# 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
In water consumption, this data indicates a general trend of decreasing water consumption across all categories from 2022 to 2023, continuing the trend from 2021. The data is aligned with certain GRI Standards and contributes to SDG target 6.4 with a focus on sustainable water management and efficiency, as indicated by the alignment with UNGC Principles 7 to 9.
# read water and waste water <-read_excel("Kering-Esg.xlsx", sheet ="water and waste")
New names:
• `` -> `...1`
• `` -> `...6`
# water consumption water_data <-data.frame(Category =c("Public Network Domestic Use", "Public Network Industrial Use", "Groundwater"),`2021`=c(618083, 64897, 270113),`2022`=c(703102, 103535, 226564),`2023`=c(657442, 94101, 211014))# Melting the data frame for easier plotting with ggplotlibrary(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
The decreasing trend in water consumption from 2021 to 2023 across different categories can be viewed as a positive indicator of increasing resource efficiency within the organization. Efficient water use reduces the environmental impact, supports the conservation of water resources, and aligns with Sustainable Development Goal (SDG) 6, which is to ensure availability and sustainable management of water and sanitation for all. And for groundwater conservation, the decrease in groundwater consumption highlights efforts towards conserving natural water sources, which is crucial for maintaining ecological balance and ensuring that water remains available for future generations. This is in line with SDG 15, which emphasizes the sustainable use of terrestrial ecosystems. Noteworthy, there is an reduction in industrial water use. The specific reduction in water used for industrial purposes suggests a focus on process optimization and technological improvements that lead to less water being needed for production processes. This can be linked to SDG 9, which promotes industry innovation and infrastructure, aiming for sustainable industries that use resources more efficiently.
And it helps to report and improve transparency: By tracking and reporting water consumption data, Kering,ltd contributes to greater transparency in environmental impact, aligning with SDG 12, which focuses on ensuring sustainable consumption and production patterns. This data can be used to communicate with stakeholders about the organization’s sustainability efforts and progress towards environmental goals.
4.2 Waste production
The waste production data shows a total reduction in waste from 2021 to 2023, indicating progress in waste management strategies.
Non-Hazardous Waste: There’s a clear downward trend from 2021 to 2023, indicating successful measures to reduce this type of waste.
Hazardous Waste: The trend was initially declining from 2021 to 2022 but saw a slight increase in 2023. The overall reduction is still positive, but the uptick in the last year could warrant further investigation.
Total Waste: Mirroring the non-hazardous waste trend, total waste production has notably decreased over the three-year period, reflecting comprehensive waste reduction across the board.
This data aligns with the Global Reporting Initiative (GRI) Standards and supports Sustainable Development Goals (SDGs) related to responsible consumption and production (SDG 12) and life below water (SDG 14.1) by reducing waste production and improving recycling rates. It also resonates with United Nations Global Compact (UNGC) Principles 7 to 9, which encourage environmental responsibility and the development of eco-friendly technologies.
The overall trend towards reduced total waste and an increase in recycling and recovery rates is a positive sign of the company’s commitment to sustainability and more efficient waste management practices. However, there is room for improvement, particularly in increasing the hazardous waste recycling and recovery rate and aiming to match or exceed the recycling and recovery performance of 2021.
4.3 Waste recycling rate
Here we have two charts depicting waste production and recycling rates over three years.
In the first chart, the bar graph illustrates the production of non-hazardous, hazardous, and total waste. There’s a noticeable overall decline in waste production from 2021 to 2023, indicating effective waste management strategies.
The second chart shows the recycling rate trends. There was a slight decrease in the recycling rate of non-hazardous waste from 2021 to 2023, with a dip in 2022. The hazardous waste recycling rate shows a drop from 2021 to 2022, but then a significant increase in 2023, suggesting improvements in the management of hazardous waste. The total recycling rate mirrors the trend in non-hazardous waste, decreasing slightly over the three years.
These visualizations provide a clear view of the company’s waste management efficiency and highlight areas where further improvements could be made, particularly in maintaining high recycling rates
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
# packaging consumption packaging_data <-data.frame(Year =c("2021", "2022", "2023"),'Paper and Cardboard'=c(29849, 19640, 13185),'Textiles'=c(2552, 442, 427),'Plastics'=c(1136, 581, 608),'Metals'=c(683, 58, 70),'Wood'=c(219, 37, 33),'Leather'=c(2, 0, 3),'Other Materials'=c(63, 6, 4),'Total Packaging'=c(34504, 20756, 14330))# Melt the data into a long formatmelted_packaging_data <-melt(packaging_data, id.vars ='Year', variable.name ='Material', value.name ='Consumption')# Create the bar plotggplot(melted_packaging_data, aes(x = Year, y = Consumption, fill = Material)) +geom_bar(stat ='identity', position =position_dodge()) +theme_minimal() +labs(title ='Packaging Consumption by Material (2021-2023)',x ='Year', y ='Consumption (tonnes)', fill ='Material') +scale_fill_brewer(palette ='Set3') # Using a color palette for better distinction
# Display the plotggsave('PackagingConsumption.png', width =10, height =6, units ='in')
The use of recycled and certified materials in paper and cardboard, as well as plastics, indicates an effort to improve sustainability in packaging materials, aligning with GRI Standards and contributing towards SDGs 12.2 and 12.5, which are concerned with sustainable management and efficient use of natural resources.The data reflects the company’s progress towards sustainability goals, particularly in reducing waste and enhancing the use of recycled and certified materials in its packaging. The dramatic reduction in total packaging consumption could be a result of strategic changes in product packaging design, material selection, or a change in production strategies with a focus on sustainability.
