layout: true <div class="my-footer"><span>Busenur Kızılaslan/Ducky Interview</span></div> <!-- this adds the link footer to all slides, depends on my-footer class in css--> --- name: xaringan-title class: right, bottom background-image: url("img/robert-bye-dOElUitX2Do-unsplash.jpg") background-size: cover # <span style="color: white;">**Footprint Calculations with** </span> # <span style="color: white;">**REMA1000 Data** </span> <br> ###<span style="color: white;">Busenur Kızılaslan</span> <!-- this ends up being the title slide since seal = FALSE--> --- class: right, middle background-image: url("img/ycol.jpg") background-size: cover <img class="circle" src="img/busenur.sarica.png" width="200px"/> ### Busenur Kızılaslan ### Research Assistant / Marmara University [<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512" class="rfa" style="height:0.75em;fill:currentColor;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> @busenurk](https://github.com/busenurk) [<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="rfa" style="height:0.75em;fill:currentColor;position:relative;"><path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"/></svg> avesis/busenurkizilaslan](https://avesis.marmara.edu.tr/busenur.sarica) [<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="rfa" style="height:0.75em;fill:currentColor;position:relative;"><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> busenur.sarica@gmail.com](mailto:busenur.sarica@gmail.com) --- class: center, middle, inverse background-image: url("img/geran-de-klerk-WJkc3xZjSXw-unsplash.jpg") background-size: cover # Thank you for your contribution to the healing of the world! <img src="img/ducky.png" width="7%" style="display: block; margin: auto;" /> --- class: left background-image: url("img/matthew-henry-2Ts5HnA67k8-unsplash.jpg") background-size: cover # Outline -------- <br> ### 🚩 Discussing the advantages and disadvantages of data <br> -- ### 🚩 Emphasizing key points --- ### 🥕 Due to the estimated global population growth to approximately 9 billion in 2050 and growing income levels, The Food and Agriculture Organization of the United Nations (FAO) suggests that a 70% increase in food production will be necessary. -- ### .heatinline[🥕 In Europe, food consumption is responsible for approximately 30% of total GHG emissions.] <br> ------- [How to Feed the World in 2050. High Level Expert Forum. Food and Agriculture Organization of the United Nations, Rome, 2009.](https://reliefweb.int/report/world/how-feed-world-2050-high-level-expert-forum-rome-12-13-oct-2009-investment) [Guinée, J., Heijungs, R., De Koning, A., Van, L., Geerken, T., Van Holderbeke, M., ... & Delgado, L. (2006). Environmental Impact of Products (EIPRO) Analysis of the life cycle environmental impacts related to the final consumption of the EU25](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.400.4293) --- class: left <br> <br> <br> ## .heatinline[How would you go about integrating REMA1000's data into footprint calculations across Norway?] <img src="https://media.giphy.com/media/3R3PNPNf35ZgAaPbBH/giphy.gif" width="20%" style="display: block; margin: auto 0 auto auto;" /> --- <br> .pull-right[ <img src="img/REMA-1000-logo_Farge.png" width="80%" style="display: block; margin: auto;" /> ] --------- ### 📌 The data includes monthly purchases by category grain, fruit & veg .heatinline[(f.veg)], meat, fish, dairy, drinks. -- ### 📌 The product location split into Norway .heatinline[(nor)], Europe .heatinline[(eur)], Outside of Europe .heatinline[(out)] -- ### 📌 Data is given on a store level, not an individual level, but we do know how many people visit .heatinline[(visitor)] the store each month. --- ## 🌎 Location based categories ----------- ### Each category is defined based on its location as Norway, Europe, and Outside of Europe. .pull-left[ ``` ## meat_nor meat_eur meat_out ## 1 1500 7800 12000 ## 2 3450 10200 10050 ## 3 6000 15000 7950 ## 4 1950 7350 11550 ## 5 3150 10500 15000 ## 6 5400 11100 12450 ``` ``` ## fish_nor fish_eur