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IB DP Digital Society HL: 5.3 Sustainable Development (STAGE TWO): Explore and investigate challenges

  • Writer: lukewatsonteach
    lukewatsonteach
  • Sep 16
  • 26 min read

Updated: Oct 29

5.3 Sustainable Development Key Findings:

  • Climate Emergency: 2024 was the hottest year on record at 1.55°C above pre-industrial levels, with fossil CO2 emissions reaching 37.4 billion tonnes (up 0.8% from 2023) and atmospheric CO2 at 422.5 parts per million - 52% above pre-industrial levels United NationsGlobal Carbon Budget.

  • Digital Carbon Impact: With 5.5 billion people having internet access in 2024, the collective digital carbon footprint accounts for nearly 3.7% of all greenhouse emissions, comparable to aviation, while IT equipment uses 3.6% of global electricity myclimateCharity Digital.

  • Resource Depletion Crisis: Natural resource extraction tripled in the past five decades and is expected to rise by 60% by 2060, with high-income countries using six times more materials per capita than low-income countries UNEPWorld Economic Forum.

  • Circular Economy Decline: Global circularity decreased from 9.1% in 2018 to 7.2% in 2023, with over 90% of extracted materials being wasted despite increased engagement with circular approaches GreenMatchUNEP.

  • E-waste Explosion: E-waste generation is rising five times faster than recycling, with 62 million tonnes generated in 2022 but only 22.3% properly recycled, leaving $62 billion worth of recoverable resources unaccounted for E-Waste MonitorUNITAR.

  • Plastic Crisis: 2,000 garbage trucks full of plastic are dumped daily into oceans, with only 9% of all plastic waste ever recycled and 79% accumulated in landfills or the environment UNEPUNEP.

  • Waste Cost Explosion: Municipal solid waste will grow from 2.1 billion tonnes in 2023 to 3.8 billion tonnes by 2050, with costs rising from $361 billion to potentially $640.3 billion without urgent action Global Waste Management Outlook 2024 | UNEP - UN Environment Programme.


5.3 Sustainable Development: STAGE TWO: Explore and investigate challenges

Students explore sources and investigate their extended inquiry focus by considering some of the following questions.

  • What is the relationship between digital systems and this challenge?

  • What is the nature and scope of this challenge in digital society?

  • What course concepts, content and contexts will be most helpful to consider with this challenge?

  • How does this challenge manifest itself at local and global levels?

  • Who are the specific people and communities affected by this challenge?

  • What are some impacts and implications related to this challenge?

Sustainable Development Challenges: Key Statistics and Insights for IB DP Digital Society

5.3 Sustainable Development

5.3A Climate Change and Action

Global Temperature Rise:

  • 2024 was the hottest year on record, with the global average near-surface temperature 1.55°C above the pre-industrial baseline (UN Climate Action Fast Facts)

  • The Earth is now between 1.34°C and 1.41°C warmer than it was in the pre-industrial era (1850-1900) (UN Climate Action Fast Facts)

  • 2015-2024 was the warmest recorded decade (UN Climate Action Fast Facts)


Greenhouse Gas Emissions Crisis:

  • The 2024 Global Carbon Budget projects fossil carbon dioxide (CO2) emissions of 37.4 billion tonnes, up 0.8% from 2023 (Global Carbon Budget 2024)

  • Atmospheric CO2 levels are set to reach 422.5 parts per million in 2024, 2.8 parts per million above 2023, and 52% above pre-industrial levels (Global Carbon Budget 2024)

  • Nations must collectively commit to cutting 42 per cent off annual greenhouse gas emissions by 2030 and 57 per cent by 2035 or the Paris Agreement's 1.5°C goal will be gone within a few years (UNEP Emissions Gap Report 2024)


Digital Carbon Footprint:

  • According to one estimate, around 5.5 billion people worldwide will have internet access in 2024. Every single search query, every streamed video and every type of cloud computing, carried out billions of times, is responsible for an ever-increasing global demand for energy (myclimate - Digital Carbon Footprint)

  • According to a 2019 study by the Shift Project, the world's collective digital carbon footprint accounted for nearly 3.7% of all greenhouse emissions, which is comparable to aviation (Charity Digital - Climate change facts 2024)

  • IT and tech equipment accounts for about 1.4% of total carbon emissions and uses about 3.6% of global electricity consumption (Charity Digital - Climate change facts 2024)


Digital Solutions and Challenges:

  • Digital technologies can be key tools to accelerate achievement of the 2030 Agenda for Sustainable Development, as they play a key role for climate monitoring, early warning systems, and overall climate adaptation and mitigation (UN News - COP29: Digital tech and AI can boost climate action)

  • Growing levels of digitization demand more energy, which raises greenhouse gas emissions. AI programmes need servers that run around the clock. These servers and the data centres that house them use a lot of electricity (UN News - COP29: Digital tech and AI can boost climate action)

  • Developing countries bear a disproportionate share of digitalization's ecological costs while reaping fewer benefits (UNCTAD Digital Economy Report 2024)


Climate Inequality:

  • The 100 least-emitting countries generate just 3% of total emissions, while the ten largest emitters contribute 68% (Charity Digital - Climate change facts 2024)

  • Between 1990-2015, the richest 1% of the world's population were responsible for more than twice as much carbon emissions as the poorest 50% of humanity (Charity Digital - Climate change facts 2024)


5.3A: CLIMATE CHANGE AND ACTION: Organisations & Policies

  • United Nations and UNFCCC - Central to global climate action, the United Nations (UN) oversees SDG implementation—including SDG 13, focused on climate action—while the United Nations Framework Convention on Climate Change (UNFCCC) manages international climate negotiations and frameworks such as the Paris Agreement.​

