Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested o...Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries(declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging(MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error(REE) of maximum sustainable yield(MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model(low REE) in all cases when the white noise level was low(coefficient of variation(CV) = 0.02). However, when the white noise level increased(CV= 0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.展开更多
China＇s energy carbon emissions are projected to peak in 2030 with approximately 110% of its 2020 level under the following conditions： 1） China＇s gross primary energy consumption is 5 Gtce in 2020 and 6 Gtce in 2...China＇s energy carbon emissions are projected to peak in 2030 with approximately 110% of its 2020 level under the following conditions： 1） China＇s gross primary energy consumption is 5 Gtce in 2020 and 6 Gtce in 2030; 2） coal＇s share of the energy consumption is 61% in 2020 and 55% in 2030; 3） non-fossil energy＇s share increases from 15% in 2020 to 20% in 2030; 4） through 2030, China＇s GDP grows at an average annual rate of 6%; 5） the annual energy consumption elasticity coefficient is 0.30 in average; and 6） the annual growth rate of energy consumption steadily reduces to within 1%. China＇s electricity generating capacity would be 1,990 GW, with 8,600 TW h of power generation output in 2020. Of that output 66% would be from coal, 5% from gas, and 29% from non-fossil energy. By 2030, electricity generating capacity would reach 3,170 GW with 11,900 TW h of power generation output. Of that output, 56% would be from coal, 6% from gas, and 37% from non-fossil energy. From 2020 to 2030, CO2 emissions from electric power would relatively fall by 0.2 Gt due to lower coal consumption, and rela- tively fall by nearly 0.3 Gt with the installation of more coal-fired cogeneration units. During 2020--2030, the portion of carbon emissions from electric power in China＇s energy consumption is projected to increase by 3.4 percentage points. Although the carbon emissions from electric power would keep increasing to 118% of the 2020 level in 2030, the electric power industry would continue to play a decisive role in achieving the goal of increase in non-fossil energy use. This study proposes countermeasures and recommendations to control carbon emissions peak, including energy system optimization, green-coal-fired electricity generation, and demand side management.展开更多
基金supported by the special research fund of Ocean University of China (201022001)
文摘Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries(declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging(MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error(REE) of maximum sustainable yield(MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model(low REE) in all cases when the white noise level was low(coefficient of variation(CV) = 0.02). However, when the white noise level increased(CV= 0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.
文摘China＇s energy carbon emissions are projected to peak in 2030 with approximately 110% of its 2020 level under the following conditions： 1） China＇s gross primary energy consumption is 5 Gtce in 2020 and 6 Gtce in 2030; 2） coal＇s share of the energy consumption is 61% in 2020 and 55% in 2030; 3） non-fossil energy＇s share increases from 15% in 2020 to 20% in 2030; 4） through 2030, China＇s GDP grows at an average annual rate of 6%; 5） the annual energy consumption elasticity coefficient is 0.30 in average; and 6） the annual growth rate of energy consumption steadily reduces to within 1%. China＇s electricity generating capacity would be 1,990 GW, with 8,600 TW h of power generation output in 2020. Of that output 66% would be from coal, 5% from gas, and 29% from non-fossil energy. By 2030, electricity generating capacity would reach 3,170 GW with 11,900 TW h of power generation output. Of that output, 56% would be from coal, 6% from gas, and 37% from non-fossil energy. From 2020 to 2030, CO2 emissions from electric power would relatively fall by 0.2 Gt due to lower coal consumption, and rela- tively fall by nearly 0.3 Gt with the installation of more coal-fired cogeneration units. During 2020--2030, the portion of carbon emissions from electric power in China＇s energy consumption is projected to increase by 3.4 percentage points. Although the carbon emissions from electric power would keep increasing to 118% of the 2020 level in 2030, the electric power industry would continue to play a decisive role in achieving the goal of increase in non-fossil energy use. This study proposes countermeasures and recommendations to control carbon emissions peak, including energy system optimization, green-coal-fired electricity generation, and demand side management.
