Spatial pattern characteristics and optimization policies of low-carbon innovation levels in the urban agglomerations in the Yellow River Basin

Abstract

This paper refines a comprehensive index of urban low-carbon innovation based on urban low-carbon innovation concept and an urban low-carbon innovation community structure and function. This index is complemented by an indicator reflecting equity in terms of urban-rural income gap, some indicators of low-carbon knowledge and technology innovation, as well as an indicator of the characteristics of a carbon circular economy, and a further refinement of the indicator of low-carbon innovation system. We use the entropy value method, kernel density analysis, gravity model, and social network analysis to study the low-carbon innovation spatial pattern in urban agglomerations (UAs) of the Yellow River Basin (YRB). Innovations in the findings are: (1) the low-carbon innovation level in the UAs in the YRB decreases in spatial distribution from the downstream to the midstream to the upstream UAs. (2) Reveal the spatial pattern laws of low-carbon innovation levels in different types of UAs in the YRB. The spatial pattern in regional level UAs is more complex and hierarchical than that in area level UAs; but there are still gaps in the hierarchy of spatial patterns of low-carbon innovation levels between UAs at the regional level, as well as between UAs at the area level. The spatial pattern of two regional-level UAs in the YRB is transitioning from a low-carbon innovation point-axis to a low-carbon innovation network structure, and the spatial pattern of one regional-level UAs is at the embryonic stage of the low-carbon innovation point-axis structure. The spatial pattern of one area-level UA is at the embryonic stage of a low-carbon innovation point-axis structure, and the spatial pattern of three area-level UAs is at the stage of low-carbon innovation pole. The spatial pattern of low-carbon innovation becomes more complex and hierarchical as we move from upstream to midstream to downstream UAs. (3) Use social network analysis and the study show that the low-carbon innovation network is dominated by the internal correlations among the UAs in the upstream, midstream, and downstream. The low-carbon innovation linkages between upstream, midstream and downstream UAs are weak. Finally, we put forward corresponding policy recommendations.