Based on the transpiration transfer coefficient (hat), the cooling potential of several typical subtropical urban vegetations was evaluated. A Ficus concinna was continuously observed to quantitatively study its characteristics of transpiration, cooling effect and carbon-reduction effect. The results showed that 1) among several typical vegetations in the study area, Ficus concinna, as a native tree species, showed the strongest cooling potential in the same environment. 2) The average daily transpiration volume of Ficus concinna was 32.48 kg per tree, which was generally high in summer and autumn and low in spring and winter. At the same time, Ficus concinna could effectively alleviate the heat island effect, especially at night in summer when the heat island effect was severe, its cooling effect made the observation area no heat island state for 86% of the time. 3) The annual carbon emission reduction from the cooling effect of individual Ficus concinna reached 1442.1 kg.

Based on a pair of black and white standard A-type evaporation pans set under the same meteorological conditions, the dynamic characteristics of meteorological elements, water temperature and evaporation of the two pans were observed. The influence of water temperature on the evaporation of the evaporation pan was explored based on the existing 6 evaporation models. The results showed that 1) the designed observation method can be used to study the effect of water temperature on the pan evaporation. The water temperature and evaporation rate between the two pans were significantly different. During the 50-day observation period, the average water temperature difference between the two pans was 0.4°C, and the average daily evaporation difference was 1.1 mm/d. 2) 1°C increase in water temperature difference between black and white pans will produce an evaporation difference of 0.808 mm/d under the same conditions of solar radiation and other meteorological elements. 3) Under the condition of rising water temperature, the estimated value of the classical evaporation model without considering the water temperature would be smaller than the actual observed value, and the error of the estimation will also increase correspondingly.

Based on the sap flow system and synchronous meteorological observation data of the typical arbor tree in the city, a transpiration estimation model for urban arbor tree was built using deep neural network. The simulation results can systematically figure out the environmental controlling factors that affect the transpiration of Ficus microcarpa in the dry or wet seasons as well as day or night. Based on the routine meteorological observation data from 91 meteorological observation stations in Shenzhen, the trained deep neural network was used to estimate the station-scale hourly transpiration characteristics of typical arbor trees in Shenzhen. The results show that 1) compared with the measured data of the sap flow system, the deep neural network can accurately simulate the transpiration change of the Ficus microcarpa at 10-minute intervals with a R² of 0.91, MAPE of 21.77%, RMSE of 0.02 mm/h. 2) The main controlling factors of urban Ficus microcarpa during the wet and dry seasons are solar radiation and air temperature in the daytime, while at night is saturated water vapor pressure deficit. 3) Urban Ficus microcarpa still has transpiration at night, and average value can be 0.03 mm/h and 0.01 mm/h in dry season and wet season, respectively. 4) There are differences among vegetation transpiration in different areas of Shenzhen, with a maximum difference of 0.10 mm/h. In general, the transpiration during the dry season is higher than that during the wet season, and the vegetation transpiration at most sites are close to 0 at night. For some specific sites, the average transpiration at night can reach 0.07 mm/h in dry season, and can reach 0.10 mm/h in the wet season. 

Based on system-generalized method of moments (SYS-GMM), this paper analyzes the influence of environmental regulation on the GTFP (green total factor productivity) of 37 industries in China during the period of 2003–2015 by mearsuring the intermediate effect of liquidity ratio. The result shows that the compound annual growth rate (CAGR) of GTFP among all industries was 6.8%. Technical development was the main contributor ameliorating GTFP. GTFP of industries above designated size goes upward and then downward in accordance with the stringency of environmental regulation. Short-term liquidity plays a role as intermediate variable, since it drops as environmental regulation gets sticter, which leads to a rise in GTFP of industries. When environmental regulation gets too strict for enterprises to comply with, their short-term liquidity ratio decreases significantly, which brings financial risks and leads to a reduction of GTFP. These conclusions are of great significance for government and enterprises to enact and comply with environmental and development strategies.

Based on the HiWATER high-density eddy covariance (EC) tower observations in Heihe Oasis in 2012, the impact of model structure differences (comparison between one-source Penman-Monteith / PM equation and two-source PM equation, or comparison between two-source PM equation and two-source three-temperature model) and parameterization differences on the evapotranspiration estimation were evaluated. The results show that, 1) compared with the two-source PM equation with a relatively complex model structure, the mean absolute percent error (MAPE) estimated by the one-source PM equation is 34%, which is more accurate than that by the two-source PM equation (40%); 2) for two kinds of two-source model with significant differences in model structure, the three-temperature model without resistance parameters has higher estimation accuracy than the PM-based equation with resistance parameters. The former has a MAPE of 18% (R²=0.85), while the PM-based equation has that of 40% (R²=0.34); 3) two one-source and one two-source resistance parameterization methods lead to different evapotranspiration estimation accuracy for the PM-based equation, with a MAPE difference of up to 6%; 4) using prior knowledge / dataset to calibrate resistance parameterization can significantly improve the estimation accuracy of one-source PM equation (MAPE can be reduced by 22%), but as model structure and parameterization complexity increase, two-source PM equation hasn’t been improved significantly after resistance parameterization calibration (MAPE is only reduced by 0.8%).

To investigate the characteristics of nighttime sap flow and its partition into nocturnal water refilling and transpiration, the Granier-type thermal dissipation probes were used to measure sap flux density in 2013 in three main species of a mixed forest in Jiuzhaigou Valley. The results showed that the ratio of nighttime sap flow to daily value mainly ranged from 0 to 30% for Betula albosinensis Burk. and Acer ginnala Maxim., from 0 to 25% for Pinus tabulaeformis Carr., and might excess 40% in some special occasions. It showed a “U-shaped” seasonal variation, with lower ratios in the mid-growing season and higher ratios in the early and late growing season. There was a significant linear relationship between nighttime sap flow and vapor pressure deficit during a clear day, indicating the occurrence of nocturnal transpiration along with refilling. The ratios of nocturnal water refilling and transpiration to the total nighttime sap flow were 80.7% and 19.3%, 81.4% and 18.6%, 63.9% and 36.1%, respectively for the three tree species.