Smart Cities & the SDGs

Why the most effective city interventions often have no processor

Session Roadmap

• Part I: SDGs, Smart Cities & the Gap
  • What the SDGs ask of cities
  • Revisiting smart cities — low-tech is smart too
  • The gap between technology and behavioural change
• Part II: Design for People
  • How to diagnose before you design
  • 8 behavioural concepts — each with a real-world example
• Closing: Synthesis & SDG Reconnection

• This session is the deliberate counterpoint to Units 3–5: we’ve spent the semester learning powerful high-tech tools; now we ask whether they are always the most effective
• The central provocation: “smart” means effective, not digital
• Part I is brief — it sets the frame. Part II is the core of the session, concept by concept.
• Ask students to keep their project neighbourhood in mind throughout — they will recognise these patterns
• This is not anti-technology; it is pro-purposeful choice

Before we talk about cities…

Let’s remember why we’re building them.

What are the SDGs?

• 17 Sustainable Development Goals. One 2030 deadline.
• Not a wishlist — a moral and political commitment made by 193 nations

SDGs are wicked problems — no single solution, no stopping rule

Cities house 55% of the world’s population, generate 80% of global GDP, and produce 70% of greenhouse gas emissions. Most SDGs will be won or lost here.

• SDGs adopted in 2015 as part of the UN 2030 Agenda — this is the professional mandate for planners
• “Legally non-binding but morally binding” — useful framing for how planners should relate to this framework
• Unit 3: SDGs are the canonical wicked problem — when is SDG 11 “done”? It never is.
• technology is never the goal — the SDG outcome is

The Urban SDGs

SDG 11

The Anchor

Sustainable Cities and Communities

• Affordable and adequate housing
• Safe and sustainable transport
• Inclusive and accessible public spaces
• Reduced environmental impact
• Resilience to climate and disaster

• SDG 11 is explicitly about cities, but it cannot be achieved in isolation
• Ask: “Which SDG is most relevant to your project neighbourhood? Which others does it connect to?”

The Urban SDGs

Connected Urban Goals

• SDG 3 — Good Health (active mobility, clean air, green space)
• SDG 10 — Reduced Inequalities (access, public space as equaliser)
• SDG 13 — Climate Action (modal shift, nature-based solutions)
• SDG 15 — Life on Land (urban biodiversity, green corridors)

• Demonstrate the connections: a cycling lane simultaneously achieves SDG 3 (health), SDG 10 (access), SDG 11 (sustainable transport), SDG 13 (emissions), SDG 15 (if tree-lined)
• This is why urban design has outsized SDG leverage — one intervention, multiple outcomes

Smart Cities: A Recap

Sold as a high-tech promise:

• Real-time sensor networks monitoring everything
• AI-powered prediction and optimisation
• Digital twins simulating urban futures
• Seamless Mobility-as-a-Service platforms

The canonical examples:

Built. Expensive.

Often: half-empty.

• Songdo (South Korea)
• Sidewalk Toronto (cancelled, 2020)
• NEOM (under construction)
• Masdar City (stalled)

• the point is not to criticise the tools, but to contextualise them

These are not technology failures — they are failures of starting with technology before understanding people

• Songdo: streets optimised for efficiency, but low occupancy for years — the optimisation that forgot human desire
• Sidewalk Toronto: Google Sidewalk Labs’ smart city project, cancelled due to privacy concerns and escalating costs
• NEOM: $500bn Saudi megaproject, highly contested on human rights and feasibility grounds

Smart ≠ High-Tech

“Smart” means effective.

• A well-placed bench that invites people to sit → smart intervention
• A narrowed road that slows cars without a sign → smart intervention
• A cycle lane that makes car trips optional → smart intervention

Low-tech IS smart city design — when it achieves the SDG outcome.

• Ask: “What does ‘smart’ mean to you?” then challenge the assumption
• This is explicitly not anti-technology — it is pro-effectiveness
• The test: does it achieve the SDG outcome? If yes, it is smart regardless of technical complexity
• Unit 5: “AI can find the pattern, but only the Planner can give it purpose” — same logic applies to all tools

The Gap

High-tech smart city projects often underperform

The technology works.

