The study, published in Frontiers in Behavioral Economics, asked a deceptively simple question: does the occasion of purchase, self-consumption versus gift-giving, change not only what people say they want but how their eyes and brains process the information on the label? Gift-giving has long been described in the consumer literature as a highly involved, emotionally loaded process. The giver faces impression management and perceived risk, amplified when the recipient’s tastes are unknown and the product itself is unfamiliar. Wine is a natural testbed because it is routinely purchased both for personal drinking and as a gift, yet almost all prior comparisons of the two occasions relied on surveys and choice experiments, which reveal what people choose but say little about the cognitive machinery underneath.

Forty-seven participants were recruited through an online screening survey and took part in laboratory sessions in Minneapolis in December 2024 and Portland in May 2025. All were at least 21 years old, had drunk wine within the past year, had normal or corrected-to-normal vision, and reported no neurological disorders. Each session lasted roughly 80 minutes and was compensated at 60 dollars. After excluding ten participants for unusable EEG recordings or incomplete eye-tracking data, the final analytical sample comprised 37 people, 18 in the self-consumption condition and 19 in the gift-giving condition. Random assignment produced statistically balanced groups on age, gender, income, education, wine expertise, and familiarity with disease-resistant grapes, and the study protocol was approved by the University of Minnesota’s Institutional Review Board.

The experimental stimuli were front labels of a fictitious white blend wine, varying systematically across four attributes: a sustainability badge with four levels (disease resistant, climate resilient, reduced agrichemicals, or no badge), a creative phrase (Friend of the Environment, Future of Wine, or none), customer reviews (three or five stars), and price ($9.99, $14.99, or $21.99 per 750 ml bottle, anchored to average US white wine prices). A non-GMO logo appeared on every label to defuse the GMO confusion documented in earlier work. A balanced fractional factorial design yielded 24 unique label combinations, and everything else on the label, brand logo, grape blend, alcohol content, color scheme, typography, was held constant. Attribute placement followed the well-documented Z-pattern of visual scanning, with badges in the top left corner and the non-GMO logo at the bottom right.

Each participant saw all 24 labels repeated 17 times, for 408 trials plus practice rounds. Every trial began with a 500-millisecond fixation cross, followed by a 4-second presentation of the label, another fixation cross, and then an untimed five-point Likert rating of purchase likelihood. The repetitions were not gratuitous: event-related potentials, the brain’s stimulus-locked voltage deflections, are extracted by averaging across many trials to push the signal above the noise. Eye movements were recorded with a Tobii Pro Fusion tracker at 60 Hz, while a 64-channel Ant Neuro EEG cap sampled brain activity at 500 Hz, with TTL markers from the presentation software synchronizing the two data streams to the millisecond. Participants sat about 68.5 centimeters from a 27-inch 4K monitor in a sound-attenuated room, and the EEG pipeline included average-mastoid referencing, bandpass and notch filtering, independent component analysis to strip out blinks and muscle artifacts, and spherical spline interpolation of noisy channels.

The behavioral results, modeled with mixed-effects Tobit regressions that accounted for the nested structure of repeated ratings within individuals, were striking. For self-consumption shoppers, customer reviews were the single most powerful driver of purchase intent, with five-star ratings carrying a coefficient of 1.44 against three stars, and all three sustainability badges significantly boosted intent, with the reduced-agrichemicals badge leading at 0.44. Price exerted a significant negative pull. Gift-givers were more selective: the climate-resilient badge dominated at 0.383, the reduced-agrichemicals badge followed at 0.270, and, notably, the disease-resistant badge lost its statistical significance entirely. A Wald test on the price-by-occasion interaction confirmed that gift-givers were significantly less price sensitive. Willingness-to-pay estimates, derived as the ratio of each attribute coefficient to the price coefficient, told the same story: the climate-resilient badge commanded a 48.2 percent premium in the gift condition, customer reviews a 20 percent premium, while the disease-resistant badge carried no significant premium at all among gift buyers.

The eye-tracking data painted a subtler picture. Fixations concentrated on the brand and badge regions across both conditions, and the presence of a badge visibly pulled attention toward itself, but differences between the two purchase occasions were modest. Self-consumers lingered slightly longer on the badge area, 500 milliseconds of total fixation versus 454 for gift-givers, and held their first fixation on customer reviews a bit longer, while gift-givers reached the non-GMO logo sooner and fixated it more often. Price attracted essentially identical visual attention in both groups. In other words, where people looked barely changed with the occasion; what changed was what happened next, inside the skull.

