Suchergebnisse

In October 2018, the European Union (EU) launched an updated bioeconomy strategy with the aim of encouraging the substitution of fossil carbon with biomass feedstock in the industry and in energy production while preserving ecosystem services. The objective of the paper is to analyse the links between the EU bioeconomy strategy and the Sustainable Development Goals (SDGs), and to assess what could be the main points of synergies and tensions between bioeconomy-related SDG targets. By semantically mapping the action plan of the 2018 EU bioeconomy strategy with the SDG targets, the paper finds that the bioeconomy strategy is aligned with 53 targets distributed in 12 of the 17 SDGs. Ex-post correlation analysis on bioeconomy-related SDGs indicators for 28 EU Member States (1990–2018) shows a predominance of synergies over trade-offs. More intense synergetic past developments (positive correlations) are found among clean energies (SDG 7), recycling (SDG 11), ecosystem preservation (SDG 15) and most of all other bioeconomy-related SDGs. Negative correlations are observed between agro-biodiversity (SDG 2), domestic material consumption of biomass (SDG 8 and 12), agriculture and industrial developments (SDG 2 and SDG 9) and a wide array of bioeconomy-related SDG indicators. The hotspots of strong correlations identified might be useful in further enrichment of ex-ante simulation models. From a policy coherence perspective, a wide range of policy instruments are already in place in the EU to foster synergies and may bring co-benefits. Policies oriented at preventing trade-offs are already in place but they have not overcome the antagonisms observed in this study yet. Change in practices, technical and technological innovations and the application of circular and 'cascading principles' are the most common fields of action.

Aim of study: With the increasing protagonism of non-tariff measures (NTMs) in trade policy, better indexes are needed to depict the prevalence and similarity of NTMs across countries for further use in trade impact assessments.Area of study: Worldwide, with special focus on the European Union (EU)Material and methods: Using the TRAINS database on NTMs, we calculated and proposed some indicators, stressing both regulatory intensity and diversity, as well as similarity of regulatory patterns between trade partners. Our application focuses on pork trade and main importers, amongst which, the EU is singled out.Main results: We found a high level of heterogeneity in NTMs' application, both, in the number and variety of measures. The bilateral similarity was relatively low, such as only 30% of sanitary and phytosanitary measures (SPS) and 20% of technical barriers to trade were shared, providing ground and incentive for discussing trade policy harmonization. Our analysis suggests that SPS regulations prevail in those sectors and countries more engaged in trade, while a negative correlation with tariffs raises protectionism concerns. Our bilateral indicators rank country pairs according to the similarity of their regulatory patterns. The EU, for instance, is closer in SPS regulations to China or USA than to Canada or New Zealand, which will require actions in the context of the bilateral trade agreements in course.Research highlights: The low similarity of regulatory patterns evidence the challenges faced by policy makers to streamline technical regulations. For an accurate representation of regulatory patterns and their impact on trade, both uni- and bilateral indicators need to be considered.

In October 2018, the European Union (EU) launched an updated bioeconomy strategy with the aim of encouraging the substitution of fossil carbon with biomass feedstock in the industry and in energy production while preserving ecosystem services. The objective of the paper is to analyse the links between the EU bioeconomy strategy and the Sustainable Development Goals (SDGs), and to assess what could be the main points of synergies and tensions between bioeconomy-related SDG targets. By semantically mapping the action plan of the 2018 EU bioeconomy strategy with the SDG targets, the paper finds that the bioeconomy strategy is aligned with 53 targets distributed in 12 of the 17 SDGs. Ex-post correlation analysis on bioeconomy-related SDGs indicators for 28 EU Member States (1990–2018) shows a predominance of synergies over trade-offs. More intense synergetic past developments (positive correlations) are found among clean energies (SDG 7), recycling (SDG 11), ecosystem preservation (SDG 15) and most of all other bioeconomy-related SDGs. Negative correlations are observed between agro-biodiversity (SDG 2), domestic material consumption of biomass (SDG 8 and 12), agriculture and industrial developments (SDG 2 and SDG 9) and a wide array of bioeconomy-related SDG indicators. The hotspots of strong correlations identified might be useful in further enrichment of ex-ante simulation models. From a policy coherence perspective, a wide range of policy instruments are already in place in the EU to foster synergies and may bring co-benefits. Policies oriented at preventing trade-offs are already in place but they have not overcome the antagonisms observed in this study yet. Change in practices, technical and technological innovations and the application of circular and 'cascading principles' are the most common fields of action.

With the rise of anti-free-trade sentiment on both sides of the Atlantic, there is a growing urgency by trade negotiators to conclude the Trans-Atlantic Trade and Investment Partnership (TTIP) negotiations. The harmonisation of non-tariff restrictions is a key component of the talks, whilst global modelling databases typically lack a price compatible representation of these measures, which lends a degree of bias to ex-ante modelling assessments. In the gravity literature, there is (limited) evidence of non-tariff ad-valorem equivalent (AVE) estimates of agriculture and food, although disaggregated agri-food activities and/or bilateral EU-US route specific estimates are still in relatively short supply. Using panel data, this study consolidates both of these issues, whilst also proposing an 'indirect' gravity method as a basis upon which to provide econometric non-tariff AVE estimates compatible with the degree of sectoral concordance typically found in global modelling databases. On a general note, the results revealed the presence of significant behind the border trade costs on both sides of the Atlantic, which exceed their tariff counterparts. Using simple aggregated averages, our estimates are comparable with 'direct' gravity method studies. Furthermore, rigorous qualitative and quantitative comparisons on a sector-by-sector basis showed that a number of bilateral non-tariff AVEs are also found to be plausible, although in some cases, with recourse to relevant policy documents and expert opinion, it is debatable whether the EU or the US is more restrictive. Further work could focus on refining the sector specificity of each gravity equation to improve the model's predictive capacity.

The concept of 'bioeconomy' is gathering momentum in European Union (EU) policy circles as a sustainable model of growth to reconcile continued wealth generation and employment with bio-based sustainable resource usage. Unfortunately, in the literature an economy-wide quantitative assessment covering the full diversity of this sector is lacking due to relatively poor data availability for disaggregated bio-based activities. This research represents a first step by employing social accounting matrices (SAMs) for each EU27 member encompassing a highly disaggregated treatment of traditional 'bio-based' agricultural and food activities, as well as additional identifiable bioeconomic activities from the national accounts data. Employing backward-linkage (BL), forward-linkage (FL) and employment multipliers, the aim is to profile and assess comparative structural patterns both across bioeconomic sectors and EU Member States. The results indicate six clusters of EU member countries with homogeneous bioeconomy structures. Within cluster statistical tests reveal a high tendency toward 'backward orientation' or demand driven wealth generation, whilst inter-cluster statistical comparisons by bio-based sector show only a moderate degree of heterogeneous BL wealth generation and, with the exception of only two sectors, a uniformly homogeneous degree of FL wealth generation. With the exception of forestry, fishing and wood activities, bio-based employment generation prospects are below non bioeconomy activities. Finally, milk and dairy are established as 'key sectors'.