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Decarbonization plans depend on the rapid, large-scale deployment of batteries to sufficiently decarbonize the electricity system and on-road transport. This can take many forms, shaped by technology, materials, and supply chain selection, which will have local and global environmental and social impacts. Current knowledge gaps limit the ability of decision-makers to make choices in facilitating battery deployment that minimizes or avoids unintended environmental and social consequences. These gaps include a lack of harmonized, accessible, and up-to-date data on manufacturing and supply chains and shortcomings within sustainability and social impact assessment methods, resulting in uncertainty that limits incorporation of research into policy making. These gaps can lead to unintended detrimental effects of large-scale battery deployment. To support decarbonization goals while minimizing negative environmental and social impacts, we elucidate current barriers to tracking how decision-making for large-scale battery deployment translates to environmental and social impacts and recommend steps to overcome them.

There are global aspirations to reach net zero emissions, which triggered the development of standards, guidance and tools to measure and manage climate action across countries, sectors and companies. However, carbon accounting inaccuracies, ambiguous emissions disclosures and unambitious climate targets are hampering these aspirations. This paper reports on the disparity between high‐level guidance and practical implementation of carbon accounting and reporting at company level. By conducting a systematic literature analysis and focussing on the English Water sector, we utilise case study data from Northumbrian Water Limited (NWL), to identify limitations in the current guidance and frameworks. The results indicate the necessity for enhanced alignment in high‐level guidance, particularly regarding the Greenhouse Gas Protocol and the Science‐Based Target Initiative. It is evident that clarity and consistency from high‐level resources are essential for climate mitigation. This paper shows, depending on the sector and company types, that different benefits are gained from using the available resources. We propose a carbon measurement and management process for the English Water sector (and beyond) to reach net zero targets and make recommendations for decision makers. This helps to understand best practices of carbon accounting and reporting and to make effective investment decisions. Consequently, we advocate for policy interventions to improve the standardisation of carbon accounting models. Harmonising international regulatory frameworks and standards is needed, which will empower organisations to effectively assess, manage and reduce their carbon footprints. [ABSTRACT FROM AUTHOR]

Recent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle —from conception to implementation—with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision‐makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML applications across their life cycle stages by leveraging the food system. We exemplify the use of a comprehensive questionnaire to delineate unfair ML bias across data bias, algorithmic bias, and selection and deployment bias categories. Finally, we provide consolidated guidance and tailored strategies to help address AI/ML unfair bias and inequity in IE applications. Specifically, the guidance and tools help to address sensitivity, reliability, and uncertainty challenges. There is also discussion on how bias and inequity in AI/ML affect other IE research and design domains, besides the food system—such as living labs and circularity. We conclude with an explanation of the future directions IE should take to address unfair bias and inequity in AI/ML. Last, we call for systemic equity to be embedded throughout IE applications to fundamentally understand domain‐specific socioecological inequities, identify potential unfairness in ML, and select mitigation strategies in a manner that translates across different research domains. [ABSTRACT FROM AUTHOR]