This chapter has provided a glimpse into results from global and regional assessments conducted over the past decades. The key points are summarized below: • Global assessments have generally concluded small changes in global prices for a doubling of CO2, with gains in developed countries balancing losses in the tropics.
Yet these conclusions have been based on a relatively small number of models, and the sources and magnitudes of uncertainty have not been well quantified. Projections for CO2 concentrations more than double preindustrial levels (280 ppm) suggest price increases and negative impacts for food security. Regional assessments in China tend to show negative effects of warming but net positive yield changes when including CO2 fertilization, though many of these studies have unusually large amounts of modeled fertilization (upwards of 40% for mid-century).
Projections tend to be negative for India and Sub-Saharan Africa even over the next few decades, indicating that these two regions face relatively large risks of crop yield losses. Given the concentration of malnourished populations in these regions (see Chapter 2), these changes are of great importance to global food security. The United States will likely experience downward pressure on maize yields, a C4 crop with limited CO2 fertilization. Yield losses from warming will likely be balanced in other crops by CO2 effects in the next few decades. Both regional and global assessments would benefit from more explicit consideration of uncertainties from a variety of sources. A particularly important source of uncertainty, among processes currently represented in models, appears to be temperature sensitivity of crops. Effects of pests, diseases, extreme effects, and ozone represent additional factors that are not currently in most models. A relatively costly but invaluable approach to quantifying uncertainties is to have multiple modeling groups perform identical experiments with different models. An alternative is to approximate uncertainty in individual model components with statistical distributions, which lends itself to rapid propagation of errors using Monte Carlo techniques.
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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.