These data were estimated for use in the bioecomomic model simulation of the rainbow trout population in the Colorado River in Marble Canyon. The initial rainbow trout abundance is a vector (RBT_intN) representing the population of rainbow trout within each river segment (151 mile long sergments) along the mainstem of the Colorado River from Lees Ferry to 151 river miles downstream. The movement matrix (MMat) is a distribution that estimates the probability that a rainbow trout wil move to any one of the 151 river segments downstream of Lees Ferry.

The methodology termed "Classification of Exports according to Bioeconomy and Traditional Sectors" categorizes exports based on origin and processing degree, focusing on biological and mineral/fossil resource economies. It classifies exports into five categories: Basic Bioeconomy Products (directly from agriculture, agro-industry, etc.), Value-added Bioeconomy Products (processed from these sectors), High Value-added Bioeconomy Manufacturing (biological-based chemicals, pharmaceuticals, etc.), Mineral and Fossil Economy (mining, fossil-based sectors), and Other Manufactures.

This methodology analyzes how Latin American countries contribute to and benefit from the bioeconomy. It suggests applications like export analysis to understand trends in biological product exports, identifying key sectors for development comparison, and assessing economic, social, and environmental impacts. It also evaluates policy effectiveness in promoting inclusive and sustainable growth.

Organized data feeds into DEA 2.1 software for Malmquist index estimation, crucial for measuring bioeconomy impacts in Latin America.

The Data was recollected by CIAT Group and Ministerio de Agricultura y Ganaderia MAG de Ecuador. This Data was facilited for the article Rice (Oryza Sativa L.) Bioeconomy: A DEA approach (VRS, CRS & Bootstrapping). The present dataset contains the original data from 612 rice farms in the five provinces of Ecuador. The dataset includes adjusted data for application in R for statistical analysis and the DEA methodology with BCC and CCR models adjusted with Bootstrap. Data were collected from 612 rice-producing farms in Ecuador during the 2019-2020 year or cycle. Details were gathered on Total Income [ti], Total Cost [tc], Total CO2 Emissions (kg CO2 eq/cycle) [te], Urea Used (kg/ha) [u], Farmer Age [age], Years of Study [Study_year], Years of Experience [experience], Land Area (ha) [area_ha], and Yield in Tons per Hectare [rend_ton_ha]. The provinces in Ecuador where the data were collected are: Guayas, El Oro, Manabi, Loja, and Los Rios.

abstract This paper presents an overview of the state of Amazonas in terms of the development of a bioeconomy strongly linked to the potential of natural resources. This discussion fits well into the search of alternatives for the state’s economy, very centralized by the Manaus Industrial Park (PIM), which becomes increasingly threatened year after year. Thus, the article presents a relationship between the potential of local offerings and technological and market demands. It also discusses the economic and technological impacts of biodiversity innovation and human resources training, needed to leverage the development of applied science to the conversion of natural products into commercial products.

The JRC - Bioeconomics dataset has been elaborated jointly by JRC and the nova-Institute (nova-Institut für politische und ökologische Innovation GmbH). This database quantifies employment, value added and turnover in the Bioeconomy and in bioeconomy sectors, namely agriculture, forestry, fishing, the manufacture of food, beverage and tobacco, the manufacture of bio-based textile, the manufacture of wood and wood products, the manufacture of paper, the manufacture of bio-based chemicals, the manufacture of bio-based pharmaceuticals, the manufacture of bioplastics, the manufacture of liquid biofuels and the production of bioelectricity. The geographical scope of this database is the EU, processeed as aggregate and at individual EU Member state level. Since the data refers to the period 2008 to 2017, two aggregates are considered: "EU-27 (2020)" (current EU Members as from 1st February 2020) and "EU-28" (referring to the EU Members between 1st July 2013 and 31st January 2020). Please note that EU-28 includes Croatia also for data before 2013

The JRC - Bioeconomics dataset has been elaborated jointly by JRC and the nova-Institute (nova-Institut für politische und ökologische Innovation GmbH). This database quantifies employment, value added and turnover in the Bioeconomy and in bioeconomy sectors, namely agriculture, forestry, fishing, the manufacture of food, beverage and tobacco, the manufacture of bio-based textile, the manufacture of wood and wood products, the manufacture of paper, the manufacture of bio-based chemicals, the manufacture of bio-based pharmaceuticals, the manufacture of bioplastics, the manufacture of liquid biofuels and the production of bioelectricity. The geographical scope of this database is the EU, processed as aggregate and at individual EU Member state level.

