Jevons Paradox

Version

v1 · 2026-08-24 · History

Prime #

942

Origin domain

Economics & Finance

Subdomain

rebound and resource dynamics → Economics & Finance

Aliases

Khazzoom Brookes Postulate

Core Idea

Jevons paradox is the structural pattern in which improving the efficiency with which a resource is used — more output per unit input — lowers the effective price of obtaining the output, raises demand for that output and for the input it requires, and so causes total resource consumption to increase rather than decrease. The pattern sits at the intersection of three structural facts: any efficiency gain that lowers the per-unit cost of using a resource is price-equivalent to a fall in that resource's price; price drops expand demand both by substitution toward the now-cheaper output and by opening new demand niches previously priced out; and when the demand expansion is more than proportional to the efficiency gain, total input use rises, the opposite of the naive prediction.^[1]

The structural commitments are precise. There is an efficiency improvement on a specific resource-using process. There is a price channel by which the improvement reduces the effective cost of the output produced from that resource. There is a demand elasticity — end-users respond to the lower effective price by consuming more output, substituting toward it, and inventing new uses. And there is an aggregate recombination — across all users and all new uses, total resource demand is summed and compared to the efficiency gain. When the rebound exceeds 100% the case is the Jevons paradox proper (super-rebound); sub-100% rebound is the more general Efficiency Rebound, the same mechanism operating at lower intensity.^[2]

The pattern's whole force is in the gap between the per-unit and the aggregate. The naive expectation — improve efficiency, save resources — is an inference from the micro level to the macro level that the structure shows to be invalid without an elasticity model. Efficiency does not merely reduce consumption per use; it changes the price landscape in which use is decided, and the aggregate response can swamp the per-unit saving.

How would you explain it like I'm…

Cheaper Means More

Imagine your car suddenly uses way less gas, so driving gets really cheap. Now everyone wants to drive all the time, and new people start driving too. So in the end the whole town burns MORE gas, not less, even though each trip uses less.

The Efficiency Backfire

Jevons Paradox is when making something more efficient ends up using MORE of a resource overall, not less. Here's how: if a machine gets better at using fuel, the thing it makes gets cheaper, so people buy a lot more of it and even find brand-new uses for it. When that extra demand grows bigger than the efficiency saved, the total fuel used goes up. It's surprising because everyone expects 'more efficient' to mean 'saves resources,' but that guess skips over how cheaper stuff makes people want way more of it.

The Rebound Paradox

Jevons Paradox is the pattern where improving the efficiency of using a resource, more output per unit of input, actually raises total consumption instead of lowering it. The logic chains three facts: an efficiency gain that cuts the per-unit cost of using a resource is price-equivalent to that resource getting cheaper; cheaper output expands demand, both by people substituting toward it and by new uses opening up that were previously too expensive; and if that demand expansion is more than proportional to the efficiency gain, total input use rises. The whole force lives in the gap between per-unit and aggregate. When the rebound is over 100% it's the Jevons paradox proper (super-rebound); a smaller rebound under 100% is the more general rebound effect, the same mechanism at lower intensity.

 

Jevons Paradox is the structural pattern in which improving the efficiency with which a resource is used lowers the effective price of the resulting output, raises demand for that output and its input, and so increases total resource consumption rather than decreasing it. It sits at the intersection of precise commitments. There is an efficiency improvement on a specific resource-using process. There is a price channel: the improvement reduces the effective cost of the output produced from that resource, making the gain price-equivalent to a fall in the resource's price. There is a demand elasticity: end-users respond to the lower effective price by consuming more, substituting toward the now-cheaper output, and inventing new uses previously priced out. And there is an aggregate recombination: total resource demand is summed across all users and new uses, then compared to the efficiency gain. When the rebound exceeds 100% it is the Jevons paradox proper (super-rebound); sub-100% rebound is the general rebound effect. The paradox's force is the gap between micro and macro: the naive expectation 'improve efficiency, save resources' is an invalid inference from the per-unit level to the aggregate without an elasticity model, because efficiency changes the price landscape in which use is decided, and the aggregate response can swamp the per-unit saving.

