Monterey Institute of International Studies

About: Monterey Institute of International Studies is a education organization based out in Monterey, California, United States. It is known for research contribution in the topics: Nuclear weapon & Higher education. The organization has 286 authors who have published 581 publications receiving 14542 citations.

The Technology Path to Deep Greenhouse Gas Emissions Cuts by 2050: The Pivotal Role of Electricity

Abstract: The Technology Path to Deep Greenhouse Gas Emissions Cuts by 2050: The Pivotal Role of Electricity James H. Williams, 1,2 Andrew DeBenedictis, 1 Rebecca Ghanadan, 1,3 Amber Mahone, 1 Jack Moore, 1 William R. Morrow III, 4 Snuller Price, 1 Margaret S. Torn 3 * Several states and countries have adopted targets for deep reductions in greenhouse gas emissions by 2050, but there has been little physically realistic modeling of the energy and economic transformations required. We analyzed the infrastructure and technology path required to meet California’s goal of an 80% reduction below 1990 levels, using detailed modeling of infrastructure stocks, resource constraints, and electricity system operability. We found that technically feasible levels of energy efficiency and decarbonized energy supply alone are not sufficient; widespread electrification of transportation and other sectors is required. Decarbonized electricity would become the dominant form of energy supply, posing challenges and opportunities for economic growth and climate policy. This transformation demands technologies that are not yet commercialized, as well as coordination of investment, technology development, and infrastructure deployment. n 2004, Pacala and Socolow (1) proposed a way to stabilize climate using existing green- house gas (GHG) mitigation technologies, vi- sualized as interchangeable, global-scale “wedges” of equivalent emissions reductions. Subsequent work has produced more detailed analyses, but none combines the sectoral granularity, physical and resource constraints, and geographic scale needed for developing realistic technology and policy roadmaps (2–4). We addressed this gap by analyzing the specific changes in infrastructure, technology, cost, and governance required to de- carbonize a major economy, at the state level, that has primary jurisdiction over electricity supply, transportation planning, building standards, and other key components of an energy transition. California is the world’s sixth largest econ- omy and 12th largest emitter of GHGs. Its per capita GDP and GHG emissions are similar to those of Japan and western Europe, and its policy and technology choices have broad rele- vance nationally and globally (5, 6). California’s Assembly Bill 32 (AB32) requires the state to reduce GHG emissions to 1990 levels by 2020, a reduction of 30% relative to business-as-usual assumptions (7). Previous modeling work we per- formed for California’s state government formed the analytical foundation for the state’s AB32 implementation plan in the electricity and natural gas sectors (8, 9). California has also set a target of reducing 2050 emissions 80% below the 1990 level, con- I Energy and Environmental Economics, 101 Montgomery Street, Suite 1600, San Francisco, CA 94104, USA. 2 Monterey Institute of International Studies, 460 Pierce Street, Monterey, CA 93940, USA. 3 Energy and Resources Group, University of Cali- fornia,& Earth Sciences Division, Lawrence Berkeley National Laboratory (LBNL),, Berkeley, CA 94720, USA. 4 Environmental Energy Technologies Division, LBNL, Berkeley, CA 94720, USA. *To whom correspondence should be addressed. E-mail: mstorn@lbl.gov sistent with an Intergovernmental Panel on Cli- mate Change (IPCC) emissions trajectory that would stabilize atmospheric GHG concentrations at 450 parts per million carbon dioxide equivalent (CO 2 e) and reduce the likelihood of dangerous an- thropogenic interference with climate (10). Work- ing at both time scales, we found a pressing need for methodologies that bridge the analytical gap between planning for shallower, near-term GHG reductions, based entirely on existing commercialized technology, and deeper, long-term GHG reduc- tions, which will depend substantially on technol- ogies that are not yet commercialized. We used a stock-rollover methodology that simulated physical infrastructure at an aggregate level, and built scenarios to explore mitigation options (11, 12). Our model divided California’s economy into six energy demand sectors and two energy supply sectors, plus cross-sectoral eco- nomic activities that produce non-energy and non-CO 2 GHG emissions. The model adjusted the infrastructure stock (e.g., vehicle fleets, build- ings, power plants, and industrial equipment) in each sector as new infrastructure was added and old infrastructure was retired, each year from 2008 to 2050. We constructed a baseline scenario from government forecasts of population and gross state product, combined with regression-based infra- structure characteristics and emissions intensities, producing a 2050 emissions baseline of 875 Mt CO 2 e (Fig. 1). In mitigation scenarios, we used backcasting, setting 2050 emissions at the state target of 85 Mt CO 2 e as a constrained outcome, and altered the emissions intensities of new in- frastructure over time as needed to meet the tar- get, employing 72 types of physical mitigation measures (13). In the short term, measure selec- tion was driven by implementation plans for AB32 and other state policies (table S1). In the long term, technological progress and rates of in- troduction were constrained by physical feasi- bility, resource availability, and historical uptake rates rather than relative prices of technology, en- ergy, or carbon as in general equilibrium models (14). Technology penetration levels in our model are within the range of technological feasibility for the United States suggested by recent assess- ments (table S20) (15, 16). We did not include technologies expected to be far from commercial- ization in the next few decades, such as fusion- based electricity. Mitigation cost was calculated as the difference between total fuel and measure costs in the mitigation and baseline scenarios. Our fuel and technology cost assumptions, including learning curves (tables S4, S5, S11, and S12, and fig. S29), are comparable to those in other recent studies (17). Clearly, future costs are very uncertain over such a long time horizon, especially for technologies that are not yet commercialized. We did not assume explicit life-style changes (e.g., vegetarianism, bicycle transportation), which could have a substantial effect on mitigation requirements and costs (18); behavior change in our model is subsumed within conservation measures and en- ergy efficiency (EE). To ensure that electricity supply scenarios met the technical requirements for maintaining reli- able service, we included an electricity system dispatch algorithm that tested grid operability. Without a dispatch model, it is difficult to de- termine whether a generation mix has infeasibly high levels of intermittent generation. We devel- oped an electricity demand curve bottom-up from sectoral demand, by season and time of day. On the basis of the demand curve, the model con- strained generation scenarios to satisfy in succes- sion the energy, capacity, and system-balancing requirements for reliable operation. The operabil- ity constraint set physical limits on the penetra- tion of different types of generation and specified the requirements for peaking generation, on-grid energy storage, transmission capacity, and out-of- state imports and exports for a given generation mix (table S13 and figs.S20 to S31). It was as- sumed that over the long run, California would not “go it alone” in pursuing deep GHG reduc- tions, and thus that neighboring states would de- carbonize their generation such that the carbon intensity of imports would be comparable to that of California in-state generation (19). Electrification required to meet 80% reduc- tion target. Three major energy system transfor- mations were necessary to meet the target (Fig. 2). First, EE had to improve by at least 1.3% year −1 over 40 years. Second, electricity supply had to be nearly decarbonized, with 2050 emissions in- tensity less than 0.025 kg CO 2 e/kWh. Third, most existing direct fuel uses had to be electrified, with electricity constituting 55% of end-use energy in 2050 versus 15% today. Results for a mitigation scenario, including these and other measures, are shown in Fig. 1. Of the emissions reductions relative to 2050 baseline emissions, 28% came from EE, 27% from decarbonization of electricity generation, 14% from a combination of energy

