return (
-      <span key={i}>
-        <span key={i} style={{ whiteSpace: 'nowrap', display: 'inline-block' }}>
-          {' '}
-          {i == authors.length - 1 ? ' and ' : ''}
-          {d}
-          {i == authors.length - 1 ? '.' : ', '}
-        </span>{' '}
-        <span> </span>
-      </span>
-    )
-  })}
+<Box sx={{ color: 'secondary', '& p': { fontSize: [2, 2, 2, 3] }, '& a': { color: 'secondary', '&:hover': { color: 'primary' }}}}>
+We are publishing responses to frequently asked technical questions about our
+recent article [Systematic over-crediting of forest offsets](/research/forest-offsets-explainer). Most of these questions can be answered by reading our [preprint](https://www.biorxiv.org/content/10.1101/2021.04.28.441870v1.article-info), including the extended methods. To make this information more accessible,
+we developed this FAQ.
+
+Prepared by {authors.map((name, i) => i === authors.length - 1 ? `and ${name}` : name).join(', ')}.
+
 </Box>
 
 ## 01 — Does the paper’s “alternative common practice” estimates lead to lower precision relative to the approach taken by the Climate Action Reserve and the California Air Resources Board?
@@ -165,17 +140,16 @@ After showing that our estimates are accurate and developing a method to avoid b
 
 > Note also that any systematic bias in our estimates of CP<sub>0</sub> relative
 > to CP<sub>ARB</sub> could potentially overestimate (or underestimate) our
-> re-crediting calculations. Specifically, if we systematically overestimated CP
->
-> <sub>0</sub>, then we underestimated over-crediting; similarly, if we
+> re-crediting calculations. Specifically, if we systematically overestimated
+> CP<sub>0</sub>, then we underestimated over-crediting; similarly, if we
 > systematically underestimated CP<sub>0</sub>, then we overestimated
 > over-crediting. As reported above, our estimates of CP<sub>0</sub> are well matched
 > to CP<sub>ARB</sub> (R² = 0.94, RMSE = 9.76), and on average were 3.2% higher
-> than CP<sub>ARB</sub>. If anything, the fact that we overestimate CP
-> <sub>ARB</sub> likely makes our overall finding of net over-crediting
-> conservative. In addition, we found no evidence for a systematic relationship
-> between error in our estimate of CP<sub>0</sub> and our estimates of crediting
-> error (r = 0.06).
+> than CP<sub>ARB</sub>. If anything, the fact that we overestimate <>CP<sub>ARB</sub></>
+> likely makes our overall finding of net over-crediting conservative.
+> In addition, we found no evidence for a systematic relationship between error in
+> our estimate of CP<sub>0</sub> and our estimates of crediting error (r =
+> 0.06).
 
 In other words, our reproduction of CARB’s common practice (CP<sub>0</sub>) is a little too high on average; and because the formula for our alternative estimate of common practice (CP<sub>NEW</sub>) divides by our re-estimate of CARB’s common practice (CP<sub>0</sub>), our alternative common practice variable is too low; and because increases in our alternative common practice result in more over-crediting, our estimate of over-crediting is conservative.
 

articles/forest-offsets-explainer/index.md

@@ -141,20 +141,11 @@ This is best illustrated in the spatial pattern of projects in the Southern Casc
     practice number that distorts ecological reality. The number in each
     subregion shows the relative carbon compared to the supersection average, in
     units of tCO₂ per acre. The western-most carbon-rich subregion (
-    <Box as='span' sx={{ color: 'green' }}>
-      green
-    </Box>
-    ) contains almost all of this supersection’s offset projects <span
-      style={{ whiteSpace: 'nowrap' }}
-    >
-      (<Triangle />)
-    </span>, which earn credits simply by having forests with higher carbon levels
-    than the supersection average. Select a project <span
-      style={{ whiteSpace: 'nowrap' }}
-    >
-      (<Triangle />)
-    </span> to see details including ID, developer, and our estimate of crediting
-    error.
+    <Green>green</Green>) contains almost all of this supersection’s offset
+    projects <Triangle />, which earn credits simply by having forests with
+    higher carbon levels than the supersection average. Select a project{' '}
+    <Triangle /> to see details including ID, developer, and our estimate of
+    crediting error.
   </FigureCaption>
 </Figure>
 
@@ -190,7 +181,7 @@ G Badgley, J Freeman, J Hamman, B Haya, A T Trugman, W R L Anderegg, D Cullenwar
 
 Please cite this web article as:
 
-G Badgley, J Freeman, J Hamman, B Haya, A T Trugman, W R L Anderegg, D Cullenward (2021) “Systematic over-crediting of forest offsets” CarbonPlan <span style={{overflowWrap: 'break-word'}}>https://carbonplan.org/research/forest-offsets-explainer</span>
+G Badgley, J Freeman, J Hamman, B Haya, A T Trugman, W R L Anderegg, D Cullenward (2021) “Systematic over-crediting of forest offsets” CarbonPlan <span style={{overflowWrap: 'break-word'}}>[https://carbonplan.org/research/forest-offsets-explainer](https://carbonplan.org/research/forest-offsets-explainer)</span>
 
 </Endnote>