  • Paris Agreement (COP21) - The legally binding treaty adopted under the UNFCCC in 2015, it sets global targets for greenhouse gas emissions and encourages countries to submit and improve their climate action plans (Nationally Determined Contributions, or NDCs).​

  • European Union (EU) - The EU is a major climate action leader, advancing climate neutrality and resilience via the European Green Deal and supporting global cooperation through initiatives like the Global Gateway and massive climate finance contributions.​

  • ASEAN - The Association of Southeast Asian Nations has its own climate coordination, the ASEAN Working Group on Climate Change (AWGCC), and regional initiatives like the ASEAN Catalytic Green Finance Facility. ASEAN member states submit climate action strategies, engage in joint policy statements, and collaborate with EU, UN, ADB, and other development partners on regional low-carbon and resilience-building projects.​

  • Climate Action Network (CAN) - A global civil society coalition active in advocacy, policy, and mobilisation for collective climate action, keeping governments accountable for their commitments.


5.3A: CLIMATE CHANGE AND ACTION CASE STUDY TOPICS:

  • Google's AI Climate Initiatives (Case Study) - Google uses artificial intelligence for climate mitigation, including fuel-efficient routing in Google Maps and its Flood Hub platform that provides real-time flood information to help communities prepare and respond to disasters.​

  • UPS ORION Route Optimisation (Case Study) - UPS adopted the AI-powered ORION system to optimise delivery routes, saving millions of gallons of fuel per year and cutting their carbon footprint significantly, demonstrating how logistics technology can drive emissions reductions.​

  • GE Digital Wind Farms (Case Study) - General Electric leveraged IoT and digital twins to boost wind farm efficiency; the system uses real-time data to optimise turbine productivity, increasing renewable energy supplies by up to 10% per farm.​

  • CarbonBright's AI Waste Management (Case Study) - The climate tech startup CarbonBright employs AI to instantly assess the environmental footprint of consumer goods and recommend emissions reductions, improving supply chain transparency and circularity.​

  • European Space Agency's Climate Change Initiative (CCI) (Case Study) - The ESA uses AI to process satellite data for improved climate change monitoring and predictions, supporting EU goals of carbon neutrality by 2050.​

  • BrainBox AI in Energy Management (Case Study) - BrainBox AI uses autonomous artificial intelligence to optimise building HVAC systems in real time, reducing energy use and greenhouse gas emissions by significant margins.​

  • VIA's Green Coding (Case Study) - VIA developed privacy-focused, sustainable software solutions for energy tracking and reduction, illustrating the link between digital innovation, data management, and climate action.


CASE STUDY 1: The AI Energy Crisis - Data Centers and Climate Impact (2024)

Date: Ongoing throughout 2024-2025


Context: As AI adoption explodes, data centers have become one of the fastest-growing sources of carbon emissions, creating a tension between technological progress and climate action.


Key Facts:

  • IEA Report (2024): Data centers consumed 460 terawatt-hours (TWh) in 2022, roughly 2% of global electricity - expected to double by 2026

  • Goldman Sachs (2024): Data center power demand to grow 160% by 2030

  • Google (2024): Company's total greenhouse gas emissions increased 48% since 2019, primarily due to AI

  • Microsoft (2024): Emissions up 29% since 2020, despite net-zero commitments

  • Training GPT-4: Estimated 50,000 MWh of electricity (equivalent to annual power consumption of 5,000 US homes)

  • Single ChatGPT query: Uses 10x more energy than a Google search


The Problem Breakdown:

Energy Consumption:

  • AI training runs require massive parallel computing for weeks/months

  • Example: Training a large language model = 502 metric tons CO₂ (equivalent to 5 average American lifetimes)

  • Inference (actually using AI) increasingly significant as billions use ChatGPT, Claude, etc.


Water Consumption:

  • Data centers use water for cooling

  • Microsoft (2024): Water consumption increased 34% in one year

  • Training GPT-3 consumed approximately 700,000 liters of clean freshwater

  • In water-scarce regions (Arizona, Nevada), this creates competition with human needs


Location Matters:

  • Data centers in coal-powered regions (China, parts of US) have 3x carbon footprint of those in renewable-powered areas (Iceland, Norway)

  • "Carbon arbitrage": Companies build where electricity is cheap, regardless of source


Digital Systems Involved:

  • AI Models: GPT-4, Claude, Gemini, LLaMA, image generation (Midjourney, DALL-E)

  • Infrastructure: NVIDIA H100 GPUs (each draws 700 watts); cooling systems; backup generators

  • Cloud Computing: AWS, Azure, Google Cloud expanding data center networks

  • Cryptocurrency: Bitcoin mining alone uses ~150 TWh annually (more than Argentina)


Stakeholders & Impacts:

The Paradox: AI is being used to solve climate change (weather prediction, grid optimization, climate modeling) but AI itself is accelerating climate change.
The Paradox: AI is being used to solve climate change (weather prediction, grid optimization, climate modeling) but AI itself is accelerating climate change.

The Paradox: AI is being used to solve climate change (weather prediction, grid optimization, climate modeling) but AI itself is accelerating climate change.


Real-World Examples:

Google's Trajectory:

  • 2019: Announced carbon-neutral since 2007; aiming for 24/7 carbon-free energy by 2030

  • 2024: Emissions up 48% due to AI; 24/7 goal increasingly out of reach

  • Company admits: "The future environmental impact of AI is complex and difficult to predict"


Microsoft's Water Crisis:

  • Built data centers in Arizona (extreme drought)

  • 2022: Used 6.4 million cubic meters of water (enough for 40,000 people annually)

  • Local criticism: "Why are we giving water to AI when people need it?"


