Greenhouse Gas (GHG) emissions is a topic of major concern worldwide. Following previous articles which provide a methodology for estimating GHG emissions associated with international trade by transport mode at the world level, in this paper, we estimate an equivalent database of GHG emissions for inter-regional trade flows within a country (Spain). To this end, we built a new database of GHG emissions for origin-destination flows between Spanish provinces during 1995-2015. For each year, we combine industry-specific flows by four transport modes (road, train, ship and aircraft) with the corresponding GHG emissions factor for each mode in tons*km, drawn from the specialized literature. With this dataset of GHG emissions, we generate and analyze the temporal, sectoral and spatial pattern of Spanish inter-regional GHG flows. We then forecast emissions for 2016-2030 and consider how transport mode shifts might produce a more sustainable freight system within the country through the substitution of environmentally friendly alternatives (railway) for specific origin-destination-product flows in high-polluting modes (road).

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The evaluation of the Single European Market requires a better knowledge of the level of integration both between and within the EU countries. While some institutions are pushing for greater integration between EU countries, others may be introducing—purposely or collaterally—additional barriers to interaction. Several reports have reported the high levels of market fragmentation prevailing within Spain. This paper aims to determine whether regional borders influenced the patterns of intra- and interregional trade between the 18 regions of Spain (Nuts 2) over a long period of time (1995–2017). While trade is more intense within regions than between them, our results suggest the presence of spatial and temporal heterogeneity in the estimated home bias. We also investigate empirically the effect that the quantity and quality of national, regional and local regulations have on the economic performance of firms, in both the industrial and the service sectors. We use different non-spatial and spatial-gravity models, which yield robust results.