5.3A Climate Change and Responsible Use of Resources
Climate and Resources as a Global Challenge
This is HL extension material (HL only): how digital systems measure the problem, use energy and materials better, and add to the footprint themselves.
Climate change is a challenge because a tonne of carbon dioxide warms the planet wherever it is released, warming plays out over decades, and this decade's choices set conditions for people not yet born. No single technology switches it off.
Framing concepts: systems (the climate is one connected system), values and ethics (a duty to future generations), and space (harm and response are both planet-wide).
Seeing the Climate
Satellites, ocean buoys, and ground sensors gather huge data streams; models turn them into projections of temperature, sea level, and extreme weather.
Through big data and computing, an invisible planet-wide process becomes measurable, so governments and scientists can plan, warn, and hold emitters to account (concepts: systems, space).
Result: faster and more trusted knowledge of the planet.
Contested: data alone changes nothing if no one acts, and the modelling itself burns significant computing power.
Doing More With Less
A smart grid senses supply and demand in real time and shifts power to match, cutting waste and letting more renewable energy in.
Big data and sensors optimise factories, buildings, transport, and farms, and support a circular economy where materials are tracked, reused, and recycled (concepts: systems, values and ethics).
Result: energy waste falls measurably and renewables fit the grid more smoothly, holding out the chance to decouple prosperity from resource use.
Contested: efficiency gains can simply fuel more consumption, and the optimising systems draw power of their own.
The Footprint of Digital Itself
Data centres and training large AI models draw huge electricity, straining power supplies in some regions.
The world discards vast electronic waste, mostly unrecycled, and every device has a carbon footprint from its materials and energy. The digital sector is part of the challenge, not a bystander.
Through values and ethics, it is dishonest to count only the emissions a technology saves while ignoring those it creates. Contested: users, tech firms, and regulators disagree on who bears the cost.
Worked Evaluation and Comparison
To judge an intervention, first name its type: mitigate, intercede, enhance, or resolve. Then weigh it against tests: equity, acceptability, cost, feasibility, innovation, ethics.
Satellite greenhouse-gas monitoring is an enhancing intervention: it strengthens our ability to see and trace emissions rather than cutting them.
Scores high on innovation (space tech as accountability tool) and equity (exposes emitters anywhere, shared openly).
Weak on cost (expensive to build and launch, some missions failed); ethics sound but value depends on the data being acted upon.
Recommendation: fund it, but tie the data straight to enforcement so a detected leak triggers a required fix, not just a headline.
Contrast: a green-computing and e-waste law (e.g. EU right-to-repair and energy-efficiency measures) intercedes, capping data-centre energy and water use and forcing repair and recycling. An intervention can be a regulation, not just an app.
Scores well on ethics and long-run cost, but hard on feasibility (global supply chains) and acceptability (firms resist limits).
Weighing a Contested Balance
Claim: digital systems are our best hope; they measure climate change, optimise away waste, and speed the shift to clean energy.
Counter-claim: the same systems are hungry for power and materials; data centres, AI, and e-waste can deepen the problem they claim to solve.
Evidence (IEA): data centres used about 415 TWh in 2024, on track for roughly 945 TWh by 2030, with AI the fastest-growing driver; their emissions near 180 Mt today could reach 300 Mt within a decade.
Read as a system, digital is neither a fix nor a disaster on its own. The net effect depends on how computing is powered, how efficiently it is built, whether waste is recovered, and whether the data is acted on.
Recommendation: power computing with clean energy, hold the sector to efficiency and e-waste standards, count a technology's full footprint, and judge every intervention by its net effect, not its promise.
Theory of Knowledge
Knowledge and uncertainty
A climate model's projection is treated as knowledge about a future that has not happened, yet models simplify a vast system and disagree at the edges.
How much certainty can a projection carry, and how should a society act under deep uncertainty about its own future?
Case study
Case study: Satellite greenhouse-gas monitoring
System: spacecraft carrying spectrometers detect and map CO2 and methane emissions from orbit (content topics: data, networks).
Specifics: the EU Sentinel programme plus methane-hunting missions from GHGSat, Carbon Mapper, and MethaneSAT pinpoint large leaks from specific oil and gas sites and publish the data; methane is short-lived, so early catches slow near-term warming.
Impacts and implications: Impacts: leaks operators once denied are detected from space and published. Implications: an enhancing intervention, but seeing a leak does not stop it, satellites are costly, and data only bites if regulators act.
Concepts: power (public data shifts leverage to regulators and citizens), systems (diffuse problem becomes pinpointed sources), values and ethics (transparency, responsibility); environmental context with political links.
Active recall
Self review
Explain what makes climate change a challenge in the HL sense.
Explain how digital systems help us see the climate and use resources more efficiently.
Explain why the digital sector is part of the climate challenge, not only a solution.
Evaluate satellite monitoring against at least three criteria, and contrast it with an interceding intervention.
Recall the IEA data-centre electricity figures and what they show about digital's net effect.
