Cities’ low carbon plans in an ‘age of austerity’: an analysis of UK local authority actions, attitudes and responses

Background: Cities play a significant role globally in creating carbon emissions but, as centers of major population, innovation and social practice, they also offer important opportunities to tackle climate change. The new challenges faced by cities in an ‘age of austerity’ and decentralist agendas present substantial challenges for coordinated multilevel governance. Results: Based on research carried out in 2011–2012, this paper examines the attitudes and responses of sustainability and climate change officers in UK cities that have prepared low carbon and climate change plans, in the context of these challenges. Using a conceptual framework that analyses ‘awareness’, ‘analysis’ and ‘actions’ (in the context of spending cuts and a new ‘decentralized’ policy agenda) this research suggests that progress on low-carbon futures for cities continues to be fragmented, with increased funding constraints, short-termism and lack of leadership acting as key barriers to progress. Conclusion: Recent UK national policies (including localism, austerity measures and new economic incentives) have not only created further uncertainties, but also scope for cities’ local innovation through policy leverage and self-governing actions.

Promoting core maths - background, evidence, plans, opportunities, and challenges

This article discusses some of the issues relating to the promotion of Core Maths to students, parents, schools and colleges and their senior leadership teams, and also to employers and higher education (HE). Some challenges are highlighted, and addressed, with suggestions for ways forward to secure the future of Core Maths and widespread adoption by all stakeholders. A summary of the background reports that led to the introduction of Core Maths, and the related educational landscape prior to its introduction, is included.

A logic-based agent that plans for extended reachability goals

Planning to reach a goal is an essential capability for rational agents. In general, a goal specifies a condition to be achieved at the end of the plan execution. In this article, we introduce nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution). We show that, when this kind of goal is considered, the temporal logic CTL turns out to be inadequate to formalize plan synthesis and plan validation algorithms. This is mainly due to the fact that the CTL`s semantics cannot discern among the various actions that produce state transitions. To overcome this limitation, we propose a new temporal logic called alpha-CTL. Then, based on this new logic, we implement a planner capable of synthesizing reliable plans for extended reachability goals, as a side effect of model checking.

Air Conditioning and Refrigeration Student Looking at Plans

A student from the New York Trade School in the Air Conditioning and Refrigeration Dept. looks at plans on top of a building. Black and white photograph contains some damage from adhesive and writing on the front.

Leveraging new plans in AgentSpeak(PL)

In order to facilitate the development of agent-based software, several agent programming languages and architectures, have been created. Plans in these architectures are often self-contained procedures with an associated triggering event and a context condition, while any further information about the consequences of executing a plan is absent. However, agents designed using such an approach have limited flexibility at runtime, and rely on the designer’s ability to foresee all relevant situations an agent might have to handle. In order to overcome this limitation, we have created AgentSpeak(PL), an interpreter capable of performing state-space planning to generate new high-level plans. As the planning module creates new plans, the plan library is expanded, improving performance over time. However, for new plans to be useful in the long run, it is critical that the context condition associated with new plans is carefully generated. In this paper we describe a plan reuse technique aimed at improving an agent’s runtime performance by deriving optimal context conditions for new plans, allowing an agent to reuse generated plans as much as possible.