Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2411.15352

Impact of consumer preferences on decarbonization of transport sector in India

Nandita Saraf·Yogendra Shastri
View original source ↗Read source document ↗Export metadata ↓

Abstract

Decarbonization of transport sector through adoption of cleaner vehicle options will depend on the environmental awareness of consumers and their priorities. This work develops and uses a system dynamics approach to understand possible adoption pathways of novel vehicle options, i.e., ethanol-blended fuel (E85), electric, and compressed natural gas (CNG) vehicles in India. A system dynamics model using a multi-nominal logit model to capture consumer choices has been previously developed for the private road transport sector of India. The model has been modified to also include annual vehicular emissions as a decisionmaking attribute in addition to the annual cost. Four different classes of consumers with different priorities to cost and annual emissions are modeled. The model coefficients are identified using historical data. Model simulations over a period of 30 years till 2050 are performed to determine possible vehicle adoption trends. Different scenarios of changing environmental awareness, policy interventions, and technology development were analyzed to achieve targets such as COP26 greenhouse gas emission reduction, ethanol blending, and EV adoption. Simulation results showed that an increase in environmental awareness resulted in the adoption of novel vehicle options by 67.42% and 22.3% of the car and two-wheeler stocks, respectively. However, rising environmental awareness was not enough to meet the target values. Scenario analysis showed that a greater share of renewables in the electricity grid, carbon tax on transport fuel, and reduced vehicle driving together can possibly achieve the GHG emissions target in 2030. Battery-related GHG emissions were shown to be very important and led to counter-intuitive trends in EV adoption. 30% EV sales by 2030 could be achieved with a greener electricity grid and carbon tax.

Explore related subjects

physics.soc-ph

Keep this discovery

BibTeXRIS

Nandita Saraf, Yogendra Shastri. 2024-11-22. Impact of consumer preferences on decarbonization of transport sector in India. https://arxiv.org/abs/2411.15352

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Energy pathway variety and the progress of the energy transition in European countries

The integration of new energy forms into existing energy infrastructure has emerged as a critical challenge in the context of the pursuit of a sustainable energy transition. One of the main challenges is understanding how this integration takes place not only from the introduction, but also as energy follows existing paths or creates new ones through which it is transformed and used by different activities. Here we introduce techniques from network science to analyse this process for the case of 29 European countries between 1992 and 2021. We study how new energy forms increase or decrease the variety (heterogeneity) of paths through the system of each country by establishing new ones and replacing or phasing out existing ones. We find that the transition to systems based on renewable energy is characterised by an initial increase in the variety of paths while the heterogeneity of paths decreases at the end of the transition, when the proportion of non-renewables in the system tends to zero. We then demonstrate that greater heterogeneity (complexity) is associated with larger annual fluctuations in the proportion of non-renewable sources in the system, establishing a direct relationship between the progress of the transition and the complexity of the energy system in which it occurs. This contributes to the understanding of general properties of the dynamics of the energy transition and effects that accelerate or deter it.

physics.soc-ph↗
arXiv2026

Fundamental limits to identifying node and tie memory in temporal networks: marginal artefacts and spreading dynamics

Temporal-network models attribute memory in contact data to either node self-excitation (branching ratio n_node) or tie reinforcement (kappa), carrying major consequences for epidemic spreading. We prove that when event initiators are observed, the two mechanisms are orthogonal: the Fisher information is block-diagonal and neither trades off against the other. In undirected proximity data, where initiators are unobserved, marginalising over them couples the mechanisms into a structural confound that survives posterior smoothing. On empirical proximity, messaging, and email records, however, a cruder failure dominates: fitted node memory is pinned to the inter-event marginal law and remains virtually invariant across latent label posterior samples (coefficient of variation below 1%). An inter-event-order shuffle test and burstiness-memory diagnostics reveal that exponential-Hawkes node memory is recovered from none, while tie reinforcement remains identifiable throughout. This near-unidentifiability is intrinsic, not an artefact of the exponential kernel: refitting flexible scale-free (sum-of-exponentials) kernels on synthetic power-law self-exciting processes fails to distinguish genuine node memory from memoryless renewal controls, with identical collapses recurring on algorithmic networks (edit bots, cloud microservices) and cortical spiking. Downstream epidemic consequences are quantitative: simulations fitted to empirical contact records under-predict outbreak sizes by up to a factor of 2.5 and shift the epidemic threshold. We conclude that observational temporal networks face a two-fold identifiability boundary: contact directionality is essential to decouple tie reinforcement, whereas heavy-tailed node self-excitation is intrinsically unidentifiable from contact timings alone.

physics.soc-ph↗
arXiv2026

Assessing extreme flood impacts on urban rail transit: A passenger-oriented, resilience-informed framework

Urban rail transit systems (URTSs) are increasingly exposed to extreme floods following heavy precipitation, yet passenger travel impacts are often assessed through delay-based indicators that overlook infeasible journeys under large-scale disruptions. This study develops a passenger-oriented, resilience-informed framework for assessing flood impacts on URTS journeys from disruption onset to recovery completion. The framework presents a novel six-category classification of journey impacts, explicitly considering rerouting, alternative station use, and a delay threshold. It is demonstrated through hourly dynamic simulations of 15 London URTS lines under 30-year, 100-year, and 1,000-year flood risk scenarios. Results indicate that severe flood disruptions lead to substantial unsatisfied demand, driven primarily by unavailable routes rather than unacceptable delays. Compared with finer behaviour adjustments, rerouting dominates travel impacts. These findings highlight the significance of moving beyond delay-based assessment and provide valuable evidence on essential behavioural mechanisms for strategic-level stress testing intended to inform URTS flood resilience intervention planning.

physics.soc-ph↗