In the European Union, smart specialization is an important concept in regional policy. Its primary aim is to achieve inclusive and sustainable economic growth. There is a lack of convenient region specific measures to operationalize smart specialization startegies (S3). The purpose of the paper is to find “indices of smart specialization” on a regional level. We propose indices that are based on (1) the rate of industrial diversification, (2) revealed comparative advantage and (3) regions’ overall relative specialization. In the empirical part, we analyze smart specialization in Finland using structural data provided by Statistics Finland for seventy sub-regions (LAU1) and 24 sub-industries in manufacturing. These industries are the most important for exports, productivity, and regional economic performance for a small country. The following indices are used in empirical evaluations: Herfindahl-Hirschman Index (HHI) for regional diversity, Balassa-Hoover Index (BHI) for revealed comparative advantage, and Region’s Relative Specialization Index (RRSI) for aggregate regional specialization differences. The concept of smart specialization is related to these measures. Index analyses reveal that many growing sub-regions have similar comparative advantages. This means inter-regional synergy, and it enables opportunities for strategic cooperation between regions. To develop smart specialization strategies for Europe’s regions, we need these kinds of empirical knowledge-based management tools and planning approaches.

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According to the Porter hypothesis, regulations on environmental emissions under certain conditions can promote ecoinnovation. This is why the technological innovation systems (TIS) theory sees regulatory pressure as a major system function critical in the takeoff phase. In other words, the design and timing of any regulation may be decisive for the regulatory outcome. The research seeks to provide empirical evidence on how the Baltic Sea Sulphur Emission Control Area (SECA) has impacted the technological innovation system within the Baltic Sea Region maritime sectors. The results (1) show that regulatory compliance gave a knowledge development that has made it possible for clean-tech companies to engage in entrepreneurial activities that created new markets, (2) empirically support the TIS theory and the Porter hypothesis, and (3) provide qualitative evidence on how businesses see environmental regulation.

The purpose of the study was to construct smart specialisation indicators for LAU-1 regions in Finland. Established indices are based on indicators of the regions' revealed comparative advantage and the degree of diversification in the sub-regional industrial structure. Furthermore, we introduce an indicator that can be used to assess the socio-economic importance (employment) of diversification and specialisation for a region. The indices data is based on Statistics Finland (2015) data for the 70 Local Administrative Unit level 1 (LAU1) sub-regions in Finland. The potential S3 indices measured here reveal the position of each sub-region's smart specialisation among the 70 sub-regions in 2015.It is common economic knowledge that manufacturing industries are the most export-oriented, highly productive and thus can approximate a region's success in international trade and competitive advantages. The study is based on three smart specialisation indices: the Herfindahl-Hirschman Index for regional resilience (HHI), the regional relative specialisation index (RRSI) based on the Balassa-Hoover Index (B-H), and the relative employment volume index in the manufacturing sector (LIMI). Through index examination, we can obtain knowledge about a region's smart specialisation status and potential. The results reveal that each sub-region has its own smart specialisation characteristics with a different risk profile. Sub-regions like Helsinki and Tampere have a similar industrial structure to Finland as a whole and are resilient: they will benefit from nationwide economic and industrial policy, and they have a good capability of resisting economic shocks. Our study reveals that there are some other similar smaller (LAU-1) sub-regions in Finland -for example Rauma. As such, it is critical that this kind of research-based basic information be taken into account when constructing sustainable strategies for regional development. Similar calculations could be performed for all regions in Europe.European Integration Studies 2018/12

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