Ireland's Data Center Boom:

  • Data centers now use 21% of Ireland's electricity (2024)

  • Threatened rolling blackouts

  • Government considering moratorium on new data centers

  • Renewable energy goals undermined by data center growth


The Cryptocurrency Comparison:

  • Bitcoin mining: 150 TWh/year, 0.5% of global electricity

  • AI could eventually use MORE than cryptocurrency

  • Unlike crypto, AI seen as "necessary" - harder to regulate


IB Connections:

  • Concepts: Systems (2.6) - interconnected energy/climate systems; Change (2.1) - rapid technological change vs. climate stability; Values & Ethics (2.7) - progress vs. sustainability

  • Content: AI (3.6) - environmental cost; Data (3.1) - data storage impact; Networks (3.4) - infrastructure requirements

  • Contexts: Environmental (4.3) - climate impact; Economic (4.2) - growth vs. sustainability; Global - developed world benefits, developing world suffers


Interventions & Evaluation:

Interventions & Evaluation
Interventions & Evaluation

Critical Analysis:

The Growth Problem: No amount of efficiency improvement can keep pace with exponential AI adoption:

  • If AI use grows 10x but efficiency improves 5x → Still 2x more energy

  • Current trajectory: AI use growing FASTER than efficiency improvements

  • "Jevons Paradox": Efficiency gains → Lower cost → More use → Higher total consumption


The Renewable Energy Myth: Tech companies claim "100% renewable" but:

  • Renewable energy purchased elsewhere (doesn't help local grid)

  • Purchased at different times (solar/wind intermittent; data centers run 24/7)

  • Creates demand that could have replaced fossil fuels elsewhere

  • "Additionality" problem: Did company create NEW renewable energy or just buy existing?


The Equity Dimension:

  • Rich countries/companies creating emissions

  • Poor countries bearing climate consequences

  • AI benefits concentrated in Global North

  • Climate impacts concentrated in Global South

  • Digital colonialism: extractive relationship


Questions for Students:

  1. Ethical: Is AI progress worth accelerating climate change? Who decides?

  2. Allocation: Should we prioritize AI for climate solutions (models, predictions) over AI for entertainment (image generation, chatbots)?

  3. Responsibility: Are tech companies accountable for Scope 2/3 emissions (grid power, user devices)?

  4. Alternatives: Could we have "slow AI" movement like "slow food"?

  5. Regulation: Should governments limit data center construction in water-scarce or coal-powered regions?


Comparative Example - The Right Way?:

Iceland's Data Center Model:

  • 100% renewable electricity (geothermal + hydro)

  • Cold climate = less cooling needed

  • Small population = minimal competition for resources

  • BUT: Can't scale globally; not every country has these advantages

  • AND: Still has embodied carbon in hardware manufacturing


Sources:

  • International Energy Agency, "Electricity 2024" report

  • Nature, "The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink" (2024)

  • MIT Technology Review, "The AI boom is pushing data centers to their energy limits"

  • Goldman Sachs, "Gen AI: Too Much Spend, Too Little Benefit?" (2024)


CASE STUDY 2: Bitcoin's Energy Crisis and the Shift to Renewables (2024)

Date: Ongoing 2024; China ban (2021) changed landscape


Context: Bitcoin mining consumes more electricity than entire countries, sparking debate about cryptocurrency's environmental cost and whether "green Bitcoin" is possible or greenwashing.


Key Facts (2024):

  • Energy Consumption: 150 terawatt-hours (TWh) annually - more than Argentina or Netherlands

  • Carbon Emissions: 65-85 million metric tons CO₂ annually (equivalent to Greece)

  • Global Electricity: Bitcoin uses 0.5-0.7% of world's electricity

  • E-waste: 30,000-35,000 metric tons annually (equivalent to small IT equipment waste of Netherlands)

  • Water Footprint: 1.65 cubic kilometers annually (cooling for mining operations)


Dramatic Shift After China Ban (2021):

  • Before 2021: China = 75% of global mining (mostly coal-powered)

  • After 2021: Mining migrated to US, Kazakhstan, Russia, Canada

  • Renewable Energy Share: Increased from 25% (2020) to 58% (2024) according to Bitcoin Mining Council

    • Critics dispute this number; Cambridge estimates 40%


How Bitcoin Mining Works (Digital Systems):

  1. Proof-of-Work: Miners compete to solve mathematical puzzles

  2. ASIC Miners: Specialized computers (e.g., Bitmain Antminer S19) running 24/7

  3. Energy Intensity: Each transaction uses ~1,500 kWh (equivalent to 50 days of average US household electricity)

  4. Difficulty Adjustment: Puzzles get harder as more miners join → always energy-intensive

  5. Winner Take All: Only one miner wins reward every 10 minutes; others' energy wasted


Digital Systems Involved:

  • ASIC Hardware: Application-Specific Integrated Circuits (efficient only for Bitcoin)

  • Cooling Systems: Industrial-scale air conditioning

  • Blockchain: Distributed ledger requiring consensus

  • Mining Pools: Coordinated mining operations

  • Smart Contracts: (Ethereum, now proof-of-stake)


Geographic Distribution & Energy Sources (2024):

Geographic Distribution & Energy Sources (2024)
Geographic Distribution & Energy Sources (2024)

Real-World Examples:

1. Texas Bitcoin Boom (2023-2024):

  • Became #1 Bitcoin mining state in US

  • Miners attracted by cheap electricity, favorable regulations

  • 2023 Summer: Heat wave → Grid stressed → Bitcoin miners paid $31.7 million to shut down temporarily

  • Result: Miners profit from NOT mining; subsidized by electricity consumers

  • Controversy: "Why are residential customers subsidizing Bitcoin?"


