The river chief system (RCS) has been innovatively implemented in Wuxi, China since 2007 for the eutrophication control of Tai Lake. In 2016, RCS was eventually promoted throughout China to reinforce river and lake protection. The success of this new river management system is generally attributed to collaboration, accountability, and differentiation effects. This research takes Foshan in the Pearl River Delta region as a case study to examine the feasibility and weaknesses in the implementation of the RCS. Prior to the formal adoption of RCS, a coordinating organization for river improvement undertaking was established in Foshan to overcome fragmentation in water management. Compared with this practice, the new RCS can strengthen the collaboration of administrative authorities and establish a considerably sophisticated and effective management structure. Emphasis on evaluation and accountability mechanisms guarantees that management goals can be achieved. However, similar to the previous one, the new system remains a temporary management practice and its outcomes depend partially on the commitment and capability of each river chief. The imperfect evaluation and accountability mechanism also weaken its long-term effectiveness in improving river water quality. Therefore, some corresponding policy instruments are needed to ensure that RCS can be implemented smoothly.

Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R²) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194,978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050.

Urban green vegetation provides amenity value for urban residents and improves the living environment. These changes in value can be reflected in the variation of house prices. This paper uses a unique housing transaction data set to estimate the impact of urban green vegetation on house values in Beijing, China, using the hedonic price model. To measure urban green vegetation, we calculated the normalized difference vegetation index (NDVI) by time-series Landsat TM 8 remote-sensing imagery. For model specification, we applied spatial lag models to address spatial spillover effects that might lead to biased estimates if ignored. Spatial hedonic results indicate that urban green vegetation has a positive effect on residential housing prices when the measurement scale of NDVI value is smaller than or equal to 18.01 acres for each block of flats, whereas greater NDVI values generate a negative effect on property values if the scale is larger. This implies that urban green vegetation within 135 m could increase house prices in the range of 7.95–10.59%, which indicates a potential 20.2–26.9 billion yuan increase in the value of the sample real estate market in Beijing.