Vilnius Eastern Partnership Summit: A Milestone in EU-Russia Relations - not just for Ukraine

The Vilnius Eastern Partnership Summit on 28-29th November represents a milestone in EU relations not just with respect to the six Eastern Partnership countries (EaP: Armenia, Azerbaijan, Belarus, Georgia, Moldova and particularly Ukraine), but also with the EU's "strategic partner" Russia. The turbulence and numerous speculations regarding expectations about the signature of the EU-Ukraine Association Agreement (comprising a Deep and Comprehensive Free Trade Agreement -- AA/DCFTA), as well as progress in initialling similar future agreements with Georgia and Moldova, have been escalating before the summit. The association agreements would bring EaP signatory countries closer to the EU: not really closer to EU membership, but closer to the application of various EU norms and standards (takeover of the "acquis communautaire") and -- significantly -- out of the Russian orbit, for the beginning at least symbolically. The last minute postponement of the EU-Ukraine AA/DCFTA signature announced by Ukraine's government just one week before the summit represents a serious setback for the EU. Though the EU has no "Plan B" and was stunned after Ukraine's announcement, life will continue after the summit and new initiatives will have to be started. What are the relevant issues and challenges and what is at stake? This note attempts to evaluate the consequences (economic and otherwise) of alternate decisions following the Vilnius Eastern Partnership Summit, reviews some of the disputed arguments and discusses selected relevant economic issues

Economic consequences of the Ukraine conflict

The Ukraine conflict is having serious consequences, but it also potentially threatens to damage the still frail economic recovery in Europe. In Ukraine, which is the main victim of the conflict, the economy may decline by up to 8% this year. In Russia, the costs of the conflict are estimated to be in the tune of 1% of GDP in 2014-2016, primarily on account on increased investment risks. The effects on the individual EU countries differ depending on their exposure to the Russian market: the Baltic States, Finland and several other EU Member States are generally most affected. The impact on Austria is expected to be relatively modest. Austria is not overly exposed to the Russian market. For the EU as a whole, there are five industries where the share of Russia in total exports exceeds 3%: textiles, pharmaceuticals, electrical equipment, machinery and transport equipment. On the assumption of a 10% loss in exports of goods and services to Russia, the estimated GDP loss would be about 0.4% for Lithuania and Estonia, and less than 0.1% for Austria. In absolute figures, Germany might lose around EUR 3 billion, followed by Italy (EUR 1.4 billion), France, Great Britain and Poland (EUR 0.8 billion each). Austria could lose close to EUR 300 million in this scenario. the estimated impact of Russia's ban on agro-food imports from the EU imposed in August 2014 is expected to be highest in the Baltics. These losses are undoubtedly painful, yet manageable (a trade decline bigger than 10% would obviously lead to greater losses). The question is whether these losses are justifiable and will achieve the desired effects - to change Russia's behavior in Ukraine and beyond

Towards Sustainable Livestock Production Systems: Analyzing Ecological Constraints to Grazing Intensity

Increasing food production from cropland and grassland is essential to meet the future food demand of a growing world population without further land-use expansion. It is estimated that until 2050, food production has to increase strongly to meet future food demands. Increasing food production from grasslands in a sustainable way (e.g., by not degrading essential ecosystem services) is important, yet requires a good understanding of the major determinants and constraints of the global livestock production systems and the associated socio-economic and ecological patterns. The spatially explicit analysis of grazing intensity (GI; e.g., the fraction of available Net Primary Production (NPP) that is consumed by grazing animals in a year) using monthly data allow us to analyse the role of seasonality for limits to grazing intensity. Seasonality creates in many regions of the world shortage and surplus periods of NPP, which can (partly) be overcome by social organization, such as the employment of storage technologies or by imports. By comparing the current livestock density to the ecologically maximum density (EMD) determined by biomass availability during shortage periods we show that management has contributed to substantial higher livestock density in many world-regions whereas in others it is still close to the EMD. Our analysis shows to which expense (e.g., length of shortage period to overcome) the increase in livestock-density comes in different world regions and where potential for further biomass extraction exists. This study contributes to an improved understanding of the systemic inter-linkages between GI, seasonal biomass supply, and socioeconomic and ecological trade-offs, and provides essential information for analyzing intensification potentials of grasslands

