| 1. |
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| 2. |
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| 3. |
- Berndes, Göran, 1966, et al.
(författare)
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Forest biomass, carbon neutrality and climate change mitigation. From Science to Policy 3
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The Paris Agreement and the EU Climate and Energy Framework set ambitious but necessary targets. Reducing greenhouse gas (GHG) emissions by phasing out the technologies and infrastructures that cause fossil carbon emissions is one of today’s most important challenges. In the EU, bioenergy is currently the largest renewable energy source used. Most Member States have in absolute terms increased the use of forest biomass for energy to reach their 2020 renewable energy targets.In recent years, the issue of ‘carbon neutrality’ has been debated with regard to the bioenergy products that are produced from forest biomass. There is no clear consensus among scientists on the issue and their messages may even appear contradictory to decision-makers and citizens. Divergence arises because scientists address the issue from different points of view, which can all be valid. It is important to find agreement on some basic principles, to inform policy makers. Guidance is also needed on how the results should be interpreted.This report provides insights into the current scientific debate on forest biomass, carbon neutrality and climate change mitigation. It draws on the science literature to give a balanced and policy-relevant synthesis, from both an EU and global perspective.
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| 4. |
- Brandt, Adam R., et al.
(författare)
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Energy Intensity and Greenhouse Gas Emissions from Tight Oil Production in the Bakken Formation
- 2016
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Ingår i: Energy & Fuels. - : American Chemical Society (ACS). - 1520-5029 .- 0887-0624. ; 30:11, s. 9613-9621
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Tidskriftsartikel (refereegranskat)abstract
- The Bakken formation has contributed to the recent rapid increase in U.S. oil production, reaching a peak production of >1.2 × 106 barrels per day in early 2015. In this study, we estimate the energy intensity and greenhouse gas (GHG) emissions from 7271 Bakken wells drilled from 2006 to 2013. We model energy use and emissions using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) model, supplemented with an open-source drilling and fracturing model, GHGfrack. Overall well-to-refinery-gate (WTR) consumption of natural gas, diesel, and electricity represent 1.3%, 0.2%, and 0.005% of produced crude energy content, respectively. Fugitive emissions are modeled for a “typical” Bakken well using previously published results of atmospheric measurements. Flaring is a key driver of emissions: wells that flared in 2013 had a mean flaring rate that was ≈500 standard cubic feet per barrel or ≈14% of the energy content of the produced crude oil. Resulting production-weighted mean GHG emissions in 2013 were 10.2 g of CO2 equivalent GHGs per megajoule (henceforth, gCO2eq/MJ) of crude. Between-well variability gives a 5–95% range of 2–28 gCO2eq/MJ. If flaring is completely controlled, Bakken crude compares favorably to conventional U.S. crude oil, with 2013 emissions of 3.5 gCO2eq/MJ for nonflaring wells, compared to the U.S. mean of ≈8 gCO2eq/MJ.
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| 5. |
- Brown, Stephen P.A., et al.
(författare)
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Recent Developments at Energy Policy
- 2019
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Ingår i: Energy Policy. - : Elsevier BV. - 0301-4215. ; 133
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Brown, Stephen, et al.
(författare)
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The continuing evolution of Energy Policy
- 2020
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Ingår i: Energy Policy. - : Elsevier BV. - 0301-4215. ; 139
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- As the world confronts the Covid-19 pandemic, we hope that all of you are doing well. We know that many lives have been greatly disrupted, and that world economic activity is slowing and maybe declining in some places. We have read reports that energy consumption has been greatly affected by the slowdown in world economic activity—likely contributing to the sharp plunge in oil prices earlier this year. We do not know how long this pandemic may last. As we look forward to the end of the pandemic and a recovering world economy, however, we wonder if and how energy systems may have to be transformed, and whether new energy policy needs and approaches will emerge. Will we see any change in the trajectory of adopting sustainable energy systems and reducing carbon emissions? In the academic world, many of us are now teleworking and teaching our courses online. This transition has proved time consuming—so we want to thank our many reviewers who are staying on or close to schedule. So far, Energy Policy has been mostly unaffected by the pandemic, but we must recognize that the Elsevier employees who are responsible for the operations side of the journal may at some time be affected by Covid-19. In the meantime, we want to keep you informed about some recent developments regarding Energy Policy, including a little about its history and our editorial priorities.
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| 7. |
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| 8. |
- DeCarolis, Joseph, et al.
