# Demonstraing and visualizing the composition of GHG emission in different scope in 2023 ghg_emissions_2023 <-c(Scope1 =14918, Scope2 =7923, Scope3 =2573898)# Names for the bars (scopes)names(ghg_emissions_2023) <-c("Scope 1", "Scope 2", "Scope 3")# Create the bar graphbarplot(ghg_emissions_2023, main ="GHG Emissions by Scope for 2023", xlab ="Scope", ylab ="Emissions (tCO2e)", col =c("grey", "grey", "grey"), ylim =c(0, max(ghg_emissions_2023) *0.1)) text(x =barplot(ghg_emissions_2023, plot =FALSE),y = ghg_emissions_2023 +100000, labels =formatC(ghg_emissions_2023, format ="d", big.mark =","), col ="black")
From the dataset and graph, the relatively low figures for Scope 1 and Scope 2 emissions suggest effective management of direct emissions from Kering and indirect emissions from the generation of purchased electricity. This could indicate that Kering has taken steps to reduce its direct carbon footprint through energy-efficient operations and possibly the use of renewable energy sources in fashion industry.
And the drastic difference with Scope 3 emissions dominating the chart indicates that the majority of Kering’s carbon footprint lies in its value chain. This includes activities such as raw material production, product manufacturing, transportation, and end-of-life treatment. This implies that while Kering may have a degree of control over its direct operations, there is a significant environmental impact embedded in the broader supply chain.
We know that Kering is a renowned global luxury group that owns several high-profile fashion brands. In the context of the fashion industry, Kering’s substantial Scope 3 emissions, as depicted in the GHG Emissions by Scope graph for 2023, reflect the broader challenges the industry faces when it comes to sustainability and environmental impact.
1.2 Trend of change in different years 2021, 2022, 2023
emissions_data <-data.frame(Year =rep(c(2021, 2022, 2023), each =3),Scope =rep(c("Scope 1", "Scope 2", "Scope 3"), times =3),Emissions =c(# Scope 1 emissions for 2021-202319281, 21660, 14918,# Scope 2 emissions for 2021-202311227, 7598, 7923,# Scope 3 emissions for 2021-20232492861, 2935728, 2573898 ))# Plotting the trend of GHG emissions over the years 2021-2023ggplot(emissions_data, aes(x = Year, y = Emissions, color = Scope)) +geom_line(aes(group = Scope), size =1) +# Draw linesgeom_point(size =2) +# Add pointsscale_y_log10(labels = scales::comma) +# Log scale for y-axis and formatted numbers with commastheme_minimal() +# Minimal themelabs(title ="Trend of GHG Emissions by Scope (2021-2023)",x ="Year",y ="Emissions (tCO2e)",color ="Scope") +scale_color_manual(values =c("Scope 1"="pink", "Scope 2"="orange", "Scope 3"="purple")) # Custom colors for each scope
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
Part 2: Energy Consumption in the process of production for Kering
# Using pie chart to demonstrate the components of the renewable energies, and the ratio of renewable energies in total energy in 2023. renewable_energy_2023 <-data.frame(Source =c("Electricity purchased ","Electricity produced","Biomass"),Energy =c(266680, 14531, 664) # The values for 2023)# Calculating percentages for labelsrenewable_energy_2023$Percentage <-with(renewable_energy_2023, Energy /sum(Energy) *100)# Creating a pie chartggplot(renewable_energy_2023, aes(x ="", y = Percentage, fill = Source)) +geom_bar(width =10, stat ="identity", color ="white") +coord_polar("y", start =0) +theme_void() +scale_fill_manual(values =c("#69b3a2", "#404080", "light blue")) +geom_text(aes(label =paste0(round(Percentage, 1), "%")), position =position_stack(vjust =0.7)) +labs(title ="Composition of Renewable Energy Sources in 2023")
2.2 trend of change in renewable energy over the years
years <-c(2021, 2022, 2023)renewable_energy <-c(235122, 270780, 281875) # in MWhnonrenewable_energy <-c(90686, 78866, 69255) # in MWhenergy_data <-data.frame(Year =rep(years, times =2),Energy =c(renewable_energy, nonrenewable_energy),Type =rep(c("Renewable", "Nonrenewable"), each =length(years)))# Creating the line graphggplot(energy_data, aes(x = Year, y = Energy, color = Type)) +geom_line(aes(group = Type), size =1) +# Draw linesgeom_point(size =2) +# Add points to highlight the data pointsscale_y_continuous(labels = scales::comma) +# Format the y-axis labels with commaslabs(title ="Trend of Renewable vs Nonrenewable Energy (2021-2023)",x ="Year",y ="Energy Consumption (MWh)",color ="Energy Type") +theme_minimal() # Using a minimal theme for a clean look
Part 3: Sustainable Sourcing-traceability of raw materials
# A tibble: 11 × 9
...1 `Unit/Unité` `2023` `2022` `2021` ...6 `GRI Standards` `SDG targets`
<chr> <chr> <dbl> <dbl> <chr> <lgl> <chr> <chr>
1 Tracea… <NA> NA NA <NA> NA <NA> <NA>
2 Leather % 0.98 0.96 0.93 NA 304-2 12.2, 15.1, …
3 Wool % 0.97 0.95 0.96 NA 304-2 12.2, 15.1, …
4 Cashme… % 0.97 0.96 0.99 NA 304-2 12.2, 15.1, …
5 Silk % 0.97 0.97 0.94 NA 304-2 12.2, 15.1, …
6 Gold % 1 1 1 NA 304-2 12.2, 15.1, …
7 Cotton % 0.95 0.94 0.96 NA 304-2 12.2, 15.1, …
8 Paper/… % 0.96 0.99 NA NA 304-2 12.2, 15.1, …
9 Precio… % 1 0.94 0.96 NA 304-2 12.2, 15.1, …
10 Cellul… % 0.6 0.77 0.65 NA 304-2 12.2, 15.1, …
11 Total … % 0.97 0.95 0.9 NA 304-2 12.2, 15.1, …
# ℹ 1 more variable: `UNGC Principles` <chr>
The fashion industry is known for its intensive use of raw materials, some of which can have significant environmental impacts. Kering’s role may involve driving the adoption of sustainable sourcing practices across the industry, especially given its influence and leadership position.
There’s an opportunity for Kering to invest in innovative materials and processes that are less carbon-intensive. Advancements in materials like lab-grown leather, recycled fabrics, and other sustainable alternatives could be key areas of development.
In addition, supply chain management is important, like shown in scope 3 emissions, it suggests the need for Kering or other Fashion company to continue developing innovative and sustainable supply chain solutions. This could include enhancing traceability, promoting the use of low-impact materials, and investing in sustainable practices at the supplier level.
Part 4: Water and waste
# read water and waste water <-read_excel("Kering-Esg.xlsx", sheet ="water and waste")
New names:
• `` -> `...1`
• `` -> `...6`
# water consumption # percentage of water discharged over the years
4.2 Waste production
4.3 Waste recycling rate
Part 5: Paper and packaging
In terms of packaing and recycling, it is essential for company in fashion industry to embrace circular business models such as resale, rental, and recycling could significantly impact reducing Scope 3 emissions. Kering can lead this transformation in the luxury fashion sector
# paper consumption # shows types of paper# packaging consumption
