Reisinger et al. , for example, estimated that the 100-year Absolute Global Warming Potential of CO2 from 2000 to 2100 could decrease by 2 to 36 % under various GHG concentration scenarios. Second, a series of articles have attempted to synchronize the temporal system boundary under which life cycle emissions are taken into account and the time horizon under which characterization factors are derived. For example, if GWP100 is to be used, one can set the temporal system boundary to the next 100 years and account for the radiative forcing effects that occur within that time horizon . One of the rationales is that the efforts to reduce GHG emissions today is perhaps more valuable than those in the future because climate change may bring about irreversible damages to the planet . In this class of literature, simple climate-carbon cycle model like Bern model or simple first-order decay model is used to calculate atmospheric load of GHGs over time, and corresponding radiative forcing . Background concentrations of GHGs are, however, generally assumed to be constant in the literature. Third, some argues that future climate change impacts should be discounted at certain rates using the net present value approach . These approaches use different rationales and involve varying degrees of subjectivity in, e.g., the choice of emission scenarios and discount rates. For the sake of simplicity, however, these approaches are collectively referred to as dynamic characterization method in this paper. The objective of this study is to re-examine corn ethanol’s CPT,best grow pots taking into account the potential yield differences of converted land and technological advances within the corn ethanol system. We also examine how dynamic characterization of GHG emissions changes the CPT using one particular approach as an example.
We focus on conversion of CRP land primarily for ease of comparison with previous studies and also because there is evidence indicating that conversion of CRP land to cornfield has occurred with the expansion of corn production in the past decade . We start with estimating the amount of annual carbon savings that can be generated by corn ethanol from an average cornfield and how the amount changes over time. For this analysis, we use the Bio-fuel Analysis Meta-Model with several modifications . Specifically, because the base year of EBAMM is 2001 , we incorporate into the model historical data on the process inputs and outputs of corn growth and ethanol conversion for 2005 and 2010 to reflect the system’s productivity improvements in the past decade . We project further productivity improvements to 2020 using projections in the Greenhouse gases, Regulated Emissions and Energy use in Transportation model . We assume that technology advancement stabilizes after 2020 . Detailed information is provided in Appendix B. We then incorporate yield differences into the model to approximate the amount of annual carbon savings that the CRP-corn ethanol system provides. The CRP program, established by the Food Security Act of 1985, is intended to retire highly erodible and environmentally sensitive cropland from production . Because highly erodible land is less productive in general, the program enrolls land with lower productivity indirectly . Additionally, due in part to the early payment scheme— the maximum acceptable rental rates—farmers tended to offer their low-quality land for CRP consideration while retaining productive land for continuous cultivation. As a result, CRP land appears less productive than other types of cropland, including land that shifts into or out of the cultivated cropland from less, other intensive uses.
Direct measurements of crop yield on CRP land are scarce, but measurements of crop yield on marginal land, including CRP and shifting land, can be used as indications of the relative yield differences between CRP land and average croplands . Estimates of the ratio of marginal to average yield for US corn based on different methods range from 47 to 82 % . This range is consistent with estimates on the global scale . In our analysis, we test how different MtA yield ratios affect ethanol’s CPT. We also identify the best scenario for CRP land with comparable fertility because the CRP program can sometimes retire highly productive land . For the carbon debt caused by CRP land conversion, we use the field measurement by Gelfand et al. , estimated at 68 Mg CO2e ha−1, with the assumption that no-till practices are used for corn farming after land conversion. This estimate is similar to that by Fargione et al. for CRP land conversion, i.e., 69 Mg CO2e ha−1. Following Fargione et al., 83 % of the total carbon debt is allocated to ethanol and 17 % to coproducts, primarily distiller grains with solubles , based on their economic values . As discussed in the Introduction section, a number of approaches to accounting for emission timing effects in LCA and carbon accounting are reported in the literature with various rationales.Dynamic characterization uses temporally specific emissions and characterization factors instead of using time-integrated life cycle inventory and characterization factors . In general, emissions and characterization factors are calculated for each annual time-step and summed up to produce a cumulative impact over a certain period of time. Just like any other characterization methods, dynamic characterization approaches are not free from subjective choices. In this study, we select 100 years as the time horizon, and following the approach by Levasseur et al. , we then calculate the cumulative radiative forcing , over the 100-year time horizon, for 1 kg CO2 emitted in different years.
