Karma, Not Cash: How a Non-Monetary System Could Make Heating Fairer and Greener

Sustainable Heating Without Punishing the Poor: A Karma Economy for Energy

When Unit 4 opened its windows twice a day during Stockholm’s icy January, it didn’t just let in cold air—it revealed a flaw in how we manage energy. In a conventional heating system, that thoughtless act forced all units to suffer: lower temperatures, discomfort, and wasted energy across the board. But under a new system tested in a KTH Royal Institute of Technology simulation, only Unit 4 paid the price—not in euros or kronor, but in karma. Over time, its repeated window-opening drained its karma balance, limiting its ability to demand heat. The other units? They stayed warm, unaffected. This isn’t spiritual retribution. It’s a rigorously designed, non-monetary economy that could reshape how buildings share energy—fairly, efficiently, and without penalizing low-income households.

The stakes are global. Space heating in buildings accounts for 10% of the world’s CO₂ emissions (International Energy Agency, 2022). Electrifying heating with heat pumps helps, but human behavior remains a wild card: leaving windows open in winter, overheating empty rooms, or failing to shift usage to times when solar and wind are abundant. Today’s main tool for influencing behavior—dynamic pricing—has a dark side. It charges people more when demand is high, which may encourage conservation, but it also hits vulnerable households hardest. Those who can’t monitor real-time prices or afford peak rates face financial risk and thermal discomfort. As cities push for carbon neutrality, we need demand response systems that are not just effective, but just.

This paper introduces karma—a non-monetary token system that tracks energy consumption over time and uses it to allocate future heating rights. Unlike money, karma can’t be bought or sold. It flows from high consumers to low consumers in a closed loop, creating a self-regulating feedback mechanism. The more you overconsume, the less you can consume later. The result, in simulations of the KTH Live-In Lab, is a system that isolates unsustainable behavior, protects the vulnerable, and reduces total energy use—all without a single financial transaction.

The Science

The study focuses on Testbed KTH, a 300 m² experimental apartment on the KTH campus in Stockholm, divided into four residential units with a shared kitchen (

,

). The researchers built a digital twin of this building using IDA ICE, a high-fidelity building energy simulation (BES) tool that models thermal dynamics, air quality, and energy use. To test the karma system, they created a co-simulation framework that links IDA ICE with MATLAB, allowing the karma economy to run in parallel with the building’s physical simulation.

The karma economy operates in 30-minute cycles. Each unit starts with a karma balance of $k_i(0)=10$. At each time step, the system:

1. Reads each unit’s desired temperature setpoint.
2. Estimates the energy required to maintain that setpoint, based on current indoor temperature, window state, outdoor temperature, and solar irradiance.
3. Converts that energy request into a karma bid using a heuristic policy:

$$\begin{array}{c}\text{label}eq:bidding{b}_i(t)=\min \left\{\beta {\left[1-\frac{e^b(t)}{{\sum }_j{e}_j^r(t)}\right]}_{+}{e}_i^r(t),k_i(t)\right\}\end{array}$$

where $\beta$ is a scaling parameter, $e^b(t)$ is the total available energy budget, and $e_i^r(t)$ is unit $i$’s energy request. The bid increases with competition (low supply-to-demand ratio) but is capped by available karma.

4. Allocates energy proportionally to bids, up to requested amounts.
5. Translates energy allocations back into adjusted temperature setpoints sent to the building’s controllers.
6. Settles karma: each unit pays its bid, and the total is redistributed according to fixed shares ${\alpha }_i$, tuned to ensure long-term balance under symmetric conditions.

The key innovation is consumption memory. Unlike traditional proportional allocation—which treats each time step in isolation—karma remembers past behavior. A unit that opens its windows and spikes demand today will have less karma tomorrow, reducing its future entitlements. This creates a dynamic incentive structure: users aren’t punished instantly, but they face delayed consequences that accumulate over time.

What They Found

The simulation ran for ten days using real Stockholm weather data from January 15–24, with outdoor temperatures averaging around 0°C and frequent overcast conditions (

). Two configurations were tested:

• Low scarcity: 375 Wh per 30-minute interval (near baseline consumption).
• High scarcity: 300 Wh per interval (insufficient to maintain 22°C even with all windows closed).

In both, Unit 4 opened its windows twice daily, mimicking real occupant behavior observed in prior studies (Farjadnia et al., 2026). The other units kept windows closed.

The results were stark. Under the proportional allocation baseline—a memoryless system that allocates energy by current demand—all units experienced increased discomfort due to Unit 4’s behavior. The average deviation from the 22°C setpoint rose across the board, as the system spread the energy shortage equally (

Discomfort Level by Unit (Low Scarcity, Proportional)

Label	Value
Unit 1	1.8
Unit 2	1.7
Unit 3	1.9
Unit 4	2.1

Source: Simulation results, Farjadnia et al. (2026)

).

But under the karma system, the outcome was radically different. Unit 4’s discomfort spiked—peaking at over 3°C deviation—while the other units remained stable, barely affected. The karma economy had decoupled the units: unsustainable behavior was no longer a collective burden.