(författare)
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Formalizing best practice for energy system optimization modelling
- 2017
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Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 194, s. 184-198
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Forskningsöversikt (refereegranskat)abstract
- Energy system optimization models (ESOMs) are widely used to generate insight that informs energy and environmental policy. Using ESOMs to produce policy-relevant insight requires significant modeler judgement, yet little formal guidance exists on how to conduct analysis with ESOMs. To address this shortcoming, we draw on our collective modelling experience and conduct an extensive literature review to formalize best practice for energy system optimization modelling. We begin by articulating a set of overarching principles that can be used to guide ESOM-based analysis. To help operationalize the guiding principles, we outline and explain critical steps in the modelling process, including how to formulate research questions, set spatio-temporal boundaries, consider appropriate model features, conduct and refine the analysis, quantify uncertainty, and communicate insights. We highlight the need to develop and refine formal guidance on ESOM application, which comes at a critical time as ESOMs are being used to inform national climate targets.
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| 9. |
- DeCarolis, Joseph, et al.
(författare)
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Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts
- 2020
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Ingår i: Joule. - : Elsevier BV. - 2542-4351. ; 4:12, s. 2523-2526
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/. DeCarolis et al. articulate the benefits of forming collaborative teams with a wide array of disciplinary and domain expertise to conduct analysis with macro-energy system models. Open-source models, tools, and datasets underpin such efforts by enabling transparency, accessibility, and replicability among team members and with the broader modeling community.
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| 10. |
- Kumar, Anikender, et al.
(författare)
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Effects of low-carbon energy adoption on airborne particulate matter concentrations with feedbacks to future climate over California
- 2020
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Ingår i: Journal of Geophysical Research: Atmospheres. - 2169-8996 .- 2169-897X. ; 125:16
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Tidskriftsartikel (refereegranskat)abstract
- California plans to reduce emissions of long‐lived greenhouse gases (GHGs) through adoption of new energy systems that will also lower concentrations of short‐lived absorbing soot contained in airborne particulate matter (PM). Here we examine the direct and indirect effects of reduced PM concentrations under a low‐carbon energy (GHG‐Step) scenario on radiative forcing in California. Simulations were carried out using the source‐oriented WRF/Chem (SOWC) model with 12 km spatial resolution for the year 2054. The avoided aerosol emissions due to technology advances in the GHG‐step scenario reduce ground level PM concentrations by ~8.85% over land compared to the Business as Usual (BAU) scenario, but changes to meteorological parameters are more modest. Top of atmospheric forcing predicted by the SOWC model increased by 0.15 W m‐2, surface temperature warmed by 0.001 K, and planetary boundary layer height (PBLH) increased by 2.20 cm in the GHG‐Step scenario compared to the BAU scenario. PM climate feedbacks are small because the significant changes in ground level PM concentrations associated with the GHG‐Step scenario are limited to the first few hundred meters of the atmosphere, with little change for the majority of the vertical column above that level. As an order‐of‐magnitude comparison, the long‐term effects of global reductions in GHG emissions (RCP8.5 – RCP4.5) lowered average surface temperature over the California study domain by approximately 0.76 K. The effects of long‐lived climate pollutants such as CO2 are much stronger than the effects of short‐lived climate pollutants such as PM soot over California in the year 2054.
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| 11. |
- Liao, Yuan, 1991, et al.
(författare)
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A Mobility Model for Synthetic Travel Demand from Sparse Traces
- 2022
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Ingår i: IEEE Open Journal of Intelligent Transportation Systems. - 2687-7813. ; 3, s. 665-678
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Tidskriftsartikel (refereegranskat)abstract
- Knowing how much people travel is essential for transport planning. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these data suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and accessible data, this study proposes a mobility model that fills the gaps in sparse mobility traces from which one can later synthesise travel demand. The proposed model extends the fundamental mechanisms of exploration and preferential return to synthesise mobility trips. The model is tested on sparse mobility traces from Twitter. We validate our model and find good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and Saõ Paulo, Brazil, compared with a benchmark model using a heuristic method, especially for the most frequent trip distance range (1-40 km). Moreover, the learned model parameters are found to be transferable from one region to another. Using the proposed model, reasonable travel demand values can be synthesised from a dataset covering a large enough population of very sparse individual geolocations (around 1.5 geolocations per day covering 100 days on average).
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| 12. |
- Liao, Yuan, 1991, et al.
(författare)
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Disparities in travel times between car and transit: Spatiotemporal patterns in cities
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Cities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: R<1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.
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| 13. |
- Liao, Yuan, 1991, et al.