Objective: to propose an analytical framework that integrates the social technology approach into the concept of sociobioeconomy, as its compatible technological aspect, based on the concept of social technological system. Theoretical approach: The articulation of sociobioeconomy concept with that of social technological system is developed. The concept of sociobioeconomy is discussed in light of the three predominant views of bioeconomy (biotechnological, bioresources and bioecological), with the proposition of a conceptual-analytical framework. The social technological system concept is revised to integrate, in a transversal way, with sociobioeconomy. The framework combines dimensions of the social technological system (sociopolitical-cognitive; territorial and socioeconomic sustainability) with the dimensions of sociobioeconomy (social, bio-territorial and economic). Method: The proposed framework was applied in a case study of a potential social technological system in sociobioeconomy activities in the rubber production chain in the Amazon. Data collection included documentary research, interviews and a technical visit. Results: The case study presented the integration of three solutions based on social technology, constituting several properties of a social technological system. The social technological system demonstrated dialogued with the dimensions proposed for sociobioeconomy, proving to be compatible with it. The identification of evidence in the case of several properties of the framework allowed us to consider an initial analytical feasibility for the proposed framework. Conclusions: In conceptual terms, the framework has advanced the analytical detailing of the sociobioeconomy, including its technological aspect, proposing its approximation with social technology. In practical terms, the framework can assist in the development of sociobioeconomy initiatives.

The database contains 487 quantified enzymes production examples using cereals as a substrate in 209 experiments (some experiment include several enzymes). It covers both single (cereal) and mixed substrates (cereal plus other substrates) from food waste and agri-industrial residues. Main substrates are wheat (53 experiments), rice (22 experiments) and corn (22 experiments). The most common enzymes are xylanase, CMCase, FPase. The dataset consists of separated columns with details on the organisms; yield; unit; incubation time; pretreatment; nutritive or inducing supplement; optimization method; and other parameters and details, with references to scientific articles. The database was structured in a way that makes it possible to filter any parameter (for example it is possible to select only rice stray as substrates, only xylanase, only certain units to make a more appropriate data comparison, only yield above a certain number, and combination of such parameters.). Each experiment includes data on the reference in the last column. The full reference list is in the last sheet of the dataset. The file includes an introduction to the database with a description of each column and abbreviations. This database was produced as part of the research for “The review of enzyme production from cereal residues via solid state fermentation with fungi” Joseph Bourgine1, Dominika Alexa Teigiserova2,3, Marianne Thomsen2,3* 1 University Nantes, Department of Process and Bioprocess Engineering, Rue Christian Pauc, CS 50609, 44300 Nantes, France 2 Research Group on EcoIndustrial System Analysis, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Postboks 358, DK-4000, Roskilde, Denmark 3 Aarhus University Centre for Circular Bioeconomy *Corresponding Author email: mth@envs.au.dk The research was performed as part of the Horizon 2020 project DECISIVE (Decentralised valorisation of biowaste) under grant agreement N° 689229. The work was further supported by Aarhus University Centre for Circular Bioeconomy and Aarhus University Graduate School of Science and Technology.

This dataset contains MS Excel spreadsheet code used to analyze an integrative model that illustrates the inherent trade-offs that will arise among the competing values for landscape space in a boreal forest ecosystem involving interactions among the main trophic compartments of an intact boreal ecosystem, aka “nature†. The model accounts for carbon accumulation via biomass growth of forest trees (timber), carbon loss due to controls from moose herbivory that varies with moose population density (hunting), and soil carbon inputs and release, which together determine net ecosystem productivity (NEP), a measure of carbon sink strength of the ecosystem. We examine how controls on carbon dynamics are altered by forest management for timber harvest, and by moose hunting. We link the ecological dynamics with an economic analysis by assigning a price to carbon stored within the intact boreal forest ecosystem. We then weigh these carbon impacts against the economic benefits of timber production..., The Excel spreadsheet converts the analytical model into code to numerically calculate carbon benefits. Data in the article figures are generated using the spread code., , # Excel code for: Trading off nature for nature-based solutions: The bioeconomics of forest management for wildlife, timber and carbon

https://doi.org/10.5061/dryad.j0zpc86p1

CODE AND DATA FROM

Trading off nature for nature-based solutions: the bioeconomics of forest management for wildlife, timber and carbon

