1,721,174 research outputs found
What Can Driving Patterns Reveal About the Suitability of PEVs in Sweden? Analysis and Policy Implications
No full textSweden has ambitious climate goals. For the transport sector the goals is a 70% reduction of emissions from domestic transport by 2030 relative to 2010 levels. Even if Sweden has not set any specific goals for PEVs, electrification of the transport sector is one of the major strategies to achieve the climate goals. In this chapter we analyze the suitability of PEVs with a special emphasis on BEVs, in Sweden. The main approach has been to study driving patterns and how these relate to the limited range of a BEV. Special emphasis has been set on two-car households since these are potential early adopters. Data on driving patterns is complemented with interviews of households that have trialed a BEV for at least 3 months. We find that the longer driving distances in Sweden might make it more challenging for BEVs to be adopted. On the plus side the economic conditions in Sweden with low electricity prices, higher fuel taxes and subsidies make the BEV economically viable from a TCO perspective. The combination of these factors might explain the relatively high shares of sales of PEVs in Sweden (5.3% sales share in 2017) and why these are dominated by PHEVs. The chapter also looks at the lessons learned from the failed introduction of flex-fuel vehicles and finds that is important to maintain the economic advantages of PEVs through, e.g., higher fuel taxes and that PEVs should have a relative advantage for the user that goes beyond their environmental performance
Public charging facility requirements for long-haul trucks in the EU: a trip chain approach
No full textAbstract of the research:
This research presents a trip chain-based model that evaluates the LBET's charging requirements in the year 2030 for the European continent. Following EU truck driver regulations, the research converts a four-step origin-destination (OD) matrix into LHT trip chains. We show that in our main case scenario of 15% LBET share, a minimum of 28800 slow (≤ 100 kW) and 9800 fast (≥ 1 mW) charging points are required to meet that energy demand, corresponding to daily energy requirements of 112 GWh. On average, the slow to fast charging points ratio is 3. Fast and slow charging points serve 12 and 2 LBETs daily, respectively. Our model suggests that it will be necessary to place charging stations every 25-35 km on highways where demand for charging is required.
The methodology:
We develop a method for placement of charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by LHT is identified using the ETISplus dataset. We develop a travel pattern for the long haul truck (LHT) to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving LHT. Break and rest locations for all moving LHTs are aggregated to suggest energy requirements if assuming these LHTs are BETs. The aggregated energy to charge stopped LBETs is used to identify the number and type of chargers within each suggested charging station.
Datasets details
The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers and Planned transport infrastructure.
The first dataset titled 'ChargerLocations' contains infromation about the locations of suggested charging stations, number and type of chargers, and number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:
Name
Description
Data Type
Unit
DTN30/MainDTN
number of electrified trucks in 2030
integer
number
ChE30
charged energy in Mega watt-hour from all charging (fast and slow)
float
Mega watt-hour
ChERM
charged energy in Megawatt hour with slow charging only (rest)
float
Mega watt-hour
MDTN_R
number of electrified trucks using slow chargers (rest)
integer
number
ChEBM
charged energy in Megawatt hour with fast charging only (break)
float
Mega watt-hour
MDTN_B
number of electrified trucks using fast chargers (break)
integer
number
NSCh2pD
number of slow chargers
integer
number
NFCh30m
number of fast chargers
integer
number
TotCha
Total number of chargers
integer
number
The second dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):
Name
Description
Data Type
Unit
ID_origin_region
Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3
Integer (9digits)
-
Name_origin_region
National name of NUTS-3 region of origin
String
-
ID_destination_region
Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3
Integer (9digits)
-
Name_destination_
region
National name of NUTS-3 destination region
String
-
Edge_path_E_road
List of the network edge IDs of the shortest path between the O-D pair, determined with Dijkstra's algorithm
String
-
Distance_from_origin_
region_to_E_road
Distance from the geometric centre of the origin region to the closest network node
Float
Kilometres [km]
Distance_within_E_
road
Distance of the shortest edge path between the O-D pair
Float
Kilometres [km]
Distance_from_E_
road_to_destination_
region
Distance from the geometric centre of the destination region to the closest network node
Float
Kilometres [km]
Total_distance
Sum of Distance_from_origin_region_to_E_road, Distance_within_E_road and Distance_from_E_road_to_destination_region
Float
Kilometres [km]
Traffic_flow_trucks_
2010
Number of trucks that drive between the O-D pair in 2010
Float
Number of trucks
Traffic_flow_trucks_
2019
