**5. Discussions**

Table 2 proves that Hypotheses H1 and H3 (H3a, H3b, H3c and H3d) are valid, while H2 and H4 (H4a, H4b, H4c and H4d) are not. This suggests that (1) innovation agglomeration contributes to the reduction of industrial emissions in the region but does not have a significant effect on its surrounding regions; (2) it is feasible to enhance the effect of innovation agglomeration on industrial pollution reduction by land use transition on the region, but this effect does not have a significant influence on the surrounding regions. Specifically: hypothesis H3a holds, which suggests that an increase in the human capital dimension will, on the one hand, help to drive land use transition and thus strengthen the reduction of industrial pollution emissions from innovation agglomeration; on the other hand, the governmen<sup>t</sup> will be more active in implementing strategies to reduce emissions and retain elite talent. Based on the weight of the secondary indicators of human capital dimension, it can be further argued that full-time equivalent of R & D personnel plays the greatest role, followed by population density and finally average educational level. This enlightens us that increasing the workload of R & D personnel and attracting the inflow of foreign population can bring greater benefits. Hypothesis H3b holds, which suggests that an increase in the material capital dimension will help to remove financial barriers to research, ease the "difficulty of financing" the use of environmental equipment by firms, accelerate land use transition and reduce industrial pollution emissions. In addition, the demand of capitalists for adequate returns on capital will also increase the level of land use in the region, thus promoting land use change and reducing industrial pollution emissions. Based on the weight of the secondary indicator of material capital dimension, it can be further argued that although the role of internal expenditure of R & D funds in reducing emissions is stronger than that of financial institutions density, both are very important. This reveals to us that althoughR&D expenses are important, the role of financial institutions in financing should not be neglected, which reduces the difficulty of innovative enterprises in obtaining funds. Hypothesis H3c holds, indicating that the upgrading of the urban function dimension is conducive to the transition and upgrading of industries in the region, achieving land use transition while also reducing industrial pollution emissions and, on the other hand, encouraging the governmen<sup>t</sup> to build more humane and innovative infrastructure, thus accelerating land use transition. Based on the weight of the secondary indicators of the urban function dimension, it can be further argued that industrial structure evolution is the main force in promoting innovation and achieving industrial pollution reduction, which reveals to us that promoting industrial structure upgrading and accelerating the service-oriented economy is a triple-win path of accelerating land use transition, promoting innovation and achieving environmental protection. Hypothesis H3d holds that the government's efforts help to accelerate land use transition and reduce industrial pollution emissions. Based on the weights of the secondary indicators of the governmen<sup>t</sup> dimension, it can be further argued that, on the one hand, the government, by supporting innovation development, has the guidance to promote land use transition and strengthen innovation agglomeration, thus reducing industrial pollution; on the other hand, the government's greening efforts help retain elites and provide an environmental basis for innovation agglomeration, which also indirectly curbs industrial pollution emissions.

For other variables, the direct effect coefficient of Per\_GDP is negative and passes the 10% significance level test. The indirect effect coefficient was also negative and passed the 10% significance level test. This indicates that an increase in per capita GDP within a region not only helps to reduce industrial pollution emissions in the region but also helps to reduce emissions in neighbouring regions. This is consistent with the findings of Buryn et al. [51]. The coefficient of the direct effect of FDI is negative and passes the 10% significance level test. However, its indirect effect is not significant. This suggests that as the Chinese and the governmen<sup>t</sup> have become more environmentally conscious, the role of foreign investors in increasing emissions has been reversed and has instead helped to reduce industrial pollution, which is consistent with Jorgenson's findings [52]. This is because China is focusing on bringing in high quality FDI (advanced production technologies) and gradually abandoning its role as a "processing plant". The direct effect coefficient of energy structure is negative and passes the 10% significance level test. The indirect effect coefficient is also negative and passes the 10% significance level test. This indicates, when the proportion of traditional coal energy drops, that the proportion of other relatively clean energy (wind power, hydroelectric power, etc.) in China has risen, thereby affecting industrial pollution emissions. This is consistent with the results of Wang et al.'s study [53], implying the importance of energy structure transformation in reducing industrial pollution.