2. Greenidge Generation (New York):

  • Converted natural gas power plant to Bitcoin mine

  • Reversed planned closure by mining Bitcoin

  • Impact: Increased emissions from 41,000 tons (2019) to 243,000 tons (2022)

  • Environmental groups sued; permit revoked 2024

  • Significance: Bitcoin incentivizing fossil fuel plant operation


3. Iceland's Renewable Bitcoin:

  • 100% renewable energy (geothermal + hydro)

  • Low temperatures reduce cooling costs

  • "Green Bitcoin" marketing

  • BUT: Energy could heat homes, power industries, or be exported

  • Opportunity cost: Is Bitcoin best use of renewable energy?


4. Crusoe Energy (Flared Gas Mining):

  • Mines Bitcoin using natural gas that would otherwise be flared (burned/wasted) at oil wells

  • Marketing: "Reducing emissions by using waste energy"

  • Reality: Extends life of fossil fuel operations; prevents transition to renewables

  • Evaluation: Better than flaring but worse than leaving gas in ground


The "Green Bitcoin" Debate:

Pro Arguments:

  1. Renewable Energy Buyer of Last Resort: Bitcoin miners will buy excess renewable energy (e.g., solar midday) that would otherwise be curtailed (wasted)

  2. Flexible Load: Miners can shut down during peak demand, stabilizing grid

  3. Funding Renewable Development: Mining revenue funds new renewable projects in remote areas

  4. Better Than Before: Shift from 75% fossil (2020) to 40-58% renewable (2024) is progress


Counter Arguments:

  1. Opportunity Cost: Every kWh used for Bitcoin could power homes, hospitals, electric vehicles

  2. Greenwashing: "58% renewable" claim disputed; includes hydro in rainy season only

  3. Jevons Paradox: Cheaper renewable energy → More mining → Higher total energy use

  4. Moral Hazard: Creates financial incentive to overproduce electricity

  5. Carbon Accounting Trick: Miners buy renewable energy credits without reducing grid emissions


The Ethereum Comparison (Critical Context):

September 2022: Ethereum switched from Proof-of-Work to Proof-of-Stake

  • Energy Reduction: 99.95% decrease in energy consumption

  • Before: 112 TWh/year (similar to Bitcoin)

  • After: 0.01 TWh/year (2,600 times less)

  • Proof: Alternative consensus mechanisms exist that don't require massive energy


Bitcoin Response:

  • "Proof-of-Work is essential for security"

  • "Ethereum's security is unproven"

  • BUT: Critics argue this is defending profitable mining infrastructure, not technical necessity


IB Connections:

  • Concepts: Values & Ethics (2.7) - profit vs. environment; Systems (2.6) - complex energy-blockchain interaction; Power (2.4) - who benefits from energy use

  • Content: Algorithms (3.2) - proof-of-work; Networks (3.4) - distributed ledger; Data (3.1) - blockchain transaction records

  • Contexts: Economic (4.2) - speculation vs. utility; Environmental (4.3) - climate impact; Global - developed world speculation, developing world suffers


Interventions & Effectiveness:

Interventions & Effectiveness
Interventions & Effectiveness

Critical Analysis:

The Fundamental Question: Is ANY amount of energy justified for Bitcoin, which processes 7 transactions per second (vs. Visa's 65,000/second) and is primarily used for speculation rather than payments?


Economic Perspective:

  • Bitcoin market cap: ~$1 trillion (2024)

  • Energy cost: ~$10-15 billion annually

  • Conclusion: Profitable for miners; expensive for planet


Three Positions:

1. Bitcoin Maximalists:

  • "Bitcoin is revolutionary financial freedom"

  • "Energy use is justified for decentralization and security"

  • "Driving renewable energy innovation"


2. Moderate Critics:

  • "Bitcoin wastes energy but greenwashing is worse"

  • "Should pay full environmental cost (carbon tax)"

  • "Better than proof-of-work alternatives exist"


3. Strong Critics:

  • "Bitcoin provides no social value worth its energy cost"

  • "Should be banned like China did"

  • "Massive misallocation of renewable energy"


Questions for Students:

  1. Value Judgment: Does Bitcoin's decentralization value justify its energy cost?

  2. Alternatives: If Ethereum can use 99.95% less energy, why can't Bitcoin?

  3. Regulation: Should governments restrict crypto mining to protect climate goals?

  4. Green Claims: Is renewable-powered Bitcoin actually "green" given opportunity costs?

  5. Future: If Bitcoin price rises 10x, would energy use rise proportionally?


Sources:

  • Cambridge Bitcoin Electricity Consumption Index (2024)

  • Bitcoin Mining Council reports (disputed by critics)

  • Digiconomist, "Bitcoin Energy Consumption Index"

  • Nature Communications, "Bitcoin emissions alone could push global warming above 2°C" (controversial paper)

  • IEEE Spectrum, "The Frustrating, Maddening, All-Consuming Bitcoin Energy Debate"

5.3 B Use of Resources

Resource Extraction Crisis:

  • Extraction of the Earth's natural resources tripled in the past five decades, related to the massive build-up of infrastructure in many parts of the world and the high levels of material consumption (UNEP Global Resources Outlook 2024)

  • Material extraction is expected to rise by 60 per cent by 2060 and could derail efforts to achieve not only the Sustainable Development Goals (UNEP Global Resources Outlook 2024)

