paper values update + MET

paper/PaperCEUS/PaperCEUS.Rmd

@@ -20,7 +20,7 @@ address:
 abstract: |
   A high proportion of car trips can be replaced by a combination of public transit and cycling for the first-and-last mile. This paper estimates the potential for cycling combined with public transit (PT) as a substitute for car trips in the Lisbon metropolitan area and assesses its socio-environmental impacts using open data and open source tools.
   A decision support tool that facilitates the design and development of a metropolitan cycling network was developed (_biclaR_). The social and environmental impacts were assessed using the _HEAT for Cycling_ and the _HEAT as a Service_ tools. The impacts of shifting car trips to PT were also estimated and monetized.
-  The results indicate that 10% of trips could be made by bicycle + PT combination. Shifting to cycling for the first-and-last mile stages can reduce annual CO~2~eq emissions from 6,000 tons/day, with benefits over 10 years of at least €230 million. For the PT leg, the transfer from car avoids of up to 20,500 tons of CO~2~eq emissions per year.
+  The results indicate that 10% of trips could be made by bicycle + PT combination. Shifting to cycling for the first-and-last mile stages can reduce annual CO~2~eq emissions from 3,000 to 7,500 tons/day, while for the PT leg, the transfer from car avoids of up to 20,500 tons of CO~2~eq emissions per year. The estimated socio-environmental benefits are of €125 million to €325 million over 10 years.
   This evidence can support policymakers to prioritize interventions that reduce the reliance on private motor vehicles.
   \
 keywords: 
@@ -65,7 +65,6 @@ linkcolor: blue
 ---
 
 <!-- TODO: -->
-<!-- check if HEAT estimates remain the same (the tool is not working atm) -->
 <!-- should we ignore the social benefits component? and only estimate and add the environmental ones? or estimate the car to PT transfer Social benefits? maybe to the next paper? -->
 <!-- should we keep both direct and safe routing is is it just noise? -->
 
@@ -79,8 +78,6 @@ library(dplyr)
 
 # Introduction
 
-<!-- **full paper**: 4-6 pages in length (typically up to 3,000 words). we have 2849 words before references, or 2278 words without tables -->
-
 Combining public transportation (PT) and cycling for the first and last mile in metropolitan areas can significantly replace private car trips [@MARTENS2007326; @RIETVELD200071].
 This approach requires interventions and programs to make bicycling more appealing, and the resulting public investments can have significant social and environmental benefits.
 <!-- Many studies refer to the potential of cycling and bike-sharing to replace car and PT trips, but not to the potential of serving as first and last miles. --> 
@@ -158,24 +155,21 @@ Different routing profiles enable decision-makers to plan for different bicycle
 
 [^4]: see [docs.conveyal.com/learn-more/traffic-stress](https://docs.conveyal.com/learn-more/traffic-stress).
 
-<!-- The quietness level^[see https://www.cyclestreets.net/help/journey/howitworks/#quietness] was another indicator estimated by [CycleStreets](https://www.cyclestreets.org/) for each route section, based on road network variables xisting on OpenStreetMap labels, such as the number of lanes, maximum allowed speed, trees, or road hierarchy. Values vary on a scale of 0 to 100, where zero corresponds to the least safe level for cycling and 100 corresponds to the safest and quietest level, usually corresponding to sections that already include segregated cycling infrastructure away from motorized traffic.  I THINK THIS IS NOT RELEVANT FOR THIS SHORT PAPER, AS IT IS MOST USEFUL FOR VIZUALIZATION PURPOSES ON BICLAR TOOL-->
-
 The `r5r` model used the OpenStreetMap road network and the GTFS metropolitan data aggregated and validated.
 This information is crucial for an accurate PT trip and route estimation.
 A digital elevation model, from the European Space Agency's COPERNICUS mission, was used to include street gradient information, as a weight in cycling routing. <!--  with a 25 m spatial resolution, -->
 The cycling potential trips for the two national strategic targets (4% and 10%) were estimated from the 2017 cycling and car trips (both as a driver and as a passenger), the baseline scenario.
 
