PIRE: Deeply Decarbonizing Global Industrial Supply Chains: Technology, Organizational Practices, and Institutional Design

PIRE:全球工业供应链深度脱碳:技术、组织实践和制度设计

基本信息

  • 批准号:
    2230743
  • 负责人:
  • 金额:
    $ 149.96万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-01-01 至 2025-12-31
  • 项目状态:
    未结题

项目摘要

Drastically reducing greenhouse gas (GHG) emissions from industrial production worldwide while limiting adverse impacts on workers and communities is a major societal challenge. The industrial sector accounts for around one-third of global GHG emissions, with cement, chemicals and plastics, and iron and steelmaking being the three largest contributing industries. Decarbonizing current processes is complicated by the challenge of replacing fossil fuels as a heat source and implies changes in labor requirements, which could disproportionately impact energy communities and vulnerable members of the energy workforce. Furthermore, a large share of industrial sector output is traded internationally, potentially disadvantaging producers that face stringent domestic climate policies and encouraging relocation of production to less regulated markets. By engaging students and researchers at centers of excellence and a broad, diverse network of multi-sector, multi-stakeholder partners in China, Germany, South Africa, and the United States, this partnership will use data science and case study analysis to generate innovative solutions to the technological and societal challenges of industrial decarbonization. These solutions will be rooted in a deep contextual and data-driven understanding of production geographies and their connected communities in major centers of industrial activity worldwide. The shared insights and global awareness developed in this partnership will support national health, prosperity, and welfare in a clean energy transition. The goal of the Industrial Decarbonization Analysis, Benchmarking, and Action (INDABA) partnership is to integrate global expertise in materials science and engineering, data science, economics, and decision analysis in a convergence approach to address the challenge of decarbonizing industrial supply chains. Partnership activities bring together research teams in China, Germany, South Africa, and the United States to develop data and analysis, to conduct case study comparisons of regional approaches, and ultimately to form a shared understanding of global opportunities to demonstrate technologies and infrastructure to enable deep decarbonization. Specifically, the partnership will use machine learning techniques to illuminate drivers of industrial greenhouse gas (GHG) emissions at the plant and process level in diverse global settings and develop use-inspired approaches to evaluate plant- and region-specific options for decarbonization considering technical, economic, and societal impacts. The outcome of this research will be the development of a novel global dataverse and the advancement of machine learning approaches to examine GHG emissions drivers for industrial plants in three sectors, cement, chemicals and plastics, and iron and steelmaking. Through international collaboration, participants in the partnership will examine how industrial decarbonization investments can be most effectively customized or scaled within country-, region-, and plant-specific contexts. This partnership will further accelerate industrial decarbonization by educating diverse audiences via new curriculum, policy memos, and publications. Together these activities will lay the foundation for technology demonstrations and coordinated institutional design to advance global efforts to mitigate climate change.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
大幅度减少全球工业生产的温室气体排放,同时限制对工人和社区的不利影响,是一项重大的社会挑战。工业部门约占全球温室气体排放量的三分之一,其中水泥、化学品和塑料以及钢铁和炼钢是三个最大的贡献行业。脱碳目前的过程是复杂的挑战,取代化石燃料作为热源,并意味着劳动力需求的变化,这可能会不成比例地影响能源社区和能源劳动力的弱势成员。此外,工业部门产出的很大一部分是在国际上交易的,这可能会使面临严格的国内气候政策的生产商感到不安,并鼓励将生产转移到监管较少的市场。通过吸引卓越中心的学生和研究人员以及中国,德国,南非和美国的多部门,多利益相关者合作伙伴的广泛,多样化的网络,这种伙伴关系将使用数据科学和案例研究分析,为工业脱碳的技术和社会挑战提供创新解决方案。这些解决方案将植根于对生产地理位置及其在全球主要工业活动中心的连接社区的深刻背景和数据驱动的理解。 工业脱碳分析、基准和行动(INDABA)伙伴关系的目标是整合材料科学与工程、数据科学、经济学和决策分析等领域的全球专业知识,以融合的方式应对工业供应链脱碳的挑战。伙伴关系活动将中国、德国、南非和美国的研究团队聚集在一起,开发数据和分析,对区域方法进行案例研究比较,并最终形成对全球机会的共同理解,以展示技术和基础设施,实现深度脱碳。具体而言,该合作伙伴关系将使用机器学习技术来阐明不同全球环境中工厂和工艺层面的工业温室气体(GHG)排放驱动因素,并开发基于使用的方法来评估工厂和区域特定的脱碳选择,同时考虑技术,经济和社会影响。这项研究的成果将是开发一个新的全球数据宇宙,并推进机器学习方法,以研究水泥、化工和塑料以及钢铁和炼钢三个行业工业工厂的温室气体排放驱动因素。通过国际合作,该伙伴关系的参与者将研究如何在国家,地区和工厂特定的背景下最有效地定制或扩展工业脱碳投资。这一伙伴关系将通过新课程、政策备忘录和出版物教育不同的受众,进一步加速工业脱碳。这些活动将共同为技术示范和协调机构设计奠定基础,以推动全球减缓气候变化的努力。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Valerie Karplus其他文献

