ECOScore: Multi-Context Sustainability Metric

• ECOScore is a family of quantitative metrics that assess eco-efficiency and sustainability by mapping operational behavior or lifecycle proxies into a scalar rating.
• It integrates diverse data—from real-time emissions in energy storage to resource utilization in hardware design—to drive carbon-aware decision-making.
• The score facilitates benchmarking and optimized operational strategies by normalizing environmental performance against clear system boundaries and baselines.

ECOScore is a score-like construct used in several research contexts to quantify environmental performance, eco-efficiency, or decision priority. In the literature considered here, the term is explicit in some cases and implicit in others. For electrical energy storage systems, it can be built from real-time marginal emission intensity, operational emissions accounting, and Emission Performance Credits; for eFPGA-augmented SoCs, it is a composite RTL/IP partitioning score; for machine-learning inference, an analogous construct appears as the Environmental Sustainability Score; and adjacent work uses comparable environmental ratings based on product carbon footprint, accommodation energy performance, eco-driving behavior, or spatial ecosystem-service valuation (Yao et al., 19 Jun 2025, Tashdid et al., 6 Aug 2025, Minoza et al., 10 Nov 2025, Spillo et al., 17 Feb 2026, Bentley et al., 2024, Hadjigeorgiou et al., 5 Jun 2025, Shao et al., 2021).

1. Terminological scope and research uses

In the cited literature, ECOScore does not denote a single universal standard. Rather, it denotes a family of quantitative constructs that map operational behavior, structural attributes, or lifecycle proxies into a scalar sustainability- or eco-efficiency-oriented signal. In some works the term is explicit, while in others the same role is played by a score-like metric with a different label. This suggests that ECOScore is best understood as a methodological pattern: a normalized representation of eco-relevant performance, tied to a clearly specified system boundary, baseline, and optimization target (Yao et al., 19 Jun 2025, Tashdid et al., 6 Aug 2025, Minoza et al., 10 Nov 2025, Spillo et al., 17 Feb 2026, Bentley et al., 2024, Hadjigeorgiou et al., 5 Jun 2025).

Context	Basis of the score	Function
Electrical energy storage	MEI, net operational emissions, EPCs	Carbon-aware dispatch and crediting
ECOLogic SoCs	Adaptability, piracy threat, performance tolerance, resource fit	ASIC/eFPGA partitioning
ML inference	Effective parameters per gram of CO$_2$	Cross-hardware sustainability comparison
Product, building, and mobility ratings	PCF, EcoGrade factors, PCI or SOM clusters	Recommendation, guidance, or benchmarking

A central distinction across these uses is whether the score is based on direct physical flows or on a proxy. The ESS-oriented storage framework is operational and marginal, because it tracks charge/discharge decisions against time-varying grid marginal emission intensity. The ML inference ESS is operational but normalized by model capacity. Eco-Amazon is lifecycle-oriented because it uses cradle-to-grave product carbon footprint estimates. EcoGrade is address-specific and multi-factor. ECO+ in autonomous driving uses Positive Control Input as a surrogate for energy and fuel consumption (Yao et al., 19 Jun 2025, Minoza et al., 10 Nov 2025, Spillo et al., 17 Feb 2026, Bentley et al., 2024, Hadjigeorgiou et al., 5 Jun 2025).

2. Carbon-aware electrical energy storage ECOScore

The most explicit ECOScore-like formulation in the provided material is the framework built around “Emission-Aware Operation of Electrical Energy Storage Systems” (Yao et al., 19 Jun 2025). Its stated objective is to create a practical, operational carbon-accounting framework that quantifies the indirect emissions caused or avoided by ESS operation, defines Emission Performance Credits, and enables ESS, down to the prosumer level, to participate in compliance carbon markets. The framework is agnostic to ESS size and connection level, covering grid-scale ESS, distribution-level ESS, and behind-the-meter or prosumer ESS.

Its core signal is the grid’s real-time marginal emission intensity. The paper proposes, for the first time, a mechanism to calculate the grid’s real-time marginal emission intensity for the Ontario system. Residual demand is defined as

$$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$

where non-dispatchable or baseload resources are removed from total demand. Using Ontario IESO hourly data from October 2024 to April 2025, cubic regressions are fitted for gas generation, hydro generation, and net imports against residual demand; these are then approximated by 15 piecewise-linear segments, each 1,000 MWh wide, plus extremes. Segment-wise MEI is

$m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$

with gas at $0.37$ tCO$_2$e/MWh, imports at $0.44$ tCO$_2$e/MWh when net imports are positive, and hydro at $0$. Hourly MEI is then assigned by

$$\rho_t^{\text{G}} = m_s \quad \text{where} \quad P_t^{\text{R,D}} \in \text{Segment } s.$$

The resulting signal is hourly and system-wide rather than nodal (Yao et al., 19 Jun 2025).

This framework links ESS operation directly to carbon accounting. Charging draws electricity at the contemporaneous MEI and therefore causes indirect emissions; discharging offsets marginal generation at the contemporaneous MEI and therefore produces avoided emissions. A positive net effect yields EPCs, while a negative net effect produces a “dirty” ESS trajectory that would not earn EPCs. A plausible implication is that ECOScore, in this domain, is not a static label for installed capacity but a rating of real operational behavior under time-varying marginal carbon conditions (Yao et al., 19 Jun 2025).

