The A2 vision focuses primarily on Low Density Residential development, while the B1 scenario is relatively evenly split between Low, Medium, and High Density types, and AB32-Plus favors Medium and High densities. In addition to modeling these three scenarios, we examined additional versions of A2 and AB32-Plus in which we held population constant at the B1 level, and in which we held population, energy efficiency for both homes and vehicles, and utility-portfolio assumptions constant. This step provides a more analogous comparison of the land use influence within the three scenarios.After modeling urban-growth footprints for the A2, B1, and AB32-Plus scenarios, we calculated two main categories of GHG emissions for the new urbanization produced by each scenario. These calculations help provide a ballpark sense of the magnitude of emissions variations that can result from different policy approaches. For the sake of simplicity, we focused on emissions from the operation of motor vehicles and residential structures, not their life cycle emissions from construction and materials, because operating emissions are likely to be a large majority of the total in both cases and thus estimate the emissions trade offs of different urbanization trajectories. Within the transportation category of GHG emissions, many factors potentially affect individual travel decisions within a given type of urban location, including: land use mix and densities in the surrounding area; the availability, attractiveness, and price of alternative travel modes; the nature of the travel route network, including available route choices and congestion; social pressures, influences, and incentives; and self-selection of residents living in that type of urban location.
An extensive field of travel modeling has attempted to take many of these variables into account ,plastic gardening pots usually projecting travel in the future based on changes to current conditions. But given that travel, like many other behavior choices, is highly multi-determined, the process is problematic. Even within time frames of 20 years or less, travel forecasts are often highly inaccurate , and have had particular difficulties in incorporating variables related to land development and urban design. For a longer time frame such as 2050, social factors, economic conditions, and behavioral changes are likely to play a larger role, changing travel demand in unpredictable ways and making modeling even more problematic. Accordingly, we have chosen here to keep our calculations to a very basic level, simply extrapolating travel based on the existing range of travel differences between residents in areas of different densities. Household travel surveys done by SACOG show that household vehicle miles travelled vary by a factor of 6 between households in low-density and high-density locations . Some of this difference may be due to household size, composition, and demographics, but much is probably due to accessibility factors , including proximity to jobs, shopping, and schools, and alternative transportation modes. All of these environmental variables can be assumed to vary in unison: the B1 and AB32-Plus storylines assume improved balance of jobs, housing, and shopping within communities; improved bicycle, pedestrian, and public-transit options; rising gas prices and/or carbon taxes; and other economic incentives such as higher parking and road-use charges. Likewise, we can assume that these multiple changes tend to influence resident behavior in synergistic ways; for example, individuals drive less in a dense urban environment because people discover alternatives and are influenced by their peers. Thus, the assumption of a strong difference in driving between low- and high-density environments in 2050 for purposes of back casting scenarios seems reasonable.
Transportation emissions also depend on the fuel efficiency of motor vehicles. The average fuel efficiency of American vehicles remained more or less unchanged from the mid-1980s through the early 2010s, and so for purposes of illustration we assumed only modest further improvements in the A2 scenario until 2050. In the B1 scenario, we assumed additional efficiency increases of 2% a year , which would plausibly be brought about through improvements in the US national CAFE standards. For the AB32-Plus scenario, we assumed improvements of 4% a year . These assumptions are reasonable given recent efficiency improvements such as the spread of hybrid vehicle technologies. Despite accelerating globalization, food security in most of the developing world depends upon local food production. Most rural citizens in developing nations are involved in agriculture . Also, most locally produced goods are consumed locally, so increasing local productivity and slowing population growth remains a central food security issue . Over the past few years, energy price increases, bio-fuels and food scarcity have led to higher global food prices and price volatility.Rapidly increasing consumer prices limit food access. Increased price volatility reduces the benefit that small scale farmers derive from higher producer prices. Bio-fuels create competition between poor people in the developing world and energy consumers in the developed world. While higher priced commodities can bring direct benefits to farmers, these benefits will not be attained without significant and sustained investments in agriculture to increase production and in programs that reduce price volatility. High and volatile food prices make local production even more important for food insecure regions. With high prices and continually increasing transportation costs, producing more locally will become an important source of vitality for programs focused on reducing poverty .
