The creation of a common European currency has been scrutinized in the context of optimum currency area theory since its origin in Mundell (1961). The debate gained particular prominence in light of the ’endogeneity’ theorem (Frankel and Rose, 1998), which argues that, once two countries establish a common currency, their economic structures and cycles increasingly align due to strengthening intra-industry trade. By contrast, the ’specialization’ theorem (Eichengreen, 1992; Krugman, 2013) argues that the creation of a currency union will predominantly increase inter-industry trade, ultimately lowering business cycle correlation. To test these views, we establish several indices of bilateral trade intensity across EU members using input-output data, measuring gross and so-called ’value-added’ trade, which also considers the contribution of intermediary goods in the production of final exports. The results of the fixed effect panel framework indicate a strong and robust empirical relationship between growth correlations and intra-industry trade, much in line with both Mundell’s and Frankel and Rose’s theories. However, we cannot establish a similarly robust relationship between total trade intensity and growth correlations. We reconcile these results by identifying a statistically significant relationship between economic alignment and trade when only considering industrial production, highlighting the importance of pan-European industrial supply chains for European economic integration. In contrast to the EU-28, the Eurozone does not display properties of an Optimum Currency Area. We partially attribute this to the Eurozone periphery, which shows a high degree of misalignment within itself; excluding the Eurozone periphery from our initial regressions further improves the results.

Which renewable energy (RE) policy instrument is most effective in expanding the international diffusion of RE and what is the role of innovation? We consider rich policy and patent data for 189 countries and territories to investigate these diversely debated questions for wind and solar photovoltaic capacities. This allows us, firstly, to contribute to the limited evidence on the effect of RE innovation on RE diffusion and its interrelated influence with RE support policies. Secondly, we can evaluate the disentangled individual policies' effectiveness in a broad instrument-country context. Thirdly, we control for the inherent endogeneity of policy instruments and innovation. We find that RE innovation, which appears to be largely policy-induced, is among the most promising ways to increase RE capacities. The most effective policy instruments tend to be quotas with certificate trading, tendering, and fiscal instruments that provide specific investment support, i.e. investment tax credits and capital subsidies. Less tangible and projectable measures, such as the most commonly implemented sales-related tax reductions and RE targets, are least effective. While interactions between instruments influence the composition of a well-designed policy mix, there are also differences in the policies' effectiveness and role of innovation depending on the countries' level of development.

Greenhouse gas emission (GHGE) taxes on food products have recently been proposed as means to help reduce agricultural emissions. Numerous authors have calculated potential GHGE reductions in case such a tax was implemented in certain countries or regions. They did however assume a reduced production of GHGE-intense foods equal to the decline in demand induced by the tax. This omits, however, possible increases of net-exports that might offset such a demand reduction. Herein, the market dynamic behind this so-called “emission leakage” is explained and its effect quantified for a greenhouse gas tax in the European Union. We use the European Forest and Agricultural Sector Optimization Model for the quantitative analysis and simulate a greenhouse gas tax on all food products, based on their individual emission levels. The partial equilibrium model covers all world regions and hence the tax's effects on international trade of agricultural commodities can be examined. It was found that 43% of the greenhouse gas reduction indicted by a domestic consumption reduction is lost through emission leakage. This already includes the mitigating effects of a production shift from inefficient to efficient producers that is another consequence of increased exports from the European Union. A greenhouse gas emission tax on food products is hence much less efficient than previously proposed, if it is not introduced globally or trade is not restricted.

Using province-level data for South Korea, we analyze the dynamic relationship between economic growth and several energy parameters. Specifically, we decompose the growth effect into scale, composition, and technique effects, and control for regional spillovers through the use of a dynamic GMM estimator for spatial panel data models. The analyzed period, ranging from 2000 to 2017, allows us to look for changes in the regional growth effects following the implementation of the National Strategy for Green Growth in 2009. Our estimates show that the scale and composition effect tended to increase both per capita final energy use and energy intensity, outweighing reductions through the technique effect. In contrast, when considering renewable energy production, the scale and technique effect increased and the composition effect decreased the corresponding figures. Thereby, the technique effect was the main driver of increases in renewable energy production. Despite the larger, yet comparatively small share of renewables in Korea’s energy mix, no considerable change of the growth effects can be observed since 2009. Therefore, to reduce the risks for the economy and achieve the political objectives of the green growth strategy throughout the whole country and in a timely manner, a stronger commitment seems to be required.

