Agricultural insurance is instrumental in consolidating the gains of poverty alleviation and advancing rural revitalization. It significantly aids in the efficient allocation of agricultural production factors, which in turn enhances agricultural output and bolsters the evolution of modern agriculture. Therefore, utilizing data from 583 household surveys and employing endogenous transformation and intermediary effect models, this paper analyzes the production factor allocation effect and specific mechanism of agricultural insurance. It focuses on small-scale farmers and new agricultural operators, exploring how insurance contributes to the advancement of modern agricultural practices. The results show the following: (1) Agriculture insurance can significantly affect the agricultural scale input behavior of farmers such as land input scale and input scale, agricultural machinery application behavior such as the degree of mechanization and water conservancy application, agricultural technology adoption behavior, and planting structure selection behavior, thereby helping to modernize agriculture. (2) There is heterogeneity in the impact of agriculture insurance on the allocation of production factors for small farmers and new agricultural operators. For small farmers, agriculture insurance has a significant promoting effect on their agricultural machinery application behavior, agricultural technology adoption behavior, and planting structure selection behavior. For new agricultural operators, agriculture insurance significantly promotes their agricultural scale input behavior, agricultural machinery application behavior, and agricultural technology adoption behavior. (3) In terms of the mechanism of action, agriculture insurance mainly promotes agricultural scale input behavior through land transfer, facilitates agricultural machinery application behavior by purchasing agricultural machinery equipment and services, encourages agricultural technology adoption behavior by strengthening agricultural technology training, and enhances professional production levels by increasing the scale of insured planting, thereby contributing to the development of modern agriculture. Based on this, several policy suggestions have been proposed. These include enhancing the directionality of agriculture insurance policies, improving the collaborative interaction mechanism between agriculture insurance and agricultural credit financing, and adopting certain reward and punishment measures to curb moral hazard.

To achieve sustainable economic growth, a significant amount of private capital must be invested in green industries. However, risk management in the green industry stock market has drawn much attention recently due to the uncertainty and high risk present in this market. By applying the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Baruník and Křehlík, and the dynamic conditional correlation (DCC) model, this paper focuses mainly on the heterogeneity of the volatility spillovers among six green industry equities and other financial assets in China, under various market economy situations. Based on the empirical results obtained in this paper, we find that the green industry stock markets have the least impact on the gold and energy futures markets. Additionally, based on asymmetric analyses, it can be concluded that the green bond market has experienced the smallest shocks from the six green industry stock markets. By utilizing frequency-domain analyses, the energy futures market experiences the least amount of volatility from green stocks. Additionally, the COVID-19 pandemic affects the interconnectedness of markets. Prior to the COVID-19 pandemic, energy futures were the most suitable portfolio instrument for green industry stocks. When the COVID-19 pandemic occurred, however, gold proved to be the most advantageous portfolio asset. The research findings of this paper demonstrate the impact of COVID-19 on the selection of the best investment instruments for green industry stocks, which is beneficial for reducing the investment risk of green financial market participants and increasing the demand for green stock markets, while also providing practical advice for environmentally conscious investors and policymakers.

Traditional DID models overlook variations in policy intensity, causing estimation deviations from the actual situation and a limited understanding of the influence mechanism. In response, the Intensity Modified SDID Model is built to examine the influence mechanism of ETS's carbon reductions. Moreover, through model extensions, the study explores the spatiotemporal characteristics and heterogeneities of ETS’s effects. Results show that: (1) "Dual-circulation" influence mechanism is confirmed, where ETS directly contributes to carbon reductions (2.70% to 10.0% impact) through external pathways, and internal pathways continuously strengthen reduction effects, comprehensive mechanisms are thereby formed and enhanced based on interaction among internal and external pathways. (2) Reasonable ETS levels are estimated and proposed to achieve "Dual Carbon Target", constraining nationwide carbon quotas by 20 billion tons/year, increasing carbon trading volumes by 80 thousand tons/year, and elevating the carbon trading prices by 100 RMB (14 USD) per ton. (3) ETS's carbon reduction effects are identified with temporal and spatial characteristics, temporally, effects peak in the 4th period (Event₊₄) but diminish in the 5th period (Event₊₅), spatially, effects peak in areas distancing around 1000 km but disappear beyond 1500 km. (4) ETS also has synergistic effects with atmospheric pollution reduction, including industrial emissions of sulfur dioxide and smoke (dust), but are insignificant to industrial emissions of wastewater and solid waste.