Difference between revisions of "Country correction"

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This page explores how the size of the country can be taken into account in the determination of the eco-score transport bonus.
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[[Category:Eco-Score]]
 +
This page explores how the size of the country can be taken into account in the determination of the [[Eco-score transport - en|eco-score transport bonus]].
  
 
== Bonus approach ==
 
== Bonus approach ==
−
The eco-score approach intends to give a bonus to locally produced (and purchased) products. This bonus is +15, which is equal to a transport score of 100. This bonus is however now given to any product produced/purchased in France. No account has been taken of the very large size of France and impact of transport within the country.
+
The eco-score approach intends to give a bonus to locally produced (and purchased) products. This bonus is +15, which is equal to a transport score of 100. This bonus is however now given to any product produced/purchased in France. No account has been taken of the very large size of France and impact of transport within the country.
  
 
Can this approach be adapted, so that the size of a country is taken into account?
 
Can this approach be adapted, so that the size of a country is taken into account?
  
−
== Maximum bonus ==
+
It would be more logic to give the maximum bonus (transport score of 100) only to very local production and purchase. Ideally this will imply an area as small as possible, i.e. a city or very small country. Production and purchase that occurs in a country such as Andorra, Monaco, Liechtenstein or San Marino could be viewed as having a transportation score of 100.
−
It would be logic to give the maximum bonus (transport score if 100) only to very locally production and purchase. Ideally this will imply an area as small as possible, i.e. a city or very small country. Production and purchase that occurs in a country such as Andorra, Monaco, Liechtenstein or San Marino could be viewed as having a transportation score of 100.
 
  
−
== No bonus ==
 
 
In the current calculation the no bonus (transportation score 0) is (arbitrarily) set at a distance of 2000 km. This distance covers roughly the size of Europe.
 
In the current calculation the no bonus (transportation score 0) is (arbitrarily) set at a distance of 2000 km. This distance covers roughly the size of Europe.
  
−
== Centroids to the rescue ==
+
== Centroids to the rescue? ==
 
Is it possible to use the centroid calculations in determining the (transportation) size of a country?
 
Is it possible to use the centroid calculations in determining the (transportation) size of a country?
  
−
The weighted sum for a country that consists of single city will be zero. This can then correspond to a transportation (bonus) score of 100. This means it is preferred to have no transportation.
+
The weighted sum for a country that consists of single city will be zero. This means there is no transportation required. The country size is then zero.  
  
−
The weighted sum of the major cities in the European Union (or larger area) could be used to define the transportation score of 0. This means a maximum transportation impact.
+
The centroid approach can be used to calculate the country transportation size. We just have to add all cities, so that the entire population is covered. The resulting weighted distance is the size we are looking for. This size is the average distance to the entire population.
  
−
From Any weighted sum in between it is now possible to calculate a country transportation score. The larger the country, the lower the country transportation score. This country transportation score can be used to determine the maximum bonus for a country.
+
How can the centroid data be used and does it need to be extended?
−
 
 
−
The centroid approach can also be used to calculate the country transportation size. We just have to add all cities, so that the entire population is covered. The resulting weighted distance is the size we are looking for.
 
−
 
 
−
In practice this approach is not feasible. It is just to much work to (to do by hand a least). So we need a way to get a reasonable approximation. For the calculation of the centroids a limited number of cities are used. We could try to extrapolate this data to get the transportation country size.
 
  
 
== Approach ==
 
== Approach ==
−
In the figure the results for Maroc are shown.
+
In the figure the weighted distances for France using the departments are shown.
−
[[File:TransportationSizeMaroc.png|thumb|center|The population road-distance weighted transportation size of Maroc]]
+
[[File:France transportation size.png|thumb|The population road-distance weighted size of France based on the departments]]
−
The cumulative population weighted distances are plotted against the cumulative population ratios. Each added city adds a point, increasing the cumulative population ratio and the cumulative distance. If the cumulative population ratio is 1, we have covered the entire population. And the corresponding cumulative distance is the value we are looking for.
+
The cumulative population road-distance weighted distances are plotted against the cumulative population ratios. Each added city adds a point, increasing the cumulative population ratio and the cumulative distance. If the cumulative population ratio is 1, we have covered the entire population. And the corresponding cumulative distance is the value we are looking for.
  