We examine the energy-food nexus using the dependence-switching copula model. Specifically, we look at the dependence for four distinct market states, such as, increasing oil–increasing commodity, declining oil–declining commodity, increasing oil–declining commodity, as well as declining oil–increasing commodity markets. Our results support the argument that the crash of oil markets and agricultural commodities happen at the same time, especially during crisis period. However, the same is not true during times of normal economic conditions, implying that investors cannot make excess profits in both agricultural and oil markets at once. Furthermore, our analysis suggests that the return chasing effect dominates for all commodities on maximum occasions. The CoVaR and ΔCoVaR results indicate important risk spillover from oil to agricultural markets, especially around the financial crisis.

The paper complements the few regional studies on the sustainability–competitiveness nexus by providing a novel composite index of sustainable competitiveness for 272 European regions in 28 European countries. Principal component factor analysis is combined with a variance-based structural equation model to create a statistically reliable index, which overcomes the methodological issues of previous studies. Especially, the use of the latter also allows estimation the cause–effect relationships between the different pillars of sustainable competitiveness, where empirical evidence is scarce. The paper shows that favorable ecological, social, and economic eironments can jointly contribute to facilitating long-term sustainable competitiveness outcomes. Thereby, the progress in one dimension is not necessarily at the expense of another dimension of sustainable competitiveness. The proposed index reveals important insights for policymakers into the sustainable competitiveness trajectory of European regions. Region-specific plans for action can be derived and new policy conclusion can be drawn from the index. Keywords: Sustainable regional competitiveness; composite index; Beyond-GDP; structural equation modeling; European studies

In the context of the 4th industrial revolution, artificial intelligence (AI) and environmental challenges, this study investigates the role of AI, robotics stocks and green bonds in portfolio diversification. Using daily data from 2017 to 2020, we employ tail dependence as copulas and the Generalized Forecast Error Variance Decomposition to examine the volatility connectedness. Our results suggest that, first, portfolios consisting of these assets exhibit heavy-tail dependence which implies that in the times of economic turbulence, there will be a high probability of large joint losses. Second, volatility transmission is higher in the short term, implying that short-term shocks can cause higher volatility in the assets, but in the long run, volatility transmission decreases. Third, Bitcoin and gold are vital assets for hedging, though the Bitcoin is also affected by its past volatility, a feature it shares with green bonds and NASDAQ AI. During economic downturns, gold may act as a safe haven, as its shock transmission to NASDAQ AI is just around 1.41%. Lastly, the total volatility transmission of all financial assets is considerably high, suggesting that the portfolio has an inherent self-transmitting risk which requires careful diversification. The NASDAQ AI and general equity indexes are not good hedging instruments for each other.

We discuss the relative merits of various indicators of international price competitiveness from a conceptual point of view, and empirically check which indicator serves best to predict real exports in the long run. To this end, a panel cointegration analysis is conducted, augmented by a forecast exercise. In the latter, repeated sampling techniques are used in order to avoid arbitrary sample splits. In line with the theoretical reasoning, we find that broad price- and cost-based indicators are to be preferred to narrow price-based measures such as CPI- or PPI-deflated real exchange rates. Furthermore, the evidence points towards using world trade as external activity variable instead of GDP- or real imports-based measures.

International trade and economic development affect air emissions. Previous studies have decomposed their effects into scale, composition, and technique effects. While the scale and composition effects occur through market responses, the technique effect is a policy-stringency influence through the mix of eironmental policies. This study analyzes whether the market or policy-stringency effects are more prominent. Previous studies have been unable to adequately separate the market and policy-stringency effects. To independently measure the technique effect, we use two indicators of policy stringency, i.e. shadow prices of energy and industrial energy prices. These policy stringency measures are treated as endogenous. The effects on six types of air emissions are estimated utilizing a sector-specific, international panel dataset that includes newly industrialized and former transition economies. The empirical results show that the major source of emissions reductions is the policy-stringency effect through carbon-related policies. Pollution offshoring to countries with weaker carbon-related regulation has a minor role in the reduction of air emissions. Keywords: Air pollution, policy stringency, pollution offshoring, energy prices