Monthly pork price forecasting method based on Census X12-GM(1,1) combination model

Background In recent years, the price of pork in China continues to fluctuate at a high level. The forecast of pork price becomes more important. Single prediction models are often used for this work, but they are not accurate enough. This paper proposes a new method based on Census X12-GM(1,1) combination model. Methods Monthly pork price data from January 2014 to December 2020 were obtained from the State Statistics Bureau(Mainland China). Census X12 model was adopted to get the long-term trend factor, business cycle change factor and seasonal factor of pork price data before September 2020. GM (1,1) model was used to fit and predict the long-term trend factor and business cycle change factor. The fitting and forecasting values of GM(1,1) were multiplied by the seasonal factor and empirical seasonal factor individually to obtain the fitting and forecasting values of the original monthly pork price series. Results The expression of GM(1,1) model for fitting and forecasting long-term trend factor and and business cycle change factor was X(1)(k) = −1704.80e−0.022(k−1) + 1742.36. Empirical seasonal factor of predicted values was 1.002 Using Census X12-GM(1,1) method, the final forecast values of pork price from July 2020 to December 2020 were 34.75, 33.98, 33.23, 32.50, 31.78 and 31.08 respectively. Compared with ARIMA, GM(1,1) and Holt-Winters models, Root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) of Census X12-GM(1,1) method was the lowest on forecasting part. Conclusions Compared with other single model, Census X12-GM(1,1) method has better prediction accuracy for monthly pork price series. The monthly pork price predicted by Census X12-GM(1,1) method can be used as an important reference for stakeholders.

The Ascending and Fading of a Progressive Policy Instrument: The Climate Change Factor in Southern Germany

The climate change factor (CCF) is a precautionary instrument for technical flood protection that was introduced in Southern Germany in the early 2000s. The CCF was designed as a surcharge value to be added to all new technical flood protection facilities, such as dams, protection walls, and retention areas. This paper deconstructs the conditions and processes that led to the creation of this new policy instrument. Following the instrument choice framework, the paper analyzes in a heuristic manner, the institutions, actors, discourses, and decision context that were part of this process from the early 1990s to 2004, when the instrument was introduced. In order to better understand the scope of this regional instrument, the paper also briefly depicts four non-representative cases of flood risk and protection management, where the instrument was either applied or avoided. The article closes with an assessment of the CCF, concluding that the innovativeness of this instrument faded once the overarching sectoral paradigm shifted from technical flood protection to more comprehensive flood risk management.