China's coal market is now in the midst of a radical restructuring that has the potential to change how coal is produced, traded and consumed both in China and the rest of the world.  The restructuring aims to integrate the coal and power sectors at giant "coal-power bases" that combined would churn out more coal annually than all the coal produced in the entire United States. 

Coal-power integration is now a focal point of the Chinese government's energy policy, driven by the dramatic "coal-power conflict".  Coal prices are market-based, but power prices are tightly controlled by the government.  This has caused massive losses for Chinese power generators in 2008 and 2010 and triggered government intervention in the coal market with attempts to cap the price of coal.  The pervasive conflict between coal and power is now driving the Chinese government to remake these markets.

Coal-power base policy aims to establish upwards of 14 major coal-power bases, each producing over 100 mt of coal with consuming industries on-site.  The plan envisions that roughly half of China's coal production would be produced at a handful major coal-power base sites that are controlled by key state-owned enterprises (SOEs) and the central government.    

PESD's new research analyzes China's coal-power base reforms and how they will impact Chinese and global coal markets.  Several key findings are:

First, the implementation of coal-power bases would enhance central government's control over the coal sector and over coal prices.  The government could control coal pricing in a large share of the market and mitigate power sector losses by mandating lower coal transaction prices within integrated SOEs.  Using this kind of internal transfer pricing at below market prices for up to half of China's coal would represent a meaningful shift in how coal is priced in China.  If a large share of China's coal were transacted in this manner, it might create an unofficial two-tiered pricing structure in the coal market.

Second, coal-power base policy would bring about modernization and mechanization of a larger share of China's coal production, in theory bringing larger economies of scale to the sector.  While up-front capital investment per ton produced will certainly increase, the marginal cost of coal production should decrease, all other things equal. 

Third, the massive rebalancing of China's coal market implied by coal-power bases is poised to have important impacts on the globally traded coal market.  Since 2009, China's import behavior has become a dominant factor determining the price of globally traded coal.  In simple terms, when Chinese domestic prices are higher than global prices, the country imports.  The development of coal-power bases could radically alter coal price formation in China and directly impact China's appetite for imports, and therefore has the potential to alter coal price formation globally.

Brazil has developed a large-scale commercial agricultural system, recognized worldwide for its role in domestic economic growth and expanding exports. However, the success of this sector has been associated with widespread destruction of Brazilian ecosystems, especially the Cerrado and the Brazilian Amazon rainforest, as well as environmental degradation. Brazil's agricultural development has also led to land consolidation, aggravating a historical land distribution inequality. This pattern of agricultural growth has reinforced Brazil's status as one of the world's most inequitable countries in terms of income distribution, making it difficult to assert that the nation is pursuing a sustainable development path. In order to achieve sustainable development Brazil must reconcile its increasingly productive, modern tropical agricultural system with environmental preservation, social equity, and poverty alleviation in rural and urban areas. Although a daunting task, Brazil has the opportunity to lead tropical countries in combining modernized agriculture with highly diverse and functional ecosystems. Continued improvement in socioeconomic conditions is equally important and will require stronger efforts to decrease inequalities in income and land distribution in the rural sector.

Expanding croplands to meet the needs of a growing population, changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha-1 vs. ∼63 tons·ha-1) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha-1·y-1 vs. 3.84 tons·ha-1·y-1). Therefore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions.

Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often disagree, understanding the sources of this divergence is central to building a more robust picture of climate change's likely impacts. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Although the general strengths and weaknesses of statistical models are widely understood, there has been little systematic evaluation of their performance relative to other methods. Here we use a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. The CERES-Maize model was first used to simulate historical maize yield variability at nearly 200 sites in Sub-Saharan Africa, as well as the impacts of hypothetical future scenarios of 2◦C warming and 20% precipitation reduction. Statistical models of three types (time series, panel, and cross-sectional models) were then trained on the simulated historical variability and used to predict the responses to the future climate changes. The agreement between the process-based and statistical models' predictions was then assessed as a measure of how well statistical models can capture crop responses to warming or precipitation changes. The performance of statistical models differed by climate variable and spatial scale, with time-series statistical models ably reproducing site-specific yield response to precipitation change, but performing less well for temperature responses. In contrast, statistical models that relied on information from multiple sites, namely panel and cross-sectional models, were better at predicting responses to temperature change than precipitation change. The models based on multiple sites were also much less sensitive to the length of historical period used for training. For all three statistical approaches, the performance improved when individual sites were first aggregated to country-level averages. Results suggest that statistical models, as compared to CERES-Maize, represent a useful if imperfect tool for projecting future yield responses, with their usefulness higher at broader spatial scales. It is also at these broader scales that climate projections are most available and reliable, and therefore statistical models are likely to continue to play an important role in anticipating future impacts of climate change.

Global demand for agricultural products such as food, feed, and fuel is now a major driver of cropland and pasture expansion across much of the developing world. Whether these new agricultural lands replace forests, degraded forests, or grasslands greatly influences the environmental consequences of expansion. Although the general pattern is known, there still is no definitive quantification of these land-cover changes. Here we analyze the rich, pan-tropical database of classified Landsat scenes created by the Food and Agricultural Organization of the United Nations to examine pathways of agricultural expansion across the major tropical forest regions in the 1980s and 1990s and use this information to highlight the future land conversions that probably will be needed to meet mounting demand for agricultural products. Across the tropics, we find that between 1980 and 2000 more than 55% of new agricultural land came at the expense of intact forests, and another 28% came from disturbed forests. This study underscores the potential consequences of unabated agricultural expansion for forest conservation and carbon emissions.