Suomalaisen metsänhoidon ympäristövaikutuksista on nyt saatavilla kattavaa ja laadukasta dataa kansainvälisessä ecoinvent-tietokannassa. Tämä on ensimmäinen

LCAエキスパートセンターが新たにリリースしたクラウドサービスMiLCAは、ecoinventデータを搭載し、環境負荷の算定を簡単に行えるアプリケーションです。

Ecoinvent, the world’s largest database on the environmental impact of renewables, has no data from China, even though it makes most of the world's solar panels

Ecoinvent, the world’s largest database on the environmental impact of renewables, has no data from China, even though it makes most of the world's solar panels

to paper

UCL Discovery is UCL's open access repository, showcasing and providing access to UCL research outputs from all UCL disciplines.

Ecoinvent, the world’s largest database on the environmental impact of renewables, has no data from China, even though it makes most of the world's solar panels

Newswise — Conventional LCA faces a persistent bottleneck: background databases like ecoinvent, while comprehensive, often fail to cover all activity types within a sector. Downstream fine chemicals may lack usable data even when upstream bulk chemicals are well represented. Compiling life cycle inventories requires matching each input and output to corresponding background data—a process that is costly, time-consuming, and demands extensive domain expertise. Existing machine learning solutions have tackled these challenges but remain confined to single sectors, trained on hand-crafted features for agriculture, construction, or chemicals in isolation. Due to these limitations, there is a pressing need for a generalizable approach that can bridge sectors and scale across the full spectrum of industrial activities. A research team from Tsinghua University, Shanghai E-Carbon Digital Technology Co., Ltd., Shanghai HiQ Smart Data Co., Ltd. (HiQLCD), and The University of Hong Kong, led by Professor Shanying Hu and Zhijun Gui, with Kai Zhao and Biao Luo as the first authors, has developed LCA-TextNet, a deep learning model that predicts life cycle impact assessment (LCIA) results from knowledge-based textual descriptions. The work, accepted for publication (DOI: 10.1016/j.ese.2026.100724) on June 16, 2026, in Environmental Science and Ecotechnology, represents a significant step toward cross-sector generalization in...