Policy implications of time-differentiated climate change analysis in life cycle assessment of building elements in Aotearoa New Zealand
Bibliographic record
Abstract
Abstract Purpose Climate change policies are increasingly including time-dependent carbon targets for different economic activities. However, current standards and guidelines for climate change assessment of buildings ignore these dynamic aspects and require use of static life cycle assessment (LCA). This research investigates how to better account for the timing of greenhouse gas (GHG) emissions and removals in LCAs of buildings and construction products, using a static and dynamic LCA case study of roofs, walls and floors in Aotearoa New Zealand residential dwellings. Methods Static and dynamic LCA methods were used to assess the climate change impact of two assemblies each for external walls, ground floors and roofs used in stand-alone residential dwellings in Aotearoa New Zealand. Each assembly was modelled for a life cycle extending from material production, through to element construction, operational use, and final end-of-life treatment. Results were calculated as total GWP100 results for each life cycle stage, GWP100 results disaggregated into time periods, and as instantaneous and cumulative radiative forcing up to year 190. Sensitivity analysis was undertaken for the building reference service life, exposure zone, location, and end-of-life treatment. Results and discussion Four time-related aspects were found to be particularly significant as regards their contribution to the final static LCA (sLCA) climate change results: Inclusion versus exclusion of biogenic carbon storage in landfill Modelling of end-of-life recycling activities using current versus future low or net zero carbon technologies (in module D) Building reference service life (50 versus 90 years) Choice of modelling parameters for landfilled timber and engineered wood products. Use of dynamic LCA (dLCA) enabled priorities to be identified for climate change mitigation actions in the shorter and longer term, and showed that half of the assemblies achieved net zero carbon by year 190 (timber wall, steel wall, timber floor). Conclusions Timing of GHG emissions and removals should be included in LCAs to support decision-making in the context of achieving targets set in climate change policies. In particular, LCA results should show ongoing biogenic carbon storage in landfilled timber and engineered wood products. Carbon footprint standards, guidelines and calculation tools should be prescriptive about building and construction product reference service lives, the EofL fate for different materials/products, and modelling of forestry and landfill activities, to provide a level playing field for stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".