(författare)
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Feasibility of estimating travel demand using geolocations of social media data
- 2022
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Ingår i: Transportation. - : Springer Science and Business Media LLC. - 0049-4488 .- 1572-9435. ; 49:1, s. 137-161
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Tidskriftsartikel (refereegranskat)abstract
- Travel demand estimation, as represented by an origin–destination (OD) matrix, is essential for urban planning and management. Compared to data typically used in travel demand estimation, the key strengths of social media data are that they are low-cost, abundant, available in real-time, and free of geographical partition. However, the data also have significant limitations: population and behavioural biases, and lack of important information such as trip purpose and social demographics. This study systematically explores the feasibility of using geolocations of Twitter data for travel demand estimation by examining the effects of data sparsity, spatial scale, sampling methods, and sample size. We show that Twitter data are suitable for modelling the overall travel demand for an average weekday but not for commuting travel demand, due to the low reliability of identifying home and workplace. Collecting more detailed, long-term individual data from user timelines for a small number of individuals produces more accurate results than short-term data for a much larger population within a region. We developed a novel approach using geotagged tweets as attraction generators as opposed to the commonly adopted trip generators. This significantly increases usable data, resulting in better representation of travel demand. This study demonstrates that Twitter can be a viable option for estimating travel demand, though careful consideration must be given to sampling method, estimation model, and sample size.
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| 14. |
- Liao, Yuan, 1991, et al.
(författare)
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From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data
- 2019
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Ingår i: EPJ Data Science. - : Springer Science and Business Media LLC. - 2193-1127. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences.
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| 15. |
- Liao, Yuan, 1991, et al.
(författare)
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Impacts of charging behavior on BEV charging infrastructure needs and energy use
- 2023
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 116
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Tidskriftsartikel (refereegranskat)abstract
- Battery electric vehicles (BEVs) are vital in the sustainable future of transport systems. Increased BEV adoption makes the realistic assessment of charging infrastructure demand critical. The current literature on charging infrastructure often uses outdated charging behavior assumptions such as universal access to home chargers and the "Liquid-fuel" mental model. We simulate charging infrastructure needs using a large-scale agent-based simulation of Sweden with detailed individual characteristics, including dwelling types and activity patterns. The two state-of-art archetypes of charging behaviors, "Plan-ahead" and "Event-triggered," mirror the current infrastructure built-up, suggesting 2.3-4.5 times more public chargers per BEV than the "Liquid-fuel" mental model. We also estimate roughly 30-150 BEVs served by a slow charger may be needed for non-home residential overnight charging.
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| 16. |
- Liao, Yuan, 1991, et al.
(författare)
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Predictability in Human Mobility based on Geographical-boundary-free and Long-time Social Media Data
- 2018
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Ingår i: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). - 2153-0017. - 9781728103211 ; 2018-November, s. 2068-2073
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Konferensbidrag (refereegranskat)abstract
- Understanding of predictability in human mobility benefits a broad spectrum such as urban planning and traffic forecasting. In human mobility studies, geotagged social media data are being gradually accepted as a user-contributed data source. It remains unclear to what extent we can use geotagged social media data to predict individual mobility. In the present study, a dataset is collected and applied which includes 652,945 geotagged tweets generated by 2,933 Swedish users covering time spans of more than one year (3.6 years on average). Based on such a dataset, human mobility predictability has been explored from three aspects: 1) time history of mobility range indicating how people diffuse in space, 2) entropy and the corresponding predictability of mobility, and 3) the limits of predictability dependent on geographical boundaries and mobility range. This study reveals a dataset that captures Twitter users' mobility where they routinely visit a couple of regions at most of the time and occasionally explore new regions. A 70% potential predictability is obtained by measuring the entropy of each individual's geotagged activity trajectory using a half-day time interval. The predictability's dependence on mobility range is prolonged when the observation of mobility is geographical-boundary-free which also decreases predictability.
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| 17. |
- Linero, Humberto, 1992, et al.
(författare)
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The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - 2018. It aims to create transparency through two key features: Open-Data: Assembling a comprehensive collection of publicly-available transportation data Open-Code: All code and documentation will be publicly accessible and open for modification and extension. https://github.com/transportenergy The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English).
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| 18. |
- Liu, Xiaohan, 1994, et al.