Number of trucks that drive between the O-D pair after they had been scaled to 2019
Float
Number of trucks
Traffic_flow_trucks_
2030
Number of trucks that drive between the O-D pair according to the forecast for 2030
Float
Number of trucks
Traffic_flow_tons_
2010
Number of tons that are transported between the O-D pair in 2010 according to ETISplus
Integer
Tons [t]
Traffic_flow_tons_
2019
Number of tons that are transported between the O-D pair after they had been scaled to 2019
Integer
Tons [t]
Traffic_flow_tons_
2030
Number of tons that are transported between the O-D pair according to the forecast for 2030
Integer
Tons [t]
Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))
Name
Description
Data Type
Unit
Network_Node_ID
Unique network node ID
Integer (6 digits)
-
Network_Node_X
Longitude of the location of network node
Float
Degrees
Network_Node_Y
Latitude of the location of network node
Float
Degrees
ETISplus_Zone_ID
ID of the NUTS-3 region in which the network node is located
Integer
-
Country
Unique country code of the country in which the network node is located (country codes are defined by ETISplus)
String
-
Description of variables used in the network edges list (04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))
Name
Description
Data Type
Unit
Network_Edge_ID
Unique edge ID
Integer
(7 digits)
-
Manually_Added
Determines whether an edge had been manually added to the network (1) or not (0)
Binary-integer
-
Distance
Length of the network edge
Float
Kilometres [km]
Network_Node_A_ID
Unique ID of the network node that defines one end point of the network edge
Integer
-
Network_Node_B_ID
Unique ID of the network node that defines one end point of the network edge
Integer
-
Traffic_flow_trucks_2019
Number of trucks that drive on the edge in 2019 (both highway directions combined)
Float
Number of trucks
Traffic_flow_trucks_2030
Number of trucks that drive on the edge in 2030 (both highway directions combined)
Float
Number of truck
Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach
No full text<p>Contact details:</p>
<p>wasim.shoman at chalmers.se </p>
<p>waahh7 at gmail com</p>
<p><strong>Abstract of the research:</strong></p>
<p>Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km<sup>2</sup> square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.</p>
<p><strong>The methodology:</strong></p>
<p>We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.</p>
<p><strong>Datasets details</strong></p>
<p>The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure. </p>
<p>The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:</p>
<table>
<tbody>
<tr>
<td>Name</td>
<td>Description</td>
<td>Data Type</td>
<td>Unit</td>
</tr>
<tr>
<td>DTN30/MainDTN</td>
<td> number of electrified trucks in 2030</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>ChE30</td>
<td> charged energy in Mega watt-hour from all charging (fast and slow)</td>
<td>float</td>
<td> Mega watt-hour</td>
</tr>
<tr>
<td>ChERM</td>
<td> charged energy in Megawatt hour with slow charging only (rest)</td>
<td>float</td>
<td> Mega watt-hour</td>
</tr>
<tr>
<td>MDTN_R</td>
<td> number of electrified trucks using slow chargers (rest)</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>ChEBM</td>
<td> charged energy in Megawatt hour with fast charging only (break)</td>
<td>float</td>
<td> Mega watt-hour</td>
</tr>
<tr>
<td>MDTN_B</td>
<td> number of electrified trucks using fast chargers (break)</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>NSCh2pD</td>
<td> number of slow chargers</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>NFCh30m</td>
<td> number of fast chargers</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>TotCha</td>
<td> Total number of chargers</td>
<td>integer </td>
<td>number</td>
</tr>
</tbody>
</table>
<p> </p>
<p>The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format. </p>
<table>
<tbody>
<tr>
<td>
<p><strong>Name</strong></p>
</td>
<td>
<p><strong>Description</strong></p>
</td>
<td>
<p><strong>Data Type</strong></p>
</td>
<td>
<p><strong>Unit</strong></p>
</td>
</tr>
<tr>
<td>
<p>ID_origin_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_origin_region</p>
</td>
<td>
<p>National name of NUTS-3 region of origin</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>ID_destination_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_destination_<br>
region</p>
</td>
<td>
<p>National name of NUTS-3 destination region</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Rest</p>
</td>
<td>
<p>A value of ”1” indicates a rest stop</p>
</td>
<td>
<p>Boolean</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Break</p>
</td>
<td>
<p>A value of ”1” indicates a break stop</p>
</td>
<td>
<p>Boolean</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>ChaDisKM</p>
</td>
<td>
<p>Charged range within a trip for stopped the truck</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>km</p>
</td>
</tr>
<tr>
<td>
<p>ChaEnekWh</p>
</td>
<td>
<p>Charged energy within a trip for stopped the truck</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>KWh</p>
</td>
</tr>
<tr>
<td>
<p>MainDTN</p>
</td>
<td>
<p>Number of stopped trucks for the main electrification scenario (15%)</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>number</p>
</td>
</tr>
<tr>
<td>
<p>ChE30M</p>
</td>
<td>
<p>Charged energy for all stopped trucks</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>MWh</p>