Valerie Karplus的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

相似海外基金

Deeply analyzing MHC class I-restricted peptide presentation mechanistics across alleles, pathways, and disease coupled with TCR discovery/characterization
深入分析跨等位基因、通路和疾病的 MHC I 类限制性肽呈递机制以及 TCR 发现/表征
  • 批准号:
    10674405
  • 财政年份:
    2023
  • 资助金额:
    $ 149.96万
  • 项目类别:
How can people connect more deeply through self-disclosure? Testing the linguistic, nonverbal, and neural mechanisms of successful communication
人们如何通过自我表露更深入地联系?
  • 批准号:
    2314423
  • 财政年份:
    2023
  • 资助金额:
    $ 149.96万
  • 项目类别:
    Standard Grant
Enhancing Engineering Students’ Ability to Think Deeply about their Learning Through Formal and Continuous Reflection
提高工科学生通过正式和持续的反思深入思考学习的能力
  • 批准号:
    2235227
  • 财政年份:
    2023
  • 资助金额:
    $ 149.96万
  • 项目类别:
    Standard Grant
Gprasp2 expression identifies a deeply quiescent hematopoietic stem cells subset with superior stemness and self-renewal
Gprasp2 表达鉴定出具有卓越干性和自我更新能力的深度静止造血干细胞亚群
  • 批准号:
    10751567
  • 财政年份:
    2023
  • 资助金额:
    $ 149.96万
  • 项目类别:
Genetic relationships between PTSD and Alcohol Use Disorder: Integrating GWAS and Deeply Phenotyped Longitudinal data.
PTSD 和酒精使用障碍之间的遗传关系:整合 GWAS 和深度表型纵向数据。
  • 批准号:
    10672457
  • 财政年份:
    2022
  • 资助金额:
    $ 149.96万
  • 项目类别:
Genetic relationships between PTSD and Alcohol Use Disorder: Integrating GWAS and Deeply Phenotyped Longitudinal data.
PTSD 和酒精使用障碍之间的遗传关系:整合 GWAS 和深度表型纵向数据。
  • 批准号:
    10418931
  • 财政年份:
    2022
  • 资助金额:
    $ 149.96万
  • 项目类别:
Validating establishment of functional safety in skin interface with deeply porous transcutaneous pylon for direct skeletal attachment of limb prostheses
验证用于肢体假肢直接骨骼附着的深孔经皮塔皮肤界面功能安全性的建立
  • 批准号:
    10324937
  • 财政年份:
    2021
  • 资助金额:
    $ 149.96万
  • 项目类别:
Identifying the genetic causes of depression in a deeply phenotyped population from South Korea
确定韩国深层表型人群抑郁症的遗传原因
  • 批准号:
    10654686
  • 财政年份:
    2021
  • 资助金额:
    $ 149.96万
  • 项目类别:
Identifying the genetic causes of depression in a deeply phenotyped population from South Korea
确定韩国深层表型人群抑郁症的遗传原因
  • 批准号:
    10470895
  • 财政年份:
    2021
  • 资助金额:
    $ 149.96万
  • 项目类别:
Validating establishment of functional safety in skin interface with deeply porous transcutaneous pylon for direct skeletal attachment of limb prostheses
验证用于肢体假肢直接骨骼附着的深孔经皮塔皮肤界面功能安全性的建立
  • 批准号:
    10493303
  • 财政年份:
    2021
  • 资助金额:
    $ 149.96万
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了