A physically extended EEIO framework for material efficiency assessment in United States manufacturing supply chains
- URL: http://arxiv.org/abs/2510.15121v1
- Date: Thu, 16 Oct 2025 20:26:41 GMT
- Title: A physically extended EEIO framework for material efficiency assessment in United States manufacturing supply chains
- Abstract summary: A physical assessment of material flows in an economy can support the development of sustainable decarbonization and circularity strategies.<n>Here we describe a new physical extension for the U.S. Department of Energy's (DOE's) EEIO for Industrial Decarbonization (EEIO-IDA) model.<n>This work is designed to align with existing DOE frameworks and tools, including the EEIO-IDA tool.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A physical assessment of material flows in an economy (e.g., material flow quantification) can support the development of sustainable decarbonization and circularity strategies by providing the tangible physical context of industrial production quantities and supply chain relationships. However, completing a physical assessment is challenging due to the scarcity of high-quality raw data and poor harmonization across industry classification systems used in data reporting. Here we describe a new physical extension for the U.S. Department of Energy's (DOE's) EEIO for Industrial Decarbonization (EEIO-IDA) model, yielding an expanded EEIO model that is both physically and environmentally extended. In the model framework, the U.S. economy is divided into goods-producing and service-producing subsectors, and mass flows are quantified for each goods-producing subsector using a combination of trade data (e.g., UN Comtrade) and physical production data (e.g., U.S. Geological Survey). Given that primary-source production data are not available for all subsectors, price-imputation and mass-balance assumptions are developed and used to complete the physical flows dataset with high-quality estimations. The resulting dataset, when integrated with the EEIO-IDA tool, enables the quantification of environmental impact intensity metrics on a mass basis (e.g., CO$_2$eq/kg)) for each industrial subsector. This work is designed to align with existing DOE frameworks and tools, including the EEIO-IDA tool, the DOE Industrial Decarbonization Roadmap (2022), and Pathways for U.S. Industrial Transformations study (2025).
Related papers
- Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting [0.0]
AI inference services fall under Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD)<n>Current practice either omits the category entirely or applies a generic economic input-output factor calibrated to the ICT sector as a whole.<n>We propose a four-tier framework that matches estimation precision to the data organisations can realistically obtain.
arXiv Detail & Related papers (2026-06-09T10:08:36Z) - Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
arXiv Detail & Related papers (2026-03-31T13:29:20Z) - AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models [2.7946918847372277]
We propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of machine learning models.<n>We demonstrate, through theoretical analysis and empirical validation, that carbon-aware benchmarking changes the relative ranking of models.<n>Our proposal aims to shift the research community toward transparent, multi-objective evaluation and align ML progress with global sustainability goals.
arXiv Detail & Related papers (2026-02-17T21:52:48Z) - Sustainable Materials Discovery in the Era of Artificial Intelligence [3.222363676081407]
We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment.<n>The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization.
arXiv Detail & Related papers (2026-01-29T10:42:44Z) - A Sustainable AI Economy Needs Data Deals That Work for Generators [56.949279542190084]
We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality.<n>We analyze 73 public data deals and show that the majority of value accrues to aggregators.<n>We propose an Equitable Data-Value Exchange Framework to enable a minimal market that benefits all participants.
arXiv Detail & Related papers (2026-01-15T01:05:48Z) - Unified Smart Factory Model: A model-based Approach for Integrating Industry 4.0 and Sustainability for Manufacturing Systems [0.0]
The Unified Smart Factory Model (USFM) is a comprehensive framework designed to translate high-level sustainability goals into measurable factory-level indicators.<n>The model's systematic approach can reduce redundancy, minimize the risk of missing critical information, and enhance data collection.
arXiv Detail & Related papers (2025-12-11T13:30:38Z) - Advancements in synthetic data extraction for industrial injection molding [0.0]
We investigate the feasibility of incorporating synthetic data into the training process of the injection molding process.<n>Our results suggest that the inclusion of synthetic data improves the model's ability to handle different scenarios.