  • The use of material resources has increased more than ten times since 1900 and is set to double again by 2030 (UNECE Circular Economy)


Global Consumption Disparities:

  • High-income countries use six times more materials per capita and are responsible for 10 times more climate impacts per capita than low-income countries (World Economic Forum - Global sustainable resource consumption needed urgently)

  • The material footprint per capita in high-income countries is 10 times the level of low-income countries (UN Sustainable Consumption and Production)

  • If the global population reaches 9.8 billion by 2050, the equivalent of almost three planets will be required to provide the natural resources needed to sustain current lifestyles (UN Sustainable Consumption and Production)


Circular Economy Status:

  • Global circularity has decreased from 9.1% in 2018 to 7.2% in 2023, a 21% drop over five years (GreenMatch - Environmental Impact of Circular Economy)

  • The world population consumes over 100 billion tonnes of materials every year. Over 90% of all materials extracted and used are wasted (GreenMatch - Environmental Impact of Circular Economy)

  • World circularity is about 8%. Despite an increased engagement globally with circular approaches, material consumption has, in fact, increased rather than declined over the years 2018-2023 (UNEP - Circularity – accelerating sustainable consumption)


Digital Resource Impact:

  • Data centers require enormous amounts of energy and water for cooling

  • Manufacturing of digital devices requires rare earth elements and creates significant environmental impact

  • The circular economy could unlock $4.5 trillion of economic growth and create six million new jobs, many of which will be in developing countries (UNEP - Circularity – accelerating sustainable consumption)


Business Response:

  • 49% of manufacturers cited becoming a circular business as part of their strategy in 2024 (up from 44% in 2023) (GreenMatch - Environmental Impact of Circular Economy)

  • 50% made operational changes to become more circular in the last three years (GreenMatch - Environmental Impact of Circular Economy)


CASE STUDY 3: Fairphone 5 and the Right to Repair Movement (2024)

Date: Fairphone 5 released August 2023; movement ongoing through 2024


Context: While e-waste grows 5x faster than recycling, Fairphone attempts to prove sustainable electronics are commercially viable, challenging the planned obsolescence model that dominates the industry.


Key Facts:

Fairphone 5 Specifications:

  • 10-year software support: Longest in industry (vs. 2-5 years typical)

  • 10-year spare parts availability: Company commits to parts availability

  • 10/10 iFixit Repairability Score: Only phone to achieve perfect score

  • Modular design: 10 repairable components (battery, screen, cameras, ports, speakers)

  • Fair materials: Fairtrade gold, recycled plastics, conflict-free minerals

  • Living wage: Premium paid to factory workers

  • Price: €699 (~$750 USD)


The Problem It Addresses:

Planned Obsolescence:

  • Average smartphone lifespan: 2.5 years globally

  • Technical lifespan: Could be 10+ years with repairs

  • Gap: 7.5 years of potential use wasted

  • Primary reasons for replacement:

    1. Battery degradation (40%)

    2. Broken screen (25%)

    3. Software no longer supported (20%)

    4. "Slow" performance (10%)

    5. Fashion/desire for new model (5%)


E-Waste Impact:

  • Global: 62 million tonnes (2022) → 82 million tonnes projected (2030)

  • Recycling rate: Only 22.3%

  • Valuable materials lost: $62 billion worth annually

  • One smartphone contains: Gold, silver, copper, rare earth elements, cobalt, lithium

  • Environmental cost of new phone: 85kg CO₂ equivalent (mostly in manufacturing)

  • Repair vs. Replace: Repair = ~5kg CO₂; Replace = 85kg CO₂


Digital Systems & Technologies Involved:

Modular Design:

  • Screwless assembly (reusable clips and sustainable Torx screws)

  • Color-coded components for easy identification

  • QR codes link to video repair guides

  • Standardized connections (not proprietary)


Software Longevity:

  • Android OS with long-term support (unusual - most phones 2-3 years)

  • Partnership with Qualcomm for extended chip support

  • Open-source friendly (can install alternative OS)


Supply Chain Transparency:

  • Blockchain-based material tracing (pilot program)

  • Fair cobalt from Congo (artisanal mining with premium paid)

  • Fairtrade certified gold

  • Recycled tungsten, tin, rare earth elements


Digital Education:

  • Online repair community forum

  • Video tutorials for every component

  • Spare parts ordering system

  • Software troubleshooting database


Stakeholders & Impacts:

Stakeholders & Impacts
Stakeholders & Impacts

Real-World Outcomes & Challenges:

Success Indicators:

  • Sales: 400,000+ units (2024 estimate)

  • B-Corp Certified: Highest social/environmental standards

  • iFixit Partnership: Technical validation

  • Growing Movement: Inspired similar projects (Framework laptop, Valve Steam Deck)

  • Legislative Impact: Cited by EU in Right to Repair directive


Challenges:

1. Performance Perception:

  • Fairphone 5: Mid-range specs (Qualcomm QCM6490)

  • Competitors: Flagship specs (Snapdragon 8 Gen 3, Apple A17 Pro)

  • Consumer perception: "Ethical = Low performance"

  • Reality: Sufficient for 95% of users but fails marketing test


2. Ecosystem Lock-In:

  • No Fairphone equivalent for tablets, laptops, watches

  • Apple/Samsung ecosystems (iCloud, Galaxy ecosystem) create switching costs

  • Harder to convince someone with $3,000 invested in ecosystem


3. Scale Economics:

  • Small production volume = higher unit costs

  • Cannot compete on price with mass manufacturers

  • Catch-22: Need scale to reduce costs; need low costs to achieve scale


4. Fashion Factor:

  • Smartphones as status symbols

  • 2-year upgrade cycle culturally normalized

  • Sustainability less important than "having latest model" for many consumers


5. Carrier Subsidies:

  • In US, carriers subsidize new phones every 2 years

  • Makes new flagship phone seem "free" vs. $750 for Fairphone

  • Economic incentives favor replacement


6. Component Availability:

  • Qualcomm doesn't usually provide 10-year chip support

  • Required special negotiation

  • Could be discontinued if Qualcomm changes policy


The Right to Repair Movement (Broader Context):

