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Since 2015, the intended climate actions of the Paris Agreement signatories have been reported as nationally determined contributions (NDC). These climate actions are fully aligned with the 13th Sustainable Development Goal (SDG) which calls for urgent action to combat climate change. The same, however, cannot be said for their relation to the other 16 SDGs of the 2030 Agenda for Sustainable Development, since climate action can either enhance or compromise the prospects for SDG implementation. In light of this challenge, this paper proposes a simple method for quantifying the synergies and trade-offs between national climate actions and the SDGs. The method, referred to as Q-SCAN, makes use of a seven-step scale and the SDG Climate Action Nexus tool. The effectiveness of the method has been demonstrated on a case study of North Macedonia, a non-Annex I, Western Balkan country with a coal-intensive energy system. Based on the experience in the preparation of the country’s enhanced NDC, the paper elaborates how the method can be used to contribute to the alignment of the national climate actions with the SDGs and how it can be used to improve stakeholder engagement.

Effective societal responses to rapid climate change in the Arctic rely on an accurate representation of region-specific ecosystem properties and processes. However, this is limited by the scarcity and patchy distribution of field measurements. Here, we use a comprehensive, geo-referenced database of primary field measurements in 1,840 published studies across the Arctic to identify statistically significant spatial biases in field sampling and study citation across this globally important region. We find that 31% of all study citations are derived from sites located within 50 km of just two research sites: Toolik Lake in the USA and Abisko in Sweden. Furthermore, relatively colder, more rapidly warming and sparsely vegetated sites are under-sampled and under-recognized in terms of citations, particularly among microbiology-related studies. The poorly sampled and cited areas, mainly in the Canadian high-Arctic archipelago and the Arctic coastline of Russia, constitute a large fraction of the Arctic ice-free land area. Our results suggest that the current pattern of sampling and citation may bias the scientific consensuses that underpin attempts to accurately predict and effectively mitigate climate change in the region. Further work is required to increase both the quality and quantity of sampling, and incorporate existing literature from poorly cited areas to generate a more representative picture of Arctic climate change and its environmental impacts.

The number of natural disasters is growing. A considerable number of them derives directly from climate changes and generates a deep impact on cultural heritages. As a result, the need for solutions able to cope with uncertain weather conditions and increased natural disasters is getting urgent. STORM is a European Research and Innovation Action co-funded in the H2020 framework that is aimed at creating intelligent tools which will gather data from libraries, sensors and crowd-sensing techniques in order to enable cultural heritage stakeholders (e.g., the organisations which have to manage the sites) to implement the most appropriate actions in the prevention, response and recovery phases of emergencies which could impact on cultural heritages. More specifically, the project aims at addressing those risks deriving from climate change, which in the near future is going to worsen most of the present hazards, as flash floods, heat waves and forest fires. As such, the approach adopted by the project is particularly fit for the purpose of being integrated with smart city systems which are increasingly growing in number and variety. Even if safety issues are not considered central in the overall concept of smart city, it’s plain that the huge quantity of data that can be gathered and processed in any future STORM-like urban cultural heritage compound should feed into the available smart city systems. On the other way round, the mass of data produced or processed by smart city systems have the potential to impact dramatically on emergency management and prevention activities to be implemented by cultural heritage stakeholders. The paper will illustrate the approach that the STORM project is adopting to mitigate the impact of climate changes on cultural heritages and the mutual benefits which could derive from an integration of the STORM outcomes with smart cities systems through the use of standard emergency data exchange protocols and an integrated framework aimed at improving existing processes related to the three identified areas: Prevention, Response and Policy

Abstract The feasibility of reusing waste materials as an inexpensive sorbent to remove volatile organic compounds from gaseous waste streams has been demonstrated. Ashes from wood-chips were tested as sorbent materials for VOCs removal with a PTR-ToF-MS instrument. Both scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDX) and BET analysis were used to identify the structural characteristics, elemental composition and surface area of the tested ashes respectively. Most of the tentatively identified compounds were less strongly adsorbed by wet ash: thiols, siloxanes, carbonyl compounds and terpenes. Hydrogen sulfide and alcohols show improving removal performance in wet conditions. These results are related to the water solubility properties. Siloxanes were tentatively identified and monitored with PTR-ToF-MS. This demonstrates how this instrument is a suitable tool for simultaneously providing a multitude of analysis for rapid in situ monitoring of fuel contaminants. Considering the low cost, and the recycling of environmental pollutants, wood ashes are a possible choice for VOCs removal from biogas.

