Sustainable Development Goal 12.2 aims to achieve the sustainable management and efficient use of natural resources by 2030. Its target indicator, the material footprint, is a key macroeconomic indicator for impacts related to resource extraction. On the microeconomic scale, it is complemented by the product material footprint (PMF). While previous methods relied on generic characterization factors for resource extraction, this study introduces a method for the direct calculation of PMFs based on resource and other material flows included in life cycle inventories (LCI). It aims to evaluate the suitability of the LCI database ecoinvent, by comparing direct PMF calculation to a benchmark method based on characterization factors. It could be shown that ecoinvent suffices to assess the first part of the PMF - the indicator Raw Material Input (RMI), which focuses on material extraction put to an economic use. However, the results of two approaches deviate for some metals due to geographical misalignment or differences in the source data. The database further lacks information regarding unused material (both biotic and abiotic) which hampers the quantification of the second part of PMF: Total Material Requirement (TMR). Unused resource extraction still requires external data in many cases because no information is given in the database. Calculating PMF directly from LCI has advantages regarding consistency, transparency and geographical resolution, which is why we consider this a valuable approach for direct RMI quantification. However, for direct TMR quantification, ecoinvent is not suitable - yet.
The climate benefit of biofuels depends on how well their production offsets the biogenic carbon emitted by their use. It is commonly assumed that carbon uptake during feedstock growth renders biofuels carbon neutral aside from ancillary fossil carbon and other production-related greenhouse gas (GHG) emissions. This assumption of a feedstock offset is retained in the lifecycle assessment (LCA) models adopted by policymakers for renewable fuels. However, consideration of land-use change (LUC) and other economically induced effects has revealed carbon flow changes more complex than those of a feedstock offset. This paper presents a relatively simple stock-and-flow model that offers a conceptual understanding of the carbon-related dynamics of biofuel use. The model focuses only on key carbon exchanges, omitting the ancillary emissions while capturing the economic impacts of increased crop yields, cropland expansion with LUC, decreased food demand and petroleum market rebound. Applied to a stylized scenario of corn ethanol use, the model transparently depicts effects and trade-offs previously characterized by LCA and other complex methods. It reframes biofuel carbon neutrality in terms of distinct market-mediated mechanisms of net uptake that can offset the biogenic carbon emitted from biofuel use while highlighting the dynamics of the atmospheric carbon response. As a complement to LCA, stock-and-flow analysis can be informative for policymakers and valuable for educational purposes.
Life cycle assessment (LCA) is a key method for environmental impact analysis. In this context, different methods for hybrid LCA (HLCA) have been proposed with the aim of combining bottom-up data from process-based life cycle inventories with top-down data from environmentally extended input–output tables. This is done to overcome limitations of data coverage and aggregation: Process inventory data, though detailed, may underestimate flow magnitudes or miss flows entirely, potentially underestimating environmental impacts. On the other hand, input–output data is by definition complete for intermediary flows, but highly aggregated into a small number of economic sectors and generally does not capture the use-phase of products. Combining these data sources would give a more complete picture of the environmental impact associated with products or services. Four main methods are currently recognized in literature: The integrated, tiered, matrix-augmentation and path-exchange hybrid methods. However, no consensus has emerged on the underlying mathematical differences and their applicability. Here, we propose a unifying theory of HLCA. First, we show that all methods can be expressed in the same matrix form. Using this insight, we show that instead of four, only two distinct methods exist: the tiered and the integrated methods. In particular, the matrix-augmentation method is algebraically identical to the integrated method, and the path-exchange method is the tiered method with inferred rather than compiled flows. Method names alone are therefore unreliable guides to what a study actually computes, and to whether two studies can be compared. We show how both the integrated and tiered method can be used to account for “known unknown” and “unknown unknown” data and how economic equilibrium can be maintained through balancing conditions. Finally, we offer recommendations on the optimal selection and application of each method. This constitutes an important step toward a unified methodological framework for hybrid life cycle assessment.
Product lifetime is a critical parameter in dynamic material flow analysis, but it is commonly treated as fixed and independent of upgraded product penetration. This assumption may overlook how the penetration of upgraded products reshapes the retirement behavior and in-use lifetime of conventional products, thereby affecting carbon emissions during technology transitions. Here, we develop a multi-stock-driven model to capture penetration-induced lifetime changes by explicitly linking the stock evolution of conventional and upgraded products. The model distinguishes between outflows determined by lifetime distributions and additional outflows induced by the adoption of upgraded products. We apply this model to China’s transition from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) from 2005 to 2060. Under the stock, penetration, and retirement assumptions, the model estimates that EV penetration is associated with 230 million penetration-induced early retirements of ICEVs, accounting for 55
Artificial intelligence (AI) supports apparel concept generation, sewing-pattern engineering, virtual prototyping, production planning, consumer evaluation, and circular information systems. This study examines when these capabilities support sustainability-relevant decisions without compromising feasibility, aesthetics, market viability, or accountability. A structured narrative review organizes evidence by lifecycle stage, sustainability problem, and validation maturity. Four separately analyzed modules provide empirical anchoring: 4200 garment-design records; criterion-specific assessments by 28 specialists of 350 designs; a 3 × 2 × 2 consumer experiment with 864 recruited and 858 retained participants; and 426 enterprise pattern records with measured cutting-waste rates. The reported consumer cell means suggest higher purchase intention for co-created stimuli and labeled products, and lower intention at the higher price premium. Approximate Type-II ANOVA was reconstructed from the reported participant-level cell counts, means, and SDs; its precision is limited by rounding of those inputs. Within the observed enterprise, quarter, and product mix, ridge regression had reported test RMSE = 0.049 and MAE = 0.038, compared with baseline RMSE = 0.096. The AI-Sustainable Apparel Integration (ASAI) framework combines three structural pillars—data and technical integration, circular design performance, and validation and governance—with four evaluation dimensions: digital plausibility, design feasibility, market viability, and verified sustainability performance. Direct sustainability verification is limited to production-stage cutting waste; broader lifecycle outcomes require separate validation.