• Sensors sense
• Algorithms optimise
• Dashboards display etc..

The city doesn’t change.

• People still drive
• Pollution still rises
• Inequality persists

The missing variable is not the system.

It is the people in it.

• Key failure modes: digital divide (who can access smart city services?), high maintenance costs (who pays in Year 5?), top-down imposition (no community ownership), surveillance creep

• Sidewalk Toronto: partly collapsed due to data sovereignty — people didn’t want to be monitored as a precondition for urban services

• Songdo: optimised for flow, but without human desire — result: well-connected ghost city for years

• Pause here: “Can you think of a smart city project that measurably changed how ordinary people live?”

• This is the bridge: we’ve diagnosed the problem — now Part II presents the alternative

Design for People

The most powerful urban tool has no processor.

What is “Low-Tech”?

It is not primitive. It is human-centred.

Low-tech design is:

• Accessible without a device or signal
• Durable works in 10, 20, 50 years
• Legible understood without a manual
• Maintainable at low cost

Fails gracefully when it fails at all

Who needs a smartphone to use a bench?

Who gets excluded when the Wi-Fi goes down?

Who can maintain it after the consultant leaves?

If the answer is “everyone can use it” — you have a strong foundation.

• “Fails gracefully” is critical: a broken smart sensor gives no data; a worn cobblestone still slows a car
• This is an equity argument: low-tech is more equitable because access is not gated by digital literacy or device ownership
• The maintenance gap: many smart city projects degrade after Year 5 when maintenance budgets are cut; low-tech ages more robustly
• Ask: “Think of a street that works well for everyone — what makes it work? Is any of it digital?”

Diagnose Before You Design

Solutions can be low-tech. Diagnosis can be hybrid.

Phase 1: Understand

What is actually happening here?

How do people move, gather, avoid? What paths do they really take? What spaces do they never use?

Phase 2: Design

What is the minimum effective intervention?

Work with observed behaviour — not assumed behaviour.

“don’t we need data before we can design well?” - Yes — but data doesn’t require expensive custom infrastructure

• Principle: appropriate technology at each phase. Diagnosis may use lightweight data tools; the intervention itself can still be human-centred.
• “The planner’s role is gatekeeper” — gatekeeping starts with asking the right questions before choosing the tool

Diagnose Before You Design

Solutions can be low-tech. Diagnosis can be hybrid.

Low-Cost Diagnostic Tools

• Walking audits

  — J. Gehl methodology: go out, count, observe, record

• Desire line mapping

  — where are paths worn?

• Participatory community mapping

  — residents as data collectors

• Open data

  Sentinel satellite (free ESA), OpenStreetMap, Strava Metro cycling flows

• Time-lapse cameras

• Jan Gehl Architects: trained observers with clipboards, recording behaviour at different times of day. Simple, rigorous, replicable. Used in cities worldwide including Oslo.
• Sentinel satellite: ESA’s freely available remote sensing data — accessible to any municipality with internet access
• Strava Metro: aggregated, anonymised cycling/running data — available to cities at low cost

Desire Lines

When planners build straight paths and people carve diagonal ones

Who is wrong?

“Desire lines are the city’s unwritten design critique.”

The worn path across the grass is not vandalism. → It is data.

What desire lines reveal

• How people actually navigate
• Where friction exists in the planned environment
• The gap between designed intent and lived experience

• Some universities (e.g. Ohio State) specifically delayed paving until behaviour could be observed first

• Connect to the diagnostic slide: desire lines are free behavioural data, visible to anyone paying attention

• Every desire line is a planning failure — and a design opportunity waiting to be claimed

• Ask: “Can you think of a desire line you walk regularly? What does it tell you about the original design?”

Woonerfs & Shared Space

“If you treat drivers like idiots, they act like idiots.”

Hans Monderman, Traffic Engineer (Netherlands)

• 2003: removed ALL traffic lights, signs, and kerbs from busy intersections
• Drivers were forced to negotiate — make eye contact — pay attention

Speeds dropped. Accidents fell. Pedestrians felt safer.

Uncertainty = attention = care.

Design that reads desire rather than overriding it.

