The key role of historic path-dependency and competitor imitation on the electricity sector low-carbon transition

Market players in the energy sector transition are heterogeneous, have bounded rationality and are influenced by their own past failures, as well as imitating the successes of their competitors. However this agent heterogeneity and complex behaviour in investment choices is not taken into account in traditional energy-economy models used to inform energy sector policies. By using BRAIN-Energy, an agent-based model of investment in electricity generation, which enables to study the impact of actors’ heterogeneous characteristics on the transition pathways of the UK, German and Italian electricity sectors, this paper shows how historic path-dependency in investment choices displaces low-carbon in favour of high-carbon investments under a weak regulatory framework. By contrast, imitation can help the diffusion of renewable technologies, through a self-reinforcing positive feedback when government subsidies to low-carbon investments are in place

The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors

Achieving electricity sector transitions consistent with stringent climate change mitigation under the Paris Agreement requires a careful understanding both of the coordinating role of national governments and of its interactions with the heterogeneous market players who will make the low-carbon investments in the electricity sector. However, traditional energy models and scenarios generally assume exogenous policy targets and fail to capture this co-evolution between policy-makers and heterogeneous private and public investors. This paper uses BRAIN-Energy, a novel agent-based model of investment in electricity generation to simulate and contrast government and investor dynamics in the transition pathways of the UK, German and Italian electricity sectors. Key findings show that a successful transition – which achieves the energy policy “trilemma” (low carbon, secure, affordable) – requires the co-evolution of the policy dimension (strong and frequently updatable CO2 price, renewable subsidies and capacity market) with the strategies of the heterogeneous market players. If this dynamic balance is maintained then incentives are politically feasible and suppliers learn and evolve (in what we term a virtuous cycle). If either the incentives are too weak to drive learning or too expensive so the policy regime collapses, then the transition fails on one of its key dimensions (in what we term a vicious cycle). Getting this balance right is harder in risky markets that also have players with more pronounced bounded rationality and path dependence in how they make investments

The impact of heterogeneous market players with bounded-rationality on the electricity sector low-carbon transition

The energy sector transition requires large financial investments in low-carbon generation technologies, to be delivered by a variety of actors with heterogeneous characteristics. Real-world actors have bounded-rationality, reflected by their limited foresight and heterogeneous expectations, and as past trends influence their investments. Agent-based models are highly suitable modelling frameworks to study such realistic and complex energy transition dynamics. This paper introduces BRAIN-Energy, a novel agent-based model which explicitly allows to explore the impacts of actors' heterogeneous characteristics, and of their interactions, on the transition pathways of the UK, German and Italian electricity sectors. Results show that actors' heterogeneous characteristics pose barriers to effective decarbonisation efforts, affect the speed of the transition, and impact the transition's security of supply and affordability dimensions. Limited foresight and path-dependency lead to investment cycles (both virtuous and vicious). The country comparison highlights how such effects are stronger in markets with more heterogeneous market players

The influences of non-optimal investments on the scale-up of smart local energy systems in the UK electricity market

Rapid and deep decarbonisation of electricity systems is critical in many pathways to meet net-zero emissions by 2050. Smart local energy systems (SLES) have been touted as key for both a rapid scale-up of renewable electricity and flexibility for stability in decarbonised electricity systems. A novel agent-based model – incorporating local investor and governance agents, improved temporal resolution, and demand-side flexibility – was used to investigate strategic decision making in the scale-up of SLES. From the perspective of this model, key modelling insights include: SLES investors, initially supported by local governments, can successfully boost the uptake of renewable energy up to 80% of total generation; SLES scale-up significantly erodes the market share and profitability of incumbent utilities, however national level agents are still key for capital-intensive low carbon plants; Demand-side response facilitates balancing electricity supply and demand, but it can result in non-optimal policy agents postponing required incentives for heterogeneous investor agents to build new low carbon plants; National carbon prices (in conjunction with local SLES and technology support mechanisms) are needed to maintain overall system stability. Therefore, understanding the critical role of non-optimal investor decision making is key to fully understand the drivers and implications of a rapid scale-up of SLES

Modelling governance for a successful electricity sector decarbonisation

Early and deep electricity decarbonisation is critical to achieve the overall energy transition target of net-zero emissions by 2050. This paper extends an electricity agent based model to capture the inter-dependence of consistent governance with underpinning societal pressure and resultant investment strategies. Results show only with the strongest level of governance – reflected in the range of national/local policy mechanisms used, and their strength/timing when interim targets are met/missed – can near-zero electricity emissions be met well before 2050. Strong governance can also ensure a stable electricity system, with consistent policies mitigating the intensity of any investment cycles. Strong governance entails higher capital investments, but these can deliver lower electricity prices in the long-term. And strong governance means that a successful electricity decarbonisation does not need to be built solely on existing incumbents, but also via local cooperatives to aggregate household financing and demand side management. However, with inconsistent governance, a vicious cycle ensues with a weak rationale to enact ambitious policies at both the local and national levels, significant inertia in new electricity investments, and hence “failure” scenarios of decarbonisation. This challenges the prior findings of optimistic achievement of electricity decarbonisation scenarios by standard techno-economic optimisation models

Stima speditiva degli scenari di danno sismico atteso per edifici in muratura mediante l’utilizzo di curve di Probit

Nel presente lavoro si presentano le curve di vulnerabilità degli edifici in muratura ricavate dall’analisi dei dati del terremoto del Friuli del 1976. In particolare le curve sono ricavate tramite l’analisi di Probit, una tecnica statistica largamente in uso nel campo delle analisi di rischio industriale e più in generale nei settori in cui si studiano fenomeni governati da una legge dose-effetto di tipo sigmoidale. Nell’articolo si mostra come tali curve possono essere direttamente impiegate per valutazioni previsionali di danno atteso su scala territoriale in quanto consentono di ricavare in modo relativamente semplice e rapido la percentuale di edifici che, sottoposti ad una determinata azione sismica, subiscono un determinato livello di danno strutturale espresso nella scala EMS-98. Nel lavoro si propone inoltre una metodologia di stima più ampia dello “scenario di danno”, basata sull’uso di una matrice di correlazione che lega l’indice di danno EMS-98 agli effetti indiretti in termini di giudizio di agibilità, riparabilità e probabilità di vittime associate. Tale matrice, anch’essa messa a punto sulla base dei dati del terremoto del Friuli, è stata validata e calibrata sui dati dalle schede AeDES compilate in occasione del terremoto Umbria-Marche

Relating basic properties of bright early-type dwarf galaxies to their location in Abell 901/902

We present a study of the population of bright early-type dwarf galaxies in the multiple-cluster system Abell 901/902. We use data from the STAGES survey and COMBO-17 to investigate the relation between the color and structural properties of the dwarfs and their location in the cluster. The definition of the dwarf sample is based on the central surface brightness and includes galaxies in the luminosity range -16 >= M_B >~-19 mag. Using a fit to the color magnitude relation of the dwarfs, our sample is divided into a red and blue subsample. We find a color-density relation in the projected radial distribution of the dwarf sample: at the same luminosity dwarfs with redder colors are located closer to the cluster centers than their bluer counterparts. Furthermore, the redder dwarfs are on average more compact and rounder than the bluer dwarfs. These findings are consistent with theoretical expectations assuming that bright early-type dwarfs are the remnants of transformed late-type disk galaxies involving processes such as ram pressure stripping and galaxy harassment. This indicates that a considerable fraction of dwarf elliptical galaxies in clusters are the results of transformation processes related to interactions with their host cluster.Comment: 12 pages, 8 figures, accepted for publication in A&A, typo corrected in abstrac