The Sustainable Development Goals (SDGs) guide towards sustainability across a broad palette of aspects. Since their adoption by the United Nations in 2015, many organizations have started using them to relate their own sustainability performance. However, there exists no consensus nor science-based methods for performing such assessments, hence bringing the risk of cherry-picking and arbitrary target-setting among the 17 goals and potentially compromising the robustness, usefulness, and credibility of SDG assessment. To address this, we propose a framework that enables to systematically evaluate or select indicators for assessing and monitoring SDG performance at different scales. A critical review of sustainability indicator selection criteria was performed, forming the basis for recommendations of existing criteria. Two separate applications of the framework are operationalized, namely the evaluation of existing indicator sets and the selection of new indicators, respectively. The guide is tested and demonstrated on two different proof-of-concept cases representing the global level and the company level. The results demonstrate that the proposed framework allows decision-makers to systematically evaluate and select SDG indicators for different scopes, offering stronger scientifically founded indicator sets and reducing unintentional bias. Furthermore, the framework still provides the flexibility of tailoring indicators in the selection process, allowing the user to freely select the indicators to be tested. The framework and criteria are central inputs for the indicator selection phase and represent an essential step towards a full-fledged, consistent framework for SDG performance assessments. 

Selecting indicators for measuring progress towards sustainable development goals at the global, national and corporate levels

Scope 3 greenhouse gas (GHG) emissions are the most relevant element of a company's total emissions since they account for more than eighty percent. However, they are difficult to calculate since many stakeholders in the value chain are involved and emission data are usually not shared among them. Sustainable finance could provide a link to this discussion by providing data, digital data infrastructures and evaluation instruments. However, the existing research today is either limited to analyzing the levels of scope 3 emissions or to calculating them based on different measurement methods. How to implement scope 3 emissions reporting by solving the data sharing challenge remains mainly unexplored. This paper aims to close this gap by developing an approach, which chooses sustainable finance as a connecting element that (1) combines different calculation methods, (2) integrates cross-value chain data from different stakeholders and (3) combines primary and secondary data in a single model. The approach was developed in a prototype that uses real world data from collaboration with the UN-convened Net-Zero Asset Owner Alliance to evaluate its applicability. The findings of the prototype indicate that a digital data infrastructure can improve the calculation of scope 3 GHG emissions by improving data availability, accessibility and reliability and at the same time shows that the calculations are only as good as the data, which fuels this calculation. With this, the paper contributes to the theoretical and practical discussion about scope 3 GHG emission data. 

Decreasing the impact of climate change in value chains by leveraging sustainable finance

Abstract Aim The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR (findability, accessibility, interoperability, and reuse) data principles. Methods This study investigates how is LCA data from publicly funded research currently shared. Firstly, the focus is on life cycle inventory data shared in journal articles. Given that FAIR data sharing is not only the responsibility of the LCA practitioner, this study further investigates guidelines (e.g., data sharing standards and data management plans) and infrastructure (repositories, data formats, and nomenclature) to identify the tools and services available to LCA community which are essential to enable FAIR data sharing. Results The study identifies that although there is growing awareness to improve data sharing practices, implementation of FAIR guidelines for data sharing is seldom seen in practice. LCA studies that adhere to FAIR principles are primarily due to use of generic data repositories which provide tools to support data sharing. However, there is no guidance on how LCA specific data should be shared to ensure its findability, accessibility, interoperability, and reusability. This study suggests a workflow to enable FAIRification of LCA data. In addition, the study recommends further efforts within the LCA community on skill and technology development, strategic funding, and recognition of the best practices in relation to data sharing. Conclusion In conclusion, this study highlights the necessity of data sharing incentives, guidelines, and platforms/repositories specific for the LCA community. 

Can LCA be FAIR? Assessing the status quo and opportunities for FAIR data sharing


Purpose
This study views Net-Zero as a dynamic capability for decarbonising supply chains (SCs). This study aims to investigate the relationship between three information processing-related capabilities (supply chain visibility [SCV], supply chain integration [SCI] and big data analytics [BDA]) as its antecedents and SC performance as its competitive advantage outcome.


Design/methodology/approach
The authors conceptualise a research model grounded in the literature based on dynamic capabilities and information processing views. The study uses a structural equation modelling technique to test the hypotheses’ relationship using the survey data from 311 industrial enterprises.


Findings
The results show that SCI and BDA positively and directly influence the Net-Zero capability (NZC). No significant direct impact is found between SCV and NZC. BDA fully mediates SCV and partially mediates SCI in their relationship with NZC. The results also confirm that NZC positively impacts SC performance (SCP).


Originality/value
This study contributes to operations management and SC literature by extending the knowledge about Net-Zero SCs through an empirical investigation. In particular, the study suggests BDA is essential to enhance NZC as SCV alone does not significantly contribute. The study also documents the benefit of NZC on SCP, which can encourage more volunteer actions in the industry.


The relationship between information processing capabilities, Net-Zero capability and supply chain performance

ABSTRACT Sharing and reusing data across organisations is central to the European data strategy and its transformation towards creating a data-driven economy. Currently, data sharing, when it occurs, is typically facilitated through centralised platforms or bilateral integration solutions, which are increasingly failing to meet requirements for flexibility, trustworthiness, and self-determination over shared data. Data spaces have emerged as a promising solution, built on distributed and shared infrastructure and governance frameworks. However, widespread adoption requires consensus on the necessary organisational and technical capabilities. This paper proposes a data service model and design principles for data services to clarify choices and guide decision-making. We adopt a novel perspective by conceptualising data spaces as meta-organisations to guide an Action Design Research study, utilising privileged access to key large-scale European data space projects in the mobility and automotive sectors. This paper not only develops a nascent design theory for data spaces, but also offers a practical framework, breaking down complex challenges into actionable, scientifically validated components – for further growth and adoption of data spaces.

Data spaces as meta-organisations

Abstract This paper deals with data sharing among firms along a supply chain for the calculation of Scope 3 emissions. Scope 3 emissions are indirect emissions produced along a firm’s supply chain. They represent the majority of most firms’ carbon footprint. Current estimations of Scope 3 emissions are largely based on industry averages and other approximations, which leads to several disadvantages. By contrast, primary data sharing along the supply chain would provide firms with precise measures of Scope 3 emissions (that are eventually necessary for decarbonizing supply chain emissions beyond industry averages). For that, firms need access to data and information from their suppliers that are not under their control. We review the benefits of and three main obstacles to such data sharing: legal and regulatory challenges, missing interoperability, and data privacy concerns. Finally, we discuss initiatives and approaches for overcoming these obstacles. 

/pdf/supply-chain-data-sharing-for-scope-3-emissions-13v7lyti.pdf

Supply-chain data sharing for scope 3 emissions

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Aurel Stenzel

Author Tools

Chat about Author

Papers

Supply-chain data sharing for scope 3 emissions