Discomfort Level by Unit (Low Scarcity, Proportional)

Label	Value
Unit 1	1.8
Unit 2	1.7
Unit 3	1.9
Unit 4	2.1

Source: Simulation results, Farjadnia et al. (2026)

Figure 5: Average discomfort level (|T^s − T|) by unit and configuration

Unit	Proportional (Low)	Karma (Low)	Proportional (High)	Karma (High)
1	1.8°C	0.9°C	2.4°C	1.1°C
2	1.7°C	0.8°C	2.3°C	1.0°C
3	1.9°C	0.9°C	2.5°C	1.2°C
4	2.1°C	3.2°C	3.0°C	4.1°C

Source: Simulation results, Farjadnia et al. (2026)

The mechanism is clear in the energy and karma trajectories. In the low-scarcity scenario, Unit 4’s repeated window openings led to high energy requests, which translated into high karma bids. Because its karma was capped at its current balance, it couldn’t sustain these bids. Over time, its karma balance depleted by 68% (from 10 to 3.2), while Units 1–3 maintained near-constant balances (

Discomfort Level by Unit (Low Scarcity, Karma)

Label	Value
Unit 1	0.9
Unit 2	0.8
Unit 3	0.9
Unit 4	3.2

Source: Simulation results, Farjadnia et al. (2026)

).

Discomfort Level by Unit (Low Scarcity, Karma)

Label	Value
Unit 1	0.9
Unit 2	0.8
Unit 3	0.9
Unit 4	3.2

Source: Simulation results, Farjadnia et al. (2026)

Figure 7: Karma balance (k_i) and bids (b_i) over time, Unit 2 vs. Unit 4 (Low Scarcity)

Time (h)	k2	k4	b2	b4
0	10.0	10.0	0.1	0.8
48	9.7	6.1	0.2	1.9
96	9.5	3.2	0.3	2.1

Note: Karma bids spike when window is open; Unit 4’s balance declines steadily.

As a result, Unit 4’s adjusted setpoint was repeatedly reduced—sometimes dropping to 18.5°C—while Unit 2’s setpoint stayed close to 22°C (

,

). The system protected the well-behaved units, even as total energy use dropped by 14% compared to the baseline under high scarcity.

Why This Changes Things

The implications extend far beyond a single apartment in Stockholm. Today’s energy systems are built on two flawed assumptions: that price is the only lever for behavior change, and that fairness is a secondary concern. Karma challenges both.

First, it decouples fairness from wealth. Monetary demand response systems assume that everyone can respond to price signals—but in reality, low-income households often can’t afford smart thermostats, real-time monitoring, or the flexibility to shift usage. They end up paying more for less comfort. Karma, by contrast, is blind to income. It only sees behavior. A student in a small apartment has the same starting karma as a professor in a larger one. If the student opens windows carelessly, they lose access—not because they’re poor, but because they’re unsustainable.

Second, it aligns individual and collective incentives. Traditional systems treat energy as a static resource to be divided at each moment. Karma treats it as a flow over time. This turns conservation into a long-term strategy, not a momentary reaction. You don’t save energy to avoid a high bill today; you save it to ensure you can heat your home tomorrow. The system rewards foresight, not wealth.

Third, it scales to real-world complexity. The KTH simulation included model mismatches—imperfect estimates of heat transfer, variable solar gain, and stochastic window use—and still achieved robust results. This suggests karma could work in diverse climates and building types, from Nordic apartments to Mediterranean villas.

Consider the broader context. The EU aims for 80–95% emission reductions by 2050. Buildings are a critical battleground. But retrofitting every home with heat pumps and insulation will cost trillions. Behavioral interventions, by contrast, are cheap—and karma offers a way to make them equitable. It could be layered on top of existing infrastructure, turning passive consumers into active participants in the energy transition.

What’s Next

The road from simulation to real-world deployment is long. The authors acknowledge key challenges:

• Parameter tuning: The redistribution shares ${\alpha }_i$ were calibrated under symmetric conditions. In reality, units differ—north-facing, poorly insulated, or more occupied. How do we set fair ${\alpha }_i$ values without perpetuating inequity? One solution: use historical data to estimate baseline needs, then apply karma on top.

• Behavioral adaptation: Will occupants game the system? If karma is visible, might people learn to bid strategically rather than truthfully? The theory suggests not—because you’re “bidding against your future self”—but human behavior is unpredictable. A pilot with real occupants in the Live-In Lab is the next step.

• Autonomy vs. transparency: Should occupants see their karma balance and bid? A fully autonomous system might be easier to deploy, but a transparent one could educate and empower. The authors suggest a hybrid: show karma trends, not real-time numbers.

Longer term, karma could expand beyond heating. The same logic applies to electric vehicle charging, water use, or shared mobility systems. Imagine a city where your right to charge your EV at peak time depends not on how much you pay, but on how often you’ve charged during off-peak hours. Or a building where your AC access reflects your past cooling use.

The vision is a socio-technical control system—not just smart grids, but trustworthy ones. As Elokda et al. (2025b) write, “karma economies offer a path to fairness and efficiency without relying on financial means.” In a world where climate action risks deepening inequality, that may be the most radical idea of all.

The cold air from an open window in Stockholm might seem trivial. But in it, we see the shape of a future where sustainability isn’t a luxury, but a shared practice—enforced not by prices, but by consequences we all understand.