Espresso coffee grounds constitute a residue which is produced daily in considerable amounts, and is often pointed out as being potentially interesting for plant nutrition. Two experiments (incubations and field experiments) were carried out to evaluate the potential nitrogen (N) and phosphorus (P) supply for carrot (Daucus carota L.), spinach (Spinacea oleracea L.) and lettuce (Lactuca sativa L.) nutrition.Immobilisation of nitrogen and phosphorus was detected in all the incubations and, in the field experiments, germination and yield growth were decreased by the presence of espresso coffee grounds, in general for all the species studied.The study showed an inhibition of N and P mineralisation and a reduction of plant germination and growth. Further research is required to determine whether this is related to the immobilising capacity of the residue or possibly due to the presence of caffeine.

This study assesses the socio-economic impacts in terms of value added, imports and employment of sugarcane-derived bioethanol production in Northeast (NE) Brazil. An extended inter-regional Input–Output (IO) model has been developed and is used to analyse three scenarios, all projected for 2020: a business-as-usual scenario (BaU) which projects current practices, and two scenarios that consider more efficient agricultural practices and processing efficiency (scenario A) and in addition an expansion of the sector into new areas (scenario B). By 2020 in all scenarios, value added and imports increase compared to the current situation. The value added by the sugarcane–ethanol sector in the NE region is 2.8 billion US$ in the BaU scenario, almost 4 billion US$ in scenario A, and 9.4 billion US$ in scenario B. The imports in the region will grow with 4% (BaU scenario), 38% (scenario A) and 262% (scenario B). This study shows that the large reduction of employment (114,000 jobs) due to the replacement of manual harvesting by mechanical harvesting can be offset by additional production and indirect effects. The total employment in the region by 2020 grows with 10% in scenario A (around 12,500 jobs) and 126% in scenario B (around 160,000 jobs). The indirect effects of sugarcane production in the NE are large in the rest of Brazil due to the import of inputs from these regions. The use of an extended inter-regional IO model can quantify direct and indirect socio-economic effects at regional level and can provide insight in the linkages between regions. The application of the model to NE Brazil has demonstrated significant positive socio-economic impacts that can be achieved when developing and expanding the sugarcane–ethanol sector in the region under the conditions studied here, not only for the NE region itself but also for the economy of the rest of Brazil.

Pollination and soil fertility are important ecosystem services to agriculture but their relative roles and potential interactions are poorly understood. We explored the combined effects of pollination and soil fertility in sunflower using soils from a trial characterized by different long-term input management in order to recreate plausible levels of soil fertility. Pollinator exclusion was used as a proxy for a highly eroded pollination service. Pollination benefits to yield depended on soil fertility, i.e., insect pollination enhanced seed set and yield only under higher soil fertility indicating that limited nutrient availability may constrain pollination benefits. Our study provides evidence for interactions between above- and belowground ecosystem services, highlighting the crucial role of soil fertility in supporting agricultural production not only directly, but also indirectly through pollination. Management strategies aimed at enhancing pollination services might fail in increasing yield in landscapes characterized by high soil service degradation. Comprehensive knowledge about service interactions is therefore essential for the correct management of ecosystem services in agricultural landscapes.

This study regards the estimate of olive oil pruning biomass. The objective is to increase the knowledge of pruning biomass availability to business purposes. Having recognized the importance of both uncertainty and dynamics, we first estimate the probability distribution of the yields in a small farms sample and then determine the related expected value of pruning olive oil yield. We use these estimates to draw microscenarios illustrating the dynamics of the pruning biomass yields based on the enlargement of the number of trees per hectare (intensification) and of the olive oil crop area (specialization). The results show how intensification and specialization determine a mosaic pattern of changes in the pruning yields probabilities. The resulting estimate of the pruning biomass availability is similar to those provided in literature for the Italian case. Across the Italian regions, the difference between the expected pruning yield and a theoretical yield index decreases as the specialization increases. Two validity boundaries of the model are presented.The study concludes noting that, unless they are associated, intensification and specification not systematically augment the average olive oil pruning biomass yield in a territory and that the yields increase appears to be more probable beyond intensification large values.