Options for reforming agricultural subsidies from health, climate, and economic perspectives

Agricultural subsidies are an important factor for influencing food production and therefore part of a food system that is seen as neither healthy nor sustainable. Here we analyse options for reforming agricultural subsidies in line with health and climate-change objectives on one side, and economic objectives on the other. Using an integrated modelling framework including economic, environmental, and health assessments, we find that on a global scale several reform options could lead to reductions in greenhouse gas emissions and improvements in population health without reductions in economic welfare. Those include a repurposing of up to half of agricultural subsidies to support the production of foods with beneficial health and environmental characteristics, including fruits, vegetables, and other horticultural products, and combining such repurposing with a more equal distribution of subsidy payments globally. The findings suggest that reforming agricultural subsidy schemes based on health and climate-change objectives can be economically feasible and contribute to transitions towards healthy and sustainable food systems.

Integrated modelling method of bonding strength and residual life prediction for selective repair structure of aluminium alloy

To accurately describe and predict the overall strength and residual life of selective repair bonded structures, an integrated simulation model of crack propagation including bonding strength is established. Based on two methods, an integrated simulation model including a cohesive zone method model for predicting the residual life of a selective repair structure is established. By comparing the computational efficiency and accuracy of both the stress intensity factor and residual life of selective repair structures using different calculation methods, the modelling scheme is optimised. Based on this optimised scheme, the effect of adhesive thickness on the stress intensity factor and residual life of the repair structure is analysed. FM94 adhesive measuring 0.2–0.4 mm thickness is used to decrease the stress intensity factor and improve the remaining life such that material utilisation efficiency is guaranteed.

Exploratory Study on Modelling Agricultural Carbon Emissions in Ireland

In 2020 Ireland missed its EU climate emissions target and without additional measures will not be on the right trajectory towards decarbonisation in the longer 2030 and 2050 challenges. Agriculture remains the single most significant contributor to overall emissions in Ireland. In the absence of effective mitigating strategies, agricultural emissions have continued to rise. The purpose of the review is to explore current research conducted in Ireland regarding environmental modelling within agriculture to identify research gap areas for further research. 10 models were selected and reviewed regarding modelling carbon emissions from agriculture in Ireland, the GAINS (Air pollution Interactions and Synergies) model used for air pollutants, the JRC-EU-TIMES, (Joint Research Council-European Union-The Integrated MARKAL-EFOM System) and the Irish TIMES model used for energy, the integrated modelling project Ireland (GAINS & TIMES), the environmental, economic model ENV-Linkages and ENV-Growth along with the IE3 and AGRI-I models. The review found that data on greenhouse gas emissions for 2019 reveals that emissions can be efficiently lowered if the right initiatives are taken. More precise emission factors and adaptable inventories are urgently needed to improve national CO2 reporting and minimise the agricultural sector’s emissions profile in Ireland. The Climate Action Delivery Act is a centrally driven monitoring and reporting system for climate action delivery that will help in determining optimal decarbonisation from agriculture in Ireland. Multi-modelling approaches will give a better understanding of the technology pathways that will be required to meet decarbonisation ambitions.

Utility of Human Footprint Pressure Mapping for Large Carnivore Conservation: The Kafue-Zambezi Interface

Proxies and indicators to monitor cumulative human pressures provide useful tools to model change and understanding threshold pressures at which species can persist, are extirpated, or might recolonize human-impacted landscapes. We integrated modelling and field observations of human pressure variables to generate a site-specific, fine scale Human Footprint Pressure map for 39,000 km2 of rangelands at the Kafue–Zambezi interface—a key linkage in the Kavango-Zambezi Transfrontier Conservation Area. We then modelled Human Footprint Pressure against empirically derived occurrence data for lion (Panthera leo), leopard (Panthera pardus), and spotted hyena (Crocuta crocuta) to generate Human Footprint Pressure threshold ranges at which each species were persisting or extirpated within ten wildlife managed areas linking Kafue National Park to the Zambezi River. Results overcame many limitations inherent in existing large-scale Human Footprint Pressure models, providing encouraging direction for this approach. Human Footprint Pressure thresholds were broadly similar to existing studies, indicating this approach is valid for site- and species-specific modelling. Model performance would improve as additional datasets become available and with improved understanding of how asymmetrical and nonlinear threshold responses to footprint pressure change across spatial-temporal scales. However, our approach has broader utility for local and region-wide conservation planning where mapping and managing human disturbance will help in managing carnivore species within and without protected area networks.

