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Soil carbon is currently being monitored in European national greenhouse-gas (GHG) inventories. Reviewing the data and methods, we find that unreported losses could be around 70 MtCO2 yr–1 in croplands, and unreported gains could be around 15 MtCO2 yr–1 in grasslands and 45 MtCO2 yr–1 in forests. The share of European Union (EU) forest area for which soil carbon is being accurately reported is at most 33%, and more likely close to 24%. Accuracy is even worse for grasslands and croplands. Widespread adoption of key carbon-farming practices (peatland restoration, agroforestry, substituting maize with grass) could remove an additional 150–350 MtCO2 yr–1. Yet, if effective policies lead to realizing this potential, current GHG inventories would not capture their climate mitigation benefits.Increasing carbon storage through soils could help mitigate climate change and is an important part of many countries’ strategies. The authors review current soil carbon monitoring in the European Union for greenhouse-gas inventories and find that current practices are not accurate enough to measure climate benefits.

The future trajectory of the carbon sink influences how forests should be managed for climate-change mitigation. If the world’s forests remain net absorbers, conservation would be appealing. Preserved mature forests would absorb almost as much carbon as younger ones. Because decomposing harvest residues and roots add immediately to the CO2 emissions, and it takes decades for increased use of wood products to compensate, avoiding harvest could generate extra climate benefits, at least in the short run. Conversely, if mature forests become carbon sources, increased harvesting may be the best itigation option. Harvesting would reduce losses from decomposition while promoting wood as a fossil-fuel substitute. Today’s forest management is more of a gamble than a scientific debate. By following ‘no-regret’ strategies, we can buy time while we learn more. The future of the world’s forest should not depend on tossing a coin.

Cities currently covering only a very small portion (< 3 %) of the world's land surface directly release to the atmosphere about 44 % of global energy-related CO2, but they are associated with 71–76 % of CO2 emissions from global final energy use. Although many cities have set voluntary climate plans, their CO2 emissions are not evaluated by the monitoring, reporting, and verification (MRV) procedures that play a key role for market- or policy-based mitigation actions. Here we analyze the potential of a monitoring tool that could support the development of such procedures at the city scale. It is based on an atmospheric inversion method that exploits inventory data and continuous atmospheric CO2 concentration measurements from a network of stations within and around cities to estimate city CO2 emissions. This monitoring tool is configured for the quantification of the total and sectoral CO2 emissions in the Paris metropolitan area (∼ 12 million inhabitants and 11.4 TgC emitted in 2010) during the month of January 2011. Its performances are evaluated in terms of uncertainty reduction based on observing system simulation experiments (OSSEs). They are analyzed as a function of the number of sampling sites (measuring at 25 m a.g.l.) and as a function of the network design. The instruments presently used to measure CO2 concentrations at research stations are expensive (typically ∼ EUR 50 k per sensor), which has limited the few current pilot city networks to around 10 sites. Larger theoretical networks are studied here to assess the potential benefit of hypothetical operational lower-cost sensors. The setup of our inversion system is based on a number of diagnostics and assumptions from previous city-scale inversion experiences with real data. We find that, given our assumptions underlying the configuration of the OSSEs, with 10 stations only the uncertainty for the total city CO2 emission during 1 month is significantly reduced by the inversion by ∼ 42 %. It can be further reduced by extending the network, e.g., from 10 to 70 stations, which is promising for MRV applications in the Paris metropolitan area. With 70 stations, the uncertainties in the inverted emissions are reduced significantly over those obtained using 10 stations: by 32 % for commercial and residential buildings, by 33 % for road transport, by 18 % for the production of energy by power plants, and by 31 % for total emissions. These results indicate that such a high number of stations would be likely required for the monitoring of sectoral emissions in Paris using this observation–model framework. They demonstrate some high potential that atmospheric inversions can contribute to the monitoring and/or the verification of city CO2 emissions (baseline) and CO2 emission reductions (commitments) and the advantage that could be brought by the current developments of lower-cost medium precision (LCMP) sensors.

