This story connects climate and healthcare datasets to show a broader risk pattern. The temperature and emissions data show the environmental pressure Australia faces, while the emergency department and elective surgery data show the existing pressure on the health system. These datasets do not prove that climate change directly caused recent hospital demand. Instead, they show why rising heat is important for health-system planning: climate risk is growing while hospital capacity is already stretched.
Australia has warmed clearly since 1950.
The long-term temperature trend shows that hotter years are becoming part of Australia’s climate story. The highlighted point marks 2019, the hottest year in this dataset. This chart intentionally uses a single variable as a deliberate narrative choice — establishing the foundational climate argument before more complex multivariate charts follow. The data is drawn from the Copernicus Climate Change Service via Our World in Data, covering annual average surface temperatures from 1950 to 2024. The dashed trend line makes the warming direction undeniable, while the 2019 highlight connects the statistics to a year many Australians remember through catastrophic bushfires and broken heat records. The interactive tooltip allows readers to explore exact temperatures for any individual year, turning a static trend into an exploratory tool. The y-axis does not begin at zero as temperature data operates within a naturally narrow range, and the orange and blue colour combination is distinguishable for most forms of colour vision deficiency.
This matters because climate pressure does not arrive in an empty system. Emergency departments are already managing millions of presentations each year.
Emergency departments are handling millions of presentations each year.
Across Australia, emergency departments remain under heavy pressure. This chart compares emergency department presentation numbers by state and territory from 2020–21 to 2024–25. It is multivariate, plotting two dimensions simultaneously — state and time — revealing that pressure is systemic across every jurisdiction, not concentrated in one or two large states. This follows a deliberate “problem → consequence” narrative structure: Chart 1 established the climate risk; Chart 2 shows the health system receiving that risk is already stretched. The data comes from the AIHW Emergency Department Care report 2024–25. The 2020–21 starting point coincides with COVID-19 disruptions, which suppressed some presentations — readers should interpret early figures with that context in mind. The interactive tooltip allows readers to isolate individual states across overlapping lines, making the chart genuinely exploratory rather than visually cluttered. The comma-formatted y-axis and rotated x-axis labels are accessibility decisions for a general public audience, consistent with The Conversation’s 600px publication standard.
Health-system planning also needs to consider who uses emergency care. Age and sex patterns help identify groups that may require more targeted support during climate-related health events.
Older Australians are a key group in health-system planning.
This chart does not show heat-related illness directly. Instead, it shows which age groups already use emergency departments heavily, helping explain why climate resilience must include hospital capacity and older Australians. This chart is multivariate, crossing three variables simultaneously — age group, sex, and presentation volume — making it one of the most analytically rich in the dashboard. The data comes from the AIHW Emergency Department Care report 2024–25. It is worth noting that the dataset uses binary sex categories and does not capture gender diversity, a limitation relevant for The Conversation’s academically literate readership. The editorial hook sits in a striking contrast: very young children (0–4) and older Australians (65+) dominate ED demand from opposite ends of the age spectrum, for entirely different reasons. Critically, the 65+ cohort is also the group most physiologically vulnerable to heat stress, cardiovascular strain, and dehydration during extreme heat events — showing precisely where the system will feel most pressure as temperatures rise. The horizontal grouped layout, manual age ordering, and interactive tooltip all support readability and precision for a general public audience. The chart is rendered at 600px width in line with The Conversation’s publication standard, and the orange and blue colour combination is distinguishable for most forms of colour vision deficiency.Emergency care is only one part of the system. Elective surgery waiting times show that pressure also exists elsewhere in hospital care.
Elective surgery waits show another pressure point.
Emergency demand is only one part of the health-system picture. Elective surgery waiting times show how much pressure already exists across states and territories. This chart is multivariate, plotting three variables simultaneously — state, wait time measure (median vs 90th percentile), and days waited. Showing both measures together is analytically important: the median reveals what a typical patient experiences, but the 90th percentile exposes the long tail of disadvantage that the median alone conceals, where one in ten patients waits an extraordinarily long time for care classified as non-emergency but still medically necessary.