Probabilistic Economical Evaluation for Business Decisions Through Integrated Uncertainty Assessment and Reliable Ensemble-Based Production Forecasts

Economic evaluation of exploration and production projects ensures a positive return for asset operators and stakeholders and evaluates risk in field development decisions related to both reservoir model uncertainties and fluctuations in oil and gas prices. Traditionally, such evaluation is performed manually and deterministically using single or limited number of cases (limited number of reservoir models and few values of economic parameters). Such traditional approach does not integrate seismic-to-simulation reservoir model uncertainties, the reservoir model used is often unreliable due to inconsistent property modifications during the history matching process, full span of prediction uncertainty isn't properly propagated for economic evaluation and the whole process is not fully automated. This paper presents an integrated and automated forward modelling approach where static and dynamic models are connected to integrate the impact of uncertainties at the different modelling stages (seismic interpretation through geological modelling to dynamic simulation and further to economic evaluations). The approach is demonstrated using synthetic 3D model data mimicking a real North Sea field. It starts by building an integrated modelling workflow that can capture the various reservoir model uncertainties at different stages to automatically generate multiple probable model realisations. Proxy models are constructed and used to refine the history match in successive batches. For each prediction development scenario, prediction probabilities are estimated using posterior ensemble of geologically consistent runs that matches historical observed data. The ensemble of reservoir models is automatically evaluated against different possible economic scenarios. The approach presents a seamless and innovative workflow that benefits from new-generation hardware and software, enables faster simultaneous realisations, produces consistent and more reliable reservoir models. Probabilistic economic evaluation concept is implemented to calculate the statistical probabilities of economic indicators.

The effect of accounting for public holidays on the skills of the atmospheric composition model SILAM v.5.7

This study quantifies the impact of emission changes during public holidays on air quality (AQ) and analyses the added value of accounting for the holidays in AQ modelling. Spatial and temporal distributions of atmospheric concentrations of the major air pollutants (the main focus was on NO2, but we also included O3, CO, PM2.5, and SO2) were considered at the European scale for all public holidays of 2018. Particular attention was paid to the events with the most pronounced continental- or regional-scale impact: Christmas and New Year, Easter, May Day vacations, and the last days of Ramadan. The simulations were performed with the chemistry transport model SILAM v.5.7 (System for Integrated modeLling of Atmospheric coMposition). Three model runs were made: the baseline with no treatment of holidays, the run considering holidays as Sundays, and the run forcing 80 % reduction in emissions during holidays for the weekday-sensitive sectors. The emission scaling was applied on a country basis. The model predictions were compared with in situ observations collected by the European Environment Agency. The experiment showed that even conservative treatment of official holidays has a large positive impact on NOx (up to 30 % of reduction in the bias inhomogeneity during the holiday days) and improves the CO, PM2.5, and O3 predictions. In many cases, the sensitivity simulations suggested a greater emission reduction than the level of Sundays. An individual consideration of the holiday events in different countries may further improve their representation in the models: specific diurnal pattern of emissions, additional emission due to fireworks, and different driving patterns.

Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records

The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements.

Adequacy of ecosystem services assessment tools and approaches to current policy needs and gaps in the European Union Overseas entities

The paper presents the current policy needs and gaps identified in the European Union (EU) Outermost Regions and Overseas Countries and Territories to implement Mapping and Assessment of Ecosystem and their Services (MAES) methodology. Then, a selection of the most appropriate tools and methods for mapping and assessing ecosystem services (biophysical, economic, socio-cultural – and decision-support) is provided to address local needs. Using a performance matrix to assess the effectiveness, efficiency and sustainability of these tools, key factors required to facilitate the implementation of an ecosystem services framework are identified by considering local needs and possibilities in terms of data availability, mapping support, ecosystem services assessment and decision-support. Our results show how effective and accurate various methods (e.g. process-based models, integrated modelling and most Decision-Supporting Tools) can be, or how efficient other methods are (e.g. value transfer, spatial proxy methods and replacement cost) in data-scarce regions. Participatory approaches score well in terms of sustainability as they allow the assessment of multiple ecosystem services (covering the biophysical, economic and social-cultural components of the assessment) with local stakeholders' contribution, therefore contributing to the awareness-raising dimension. There is no one-size-fits-all approach. Instead, there is a need for flexible, guidance-based ecosystem services mapping and assessment approaches in the EU Overseas entities to facilitate MAES implementation and to adapt and integrate those methods into scenario analysis and decision-supporting tools for better uptake of MAES outputs at the decision-making and policy levels in the EU Overseas entities.