</td>
</tr>
<tr>
<td>
<p>ChERM</p>
</td>
<td>
<p>Charged energy for the trucks stopping for rest</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>MWh</p>
</td>
</tr>
<tr>
<td>
<p>MDTN_R</p>
</td>
<td>
<p>Number of trucks stopping for rest</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>number</p>
</td>
</tr>
<tr>
<td>
<p>ChEBM</p>
</td>
<td>
<p>Charged energy for the trucks stopping for break</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>MWh</p>
</td>
</tr>
<tr>
<td>
<p>MDTN_B</p>
</td>
<td>
<p>Number of trucks stopping for break</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>number</p>
</td>
</tr>
<tr>
<td>
<p>geometry</p>
</td>
<td>
<p>X, Y coordinates</p>
</td>
<td>
<p>geometry</p>
</td>
<td>
<p>-</p>
</td>
</tr>
</tbody>
</table>
<p> </p>
<p> </p>
<p>The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):</p>
<table>
<tbody><tr>
<th>
<p><strong>Name</strong></p>
</th>
<th>
<p><strong>Description</strong></p>
</th>
<th>
<p><strong>Data Type</strong></p>
</th>
<th>
<p><strong>Unit</strong></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p>ID_origin_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_origin_region</p>
</td>
<td>
<p>National name of NUTS-3 region of origin</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>ID_destination_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_destination_<br>
region</p>
</td>
<td>
<p>National name of NUTS-3 destination region</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Edge_path_E_road</p>
</td>
<td>
<p>List of the <em>network edge IDs</em> of the shortest path between the O-D pair, determined with Dijkstra's algorithm</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Distance_from_origin_<br>
region_to_E_road</p>
</td>
<td>
<p>Distance from the geometric centre of the origin region to the closest network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Distance_within_E_<br>
road</p>
</td>
<td>
<p>Distance of the shortest edge path between the O-D pair</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Distance_from_E_<br>
road_to_destination_<br>
region</p>
</td>
<td>
<p>Distance from the geometric centre of the destination region to the closest network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Total_distance</p>
</td>
<td>
<p>Sum of <em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em> and <em>Distance_from_E_road_to_destination_region</em></p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_trucks_<br>
2010</p>
</td>
<td>
<p>Number of trucks that drive between the O-D pair in 2010</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Number of trucks</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_trucks_<br>
2019</p>
</td>
<td>
<p>Number of trucks that drive between the O-D pair after they had been scaled to 2019</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Number of trucks</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_trucks_<br>
2030</p>
</td>
<td>
<p>Number of trucks that drive between the O-D pair according to the forecast for 2030</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Number of trucks</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_tons_<br>
2010</p>
</td>
<td>
<p>Number of tons that are transported between the O-D pair in 2010 according to ETISplus</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>Tons [t]</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_tons_<br>
2019</p>
</td>
<td>
<p>Number of tons that are transported between the O-D pair after they had been scaled to 2019</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>Tons [t]</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_tons_<br>
2030</p>
</td>
<td>
<p>Number of tons that are transported between the O-D pair according to the forecast for 2030</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>Tons [t]</p>
</td>
</tr>
</tbody>
</table>
<p>Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p>
<table>
<tbody><tr>
<th>
<p><strong>Name</strong></p>
</th>
<th>
<p><strong>Description</strong></p>
</th>
<th>
<p><strong>Data Type</strong></p>
</th>
<th>
<p><strong>Unit</strong></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p>Network_Node_ID</p>
</td>
<td>
<p>Unique network node ID</p>
</td>
<td>
<p>Integer (6 digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Network_Node_X</p>
</td>
<td>
<p>Longitude of the location of network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Degrees</p>
</td>
</tr>
<tr>
<td>
<p>Network_Node_Y</p>
</td>
<td>
<p>Latitude of the location of network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Degrees</p>
</td>
</tr>
<tr>
<td>
<p>ETISplus_Zone_ID</p>
</td>
<td>
<p>ID of the NUTS-3 region in which the network node is located</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Country</p>
</td>
<td>
<p>Unique country code of the country in which the network node is located (country codes are defined by ETISplus)</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
</tbody>
</table>
<p>Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p>
<table>
<tbody><tr>
<th>
<p><strong>Name</strong></p>
</th>
<th>
<p><strong>Description</strong></p>
</th>
<th>
<p><strong>Data Type</strong></p>
</th>
<th>
<p><strong>Unit</strong></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p>Network_Edge_ID</p>
</td>
<td>
