The financing strategies for a coal-electricity supply chain in which the coal company has capital constraint and faces yield uncertainty were studied. We propose an advance payment mechanism: in the coal company'...The financing strategies for a coal-electricity supply chain in which the coal company has capital constraint and faces yield uncertainty were studied. We propose an advance payment mechanism: in the coal company's initial production period, the electricity company provides advance payment to the coal company, and the coal company pays interest to the electricity company as the risk compensation. The optimal operation strategies for the coal company and the electricity company under the advance payment mechanism are derived and compared with those under the bank loan financing case. We find that,the expected profit functions of the coal company and the electricity company under the advance payment mechanism are the same with those under the case that the coal company has enough capital;under the advance payment mechanism, the profits of the coal company and the electricity company are higher than those under the bank financing case. We also discuss the compensation interest rate of the advance payment and the ordering and production quantities under the advance payment mechanism.展开更多
To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） v...To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.展开更多
Measuring the impacts of uncertainties identified from different global climate models(GCMs),representative concentration pathways(RCPs),and parameters of statistical crop models on the projected effects of climate ch...Measuring the impacts of uncertainties identified from different global climate models(GCMs),representative concentration pathways(RCPs),and parameters of statistical crop models on the projected effects of climate change on crop yields can help to improve the availability of simulation results.The quantification and separation of different sources of uncertainty also help to improve understanding of impacts of uncertainties and provide a theoretical basis for their reduction.In this study,uncertainties of maize yield predictions are evaluated by using 30 sets of parameters from statistical crop models together with eight GCMs with reference to three emission scenarios for Jilin Province of northeastern China.Regression models using replicates based on bootstrap resampling reveal that yields are maximized when the optimum average growing season temperature is 20.1°C for 1990–2009.The results of multi-model ensemble simulations show a maize yield reduction of 11%,with 75%probability for 2040–69 relative to the baseline period of 1976–2005.We decompose the variance so as to understand the relative importance of different sources of uncertainty,such as GCMs,RCPs,and statistical model parameters.The greatest proportion of uncertainty(>50%)is derived from GCMs,followed by RCPs with a proportion of 28%and statistical crop model parameters with a proportion of 20%of total ensemble uncertainty.展开更多
基金supported by the Project of Humanities and Social Sciences of Ministry of Education of China (No.16YJC630090)
文摘The financing strategies for a coal-electricity supply chain in which the coal company has capital constraint and faces yield uncertainty were studied. We propose an advance payment mechanism: in the coal company's initial production period, the electricity company provides advance payment to the coal company, and the coal company pays interest to the electricity company as the risk compensation. The optimal operation strategies for the coal company and the electricity company under the advance payment mechanism are derived and compared with those under the bank loan financing case. We find that,the expected profit functions of the coal company and the electricity company under the advance payment mechanism are the same with those under the case that the coal company has enough capital;under the advance payment mechanism, the profits of the coal company and the electricity company are higher than those under the bank financing case. We also discuss the compensation interest rate of the advance payment and the ordering and production quantities under the advance payment mechanism.
基金supported by the National Natural Science Foundation of China (41401491,41371396,41301457,41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2016-X38)+1 种基金the Agricultural Scientific Research Fund of Outstanding Talentsthe Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.
基金Supported by the National Natural Science Foundation of China(41505097)Basic Research and Operation Funds of Chinese Academy of Meteorological Sciences(2017Z004)
文摘Measuring the impacts of uncertainties identified from different global climate models(GCMs),representative concentration pathways(RCPs),and parameters of statistical crop models on the projected effects of climate change on crop yields can help to improve the availability of simulation results.The quantification and separation of different sources of uncertainty also help to improve understanding of impacts of uncertainties and provide a theoretical basis for their reduction.In this study,uncertainties of maize yield predictions are evaluated by using 30 sets of parameters from statistical crop models together with eight GCMs with reference to three emission scenarios for Jilin Province of northeastern China.Regression models using replicates based on bootstrap resampling reveal that yields are maximized when the optimum average growing season temperature is 20.1°C for 1990–2009.The results of multi-model ensemble simulations show a maize yield reduction of 11%,with 75%probability for 2040–69 relative to the baseline period of 1976–2005.We decompose the variance so as to understand the relative importance of different sources of uncertainty,such as GCMs,RCPs,and statistical model parameters.The greatest proportion of uncertainty(>50%)is derived from GCMs,followed by RCPs with a proportion of 28%and statistical crop model parameters with a proportion of 20%of total ensemble uncertainty.