• Monderman was a traffic engineer — not a radical. His insight came from observing how drivers behave differently in unmarked rural lanes vs. heavily-signed urban roads

• The Laweiplein intersection in Drachten: car speeds dropped from 30km/h average to under 20km/h after removal of all signals; accidents reduced

• Woonerf (living street): Dutch concept giving pedestrians legal priority; speed controlled by design (narrowing, trees, varied paving), not signage

• Direct link to desire lines: Monderman observed how people actually navigate before redesigning

Nudge Theory

“A nudge is any aspect of the choice […] that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their […] incentives.”

• People do not make rational choices in a vacuum
• They respond to defaults, framing, salience, and friction
• Change the environment → change the behaviour
• No mandate. No fine. No app.

Thaler & Sunstein, Nudge (2008)

• Richard Thaler received the Nobel Prize in Economics in 2017 for behavioural economics

• Classic non-urban examples: organ donation default switches (opt-out vs. opt-in); the fly painted in Schiphol airport urinals (reduced spillage 80%)

• Urban implication: you don’t need to convince people to make better choices; make better choices the default or the easiest option

• A bike lane doesn’t tell anyone not to drive — it makes cycling a visible, safe, obvious alternative

• Connection to planning ethics: nudge is non-coercive, but planners must still ask: who benefits from the default we are setting?

The Bench Test

William H. Whyte, The Social Life of Small Urban Spaces (1980)

Filmed New York City plazas systematically for years.

Revolutionary findings for their simplicity:

• People sit where there is sun
• People sit where they can watch activity
• People sit where they can face each other
• Movable chairs > fixed benches agency matters

The best public spaces are designed from observation, not assumption.

• Whyte’s method: time-lapse cameras, behavioural observers, systematic annotation — all available in 1980, zero digital technology required
• Conclusion: plazas that ignored sun angles, sightlines, and seat movability consistently failed; the fix was almost always cheap
• Direct application of nudge theory: the bench IS the choice architecture — where it points, what it faces, whether it can move all shape behaviour without any instructions

Levers of Behavioural Change

EAST Framework — Behavioural Insights Team (UK)

These are the levers planners pull — with or without technology.

• E — Easy Remove friction from the desired behaviour.

• A — Attractive Make the good option visible and appealing.

• S — Social Leverage norms: what do others do here?

• T — Timely Intervene at the right moment.

• The Behavioural Insights Team (BIT) was set up by the UK Government in 2010 — the original “Nudge Unit”
• EAST emerged from applying behavioural economics to public policy across dozens of domains
• For urban design: EASY = protected cycling lane (not sharing with lorries). ATTRACTIVE = well-lit, tree-lined route. SOCIAL = seeing other cyclists/pedestrians making the same choice. TIMELY = infrastructure present when someone moves to a new neighbourhood before car habits form.
• Ask students to apply EAST to a local Oslo example — this grounds the framework quickly

Bogotá’s Ciclovía

Every Sunday, 75 km of streets close to motor traffic.

2 million people walk, cycle, dance, and exercise.

No app. No sensor. No algorithm.

Just: a policy decision + a bollard.

EAST in action

• E: Streets are safe — cars excluded
• A: Festive, colourful, communal
• S: Everyone is here — 2 million people
• T: Weekly ritual — it is just what Sundays mean

Started in 1976. Still running. Zero technology required.

• Ciclovía predates the smartphone, the smart city concept, and the SDGs — and achieves multiple SDG outcomes every week

• Cost: coordination staff and bollards = tiny fraction of any smart mobility initiative

• Measurable impact: improved cardiovascular health, reduced Sunday emissions, public space equity (anyone can participate regardless of income or device)

• The political lesson: Ciclovía required a mayor willing to take road space from cars — not a tech problem, a political will problem

• Replicated in 100+ cities worldwide under the “Open Streets” model

• Ask: “Could an app achieve what Ciclovía achieves? Why or why not?”

Eyes on the Street

“The bedrock attribute of a successful city district is that a person must feel personally safe and secure on the street among all these strangers.”