That is where the EEG results became decisive. In the self-consumption condition, all three badges significantly increased the amplitudes of the P100, an early marker of automatic visual attention peaking around 100 milliseconds; the P300, a mid-latency wave reflecting attentional allocation and working-memory updating; and the late positive potential, a sustained signature of conscious emotional and evaluative processing, while reducing the conflict-related N200 and the semantic-integration N400. In the gift-giving condition, the same badges produced even larger amplitudes across P100, P300, N400, and LPP, and scalp maps showed heightened neural activity in the gift group, especially in the later 600-to-2,000-millisecond window, consistent with deeper evaluative engagement. The climate-resilient badge stood out again: it produced the strongest P100 and P300 responses in gift-givers and the largest reductions in N200 and N400 among self-consumers, suggesting the message was processed with unusual fluency, perhaps because climate resilience is a broader and more familiar environmental concept than disease resistance.

Price told a fascinating double story. Behaviorally, gift-givers were less price sensitive, in line with the social-desirability dynamics of gift exchange, where the giver wants to signal generosity and care. But neurally, higher prices in the gift condition increased N200 and N400 amplitudes and decreased P300, a pattern the authors interpret as conflict monitoring and semantic dissonance: the brain registering tension between generosity norms and cost, between the desire to impress and the pain of paying. Customer reviews, by contrast, behaved like a cognitive heuristic rather than an emotional trigger; they dominated stated preferences and eased semantic integration in self-consumers, as shown by reduced N400, but left the emotional LPP largely untouched.

The authors are careful to frame their conclusions as preliminary and hypothesis-generating. Thirty-seven participants is a small sample by conventional standards, even if typical for EEG research; multiple comparisons across many ERP components and eye-tracking metrics raise the possibility of inflated false-positive risk; repeated exposure to the same labels may have induced habituation or fatigue; and the study included no direct manipulation check confirming how participants actually interpreted the disease-resistant badge, leaving open the possibility that some responses tracked the semantic salience of the word disease itself. The laboratory setting, a single fictitious label design, and hypothetical rather than incentive-compatible purchases further limit generalizability. Still, the practical implications are concrete: sustainability messaging, particularly the climate-resilient framing, consistently strengthened both behavioral and neural responses across occasions, and positioning unfamiliar sustainable wines as gifts may be an effective launch strategy, since gift-givers scrutinize messaging more carefully, pay more, and reward clear, interpretable environmental claims. Customer reviews emerged as a cheap, credible lever for reducing uncertainty around novel products. More broadly, the study demonstrates the payoff of multimodal consumer neuroscience: the eyes showed almost no difference between buying for oneself and buying for another, but the brain, measured in milliseconds, told a story of two fundamentally different acts of evaluation.

Subject of Research: Neural and attentional differences in consumer preferences for novel disease-resistant wines between gift-giving and self-consumption purchase occasions

Article Title: Consumer preferences for novel products: eye-tracking and EEG insights into gift-giving vs. self consumption

Article References: Parasuram, U., Yue, C., Gallardo, R. K., Steede, G., Eisenreich, B. R., & Clark, M. (2026). Consumer preferences for novel products: eye-tracking and EEG insights into gift-giving vs. self consumption. Frontiers in Behavioral Economics, 5, Article 1871704. https://doi.org/10.3389/frbhe.2026.1871704

Image Credits: AI Generated

DOI: 10.3389/frbhe.2026.1871704

Keywords: consumer neuroscience, EEG, eye-tracking, gift-giving, self-consumption, disease-resistant wine, sustainability badges, willingness to pay, event-related potentials, wine labels, conjoint analysis, behavioral economics

The scale of the finding matters far beyond Germany. The researchers calculate that if the roughly 20 percent rebound effect were replicated globally under widespread rooftop PV adoption, it could add more than 2,000 terawatt-hours of extra household electricity demand by 2050, assuming about 30 percent of installed solar capacity sits on roofs. For comparison, the entire European Union consumed 2,729 terawatt-hours of electricity in 2024. In other words, the behavioral response to free solar power is not a rounding error in the energy transition; it is system-relevant, with direct consequences for grid infrastructure, storage requirements, backup generation capacity, and the life-cycle emissions of the electricity sector.