Structural Signature

the efficiency improvement on a resource-using process — the price channel translating the gain into a lower effective cost of output — the demand elasticity responding to that lower price — the latent-demand niches the price drop unlocks — the aggregate recombination summing use across all users and new uses — the per-unit-versus-aggregate gap that flips the sign

The pattern holds when each of the following is present:

1. An efficiency improvement. A specific process is made to yield more output per unit of input — a genuine, measurable per-unit gain.

2. A price channel. The efficiency gain is price-equivalent: it lowers the effective cost of obtaining the output, whether that cost is money, time, attention, risk, or effort.

3. A demand elasticity. Users respond to the lower effective price by consuming more output and substituting toward it; the magnitude of this response is the load-bearing parameter.

4. Latent-demand unlocking. The price drop opens uses previously priced out — new users, new applications, new niches — a qualitative expansion, not merely a quantitative one.

5. An aggregate recombination. Total input use is summed across all existing and newly-feasible uses and compared to the efficiency gain.

6. A per-unit-versus-aggregate sign condition. When the demand expansion is more than proportional to the gain (rebound above 100%), total input use rises — the Jevons paradox proper; sub-100% rebound is the same mechanism at lower intensity.

These compose so that the inference from a micro per-unit saving to a macro reduction is invalid without an elasticity model: efficiency reshapes the price landscape in which use is decided, and the aggregate response can swamp the per-unit saving.

What It Is Not

• Not the general efficiency_rebound. Efficiency Rebound is the same mechanism at any intensity — efficiency lowers effective price, demand partly responds, and some saving can survive. Jevons paradox is the super-rebound special case where the response exceeds the gain (rebound above 100%) and total use rises; it is the sign-flip, not the whole rebound family.
• Not increasing_returns. Increasing returns is a supply-side dynamic where output per input rises with scale or adoption; Jevons is a demand-side dynamic where an efficiency gain lowers effective price and induces consumption. The two can co-occur but name different couplings.
• Not diminishing_returns. Diminishing returns describes falling marginal output as an input is added; Jevons describes total input use rising because efficiency unlocked demand — a different quantity moving in a different direction.
• Not price_elasticity alone. Elasticity is the demand-response parameter the prime depends on, but Jevons is the full generator — efficiency, effective-price drop, demand response, latent-niche unlock, aggregate recombination — not the elasticity in isolation.
• Not peltzman_effect (risk compensation) per se. Risk compensation is a behavioural cousin where a safety gain elicits offsetting risk-taking; it shares the rebound skeleton but is a narrower, behaviour-specific instance, whereas Jevons spans any "price" currency (money, time, attention).^[3]
• Common misclassification. Inferring macro reduction from a micro efficiency gain by multiplying the per-unit saving by current usage as if quantity were fixed. The tell: has the demand response been modelled, or has the rebound simply been assumed away?

Broad Use

• Energy and resources — the origin: efficiency improvements in resource-driven power expand the applications and lower the price, raising total resource consumption rather than sparing reserves.^[1]
• Transport and induced demand — a wider road lowers the time-cost of travelling a corridor; demand expands until congestion returns; the substrate is travel-time rather than fuel, but the structure is identical.^[4]
• Computing — each generation's fall in cost-per-operation expands use into previously infeasible applications, and total energy use rises despite enormous per-operation efficiency gains.^[5]
• Attention and information — better filters reduce the cost per relevant item; users consume more total information; algorithmic efficiency in selecting content increases rather than decreases time spent.^[6]
• Water and food — more efficient irrigation per unit yield is offset by expanded cropped area and thirstier crops; cheaper food expands total caloric consumption.^[7]
• Risk and safety — safer equipment or routes can elicit riskier behaviour that consumes the safety gain, a behavioural cousin of the same rebound dynamic.

Clarity

Naming a phenomenon as Jevons paradox, rather than as "we expected savings and got increases," disciplines the analyst to specify four things: which efficiency — per what input, per what unit of output; which price channel — does the gain show up as a lower money price, time-cost, attention-cost, or risk-cost; which elasticity — how does demand respond to the effective price drop; and which aggregation level — micro, where this user consumes less input, versus macro, where the system consumes more. Each of these is a question the naive savings narrative never asks, and each can change the sign of the predicted result.

