Technologies, Policies, and Measures for Mitigating Climate Change

This Technical Paper provides an overview and analysis of technologies and measures to limit and reduce greenhouse gas (GHG) emissions and to enhance GHG sinks under the United Nations Framework Convention on Climate Change (FCCC). The paper focuses on technologies and measures for the countries listed in Annex I of the FCCC, while noting information as appropriate for use by non- Annex I countries. Technologies and measures are examined over three time periods -- with a focus on the short term (present to 2010) and the medium term (2010-2020), but also including discussion of longer-term (e.g., 2050) possibilities and opportunities. For this analysis, the authors draw on materials used to prepare the IPCC Second Assessment Report (SAR) and previous IPCC assessments and reports. The Technical Paper includes discussions of technologies and measures that can be adopted in three energy end-use sectors (commercial/residential/institutional buildings, transportation, and industry), as well as in the energy supply sector and the agriculture, forestry, and waste management sectors. Broader measures affecting national economies are discussed in a final section on economic instruments. A range of potential measures are analyzed, including market-based programs; voluntary agreements; regulatory measures; research, development, and demonstration (RD&D); taxes on GHG emissions; and emissions permits/quotas. It should be noted that the choice of instruments could have economic impacts on other countries. The paper identifies and evaluates different options on the basis of three criteria. Because of the difficulty of estimating the economic and market potential (see Box 1) of different technologies and the effectiveness of different measures in achieving emission reduction objectives, and because of the danger of double-counting the results achieved by measures that tap the same technical potentials, the paper does not estimate total global emissions reductions. Nor does the paper recommend adoption of any particular approaches

Towards a Universal Data Provenance Framework Using Dynamic Instrumentation

The advantage of collecting data provenance information has driven research on how to extend or modify applications and systems in order to provide it, or the creation of architectures that are built from the ground up with provenance capabilities. In this paper we propose a universal data provenance framework, using dynamic instrumentation, which gathers data provenance information for real-world applications without any code modifications. Our framework simplifies the task of finding the right points to instrument, which can be cumbersome in large and complex systems. We have built a proof-of-concept implementation of the framework on top of DTrace. Moreover, we evaluated its functionality by using it for three different scenarios: file-system operations, database transactions and web browser HTTP requests. Based on our experiences we believe that it is possible to provide data provenance, transparently, to any layer of the software stack

Understanding collaborative studies through interoperable workflow provenance

The provenance of a data product contains information about how the product was derived, and is crucial for enabling scientists to easily understand, reproduce, and verify scientific results. Currently, most provenance models are designed to capture the provenance related to a single run, and mostly executed by a single user. However, a scientific discovery is often the result of methodical execution of many scientific workflows with many datasets produced at different times by one or more users. Further, to promote and facilitate exchange of information between multiple workflow systems supporting provenance, the Open Provenance Model (OPM) has been proposed by the scientific workflow community. In this paper, we describe a new query model that captures implicit user collaborations. We show how this model maps to OPM and helps to answer collaborative queries, e.g., identifying combined workflows and contributions of users collaborating on a project based on the records of previous workflow executions. We also adopt and extend the high-level Query Language for Provenance (QLP) with additional constructs, and show how these extensions allow non-expert users to express collaborative provenance queries against this model easily and concisely. Furthermore, we adopt the Provenance Challenge 3 (PC3) workflows as a collaborative and interoperable usecase scenario, where different stages of the workflow are executed in three different workflow environments - Kepler, Taverna, and WSVLAM. Through this usecase, we demonstrate how we can establish and understand collaborative studies through interoperable workflow provenance