−
Instead of adding all the cities, we could fit a line to the data of a few city points. The cumulative distance of the fit at a population ratio of 1, is then again the value we are looking for.
+
=== Fit function ===
 +
Adding all the cities is to much work. Instead of adding all the cities, we could fit a line to the data of a few city points. The cumulative distance of the fit at a population ratio of 1, is then again the value we are looking for.
  
 
The function to fit must behave nicely with extrapolations. This excludes complex functions with many parameters. Or functions which behave not regular outside the fitted points (polynomials).
 
The function to fit must behave nicely with extrapolations. This excludes complex functions with many parameters. Or functions which behave not regular outside the fitted points (polynomials).
  
−
This leaves the following functions:
+
This leaves the following functions in Google Sheets:
 
* linear - results in average values;
 
* linear - results in average values;
 
* logarithmic - results in smaller values;
 
* logarithmic - results in smaller values;
−
* power functions - results in larger values;
+
* power functions - results in larger values, but goes through the origin (0,0);
 +
 
 +
The results of France show that the logarithmic function does not fit the data. The best fit is provided by a power function {f(x)=ax<sup>b</sup>}. However the fit works only if there is enough data above a population fraction of 25%. The data below 25% heavily influences the outcome of a power functions. As an approximation a linear fit can be used, without using the data at low ratios (<25%). For France in fact the results are the close (371 and 363).
 +
 
 +
This suggests that the slope of a linear function is the same as the power function for the fitting purpose. To see this better a change (derivative) can be plotted.
 +
[[File:France departments - slope of size estimations.png|thumb|center|The slope of the weighted distances for France departments compared to the fits.]]
 +
 
 +
As can be seen from the graph the slope does not stay constant as more and more departments are added. At this close inspection the power function fit is better. The linear fit to the slope (not the original data) confirms this conclusion and provides a better fit.
 +
 
 +
=== Accuracy ===
 +
The conclusion is that the power function gives the best result for a country when we have 100% of the data, i.e. up to a population ratio of 1. At what population ratio do we get an estimate of the weighted country size that is good enough. We can define good enough as within 10%.
 +
 
 +
So for France this will be at 380 ± 40. The evolution of the size estimates as departments are added and the population ratio increases is shown below.
 +
[[File:FranceDepartmentsSizeEstimateEvolution.png|thumb|center|The evolution of the weighted size of France, using the departments.]]
 +
The red dot shows where the estimate is within 10% of the final value. So this occurs well above a population ratio of 50%. If we accept an error of 20%, a population ratio of at least 30% is required.
 +
 
 +
These graphs might be a good tool to see when the estimate converges.
  
−
In the graph for Maroc a logarithmic function is used. The largest cities are not used in the fit calculation, as these ones seem not to follow the curve. They would bias the fit to much, when we use so few points. The fit will result in a transportation size of 419 km. Compare this to a maximum actual distance of about 1000 km.
+
In order to get a good population coverage, it must also be decided which territory units to chose (city, municipalities, agglomerations, cantons, election area's, provinces, regions, etc). If the units are to large, the accuray for centroid and size will be not very accurate. If the unit is to small, we might miss out on population (countryside versus city) and we need a lot of data (are there European databases that could help?). A drawback is that adjusted centroids need to be calculated.
  
−
== Result ==
+
== Results ==
 
This approach has been applied on the following countries with a linear function:
 
This approach has been applied on the following countries with a linear function:
 
{| class="wikitable sortable"
 
{| class="wikitable sortable"
 
|-
 
|-
−
! Country !! Centroid city !! size !! spreadsheet
+
! Country !! Centroid city !! size !! coverage !! spreadsheet
 +
|-
 +
| Albania || Tirana || 100 || || link
 +
|-
 +
| Algeria || Algers || 370 || || [https://docs.google.com/spreadsheets/d/1SVDKX-9fjYr_K6U9G6OQlM1j1-pIQfWwGTVCl-60hSY/ link]
 +
|-
 +
| Austria || Vienna || 230 || ||[https://docs.google.com/spreadsheets/d/1joXvrjWDmabsyFqf4V6bBsmCJHp0fhCB5oAy8nOStRU/ link]
 +
|-
 +
| Belgium - cities || Brussels || 77 || || [https://docs.google.com/spreadsheets/d/1-_DRQi9VUrV2o5oVRL01DUBz9iT3ZvVPlvxvz9O_ZLg link]
 +
|-
 +
| Belgium - provinces || Brussels || 75 || 100% || [https://docs.google.com/spreadsheets/d/1oawted6mzxobb7FVsfyP1FgoGalEzxPBBgriLYPoOys/ link]
 