 The routes were then overlaid and aggregated by segments, using [`stplanr overline()` R function](https://docs.ropensci.org/stplanr/reference/overline.html).
-<!-- For example, the estimated volumes of bicycle trips and car transfers were aggregated, and the average car speed and quietness level, weighted by the distance of the segments, were calculated. -->
 
 ## Modeling intermodality
 
 The intermodality scenario considers trips combining PT and cycling for the first and last legs.
-In a conservative approach, we have restricted our analysis to the first and last legs with a combined length of up to 5 km (for instance: 1 km from origin to interface A plus 4 km from interface B to destination) or up to 25 minutes on bike.
+In a conservative approach, we have restricted our analysis to the first and last legs with a combined length of up to 5 km (for instance: 1 km from origin to interface _A_ plus 4 km from interface _B_ to destination) or up to 25 minutes on bike.
 Furthermore, we have imposed restrictions on PT usage to include only trips with no PT transfers, and up to 2 hours (120 min).
 Additionally, we have only included PT modes that can easily accommodate bicycles, such as trains, ferries, trams, and inter-municipal bus lines equipped with bike racks (Figure \ref{fig:map1}).
 
-```{r map1, out.width="60%", fig.cap="Interfaces and lines considered, by transport mode, in the Lisbon metropolitan area", fig.align='center'}
+```{r map1, out.width="60%", fig.cap="Interfaces and lines considered, by transport mode, in the Lisbon metropolitan area.", fig.align='center'}
 knitr::include_graphics("img/map1.png", error = FALSE)
 ```
 
@@ -189,17 +183,20 @@ knitr::include_graphics("img/map2.png", error = FALSE)
 
 <!-- The impacts were assessed for the total metro area. -->
 
-For the cycling legs of the journey (first and last legs), socio-environmental impacts were estimated, using the HEAT for Cycling tool v5.2 [@HEAT] from the World Health Organization, and the [`HEATaaS` R package](https://github.com/HEAT-WHO/HEAT_heatr_api)[^5].
-The HEAT tool provided estimates on the shifting from car to cycling for a short term time horizon (i.e., one year) and the long term (i.e., ten years).
-We considered two dimensions: *social* — including the physical activity of cyclists, air pollution exposure, and road casualties; *environmental* — including CO~2~eq emissions and other pollutants. 
+For the *cycling legs of the journey* (first and last legs), socio-environmental impacts were estimated, using the HEAT for Cycling tool v5.2 [@HEAT] from the World Health Organization, and the [`HEATaaS` R package](https://github.com/HEAT-WHO/HEAT_heatr_api)[^5]. 
+The use of this package made it possible to run multiple scenarios with few changes in input values, making the interaction with HEAT more reliable when reproducing runs.    
+The HEAT tool provided estimates on the shifting from car to cycling for a short term time horizon (i.e., one year) and the long term (i.e., ten years). It estimates the differences between two considered scenarios. In this case: one baseline scenario, with data from the mobility survey, and one cycling potential scenario in which targets of 4% and 10% of cycling levels were achieved, transferred from car trips.
+We considered two dimensions: *social* — including the physical activity, air pollution exposure, and road casualties; and *environmental* — including CO~2~eq emissions and other pollutants. 
 
 [^5]: `HEATaaS` is under development. For more information contact [heatwalkingcycling.org](https://heatwalkingcycling.org).
 
 <!-- For the full paper, consider refer the input HEAT values (tabela 6) -->
-<!-- Explain why the use of the HaaS tool instead of the website (3 scenarios, 18 municipalities, 2 routing profiles) -->
+
+<!-- include a map or image with the legs and all trip? by mode  -->
+
 <!-- Also consider put the FE formula and emmission factor values - see report -->
 
-For the second leg of the journey, we estimate the additional environmental impacts of shifting car trips to PT (between the PT interfaces).
+For the *second leg of the journey*, we estimate the environmental impacts of shifting car trips to PT (between the PT interfaces).
 
 To estimate the car emissions, we used the EMEP/EEA's COPERT software v5 methods and reference values [@COPERT] for a Tier 3 detail level. 
 We used a family-size vehicle, EURO standard, and gasoline or diesel fuel.
@@ -307,7 +304,7 @@ knitr::kable(summary21 |> select(-Baseline),
 
 <!-- These findings indicate that transferring car trips to a combination of bicycle and PT could be nearly as substantial, if not equally significant, as the shift towards bicycle-only trips. WE CANNOT SHOW THIS IN THIS PAPER WITH ONLY SCENARIO 3 DATA --> 
 
-Table \ref{tab:summary22} presents the results of the avoided emissions and its monetization for the second leg of the journey, by replacing car trips with potential TP trips.
+Table \ref{tab:summary22} presents the results of the avoided emissions and its monetization for the second leg of the journey, by replacing car trips with potential TP trips.  
 <!-- We estimate a reduction of 8,702 tons of CO2eq emissions by substituting motorized trips relying on fossil fuels and electricity. These estimations used an electricity production life cycle approach. -->
 <!-- this table could be split into Social benefits and Environmental benefits -->
 Regarding the PT segment, the shift from private car would lead to the mitigation of CO~2~ equivalent emissions to 8,500 to 20,800 tons annually, valued in €1.4 million to €3.5 million yearly.
@@ -348,7 +345,8 @@ summaryall = summary0 |>
   mutate(CO2eqT = CO2eq + CO2eq_TP,
          value1 = Environmenal_only + Value + Social, # this is CORRECT
          value10 = Environmenal10_only + Value*10*rate + Social10) |> # this is CORRECT
-  select(Target, Routing, CO2eqT, value1, value10)
+  select(Target, Routing, CO2eqT, value1, value10) |> 
+  arrange(desc(Target))
 
 knitr::kable(summaryall,
       digits = 0,
@@ -362,23 +360,24 @@ knitr::kable(summaryall,
 