— Jane Jacobs, The Death and Life of Great American Cities (1961)

• Mixed use creates continuous activity at different hours — always someone watching
• Short blocks create more intersections, more movement, more eyes
• Active frontages: shops, cafés, entrances at street level

Safety without cameras — natural surveillance as emergent design property

• Writing in 1961 against Robert Moses and the urban renewal movement destroying mixed-use neighbourhoods for highways and towers

• The “sidewalk ballet”: the informal, choreographed mix of people that makes streets safe emerges from mixed use — not from surveillance cameras

• Contrast: suburban cul-de-sacs designed for cars, empty of people for most hours — paradoxically less safe than a busy mixed-use street

• SDG 11 (sustainable cities) and SDG 10 (equality): the street as universal public space, but it only works if people are in it

Tactical Urbanism

Cheap. Reversible. Testable.

Activate the street first. Then build.

PARK(ing) Day

(San Francisco, 2005) - 1 parking space → micro-park for one day - Originally guerrilla activism; now a global annual event in 1,000+ cities

Paint-on bike lanes

(Bogotá, NYC Broadway, etc..) - Test a cycle route with paint before committing to concrete - Measure use. Adjust. Then build.

Times Square, NY

2009 — NYC DOT temporarily closed Broadway to cars. (J.SadiK Khan) Result: foot traffic increased, air quality improved, businesses reported higher sales. 2010: made permanent.

• Tactical urbanism: low-cost, high-speed, temporary interventions that generate behavioural data and community buy-in before major investment

• Rebar studio (San Francisco) created PARK(ing) Day to demonstrate that street space could be reclaimed cheaply; it became a global movement

• Times Square: the transformation was tested before full investment — a politically risky decision made reversible by starting small

• Connection to desire lines and the diagnostic phase: tactical urbanism IS a combined diagnostic and design method — you learn what works by testing cheaply

• Ask: “What could you test in your project neighbourhood with a few cones, some chairs, and paint?”

The 15-Minute City

Carlos Moreno — Paris

The idea: every essential service within 15 minutes on foot or bicycle.

Not a technology. Not an app.

A zoning and density policy.

When the destination is reachable, the car becomes optional.

Behaviour changes without mandating it.

• Carlos Moreno, professor at the Sorbonne, formalised the concept; Paris Mayor Hidalgo adopted it as city policy after COVID-19

• Key insight: the car was not always necessary — it was made necessary by planning decisions (zoning separation, suburban sprawl). Reverse the decisions, reduce the car.

• This is restoration, not innovation: it describes the structure of pre-automobile European cities including Oslo’s older neighbourhoods

• Some controversy: right-wing commentators labelled it a “15-minute prison” — worth acknowledging and discussing

• Connect to SDG 3 (health through active transport), SDG 13 (reduced emissions), SDG 11 (sustainable mobility)

CPH Cycling Revolution

62% of Copenhagen residents cycle daily.

This did not happen because of a routing app.

It happened because of 40 years of sustained investment in cycling infrastructure beginning with the 1970s oil crisis.

• Protected lanes on every major street
• Cyclist-priority traffic signals
• Integration with public transport
• Annual “Cycling Account” measuring returns

The sequence that matters:

1. Build the infrastructure
2. Behaviour shifts
3. Culture normalises
4. Then monitor and optimise

Technology followed the behaviour.

Behaviour followed the design.

• In 1970, most Copenhageners drove. The 1973 oil crisis was the political catalyst for a decision to invest in cycling.

• Each year: more infrastructure, higher cycling share. By the 1990s cycling was culturally normal. By 2010, majority choice.

• The lesson: don’t start with the monitoring platform; start with the infrastructure

• Copenhagen “Cycling Account”: annual report measuring health, economic, and environmental returns — a model for evidence-based planning

• SDG 3 (health), SDG 11 (sustainable transport), SDG 13 (reduced emissions): achieved through asphalt, paint, and political will over 40 years

Nature-Based Solutions

Working with natural processes — not engineering around them

Hard infrastructure:

• Concrete drainage channels
• Underground stormwater tunnels
• Sensor networks managing flow
• High maintenance cost
• Single function

Nature-Based Solutions:

• Bioswales and retention ponds
• Green roofs and permeable surfaces
• Urban forests as flood buffers
• Lower long-term maintenance cost
• Multi-functional: flood control + cooling + biodiversity + wellbeing

