To clarify the relations between low-carbon green transition, consumption upgrading, and industrial structure change, this paper firstly builds a dynamic model of the three, then uses the PVAR Model and panel data of 30 Chinese provinces from 2008 to 2020 to carry out empirical study from the rationalization and upgrading dimensions of industrial structural change, respectively. The results are as follows: (1) Low-carbon green transition and consumption upgrading are Granger causes of each other. In this causal relationship, low-carbon green transition hinders consumption upgrading, but consumption upgrading significantly promotes low-carbon green transition. (2) Low-carbon green transition plays a facilitating and hindering role in industrial structure rationalization and upgrading, respectively. However, from the different dimensions of industrial structure change, only industrial structure upgrading has a significant reverse hindering effect on low-carbon green transition, and the reverse effect of industrial structure rationalization on low-carbon green transition is not significant. (3) Consumption upgrading has a hindering and promoting effect on the rationalization of industrial structure in the short- and long-run respectively, and a promoting and hindering effect on the industrial structure upgrading respectively; however, only industrial structure upgrading significantly promotes consumption upgrading in the opposite direction, while industrial structure rationalization has no significant effect on consumption upgrading. These findings propose some suggestions such as advocating the new way of green consumption, constructing and improving the green whole industry chain, and strengthening the synergy between imitative innovation and independent innovation.

To explore the relationship between consumption upgrading and agricultural green total factor productivity in the context of green and high-quality development of agriculture in China. Based on the construction of a composite index of consumption upgrading and the Malmquist index of non-expected output in the SBM-DEA model to measure agricultural green total factor productivity, this paper uses the PVAR model and panel data from 30 Chinese provinces from 2008 to 2020 to empirically analyze the mechanism of the effect of consumption upgrading on agricultural green total factor productivity under high-quality development. The results are as follows: (1) Both the real economy and consumption upgrading are ahead of the change in agricultural green total factor productivity and have a negative short-run impact on agricultural green total factor productivity but a continuous boosting effect in the long-run. (2) In terms of specific impact paths, the real economy boosts agricultural green total factor productivity through technical efficiency and technical change paths and has a negative impact through scale efficiency, whereas consumption upgrading has inhibitory and sustained promotional effects in the short- and long-run, respectively, through technical efficiency and technical change paths and has opposite impact effects in the scale efficiency path.

To investigate Chinese residents’ consumption capacity and consumption upgrading, this study uses the Moran index, the Dagum Gini coefficient method, and the SPVAR model to analyze the spatial-temporal differentiation and interactions between the two; the entropy method is employed to measure the development level of both factors. As per the results, from 2008 to 2020, the levels of consumption capacity and consumption upgrading have increased rapidly and are distinctly unbalanced, but there is a clear trend of convergence between the developments in each province, and both show spatial agglomeration, mainly in the “high–high” and “low–low” clusters. The “high–high” clusters are primarily concentrated in the more economically developed eastern coastal and central fast-rising regions, with inter-regional differences being the main reason for the regional differences between the two. In terms of spatial interaction, the two are Granger causalities, with positive feedback effects that promote each other, and have a “center–periphery” spatial spillover effect, where the center generates spatial spillover and also suffers reverse shocks from the periphery, while the shock effect is limited by inter-provincial economic ties and geographical distance.