 ```
 
-<!-- FOR OTHER PAPER: separate the Socio impacts from the Environmental ones? It shouldn't be ok to sum them -->
+<!-- FOR OTHER PAPER: separate the Socio impacts from the Environmental ones? -->
 Shifting from car to cycling + PT can reduce annual CO~2~eq emissions by 14,000 to 36,000 tons per year. 
-The 10-year socio-environmental benefits account for €235 million to €620 million, depending on the cycling targets.
-
-The social impacts represent 98% of the socio-environmental benefits (in value) from replacing car trips to bicycle in first-and-last legs. For the PT segment, we did not estimate the social impacts from substituting car trips, although its health benefits would not be as high as shifting to cycling.<!-- We should estimate the social impacts from replacing Car to PT in the second leg, for the other paper --> <!--Overall there was very little evidence available on mode switch to public transport. -->
-
-The emissions of CO~2~eq that are avoided during both the initial and final journey segments account for about 75% of the emissions avoided during the PT segment. This finding, while expected -- due the zero cycling emissions, should not be overlooked when promoting the PT use.
+The 10-year socio-environmental benefits account for €125 million to €325 million, depending on the cycling targets.
+
+The environmental impacts represent less than 2% of the socio-environmental benefits (in value) from replacing car trips to bicycle in first-and-last legs. 
+For the PT segment, we did not estimate the social impacts from substituting car trips. 
+One of the main socio-environmetal benefits, valued after monetization, comes from the increase in physical activity [@Felix2023ES].
+Although are also social benefits form shifting car trips to PT, its health benefits would not be as high as shifting to cycling. 
+The literature shows that the Metabolic Equivalent Tasks (MET) for "riding in a bus or a train" is 1.3 plus the "walking for transportation" as 3.5, while "driving a car" is 2.5 [@MET2011].
+The difference between these activities is not very obvious when compared to cycling, whose equivalent MET is between 6.8 to 7.5.
+Nevertheless, future works should also encompass the estimation of the social impacts for the PT leg of the journey, shifting from car.
+ <!--Overall there was very little evidence available on mode switch to public transport. -->
+
+The emissions of CO~2~eq that are avoided during both the initial and final journey segments account for about 74% of the emissions avoided during the PT segment. This finding, while expected -- due the zero cycling emissions, should not be overlooked when promoting the PT use.
 Improving the safe accessibility to PT interfaces to cyclists and providing bicycle-friendly amenities such as parking facilities can potentially lead to a higher reduction in CO~2~eq emissions, compared to a scenario where individuals shift from car travel to car + PT combination.
 
 Our findings suggest that cycling and PT *in combination* could viably replace 10% of current LMA trips, with an additional 6% of PT journeys prone to further substitution. 
-<!-- repeated? not correct -->
-
-There are also social benefits form shifting car trips to PT, nevertheless those were not estimated in this research.
-One of the main socioenvironmetal benefits, valued after monetization, comes from the increase in physical activity [@Felix2023ES].
-The literature shows that the Metabolic Equivalent Tasks (MET) for "riding in a bus or a train" is 1.3 plus the "walking for transportation" is 3.5, while driving a car is 2.5. see https://golf.procon.org/met-values-for-800-activities/
-The difference is not that much between them.
-Future works should also encompass the estimation of the social impacts for the PT leg of the journey, shifting from car.
+<!-- repeated? -->
 
 
 # Conclusion