How can you handle conflicting data sources in LCA for Sustainability?

This is mandatory to maintain focus and alignment with company’s goals and strategies. With that in mind e wry area should understand their importance as mostly the importance of the data each one will be sharing. A workshop with experts will help with that step.

Aquí algunas claves: Además de la identificación de las unidades de medida, hacerlas dinámicas y tener en cuenta sus posibles cambios para la explotación es un must. Según nuestra experiencia, un gestor de unidades de medida ahorra tiempo y recursos en la explotación de los datos a posteriori. Definimos los límites a través de un sistema de perímetros o networks que se pueden organizar según diferentes criterios: categoría, área operacional, geografía, etc. Para fijar las categorías de impacto hay que alinearse con un estándar determinado y funcionar a partir del mismo.

There are cases where an organisation releases a LCA report to support their design choices. Unless the content is peer-reviewed by external bodies, the information presented should be further investigated, if possible with forms of primary research.

Understanding the methodology for pulling data will help deciding whether the information is aligned to the company’s needs or not. External sources should be studied in depth to make sure the result is accurate and auditable. When they follow those patterns they can help you build your own set of kpi.

It is true that not all data sources are created equal. so taking a one-size-fits-all approach to data cleansing and consolidation may rule out key sources of vital information. The skills of the data wranglers and scientists play a pivotal role in ensuring we get the right data and insights from the available sources. Then taking the necessary steps to trace them back to the source is an investigative step for Key aspects that may drive strategic decision making.

Evaluating data quality is crucial. For example, leveraging smart metering in the field of RE establishes standardized real-time energy data flows, improving accuracy and automation versus manual tracking. This eases reporting burdens while enabling efficiency opportunities. Technology implementation exemplifies how data innovations beyond risk mitigation strengthen operations and value creation while paving the way for a measurable success roadmap!

Data quality indicators are measurable criteria used to assess the quality of data. They help evaluate data for accuracy, completeness, consistency, timeliness, reliability, validity, uniqueness, relevance, precision, and conformity, ensuring data is fit for its intended purpose.

Experts can be not only helpful but necessary to your LCA. Sometimes we need a hand engaging areas and showing how important accuracy is for realizable communication and that’s when someone from outside can step in and add with their experience and expertise.

La identificación de los puntos débiles de la recopilación de datos alimentará a la mejora a futuro y al rediseño de procesos. En un informe de sostenibilidad es importante entender y explicar por qué hemos elegido x datos. También trazarlos y organizarlos para entender su fiabilidad y relevancia. Por ejemplo, si evaluamos el impacto de la distribución de nuestro producto, elegimos datos desde el punto de fabricación, al punto de venta y a su uso y deshecho. También podemos tener en cuenta (según la capacidad de cada recopilador) como hemos recogido estos datos, si hemos sido capaces de asegurarlos, si disponemos de datos calculados, etc.

I concur with Reji on the immense value of transparency in documenting data selection rationales in LCA reporting. In my experience, this rigor not only bolsters credibility but can uncover efficiency opportunities. For example, by tracing inconsistencies back to analog tracking methods, one client targeted metering upgrades. Their resulting performance visibility strengthened operations while easing reporting burdens - a win-win other contributors have wisely advocated for as well. Overall kudos to the author on outlining pragmatic steps to transform data from risk to value creation through stakeholder alignment.

Triangulation- Seek additional data sources to support or contradict the conflicting information. Utilise reputable databases, scientific literature, or industry standards for reference. Cross-check data across different sources to identify inconsistencies and outliers. Sensitivity Analysis- Evaluate how changing the conflicting data points affects the overall LCA results. Perform sensitivity analysis to assess the influence of different data choices on key sustainability indicators. Transparency & Documentation- Clearly explain how conflicting data sources were identified and addressed in the LCA report. Document the rationale behind the chosen data and the assumptions made.

Reevaluación de proveedores, establecimiento de estándares claros y requerimiento de informes periódicos, colaboración con proveedores para la mejora de prácticas y aseguramiento de la trazabilidad de la información. Alineación con los ODS, identificación de áreas de mejora, involucración de todos los departamentos y creación de un comité de sostenibilidad. Elaboración de un plan de transición hacia fuentes de energía renovables, identificación de oportunidades de ahorro energético, etc. Aplicando estrategias parecidas a estas es posible adquirir una ventaja competitiva y reforzar la propuesta de valor para los grupos de interés, además de reducir los costes a medio plazo.