Arabic Natural Language Processing: Challenges and Solutions

Abstract: The Arabic language presents researchers and developers of natural language processing (NLP) applications for Arabic text and speech with serious challenges. The purpose of this article is to describe some of these challenges and to present some solutions that would guide current and future practitioners in the field of Arabic natural language processing (ANLP). We begin with general features of the Arabic language in Sections 1, 2, and 3 and then we move to more specific properties of the language in the rest of the article. In Section 1 of this article we highlight the significance of the Arabic language today and describe its general properties. Section 2 presents the feature of Arabic Diglossia showing how the sociolinguistic aspects of the Arabic language differ from other languages. The stability of Arabic Diglossia and its implications for ANLP applications are discussed and ways to deal with this problematic property are proposed. Section 3 deals with the properties of the Arabic script and the explosion of ambiguity that results from the absence of short vowel representations and overt case markers in contemporary Arabic texts. We present in Section 4 specific features of the Arabic language such as the nonconcatenative property of Arabic morphology, Arabic as an agglutinative language, Arabic as a pro-drop language, and the challenge these properties pose to ANLP. We also present solutions that have already been adopted by some pioneering researchers in the field. In Section 5 we point out to the lack of formal and explicit grammars of Modern Standard Arabic which impedes the progress of more advanced ANLP systems. In Section 6 we draw our conclusion.