What makes the study unusual is its data architecture. The authors combined the German Socio-Economic Panel, a longitudinal survey of roughly 19,000 households conducted annually, with a purpose-built online survey of more than 2,200 respondents, about half of them PV owners. Because the panel data record only electricity drawn from the public grid, they miss the self-consumed portion of solar generation, which is precisely the cheap electricity most likely to trigger extra use. To close that gap, the team built a feedforward neural network with a single hidden layer of twelve neurons, trained on survey responses, to reconstruct total household consumption from grid withdrawals, PV generation, dwelling size, household size, income, and indicators for electric vehicles and electric heating. Predictions were constrained to physically plausible bounds: total consumption had to be at least as large as grid withdrawals and no larger than grid withdrawals plus generation.

Identifying a causal effect rather than a correlation required careful design. The researchers applied Mahalanobis distance matching to pair each adopting household with comparable non-adopters based on baseline electricity use, dwelling size, household size, and income, producing a matched sample of 6,530 cases across 775 unique households. They then used a difference-in-differences framework with household and year fixed effects, which strips out time-invariant household differences and economy-wide trends, isolating the behavioral change attributable to PV adoption itself. Crucially, the authors followed a strict rebound definition, excluding simple fuel switching, such as replacing a gas boiler with a heat pump, so the measured effect reflects genuine increases in the consumption of energy services rather than electrification of other end uses.

The headline result held up under extensive stress testing. The generation-based rebound effect ranged from 20.1 to 21.6 percent across specifications, and alternative neural network architectures produced aggregated estimates between 15.0 and 24.7 percent, all statistically significant. The effect was robust to dropping covariates, changing standard error assumptions, and splitting the sample. One exception was instructive: when the researchers restricted the sample to typical residential system sizes, excluding installations generating more than 15,000 kilowatt-hours per year, the rebound estimate fell significantly. That makes sense, the authors note, because very large systems are usually operated as income-producing assets rather than as a substitute for household demand, so their high generation volumes mechanically depress the ratio.

The most striking insight, however, lies beneath the average. Using a Mundlak decomposition, which separates within-household variation from stable between-household differences, the team showed that the rebound effect is strongly conditioned by how much electricity a household consumed before installing panels. A hypothetical household with zero pre-installation consumption and an extremely oversized system would show a rebound of nearly 35 percent. For a more typical household consuming 3,000 kilowatt-hours before adoption, the effect drops to about 25.4 percent. The downward-sloping relationship crosses zero at roughly 11,044 kilowatt-hours of pre-consumption, above which some households may actually reduce their total electricity use after going solar, a pattern consistent with saturation: homes that already use a lot of energy have less behavioral room to expand.

Efficiency tells a parallel story. The researchers defined household efficiency as the gap between predicted consumption, based on income, living space, and occupancy, and actual observed consumption before adoption. Households that consumed less than predicted were classified as efficient, and those efficient households rebounded almost twice as strongly as their less efficient counterparts, with an effect of about 21.6 percent versus 12.9 percent. The efficiency gradient persisted even among owners of oversized systems, suggesting that rebound is driven less by the sheer abundance of cheap solar electricity and more by behavioral flexibility. Efficient households, the authors argue, were likely cost-sensitive before adoption, keeping consumption artificially constrained, and the arrival of free rooftop power relaxed those previously binding limits.

Notably, the size of the PV system relative to baseline consumption did not change the rebound elasticity itself. Oversized systems did produce substantially larger absolute increases in post-adoption consumption, nearly 934 kilowatt-hours more on average than undersized systems, but that difference simply reflects their higher generation volumes. The marginal effect of roughly 20 percent was remarkably stable across system dimensions. This matters for modelers: it means the system-level impact of a solar installation depends on who adopts it, not just how big it is. Deployment strategies concentrated among efficient, previously low-consuming households will generate more rebound-induced demand than the same capacity installed among high-consumption homes.

The findings echo patterns documented elsewhere in energy research, including the prebound effect in building renovation, where households that under-consume relative to their building’s characteristics show the strongest post-renovation rebound. Similar dynamics appear in mobility and agricultural water use. Recent work on Swiss households suggests the mechanism is primarily price-driven, with income effects and moral licensing playing minor roles, and the German evidence is consistent with that interpretation: households respond rationally to the collapse of marginal electricity costs, not to some vague feeling of environmental virtue. Because feed-in tariffs in Germany now fall below retail prices, self-consumption is strongly incentivized, and comparable net-billing regimes in California, China, Japan, and much of Europe suggest the results travel well beyond the German case.