The clarity cost is sharp. A great many efficiency policies assume a linear relationship between efficiency and total use; the Jevons frame forces the analyst to predict the rebound before claiming the savings. This converts a confident assertion ("the efficient option will reduce consumption") into a conditional one ("it will reduce consumption only if the rebound is below 100%, which depends on the demand elasticity and on whether the price drop opens new niches"). The reframing is uncomfortable precisely because it withdraws a reassuring inference, but it is the inference the structure shows to be unwarranted, and the discipline of naming the four parameters is what prevents the error.

Manages Complexity

The pattern compresses a long list of "the efficiency improvement did not deliver the savings we expected" stories into a small structural schema and a small intervention catalogue. The schema is the three-step generator: efficiency raises output per input, which lowers the effective price of the output, which raises demand. The interventions all act on the same point — the price channel — and form a transferable family. Couple the efficiency gain to a price floor or cap so the price drop does not translate into demand growth. Quota the input directly with hard caps on the resource itself. Set standards on the output rather than on per-unit efficiency. Model the new demand niches rather than only the per-unit saving. Time the policy so the efficiency gain arrives together with the price-floor mechanism rather than before it.

This compression has real predictive economy. Rather than studying energy, transport, computing, water, and attention as separate domains each with its own surprising rebound, the analyst recognises one structure parameterised by domain-specific elasticities, and reaches the intervention catalogue directly. The catalogue's unifying insight — that the leverage point is the price channel, not the per-unit efficiency — tells the designer where to act in any instance: capping or pricing the input breaks the translation from efficiency to demand, while improving efficiency alone, however well-intentioned, leaves the rebound mechanism untouched and can make total consumption worse.

Abstract Reasoning

The pattern licenses inferences the linear-efficiency frame cannot reach. Per-unit savings are not total savings: the leap from a microeconomic efficiency gain to a macro reduction is invalid without an elasticity model, and the structure makes the missing premise explicit. Efficiency unlocks latent demand: the demand that was price-rationed before the gain becomes feasible after, which is a qualitative expansion — new uses, new users, new niches — not merely a quantitative one, so modelling only the existing uses understates the rebound. The substrate need not be money: time-cost, attention-cost, risk-cost, and effort-cost are all "prices" subject to the same rebound dynamic, which is why the pattern reaches far beyond energy economics into traffic, information, and behaviour.

Two further inferences follow. Behavioural offsets are partial rebound: risk compensation in safety, moral licensing in ethics, and similar effects share the structural skeleton — a gain in one currency is partly spent on more of the behaviour that the gain was meant to reduce. And the right policy is composite: efficiency plus a price-floor, not efficiency alone, because only the composite acts on the channel through which efficiency becomes demand. The reasoner who holds the structure in mind anticipates rebound as a default expectation rather than discovering it as a surprise, and asks for the elasticity before crediting any efficiency intervention with savings — which is the characteristic discipline a prime is meant to install.

Knowledge Transfer

The intervention catalogue and the analytic frame travel cleanly, and the transfers are structural predictions rather than analogies. Energy economics into transport planning: the prediction that road expansion induces demand rather than relieving congestion is a direct Jevons application, and the policy response — congestion pricing — is the price-floor remedy ported intact, since a toll restores the price the efficiency gain removed.^[4] Transport into computing: scaling capacity for ambitious workloads induces more ambitious workloads that consume the capacity, the same rebound dynamic, so capacity expansion alone does not bound total resource use. Resource economics into behavioural design: a feature that makes a desired behaviour easier may cause offsetting expansion of that behaviour, requiring a structural cap rather than a per-instance efficiency. Engineering efficiency into health behaviour: a safer product encourages riskier use, requiring a usage cap rather than only safer hardware.

The transferable form is a single recipe: predict the elasticity, model the rebound, add the structural cap, then measure total use rather than per-unit use. That recipe carries the same intervention — cap the input directly, do not merely improve per-unit consumption — across at least eight substrates that share the generator (efficiency, effective-price drop, demand response, total use) while differing only in their parameters. The cross-domain reach is the load-bearing argument for the pattern's status: the same structural moves predict and explain rebound in energy, transport, computing, attention, water, food, and behaviour, with the same corrective lever in each. The pattern's economic-historical origin gives it a price-and-elasticity vocabulary that is core to the mechanism rather than incidental, which is why it reads as framed: the structure is genuinely portable, but stating it without the language of effective price and demand elasticity is awkward, so the vocabulary travels with it even as the structure recurs across substrates that are not markets.