Technical Description of the IIASA Model Cluster

The quantitative analysis of REDD supply schedules were carried out in a global total land-use context. The Global Model cluster combines geographically explicit biophysical models with economic modeling. The model cluster covers all land-use types and thus allows for fully integrated analysis of competitive interactions between different land uses and land use change types. Combining the different models allows for geographic explicit analysis of REDD policies in a global context. The geographic explicit analysis of REDD policy options is carried out using the G4M (former DIMA) model (e.g. Rokitiansky et al., 2007; Kindermann et al., 2006, 2008b). G4M is driven by exogenous market price assumptions for land and commodities without taking market feedbacks into account. The partial equilibrium model GLOBIOM generates endogenous prices. GLOBIOM has global geographic coverage and accounts for all land uses and thus allows for REDD policy analysis in a wider land use and global change context. When the two models are coupled the G4M model serves a double purpose. First it informs GLOBIOM on basic biophysical forest growth information and engineering costing of various forest management options. Second, results from GLOBIOM, such as endogenous commodity and land prices and trade, are used as exogenous drivers for the geographically explicit modeling using G4M. In the latter G4M becomes a "sophisticated" downscaling algorithm for GLOBIOM results facilitating "visual validation" of results and geographic REDD hot spotting. In the following the two models are described. In the description of G4M we provide a detailed description of the improved carbon accounting and calibration methods departing from (Kindermann et al., 2006). Changes in the calibration methodology have necessarily created considerable differences in baseline emissions and thus REDD costs as published in (Kindermann et al., 2006). Baselines in (Kindermann et al., 2006) are determined mainly by future GDP and population development assuming low institutional barriers for expansion of the agricultural and forestry sectors whereas the latter is mainly driven by the continuation of historical emissions and the continuation of institutional barriers of agricultural and forestry sector development. The version of G4M presented in this document was calibrated to the global emissions estimates provided by the IPCC while the one in (Kindermann et al., 2006) was calibrated to the estimates provided by global analysis using remote sensing methods. Differences in the results of these two model versions of G4M provide valuable insights on the impact of changes in methodologies on REDD costs. The description of GLOBIOM is provided with less detail due to space limitations

Vilnius Eastern Partnership Summit: Milestone in Eu-Russia relations - Not just for Ukraine

The Vilnius Eastern Partnership Summit on 28-29th November 2013 represents a milestone in EU relations not just with respect to the six Eastern Partnership countries (EaP Armenia, Azerbaijan, Belarus, Georgia, Moldova and particularly Ukraine), but also with the EU's "strategic partner" Russia. The turbulence and numerous speculations regarding expectations about the signature of the EU-Ukraine Association Agreement (comprising a Deep and Comprehensive Free Trade Agreement -- AA/DCFTA), as well as progress in initialling similar future agreements with Georgia and Moldova, have been escalating before the summit. The association agreements would bring EaP signatory countries closer to the EU not really closer to EU membership, but closer to the application of various EU norms and standards (takeover of the "acquis communautaire") and -- significantly -- out of the Russian orbit, for the beginning at least symbolically. The last minute postponement of the EU-Ukraine AA/DCFTA signature announced by Ukraine's government just one week before the summit represents a serious setback for the EU. Though the EU has no "Plan B" and was stunned after Ukraine's announcement, life will continue after the summit and new initiatives will have to be started. What are the relevant issues and challenges and what is at stake? This note attempts to evaluate the consequences (economic and otherwise) of alternate decisions following the Vilnius Eastern Partnership Summit, reviews some of the disputed arguments and discusses selected relevant economic issues

Global High-resolution Land-use Change Projections: A Bayesian Multinomial Logit Downscaling Approach Incorporating Model Uncertainty and Spatial Effects