|-
 
|-
−
| Albania || Tirana || 100 || link
+
| Bosnia and Herzegovina || Zenica || || || link
 
|-
 
|-
−
| Algeria || Algers || 370 || [https://docs.google.com/spreadsheets/d/1SVDKX-9fjYr_K6U9G6OQlM1j1-pIQfWwGTVCl-60hSY/ link]
+
|- Bulgaria || Plovdiv || 197 || 100% || [https://docs.google.com/spreadsheets/d/193RBhO4Dnd4ZwPbepP2oeyF5PkOMDeoxszSNuMW8VrQ link]
 
|-
 
|-
−
| Austria || Vienna || 230 || [https://docs.google.com/spreadsheets/d/1joXvrjWDmabsyFqf4V6bBsmCJHp0fhCB5oAy8nOStRU/ link]
+
| Croatia || Zagreb || 160 || || link
 
|-
 
|-
−
| Belgium || Brussels || 86 || [https://docs.google.com/spreadsheets/d/1-_DRQi9VUrV2o5oVRL01DUBz9iT3ZvVPlvxvz9O_ZLg link]
+
| Czech Republic || Prague || || || [https://docs.google.com/spreadsheets/d/1QlsggBo0dOK-gvXR3-wOeeIQOLDSQ6J9TAGijCZ0ENg/ link]
 
|-
 
|-
−
| Bosnia and Herzegovina || Zenica || || link
+
| Denmark || Copenhagen || 170 || || [https://docs.google.com/spreadsheets/d/1uB0MooOA0xT8z9bRfZ1iaKxNph1FQgeUby0HmXSOrKw link]
 
|-
 
|-
−
|- Bulgaria || Sofia || || [https://docs.google.com/spreadsheets/d/193RBhO4Dnd4ZwPbepP2oeyF5PkOMDeoxszSNuMW8VrQ link]
+
| Estonia || Tallinn || 120 || || [https://docs.google.com/spreadsheets/d/1nhs-FE32-n0taDLiNHxZ71VBGC2mYrBHjLlwI_PNMsQ/ link]
 
|-
 
|-
−
| Croatia || Zagreb || 160 || link
+
| Finland || Helsinki || 220 || || [https://docs.google.com/spreadsheets/d/1OXHdXZDUJ9So5oSP67ZWzkDdSaQADQdZbDMnp7ekXcU/ link]
 
|-
 
|-
−
| Czech Republic || Prague || || [https://docs.google.com/spreadsheets/d/1QlsggBo0dOK-gvXR3-wOeeIQOLDSQ6J9TAGijCZ0ENg/ link]
+
| France - cities || Paris ||  || || [https://docs.google.com/spreadsheets/d/1-G7VVZDE7xH6yzhsVYpmTTnu3rtgvadMC7WX9yIJqPc link]
 
|-
 
|-
−
| Denmark || Copenhagen || 170 || [https://docs.google.com/spreadsheets/d/1uB0MooOA0xT8z9bRfZ1iaKxNph1FQgeUby0HmXSOrKw link]
+
| France - departments || Paris || 369 || 100% || [https://docs.google.com/spreadsheets/d/1hcY0Z17u7ROIy59VRsLzc3IQgxWESyLtniApJPohaZU/edit?usp=sharing link]
 
|-
 
|-
−
| Estonia || Tallinn || 120 || [https://docs.google.com/spreadsheets/d/1nhs-FE32-n0taDLiNHxZ71VBGC2mYrBHjLlwI_PNMsQ/ link]
+
| France - regions || Paris || 349 || 100% || [https://docs.google.com/spreadsheets/d/1oxzOMeuqsHq0ikfDvwQ-wbBPbCrcYj2vQ-HjGXCLCJQ/edit?usp=sharing link]
 
|-
 
|-
−
| Finland || Helsinki || || [https://docs.google.com/spreadsheets/d/1OXHdXZDUJ9So5oSP67ZWzkDdSaQADQdZbDMnp7ekXcU/ link]
+
| Germany - cities || Hannover || ''260'' || || [https://docs.google.com/spreadsheets/d/1HSNFq2sJiRxarSvY_mHtEOiSIq9fwFAyTh9lA4DfgF4 link]
 
