Decommissioning of offshore oil and gas facilities: a comparative assessment of different scenarios

A material and energy flow analysis, with corresponding financial flows, was carried out for different decommissioning scenarios for the different elements of an offshore oil and gas structure. A comparative assessment was made of the non-financial (especially environmental) outcomes of the different scenarios, with the reference scenario being to leave all structures in situ, while other scenarios envisaged leaving them on the seabed or removing them to shore for recycling and disposal. The costs of each scenario, when compared with the reference scenario, give an implicit valuation of the non-financial outcomes (e.g. environmental improvements), should that scenario be adopted by society. The paper concludes that it is not clear that the removal of the topsides and jackets of large steel structures to shore, as currently required by regulations, is environmentally justified; that concrete structures should certainly be left in place; and that leaving footings, cuttings and pipelines in place, with subsequent monitoring, would also be justified unless very large values were placed by society on a clear seabed and trawling access

Developing automated methods to estimate spectrally resolved direct normal irradiance for solar energy applications

We describe four schemes designed to estimate spectrally resolved direct normal irradiance (DNI) for multi-junction concentrator photovoltaic systems applications. The schemes have increasing levels of complexity in terms of aerosol and circumsolar irradiance (CSI) treatment, ranging from a climatological aerosol classification with no account of CSI, to an approach which includes explicit aerosol typing and type dependent CSI contribution. When tested against ground-based broadband and spectral measurements at five sites spanning a range of aerosol conditions, the most sophisticated scheme yields an average bias of þ 0:068%, well within photometer calibration uncertainties. The average spread of error is 2:5%. These statistics are markedly better than the climatological approach, which carries an average bias of 1:76% and a spread of 4%. They also improve on an intermediate approach which uses Angstrom€ exponents to estimate the spectral variation in aerosol optical depth across the solar energy relevant wavelength domain. This approach results in systematic under and over-estimations of DNI at short and long wavelengths respectively. Incorporating spectral CSI particularly benefits sites which experience a significant amount of coarse aerosol. All approaches we describe use freely available reanalyses and software tools, and can be easily applied to alternative aerosol measurements, including those from satellite

The Circular Economy: What, Why, How and Where

This paper was prepared as a background document for an OECD/EC high-level expert workshop on “Managing the transition to a circular economy in regions and cities” held on 5 July 2019 at the OECD Headquarters in Paris, France. It sets a basis for reflection and discussion. The background paper should not be reported as representing the official views of the European Commission, the OECD or one of its member countries. The opinions expressed and arguments employed are those of the author(s)

Call Me Caitlyn: Making and making over the 'authentic' transgender body in Anglo-American popular culture

A conception of transgender identity as an ‘authentic’ gendered core ‘trapped’ within a mismatched corporeality, and made tangible through corporeal transformations, has attained unprecedented legibility in contemporary Anglo-American media. Whilst pop-cultural articulations of this discourse have received some scholarly attention, the question of why this 'wrong body' paradigm has solidified as the normative explanation for gender transition within the popular media remains underexplored. This paper argues that this discourse has attained cultural pre-eminence through its convergence with a broader media and commercial zeitgeist, in which corporeal alteration and maintenance are perceived as means of accessing one’s ‘authentic’ self. I analyse the media representations of two transgender celebrities: Caitlyn Jenner and Nadia Almada, alongside the reality TV show TRANSform Me, exploring how these women’s gender transitions have been discursively aligned with a cultural imperative for all women, cisgender or trans, to display their authentic femininity through bodily work. This demonstrates how established tropes of authenticity-via-bodily transformation, have enabled transgender to become culturally legible through the wrong body trope. Problematically, I argue, this process has worked to demarcate ideals of ‘acceptable’ transgender subjectivity: self-sufficient, normatively feminine, and eager to embrace the possibilities for happiness and social integration provided by the commercial domain

Exploring the financial and investment implications of the Paris Agreement

A global energy transition is underway. Limiting warming to 2°C (or less), as envisaged in the Paris Agreement, will require a major diversion of scheduled investments in the fossil-fuel industry and other high-carbon capital infrastructure towards renewables, energy efficiency, and other low or negative carbon technologies. The article explores the scale of climate finance and investment needs embodied in the Paris Agreement. It reveals that there is little clarity in the numbers from the plethora of sources (official and otherwise) on climate finance and investment. The article compares the US$100 billion target in the Paris Agreement with a range of other financial metrics, such as investment, incremental investment, energy expenditure, energy subsidies, and welfare losses. While the relatively narrowly defined climate finance included in the US$100 billion figure is a fraction of the broader finance and investment needs of climate-change mitigation and adaptation, it is significant when compared to some estimates of the net incremental costs of decarbonization that take into account capital and operating cost savings. However, even if the annual US$100 billion materializes, achieving the much larger implied shifts in investment will require the enactment of long-term internationally coordinated policies, far more stringent than have yet been introduced.</i

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p