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openaire: EC/H2020/821124/EU//NAVIGATE Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45-61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

Submitted by Marlon Silva (marlonviniciuspacheco@gmail.com) on 2021-02-19T16:18:53Z No. of bitstreams: 1 GerdBrantesAngelkorte.pdf: 2415145 bytes, checksum: 6533bea7ac867a8c5dbd3bc7bd6cae86 (MD5) Made available in DSpace on 2021-02-19T16:18:53Z (GMT). No. of bitstreams: 1 GerdBrantesAngelkorte.pdf: 2415145 bytes, checksum: 6533bea7ac867a8c5dbd3bc7bd6cae86 (MD5) Previous issue date: 2019-02 O setor agropecuário brasileiro é um dos maiores do mundo e apresenta grande potencial de produção de fontes bioenergéticas de baixo carbono. Porém, com esse potencial, existem preocupações com os meios produtivos, quantidades de fertilizantes, defensivos químicos, água para irrigação e dessedentação animal, além das emissões de GEE do setor. O objetivo desse estudo foi aperfeiçoar a modelagem agropecuária brasileira existente no modelo BLUES, implementando alto nível de detalhamento através da elaboração de novas tecnologias agrícolas capazes de retratar o estado atual do setor, propiciando a análise de potenciais trade-offs entre produtividade agrícola e intensidade energética, hídrica e no uso de insumos químicos. Foram implementadas no modelo três novas tecnologias agrícolas (Padrão Histórico, Alta Produtividade e Verde+), as tecnologias pecuárias foram atualizadas integrando dados de dessedentação animal e consumo de alimentos da agricultura, foram elaboradas novas restrições hídricas, área propícia para a expansão agrícola e produção de insumos orgânicos. Também se compararam os modelos antes e após à implementação do novo módulo agropecuário em diferentes cenários de aquecimento global. Identificou-se que o alto nível de detalhamento foi fundamental para a redução das emissões de GEE e do uso de BECCS e que com a implementação de novas tecnologias de alta produtividade, o modelo migrou para esse tipo de tecnologia ao invés de abrir novas fronteiras agrícolas em áreas de floresta nativa. Além disso, houve melhorias significativas nos SDGs referentes a pobreza (1), fome (2), água (6), emissões de GEE (13) e degradação do solo e biodiversidade (15). The brazilian agricultural sector is one of the largest in the world and presents a great potential for production of low carbon bioenergetics supplies. However, there are concerns with productive means, fertilizers quantities, chemical defensives, water for irrigation and animal use, besides the GHG emissions. The purpose of this study was to improve the current brazilian agricultural modelling inside BLUES, establishing a high level of detail through the creation of new agricultural technologies capable of representing the current state of the sector, enabling the analysis of potential trade-offs between agricultural productivity and energy, hydro and chemical inputs intensities. Three new agricultural technologies were developed (Historic Pattern, High Productivity and Green+), the livestock technologies were updated by integrating data from water for animal use and agriculture food consumption, it was developed new hydro restraints, area for agriculture expansion and organic inputs production. The models were also compared before and after the implementation of the new agricultural module in different scenarios of global warming. It was perceived that the high level of detail was essential for reducing GHG emissions and the use of BECCS, also with the implementation of high productivity technologies, the model identified as advantageous this kind of technology, instead of opening new agriculture frontiers in native forest areas. Moreover, there were significant improvements on SDGS concerning poverty (1), hunger (2), water (6), GHG emissions (13) and land degradation and biodiversity (15).