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Within sustainable transportation systems, the adoption of electric vehicles (EVs) is considered as a potential solution to problems such as energy security, climate change, urban-air quality, and public health. This EV-adoption process is a complex system that considers the relationships between the dimensions of sustainability, car-ownership transition to EVs, market dynamics, and the government. Several models have been designed to address this system. However, these models have only partially developed these relationships because they did not consider all the components or relationships between the elements of this process. This article proposes a systemic approach based on previous models, which integrates all the components and relationships and incorporates the analysis of sustainability into the EV-adoption process. The system structure of this EV-adoption process is presented and discussed. This approach deepens the understanding of the synergies between market dynamics and sustainability, allowing to model the system more broadly. It can thus help in the sustainable and integrated evaluation of different policy scenarios, thereby contributing to a clearer understanding of the dynamic behavior and leverage points of the system. [ABSTRACT FROM AUTHOR]

According to the available guidelines, good practices for calculating nitrous oxide (N 2 O) emission factors (EFs) for livestock excreta and manure application include that sampling duration should be of at least one year after the nitrogen (N) application or deposition. However, the available experimental data suggest that in many cases most emissions are concentrated in the first months following N application. Therefore resources could be better deployed by measuring more intensively during a shorter period. This study aimed to assess the contribution of the N 2 O flux in the period directly after N application to the annual net emission. We used a database of 100 year-long plot experiments from different excreted-N sources (dung, urine, farmyard manure and slurry) used to derive EFs for the UK and Ireland. We explored different shorter potential measurement periods that could be used as proxies for cumulative annual emissions. The analysis showed that the majority of emissions occur in the first months after application, especially in experiments that i) had urine as the N source, ii) had spring N application, iii) were conducted on fine-textured soils, or iv) showed high annual emissions magnitude. Experiments that showed a smaller percentage of emissions in the first months also had a low magnitude of annual net emissions (below 370 gN 2 O–N ha−1 year−1), so the impact of measuring during a shorter period would not greatly influence the calculated EF. Accurate EF estimations were obtained by measuring for at least 60 days for urine (underestimation: 7.1%), 120 days for dung and slurry (4.7 and 5.1%) and 180 days for FYM (1.4%). At least in temperate climates, these results are promising in terms of being able to estimate annual N 2 O fluxes accurately by collecting data for less than 12 months, with significant resource-saving when conducting experiments towards developing country-specific EFs. • Sampling period to calculate country-specific N 2 O emission factors was revised. • We analyzed data from 100 year-long IPCC compliant manure and excreta experiments. • A high proportion of annual emissions occurred in the months directly after N application. • Accurate EFs were measured at 60–180 days, with significant resource saving. [ABSTRACT FROM AUTHOR]

Unravelling the impacts of climate change on agriculture becomes increasingly important, as the rates and magnitude of its effects are accelerating. Current estimates of the consequences of climate change on nitrous oxide (N 2 O) emissions remain largely uncertain; there is a need for more consistent and comprehensive assessments of this impact. In this study we explored the implications of two IPCC climate change projections (high and medium emissions scenarios) on N 2 O emissions from South West England grasslands for the time slices of a baseline, the 2020s, the 2050s and the 2080s, employing a process-based model (SPACSYS). The model was initially calibrated and validated using datasets collected from three grassland sites of the region. Statistical analysis showed that simulated results had no significant total error or bias compared to measured values. We found a consistent increase in N 2 O emissions of up to 94% under future climate change scenarios compared to those under the baseline, and warming rather than precipitation variability was the overriding factor controlling the N 2 O rise. Modelling fertilizer forms showed that replacing ammonium-nitrate fertilizers with urea or slurry significantly reduced N 2 O emissions (c. 30%). Our study highlights the urgent necessity to adopt viable N 2 O mitigation measures now in order to avoid higher emissions in the future. [ABSTRACT FROM AUTHOR]

Carbon (C) and nitrogen (N) process-based models are important tools for estimating and reporting greenhouse gas emissions and changes in soil C stocks. There is a need for continuous evaluation, development and adaptation of these models to improve scientific understanding, national inventories and assessment of mitigation options across the world. To date, much of the information needed to describe different processes like transpiration, photosynthesis, plant growth and maintenance, above and below ground carbon dynamics, decomposition and nitrogen mineralization. In ecosystem models remains inaccessible to the wider community, being stored within model computer source code, or held internally by modelling teams. Here we describe the Global Research Alliance Modelling Platform (GRAMP), a web-based modelling platform to link researchers with appropriate datasets, models and training material. It will provide access to model source code and an interactive platform for researchers to form a consensus on existing methods, and to synthesize new ideas, which will help to advance progress in this area. The platform will eventually support a variety of models, but to trial the platform and test the architecture and functionality, it was piloted with variants of the DNDC model. The intention is to form a worldwide collaborative network (a virtual laboratory) via an interactive website with access to models and best practice guidelines; appropriate datasets for testing, calibrating and evaluating models; on-line tutorials and links to modelling and data provider research groups, and their associated publications. A graphical user interface has been designed to view the model development tree and access all of the above functions. [ABSTRACT FROM AUTHOR]