Legislative Progress (2023-2024):

European Union:

  • Ecodesign for Sustainable Products Regulation (2024): Requires smartphones/tablets to:

    • Have replaceable batteries (by 2027)

    • Provide spare parts for 7 years

    • Offer software updates for 5 years

    • Include repairability scores

  • Right to Repair Directive (2024): Consumers can demand repair vs. replacement within warranty


United States:

  • New York (2022): First state to pass Right to Repair for electronics

  • California (2023): Broad Right to Repair law

  • Federal: Multiple bills introduced; tech industry lobbying heavily against


Australia:

  • Right to Repair legislation (2022): Requires parts availability


Impacts:

  • Apple announced Self Service Repair program (2022) - direct response to legislative pressure

  • Samsung expanded repair network

  • Google made Pixel phones more repairable


BUT: Critics say these are "compliance theater" - technically meet law but design makes repair impractical


Industry Opposition Tactics:

1. "Safety" Argument:

  • "Third-party repairs risk fires/explosions"

  • "Only certified technicians should repair"

  • Counter: No evidence of meaningful risk increase; certification could be offered


2. "Intellectual Property":

  • "Repair manuals are trade secrets"

  • "Diagnostic software is proprietary"

  • Counter: EU law now requires disclosure


3. "Security" Argument:

  • "Device encryption requires manufacturer-only parts"

  • "Third-party screens could have malware"

  • Counter: Security can be maintained with proper authentication


4. Lobbying:

  • Tech industry spent $100+ million lobbying against Right to Repair (2020-2024)

  • Apple alone: $9.5 million/year

  • Trade associations (Chamber of Commerce, etc.) amplify messaging


IB Connections:

  • Concepts: Systems (2.6) - circular economy model; Values & Ethics (2.7) - sustainability vs. profit; Change (2.1) - challenging business models

  • Content: Hardware (implied in 3.E) - device construction; Data (3.1) - supply chain tracking; Networks (3.4) - repair communities

  • Contexts: Economic (4.2) - alternative business model; Environmental (4.3) - resource conservation; Social (4.7) - consumer empowerment


Interventions & Effectiveness:

Interventions & Effectiveness
Interventions & Effectiveness

Critical Questions:

1. Economic Model Sustainability: If everyone kept phones 10 years, smartphone industry would shrink 75%. Is this:

  • Necessary for environmental sustainability?

  • Economic disaster (jobs, innovation)?

  • Both?


2. Innovation Trade-Off: Does planned obsolescence drive innovation (need to entice upgrades) or inhibit it (good enough)?


3. Developed vs. Developing World:

  • Developing world: Benefits from cheap used electronics (even if short-lived)

  • Developed world mandate for durability: Raises prices, reducing access

  • How to balance?


4. Consumer Responsibility: Is problem:

  • Manufacturers designing for obsolescence?

  • Consumers choosing convenience over sustainability?

  • System that makes sustainable choice prohibitively difficult?


5. Sufficiency: Even a 10-year smartphone uses resources. Should we question constant connectivity itself?


Best Practice Example - Framework Laptop:

Similar to Fairphone but for laptops:

  • Fully modular (upgrade CPU, GPU, storage, ports)

  • 10/10 repairability score

  • Mainstream performance (Intel/AMD latest gen)

  • Successfully growing (raised $18M; expanded to 50+ countries)

  • Key Difference: Laptop market less fashion-driven than phones; businesses value durability

  • Lesson: Repairability MORE viable in some product categories


Sources:

  • Fairphone Impact Report 2023

  • iFixit repairability scores and analysis

  • EU Ecodesign for Sustainable Products Regulation (2024)

  • Right to Repair movement documentation

  • Nature Sustainability, "Environmental benefits of smartphone longevity"

5.3C Managing Pollution and Waste

Electronic Waste Crisis:

  • The world's generation of electronic waste is rising five times faster than documented e-waste recycling (UNITAR Global E-waste Monitor 2024)

  • The 62 million tonnes of e-waste generated in 2022 would fill 1.55 million 40-tonne trucks, roughly enough trucks to form a bumper-to-bumper line encircling the equator (UNITAR Global E-waste Monitor 2024)

  • Less than one quarter (22.3%) of the year's e-waste mass was documented as having been properly collected and recycled in 2022, leaving US$ 62 billion worth of recoverable natural resources unaccounted for (UNITAR Global E-waste Monitor 2024)


E-waste Projections:

  • Worldwide, the annual generation of e-waste is rising by 2.6 million tonnes annually, on track to reach 82 million tonnes by 2030, a further 33% increase from the 2022 figure (E-waste Monitor 2024)

  • By 2030, e-waste generation is projected to increase to 82 billion kilograms, up from 62 billion kilograms in 2022 (UN Sustainable Consumption and Production)

  • The report foresees a drop in the documented collection and recycling rate from 22.3% in 2022 to 20% by 2030 (E-waste Monitor 2024)


Plastic Pollution Crisis:

  • Every day, the equivalent of 2,000 garbage trucks full of plastic are dumped into the world's oceans, rivers, and lakes (UNEP Plastic Pollution)