The data comes from the AIHW Elective Surgery Waiting Times report 2024–25. The dashed orange reference line at 365 days provides an immediate, intuitive benchmark — any bar approaching or crossing that line represents a serious patient-experience concern that a general public audience can grasp without needing specialist health knowledge. The climate connection here is deliberate: heat-related illness can convert elective patients into emergency patients, for example when a patient waiting for cardiac surgery experiences a heat-induced cardiac event. A system where waitlists are already stretched has no buffer capacity to absorb that kind of additional demand. The interactive tooltip allows readers to read exact wait times for each state and measure, which is essential in a grouped bar chart where bars of similar height are otherwise difficult to distinguish precisely. The chart is rendered at 600px width in line with The Conversation’s publication standard, and the three-colour fill palette — navy for median, blue for 90th percentile, and orange reserved for the one-year benchmark — keeps the two main metrics visually distinct while maintaining colour consistency across the dashboard.The final chart returns to the climate context. Australia’s per-person emissions and temperature anomalies show why health-system resilience and emissions reduction need to be discussed together.
Emissions have fallen, but warming has not disappeared.
Australia’s per-person CO₂ emissions have declined from earlier peaks, but temperature anomalies remain high. The two lines are scaled so their long-term patterns can be compared in one chart. This chart is multivariate, combining two independent climate indicators — per-capita CO₂ emissions and annual temperature anomaly — on a single scaled axis to enable direct comparison of their trajectories from 1990 to 2024. Presenting both indicators together forces the reader to confront their divergence simultaneously, which is the chart’s central analytical insight: emissions are falling, but the warming they already contributed to has not reversed.
The data comes from two sources joined by year — the Copernicus Climate Change Service for temperature anomalies and the Global Carbon Project via Our World in Data for per-capita CO₂ emissions. Both variables are min-max normalised to a 0–1 scale so their long-term patterns can be compared despite having completely different units. Importantly, the y-axis is clearly labelled “Scaled value (0 = lowest, 1 = highest)” to ensure transparency — a value of 0 on the CO₂ line means the lowest emissions within this dataset’s window, not zero emissions, and readers should interpret it accordingly. The interactive tooltip addresses this further by displaying real values on hover — actual degrees Celsius or tonnes per person — rather than the abstract scaled figures shown on the axis.
The vertical dashed line marking 2019 deliberately echoes Chart 1, creating a narrative thread that bookends the dashboard’s climate argument. The same year that appeared as the hottest on record in the opening chart reappears here as a reference point connecting emissions trajectory to temperature outcome. Australia’s per-person emissions have fallen by roughly a third since the early 2000s, yet temperature anomalies remain near historic highs — a reflection of committed warming, the scientifically important concept that temperatures continue rising even after emissions begin to fall, due to the lag between atmospheric CO₂ and its full thermal effect. Naming that concept gives The Conversation’s academically literate readership something substantive to take away. The chart is rendered at 600px width in line with The Conversation’s publication standard, with navy used for temperature anomaly and orange for CO₂ emissions, consistent with the colour logic applied across the full dashboard.The data does not prove that every extra emergency department visit is caused by climate change. Instead, the warning is about pressure building from two directions: Australia is getting hotter, while the health system is already carrying heavy demand. Preparing for climate change therefore means preparing hospitals, protecting older Australians, and reducing the emissions that contribute to long-term warming.
Australian Institute of Health and Welfare. (2025). Emergency department care 2024–25. AIHW. https://www.aihw.gov.au/reports-data/myhospitals/sectors/emergency-department-care
Australian Institute of Health and Welfare. (2025). Elective surgery waiting times 2024–25. AIHW. https://www.aihw.gov.au/reports-data/myhospitals/sectors/elective-surgery
Copernicus Climate Change Service. (2024). Surface air temperature [Dataset]. European Centre for Medium-Range Weather Forecasts. https://ourworldindata.org/grapher/average-annual-surface-temperature
Ritchie, H., Rosado, P., & Roser, M. (2023). CO₂ and greenhouse gas emissions. Our World in Data. https://ourworldindata.org/grapher/co-emissions-per-capita
Ritchie, H., & Roser, M. (2024). Temperature anomaly. Our World in Data. https://ourworldindata.org/grapher/annual-temperature-anomalies
This assignment used Claude (Anthropic, 2025) for code review and narrative feedback. All visualisations were built and finalised by the author in R.
Anthropic. (2025). Claude (claude-sonnet-4-6) [Large language model]. https://www.anthropic.com