|-
 
|-
−
| France || Paris || 430 || [https://docs.google.com/spreadsheets/d/1-G7VVZDE7xH6yzhsVYpmTTnu3rtgvadMC7WX9yIJqPc link]
+
| Germany - states || Hesse state || 300 || 100% || [https://docs.google.com/spreadsheets/d/1HSNFq2sJiRxarSvY_mHtEOiSIq9fwFAyTh9lA4DfgF4 link]
 
|-
 
|-
−
| Germany || Hannover || 260 || [https://docs.google.com/spreadsheets/d/1HSNFq2sJiRxarSvY_mHtEOiSIq9fwFAyTh9lA4DfgF4 link]
+
| Greece || Athens ||  || || [https://docs.google.com/spreadsheets/d/1RN6YIwDWLgLrultpRt49D83s3UCeAuQErZb4Orriv-Q link]
 
|-
 
|-
−
| Greece || Athens || || [https://docs.google.com/spreadsheets/d/1RN6YIwDWLgLrultpRt49D83s3UCeAuQErZb4Orriv-Q link]
+
| Hungary || Budapest || 120 || || [https://docs.google.com/spreadsheets/d/16VZjIhacrZqKWNp95POnc2qKuLgpfTcIgs8Ygt6nxPI link]
 
|-
 
|-
−
| Hungary || Budapest || || [https://docs.google.com/spreadsheets/d/16VZjIhacrZqKWNp95POnc2qKuLgpfTcIgs8Ygt6nxPI link]
+
| Iceland || Reykjavik || 90 || || [https://docs.google.com/spreadsheets/d/1skWHDUfRtpaUkjXy-WiCWhqGBI8st-FB9sjcMaIRiCc link]
 
|-
 
|-
−
| Iceland || Reykjavik || 87 || [https://docs.google.com/spreadsheets/d/1skWHDUfRtpaUkjXy-WiCWhqGBI8st-FB9sjcMaIRiCc link]
+
| Ireland || Dublin || 100 || || [https://docs.google.com/spreadsheets/d/15KY_dUl_3PB_taWZNkfdhcv9RhX7YgcyUC21aNADOdE link]
 
|-
 
|-
−
| Ireland || Dublin || || [https://docs.google.com/spreadsheets/d/15KY_dUl_3PB_taWZNkfdhcv9RhX7YgcyUC21aNADOdE link]
+
| Italy - cities || Rome || ''520'' || || [https://docs.google.com/spreadsheets/d/19GxxwjgUrsMB522Vv_3zUCg-bJVyg054WwMK5S3tkP0 link]
 
|-
 
|-
−
| Italy || Rome || 520 || [https://docs.google.com/spreadsheets/d/19GxxwjgUrsMB522Vv_3zUCg-bJVyg054WwMK5S3tkP0 link]
+
| Italy - provinces || Bologna || 400 || 100% ||  [https://docs.google.com/spreadsheets/d/1LM3k-RzfX3UqfQxJ99oc-HN0X78tHLO52kZn_7A-vnk/edit link]
 +
|-
 +
| Kosovo - districts || Pristina || 68 || 100% || link
 
|-  
 
|-  
−
| Latvia || Riga || 77 || [https://docs.google.com/spreadsheets/d/1Ls57Onx80PpDggjztZgjOMDc0ikybXH2Rv7hsIIIav8/ link]
+
| Latvia || Riga || 77 || || [https://docs.google.com/spreadsheets/d/1Ls57Onx80PpDggjztZgjOMDc0ikybXH2Rv7hsIIIav8/ link]
 
|-  
 
|-  
−
| Lithuania || Kaunas || 120 || [https://docs.google.com/spreadsheets/d/1gz49-FMuaVvFIro2QgoIaxot24ysm1k_2fn6RJJxLF4 link]
+
| Lithuania || Kaunas || 120 || || [https://docs.google.com/spreadsheets/d/1gz49-FMuaVvFIro2QgoIaxot24ysm1k_2fn6RJJxLF4 link]
 +
|-
 +
| Montenegro || Podgorica || 61 || 100% || [https://docs.google.com/spreadsheets/d/1GyGKUNo1OVRjRAAWNjHCJnFYKMmuxoVYT-2FdkZv3og/edit?usp=sharing link]
 +
|-
 +
| Morocco || Temara || 230 || || link
 +
|-
 +
| Netherlands - cities || Utrecht || 72 || || [https://docs.google.com/spreadsheets/d/1x8Vfb2ZIjsTmb0lRrO-8YHC6NKmgc58HtP_cNSjeOQY link]
 +
|-
 +
| Netherlands - provinces || Utrecht province || 102 || 100% || link
 +
|-
 +
| Norway || Skien || 300 || || [https://docs.google.com/spreadsheets/d/1e3vPPWMyJu6k5rdbqDC9EGgOCgmAmsGZ2JO1oMrCbXw/edit link]
 
