Meta-analysis of nature conservation values in Asia & Oceania: Data heterogeneity and benefit transfer issues

We conduct a meta-analysis (MA) of around 100 studies valuing nature conservation in Asia and Oceania. Dividing our dataset into two levels of heterogeneity in terms of good characteristics (endangered species vs. nature conservation more generally) and valuation methods, we show that the degree of regularity and conformity with theory and empirical expectations is higher for the more homogenous dataset of contingent valuation of endangered species. For example, we find that willingness to pay (WTP) for preservation of mammals tends to be higher than other species and that WTP for species preservation increases with income. Increasing the degree of heterogeneity in the valuation data, however, preserves much of the regularity, and the explanatory power of some of our models is in the range of other MA studies of goods typically assumed to be more homogenous (such as water quality). Subjecting our best MA models to a simple test forecasting values for out-of-sample observations, shows median (mean) forecasting errors of 24 (46) percent for endangered species and 46 (89) percent for nature conservation more generally, approaching levels that may be acceptable in benefit transfer for policy use. We recommend that the most prudent MA practice is to control for heterogeneity in regressions and sensitivity analysis, rather than to limit datasets by non-transparent criteria to a level of heterogeneity deemed acceptable to the individual analyst. However, the trade-off will always be present and the issue of acceptable level of heterogeneity in MA is far from settle

Forecasting of paddy production in Sri Lanka: a time series analysis using ARIMA model

Forecasting of paddy production is a need for planning purposes and import policy of rice should be based on such forecasts. Even though Sri Lanka has achieved self sufficiency in rice the expenditure on rice sector has increased continuously. The objectives of this study are to investigate the past, present and future trends of paddy production in Sri Lanka and to develop a time series model to detect the long term trend and prediction for future changes of paddy production for the three leading years. Autoregressive Integrated Moving Average (ARIMA) was used to fit the data set which is complementary to the trend regression approach and forecasting of the concerned variable to the near future. Time series forecasting analysis utilized the secondary data of the Department of Census and Statistics of Sri Lanka for the period of 1952 to 2010. Non-stationarity in mean was corrected through differencing of the data of order 1. ARIMA (2, 1, 0) was the most suitable model used as this model has the lowest AIC and BIC values. The Mean Absolute Percentage Error (MAPE) for paddy production was 10.5. The forecasts for paddy production during 2011 to 2013 were 4.07, 4.12 and 4.22 million Mt respectively, and the production for the year 2011 and 2012 was lower than in 2010. However in later year 2013 the production was higher. This model can be used by researchers for forecasting of paddy production in Sri Lanka. But, it should be updated continuously with incorporation of recent data