fish_out ## 1 4200 7950 9900 ## 2 8250 11100 12750 ## 3 4500 6000 12000 ## 4 9150 10800 9900 ## 5 3150 10050 14250 ## 6 12000 11400 12750 ``` ] .pull-right[ ``` ## grain_nor grain_eur grain_out ## 1 2550 5460 11610 ## 2 8550 10860 5910 ## 3 3600 6660 13860 ## 4 9000 6660 9510 ## 5 3000 6660 11610 ## 6 8550 14610 12060 ``` ``` ## drinks_nor drinks_eur drinks_out ## 1 6150 9900 10500 ## 2 3600 7200 10200 ## 3 5400 10350 7800 ## 4 3300 9150 15000 ## 5 4050 14250 13950 ## 6 4800 6750 11400 ``` ] --- ## Get a glimpse of data ------ ``` ## 'data.frame': 144 obs. of 22 variables: ## $ month : Factor w/ 12 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ... ## $ grain_nor : num 2550 8550 3600 9000 3000 8550 5700 5400 3900 2850 ... ## $ grain_eur : num 5460 10860 6660 6660 6660 ... ## $ grain_out : num 11610 5910 13860 9510 11610 ... ## $ f.veg_nor : num 4650 3900 4350 5400 900 3300 4950 2400 1950 5850 ... ## $ f.veg_eur : num 14550 5550 5850 13500 7050 ... ## $ f.veg_out : num 5250 6150 6150 5400 9450 5850 5100 15000 6900 13200 ... ## $ meat_nor : num 1500 3450 6000 1950 3150 5400 2850 5850 5850 3450 ... ## $ meat_eur : num 7800 10200 15000 7350 10500 ... ## $ meat_out : num 12000 10050 7950 11550 15000 ... ## $ fish_nor : num 4200 8250 4500 9150 3150 ... ## $ fish_eur : num 7950 11100 6000 10800 10050 ... ## $ fish_out : num 9900 12750 12000 9900 14250 ... ## $ dairy_nor : num 7800 8700 7650 4950 3300 7800 5100 8850 5700 5100 ... ## $ dairy_eur : num 13200 8850 12750 13950 13950 ... ## $ dairy_out : num 13350 6000 8550 14100 11100 ... ## $ drinks_nor: num 6150 3600 5400 3300 4050 4800 3900 5100 6750 7950 ... ## $ drinks_eur: num 9900 7200 10350 9150 14250 ... ## $ drinks_out: num 10500 10200 7800 15000 13950 ... ## $ visitor : num 2850 3000 3900 3600 3450 2700 3300 1650 4350 3750 ... ## $ city : Factor w/ 3 levels "Oslo","Bergen",..: 1 1 1 1 1 1 1 1 1 1 ... ## $ store : Factor w/ 4 levels "st1","st2","st3",..: 1 1 1 1 1 1 1 1 1 1 ... ``` --- ## Example dataset ------- ``` ## month grain_nor grain_eur grain_out drinks_nor visitor city store ## 1 1 2550 5460 11610 6150 2850 Oslo st1 ## 2 2 8550 10860 5910 3600 3000 Oslo st1 ## 3 3 3600 6660 13860 5400 3900 Oslo st1 ## 4 4 9000 6660 9510 3300 3600 Oslo st1 ## 5 5 3000 6660 11610 4050 3450 Oslo st1 ## 6 6 8550 14610 12060 4800 2700 Oslo st1 ## 7 7 5700 11010 14160 3900 3300 Oslo st1 ## 8 8 5400 5910 13710 5100 1650 Oslo st1 ## 9 9 3900 7410 6210 6750 4350 Oslo st1 ## 10 10 2850 6360 5760 7950 3750 Oslo st1 ## 11 11 8550 6360 6810 4200 1500 Oslo st1 ## 12 12 8100 8010 12060 4050 2550 Oslo st1 ## 13 1 4350 14760 8760 6450 1650 Oslo st2 ## 14 2 8700 6810 12210 5100 1950 Oslo st2 ## 15 3 4350 5910 5910 3450 3750 Oslo st2 ## 16 4 2550 14160 13560 4050 2850 Oslo st2 ## 17 5 5550 6210 14010 4200 2250 Oslo st2 ## 18 6 4050 7710 5310 4050 3600 Oslo st2 ## 19 7 8700 10710 9810 8100 4350 Oslo st2 ## 20 8 6450 8760 8460 6150 2550 Oslo st2 ## 21 9 4200 8760 14160 6000 3900 Oslo st2 ## 22 10 3150 13560 8610 8100 1500 Oslo st2 ## 23 11 4800 11160 11760 3750 3300 Oslo st2 ``` --- <br> ### We are currently in the process of integrating another factor - .heatinline[the percentage of food which is bought locally.] -- .pull-left[ ### In this scenario, we are interested in only <u>food bought by customers.</u> If we want to integrate the percentage of food that is bought locally with this dataset, we need to learn how much food is wasted in the REMA1000.] .pull-right[ <img src="https://media.giphy.com/media/621xh7AP4wDraEgdLt/giphy.gif" width="60%" style="display: block; margin: auto;" /> ] --- <br> ### Location information in the dataset is an advantage because it allows us to add the effect of transportation to our model. <br> -- ### .heatinline[Internal transport information is also important for footprint calculation. If we can access this type of detailed data, our model could be more reliable.] <br> ----- [Górny, K., Idaszewska, N., Sydow, Z., & Bieńczak, K. (2021). Modelling the carbon footprint of various fruit and vegetable products based