arXiv Detail & Related papers (2025-11-11T11:19:54Z) - Understanding the Influence of Synthetic Data for Text Embedders [52.04771455432998]
We first reproduce and publicly release the synthetic data proposed by Wang et al.<n>We critically examine where exactly synthetic data improves model generalization.<n>Our findings highlight the limitations of current synthetic data approaches for building general-purpose embedders.
arXiv Detail & Related papers (2025-09-07T19:28:52Z) - Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings [0.4681310436826459]
This paper presents the design, implementation, and evaluation of the AirQo data pipeline.<n>It is built using open-source technologies such as Apache Airflow, Apache Kafka, and Google BigQuery.<n>We demonstrate the pipeline's ability to ingest, transform, and distribute millions of air quality measurements monthly from over 400 monitoring devices.
arXiv Detail & Related papers (2025-08-20T06:19:27Z) - Description and Comparative Analysis of QuRE: A New Industrial Requirements Quality Dataset [2.2087818411607225]
This paper introduces QuRE, a new dataset comprising 2,111 industrial requirements that have been annotated through a real-world review process.<n>Previously used for over five years as part of an industrial contract, this dataset is now being released to the research community.
arXiv Detail & Related papers (2025-08-12T11:51:15Z) - SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting [22.82938587928396]
We introduce SustainableQA, a novel dataset and a scalable pipeline that generates comprehensive QA pairs from corporate sustainability and annual reports.<n>This results in a robust dataset of over 195,000 diverse factoid and non-factoid QA pairs.<n>SustainableQA proves to be a highly effective resource for developing benchmarking and advanced knowledge assistants capable of navigating complex sustainability compliance data.
arXiv Detail & Related papers (2025-08-05T02:03:59Z) - A Hybrid Game-Theory and Deep Learning Framework for Predicting Tourist Arrivals via Big Data Analytics and Opinion Leader Detection [0.0]
This paper proposes a novel non-linear hybrid approach for forecasting international tourist arrivals in two different contexts.<n>The method integrates multiple sources of Internet big data and employs an innovative game theory-based algorithm to identify opinion leaders on social media platforms.<n> Experimental results demonstrate that this approach outperforms existing state-of-the-art techniques.
arXiv Detail & Related papers (2025-07-04T09:17:17Z) - Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map [50.21082069320818]
We propose a novel diffusion-based pipeline for generating high-fidelity industrial datasets with minimal supervision.<n>Our approach conditions the diffusion model on enriched bounding box representations to produce precise segmentation masks.<n>Results demonstrate that diffusion-based synthesis can bridge the gap between artificial and real-world industrial data.
arXiv Detail & Related papers (2025-05-06T15:21:36Z) - Modeling supply chain compliance response strategies based on AI synthetic data with structural path regression: A Simulation Study of EU 2027 Mandatory Labor Regulations [3.5516803380598074]
This paper constructs a methodological framework that integrates the AI synthetic data generation mechanism and structural path regression modeling.<n>It is concluded that AI synthetic data combined with structural path modeling provides an effective tool for high-intensity regulatory simulation.
arXiv Detail & Related papers (2025-05-04T11:39:56Z) - Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
arXiv Detail & Related papers (2024-08-02T10:47:10Z) - Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing [56.61654656648898]
We propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing.
We analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.
arXiv Detail & Related papers (2024-01-19T11:04:14Z) - A Generative Approach for Production-Aware Industrial Network Traffic
Modeling [70.46446906513677]
We investigate the network traffic data generated from a laser cutting machine deployed in a Trumpf factory in Germany.
We analyze the traffic statistics, capture the dependencies between the internal states of the machine, and model the network traffic as a production state dependent process.
We compare the performance of various generative models including variational autoencoder (VAE), conditional variational autoencoder (CVAE), and generative adversarial network (GAN)
arXiv Detail & Related papers (2022-11-11T09:46:58Z) - Deep Learning based pipeline for anomaly detection and quality
enhancement in industrial binder jetting processes [68.8204255655161]
Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space.
This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
arXiv Detail & Related papers (2022-09-21T08:14:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.