(författare)
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Electric bus charging scheduling problem considering charging infrastructure integrated with solar photovoltaic and energy storage systems
- 2024
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Ingår i: Transportation Research Part E: Logistics and Transportation Review. - 1366-5545. ; 187
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Tidskriftsartikel (refereegranskat)abstract
- Bus fleet electrification is crucial in reducing urban mobility carbon emissions, but it increases charging demand on the power grid. This study focuses on a novel battery electric bus (BEB) charging scheduling problem involving solar photovoltaic (PV) and battery energy storage facilities. A mixed integer linear programming model is formulated to schedule BEB charging and control solar PV energy simultaneously. The model handles a range of realistic considerations, including heterogeneous BEBs regarding battery capacities, peak net charging power costs, flexible charging powers, and multi-route-multi-depot scheduling. A key point of our model is the introduction of variable charging power decisions designed to align BEB charging demands with solar PV production. The optimization objective is to minimize the sum of charging costs, carbon emission costs, energy storage costs, and revenue (negative cost) from solar PV energy sales. The model empowers public transport agencies to swiftly generate daily BEB charging schedules given daily solar and weather variations. A case study is performed in Beijing, China, utilizing actual bus trajectory data, weather conditions, solar irradiance, and detailed built environment data of bus depots. The results show that the proposed model can significantly reduce the operating cost and shift the charging loads by improving solar PV energy utilization.
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| 19. |
- Liu, Xiaohan, 1994, et al.
(författare)
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Transforming public transport depots into profitable energy hubs
- 2024
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Ingår i: Nature Energy. - 2058-7546. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- Transportation is undergoing rapid electrification, with electric buses at the forefront of public transport, especially in China. This transition, however, could strain electricity grids. Using a large-scale dataset with over 200 million global positioning system records from 20,992 buses in Beijing, we explore the technical, economic and environmental implications of transforming public transport depots into renewable energy hubs. Here we show that solar photovoltaic reduces the grid’s net charging load by 23% during electricity generation periods and lowers the net charging peak load by 8.6%. Integrating energy storage amplifies these reductions to 28% and 37.4%, respectively. Whereas unsubsidized solar photovoltaic yields profit 64% above costs, adding battery storage cuts profits to 31% despite offering grid benefits. Negative marginal abatement gains for CO2 emissions underscore the economic sustainability. Our findings provide a model for cities worldwide to accelerate their commitments towards sustainable transport and energy systems.
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| 20. |
- Masnadi, Mohammad S., et al.
(författare)
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Global carbon intensity of crude oil production
- 2018
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 361:6405, s. 851-853
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Tidskriftsartikel (refereegranskat)abstract
- Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ∼15 to 40% of the “well-to-wheels” life-cycle greenhouse gas (GHG) emissions of transport fuels (1). Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions (2). Yet, while regulations are beginning to address petroleum sector GHG emissions (3–5), and private investors are beginning to consider climate-related risk in oil investments (6), such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE (7, 8), supplementary materials (SM) 1.1]. Here, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally—and to identify major drivers of these emissions.
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| 21. |
- Morfeldt, Johannes, 1983, et al.
(författare)
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If Electric Cars Are Good for Reducing Emissions, They Could Be Even Better with Electric Roads
- 2022
-
Ingår i: Journal of Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 56:13, s. 9593-9603
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Tidskriftsartikel (refereegranskat)abstract
- This research investigates carbon footprint impacts for full fleet electrification of Swedish passenger car travel in combination with different charging conditions, including electric road system (ERS) that enables dynamic on-road charging. The research applies a prospective life cycle analysis framework for estimating carbon footprints of vehicles, fuels, and infrastructure. The framework includes vehicle stock turnover modeling of fleet electrification and modeling of optimal battery capacity for different charging conditions based on Swedish real-world driving patterns. All new car sales are assumed to be electric after 2030 following phase-out policies for gasoline and diesel cars. Implementing ERS on selected high-traffic roads could yield significant avoided emissions in battery manufacturing compared to the additional emissions in ERS construction. ERS combined with stationary charging could enable additional reductions in the cumulative carbon footprint of about 12–24 million tons of CO2 over 30 years (2030–2060) compared to an electrified fleet only relying on stationary charging. The range depends on uncertainty in emission abatement in global manufacturing, where the lower is based on Paris Agreement compliance and the higher on current climate policies. A large share of the reduction could be achieved even if only a small share of the cars adopts the optimized battery capacities.
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| 22. |
- Pop, Paul, et al.
(författare)
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Special Session : Digital Technologies for Sustainability - Research Challenges and Opportunities
- 2023
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Ingår i: Proceedings - 2023 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2023. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 18-23
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Konferensbidrag (refereegranskat)abstract
- This paper presents an integrative exploration of key digital technologies instrumental in enabling the global green transition. In view of ambitious climate neutrality targets, it underscores the complex interplay between these technologies, policy implications, research challenges, and real-world applications in building a sustainable, low-carbon solutions. From a policy perspective, the paper delves into the systemic opportunities and challenges, providing valuable insights for various stakeholders. The discussion also encompasses pivotal research areas necessitated by the convergence of digital technologies and sustainable practices, emphasizing the need for a multi-disciplinary approach. Further, the paper identifies the practical applications of digital technologies across a range of sectors, highlighting several illustrative case studies, drawing lessons for future implementations. It concludes with forward-looking recommendations, stressing the significance of coherent policy coordination, public engagement, and global cooperation in furthering the digital-led green transition.