In the European context, geographical indications (GIs) are tools that contribute to the achievement of rural development policy objectives. In this article, we propose that GI value chains produce positive environmental, social and economic benefits, defined as Public Goods (PGs), resulting from the rules defined in the Code of Specifications (CoS). This article reports the main results of the Strength2food H2020 project, designed to assessing the impact of GIs (through their CoSs) on agri-food system sustainability. Specifically, this report highlights that GI CoSs may generate PGs through the rules codified in CoSs presented as good practices in the production of PGs for other GI systems. Some final recommendations are proposed from the analysis of those good practices which contribute to the generation of PGs and, consequently, to the improvement of a sustainable rural development process. Case studies analysed show that generation of PGs requires both an internal and external intervention. The former intervention implies governance strategies for GI territorial systems and value chains that can improve the production of PGs. The latter intervention entails consumers and other stakeholder communication strategies to raise awareness regarding PG generation. These interventions will ultimately increase the social value of GIs.

In this paper we analyze research gaps and identify new directions of research in relation to a number of facets of the Paris Agreement, including the new 1.5 °C objective, the articulation between near-term and long-term mitigation pathways, negative emissions, verification, climate finance, non-Parties stakeholders, and adaptation.

The aim of this study is to analyze the behavior of French consumers with respect to food products under various quality labels (organic, label rouge, and geographical indications). In particular, we investigate if consumers who purchase once a product under a given label tend to purchase a large fraction of this product (and other products) under the same label. Using a large scanner database, the regularity of quality-food consumption is analyzed through the relative frequency of conventional and quality purchases. The respective roles in regular consumption of product attributes, availability, and household characteristics are then examined using a random utility model. Regular organic consumers purchase around 28% of the organic market value, with variations depending on products. We find that product attributes are more related to regular organic behavior than household characteristics. In particular, product availability and product family (vegetables, eggs, milk, etc.) play a key role whereas lowprice organic products are not associated with more regular consumption. Acknowledging the existence of regularity in organic consumption and understanding its variation between product categories should lead public policies to more often target specific products in order to develop quality-food consumption.

The aim of this study is to analyze the behavior of French consumers with respect to food products under various quality labels (organic, label rouge, and geographical indications). In particular, we investigate if consumers who purchase once a product under a given label tend to purchase a large fraction of this product (and other products) under the same label. Using a large scanner database, the regularity of quality-food consumption is analyzed through the relative frequency of conventional and quality purchases. The respective roles in regular consumption of product attributes, availability and household characteristics are then examined using a random utility model.Regular organic consumers purchase around 28% of the organic market value, with variations depending on products. We find that product attributes are more related to regular organic behavior than household characteristics. In particular, product availability and product family (vegetables, eggs, milk, etc.) play a key role whereas low-price organic products are not associated with more regular consumption.Acknowledging the existence of regularity in organic consumption and understanding its variation between product categories should lead public policies to more often target specific products in order to develop quality-food consumption.

Aim: Dynamic global vegetation models (DGVMs) use a discretization of forest vegetation based on plant functional types (PFTs). The physiological and ecological parameters used to model a given PFT are usually fixed, being defined from point-based observations, while model applications are often grid-based. This rigid approach causes spatial biases in the results of DGVMsimulated productivity and biomass-related variables. We aim to overcome this limitation with a new approach that uses a hierarchical classification of forest PFT parameters from traits retrieved from the literature and from the TRY global database of plant traits. This approach is applied to temperate conifers in the ORCHIDEE-FM DGVM, which has previously been shown to produce systematic biases in the simulation of biomass and biomass increments. Location: Temperate coniferous forests in France. Methods: The five major coniferous species in France, Abies alba, Picea abies, Pinus pinaster, Pinus sylvestris and Pseudotsuga menziesii, were grouped objectively into PFTs within the ORCHIDEE-FM DGVM using a hierarchical classification based on 12 key attributes related to photosynthesis, phenology and allometric relationships. Results: We show that the single PFT covering all temperate coniferous forests used by default in ORCHIDEE-FM could be replaced by two representative subcategories defined by grouping species-level data without necessarily having to adopt a set of parameters for each species. The definition of new temperate conifer PFTs with this approach allows us to reduce the spatial heterogeneity by 40% on average in model–measurement misfit for stand volume, growth and stand density at the regional scale. Main conclusions: The proposed approach to improve the representation of PFTs in DGVMs, while keeping the number of different PFTs manageable, is promising for application to regions where a single PFT can correspond to a number of different species.