2.-0 LCA Consultants (Denmark)

Energy and non-energy use of fossil carbon-based fuels and associated emissions have been extensively studied, but the retention and accumulation of fossil carbon in the technosphere are less understood. This study uses retrospective dynamic material flow modeling to map the flows related to fossil carbon in durables between the years 1995 and 2019 using monetary multi-regional supply-use tables for 1995–2019 and multi-regional hybrid supply-use tables for 2011. In 2011, 91% of the extracted fossil carbon flowed directly to the atmosphere, with 9% accumulating in the technosphere, primarily in construction, manufacturing, and households. From 1995 to 2019, 8.4 Gt of fossil carbon (i.e., 30.8 Gt of CO₂ equiv) accumulated in all human-made artifacts, with most remaining in use and some ending up in landfills, where decomposition exceeds 50 years. This study lays a critical foundation for future research focused on reducing fossil carbon reliance by curbing its inflow and slowing its throughput in the technosphere.

Life cycle assessment (LCA) and environmentally extended input output analysis (EEIOA) are two widely used approaches to assess the environmental impacts of products and services with the aim of providing decision support. Here, we compare carbon footprint (CF) results for products and services in the ecoinvent 3.4 cut-off and the hybrid version of EXIOBASE. While we find that there is good agreement for certain sectors, more than half of the matched products differ by more than a factor 2. Best fits are observed in the energy, manufacturing, and agricultural sectors, although deviations are substantial for renewable energy. Poorer fits are observed for waste treatment and mining sectors. Both databases have a limited differentiation in the service sector. Differences can, to some degree, be explained by methodological differences, such as system boundaries and approaches used to resolve multi-functionality, and data differences. The common finding that, due to incomplete economic coverage (truncation error), LCA-based CFs should be lower than EEIOA-based CFs, could not be confirmed. The comparison of CFs from LCA and EEIOA databases can provide additional insights into the uncertainties of CF results, which is important knowledge when guiding decision makers. An approach that uses the coefficient of variation to identify strategic database improvement potentials is also presented and highlights several product groups that could deserve additional attention in both databases. Further strategic database improvements are crucial to reduce uncertainties and increase the robustness of decision support that the industrial ecology community can provide for the economic transformations ahead of us. This article met the requirements for a gold-gold JIE data openness badge described at http://jie.click/badges.

Due to increased policy attention on circular economy strategies, many studies have quantified material use and recovery at national and global scales. However, there has been no quantitative analysis of the unrecovered waste that can be potentially reintegrated into the economy as materials or products. This can be interpreted as the gap of material circularity. In this paper we define the circularity gap of a country as the generated waste, plus old materials removed from stocks and durable products disposed (i.e. stock depletion), minus recovered waste. We estimated the circularity gap of 43 nations and 5 rest of the world regions in 2011, using the global, multiregional hybrid-units input-output database EXIOBASE v3.3. Our results show the trends of circularity gap in accordance to each region. For example, the circularity gaps of Europe and North America were between 1.6–2.2 tonnes per capita (t/cap), which are more than twice the global average gap (0.8 t/cap). Although these regions presented the major amount of material recovery, their circularity gaps were mostly related to the levels of stock depletion. In Africa and Asia-Pacific regions, the circularity gap was characterized by a low degree of recovery and stock depletion, with high levels of generated waste. Moreover, we discuss which intervention types can be implemented to minimize the circularity gap of nations.

Clustering is a technique for dividing a set of similar objects into same groups and dissimilar objects into different groups. Among different clustering algorithms, the K-means algorithm is considered as the most popular due to its simplicity. However, the outcome from the K-means algorithm is highly sensitive to the initial centroid selection. As a consequence, the selection of initial centroids in the K-means algorithm plays a crucial part in accuracy and efficiency. To select the initial centroids more effectively, in this paper, we propose a new method based on radial and angular coordinates. To check the feasibility of the proposed method, we compare our method with the standard K-means algorithm. For the comparison, we use synthetic data sets with different size of instances and number of clusters. The experiment shows that in most of the cases the proposed method clearly dominates over the standard K-means algorithm in terms of execution time and required number of iterations.

With the publication of the two new standards, ISO 14040 and ISO 14044, the former four standards ISO