Contents lists available at ScienceDirect Energy Policy journal homepage: www.elsevier.com/locate/enpol Reaching carbon neutral transport sector in Denmark – Evidence from the incorporation of modal shift into the TIMES energy system modeling framework Jacopo Tattini a, ⁎ , Maurizio Gargiulo b , Kenneth Karlsson a a DTU Management Engineering, Produktionstorvet, Building 426, 2800 Kgs. Lyngby, Denmark b E4SMA, Via Livorno, 60, 10144 Turin, Italy ARTICLE INFO Keywords: Modal shift TIMES model Integrated Danish energy system Transport Behaviour ABSTRACT Energy/Economy/Environment/Engineering (E4) models have been rarely apt to represent human behaviour in transportation mode adoption. This paper contributes to the scientific literature by using an E4 model to analyse the long-term decarbonisation of the Danish transport sector. The study is carried out with TIMES-DK, the integrated energy system model of Denmark, which has been expanded in order to endogenously determine modal shares. The methodology extends the technology competition within the modes to competition across modes by aggregating the passenger modal travel demands into demand segments based on the distance range. Modal shift is based not only on the levelised costs of the modes, but also on speed and infrastructure re- quirement. Constraints derived from the National Travel Survey guarantee consistent travel habits and avoid unrealistic modal shifts. The comparison of model versions with and without modal shift identifies its positive contribution to the fulfilment of the Danish environmental targets. Four sensitivity analyses on the key variables of modal shift assess how their alternative realizations affect the decarbonisation of the transport sector and enable shifting away from car. The results indicate that less strict travel time budget (TTB) and increased speed of public bus lead to a more efficient decarbonisation by 2050. 1. Introduction Transport is a fundamental driver of economy and society and it plays a primary role in supporting economic growth and quality of life. Nonetheless, transport is also responsible for many externalities at local, regional and global levels. At the local scale, transport is re- sponsible for accidents, road damage, vibration, noise and congestion (Santos et al., 2010). At the regional scale, transport is responsible for emitting several air pollutants affecting human health. A widely dis- cussed global externality is transport's contribution to climate change. Since 1970 greenhouse gas (GHG) emissions from the transport sector have more than doubled, increasing at the fastest rate among all the end-use energy sectors (Sims et al., 2014). In 2010, transport accounted for approximately 23% of energy-related CO 2 emissions worldwide (International Energy Agency, 2009) and about 36% in Denmark (Nordic Energy Research and International Energy Agency, 2016). So far, the efforts to reduce transport GHG emissions by improving pow- ertrain efficiency and fuel standards have been offset by the increase of transport activity. Moreover, alternative fuelled vehicles (AFV) still require policy support to gain a significant market share (Mulholland et al., In preparation). An evidence is that the derogation of the vehicle registration tax (VRT) towards electric vehicles (EVs) in Denmark has seen a fall in their sale in 2016 (European Environmental Agency, 2017). Besides, the International Energy Agency (IEA) (2009) estimates that 2050 worldwide car ownership could triple, while freight transport by truck and aviation could increase four-fold, thus leading to a dou- bling of energy use in transport. In order to reverse this tremendous trend, the IEA proposes a combination of both technological and be- havioural measures: avoid, shift, improve and switch (International Energy Agency, 2012). Avoid entails mitigating the mobility demands by, for instance, densifying the urban structure, teleworking and virtual mobility. Shift consists in increasing the market shares of low-carbon modes, fostered by e.g. improving the level of service (LoS) of public transport and deploying biking infrastructure. Improve focuses on en- hancing the vehicle efficiency by decreasing its weight, increasing the occupancy and load factor and developing advanced engines. Switch consists in substituting oil-based fuels with low-carbon fuels. In this paper, we investigate transport-related issues through the lens of an E4 optimization model, specifically a TIMES/MARKAL model. Such energy system models are valuable tools for long-term https://doi.org/10.1016/j.enpol.2017.11.013 Received 1 February 2017; Received in revised form 1 November 2017; Accepted 5 November 2017 ⁎ Corresponding author. E-mail address: jactat@dtu.dk (J. Tattini). Energy Policy 113 (2018) 571–583 0301-4215/ © 2017 Elsevier Ltd. All rights reserved. T