Transport in cities is one of the most important sources of emissions. Electromobility is an essential element in the catalogue of activities of local authorities aimed at combating climate change. Over the years trolleybus transport has been characterised by both phases of development and regression and is still an essential component of zero-emission urban transport in about 300 cities worldwide. The development of electricity storage technology, especially in the form of a battery, has opened up new prospects for this mode of transport. A trolleybus equipped with a battery (in-motion charging technology) gains unique characteristics for operation independent of the catenary. This study presents the approach for assessing the development of in-motion charging for trolleybuses in all Polish cities operating this means of transport. A set of KPIs has therefore, been set and analysed. The analysis covers a comparison between 2014 and 2019, aimed at showing the development of technological innovations in this field. The results clearly show that in-motion charging technology leads to the development of trolleybus transport, although this development has mainly a qualitative dimension. A key factor determining the development of trolleybus transport using in-motion charging technology is progress in the development of traction batteries.

The article examines the innovativeness of enterprises in 69 Polish NUTS3 sub-regions in 2014. The analysis is based on unpublished regional data from the Polish Central Statistical Office covering the following variables: the share of enterprises which have incurred outlays for innovative activities, the share of enterprises implementing process or product innovations, the share of companies collaborating in the field of innovation, and the share of new or modernized products in total production sold in industrial companies. The analysis focuses on building rankings and cluster analysis of NUTS3 regions. As research methods, the author uses selected methods of multidimensional comparative analysis, principal component analysis and the hierarchical Ward's method. The results show that there are substantial differences among NUTS3 sub-regions as regards innovativeness of enterprises. The low level of cooperation does not foster innovation. Innovation outputs of enterprises are also unsatisfactory. The highest variation is seen in the share of new or modernized products in total production sold in industrial companies. The final effect of the cluster analysis is the division of regions into 7 groups. In the case of units where innovation inputs are not reflected in innovation outputs, it would be useful to explore regional and local factors influencing those relations. Further research is still needed.

No abstract

SummarySmart specialisation constitutes an important framework for regional innovation policy making in the EU. According to the EU documents, the process of smart specialisation can be applied in all types of regions: the most developed ones as well as the least innovative ones. It is thus of great importance for regions with a low level of innovation performance due to numerous innovation challenges faced by these regions (such a situation can be observed in the case of four least innovative regions in Poland).The main objective of the paper is to assess the advancement of the least innovative Polish regions in the process of smart specialisation. In order to achieve this objective, the following detailed objectives are expected to be met: 1. presentation of innovation challenges for the least developed regions in Europe; 2. selection of the least innovative Polish regions on the basis of four indicators; 3. assessment of the advancement in the smart specialisation process in selected regions with reference to their economic, social and innovation potential. As research methods, the authors used descriptive analysis, analysis of strategic documents, case studies analysis and statistical analysis. As results from the analysis, the approach to the identification process of smart specialisations in the least innovative Polish regions was diverse. Depending on the maturity level of work on updating Regional Innovation Strategies, awareness of competitive advantages at the sectoral and technological level, used methods, different concepts of their identification have been adopted.

Observation that regional factors could influence innovative capacity of firms caused interest growth of innovation analysis at regional level. The objective of the paper is to measure and compare INPUT and OUTPUT innovativeness of 35 NUTS-2 Visegrad Group (V4) regions in the years 2004-2009. The indexes of regional innovativeness are based on synthetic measure. The variables correspond with the variables proposed in Regional Innovation Scoreboard. The first part of the paper contains a survey of innovativeness measures. In the next part I apply INPUT-OUTPUT analysis. The research procedure consist of three steps. The construction of matrix of regional innovativeness data was the first step. The next step was the measurement of innovation indexes. The last step was the comparison of regions based on INPUT and OUTPUT indexes. The results show that there have been and continue to be substantial differences among the V4 regions as regards innovativeness. Differences are particularly visible in case of capital regions, which are characterised by the highest INPUT and OUTPUT indexes (except for mazowieckie). In 2009, high indexes have also 2 Czech regions: Strední Cechy and Jihovýchod. In the V4, high value of INPUT index not always corresponds to high value of OUTPUT index. The most numerous group consisted of regions with medium values of both indexes. The lowest OUTPUT indexes were recorded for Polish Eastern regions. To the group characterised by low or medium INPUT indexes and high OUTPUT indexes belonged mainly Czech and Hungarian regions. In Slovak regions low INPUT indexes corresponded to medium OUTPUT indexes. Analysing the results, one should not forget that they are based on seven variables, which are a resultant of – in some measure – random choice and data accessibility. However it should not underrate the importance of this research.