Data from farmer-managed fields have not been used previously to disentangle the impacts of daily minimum and maximum temperatures and solar radiation on rice yields in tropical/subtropical Asia. We used a multiple regression model to analyze data from 227 intensively managed irrigated rice farms in six important riceproducing countries. The farm-level detail, observed over multiple growing seasons, enabled us to construct farm-specific weather variables, control for unobserved factors that either were unique to each farm but did not vary over time or were common to all farms at a given site but varied by season and year, and obtain more precise estimates by including farm-and site-specific economic variables. Temperature and radiation had statistically significant impacts during both the vegetative and ripening phases of the rice plant. Higher minimum temperature reduced yield, whereas higher maximum temperature raised it; radiation impact varied by growth phase. Combined, these effects imply that yield at most sites would have grown more rapidly during the high-yielding season but less rapidly during the low-yielding season if observed temperature and radiation trends at the end of the 20th century had not occurred, with temperature trends being more influential. Looking ahead, they imply a net negative impact on yield from moderate warming in coming decades. Beyond that, the impact would likely become more negative, because prior research indicates that the impact of maximum temperature becomes negative at higher levels. Diurnal temperature variation must be considered when investigating the impacts of climate change on irrigated rice in Asia.T he impacts of temperature and solar radiation on rice yield remain imperfectly understood, despite decades of agronomic research. Current knowledge is based primarily on field trials and greenhouse experiments. These experimental studies indicate that increased temperature (1-4) and decreased radiation (1, 3, 5) can reduce yield, with the impacts varying across the plant's three growth phases (vegetative, establishment to panicle initiation; reproductive, panicle initiation to flowering; ripening, flowering to mature grain). Unresolved issues remain with respect to the relative impacts of temperature during daytime (T max ) vs. nighttime (T min ), potentially confounding impacts of temperature and radiation, and the magnitude of impacts in nonexperimental settings. Here, we investigate these issues by analyzing data from the largest farm-level rice study conducted in Asia since the mid-1980s. We use disaggregated data from farmer-managed fields to disentangle the impacts of T min , T max , and solar radiation on rice yield.With few exceptions (6, 7), most statistical studies on temperature and rice yield have focused on the impact of daily mean temperature (T ave ), despite evidence that that the effects of T min and T max on crop phenological development and physiological processes differ (4). It is well-established that extremely high levels of T max during flowering...
Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.JEL classification numbers and key words: Q54 [Climate Change, Impacts Estimation]
Our results suggest that the anticipated path of China's Carbon Dioxide (CO 2 ) emissions has dramatically increased over the last five years. The magnitude of the projected increase in Chinese emissions out to 2010 is several times larger than reductions embodied in the Kyoto Protocol. Our estimates are based on a unique provincial level panel data set from the Chinese Environmental Protection Agency. This dataset contains considerably more information relevant to the path of likely Chinese greenhouse gas emissions than national level time series models currently in use. Model selection criteria clearly reject the popular static environmental Kuznets curve specification in favor of a class of dynamic models with spatial dependence.
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