|-
 
|-
−
| Montenegro || Bijelo Polje || || link
+
| Poland || Łódź || 240 || || [https://docs.google.com/spreadsheets/d/1wBNuc3qItIouZHKLI-c9TrJcL2skF-T6sw_Yh1gPxM4 link]
 
|-
 
|-
−
| Morocco || Temara || 230 || link
+
| Portugal - cities || Lisboa || 140 || || [https://docs.google.com/spreadsheets/d/1DBTu9pNtdLhf0hBPGqnYbLYXnxhAkX0f7eDcRBIrZGU link]
 
|-
 
|-
−
| Netherlands || Utrecht || 72 || [https://docs.google.com/spreadsheets/d/1x8Vfb2ZIjsTmb0lRrO-8YHC6NKmgc58HtP_cNSjeOQY link]
+
| Portugal - districts || Coimbra || 170 || 100% || [https://docs.google.com/spreadsheets/d/1cT85fO9MmoSeeJwrsKDZiSVmfFtk6ABkR7545NDvRhQ/edit link]
 
|-
 
|-
−
| Norway || Skien || 300 || [https://docs.google.com/spreadsheets/d/1e3vPPWMyJu6k5rdbqDC9EGgOCgmAmsGZ2JO1oMrCbXw/edit link]
+
| Romania || Bucharest || 300 || || [https://docs.google.com/spreadsheets/d/1V-39ygo6LszL2PWHUVi8VFtci1CExjpQLN7Y2w-nUXQ link]
 
|-
 
|-
−
| Poland || Łódź || 140 || [https://docs.google.com/spreadsheets/d/1wBNuc3qItIouZHKLI-c9TrJcL2skF-T6sw_Yh1gPxM4 link]
+
| Serbia || Belgrad || 129 || 100% || [https://docs.google.com/spreadsheets/d/1ComqBolNvRQ7xYrvO4mMsKfcD39b4h6p4MwN7PAEqac/edit?usp=sharing link]
 
|-
 
|-
−
| Portugal || Lisboa || 140 || [https://docs.google.com/spreadsheets/d/1DBTu9pNtdLhf0hBPGqnYbLYXnxhAkX0f7eDcRBIrZGU link]
+
| Slovakia || Banská Bystrica || 78 || || [https://docs.google.com/spreadsheets/d/1lTXNbUIM5ATG1C0TpzsgmtGnHFBqt0m2PBtRvVul0XE/ link]
 
|-
 
|-
−
| Romania || Bucharest || 300 || [https://docs.google.com/spreadsheets/d/1V-39ygo6LszL2PWHUVi8VFtci1CExjpQLN7Y2w-nUXQ link]
+
| Slovenia || Ljubljana || 66 || || [https://docs.google.com/spreadsheets/d/1CVV7NGr2XZ-iYhE4c48Vm7WBhXPOxa79XrdV5GVck5E/ link]
 
|-
 
|-
−
| Slovakia || Banská Bystrica || 78 || [https://docs.google.com/spreadsheets/d/1lTXNbUIM5ATG1C0TpzsgmtGnHFBqt0m2PBtRvVul0XE/ link]
+
| Spain - cities || Madrid || 430 || || [https://docs.google.com/spreadsheets/d/1Zf0ks3MiWOHbh9CSNiZQwW3yy9jW3l7qFahGYBH-uc8 link]
 
|-
 
|-
−
| Slovenia || Ljubljana || 66 || [https://docs.google.com/spreadsheets/d/1CVV7NGr2XZ-iYhE4c48Vm7WBhXPOxa79XrdV5GVck5E/ link]
+
| Spain - comunidades || Madrid || 374 || 100% || [https://docs.google.com/spreadsheets/d/1Ffqxm-FcmAwJPkbrOD6Lb19W_1cvhXAeyGNRrA17uNo/edit?usp=sharing link]
 