Examples

Formal/abstract

Induced travel demand on a congested road is the cleanest case where the substrate is not energy but time, and it can be worked quantitatively. The efficiency improvement is widening a highway from two lanes to three on a congested corridor — a genuine per-trip gain in throughput.^[8] The price channel is travel time: more lanes lower the time-cost of using the corridor at the margin. The demand elasticity is the load-bearing parameter: lower time-cost induces drivers who previously avoided the corridor (taking transit, travelling off-peak, or not travelling) to return to it. The latent-demand niches are qualitative — trips that were simply not worth making at the old congestion level become worth making at the new lower cost, a new-use expansion, not merely more of the existing trips. The aggregate recombination sums all trips, old and newly-induced; the per-unit-versus-aggregate sign condition is the paradox: when the induced demand is more than proportional to the capacity gain, total vehicle-miles rise and congestion returns to its prior level, the widened road now carrying more cars at the same speed.^[8] The structure makes the missing premise explicit — the inference "more lanes, less congestion" is invalid without an elasticity model — and prescribes the corrective lever precisely: act on the price channel that the efficiency gain removed, by congestion pricing, which restores the time-or-money cost the extra lane eliminated.^[9] Widening alone, however well-intentioned, leaves the rebound mechanism untouched.

Mapped back: The extra lane is the efficiency improvement, travel-time is the price channel, returning and induced drivers are the demand response, and congestion pricing is the structural cap on the channel — the Jevons paradox operating on time rather than fuel.

Applied/industry

Computing efficiency is the modern industrial instance, where the substrate is energy and the rebound has run for decades. The efficiency improvement is each generation's fall in energy-per-operation — chips delivering more computation per joule. The price channel is the effective cost of computation: cheaper operations lower the cost of any compute-driven product. The demand elasticity is enormous: as cost-per-operation falls, applications that were economically infeasible — large-scale model training, ubiquitous video streaming, always-on services — become viable and proliferate. The latent-demand unlocking is the qualitative engine: entire product categories that could not have existed at the old cost-per-operation appear, a new-use expansion that dwarfs the per-operation saving. The aggregate recombination sums energy across all uses old and new; the sign condition flips — despite per-operation efficiency gains of many orders of magnitude, total energy consumed by computing has risen, because the demand expansion outpaced the per-unit gain.^[5] The intervention catalogue ports intact: capping the input directly (a hard energy budget or carbon price on data-centre power) acts on the channel, whereas merely improving chip efficiency leaves the rebound untouched and can worsen total use. The same structure governs efficient irrigation (per-unit water savings offset by expanded cropped area and thirstier crops, requiring a water quota rather than only better drip lines) and safety equipment (safer gear eliciting riskier behaviour that consumes the safety margin, requiring a usage cap rather than only better hardware).

Mapped back: The efficiency gain is more computation per joule, the price channel is cheaper compute, newly-feasible applications are the unlocked latent demand, and the energy budget or carbon price is the structural cap — total resource use rising despite per-unit efficiency, the canonical Jevons sign-flip, corrected only at the input channel.

Structural Tensions

T1 — Per-Unit Saving versus Aggregate Use (scalar). The prime's whole force is the gap between the micro saving and the macro outcome: efficiency reduces consumption per use while raising total use through expanded demand. The boundary is the rebound magnitude — below 100% the saving partly survives, above it the sign flips. The characteristic failure is inferring macro reduction from a micro gain without an elasticity model, crediting an efficiency policy with savings the rebound erased. Diagnostic: has the demand response been modelled, or has the per-unit improvement simply been multiplied by current usage as if quantity were fixed?

T2 — Efficiency Lever versus Cap Lever (coupling). The leverage point is the price channel, not the per-unit efficiency; improving efficiency alone leaves the efficiency-to-demand translation intact, while capping or pricing the input breaks it. The two interventions pull against intuition, which favours the efficiency improvement as obviously good. The failure mode is pursuing efficiency as a conservation strategy while declining the cap that would actually bound total use — and thereby worsening consumption. Diagnostic: does the policy act on the input quantity (a quota or price) or only on conversion efficiency? Only the former bounds the aggregate.

