Low-Carbon Technologies in the Post-Bali Period: Accelerating their Development and Deployment. CEPS ECP Report No. 4, 4 December 2007

This report analyses the very broad issue of technology development, demonstration and diffusion with a view to identifying the key elements of a complementary global technology track in the post-2012 framework. It identifies a number of immediate and concrete steps that can be taken to provide content and a structure for such a track. The report features three sections dealing with innovation and technology, investment in developing countries and investment and finance, followed by an analysis of the various initiatives being taken on technology both within and outside the United Nations Framework Convention on Climate Change (UNFCCC). A final section presents ideas for the way forward followed by brief concluding remarks

Making the most of the G8+5 Climate Change Process: Accelerating Structural Change and Technology Diffusion on a Global Scale. CEPS Task Force Reports, 5 June 2008

Under the chairmanship of Gunnar Still, Senior Vice President and Head of Environment Division at ThyssenKrupp, CEPS organized a Task Force to explore possible initiatives within the context of the G8+5 dialogue on tackling climate change. This report identifies a number of concrete measures that could reduce greenhouse gas (GHG) emissions, while at the same time stimulating structural change and technology development and diffusion. It calls for supporting action-based approaches, which are essential to achieve the necessary reductions in GHG emissions, inform the post-2012 negotiations and address the most urgent issues such as surging energy demand and the need for clean energy technologies in emerging economies. An action-based approach can be regarded as a way of integrating targets and timetables, as they are agreed, with consistent and comparable policies and measures. With a view to a long-term climate strategy, this report attempts to present a portfolio of actions that can be implemented and accelerated on a global scale – especially in the G8+5 countries and the EU, and could become a basis on which developed and developing countries can cooperate

Mitigating Greenhouse Gases in Agriculture

Climate change has severe adverse effects on the livelihood of millions of the world’s poorest people. Increasing temperatures, water scarcity and droughts, flooding and storms affect food security. Thus, mitigation actions are needed to pave the way for a sustainable future for all. Currently, agriculture directly contributes about 10-15 percent to global greenhouse gas (GHG) emissions. Adding emissions from deforestation and land use change for animal feed production, this rises to about 30 percent. Scenarios predict a significant rise in agricultural emissions without effective mitigation actions. Given all the efforts undertaken in other sectors, agriculture would then become the single largest emitter within some decades, and without mitigation in agriculture, ambitious goals, such as keeping global warming below two degrees may become impossible to reach. The main agricultural emission sources are nitrous oxide from soils and methane from enteric fermentation in ruminants. In addition, conversion of native vegetation and grasslands to arable agriculture releases large amounts of CO2 from the vegetation and from soil organic matter. The main mitigation potential lies in soil carbon sequestration and preserving the existing soil carbon in arable soils. Nitrous oxide emissions can be reduced by reduced nitrogen application, but much still remains unclear about the effect different fertilizer types and management practices have on these emissions. Methane emissions from ruminants can only be reduced significantly by a reduction in animal numbers. Sequestration, finally, can be enhanced by conservative management practices, crop rotation with legumes (grass-clover) leys and application of organic fertilizers. An additional issue of importance are storage losses of food in developing and food wastage in developed countries (each about 30-40 percent of end products). Thus, there are basically five broad categories of mitigation actions in agriculture and its broader context: zz reducing direct and indirect emissions from agriculture; zz increasing carbon sequestration in agricultural soils; zz changing human dietary patterns towards more climate friendly food consumption, in particular less animal products; zz reducing storage losses and food wastage; zz the option of bioenergy needs to be mentioned, but depending on the type of bioenergy several negative side-effects may occur, including effects on food security, biodiversity and net GHG emissions. Although there are many difficulties in the details of mitigation actions in agriculture, a paradigm of climate friendly agriculture based on five principles can be derived from the knowledge about agricultural emissions and carbon sequestration: zz Climate friendly agriculture has to account for tradeoffs and choose system boundaries adequately; zz it has to account for synergies and adopt a systemic approach; zz aspects besides mitigation such as adaptation and food security are of crucial importance; zz it has to account for uncertainties and knowledge gaps, and zz the context beyond the agricultural sector has to be taken into account, in particular food consumption and waste patterns. Regarding policies to implement such a climate friendly agriculture, not much is yet around. In climate policy, agriculture only plays a minor role and negotiations proceed only very slowly on this topic. In agricultural policy climate change mitigation currently plays an insignificant role. In both contexts, some changes towards combined approaches can be expected over the next decade. Its 13 is essential that climate policy adequately captures the special characteristics of the agricultural sector. Policies with outcomes that endanger other aspects of agriculture such as food security or ecology have to be avoided. Agriculture delivers much more than options for mitigating greenhouse gas emissions and serving as a CO2 sink. We close this report with recommendations for the five most important goals to be realized in the context of mitigation and agriculture and proposals for concrete actions. First, soil organic carbon levels have to be preserved and, if possible, increased. Governments should include soil carbon sequestration in their mitigation and adaptation strategies and the climate funds should take a strong position on supporting such practices. Second, the implementation of closed nutrient cycles and optimal use of biomass has to be supported. Again, governments and funds should act on this. Policy instruments for nitrate regulation are a good starting point for this. As a third and most effective goal, we propose changes in food consumption and waste patterns. Without a switch to attitudes characterized by sufficiency, there is a danger that all attempts for mitigation remain futile. Finally, there are two goals for research, namely to develop improved knowledge on nitrous oxide dynamics, and on methods for assessment of multi-functional farming systems. Without this, adequate policy instruments for climate friendly agriculture and an optimal further development of it are not possible

Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning

Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, $V_1$ and $V_2$) but parallel data is available between each of these views and a pivot view ($V_3$). We propose a model for learning a common representation for $V_1$, $V_2$ and $V_3$ using only the parallel data available between $V_1V_3$ and $V_2V_3$. The proposed model is generic and even works when there are $n$ views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages $L_1$,$L_2$,...,$L_n$ using a pivot language $L$ and (ii) cross modal access between images and a language $L_1$ using a pivot language $L_2$. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work.Comment: Published at NAACL-HLT 201

An overview of current status of carbon dioxide capture and storage technologies

AbstractGlobal warming and climate change concerns have triggered global efforts to reduce the concentration of atmospheric carbon dioxide (CO2). Carbon dioxide capture and storage (CCS) is considered a crucial strategy for meeting CO2 emission reduction targets. In this paper, various aspects of CCS are reviewed and discussed including the state of the art technologies for CO2 capture, separation, transport, storage, leakage, monitoring, and life cycle analysis. The selection of specific CO2 capture technology heavily depends on the type of CO2 generating plant and fuel used. Among those CO2 separation processes, absorption is the most mature and commonly adopted due to its higher efficiency and lower cost. Pipeline is considered to be the most viable solution for large volume of CO2 transport. Among those geological formations for CO2 storage, enhanced oil recovery is mature and has been practiced for many years but its economical viability for anthropogenic sources needs to be demonstrated. There are growing interests in CO2 storage in saline aquifers due to their enormous potential storage capacity and several projects are in the pipeline for demonstration of its viability. There are multiple hurdles to CCS deployment including the absence of a clear business case for CCS investment and the absence of robust economic incentives to support the additional high capital and operating costs of the whole CCS process

Principles in Patterns (PiP) : Evaluation of Impact on Business Processes

The innovation and development work conducted under the auspices of the Principles in Patterns (PiP) project is intended to explore and develop new technology-supported approaches to curriculum design, approval and review. An integral component of this innovation is the use of business process analysis and process change techniques - and their instantiation within the C-CAP system (Class and Course Approval Pilot) - in order to improve the efficacy of curriculum approval processes. Improvements to approval process responsiveness and overall process efficacy can assist institutions in better reviewing or updating curriculum designs to enhance pedagogy. Such improvements also assume a greater significance in a globalised HE environment, in which institutions must adapt or create curricula quickly in order to better reflect rapidly changing academic contexts, as well as better responding to the demands of employment marketplaces and the expectations of professional bodies. This is increasingly an issue for disciplines within the sciences and engineering, where new skills or knowledge need to be rapidly embedded in curricula as a response to emerging technological or environmental developments. All of the aforementioned must also be achieved while simultaneously maintaining high standards of academic quality, thus adding a further layer of complexity to the way in which HE institutions engage in "responsive curriculum design" and approval. This strand of the PiP evaluation therefore entails an analysis of the business process techniques used by PiP, their efficacy, and the impact of process changes on the curriculum approval process, as instantiated by C-CAP. More generally the evaluation is a contribution towards a wider understanding of technology-supported process improvement initiatives within curriculum approval and their potential to render such processes more transparent, efficient and effective. Partly owing to limitations in the data required to facilitate comparative analyses, this evaluation adopts a mixed approach, making use of qualitative and quantitative methods as well as theoretical techniques. These approaches combined enable a comparative evaluation of the curriculum approval process under the "new state" (i.e. using C-CAP) and under the "previous state". This report summarises the methodology used to enable comparative evaluation and presents an analysis and discussion of the results. As the report will explain, the impact of C-CAP and its ability to support improvements in process and document management has resulted in the resolution of numerous process failings. C-CAP has also demonstrated potential for improvements in approval process cycle time, process reliability, process visibility, process automation, process parallelism and a reduction in transition delays within the approval process, thus contributing to considerable process efficiencies; although it is acknowledged that enhancements and redesign may be required to take advantage of C-CAP's potential. Other aspects pertaining to C-CAP's impact on process change, improvements to document management and the curation of curriculum designs will also be discussed

Detecting Visual Relationships with Deep Relational Networks

Relationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem, considering each type of relationship (e.g. "ride") or each distinct visual phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with significant difficulties caused by the high diversity of visual appearance for each kind of relationships or the large number of distinct visual phrases. We propose an integrated framework to tackle this problem. At the heart of this framework is the Deep Relational Network, a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships. On two large datasets, the proposed method achieves substantial improvement over state-of-the-art.Comment: To be appeared in CVPR 2017 as an oral pape