Mining Rewards Revisited
One of Glow's founding insights is rooted in the power of protocol rewards tied to measurable outcomes. Bitcoin's block reward famously set off a global race to deploy large amounts of specialized compute, and networks like Filecoin incentivized petabytes of verifiable global storage capacity. Blockchain networks have repeatedly demonstrated their ability to build and orchestrate large-scale infrastructure networks.
While these networks have different objectives, their rewards frameworks are structurally similar. Protocols define a measure of performance, regularly evaluate performance against that measure (e.g., storage capacity in Filecoin), and then distribute protocol rewards based on each participant's relative output. This creates a feedback loop in which better performance leads to greater rewards, which in turn incentivizes further optimization and improvement.
Subtle Incentive Incompatibilities
Glow draws inspiration from this established rewards structure and points its protocol rewards towards solar farm deployments. In particular, Glow cares about incentivizing new solar deployments that maximize their lifetime carbon displacement. This makes Glow different from traditional blockchain networks: while networks like Bitcoin and Filecoin require ongoing performance measurement to ensure network reliability, Glow focuses on lifetime impact and is largely unconcerned with short-term performance variability like weather-driven fluctuations in solar output.
If Glow were to directly mirror mining's performance-based model for solar output, it would track each farm's carbon displacement weekly and distribute rewards proportionally. A farm generating 10% of that week's carbon displacement would receive 10% of the performance-based rewards. This appears fair at first glance, but when applied to solar economics, it introduces subtle incentive incompatibilities. Regular performance measurement exposes solar farms to income instability due to weather volatility, as a single cloudy week could crater a normally high-impact farm's income, making financing unpredictable and discouraging development in carbon-intensive regions with variable weather. Regional clustering compounds this problem: if most farms sit beneath the same weather system, "lucky" outliers elsewhere capture outsized rewards during regional weather events, even though the network's aggregate impact remains concentrated in one region.
Continuous weekly measurement shifts the competitive focus toward weather stability rather than carbon intensity. A solar farm in New Mexico with consistent sunshine appears reliably strong, while a farm in Pennsylvania's coal-heavy grid sees volatile returns due to clouds and seasonal swings despite displacing more carbon per kilowatt. This may push some developers to optimize for stable sunshine instead of maximum carbon abatement, skewing deployment toward reliably-sunny grids rather than solely carbon-intensive ones.
These rewards distortions highlight an important distinction between Glow and traditional mining networks. In Bitcoin and Filecoin, ongoing operational optimization drives meaningful performance gains; miners constantly upgrade hardware and cooling systems, while storage providers add capacity and improve network utilization. Solar farms work differently, since their performance is largely locked in at construction through location selection, equipment choice, and installation quality. Once operational, total output remains relatively fixed for decades.
Solar farm operators face strong economic incentives to operate their farms optimally. Farms are revenue-generating assets where maintenance costs are generally far lower than the revenue lost from underproducing electricity, which means that farms will reliably optimize for operating at full capacity and displacing their expected carbon. The protocol's incentives therefore need only focus on expectations rather than chasing short-term performance variations that don't reflect their underlying climate impact.
To maximize carbon displacement, Glow should not expose its incentives to short-term optimization games. Instead, the protocol should reward farms based purely on where and how they are built.
Expected Instead of Measured Output
When a solar farm joins Glow, it undergoes a comprehensive audit to estimate expected lifetime electricity production, revenues, and carbon displacement. These projections are built from satellite baselines, historical weather patterns, local carbon intensity data, and pre-construction engineering studies.
Solar farms are fundamentally large, fixed pieces of infrastructure whose performance characteristics are essentially locked in at construction. The size of the array, its geographic location, the local grid's carbon intensity, and historical weather patterns all exist as knowable inputs that are entirely within the solar farm owner's control during the planning phase. This predictable nature allows Glow to reliably project lifetime performance metrics before a single kilowatt is generated while rewarding miners and capital providers based on deliberate choices rather than variable circumstances.
This predictability gives Glow an elegant way to resolve the incentive incompatibilities described earlier by basing rewards on expected performance. Rather than compensating farms for their weekly output, which fluctuates with weather and creates perverse incentives, Glow rewards them based on what they are expected to produce over their lifetimes. Each week, farms earn rewards proportional to their expected carbon displacement, regardless of actual weather conditions.