T3 — Money Price versus Shadow Price (scopal). The "price" the efficiency gain lowers need not be money — it may be time, attention, risk, or effort, which extends the prime far beyond energy economics but blurs where it applies. Misidentifying the channel sends the corrective lever to the wrong place. The failure mode is treating a time-cost rebound (induced traffic) with a money instrument, or missing the rebound entirely because no monetary price moved. Diagnostic: in what currency does the efficiency gain reduce the effective cost of the output? The cap must be placed on that channel, not on whichever price is easiest to observe.

T4 — Marginal Rebound versus Latent-Demand Unlock (scalar). Rebound has two components — more of existing uses (marginal) and entirely new uses previously priced out (latent unlock) — and modelling only the first systematically understates the effect. The qualitative expansion (new users, new applications) can dwarf the quantitative one. The failure is projecting rebound from current users' elasticity while ignoring the new niches the price drop opens, which is where super-rebound usually lives. Diagnostic: does the rebound estimate include applications that do not yet exist at the current price, or only the demand response of present users?

T5 — Composite Policy versus Efficiency-Alone (temporal/coupling). The structurally sound response is efficiency plus a price floor, and the two must arrive together — an efficiency gain that lands before its cap simply runs the rebound in the interval. Timing is part of the design, not an afterthought. The failure mode is sequencing the efficiency improvement first and the cap "later," letting demand expand and entrench before the floor is in place, after which the cap faces a larger constituency. Diagnostic: do the efficiency gain and the price-floor mechanism deploy simultaneously, or is the cap deferred to a future the expanded demand will resist?

T6 — Rebound Regime versus Saturation Regime (limit). The Jevons sign-flip presupposes elastic, expandable demand; where demand is saturated or inelastic, efficiency genuinely conserves and the rebound is small. Applying the rebound expectation universally is itself an error. The failure mode is assuming super-rebound for a use whose demand is near-satiated, forgoing a real efficiency saving out of misplaced Jevons caution. Diagnostic: is the demand for this output still expandable (rebound risk) or near saturation (efficiency conserves)? The elasticity, not a blanket prior, decides which regime governs.

Structural–Framed Character

Jevons Paradox sits at the lower lip of the mixed-framed band on the structural–framed spectrum, at the midline — aggregate 0.5, with every diagnostic reading 0.5. It is a balanced hybrid: a genuinely portable structure wearing an economic frame heavy enough to travel with it. The structural kernel is real — an efficiency gain, a price channel, a demand elasticity, latent-niche unlocking, and an aggregate recombination that can flip the sign — and it recurs across energy, transport, computing, attention, water, and behaviour, which is why no diagnostic reads a full 1.0.

But the frame is load-bearing in a way that keeps every diagnostic off zero. Vocab_travels is 0.5 because the mechanism is most naturally stated in the language of effective price and demand elasticity; the pattern reaches a time-cost or attention-cost substrate, but stating it there still borrows the price-and-elasticity vocabulary rather than letting traffic or information narrate it natively. Evaluative_weight is 0.5 because "paradox" and the backfire framing carry a faint cautionary charge — efficiency that makes things worse — though the prime defuses it by insisting the sign depends on the elasticity. Institutional_origin is 0.5: the concept has a specific economic-historical provenance (Jevons 1865) rather than being a formal regularity. Human_practice_bound is 0.5 because the central cases are human consumption decisions and policy interventions, even though the rebound skeleton extends to a behavioural cousin like risk compensation; there is no purely physical or biological instance. Import_vs_recognize is 0.5 because invoking the prime brings an analytic frame — predict the rebound, model the elasticity, cap the input channel — rather than merely recognising a regularity already wired into the medium. The portable generator keeps it from climbing into framed territory proper; the economic vocabulary that is core to that generator, not incidental to it, is what holds it at the balanced 0.5 the frontmatter records.