3. Optimization, accounting, and credit generation

The ESS dispatch problem is formulated through net grid exchange

$$P_t^{\text{G}} = P_t^{\text{dis}} - P_t^{\text{ch}},$$

and the objective

$$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$0

where $$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$1 is electricity price, $$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$2 is carbon price, and $$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$3 is grid MEI. State-of-charge dynamics are constrained by charging and discharging efficiencies, both set to $$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$4 in the simulations, and by normalized SoC bounds. The paper studies three cases: electricity price only, carbon value only, and the full joint objective. In operational terms, the joint formulation rewards charging in low-price, low-MEI hours and discharging in high-price, high-MEI hours (Yao et al., 19 Jun 2025).

The same framework supports operational emissions accounting. The integrated explanation formalizes charging emissions as $$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$5, avoided emissions as $$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$6, and net operational effect as

$$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$7

A compatible EPC definition is then

$$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$8

with EPC revenue aligned to prevailing carbon prices. A plausible ECOScore derived from this framework would therefore use net operational emission performance, emission reduction relative to a baseline, and credit generation intensity, for example as emissions per unit of delivered energy, EPCs per unit of ESS capacity or throughput, or a normalized $$P_t^{\text{R,D}} = P_t^{\text{T,D}} - \sum_{m\in\mathcal M} P_{m,t},$$9–$m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$0 score based on peer or baseline performance (Yao et al., 19 Jun 2025).

The simulation results show why a marginal, time-varying score is necessary. For a $m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$1 MWh ESS, Case 1 yields operational revenue of $m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$2, $m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$3 tCO$m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$4, and $m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$5; Case 3 yields $m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$6, $m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$7 tCO$m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$8, and $m_s = \sum_{n\in\mathcal N} \lambda_{n,s} e_n,$9. The most important misconception addressed by these numbers is that price arbitrage alone is not an environmental proxy: the price-only strategy can increase emissions, because low-price periods can still be high-MEI periods (Yao et al., 19 Jun 2025).

4. Formal score constructions in hardware and machine learning

A distinct use of ECOScore appears in “ECOLogic: Enabling Circular, Obfuscated, and Adaptive Logic via eFPGA-Augmented SoCs” (Tashdid et al., 6 Aug 2025). Here ECOScore is not an environmental label in the narrow sense, but a quantitative scoring framework that decides which RTL/IP blocks should remain in fixed ASIC logic and which should move into embedded FPGA fabric. The score aggregates four normalized sub-metrics: adaptability, piracy threat, performance tolerance, and resource fit:

$0.37$0

Adaptability is log-normalized code churn, piracy threat combines confidentiality risk, exposure factor, and redaction potential, performance tolerance compares ASIC and eFPGA maximum frequency, and resource fit is area-based normalization. In the evaluation, the weights are $0.37$1, and the six reported ECOScore values are $0.37$2 for ASCON, $0.37$3 for SHA-256, $0.37$4 for a transformer accelerator, $0.37$5 for a CNN accelerator, $0.37$6 for an interconnect, and $0.37$7 for controller logic. This score drives selective mapping into eFPGA fabric and is associated with average retention of $0.37$8 percent ASIC-level performance, $0.37$9 ns timing slack versus $_2$0 ns in FPGA, average power reduction by $_2$1 times, and a $_2$2 percent reduction in deployment carbon footprint relative to FPGA-only implementations (Tashdid et al., 6 Aug 2025).

An explicitly environmental score appears in “ML-EcoLyzer: Quantifying the Environmental Cost of Machine Learning Inference Across Frameworks and Hardware” (Minoza et al., 10 Nov 2025). The paper defines the Environmental Sustainability Score as

$_2$3

where $_2$4 is total parameter count and $_2$5 is the quantization factor. CO$_2$6 is derived from measured energy, regional carbon intensity, and hardware-tier PUE, with PUE values of $_2$7 for CPU-only systems, $_2$8 for desktop or consumer GPUs, and $_2$9 for datacenter GPUs. The study spans more than $0.44$0 inference configurations across CPUs, consumer GPUs, and datacenter accelerators. Quantization improves ESS; huge accelerators can be inefficient for lightweight applications; and even small models can incur significant costs when implemented suboptimally. The paper also reports a sampling-rate effect on CO$0.44$1 estimation: $0.44$2 g at $0.44$3 Hz with $0.44$4 relative error, $0.44$5 g at $0.44$6 Hz with $0.44$7, and $0.44$8 g at $0.44$9 Hz as the reference. This use of ECOScore-like normalization is therefore explicitly cross-framework and hardware-aware (Minoza et al., 10 Nov 2025).