Even though real-time monitoring does not affect rewards, Glow still tracks every farm continuously. The data serves two critical purposes: improving models for future projects and measuring the protocol's actual carbon impact. Monitoring devices verify that farms are operational and feed performance data back to continuously refine projections for future audits. This real-time farm performance data is also publicly accessible at https://glow.org/weekly-reports.
This approach ensures that Glow's natural incentives push developers to maximize their total environmental impact. Operating on expectation-based rewards both simplifies financial modeling and ensures that every solar farm is optimized purely for climate impact. The only way to outperform is to exceed expectations by building projects that deliver more carbon displacement per dollar, singularly directing competition toward maximizing climate impact.
Towards Predictable Capital Allocation
Solar farms are expensive infrastructure projects requiring substantial upfront capital investment. Like most large infrastructure, they face tail risk: low-probability, high-impact events that can devastate financial returns. Catastrophic equipment failures from severe weather, fire, or other unforeseen circumstances are rare, but when they occur, they can permanently halt energy production with devastating consequences.
Under performance-based reward systems, tail risk introduces revenue uncertainty for farm operators and capital providers. When a solar farm fails, its reward earnings immediately halt while other farms continue receiving their share, redistributing the failed operator's expected rewards to competitors. Even if unlikely, the mere possibility that a substantial sum of capital could evaporate due to unforeseen and uncontrollable circumstances becomes a prohibitive barrier to entry.
Expectation-based rewards solve this problem by eliminating tail risk from the risk calculus. Glow auditors appraise each solar farm's expected lifetime performance, and protocol rewards are paid against these projections regardless of realized output. A solar farm that suffers complete failure continues earning rewards based on its original expectations, protecting both capital providers and operators from devastating losses while maintaining competitive pressure to build high-impact projects.
Consider a scenario where a solar farm with a $5 million Protocol Deposit suffers catastrophic failure six months into operation due to an unforeseen weather event that destroys most of the panels. In a traditional performance-based system, deposit recovery would immediately halt. With actual production near zero, the farm would stop earning rewards while other farms continue claiming their share. The remaining 20 years of expected production would be lost, along with most of the $5 million deposit.
Under Glow's expectation model, that same catastrophic failure would have no financial impact on Protocol Deposit recovery. Despite producing zero kilowatts after the disaster, the farm continues recovering its deposit and earning rewards based on its expected lifetime production. The protocol absorbs the actual performance shortfall across the entire network while protecting individual participants from devastating losses.
These tail risk protections don't dilute performance incentives since, as established earlier, farm operators are strongly motivated to maximize electricity output. Solar failures tend to be binary rather than gradual, and by collectively spreading the financial impact of these rare events, Glow removes the primary barrier to capital participation. Capital providers can underwrite projects against stable baselines without fearing catastrophic loss, while developers still face strong pressure to optimize for carbon displacement per dollar invested.
Expectations Improve Incentive Alignment
By paying rewards against expectations rather than short-term performance, Glow brings the incentives of builders, capital providers, and climate buyers into alignment.
Builders are rewarded for constructing projects that deliver strong lifetime carbon displacement and capital providers fund these projects with confidence, knowing that returns are tied to predictable long-term projections. Furthermore, the competitive intensity that defines crypto mining networks remains intact. Farms that beat the network baseline carbon displacement recover deposits quickly and claim surpluses from underperformers. Farms that lag lose ground and forfeit part of their deposits to more efficient operators. But the competition is now directed at what matters most: lifetime carbon displacement.
Glow is not just building solar capacity. It is building a competitive economic environment where profitability and climate impact converge. By tying performance-based rewards to expectations rather than short-term output, Glow ensures that the most profitable strategy for participants is also the one that maximizes net impact. This completes the evolution from Bitcoin's block rewards to Glow's expectation-based system. The feedback loop of performance measurement and distribution remains, but now it measures and rewards the decisions that truly matter for carbon displacement, creating a self-reinforcing cycle where better climate impact leads to greater rewards, which in turn incentivizes further optimization of solar deployment for maximum emission reductions.