Using econometric models to estimate land-use change has a long tradition in scientific literature. Recent contributions show the importance of including spatial information and of using a multinomial framework to take into account the interdependencies between the land-use classes. Few studies, however, agree on the relevant determinants of land-use change and there are no contributions so far comparing determinants on a global scale. Using multiple 5 arc minute resolution datasets of land-use change between 2000 and 2010 and taking into account the transitions between forest, cropland, grassland and all other land covers, we estimate a Bayesian multinomial logit model, using the efficient Pólya-Gamma sampling procedure introduced by Polson et al. (2013). To identify and measure the determinants of land-use change and the strength of spatial separation, our model implements Bayesian model selection through stochastic search variable selection (SSVS) priors and spatial information via Gaussian Process (GP) priors. Our results indicate that spatial proximity is of central importance in land-use change, in all regions except the pacific islands. We also show that infrastructure policy, proxied by mean time to market, seems to have a significant impact on deforestation throughout most regions. In a second step we use aggregate, supra national land-use change results from the partial equilibrium agricultural model GLOBIOM as a framework for projecting our model in ten-year intervals up to 2100 on a spatially explicit scale along multiple shared socioeconomic pathways

Investment needs for irrigation infrastructure along different socio-economic pathways

This paper conducts an assessment of the global costs for expanding, upgrading, and improving irrigation infra-structure in developing countries, along different future scenarios toward 2050. It uses the GLobal BIOsphere Man-agement Model, a partial equilibrium model of the global agricultural and forestry sectors. It examines the impacts of irrigation expansion on the agriculture and food system, from the perspective of different Sustainable Development Goals, in particular food security (goal 2), land use change and biodiversity (goal 15), greenhouse gas emissions (goal 13), and sustainable water use (goal 6). It finds that irriga-tion support policies improve food security globally and can reduce the burden on land by limiting expansion of cropland area. However, the effectiveness of irrigation to achieve a larger set of goals depends on the regional con-text. In South Asia and the Middle East and North Africa, the expansion of irrigation increases unsustainable water extraction practices. A sensitivity analysis is conducted to evaluate the uncertainty of the infrastructure costs and impacts under different socioeconomic developments, levels of radiative forcing and climate change scenarios, dietary patterns, trade openness, and efficiencies of irriga-tion systems. The findings indicate that irrigation systems could play an important role in adaptation to the most adverse climate change; however, increased water scarcity may also limit adaptation potentials

Improving Ethiopian Smallholders’ Income and Food Security: An Assessment of Alternative Policy Options

Smallholder farmers dominate food production, but also represent the largest share of people in developing countries experiencing food insecurity. In Ethiopia, agricultural growth now forms the backbone of the country’s long-term plans for economic growth. This study aims to analyze long-term changes to the agricultural sector and its consequences for the evolution of smallholder farmers under various policy scenarios. A farming typology based on the agro-ecological zone, the dominant activities, and the degree of market integration is established for this purpose. The agro-ecological zone is divided into the rainfall-sufficient and drought prone highland areas and the pastoralist lowlands. Dominant activities are either pure livestock-keeping or a combination of crops and livestock. Market integration is based on the share of agricultural output sold to the market. The resulting typology is extrapolated to all regions of Ethiopia. The spatially differentiated typology is integrated in an Ethiopia-version of Global Biosphere Management Model (GLOBIOM), a globally-consistent partial equilibrium model representing spatial land-use patterns and accounting for biophysical resource constraints. 19 crops (the standard GLOBIOM crops, teff, coffee and sesame), 4 animal types (cattle, sheep, goats and poultry) and 2 livestock products (milk and meat) are represented in the model. Projections of population and GDP growth per region are used to set up the initial demand for each product and each time step. Policies aiming to improve food security and reduce poverty are subsequently implemented. These include infrastructure and irrigation extensions as well as the improvement of access to fertilizers. Results show that the distribution of the farming systems changes across space and time under different policy scenarios. Impacts on smallholders’ poverty and food security status differ depending on the policy, enabling a spatially explicit assessment of policy options at both the local and national level