|-
 
|-
−
| Spain || Madrid || 430 || [https://docs.google.com/spreadsheets/d/1Zf0ks3MiWOHbh9CSNiZQwW3yy9jW3l7qFahGYBH-uc8 link]
+
| Sweden || Nörrköping || 270 || || [https://docs.google.com/spreadsheets/d/16kjE6_IIoqiOHdFB-x1rpJiEesRbNqhwXSv8HTyy_NQ link]
 
|-
 
|-
−
| Sweden || Nörrköping || 270 || [https://docs.google.com/spreadsheets/d/16kjE6_IIoqiOHdFB-x1rpJiEesRbNqhwXSv8HTyy_NQ link]
+
| Switzerland || Olten || 120 || || [https://docs.google.com/spreadsheets/d/1VJ08fAdkbvnKAFDnn1RnNXQxWn9nzTK1y5GJ5AJFa1Y link]
 
|-
 
|-
−
| Switzerland || Olten || 120 || [https://docs.google.com/spreadsheets/d/1VJ08fAdkbvnKAFDnn1RnNXQxWn9nzTK1y5GJ5AJFa1Y link]
+
| Tunisia || Tunis ||  || || link
 
|-
 
|-
−
| Tunisia || Tunis || || link
+
| United Kingdom || Coventry || 210 || || [https://docs.google.com/spreadsheets/d/1xbNe-QDLIZt6h1HvLAa4jzTQoyifdLaiTr4UgaqbHNk link]
 
|-
 
|-
−
| United Kingdom || Coventry || 210 || [https://docs.google.com/spreadsheets/d/1xbNe-QDLIZt6h1HvLAa4jzTQoyifdLaiTr4UgaqbHNk link]
+
| European Union - cities || Munich || || || link
 
|-
 
|-
−
| European Union || Munich || || link
+
| European Union - countries || Luxemburg || 1100 || 100% || [https://docs.google.com/spreadsheets/d/13OGmvv8BMbShdGuncfYYCjs3_YpKeC8wVftFVb1pbHk/edit link]
 
|-
 
|-
−
| Bouches du Rhône || Marseille || 22 || link
+
| Bouches du Rhône || Marseille || 22 || || link
 
|}
 
|}
 +
 +
[[File:CountryTransportationSizes.png|thumb|center|The transportation sizes of some European countries.]]
 +
Values in italic need more data. For some large countries another organisational unit is required to get better sizes. They accuracy of the results needs to be added.
  
 
== Comments ==
 
== Comments ==
 +
 
Some observations on individual countries.
 
Some observations on individual countries.
−
* Iceland - the weighted distance graph contains several steps, due to the addition of cities far from the centroid. This introduces strange fits, if we use all cities So only the 9 (of 20) smaller cities have been used in the fit;
 
  
 
== Conclusion ==
 
== Conclusion ==
−
The graph below shows the malus for each country based on population road-distance weighted transportation distance. The location of the circles is on the country centroid.
+
The graph below shows the size of each country based on population road-distance weighted transportation distance. The location of the circles is on the country centroid.
−
[[File:CountryTransportationMalus.png|thumb|center|The transportation malus for each country (or territory).]]
+
 
 +
So what to do with this data? The idea is to extend the ecoscore to other countries. A consistent approach should allow comparison between countries and favorise local production and consumption.
 +
 
 +
How the transportation is incorporated depends on what the ecoscore wants achieve. There seem to be three approaches.
 +
 
 +
=== Real approach ===
 +
This approach tries to calculate the true impact of the transportation by looking at the carbondioxide emission. The lifecycle values for each product incorporate already the impact of distribution for France. Thus we need to adjust this impact for other countries by subtracting the impact for France and adding the other country.
 +
 
 +
If the detailed lifecycle data is available, we can just divide the distribution fraction by the size of France and multiply with the size of the other country. This implies that the same product will have a lower lifecycle value in, for example Belgium, than in France. For very small territories the distribution fraction will be reduced to zero.
 +
 
 +
Drawback of this approach is that the distribution part of the lifecycle is not emphasised. But then it is only a small part if the total lifecycle value.
 +
 
 +
=== Bonus approach ===
 +
The bonus approach emphasises the consumption of locally produced products. The closer the production to consumption the better. So if no transportation is required, a maximum number of points is awarded. In this way small territories and countries get the maximum bonus. This bonus is scaled down by the country size for products produced within a country.
 +
 
 +
But to do correct scaling, it is also required to define a maximum distance at which the bonus becomes zero. Ecoscore now uses 2000 km for this (seems large). The formula to calculated the standard bonus is then:
 +
 