  • Global plastic waste generation has grown more than seven-fold in the past four decades to 360 million metric tons per year (Statista - Plastic waste worldwide)

  • Only 9% of all plastic waste ever produced has been recycled. About 12% has been incinerated, while the rest — 79% — has accumulated in landfills, dumps or the natural environment (UNEP - Our planet is drowning in plastic pollution)


Municipal Solid Waste:

  • Municipal solid waste generation is predicted to grow from 2.1 billion tonnes in 2023 to 3.8 billion tonnes by 2050 (UNEP Global Waste Management Outlook 2024)

  • In 2020, the global direct cost of waste management was an estimated USD 252 billion. When factoring in the hidden costs of pollution, poor health and climate change from poor waste disposal practices, the cost rises to USD 361 billion (UNEP Global Waste Management Outlook 2024)

  • Without urgent action on waste management, by 2050 this global annual cost could almost double to a staggering USD 640.3 billion (UNEP Global Waste Management Outlook 2024)


Digital Waste Challenges:

  • The majority of the digital footprint is caused by video streaming due to the large amounts of video data (myclimate - Digital Carbon Footprint)

  • Manufacturing and disposal of digital devices contributes significantly to e-waste

  • Current levels of technology-based Carbon Dioxide Removal (excluding nature-based means such as reforestation) only account for about one-millionth of the CO2 emitted from fossil fuels (Global Carbon Budget 2024)


Pollution Health Impact:

  • Waste consumer products such as toys, pharmaceuticals, personal care products, food additives and plastic debris can contaminate the environment with toxic compounds that get into waterways and the human food chain (World Economic Forum - Circular Economy)

  • Endocrine-disrupting chemicals, such as cadmium, asbestos and arsenic, increase health risks including cancer, cognitive conditions, obesity and reproductive impairment (World Economic Forum - Circular Economy)


Policy Response:

  • As of 2024, 530 policies related to sustainable consumption and production were submitted across 71 countries, a 6 per cent increase from 2023 (UN Sustainable Consumption and Production)

  • 81 countries had e-waste legislation in 2023, up from 78 in 2019 (UNITAR Global E-waste Monitor 2024)

  • Despite the issue of plastic pollution gaining increased attention and outrage in recent years, UN member states failed to reach an agreement in December 2024 on a legally binding global plastic treaty (Statista - Plastic waste worldwide)


CASE STUDY 4: AI-Powered Waste Sorting - AMP Robotics and the Recycling Revolution (2024)

Date: Ongoing 2024; rapid expansion phase


Context: Global recycling rates are dismal (only 22.3% of e-waste, 9% of plastics) largely because manual sorting is expensive and inaccurate. AI-powered robotic systems promise to transform recycling economics, but raise questions about job displacement and whether technology can solve problems technology created.


Key Facts:

  • AMP Robotics: Leading company; deployed 400+ AI systems globally (2024)

  • Performance: Picks 80 items/minute (2x human speed); 99% accuracy (vs. 90% human)

  • Cost: $150,000-250,000 per robot; payback period 2-3 years

  • Impact: Facilities using AMP increased recycling rates 30-50%

  • Market: $900M market (2024) → projected $3B by 2030


How It Works:

Computer Vision + Machine Learning:

  1. High-resolution cameras scan conveyor belt 60 times/second

  2. AI identifies material type (plastic #1-7, paper grades, metals, glass, contamination)

  3. Recognition includes: Brand logos, text, barcodes, material composition, degradation state

  4. Robot arm uses suction or gripper to sort items into correct bins

  5. Continuous learning: System improves accuracy with each item processed


Digital Systems Involved:

  • Neural Networks: Trained on millions of images of waste items

  • Edge Computing: Real-time processing (no cloud delay)

  • IoT Sensors: Monitor conveyor speed, bin fullness, system health

  • Data Analytics: Track contamination rates, material flows, facility efficiency

  • Cloud Platform: Aggregates data across facilities; identifies trends


The Problem It Addresses:

Why Recycling Fails Currently:

  1. Contamination: One greasy pizza box ruins entire bale of cardboard

  2. Labor: Sorting jobs dangerous (sharp objects, pathogens, heavy items); high turnover

  3. Speed: Humans can't keep pace with waste volume; items missed

  4. Accuracy: Humans misidentify materials (many plastics look similar)

  5. Economics: Labor costs make recycling unprofitable for many materials

  6. Variability: Packaging constantly changes; workers struggle to keep up


Traditional Recycling Line:

  • 10-15 human sorters per line

  • 30-40 items/minute per person

  • 85-90% accuracy

  • High injury rates (cuts, back problems, respiratory issues)

  • $35,000-45,000 annual wage per worker


AI-Powered Line:

  • 2-3 humans supervising 5-6 robots

  • 80+ items/minute per robot

  • 99% accuracy

  • Near-zero injury risk

  • Better economics enables recycling previously uneconomical materials


Stakeholders & Impacts:

Stakeholders & Impacts
Stakeholders & Impacts

Real-World Results:

1. Denver, Colorado (Alpine Waste & Recycling):

  • Before: 20% contamination rate; frequent shutdowns

  • After AMP installation: 5% contamination; 40% more material recovered

  • Economics: ROI in 2.1 years; now profitable to sort mixed plastics (previously landfilled)

  • Jobs: Reduced 12 sorters to 4 (robot supervisors); avg wage increased from $15 to $22/hr


2. Waste Management Inc. (National):

  • Deployed 150+ AMP systems across US facilities (2024)

  • Result: Diverted 150,000 tonnes from landfills annually

  • Discovery: AI identified recyclable items humans consistently missed (certain plastic films)

  • Challenge: Still 30% of facilities not automated (cost barrier)