Similar to Kendall , we further normalize the CRF results of different years by that of the year when the carbon debt occurs . This step yields a set of weights of decreasing value from 1 for year 0, 0.5 for year 60, to nearly 0 towards year 100. Finally, we assume that carbon debt occurs all at once in the year of land conversion . The assumption of instantaneous carbon loss is somewhat unrealistic but can be considered as a worst-case scenario to indicate the impact of considering emissions timing. Following the argument of Hellweg et al. , we do not further discount future emissions, a practice that is common in economics to account for time value of money. The general approach to CPT calculation outlined in this paper could, however, employ other dynamic characterization approaches discussed earlier; we select the approach by Levasseur et al. with 100-year time horizon only for the purpose of illustration.We have re-examined corn ethanol’s carbon payback time in the case of converting CRP land for corn ethanol production taking into account three factors that were neglected in previous studies: yield differences on newly converted land, productivity improvements within the corn ethanol system, and emissions timing . Our results show that CPT estimates for converting low-fertility CRP land with 50 % marginal-to-average yield ratio ranges from 65 to 88 years . For highly productive CRP land, the payback time could be reduced to less than 20 years. For CRP lands with 60–80 % of marginal-to-average yield ratio, which is considered to be a more typical case, the payback time range from 19 to 43 years . Previous estimates of 40 and 48 years of payback time are near the upper bound of our estimates. Technological advances within the corn ethanol system are the key for the CRP-corn ethanol system to be able to generate positive climate impacts. Without technological advances, CRP land with ≤80 % of marginal-to-average yield ratio would fail to provide any carbon benefits over the 100 years after the land conversion. Note that our study does not consider the reversion of land use, which, if included, would further shorten our estimates of CPT for the CRP-corn ethanol system . Overall, our study confirms the importance of understanding marginal technologies and efficiency changes in LCA ; LCAs based on a static productivity assumption may fail to recognize the long-term benefits of the technology as it matures. Also, our study demonstrates the relevance of considering the actual yield of the converted land rather than the average yield, as direct corn expansion will most likely bring marginal, less-fertile land into production. One of the key questions in bio-fuel policies is whether additional corn ethanol production would reduce GHG emissions. Therefore, LCA studies based on average data from existing corn ethanol systems fall short of offering adequate insights for the policy questions at hand. Ideally,plants in pots ideas such policy questions can be answered using a prospective model that embraces the complex dynamics between and within marginal technologies, marginal impacts, displacement mechanisms and behavioral changes.
Our analysis highlights the importance of taking the underlying dynamics into account in understanding the implications of a technology, which can be referred to as “consequential thinking” as an analytical paradigm . However, we acknowledge and admit that our analysis neglects many other factors that would influence the system. That is one of the main reasons why we believe that the term, “consequential LCA”, which implies the existence of a well-defined, operational modelthat is capable of showing the future trajectories of human-nature complexity, can be misleading . Instead, our study employs a scenario approach to answer what-if questions focusing on marginal yield and ethanol system productivity. Needless to say, our results shall be interpreted only under the assumptions employed as well as the limitations associated with them. Agriculture is essential for feeding a majority of the global population, but it has also been identified as one of the major drivers behind various global environmental degradations . For example, due to a quintupling of global fertilizer use in the past decades, agriculture has greatly disturbed the global nitrogen and phosphorus cycles . This results in a wide range of environmental issues from release of N2O, formation of photochemical smog over large regions of earth, to accumulation of excessive nutrients in estuaries and costal oceans . Agriculture dominates pesticide use , which contaminates surface and ground water and threatens human and ecological health . So also does agriculture dominate freshwater withdrawal worldwide , adding stresses where there are competing needs for water . Despite the severity of existing environmental impacts of agriculture, the challenge of addressing them is compounded by increasing global food demand . Continuous global population growth and spread of economic prosperity , mainly in developing countries, will likely drive the global food demand to double by 2050 . Over the past decade, life cycle assessment has been increasingly applied to agricultural and food products , with a number of agricultural LCA databases developed worldwide recently . LCA is a tool that quantifies products’ environmental emissions and resource use throughout the life cycle and evaluates the potential impacts they generate on human and ecological health . Impact categories evaluated in LCA span a wide range, from global warming, ozone depletion, acidification, eutrophication, to ecotoxicity, human health cancer, and non-cancer . Applications of LCA in agriculture include comparing the environmental performance of alternative products or technologies , such as organic versus conventional farming , and identifying hot spots and improvement opportunities . In particular, LCA has played an active and important role in assessing the environmental benefits of bio-energy and contributed to the making of public climate policies . As with LCA studies in general, agricultural LCAs often rely on static and single-year inventory data with commonly 5 to 10 years of data age. In Ecoinvent database, for example, the data year for U.S. Corn Farming is around 2005 and for Swiss Corn Farming is around 2000 . Literature suggests, however, that agricultural systems may be highly dynamic due in part to the increasingly changing climate and technological advances such as improved yield and energy efficiency . These factors may bring about substantial changes in the use of input materials and the yield of crops, hence substantial changes in the environmental impacts. For example, direct energy inputs per ha corn produced in the U.S. declined by about 40% between 1996 and 2005 and in the meantime corn yield increased by about 30% . In this study, we seek to evaluate if on-going changes in input use and structure of four major crops in the U.S. might have resulted in substantial changes in their environmental impacts over the past decade, focusing on regional issues such as eutrophication,acidification, and ecological toxicity.