Rapid urbanization in China greatly contributes to carbon emissions, while the industrial structure greatly contributes to changes in the variation of carbon emissions. This research addresses the impact of urbanization and industrial structure on carbon emissions from 2010 to 2018, by focusing on the Huaihe River Eco-economic Zone, which is an important economic corridor along the north–south division of China. Although many studies have focused on investigating the impact of urbanization or industrial structure on carbon emissions, few studies further addressed an analysis of the impact of both on carbon emissions, using multiple measurement models. This paper reveals the holistic and local impact of industrial structure and urbanization on carbon emissions, by integrating a threshold regression model with geographically weighted regression. The results are as follows: (1) From a holistic point of view, industrial structure and urbanization had both, single threshold, and double threshold effects on carbon emissions in the Huaihe River Eco-economic Zone. (2) From a regional perspective, the coefficients of industrial structure on carbon emissions were all positive, but the rate of increase gradually slowed down. The coefficients of urbanization on carbon emissions were all negative, reaching a maximum value of negative effect in 2013. Understanding the holistic and local impact of urbanization and industrial structure on carbon emissions provides governments with differentiated and forward-looking suggestions for mitigating carbon emissions in the Huaihe River Eco-economic Zone.

The River Chief Policy (RCP) is an innovative water resource management system in China aimed at managing water pollution and improving water quality. Though the RCP has been piloted in some river basins of China, few scholars have studied the effects of the policy. We built a differential game model under random interference factors to compare the water pollution in Chaohu Lake under the RCP and without the RCP, and we explored the conditions to ensure the effectiveness of the RCP. The results showed that: (1) The average effect of water pollution control under the RCP was greater than under non-RCP; (2) the higher the rewarding excellence and punishing inferiority coefficient ( $\theta$ ) was, the better the water pollution control effect under the RCP; (3) the greater the random interference coefficient ( $\sigma$ ) and rewarding excellence and punishing inferiority coefficient ( $\theta$ ) were, the bigger the fluctuation of the water pollution control effect was; (4) when using the stochastic differential game, when $\sigma \le 0.0403$ , $\theta \ge 0.0063$ , or $\sigma >0.0403$ , $\theta \ge 0.268$ , the RCP must be effective for water pollution control. Therefore, we can theoretically adjust the rewarding excellence and punishing inferiority coefficient ( $\theta$ ) and the random interference coefficient ( $\sigma$ ) to ensure the effective implementation of the RCP and achieve the purpose of water pollution control.