Substrate Independence

Jevons Paradox is a strongly substrate-independent prime — composite 4 / 5 on the substrate-independence scale. The generator — an efficiency gain that lowers an output's effective price, an elastic demand response, latent-niche unlocking, and an aggregate recombination that can flip the sign — is genuinely portable, and the breadth is wide: it runs on energy in resource economics, on travel-time in induced traffic demand, on energy-per-operation in computing, on attention with better content filters, on water in efficient irrigation, and on risk in safety-equipment compensation. That the "price" need not be money — time-cost, attention-cost, risk-cost all instantiate the same rebound — is what carries domain breadth and structural abstraction to 4, and the corrective lever (cap or price the input channel, do not merely improve per-unit efficiency) transfers intact across at least eight substrates, supporting the strong transfer reading. What holds the composite below a 5 is that the mechanism is most naturally stated in the language of effective price and demand elasticity, and its clearest instances are human consumption and policy decisions; there is no purely physical or biological occurrence (risk compensation is the most behaviour-distant, and it is still a behavioural cousin), so the economic vocabulary that is core to the generator travels with it rather than dropping away.

• Composite substrate independence — 4 / 5
• Domain breadth — 4 / 5
• Structural abstraction — 4 / 5
• Transfer evidence — 4 / 5

Relationships to Other Abstractions

Current abstraction Jevons Paradox Prime

Parents (1) — more general patterns this builds on

• Jevons Paradox is a kind of Efficiency Rebound Prime

  Jevons Paradox is the more-than-full Efficiency Rebound region in which demand expansion reverses the sign and raises total resource use.

Hierarchy paths (5) — routes to 5 parentless roots

• Jevons Paradox → Efficiency Rebound → Counterresponse Offset → Coupling

Show alternative paths (4)

Neighborhood in Abstraction Space

Jevons Paradox sits in a moderately populated region (51^st percentile for distinctiveness): it has near-neighbors but no dense thicket of synonyms.

Family — Unclustered & Miscellaneous (424 primes)

Nearest neighbors

• Efficiency Rebound — 0.75
• Unevenness Waste — 0.71
• Gains from Trade — 0.71
• Brandolini's Law — 0.71
• Last Mile Delivery — 0.70

Computed from structural-signature embeddings · 2026-09-10

Not to Be Confused With

The closest and most important distinction is between Jevons paradox and the general efficiency_rebound, of which it is a proper subset. Both run the identical mechanism: an efficiency gain lowers the effective price of an output, demand responds, and some of the intended per-unit saving is offset by increased use. The difference is purely one of magnitude with a qualitative consequence. When the rebound is below 100%, part of the saving survives and total input use still falls — this is ordinary Efficiency Rebound, and efficiency remains a partial conservation strategy. Jevons paradox is the sign-flipping case where the rebound exceeds 100%: the demand expansion is more than proportional to the efficiency gain, so total input use actually rises, the opposite of the naive prediction. The practical stakes of the distinction are real: a policymaker who knows only "rebound" may still expect net conservation, whereas the Jevons case warns that efficiency alone can make total consumption worse. Calling every rebound a "Jevons paradox" over-claims the sign-flip where only partial offset occurs; treating Jevons as merely "some rebound" under-states the danger of super-rebound, which usually lives in the latent-demand niches the price drop unlocks.

A second genuine confusion is with increasing_returns, the embedding-nearest catalogue prime. Increasing returns is a supply-side dynamic: as scale, output, or adoption grows, the output obtained per unit input rises (through learning, network effects, or fixed-cost amortisation), so the system gets cheaper to run as it gets bigger. Jevons is a demand-side dynamic: a given efficiency improvement lowers the effective price of an output, which induces more consumption, raising total input use. The two are easy to conflate because both feature a self-reinforcing-looking expansion and both can appear in the same growing system (cheaper computation both exhibits increasing returns to scale and induces Jevons demand). But the couplings are distinct — increasing returns is about the production function's response to scale, Jevons about the demand function's response to a price drop — and the corrective levers differ: increasing returns is managed by capacity and scale decisions, Jevons by capping or pricing the input channel through which efficiency becomes demand. Mislabelling a Jevons rebound as increasing returns sends the analyst to scale economics when the leverage is the price channel, and vice versa.