The policy implications are concrete. The authors argue against restricting solar deployment and instead recommend preserving marginal price signals after adoption, through time-varying network charges, dynamic tariffs, or residual levies on self-consumed electricity, so the effective cost of an extra kilowatt-hour never falls to zero. They also propose post-adoption feedback, such as benchmarking consumption against pre-installation levels, and restructuring subsidies as long-term monitored grants rather than one-off payments, with feed-in tariffs calibrated to pre-adoption consumption and grid value. More fundamentally, the study is a warning to life-cycle assessment and energy system modeling: treating efficiency gains as proportional demand reductions risks overstating net savings precisely where efficiency is highest. Rebound, the authors conclude, is not a curiosity at the margins of the energy transition but a central link between household behavior, infrastructure planning, and the credibility of net-zero pathways.

Subject of Research: The solar rebound effect: increased household electricity consumption following residential photovoltaic adoption

Article Title: Here comes the sun: investigating the solar rebound effect and consumption behavior in photovoltaic households

Article References: Poier, S., & Bucksteeg, M. (2026). Here comes the sun: investigating the solar rebound effect and consumption behavior in photovoltaic households. Journal of Industrial Ecology, 30(4), 1903-1917. https://doi.org/10.1007/s44498-026-00129-6

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00129-6

Keywords: solar rebound effect, photovoltaics, household energy consumption, rebound effect, energy policy, difference-in-differences, machine learning, life cycle assessment, energy transition, self-consumption, industrial ecology, Germany

The research, led by Amin Barati, Nicola Bianco, Marialaura Di Somma, and Francesco Scognamiglio, focuses on what the authors call integrated local energy communities, or ILECs. These are clusters of buildings that coordinate electricity, heating, cooling, and mobility at the neighborhood level, combining technologies such as combined heat and power units, photovoltaic panels, heat pumps, absorption chillers, batteries, and thermal storage into a single, intelligently managed ecosystem. The European Union has thrown its weight behind this model, with the REPowerEU agenda envisioning one renewable energy community per municipality, and studies suggesting that community membership can cut household energy bills by as much as 30 percent. What has been missing, the researchers argue, is a genuinely rigorous way to operate these communities once electric vehicles enter the picture.

Most previous studies, the team found, treat electric vehicles as simple, time-varying loads, blobs of extra demand that arrive and depart on schedule. Others compress entire fleets into aggregated flexibility proxies that obscure the very constraints that matter most in practice: when a specific car actually parks, how long it stays, what state its battery is in when it arrives, and what minimum charge its owner demands before leaving. The new framework takes a fundamentally different approach. Vehicles are grouped into clusters defined by battery capacity, arrival and departure times, and state-of-charge requirements, and each cluster becomes an explicit decision variable in a mixed-integer linear programming model that simultaneously optimizes the flow of electricity, heat, and cooling across the entire community.

The mathematical machinery behind the study is substantial. The model tracks the gas consumption and electrical output of a 250-kilowatt combined heat and power unit, the thermal contribution of a 600-kilowatt auxiliary boiler, the behavior of a 540-kilowatt heat pump operating in both heating and cooling modes, and the charge-discharge cycles of a 600-kilowatt-hour battery alongside thermal storage tanks. Photovoltaic generation is calculated from hourly solar irradiance data for Turin, and electricity prices are drawn from the Italian wholesale market for January 2025, with export prices conservatively assumed at half the purchase price. Crucially, the vehicles themselves can operate bidirectionally, charging from the community in grid-to-vehicle mode or discharging back in vehicle-to-grid mode, with their state of charge constrained between 20 and 80 percent of capacity and a guaranteed minimum of 80 percent at departure so that no driver is ever stranded.

To resolve the inherent conflict between saving money and saving carbon, the researchers employed a weighted-sum multi-objective approach, sweeping a weight parameter from zero to one to trace out a complete Pareto frontier of optimal trade-offs. At one extreme lies the cheapest possible operation; at the other, the lowest possible emissions; in between, a continuum of compromise strategies. The whole problem is solved with a branch-and-cut algorithm, and the results are benchmarked against a reference scenario in which heat comes entirely from conventional gas boilers and electricity entirely from the national grid, a baseline that costs 421.97 euros per day and emits 1,463.52 kilograms of carbon dioxide on a cold January day in Turin.