5. Product, building, mobility, and spatial eco-rating frameworks

Several adjacent frameworks instantiate ECOScore-like logic without standardizing on the same name. “Eco-Amazon” treats Product Carbon Footprint as a scalar item attribute measured in kilograms of cradle-to-grave CO$_2$0e and shows how it can be injected into retrieval and recommendation. The dataset covers Electronics, Home and Kitchen, and Clothing, with $_2$1 items enriched with PCF estimates from GPT-o3-mini and Gemini-2.5-flash. The paper’s re-ranking score is

$_2$2

while the accompanying explanation notes that practical implementations should invert PCF into a greenness score because lower PCF is better. Validation against $_2$3 ground-truth items shows that absolute PCF error can be substantial, but Spearman correlation and NDCG are high, which implies that the strongest use of such a score is ordinal ranking rather than certified lifecycle accounting (Spillo et al., 17 Feb 2026).

“Address-Specific Sustainable Accommodation Choice Through Real-World Data Integration” defines EcoGrade, an address-specific $_2$4–$_2$5 sustainability rating built from energy consumption, energy efficiency, green supplier, and green transport factors. The metric is validated on $_2$6 UK addresses in $_2$7 cities, and equivalence between interpolated and direct EPC-based scores is tested using TOST with an equivalence margin of $_2$8, with both one-sided $_2$9-values reported as $0$0. This is a distinct but clearly homologous instantiation of ECOScore logic: multi-factor normalization, spatial interpolation, and user-facing aggregation into a simple rating (Bentley et al., 2024).

Mobility research supplies two further score-like constructions. In “Energy Consumption Optimization for Autonomous Vehicles via Positive Control Input Minimization,” ECO+ uses

$0$1

as a convex surrogate for fuel and electric energy use; the paper proposes that an ECOScore can be built from PCI per unit distance or from PCI relative to an ECO+ optimal reference trajectory (Hadjigeorgiou et al., 5 Jun 2025). In “An Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel Consumption to Promote Eco-Driving,” an SOM-based system classifies $0$2-s driving windows into five fuel-consumption-related driving-style clusters ranging from about $0$3 to $0$4 L/100 km, with potential adjacent-cluster savings from $0$5 to $0$6, and up to $0$7 CO$0$8 reduction between efficient and inefficient styles under similar conditions. This suggests a behavior-based ECOScore grounded in cluster occupancy and personalized recommendations (Mata-Carballeira et al., 29 Jan 2025).

A broader ecological interpretation appears in spatial and sectoral sustainability work. The ExioML benchmark explicitly states that it provides the structured, multi-indicator information needed to build an ECOScore-type environmental performance metric, including direct intensity-based and footprint-based forms grounded in

$0$9

the standard EE-MRIO footprint expression (Guo et al., 2024). “Monitoring urban ecosystem service value using dynamic multi-level grids” similarly provides a monetary ESV basis that can be normalized into a spatially explicit ECOScore by grid cell, with positive and negative ecosystem-service contributions and multi-scale aggregation (Shao et al., 2021).

6. Limitations, misconceptions, and standardization challenges

Across these literatures, ECOScore is highly sensitive to boundary definition. The ESS-oriented storage framework covers operational emissions only and is marginal rather than lifecycle-based; Eco-Amazon targets cradle-to-grave PCF; EcoGrade mixes building performance with green transport accessibility; and ML-EcoLyzer’s ESS normalizes emissions by effective parameter count rather than by utility or accuracy alone (Yao et al., 19 Jun 2025, Spillo et al., 17 Feb 2026, Bentley et al., 2024, Minoza et al., 10 Nov 2025). This means that two scores with the same name may not be comparable unless their temporal granularity, spatial scope, lifecycle boundary, and normalization denominator are made explicit.

Methodological uncertainty is equally heterogeneous. The storage framework assumes accurate predictions of prices and MEI over the optimization horizon and uses system-wide rather than locational marginal emissions, with no network constraints or congestion effects in the core model (Yao et al., 19 Jun 2025). Eco-Amazon explicitly states that its PCF values are not a replacement for full LCA, that high-impact items can have large absolute errors, and that the method provides no explicit uncertainty quantification beyond averaging four runs per LLM (Spillo et al., 17 Feb 2026). ECOLogic depends on designer-selected weights and subjective confidentiality scores (Tashdid et al., 6 Aug 2025). ML-EcoLyzer uses fixed PUE values and region-based carbon and water factors, and its own measurements show that monitoring frequency affects estimated emissions (Minoza et al., 10 Nov 2025). EcoGrade, for its part, averages available factors equally and uses an empirically tuned log transformation to balance the distribution of scores (Bentley et al., 2024).

Several common misconceptions follow. First, a low market price is not a reliable environmental signal: in the ESS case, the price-only strategy increases emissions (Yao et al., 19 Jun 2025). Second, a high normalized sustainability score does not by itself imply low absolute environmental cost: ML-EcoLyzer states that ESS should be interpreted together with absolute emissions because a large model can have high ESS and still be environmentally expensive in absolute terms (Minoza et al., 10 Nov 2025). Third, ordinal ranking quality and certified physical accuracy are distinct: Eco-Amazon’s ranking metrics are strong even when absolute MAE is substantial (Spillo et al., 17 Feb 2026). This suggests that any formal ECOScore standard would need, at minimum, an explicit baseline, a declared system boundary, uncertainty reporting, and a separation between relative performance and absolute impact.