A third worth drawing is against price_elasticity, which the prime depends on but is not. Elasticity is a single parameter: the responsiveness of demand to a price change. Jevons paradox is the full structural generator in which that parameter is embedded — efficiency gain, translation into an effective-price drop, the elastic demand response, the unlocking of previously priced-out niches, and the aggregate recombination across all uses. Reducing Jevons to "high elasticity" loses the load-bearing latent-demand-unlock component, where the qualitative expansion into entirely new applications (not just more of existing ones) drives most super-rebound; an analyst who models only existing users' elasticity systematically understates the effect.

For a practitioner the distinctions decide both the prediction and the lever. Confusing Jevons with the general efficiency_rebound mis-estimates whether efficiency conserves or backfires; confusing it with increasing_returns routes the corrective to the wrong (supply) side; and confusing it with bare price_elasticity omits the new-niche expansion where the sign actually flips. Naming the effective-price channel and modelling the full rebound — including the demand that does not yet exist at the current price — is what keeps efficiency policy honest about its aggregate effect.

Solution Archetypes

Solution archetypes in the catalog that build on this prime — directly (this prime is a source ingredient) or as a related prime.

Built directly on this prime (1)

• Rebound-Aware Efficiency Governance: Pair efficiency improvements with absolute resource targets, rebound modeling, demand guardrails, and adaptive monitoring so cheaper service does not erase or reverse the intended savings.
  ▸ Mechanisms (20)

Also a related prime in 1 archetype

• Demand Curve Calibration and Response Design: Model how much of something is sought at different generalized costs, then use the calibrated response curve to guide allocation, pricing, capacity, and access decisions.

References

[1] Jevons, William Stanley. The Coal Question: An Inquiry Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal-Mines. London: Macmillan, 1865. Original statement that improved fuel efficiency lowers the cost of use and raises total coal consumption rather than economizing it. registry ↩a ↩b

[2] Khazzoom, J. Daniel. "Economic Implications of Mandated Efficiency in Standards for Household Appliances". The Energy Journal, vol. 1, no. 4 (1980): 21-40. Formalizes the efficiency-induced rebound (the Khazzoom-Brookes postulate), including the case where rebound exceeds 100% (backfire). registry ↩

[3] Peltzman, Sam. "The Effects of Automobile Safety Regulation". Journal of Political Economy, vol. 83, no. 4 (1975): 677-725. Original statement of risk compensation (the Peltzman effect): a safety gain elicits offsetting risk-taking, a behavior-specific cousin of the rebound dynamic. registry ↩

[4] Downs, Anthony. "The Law of Peak-Hour Expressway Congestion". Traffic Quarterly, vol. 16, no. 3 (1962): 393-409. States the fundamental law of road congestion / induced demand: adding road capacity lowers the time-cost of travel and induces demand until congestion returns. registry ↩a ↩b

[5] International Energy Agency. "Data Centres and Data Transmission Networks". IEA, Paris. Documents that despite strong per-task energy-efficiency gains, rapid growth in computing workloads has driven total data-centre electricity demand up (growing 12% per year over 2019-2024 and projected to roughly double 2025-2030), the computing-energy Jevons case. registry ↩a ↩b

[6] Simon, Herbert A. "Designing Organizations for an Information-Rich World" (in Martin Greenberger, ed., Computers, Communications, and the Public Interest, Johns Hopkins University Press, 1971): 37-72. Origin of the attention-economy argument: a wealth of information creates a poverty of attention, so cheaper/more-efficient information selection raises the total volume consumed and attention spent rather than economizing it. registry ↩

[7] Grafton, R. Quentin, et al. "The paradox of irrigation efficiency". Science, vol. 361, no. 6404 (2018): 748-750. Shows that higher irrigation efficiency rarely reduces water consumption because farmers expand irrigated area and switch to thirstier, higher-value crops (an income/rebound effect). registry ↩

[8] Duranton, Gilles, and Matthew A. Turner. "The Fundamental Law of Road Congestion: Evidence from US Cities". American Economic Review, vol. 101, no. 6 (2011): 2616-2652. Empirically shows that vehicle-kilometers travelled rise roughly one-for-one with lane-kilometers, so road expansion induces proportional demand. registry ↩a ↩b

[9] Vickrey, William S. "Pricing in Urban and Suburban Transport". American Economic Review, vol. 53, no. 2 (1963): 452-465. Foundational argument for congestion pricing as the corrective that restores the price the added capacity removed. registry ↩