Estimating jobs and wealth in the Bioeconomy

- The bioeconomy generates 4.1% of the EU GDP and employs 8.2% of the EU labour force. - Concomitant growth in value added and reduction in number of persons employed resulted in apparent labour productivity gains over the period 2008 - 2015. - Each bioeconomy sector follows its own dynamics, which can also differ from one EU Member State to another. - Looking at dynamic similarities across Member States can help differentiating bioeconomy strategies according to distinct Member State groups for a finer targeting. - Numbers are partly based on estimates, following nova-Institut's methodologyJRC.D.4-Economics of Agricultur

Biomass production, supply, uses and flows in the European Union: First results from an integrated assessment

The report delivers an assessment of EU biomass production, uses, flows and related environmental impacts for the sectors agriculture, forestry, fisheries and aquaculture, and algae. Quantitative estimates are derived from available data and current knowledge, yet highlighting the uncertainties and the remaining gaps. The work is framed within the JRC biomass study and is meant to support the EU bioeconomy and the related policies.JRC.D.1-Bio-econom

The Sankey Biomass Diagram: Research Brief

This Research Brief highlights the key points of the Sankey Biomass DiagramJRC.D.4-Economics of Agricultur

Biomass flows in the European Union: The EU Biomass Flows tool, version 2020

The Sankey biomass diagram is a representation of harmonised data from the various Joint Research Centre (JRC) units contributing to the BIOMASS Assessment study of the JRC . It represents the flows of biomass for each sector of the bioeconomy, from supply to uses including trade. The diagram enables deeper analysis and comparison of the different countries and sectors across a defined time series. The former Sankey biomass diagram was published in 2017 and has been used in multiple research activities and publications. Since its publication, the flexibility, analysis capabilities and user experience of the interactive tool have been improved. The new EU Biomass Flows tool was created based on the Energy Flows tool from Eurostat. The EU Biomass Flows tool displays biomass flows in Sankey diagrams and it relies on the methodology to extract and integrate data developed for the former biomass Sankey diagram. The new tool offers also an increased granularity of data for some biomass types: crop and residue production can now be shown in crop categories, animal- and plant-based food can be disaggregated into their nutrients. It has also significantly improved the visualisation of the data in charts and graphs, as well as enabling visibility of the evolution over time. Finally, users can download the full or a partial set of data. In this document, we summarise the sources and data transformation steps to create the database used to represent these biomass flows, as well as the main data gaps and challenges encountered. We also briefly discuss the main features and functionalities of the new EU Biomass Flows tool. Finally, we present some insights based on the represented data and potential future research opportunities.JRC.D.4-Economics of Agricultur

JRC agro-economic portal

DataM is the internet complement to JRC scientific output relating to the economics of agriculture. The economic aspects of agriculture encompass several areas, such as trade and productivity in agriculture, agricultural technologies (GMOs, fertilisers, etc.), the bioeconomy, climate change, food security, nutrition, developing countries, farm structure and rural development. DataM aims to promote the communication of JRC scientific results in these area through the use of the web and of interactive data visualisations. DataM also aims to provide access to the raw datasets produced by the JRC's agro-economic research activities, in line with the JRC's open data principles.JRC.D.4-Economics of Agricultur

EC data portal of agro-economic research

DataM is a portal of the European Commission's web site that gives access to a collection of data related to the scientific production of the European Commission and partners about the economics aspects of agriculture, bioeconomy, climate change, food and nutrition security and related sustainability. Datasets of this collection consist mainly in modelling data and estimates. These are outlooks about future scenarios, and calculations concerning the past that overcome the lack of official statistical data.JRC.D.4-Economics of Agricultur

Developments of economic growth and employment in bioeconomy sectors across the EU

The development of the bioeconomy—or the substitution of fossil-based materials and energy by bio-based solutions—is considered a strategic economic orientation by the European Commission and its Green Deal. This paper presents a methodology to monitor the contribution of the bioeconomy to jobs and growth within the European Union (EU) and its Member States. Classified as an “output-based” approach, the methodology relies on expert estimations of the biomass content of the bio-based materials produced in the EU and the subsequent calculation of “sectoral” bio-based shares by using Eurostat statistics on the production of manufactured goods (prom). Sectoral shares are applied to indicators of employment, and value added is reported in Eurostat–Structural business statistics. This paper updates the methodology and time series presented in 2018. The bioeconomy of the EU (post-Brexit composition) employed around 17.5 million people and generated ¿614 billion of value added in 2017. The study evidences structural differences between EU national bioeconomies, which become more pronounced over time, especially in terms of the level of apparent labour productivity of national bioeconomies. Finally, this paper describes cases of transition over the 2008–2017 period.JRC.D.4-Economics of Agricultur

Correction Ronzon, T., et al. Developments of economic growth and employment in bioeconomy sectors across the EU.

The authors would like to make the following corrections to the published paper [1]. The changes are as follows: Replacing six parts of the content in Table 1:.</p

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research