Connecting new innovations in crop research with improved outcomes in the developing world will likely require public sector investments . The recent entry into small-scale agricultural investment by the Bill and Melinda Gates Foundation has added new vibrancy to efforts focused on understanding barriers to increasing yields and sustaining production in the face of climate change. This paper explores the convergence of three different trends: changes in agricultural production, changing climate and increasing population. Our assumption in the analyses presented is that that some countries will continue to have substantially less purchasing power than others over the next few decades. Although global trade is important, we assume that regions with small agriculturally based economies today are unlikely to transform themselves into industrial nations without first improving their agricultural productivity . The collision between increasing global food demand, competition for food with developed world energy consumers and increasingly difficult production conditions means that the food security situation is likely to worsen. Climate change will potentially bring reduced rainfall over some regions, and increased rainfall over others. The impact of these changes on agriculture must be anticipated and planned for . To this end, this paper examines potential trends in per capita cereal production and rainfall. While other foods make substantial contributions to diets in many areas,blueberry pot size per capita cereal production is indicative of general food availability in most developing countries. If the current rates of yield and population increases persist, many regions will see substantial declines in cereal availability. Greenhouse gas-induced reductions in monsoonal rainfall could exacerbate these grain shortages. On the other hand, relatively modest yield improvements in the least developed nations could lead to improved levels of per capita cereal production by 2030. If done sustainably, raising yields in these poorest nations may be the most technologically feasible way of addressing global cereal demands while reducing poverty and food insecurity.This study uses five sets of data : agricultural statistics, population, observed rainfall and simulated rainfall from 10 different global climate models . Based on simple equations describing the conservation of mass, momentum, radiation and other key dynamic factors, the global climate models simulate the full atmosphere at sub-daily time steps. These models may be constrained by observed sea surface temperatures , or run combined with ocean models in full simulation mode. When constrained by observed SSTs, the models produce circulations resembling the observed atmosphere. This analysis uses 24 of these 20th century simulations from 10 models to examine how well the models recreate observed seasonal rainfall variations between 1980 and 2000 rainfall observations. Our study also examines climate change simulations for the 21st century which predict a doubling of atmospheric CO2 concentrations by 2100 . When combined with ocean models and forced by projected trends in greenhouse gasses and aerosols, climate simulations can be used to examine the impact due to anthropogenic emissions into the atmosphere. This study examines 28 simulations from a standard emission scenario—the Special Report on Emissions Scenarios, SRESA1B. Individual simulations were weighted in such a way as to give each model an equal influence.Between 1961 and 1986, global total cereal yield increased by 89%, harvested area expanded by 11% and global per capita cereal production increased enormously, with a maximum in about 1986 .
Population, meanwhile, grew by 60% over those 25 years, resulting in an increase in per capita cereal production from 284 to 372 kg of grain per person per year. The last 21 years have seen slower population growth accompanied by slower yield improvements and a 2% reduction in the total area harvested. Surprisingly, over this period according to FAO and UN data, per capita harvested area, fertilizer and seed use have all declined by 20–30% . Presumably, higher yielding seeds, double cropping of rice, irrigation and more effective farming techniques have made up for lower per capita inputs. Per capita production is now about 350 kg of cereal per person per year, 6% less than the 1986 maximum. The relative stabilization of global per capita production hides the increased use of cereals for biofuels, alcohol and meat production, as well as significant variations between regions.Four regions have more than doubled their population since 1980: Eastern Africa, Western Africa, Middle Africa and Western Asia. Central America, Southern America, Northern Africa, Southern Africa, Southern Asia, and Southeastern Asia have seen their populations increase by more than 50%. Since 1980, two out of every three people born now live in Asia. Another one out of four lives in Africa. Across most of Africa, harvested area has increased substantially more than yields . In Eastern Africa, for example, yields have only increased by 25% since 1980, while harvested area increased by 55%. In Western Africa yields increased by 42% while harvested area increased by 127%. Yields, per capita harvested area and production remain very low. Across much of the rest of the world, harvested areas have remained fairly steady and yields have been the primary driver of agricultural growth. Our evaluation of FAO statistics shows that Southern America, Northern America, Eastern, Southern and Southeastern Asia have experienced greater than 70% increases in yields since 1980. Today, huge regional disparities in yields remain the norm. In 2007, cereal yields varied by almost 600%, from ~1,000 kg ha−1 in Middle Africa, to more than 5,000 kg ha−1 in Northern America, Eastern Asia, and Europe.4 Given that global and regional tendencies in per capita harvested area and yields appear fairly predictable on 10 year scales , we may plausibly use the observed trends to project to 2030. Table 4 presents the anticipated 2007 to 2030 changes, expressed as kg of cereals per person per year, and as percent changes compared to 2007. Figure 3 shows the historical and projected per capita cereal production for selected regions. Globally, a return to per capita production of the late 1960s, when per capita production was near 327 kg per person,appears likely. Eastern Asia may return to 1980s production levels and Southern Asia to 1960s levels . Declines in heavily populated Asia could re-expose millions of people to chronic undernourishment.5 Central and Southern America may experience 18–20% declines in per capita cereal production levels. Eastern and Middle Africa, however, may be affected most, with more than 30% reductions in already low per capita cereal production levels, with Eastern Africa changing from 131 kg per year in 2007 to 84 kg per person per year in 2030. United Nations projections suggest that the population of Eastern Africa will increase by 191 million people by 2030—a net increase second only to Southern Asia. While imprecise, it is instructive to evaluate simple food balance equations, examining the number of people who could be reasonably supported by the anticipated levels of grain production . We do this by calculating the theoretical population without food . Globally, the 2030 food balance estimates suggest that as now, we will still have enough grain to maintain our human population,albeit at a low baseline value of 1,900 calories a day. This estimate, however, does not take into account grain consumption associated with livestock production, biofuels or industrial applications.