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Industrial structure and regional innovation have a significant impact on emissions. This study explores, from the multivariate coupling and spatial perspectives, the degree of coupling coordination between three factors: industrial structure, carbon emissions, and regional innovation of 97 counties in Shandong Province, China from 2000 to 2017. On the basis of global spatial autocorrelation and cold and hot spots, this article analyzes the spatial characteristics and aggregation effects of coupled and coordinated development within each region. The results are as follows. (1) The coupling degree between carbon emissions, industrial structure, and regional innovation in these counties fluctuated upward from 2000 to 2017. Coupling coordination progressed from low coordination to basic coordination. Regional differences in coupling coordination degree are evident, showing a stepped spatial distribution pattern with high levels in the east and low levels in the west. (2) During the study period, the coupling coordination showed a positive correlation in spatial distribution. Moran’s I varies from 0.057 to 0.305 on a global basis. Spatial clustering is characterized by agglomeration of cold spots and hot spots. (3) The coupling coordination exhibited significant spatial differentiation. The hot spots were distributed in the eastern part, while the cold spots were located in the western part. The results of this study suggest that the counties in Shandong Province should promote industrial structure upgrades and enhance regional innovation to reduce carbon emissions.

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Human activities and land transformation are important factors in the growth of carbon emissions. In recent years, construction land for urban use in China has expanded rapidly. At the same time, carbon emissions in China are among the highest in the world. However, little is known about the relationship between the two factors. This study seeks to estimate the carbon emissions and carbon sequestrations of various types of land based on the land cover data of 137 county-level administrative regions in Shandong Province, China, from 2000 to 2020.The study estimated the carbon emissions for energy consumption using energy consumption data and night-time light images, hence, net carbon emissions. The Tapio decoupling coefficient was used to analyze the decoupling between the net carbon emissions and construction land, and where the model for the decoupling effort was constructed to explore the driving factors of decoupling. The results showed that net carbon emissions in Shandong Province continued to increase, and the areas with high carbon emissions were concentrated primarily in specific districts of the province. The relationship between net carbon emissions and construction land evolved from an expansive negative decoupling type to a strong negative decoupling type. Spatially, most areas in the province featured an expansive negative decoupling, but the areas with a strong negative decoupling have gradually increased. The intensive rate of land use and efficiencies in technological innovation have restrained carbon emissions, and they have contributed to an ideal decoupling situation. Although the intensity of carbon emission and the size of the population have restrained carbon emissions, efforts towards decoupling have faded. The degree of land use has facilitated carbon emissions, and in recent years, efforts have been made to achieve an ideal decoupling. The method of estimation of net carbon emissions devised in this research can lend itself to studies on other regions, and the conclusions provide a reference for China, going forward, to balance urbanization and carbon emissions.

Abstract-Public safety, especially the daily traffic accident is concerned by the public. Previous studies have already discussed accident reasons associated with accidents statistically. There is a method called Innovators Marketplace on Data Jackets created by Professor Ohsawa. This method is used to externalize the value of data via stakeholders' requirement communication. This paper applied the solution from an IMDJ workshop to research this topic creatively. This novel solution suggested to do analysis on the combination of urban data and traffic accident rate to find the impact factors to the traffic accident rate in the urban system. This paper used factor analysis, structure equation modeling and data mining to construct a theoretical frame for traffic accident rate analysis for urban data. Different accident indexes, such as total number of accident, fatality rate, injury rate, and casualty rate are combined to construct a traffic accident risk evaluation model. This paper chosen the urban data as the solution from IMDJ workshop, such as population structure information, vehicle information, road characters, public traffic system information, and the other kinds of data to explore factor meaning, and to identify relationships between different factors. It segmented these urban data based on their categories, and determined accident risk for each section. By doing analysis on not only the original data but also the changing rate of these data each year, the result analytical results showed that traffic accident rate on urban data could be described by the combination of population structure, road characters, public traffic system and public facilities. These four sections affects traffic accident rate significantly during the development of urban; however, the vehicle factor does not have influence on traffic accident rate. And it proves the solution from IMDJ workshop is not only novel but also practical strongly. Making some solution from IMDJ into reality, we will find another new way to affect the world.