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| 23. |
- Ramea, Kalai, et al.
(författare)
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Integration of behavioral effects from vehicle choice models into long-term energy systems optimization models
- 2018
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Ingår i: Energy Economics. - : Elsevier BV. - 0140-9883 .- 1873-6181. ; 74, s. 663-676
-
Tidskriftsartikel (refereegranskat)abstract
- Long-term energy systems models have been used extensively in energy planning and climate policy analysis. However, specifically in energy systems optimization models, heterogeneity of consumer preferences for competing energy technologies (e.g., vehicles), has not been adequately represented, leading to behaviorally unrealistic modeling results. This can lead to policy analysis results that are viewed by stakeholders as clearly deficient. This paper shows how heterogeneous consumer behavioral effects can be introduced into these models in the form of perceived disutility costs, to more realistically capture consumer choice in making technology purchase decisions. We developed a novel methodology that incorporates the theory of a classic consumer choice model into a commonly used long-term energy systems modeling framework using a case study of light-duty vehicles. A diverse set of consumer segments (thirty-six) is created to represent observable, identifiable differences in factors such as annual driving distances and attitude towards risks of new technology. Non-monetary or “disutility” costs associated with these factors are introduced to capture the differences in preferences across consumer segments for various technologies. We also create clones within each consumer segment to capture randomly distributed unobservable differences in preferences. We provide and review results for a specific example that includes external factors such as recharging/refueling station availability, battery size of electric vehicles, recharging time and perceived technology risks. Although the example is for light-duty vehicles in the US using a specific modeling system, this approach can be implemented more broadly to model the adoption of consumer technologies in other sectors or regions in similar energy systems modeling frameworks.
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| 24. |
- Schäfer, Andreas, et al.
(författare)
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A holistic analysis of passenger travel energy and greenhouse gas intensities
- 2020
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Ingår i: Nature Sustainability. - : Springer Science and Business Media LLC. - 2398-9629. ; 3:6, s. 459-462
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Tidskriftsartikel (refereegranskat)abstract
- Transportation is a major energy consumer and emitter of greenhouse gases (GHGs). Exploring the opportunities for energy savings and GHG emissions reductions requires understanding transportation energy or GHG intensity, which is defined as energy use or GHG emissions per unit activity, here passenger-kilometres travelled. This aggregate indicator quantifies the amount of energy required or GHGs emitted to provide a generic transportation service. We show that the range of observed energy and GHG intensities of major transportation modes is remarkably similar and that occupancy explains about 70–90% of the variation around the mean; only the remaining 10–30% is explained by differences in trip distances and other factors such as technology and operating conditions. Whereas average occupancy levels differ vastly, they translate into roughly similar levels of energy and GHG intensity for nearly all major transportation modes.
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| 25. |
- Shoman, Wasim, 1990, et al.
(författare)
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A Review of Big Data in Road Freight Transport Modeling: Gaps and Potentials
- 2023
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Ingår i: Data Science for Transportation. - : Springer Science and Business Media LLC. - 2948-1368 .- 2948-135X. ; 5:2
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Tidskriftsartikel (refereegranskat)abstract
- Road transport accounted for 20% of global total greenhouse gas emissions in 2020, of which 30% come from road freight transport (RFT). Modeling the modern challenges in RFT requires the integration of different freight modeling improvements in, e.g., traffic, demand, and energy modeling. Recent developments in 'Big Data' (i.e., vast quantities of structured and unstructured data) can provide useful information such as individual behaviors and activities in addition to aggregated patterns using conventional datasets. This paper summarizes the state of the art in analyzing Big Data sources concerning RFT by identifying key challenges and the current knowledge gaps. Various challenges, including organizational, privacy, technical expertise, and legal challenges, hinder the access and utilization of Big Data for RFT applications. We note that the environment for sharing data is still in its infancy. Improving access and use of Big Data will require political support to ensure all involved parties that their data will be safe and contribute positively toward a common goal, such as a more sustainable economy. We identify promising areas for future opportunities and research, including data collection and preparation, data analytics and utilization, and applications to support decision-making.
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