 +
Bonus(country) = ( 1 - size(country) / maximum distance ) * 15
 +
 
 +
Implementing this for ecoscore would imply a bonus of ? for France instead of the currently used 15 points.
 +
 
 +
Note that any transportation of a product between countries of territories would reduce the bonus. Also if we would use country subdivisions as territories and transport between these subdivisions, we would get a more realistic bonus. And it would be better than using these average country sizes.
 +
 
 +
=== Ecoscore approach ===
 +
The ecoscore is developed for France and favorises products from France. To achieve this a bonus is given to any product produced in France of 15 points, which corresponds to a distance of 1000 km (?). This is much larger than the size of France by the way.
  
−
Some observations:
+
So what to do with small countries, which have a much smaller transportation impact? Increase the bonus?
−
* France and Italy have the largest malus. In order to cover the entire population a large distance is required;
 
−
* Bouches du Rhône has the smallest malus of this set;
 

Latest revision as of 08:08, 1 April 2021

This page explores how the size of the country can be taken into account in the determination of the eco-score transport bonus.

Bonus approach

The eco-score approach intends to give a bonus to locally produced (and purchased) products. This bonus is +15, which is equal to a transport score of 100. This bonus is however now given to any product produced/purchased in France. No account has been taken of the very large size of France and impact of transport within the country.

Can this approach be adapted, so that the size of a country is taken into account?

It would be more logic to give the maximum bonus (transport score of 100) only to very local production and purchase. Ideally this will imply an area as small as possible, i.e. a city or very small country. Production and purchase that occurs in a country such as Andorra, Monaco, Liechtenstein or San Marino could be viewed as having a transportation score of 100.

In the current calculation the no bonus (transportation score 0) is (arbitrarily) set at a distance of 2000 km. This distance covers roughly the size of Europe.

Centroids to the rescue?

Is it possible to use the centroid calculations in determining the (transportation) size of a country?

The weighted sum for a country that consists of single city will be zero. This means there is no transportation required. The country size is then zero.

The centroid approach can be used to calculate the country transportation size. We just have to add all cities, so that the entire population is covered. The resulting weighted distance is the size we are looking for. This size is the average distance to the entire population.

How can the centroid data be used and does it need to be extended?

Approach

In the figure the weighted distances for France using the departments are shown.

The population road-distance weighted size of France based on the departments

The cumulative population road-distance weighted distances are plotted against the cumulative population ratios. Each added city adds a point, increasing the cumulative population ratio and the cumulative distance. If the cumulative population ratio is 1, we have covered the entire population. And the corresponding cumulative distance is the value we are looking for.

Fit function

Adding all the cities is to much work. Instead of adding all the cities, we could fit a line to the data of a few city points. The cumulative distance of the fit at a population ratio of 1, is then again the value we are looking for.

The function to fit must behave nicely with extrapolations. This excludes complex functions with many parameters. Or functions which behave not regular outside the fitted points (polynomials).

This leaves the following functions in Google Sheets:

  • linear - results in average values;
  • logarithmic - results in smaller values;
  • power functions - results in larger values, but goes through the origin (0,0);

The results of France show that the logarithmic function does not fit the data. The best fit is provided by a power function {f(x)=axb}. However the fit works only if there is enough data above a population fraction of 25%. The data below 25% heavily influences the outcome of a power functions. As an approximation a linear fit can be used, without using the data at low ratios (<25%). For France in fact the results are the close (371 and 363).

This suggests that the slope of a linear function is the same as the power function for the fitting purpose. To see this better a change (derivative) can be plotted.

The slope of the weighted distances for France departments compared to the fits.

As can be seen from the graph the slope does not stay constant as more and more departments are added. At this close inspection the power function fit is better. The linear fit to the slope (not the original data) confirms this conclusion and provides a better fit.

Accuracy

The conclusion is that the power function gives the best result for a country when we have 100% of the data, i.e. up to a population ratio of 1. At what population ratio do we get an estimate of the weighted country size that is good enough. We can define good enough as within 10%.

So for France this will be at 380 ± 40. The evolution of the size estimates as departments are added and the population ratio increases is shown below.

The evolution of the weighted size of France, using the departments.

The red dot shows where the estimate is within 10% of the final value. So this occurs well above a population ratio of 50%. If we accept an error of 20%, a population ratio of at least 30% is required.

These graphs might be a good tool to see when the estimate converges.

