Climate Change Story

This report explores climate change through a series of interactive visualisations. The charts examine temperature trends, carbon emissions, population growth, and the relationship between population and environmental impact. Together, these visualisations provide insights into some of the key challenges associated with climate change.

Graph 1 - Global Temperature Trend

temp <- data.frame(
  Year = c(2000,2005,2010,2015,2020,2024),
  Temp = c(0.42,0.48,0.63,0.87,1.02,1.18)
)

plot_ly(
  temp,
  x = ~Year,
  y = ~Temp,
  type = "scatter",
  mode = "lines+markers"
)

Explanation

This line chart illustrates the increase in global temperature anomalies between 2000 and 2024. The upward trend shows that average global temperatures are rising over time. Scientists attribute much of this increase to greenhouse gas emissions generated by human activities such as burning fossil fuels, deforestation, and industrial production.

Rising temperatures contribute to more frequent heatwaves, droughts, bushfires, melting glaciers, and sea-level rise. The graph clearly demonstrates why climate change is considered one of the most significant environmental challenges facing the world today.

Graph 2 - CO2 Emissions by Country

co2 <- data.frame(
  Country = c("Australia","India","China","USA"),
  Emissions = c(15,2,8,14)
)

plot_ly(
  co2,
  x = ~Country,
  y = ~Emissions,
  type = "bar"
)

Explanation

This bar chart compares carbon dioxide emissions per capita among Australia, India, China, and the United States. Australia and the United States display higher emissions per person, while India has considerably lower emissions per capita.

These differences occur because countries have different levels of industrialisation, energy consumption, transportation use, and population size. The graph highlights that climate responsibility is not equally distributed and that developed countries often have a larger environmental footprint per person.

Graph 3 - Population Growth

pop <- data.frame(
  Year = c(2000,2005,2010,2015,2020,2024),
  Population = c(19,20,22,24,26,27)
)

plot_ly(
  pop,
  x = ~Year,
  y = ~Population,
  type = "scatter",
  mode = "lines+markers"
)

Explanation

This graph presents Australia’s population growth over time. The population has increased steadily, reflecting economic growth, migration, and natural population increases.

As population grows, demand for housing, transportation, healthcare, education, food, water, and energy also rises. Population growth creates opportunities for economic development but can also place pressure on natural resources and the environment if not managed sustainably.

This graph helps demonstrate the relationship between population trends and environmental planning.

Graph 4 - Population vs Emissions

bubble <- data.frame(
  Country = c("Australia","India","China","USA"),
  Population = c(27,1400,1410,340),
  Emissions = c(15,2,8,14)
)

plot_ly(
  bubble,
  x = ~Population,
  y = ~Emissions,
  color = ~Country,
  size = ~Population,
  type = "scatter",
  mode = "markers"
)

Explanation

The bubble chart explores the relationship between population size and carbon emissions. Each bubble represents a country, with larger bubbles indicating larger populations.

The chart shows that countries with large populations do not always produce the highest emissions per person. For example, India has a very large population but relatively lower emissions per capita, while Australia has a smaller population but higher emissions per person.

This demonstrates that both population size and consumption patterns influence environmental impact. The chart helps viewers understand the complexity of global climate challenges.

Graph 5 - Emission Share Treemap

plot_ly(
  labels = c("Australia","India","China","USA"),
  parents = c("","","",""),
  values = c(15,2,8,14),
  type = "treemap"
)

Explanation

The treemap provides a visual comparison of emission levels across countries. Larger rectangles indicate higher emissions, making it easy to identify major contributors at a glance.

Unlike traditional charts, the treemap uses area to represent values, helping viewers quickly understand the relative differences between countries. This visualisation reinforces the importance of international cooperation in reducing greenhouse gas emissions and achieving global climate goals.

It also highlights how emission contributions vary significantly between nations.

Conclusion

The visualisations in this report demonstrate several important aspects of climate change. Global temperatures continue to rise, reflecting the growing impact of greenhouse gas emissions. Carbon emissions vary significantly between countries, showing that environmental responsibility differs across nations.

Population growth increases pressure on resources and infrastructure, while the relationship between population and emissions reveals that lifestyle and energy consumption are also important factors.

Overall, the findings emphasise the need for sustainable development, cleaner energy sources, and international cooperation to address climate change effectively and ensure a more sustainable future. # Conclusion

The visualisations demonstrate several important aspects of climate change. Rising global temperatures, differences in carbon emissions, and increasing population pressures all contribute to environmental challenges. The findings highlight the importance of sustainable development, cleaner energy solutions, and international cooperation in addressing climate change and ensuring a sustainable future.

Acknowledgements

ChatGPT (OpenAI) was used to assist with idea generation, narrative writing, and troubleshooting R Markdown code. All outputs were reviewed, modified, and validated before inclusion in this assignment.

References

OpenAI. (2025). ChatGPT (GPT-5.5) [Large language model]. https://chatgpt.com