on a company’s internal transport data. Sustainability, 13(14), 7579](https://www.mdpi.com/2071-1050/13/14/7579) --- ### We should create new features for transportation. .saltinline[Locations of products can not be evaluated with an equal carbon footprint.] <br> .pull-left[ ### A transport assignment should be made depending on the distance between each city and location. <br> ] .pull-right[ <img src="https://media.giphy.com/media/RhkNaujdz2u6qLH7NN/giphy.gif" width="30%" style="display: block; margin: auto;" /> ] ### Obtaining information about <u>the means of transportation</u> used will also make our analysis more realistic. --- ### We need to calculate the carbon footprint for each category. In this example, an average meat carbon footprint is calculated by using this reference table. .pull-left[ <br> ### We should use .heatinline[the average footprint for each category] because we do not have detailed purchase information. ] .pull-right[ <img src="img/meateaters.png" width="100%" style="display: block; margin: auto;" /> ] ------------ [Meat Eaters Guide, Environmental Wroking Group](https://static.ewg.org/reports/2011/meateaters/pdf/methodology_ewg_meat_eaters_guide_to_health_and_climate_2011.pdf) --- ### At this point, the selection of the metric (mean or median) is important. .pull-left[ ``` ## mean.meat median.meat dif ## [1,] 10.655 9.925 0.73 ``` <br> ### 📈 As the amount of food increases, the amount of deviation will also increase. ```r dif*1000 ``` ``` ## [1] 730 ``` ] .pull-right[ <img src="img/meateaters.png" width="100%" style="display: block; margin: auto;" /> ] ------------ [Meat Eaters Guide, Environmental Wroking Group](https://static.ewg.org/reports/2011/meateaters/pdf/methodology_ewg_meat_eaters_guide_to_health_and_climate_2011.pdf) --- <br> ### If we want to compare cities, stores, or months, we need to each category values divided by the number of visitors. <br> ### We should standardize the dataset because <u>all categories do not have the same unit.</u> <br> ``` ## month grain_nor grain_eur grain_out drinks_nor visitor city store ## 1 2 8550 10860 5910 3600 3000 Oslo st1 ## 2 2 8700 6810 12210 5100 1950 Oslo st2 ## 3 2 3600 5310 13710 6750 2100 Oslo st3 ## 4 2 4800 9960 11310 3600 3450 Oslo st4 ## 5 2 3000 12060 7860 5700 3450 Bergen st1 ## 6 2 8100 8160 11010 6150 4350 Bergen st2 ## 7 2 4650 8310 7410 4200 2100 Bergen st3 ## 8 2 4800 9960 11310 3600 3450 Oslo st4 ## 9 2 6750 11460 7410 5250 3150 Trondheim st1 ``` --- ## Generalization of results --------- .pull-left[ ### Norway had a population of 5.381.326 in October 2021([Statistisk Norway](https://www.ssb.no/en/befolkning/folketall/statistikk/tettsteders-befolkning-og-areal)). <br> ### .heatinline[We have a dataset of about 28% of the population but is it enough for our analysis?] ] .pull-right[ <div id="htmlwidget-8469f051dcd34c806765" style="width:504px;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-8469f051dcd34c806765">{"x":{"visdat":{"33be3446f531":["function () ","plotlyVisDat"]},"cur_data":"33be3446f531","attrs":{"33be3446f531":{"labels":{},"values":[1043168,265470,191771,3880917],"alpha_stroke":1,"sizes":[10,100],"spans":[1,20],"type":"pie"}},"layout":{"width":450,"height":450,"margin":{"b":100,"l":50,"t":100,"r":50,"pad":4},"title":"","xaxis":{"showgrid":false,"zeroline":false,"showticklabels":false},"yaxis":{"showgrid":false,"zeroline":false,"showticklabels":false},"autosize":false,"hovermode":"closest","showlegend":true},"source":"A","config":{"showSendToCloud":false},"data":[{"labels":["Oslo","Bergen","Trondheim","Other"],"values":[1043168,265470,191771,3880917],"type":"pie","marker":{"color":"rgba(31,119,180,1)","line":{"color":"rgba(255,255,255,1)"}},"frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script> ] --- <br> ### REMA1000 dataset includes only customers from big cities (Although Stavanger is among the big cities, it is not included in the data). The dataset can not represent customers from small cities. <br> -- ### If we want to calculate footprint <u>across Norway</u>, we need to use additional datasets such as Kiwi, Meny, Coop for homogeneity. .heatinline[The data can not represent the all population of Norway.] --- ### .heatinline[Seasonality contribution should be included in the model.] -- ### ♻️ Quantification of the reduction potential from a commonly recommended option, .heatinline[‘eating seasonal’], showed that consuming tomatoes and carrots seasonally in Sweden could **reduce the carbon footprint by 30-60%.