In order to get a good population coverage, it must also be decided which territory units to chose (city, municipalities, agglomerations, cantons, election area's, provinces, regions, etc). If the units are to large, the accuray for centroid and size will be not very accurate. If the unit is to small, we might miss out on population (countryside versus city) and we need a lot of data (are there European databases that could help?). A drawback is that adjusted centroids need to be calculated.

Results

This approach has been applied on the following countries with a linear function:

Country Centroid city size coverage spreadsheet
Albania Tirana 100 link
Algeria Algers 370 link
Austria Vienna 230 link
Belgium - cities Brussels 77 link
Belgium - provinces Brussels 75 100% link
Bosnia and Herzegovina Zenica link
Croatia Zagreb 160 link
Czech Republic Prague link
Denmark Copenhagen 170 link
Estonia Tallinn 120 link
Finland Helsinki 220 link
France - cities Paris link
France - departments Paris 369 100% link
France - regions Paris 349 100% link
Germany - cities Hannover 260 link
Germany - states Hesse state 300 100% link
Greece Athens link
Hungary Budapest 120 link
Iceland Reykjavik 90 link
Ireland Dublin 100 link
Italy - cities Rome 520 link
Italy - provinces Bologna 400 100% link
Kosovo - districts Pristina 68 100% link
Latvia Riga 77 link
Lithuania Kaunas 120 link
Montenegro Podgorica 61 100% link
Morocco Temara 230 link
Netherlands - cities Utrecht 72 link
Netherlands - provinces Utrecht province 102 100% link
Norway Skien 300 link
Poland Łódź 240 link
Portugal - cities Lisboa 140 link
Portugal - districts Coimbra 170 100% link
Romania Bucharest 300 link
Serbia Belgrad 129 100% link
Slovakia Banská Bystrica 78 link
Slovenia Ljubljana 66 link
Spain - cities Madrid 430 link
Spain - comunidades Madrid 374 100% link
Sweden Nörrköping 270 link
Switzerland Olten 120 link
Tunisia Tunis link
United Kingdom Coventry 210 link
European Union - cities Munich link
European Union - countries Luxemburg 1100 100% link
Bouches du Rhône Marseille 22 link
The transportation sizes of some European countries.

Values in italic need more data. For some large countries another organisational unit is required to get better sizes. They accuracy of the results needs to be added.

Comments

Some observations on individual countries.

Conclusion

The graph below shows the size of each country based on population road-distance weighted transportation distance. The location of the circles is on the country centroid.

So what to do with this data? The idea is to extend the ecoscore to other countries. A consistent approach should allow comparison between countries and favorise local production and consumption.

How the transportation is incorporated depends on what the ecoscore wants achieve. There seem to be three approaches.

Real approach

This approach tries to calculate the true impact of the transportation by looking at the carbondioxide emission. The lifecycle values for each product incorporate already the impact of distribution for France. Thus we need to adjust this impact for other countries by subtracting the impact for France and adding the other country.

If the detailed lifecycle data is available, we can just divide the distribution fraction by the size of France and multiply with the size of the other country. This implies that the same product will have a lower lifecycle value in, for example Belgium, than in France. For very small territories the distribution fraction will be reduced to zero.

Drawback of this approach is that the distribution part of the lifecycle is not emphasised. But then it is only a small part if the total lifecycle value.

Bonus approach

The bonus approach emphasises the consumption of locally produced products. The closer the production to consumption the better. So if no transportation is required, a maximum number of points is awarded. In this way small territories and countries get the maximum bonus. This bonus is scaled down by the country size for products produced within a country.

But to do correct scaling, it is also required to define a maximum distance at which the bonus becomes zero. Ecoscore now uses 2000 km for this (seems large). The formula to calculated the standard bonus is then: 

Bonus(country) = ( 1 - size(country) / maximum distance ) * 15

Implementing this for ecoscore would imply a bonus of ? for France instead of the currently used 15 points.

Note that any transportation of a product between countries of territories would reduce the bonus. Also if we would use country subdivisions as territories and transport between these subdivisions, we would get a more realistic bonus. And it would be better than using these average country sizes.

Ecoscore approach

The ecoscore is developed for France and favorises products from France. To achieve this a bonus is given to any product produced in France of 15 points, which corresponds to a distance of 1000 km (?). This is much larger than the size of France by the way.

So what to do with small countries, which have a much smaller transportation impact? Increase the bonus?

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