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Welcome and Course Orientation

Course Overview

Foundations: What Is Uncertainty in Sustainability Data?

Identifying Data Gaps and Structuring the Problem

Mapping Data Systems and Boundaries

Strategies to Address Data Gaps

Understanding Types and Drivers of Uncertainty

Uncertainty Typology and Implications

Data Quality and Credibility

Quantifying and Propagating Uncertainty

Estimation and Quantification Techniques

Practical Methods for Real-World Reporting

Communicating Uncertainty with Clarity and Impact

Confidence Communication and Visualization

Building Trust and Transparency

Application Labs and Case Work

Lab: Carbon Footprint with Data Gaps

Lab: LCA Hot-Spot and Propagation

Knowledge Checks

Quiz 1: Core Concepts of Uncertainty

Quiz 2: Data Gaps and Quality

Quiz 3: Quantification Techniques

Quiz 4: Communication and Visualization

Quiz 5: Case-Based Application

Wrap-Up and Feedback

Capstone Summary and Next Steps

Course Feedback Survey

$ (USD)

Turn uncertainty into a strategic advantage. Managing Uncertainty in Sustainability Data: Methods, Confidence, and Transparency is a practical, tools-first course designed for professionals who build, audit, and communicate carbon and ESG numbers. You will learn how to identify data gaps across Scopes 1–3 and life cycle boundaries, choose defensible proxies, quantify uncertainty with ranges and simulations, and communicate confidence clearly—without overclaiming or greenhushing. Through concise frameworks, real-world labs, and ready-to-use templates, you’ll improve credibility and decision quality in footprints, LCAs, targets, and disclosures. We’ll demystify aleatory vs. epistemic uncertainty, data quality indicators, correlation across categories, and uncertainty propagation, then translate them into simple steps for reporting, assurance, and executive briefings. By the end, you’ll have a repeatable approach to scoping, scoring data quality, estimating intervals, running sensitivity analyses, and crafting plain-language confidence statements that build trust with stakeholders. Ideal for carbon accounting, LCA, ESG reporting, supply chain, and analytics teams looking to strengthen transparency and reproducibility while avoiding false precision.

04 - Making Sustainability Data Trustworthy

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            <p>Uncertainty is unavoidable in environmental and sustainability data, but it does not have to erode credibility or stall decisions. This course equips carbon accounting, LCA, ESG reporting, supply chain, and sustainability analytics professionals with practical methods to recognize, quantify, and communicate uncertainty with confidence and transparency. You will build a shared vocabulary—distinguishing uncertainty from variability, error, and risk—and examine how measurement limits, model choices, scenarios, sampling, and reporting boundaries shape confidence in carbon footprints, life cycle assessments, and ESG metrics. From the outset, the orientation clarifies learning objectives and outcomes: consistent terminology, pragmatic frameworks, and ready-to-use templates that help you identify data challenges, select suitable estimation techniques, and improve trust in reported numbers.</p>

<p>Early modules guide you in mapping data systems and boundaries—Scopes 1–3 and cradle-to-grave—cataloging activity data, emission factors, supplier inputs, and market vs. location-based methods. You will learn to classify gaps (missing, sparse, outdated, inconsistent, unverified, proxy-derived), prioritize them by materiality and decision-criticality, and deploy a data improvement ladder: substitute, sample, survey, model, and verify. The course builds a robust understanding of uncertainty types—aleatory vs. epistemic; parameter, model, and scenario—and how correlation across data streams and propagation through inventories and LCAs affect baselines, targets, comparisons, and claims. You will apply data quality dimensions and DQIs, use evidence hierarchies and confidence ratings, and select quantification techniques ranging from simple ranges and error bars to Monte Carlo simulation, sensitivity analysis, and scenario/bounding analyses, with guidance on when to keep it simple versus when to model.</p>

<p>Because defensible reporting is as much about communication as calculation, the course emphasizes clarity: plain-language confidence statements, calibrated phrases, and visual tools such as intervals, fan charts, gradient confidence scales, and quality icons. You will practice crafting narratives that link uncertainty to decisions—avoiding both overclaiming and greenhushing—and build disclosure templates that document assumptions, data sources, methods, limitations, and planned improvements. Practical modules address small samples, proxies, mixed data sources, aggregation without false precision, and versioning for audit and assurance. Two hands-on labs anchor the learning: a Scope 3 purchased goods case with supplier data gaps, and an LCA hot-spot and propagation exercise, each culminating in concise uncertainty statements and transparency notes suitable for management and external stakeholders.</p>

<p>Knowledge checks throughout reinforce core concepts, data quality and gaps, quantification methods, communication, and case-based application. The course concludes with a capstone summary and an action plan to prioritize the top three improvements for your organization, alongside a toolkit of reusable templates for assumption logs, confidence statements, and visuals. By the end, you will be able to diagnose uncertainty sources, select fit-for-purpose methods to quantify and propagate them, and communicate findings in a way that strengthens transparency, supports risk management and investment decisions, and builds trust in sustainability data.</p>
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