The headline findings are striking. Across three photovoltaic configurations ranging from 700 to 1,400 square meters of panels serving a community of 100 apartments and 15 vehicles, the optimized energy community cut operational costs by 45 to 52 percent and carbon dioxide emissions by 41 to 48 percent compared with the reference scenario. Doubling the photovoltaic area from the smallest to the largest configuration delivered a further 12 percent reduction in operating costs and an 11 percent reduction in emissions. An investment analysis confirmed that the transition pays for itself: the daily share of the capital cost of the additional panels, roughly 20.94 euros spread over a 30-year lifetime at a 2 percent discount rate, is more than offset by the operating savings in both economic and environmental optimization modes.

But the most revealing results concern the split personality of the parked electric car. Under pure economic optimization, the energy discharged from vehicle batteries is never used to power the community at all. Instead, every kilowatt-hour flows out to the main grid during high-price hours, generating revenue that drives the community’s daily operating cost down to 203 euros in the largest photovoltaic case. Under pure environmental optimization, the strategy inverts completely: vehicle energy is discharged exclusively for local self-consumption, the combined heat and power unit sits idle, the heat pump draws low-carbon grid electricity to cover the entire thermal load, and nothing is sold back to the grid. The optimization engine, in other words, discovers two fundamentally different roles for the same fleet of cars, and the choice between them depends entirely on what the community values most.

The researchers pushed the analysis further with a scaled-up scenario featuring 30 vehicles and 2,800 square meters of photovoltaics. Here, the economic optimum fell to just over 200 euros per day, with more than 100 euros of that achieved through the sale of vehicle flexibility alone, while the environmental optimum reached 814 kilograms of carbon dioxide. Normalized comparisons showed that doubling the fleet and the solar capacity reduced emission intensity by 3.14 percent, cut the specific energy cost by 11 percent, and lifted the community’s self-sufficiency from roughly 30 percent to over 40 percent. The volume of energy exported from vehicles during high-price hours rose 89 percent, from 280 to 530 kilowatt-hours, while energy discharged for local consumption doubled from 100 to 200 kilowatt-hours.

There is an important caveat, one the authors are careful to acknowledge. Very few electric vehicle models and alternating-current chargers on the market today are actually capable of bidirectional operation, as manufacturers have prioritized direct-current fast charging over vehicle-to-grid functionality. The framework is therefore best understood as a forward-looking assessment of what becomes possible as vehicle-to-grid-ready vehicles and chargers proliferate. It is a roadmap rather than a snapshot, quantifying the flexibility prize that awaits communities willing to invest in the enabling hardware.

The implications stretch well beyond a single neighborhood in Turin. As European cities race to decarbonize heating and transport simultaneously, the study demonstrates that sector coupling at the local level is not merely a theoretical convenience but a quantifiable economic and environmental advantage, and that the humble parked car may be the most underutilized asset in the entire energy transition. The researchers plan to extend the framework to handle uncertainty in solar generation and vehicle mobility patterns, to coordinate multiple communities, and to incorporate detailed battery degradation and maintenance costs. If their projections hold, the future of community energy may be sitting in the parking lot, fully charged and waiting to be asked for help.

Subject of Research: Multi-objective optimization of sector-coupled local energy communities using plug-in electric vehicle flexibility under varying photovoltaic installation scenarios

Article Title: Optimal operation of sector-coupled energy communities leveraging plug-in electric vehicle flexibility under different PV installation scenarios

Article References: Barati, A., Bianco, N., Di Somma, M., & Scognamiglio, F. (2026). Optimal operation of sector-coupled energy communities leveraging plug-in electric vehicle flexibility under different PV installation scenarios. Energy Reports, 16, Article 109701. https://doi.org/10.1016/j.egyr.2026.109701

Image Credits: AI Generated

DOI: 10.1016/j.egyr.2026.109701

Keywords: energy communities, vehicle-to-grid, electric vehicles, photovoltaics, sector coupling, mixed-integer linear programming, Pareto optimization, combined heat and power, heat pumps, decarbonization, self-consumption, demand flexibility