China has promoted innovation-driven and green development to unprecedented strategic heights. However, compared to the large and rapid innovation investment, total factor productivity's (TFP) growth rate has shown a downward trend. Consequently, this study assesses the inefficiency caused by resource mismatch and discusses the impact of green innovation activities on green total factor productivity (GTFP). We use a causal forest-based machine learning method to solve the endogenous problem. The empirically analyzes the observation samples of 272 prefecturelevel cities in China from 2008 to 2018 and obtains the asymptotic normality estimation on the average treatment effect (ATE). Simultaneously, clustering causal forest and ridge expressions, discusses the robustness of related problems. According to the results, (1) the effect of China's green innovation on GTFP is negative for a short time and positive for a long time; (2) the impact of green innovation activities on GTFP is subject to capital mismatch, while the statistical law of the impact of labor mismatch is not obvious but the adverse impact of resource mismatch is gradually improving; and (3), Green innovation has significantly improved China's GTFP, but it did not lead to ideal Growth rate of GTC.

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There is a strong correlation between government intervention and urban production structure in China. Particularly, the outputs of the cities partly come from the economic rent of city relational network (CRN), which is a unique regional policy and administrative hierarchy. In order to analyze the gravity flows of CRN under the nonmarket mechanism, we attempt to build a new gravity model that adopts the production sector. The new gravity produces relational data with direction, which makes it possible to use social network analysis (SNA) and overcome the endogeneity of the linear model. The empirical results show that (1) modified new gravity model can effectively capture the distribution of CRN gravity flows and the convergence of regional development in China, (2) the CRN, which especially stems from the government financial intervention, increases the share of nontradable sectors in cities, and (3) adjustment of the production sector leads to the difference of CRN gravity flows, so asymmetric flows distribution leads to the heterogeneity of regional economic performance. Cities with higher share of nontradables have relatively slower productivity growth in long-term.

Brain stimulation experiment has revealed the association law between loss aversion tendency and brainspecific nervous system, providing a solid scientific basis for deep understanding of human behavioral preferences, as well as providing another research scheme for controversial school choice. There are many uncertain risks in the process of school choice, so asymmetry appears when the decision maker's brain processes gain and loss, which causes obvious school-choice preference reversal. Based on the inherent law of brain activity studied by fMRI and through analyzing the phenomenon of human choice behavior under the admission system in China, this study proves that applicants appear limited rationality or preference reversal when the emotional brain area is hyperactive. In addition, in the priority to the first applications (PTTFA) mechanism, applicants reveal that true preference is not a dominant strategy, which will lead to market imbalances and low inefficiency.

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