• NbS (Nature-Based Solutions) is now EU policy — the EU Biodiversity Strategy mandates urban greening targets
• Key argument: hard infrastructure does one thing and requires continuous maintenance; NbS provides multiple co-benefits and tends to improve over time
• The “sponge city” concept: piloted in Wuhan (China), mandated by the Chinese government as a response to flooding — permeable surfaces, wetlands, parks as flood infrastructure
• SDG 11 (urban resilience), SDG 13 (climate adaptation), SDG 15 (biodiversity): NbS achieves all three simultaneously — the kind of multi-goal leverage that sensor systems rarely match

CPH Cloudburst Management

The world’s most celebrated cloudburst management plan.

Option A: Smart Drainage

• Sensors throughout the network
• AI-predicted stormwater routing
• Real-time flow management
• High capital and maintenance cost
• One function: handle water

Option B: Green Infrastructure ✓

• Sunken courtyards that flood deliberately
• Permeable streets and green roofs
• Water retained where it falls
• Lower long-term cost
• Also: parks, cooling, social space, biodiversity

After devastating floods in 2011, Copenhagen made a choice:

• The 2011 Copenhagen floods caused €800m in damage — a once-in-a-century event projected to become more frequent

• The Copenhagen Cloudburst Management Plan (2012) is now a global reference for climate adaptation, studied in planning schools worldwide

• 300+ individual cloudburst projects across the city — each one simultaneously a park, sports ground, or community space

• Tåsinge Plads: a former car park transformed into a cloudburst plaza that handles 150mm/hour of rain — also one of the city’s most loved small parks

• Political lesson: the city chose the more legible, community-friendly option; residents can see and use the investment; a sensor network is invisible until it fails

Synthesis

Understand

Diagnose without a six-figure platform

• Walking audits
• Desire line reading
• Community observation
• Open data

Design

Work with human behaviour

• Nudge, don’t mandate
• Make the good option easy
• Add nature as infrastructure
• Test cheaply before building

Achieve

The SDG outcome

• Healthier people (SDG 3)
• Accessible space (SDG 10)
• Sustainable mobility (SDG 11)
• Climate resilience (SDG 13)

Each of these interventions achieved an SDG outcome.

None of them required a server.

• The three columns mirror the course’s own logic: understand → design → achieve outcomes

• Ask: “Is there an example we discussed that couldn’t have been designed without technology?” — honest answer: no, but technology could help monitor and evaluate faster

• Emphasise: this is not anti-tech; it is pro-purposeful choice; the planner needs both registers

Low-Tech Outperforming High-Tech

	Low-Tech	High-Tech
Durability	Decades	5–10 year cycles
Accessibility	Universal	Requires device / data literacy
Maintenance	Low	High (and often cut)
Failure mode	Graceful	Catastrophic
Legibility	Intuitive	Requires training
SDG equity	Reaches all residents	May exclude the vulnerable

• “Graceful failure”: a worn cobblestone still slows a car; a broken enforcement camera enforces nothing
• “Maintenance often cut”: smart city infrastructure frequently degrades after Year 5 when maintenance budgets are reduced — well-documented in early smart city deployments
• The equity row is crucial: the populations most affected by urban challenges are often those most excluded from high-tech service delivery
• Both low-tech and high-tech have a legitimate place in the planner’s toolkit; the professional skill is knowing when to use which

Design for the 2030 Agenda

The principles of low-tech design are not nostalgic. They are the most durable path to the commitments we made.

1. SDGs frame the why. Design determines the how. Evaluate every intervention against its SDG contribution — not its technical sophistication.

2. Human psychology is the core competency. Desire, nudge, legibility, nature — these determine whether an intervention changes behaviour.

3. Low-tech interventions are often the most durable, equitable, and impactful. The bench. The bike lane. The stream. They outlast the platform.

4. “Smart” means effective — not digital. A city that works for all its inhabitants — with or without a signal — is a smart city.

• Encourage students to apply this to their projects: “What is the low-tech argument for your intervention? What happens if the technology layer fails?”
• The course arc closes: spectrum from AI and digital twins (Unit 5) through human-centred low-tech design (Unit 7); professional planners navigate both registers