Jonah Ury, Matthew J. Kotchen, and Oswald J. Schmitz

School of the Environment, Yale University 195 Prospect Street, New Haven, CT 06511 USA

Description of the data and file structure

Overview

This file and overview is supplemental material accompanying the article "Trading off nature for nature-based solutions: the bioeconomics of forest management for wildlife, timber and carbon." The assumptions are outlined in detail in the article's Appendix S1, and all model parameters, functions, and their literature sources are presented in Table S1. The analysis presented in the excel spreadsheet examines the interrelationships betwee...

129 views (1 recent)

This dataset provides a detailed cost analysis of three biological controllers used in pest management: Sitotroga cerealella, Trichogramma, and Chrysoperla. The data includes cost breakdowns for raw materials, labor, and other expenses involved in the production and reproduction processes of these biological agents. The cost analysis is crucial for optimizing production efficiency and establishing competitive pricing for these products.

This dashobard is associated to the paper "A Cross-Country Measurement of the EU Bioeconomy: An Input–Output Approach" developed in the context of the BIOMONITOR project financed by Horizon 2020. The study measures the development of the national income share of the bioeconomy for 27 European Union Member States plus the UK, and 16 industries of BioMonitor scope from 2005 to 2015. The paper proposes a model which includes the up- and downstream linkages using Input-Output tables. The results show that for the majority of the MS the value added of the up- and downstream sector is at the band of 40%–50% of the total bioeconomy value added and has on average increased since the financial crisis.

144 views (1 recent)

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Replication Data for: Explaining Strong and Weak Resilience Orientation in Bioeconomy Policies - A Configurational Analysis. R Script and underlying data in Stata format.

148 views (1 recent)

The file consists of country wise database of whole world related to bioeconomy typology indicators collected from different online sources with following headings which are as follows: Importance of bio-based economic sectors in agriculture, Forestry, High-tech bioeconomy and bioenergy, Natural Resources Endownment, Evaluate against indicator of Bioeconomy strategy, value added sector data, Fragile state indexes, Income inequality and gini coeffiecient, social progress index. Quality/Lineage: The data were downloaded from different sources and arranged as per the countries database in a single table. Purpose: The data was collected to analyze the bioeconomy status in the countries of the whole world.

Data for the replication of our analysis and for anyone who would want to dig into it!

This dataset collects the bioeconomy-related regulatory frameworks (including strategies, action plans, roadmaps, etc.) at the regional level (NUTS1, NUTS2, NUTS3 or a combination thereof) in the EU Member States. The work was carried out by a consortium led by Spatial Foresight with the participation of ÖIR (contract 941620-IPR-2021), commissioned by the Joint Research Centre and funded through an administrative arrangement with the Directorate General for Research and Innovation.

The efforts of the Bioeconomic Analysis for Arctic Marine Resource Governance and Policy (BAAMRGP) collaborative research team will address the need for integrated, cross-sectoral ecosystem-based ocean management in the Arctic through the valuation and optimization of Arctic marine resources across multiple levels of governance. The approach will consider not only fisheries and subsistence species, but species with indirect or non-use implications, overall biodiversity and productivity, as well as the relationship between the marine ecosystem and humans, including health, commerce, and management. The researchers from the US, Iceland, Russia, Greenland, Canada, Denmark, Norway, and New Zealand will leverage existing data sets and expertise in game theory, bioeconomic valuation and modeling, and fisheries assessment to develop game models in cooperation with a variety of stakeholders, including indigenous peoples of the north. The effort will also consider different scenarios introduced by amendments to existing conventions under the Polar Code. A PhD student will receive training in interdisciplinary science and gain international research experience during the course of this project.