Integrating science into humanitarian and development planning and practice to enhance community resilience

There has been an increased emphasis upon the application of science for humanitarian and development planning, decision-making and practice; particularly in the context of understanding, assessing and anticipating risk (e.g. HERR, 2011). However, there remains very little guidance for practitioners on how to integrate sciences they may have had little contact with in the past (e.g. climate). This has led to confusion as to which ‘science’ might be of use and how it would be best utilised. Furthermore, since this integration has stemmed from a need to be more predictive, agencies are struggling with the problems associated with uncertainty and probability. Whilst a range of expertise is required to build resilience, these guidelines focus solely upon the relevant data, information, knowledge, methods, principles and perspective which scientists can provide, that typically lie outside of current humanitarian and development approaches. Using checklists, real-life case studies and scenarios the full guidelines take practitioners through a five step approach to finding, understanding and applying science. This document provides a short summary of the five steps and some key lessons for integrating science.

Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models

We utilize energy budget diagnostics from the Coupled Model Intercomparison Project phase 5 (CMIP5) to evaluate the models' climate forcing since preindustrial times employing an established regression technique. The climate forcing evaluated this way, termed the adjusted forcing (AF), includes a rapid adjustment term associated with cloud changes and other tropospheric and land-surface changes. We estimate a 2010 total anthropogenic and natural AF from CMIP5 models of 1.9 ± 0.9 W m−2 (5–95% range). The projected AF of the Representative Concentration Pathway simulations are lower than their expected radiative forcing (RF) in 2095 but agree well with efficacy weighted forcings from integrated assessment models. The smaller AF, compared to RF, is likely due to cloud adjustment. Multimodel time series of temperature change and AF from 1850 to 2100 have large intermodel spreads throughout the period. The intermodel spread of temperature change is principally driven by forcing differences in the present day and climate feedback differences in 2095, although forcing differences are still important for model spread at 2095. We find no significant relationship between the equilibrium climate sensitivity (ECS) of a model and its 2003 AF, in contrast to that found in older models where higher ECS models generally had less forcing. Given the large present-day model spread, there is no indication of any tendency by modelling groups to adjust their aerosol forcing in order to produce observed trends. Instead, some CMIP5 models have a relatively large positive forcing and overestimate the observed temperature change.

Innovative approaches For enhancing the flow of funds into the Benefit Sharing Fund of the International Treaty on Plant Genetic Resources for Food and Agriculture: an evaluation of options

This report assesses the implications and revenue-generating potential of options for reform of the International Treaty on Plant Genetic Resources for Food and Agriculture in the context of the structure of the global seed industry and the emerging landscape of plant variety innovation for different crops. The implementation of these options would require modifications of Treaty and provisions of the Standard Material Transfer Agreements to alter the nature of payment obligations related to different categories of products, the payment rates under different options and the coverage of crops in Annex-I to the Treaty.

Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Assessing pragmatic interoperability for process alignment in collaborative working environment

This paper is an extension of our previous study on pragmatic interoperability assessment for process alignment. In this study, we conduct four case studies in industrial companies and hospitals in order to gather their viewpoints regarding the concerns when condensing process alignment in a collaborative working environment. Used techniques include interview, observation, and documentation. The collected results firstly are summarised into three layers based on our previous developed pragmatic assessment model, and then are transformed into the elements which constitutes the purposed method, and finally based on the summarised results we purpose a method for assessing pragmatic interoperability for process alignment in collaborative working environment. The method contains two parts: one gives all the elements of pragmatic interoperability that should be concerned when considering process alignment in collaborative working environment, and the other one is a supplementary method for dealing with technical concerns.

Multi-attribute collaborative filtering recommendation based on improved group decision-making

Currently researchers in the field of personalized recommendations bear little consideration on users' interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment. The experimental results show that the algorithm has a high accuracy on predicting target users' attribute preferences and has a strong anti-interference ability on deviation and incomplete values.

How remote are R&D labs? Distance factors and international innovative activities

This paper shows that value creation by multinational enterprises (MNEs) is the result of activities where geographic distance effects can be overcome. We submit that geographic distance has a relatively low impact on international research and development (R&D) investments, owing to the spiky nature of innovation, and to the unique ability of MNEs to absorb and transfer knowledge on a global scale. On the one hand, MNEs need to set up their labs as close as possible to specialized technology clusters where valuable knowledge is concentrated, largely regardless of distance from their home base. On the other, MNEs have historically developed technical and organizational competencies that enable them to transfer knowledge within their internal networks and across technology clusters at relatively low cost. Using data on R&D and manufacturing investments of 6320 firms in 59 countries, we find that geographic distance has a lower negative impact on the probability of setting up R&D than manufacturing plants. Furthermore, once measures of institutional proximity are accounted for, MNEs are equally likely to set up R&D labs in nearby or in more remote locations. This result is driven by MNEs based in Triad countries, whereas for non-Triad MNEs the effect of geographic distance on cross-border R&D is negative and significant.

Scenarios of technology adoption towards low-carbon cities

Technological change has often been presented as a readily accepted means by which long-term greenhouse gas (GHG) emission reductions can be achieved. Cities are the future centers of economic growth, with the global population becoming predominantly urban; hence, increases or reductions of GHG emissions are tied to their energy strategies. This research examines the likelihood of a developed world city (the Greater Toronto Area) achieving an 80% reduction in GHG emissions through policy-enabled technological change. Emissions are examined from 3 major sources: light duty passenger vehicles, residential buildings and commercial/institutional buildings. Logistic diffusion curves are applied for the adoption of alternative vehicle technologies, building retrofits and high performance new building construction. This research devises high, low and business-as-usual estimates of future technological adoption and finds that even aggressive scenarios are not sufficient to achieve an 80% reduction in GHG emissions by 2050. This further highlights the challenges faced in maintaining a relatively stable climate. Urban policy makers must consider that the longer the lag before this transition occurs, the greater the share of GHG emissions mitigation that must addressed through behavioural change in order to meet the 2050 target, which likely poses greater political challenges.