test.Rmd

---
title: "Revealing the paths: What were the drivers of changes in CO2-emissions from German car manufacturing?"
author: "Simon Schulte"
date: "September 13th, 2019"
bibliography: "/home/simon/Documents/PhD_PROSET/tex/PROSET.bib"
output:
  ioslides_presentation:
    widescreen: true
    smaller: true
    incremental: true
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source("./settings.R")
source("./functions.R")
```
<style>
.column-left{
  float: left;
  width: 66%;
  text-align: left;
}

.column-right{
  float: right;
  width: 33%;
  text-align: right;
}
</style>


## All CO2-emissions related to Car Manufacturing in Germany
<div class="column-left">
```{r total}
data <- fread(file.path(path2temp_results, "sectoralCarbonFP_pxp200_1123.csv"))
data[, "order" := "upstream"]
data[id == id_sector, "order" := "direct"]
data[, order := as.factor(order)]
data$order <- factor(data$order, level = c("upstream", "direct"))
data[, value := value / 1E3] # to tonnes
data[, sum(value), by = .(order, year)] %>% 
  ggplot(aes(x = year, y = V1)) + 
  geom_area(aes(fill = order)) + 
  scale_fill_viridis_d() + 
  ylab("CO2 emissions [t]") + 
  theme_bw()

```
</div>

<div class="column-right">
$e_i = e_{direct} + e_{upstream}$ 
$= s_i x_i + \mathbf{s} \mathbf{l⁰_i} x_i$
</div>


## By country

```{r bycountry}
temp <- data[, sum(value), by = .(country_name, year)] 
temp <- temp %>% 
  .[, "rank" := frankv(V1, order = -1L), by = .(year)] %>% 
  .[, "mean_rank" := mean(rank), by = country_name] %>% 
  .[mean_rank > 10, sum(V1), by = year] %>% 
  .[, "country_name" := "RoW"] %>% 
  .[] %>% 
  rbind(temp[mean_rank <= 10, 
             c("year", "country_name", "V1")], 
        .)

ggplot(temp, aes(x = year, y = V1)) + 
  geom_area(aes(fill = country_name), position = "fill") + 
  scale_fill_viridis_d() + 
  ylab("Share of total emissions") + 
  theme_bw()

```

## Where to these emissions occur?
![](plots/sectoralfp_pxp200_1123_8fps.gif)



## Revealing the paths .... 1995

```{r spa}
library(networkD3)
#list.dirs(path2temp_results)
path2model_results <- file.path(path2temp_results, 
                                "2019-09-11 17:22:46_1.70.71.405.415.416_1123_SPA")
data_list <- readRDS(file = file.path(path2model_results, 
                                      "SPAdata2analyse.RData"))
setnames(data_list$values, "value[t]", "value_t")
IDpaths <- data_list$values[, mean(rank, na.rm = TRUE), by = pathID][V1 < 100]$pathID
data_list$values[, "label" := ifelse(year == max(year), as.character(pathID), 
                                     NA_character_)]


sankeySPA(data_list, 1995, 30)

```

```{r}
DT::datatable(EB3_metadata$pxp200[, 1:2], 
              options = list(pageLength = 1)) %>% 
  DT::formatStyle(columns = 1:2, fontSize = '50%')
```


## 2016
```{r}
sankeySPA(data_list, 2016, 30)
```

```{r}
DT::datatable(EB3_metadata$pxp200[, 1:2], 
              options = list(pageLength = 1)) %>% 
  DT::formatStyle(columns = 1:2, fontSize = '50%')

```


## Structural Path Decomposition
```{r}
spd_results2print <- readRDS(file.path(path2temp_results, 
                                     "spd_results2print_CO2.RData"))

DT::datatable(spd_results2print[, -"id"], 
              options = list(scrollY = '55vh')) %>% 
  DT::formatStyle(columns = 1:7, fontSize = '50%')

```



## Uncertainty? {.build}
"[...] Results at a sectoral level should therefore be interpreted with caution." [@lenzen2010]

(Possible) sources of uncertainty in MRIO analysis: 

* Raw Data
    + Satellite accounts
    + Economic data
    + Trade data
* Compiling MRIOs
    + Dif. level of detail of national economic, environmental and trade data
    + Trade-split assumption (same import mix of a product for each sector of a country)
    + sectoral/regional aggregation 
    + one-industry-one-product assumption
    + Balancing procedure
* Prices
    + Currency conversion: Purchasing Power Parity (PPP) versus market exchange rate (MER)
    + Converting basic prices to producer prices
    + Handling of taxes of subsidies
...