3. UK Trials (Multiple Facilities):

  • Finding: AI systems 95% accurate even with UK's complex packaging

  • Surprise: Robots better at handling contaminated items (don't get disgusted/tired)

  • Limitation: Struggled with black plastics (camera visibility issue) - now solved with advanced sensors


The Job Displacement Debate:

Lost Jobs:

  • Traditional recycling facility: 50-60 workers

  • Automated facility: 15-20 workers (70% reduction)

  • Concern: 50,000+ recycling jobs in US alone; automation threatens majority

  • Particularly impacts: Immigrants, people with limited education, formerly incarcerated (recycling one of few industries that hires)


Counterarguments:

  • Safety: Recycling sorting has 2x injury rate of average manufacturing job

  • New Jobs: Robot maintenance, supervision, data analysis (but require more skills/training)

  • Enabled Recycling: Some materials only economically recyclable with automation

  • Inevitability: Choice is automate OR shut down facilities (can't compete with landfill costs)


Training Programs:

  • AMP Workforce: Partners with facilities to retrain workers

  • 6-week program: Robot operation, maintenance, data analysis

  • Result: 65% of displaced workers successfully transition to new roles

  • BUT: 35% don't (age, language barriers, lack foundational tech literacy)


The Broader Questions:

Does Better Technology Lead to Better Outcomes?

Optimistic View:

  • AI makes recycling economically viable → more materials recycled → less waste

  • Creates incentive to recycle materials previously landfilled

  • Data reveals insights (which products are unrecyclable → pressure manufacturers)


Pessimistic View:

  • Addresses symptom (sorting difficulty) not cause (overproduction, poor design)

  • "Efficiency trap": Better recycling → people feel justified consuming more

  • Jevons Paradox: Cheaper recycling → more waste generated

  • Displaces workers without addressing consumption


Rebound Effect Example:

  • City installs AI sorting → recycling rate increases 40%

  • Politicians/public celebrate "problem solved"

  • Oversight of waste REDUCTION programs decreases

  • Overall waste generation increases 20% (more packaging)

  • Net result: More absolute waste despite better recycling


IB Connections:

  • Concepts: Change (2.1) - automation transforming labor; Systems (2.6) - waste as system problem; Values & Ethics (2.7) - efficiency vs. employment

  • Content: AI (3.6) - computer vision and ML; Robots (3.7) - automated sorting; Data (3.1) - waste stream analytics

  • Contexts: Economic (4.2) - labor displacement; Environmental (4.3) - waste management; Social (4.7) - community impacts


Interventions & Effectiveness:

The Technology Trap
The Technology Trap

Critical Analysis:

The Technology Trap: AI sorting is impressive BUT:

  • Only addresses END of waste stream (after production, consumption, disposal)

  • Doesn't question: Why so much waste? Why single-use everything?

  • Risk: Technology enables continued overconsumption with "clear conscience"


The Circular Economy Illusion:

  • Global circularity: 7.2% (2024) - DOWN from 9.1% (2018)

  • Better recycling hasn't translated to circular economy

  • Why?: Recycled materials compete with cheap virgin materials (oil, etc.)

  • AI sorting doesn't solve: Virgin materials artificially cheap (subsidies, externalized costs)


The Global Equity Question:

  • Developed world: Can afford AI sorting → cleaner recycling

  • Developing world: Cannot afford → stuck with manual sorting OR import developed world's trash

  • Result: Automation widens waste management gap


Best Practice - Hybrid Model (San Francisco):

  • AI sorting for high-volume materials (plastics, paper)

  • Humans for complex decisions (unusual items, quality control)

  • Strong waste reduction programs (compost mandate, plastic bag ban)

  • Result: 80% landfill diversion (best in US)

  • Key: Technology PLUS policy PLUS culture change


Questions for Students:

  1. Job Displacement: Is it ethical to automate recycling sorting if it eliminates jobs for vulnerable populations, even if environmental benefit?

  2. Technology Solutionism: Does AI sorting address root cause (overproduction) or just symptom (sorting difficulty)?

  3. Investment Priority: Should $250K be spent on one sorting robot OR on waste reduction education programs?

  4. Global Justice: If automation makes recycling profitable in rich countries, will they stop exporting waste to poor countries? Or just recycle more valuable items and still export the rest?

  5. Sufficiency: Could we achieve better environmental outcomes by producing less waste rather than sorting waste better?


Comparative Case - India's Informal Recyclers:

  • 1.5 million people make living from manual waste sorting

  • Collect 60% of India's recyclables (more than most developed countries)

  • Cost: Nearly free (paid by selling materials)

  • Social cost: Terrible working conditions; health risks; child labor

  • Automation threat: Could displace millions with no alternative livelihood

  • Question: Is automation progress or global injustice?


Sources:

  • AMP Robotics, "The State of Recycling 2024"

  • Waste Management Inc. annual reports

  • Ellen MacArthur Foundation, "Circular Economy Report 2024"

  • ILO, "The impact of automation on waste sector employment"

  • MIT Technology Review, "AI-powered recycling"



BIBLIOGRAPHY

Climate Change and Action


Use of Resources


Digital Society: Managing Pollution and Waste

IB DP HL Digital Society students studying the Plastic Crisis: 2,000 garbage trucks full of plastic are dumped daily into oceans, with only 9% of all plastic waste ever recycled and 79% accumulated in landfills or the environment.
IB DP HL Digital Society students studying the Plastic Crisis: 2,000 garbage trucks full of plastic are dumped daily into oceans, with only 9% of all plastic waste ever recycled and 79% accumulated in landfills or the environment.

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