** -- ### Local and seasonal products have a lower carbon footprint than other products. We should keep in mind the detail when we design our model. <br> -------- [Röös, E. (2013). Analysing the carbon footprint of food (Vol. 2013, No. 2013: 56).](https://pub.epsilon.slu.se/10757/1/roos_e_130821.pdf) --- ### 💸 Locally bought food is generally more expensive than food bought overseas therefore most of the customers are inclined towards non-local products. ### We have to add this effect to our model. <br> ``` ## fish_nor fish_eur fish_out ## 1 4200 7950 9900 ## 2 8250 11100 12750 ## 3 4500 6000 12000 ## 4 9150 10800 9900 ## 5 3150 10050 14250 ## 6 12000 11400 12750 ``` <br> `$$\widehat{CF\_fish} = (w1*fish\_nor+w2*fish\_eur+w3*fish\_out)*\overline{CF\_fish}$$` where `\(w3>w2>w1\)`. --- <br> ### If we can access additional information such as; ###📎 packaging type, ###📎 water usage, ### we can improve our footprint calculations. <br> ### Outliers are another significant issue, the presence of outliers in the data should be checked before beginning the analysis. --- class: left background-image: url("img/y1.jpg") background-size: cover <img src="https://media.giphy.com/media/KGYpdymW4TkhP8qOIu/giphy.gif" width="30%" style="display: block; margin: auto 0 auto auto;" /> # Future Ideas --- <br> ### 🚀 My main field, .heatinline[fuzzy logic], can be used to incorporate uncertainty in the system into the model. There are articles on this subject in the literature. <br> [Morone, P., Falcone, P. M., & Lopolito, A. (2019). **How to promote a new and sustainable food consumption model: A fuzzy cognitive map study.** Journal of cleaner production, 208, 563-574.](https://www.sciencedirect.com/science/article/pii/S0959652618330890?casa_token=W83z0BNOELIAAAAA:CHV4RtCAoqzxbE--DE699xxVPzXBNPoCTeiUYzTcM0NaUPr4MT4GsmeGHpz_O7e1mU_i6mjIKxA) [Egilmez, G., Gumus, S., Kucukvar, M., & Tatari, O. (2016). **A fuzzy data envelopment analysis framework for dealing with uncertainty impacts of input–output life cycle assessment models on eco-efficiency assessment.** Journal of cleaner production, 129, 622-636.](https://www.sciencedirect.com/science/article/pii/S0959652616301822?casa_token=XLgNeVlKLy8AAAAA:twRUCkheoM765iShA_GHc7NdjIabi1pPG4uAJdm5_eQQk5HoyLkTF_YUlsxgE7VSUsvViTqUSKA#!) [Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2018). **Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry.** Computers & Operations Research, 89, 337-347.](https://www.sciencedirect.com/science/article/pii/S0305054816300399?casa_token=GTuCJUH5aT0AAAAA:I7AF9ofvu8rPUEqKVtZwF7AC9Xre9uLQQrlmE8G0Q6UM7HWTl5XSvsQs0dPXfWuUScC6su15Hkw) --- <br> ### 🚀 Meats are the most important part of carbon footprint. We can forecast future meat bought by converting our dataset to a supervised problem. .pull-left[ ``` ## Lag.4 Lag.3 Lag.2 Lag.1 meat_out ## [1,] 1 NA NA NA NA 12000 ## [2,] 2 NA NA NA 12000 10050 ## [3,] 3 NA NA 12000 10050 7950 ## [4,] 4 NA 12000 10050 7950 11550 ## [5,] 5 12000 10050 7950 11550 15000 ## [6,] 6 10050 7950 11550 15000 12450 ## [7,] 7 7950 11550 15000 12450 12150 ## [8,] 8 11550 15000 12450 12150 8400 ## [9,] 9 15000 12450 12150 8400 6450 ## [10,] 10 12450 12150 8400 6450 10800 ``` ] .pull-right[ <img src="img/ghg_food.png" width="80%" style="display: block; margin: auto;" /> ] -------------- [Center for Sustainable Systems, Carbon Footprint Factsheet, University of Michigan](https://css.umich.edu/factsheets/carbon-footprint-factsheet) --- class: center, middle background-image: url("img/bergen.jpg") background-size: cover # .heat[**TAKK!**]