This section has given a brief introduction to both cross-sectional as well as panel models. The advantage of a pure cross-section is that it can capture how farmers adapt to changing climatic conditions, which is very different from year-to-year weather fluctuations. The potential pitfall is that it might suffer from omitted variable bias as the coefficients of interest wrongfully pick up the effect of other variables that were incorrectly excluded from the analysis. A panel analysis offers a solution to avoid additive time-invariant omitted variables. However, it comes at a price: similar to time series models discussed in the proceeding section, such an analysis uses year-to-year weather fluctuations to identify the parameters of interest and hence might measure a very different set of possible adaptation measures. The difference between panel and time series models is that a panel forces the slope of the regression line to be constant for all groups (countries).
In general, it might be worthwhile to conduct all analyses and examine whether they differ. If they do differ, further analysis is necessary to resolve these differences.
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Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.