<p>Unique edge ID</p>
</td>
<td>
<p>Integer<br>
(7 digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Manually_Added</p>
</td>
<td>
<p>Determines whether an edge had been manually added to the network (1) or not (0)</p>
</td>
<td>
<p>Binary-integer</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Distance</p>
</td>
<td>
<p>Length of the network edge</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Network_Node_A_ID</p>
</td>
<td>
<p>Unique ID of the network node that defin
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Disrupting mobility
Full text linkPersonal mobility is facing three major innovations that have disruptive potential: electrification, shared mobility and automation. In this perspective I present each of these on their own and look at their role in disrupting the auto industry, the transport system and energy system. The largest disruptive potential lies in the combination of these three innovations, i.e., in the shared autonomous electric vehicles (SAEV). While shared mobility per se might not have the potential to truly disrupt the transport system it is necessary to steer electrification and automation in a more sustainable direction. Technology and innovations alone will not be sufficient to create a new sustainable transportation system, regulations will also be necessary
Discontinued diffusion of alternative-fueled vehicles—The case of flex-fuel vehicles in Sweden
No full textPolicymakers in many countries are facing the challenge of phasing out fossil fuels from the vehicle fleet. Until 2008, Sweden seemed to have managed a shift toward flex-fuel vehicles (FFVs), i.e. vehicles that can be driven on a combination of ethanol and gasoline. Every year, sale shares increased, reaching in 2008 almost 25% of the market. But then, the sales dropped to 5% of new sold cars in 2011. In this paper, the development of the flex-fuel market is analyzed by studying the underlying factors such as the market of FFVs and other “green vehicles,” fuel prices, national and local incentives, fueling stations, and the reporting of ethanol as a fuel in media. These factors are then analyzed through econometric analysis of a time series of share of FFVs sales in Sweden between 2002 and 2011 and descriptive statistics of municipal data of share of FFVs sales between 2005 and 2011. Findings show that fuel-efficient diesels entering the market, E85 (ethanol mixed with 15% gasoline) losing its economic advantage, and changes in the rebate structure have been the most significant factors for the decline. The effect of local incentives such as parking subsidies, fueling stations, and exemption from the congestion charging in Stockholm is harder to establish
Vilka styrmedel har ökat personbilarnas energieffektivitet i Sverige?
No full textEtt antal styrmedel har införts för att minska koldioxidutsläppen i transportsektorn. Alla styrmedel har inte haft som syfte att minska bränsleförbrukningen. Syftet med denna rapport är att ge en bild av hur utvecklingen av bilarnas energieffektivitet har sett ut, vilka styrmedel som har funnits och vilken styrning de har haft mot minskad bränsleförbrukning.
Den genomsnittliga bränsleförbrukningen av nysålda bilar i Sverige har sjunkit sen 2005. Under samma period har den genomsnittliga vikten och effekten fortsatt öka. Den största skillnaden i marknaden är ett skifte från en dominans av bensinbilar till en ökad andel dieseldrivna bilar och bilar som kan drivas på alternativa drivmedel. Den ökade andelen dieselbilar har bidragit till den minskade bränsleförbrukningen, medan för de sk flexifuelbilarna har ingen minskning av den säljviktad bränsleförbrukning skett (dock har dessa bilar lägre koldioxidutsläpp om de körs på etanol).
De styrmedel som har haft en styrande effekt mot ökad energieffektivisering är främst bränsleskatter, den nya fordonsskatten samt EU-kraven. En stor del av den minskade förbrukningen kan förklaras genom de kraftigt stigande bränslepriserna inklusive skatterna. EU-kraven har haft en styrande effekt på marknaden då de ökat tillgängligheten av fordonsmodeller med lägre förbrukning. Fordonsskatten, om än styrande är en jämförelsevis liten utgift jämfört med andra subventioner och kostnader. Miljöbilspremien har delvis haft en styrande effekt när det gäller försäljning av bilar med utsläpp under 120 g CO2/km men då bränsleförbrukningsgränsen för bilar drivna med alternativa bränslen varit så hög så har det inte funnits några incitament att välja bilar med lägre bränsleförbrukning inom denna kategori. Samtidigt har bilar som drivits med alternativa bränslen fått andra förmåner i form av parkeringssubventioner och undantag från trängselskatt. Reduktionen av förmånsvärdet för bilar har inga specifikationer i bränsleförbrukning knutna till sig och den har därmed bara en styrande effekt mot ny teknologi som alternativa drivmedel och hybriddrift. Det enda nationella styrmedlet mot ökad effektivisering av bilar som kan köras på alternativa drivmedel är fordonskatten. Det låga trycket på att effektivisera alternativdrivna bilar försvagas ännu mer när man väljer att ersätta miljöbilspremien med 5 års fordonsskattebefrielse.
Styrmedlen har haft fördel av timingen då det genom medias intresse för klimatfrågan har funnits ett intresse och en vilja att göra något. Under samma period har också bränslepriserna ökat kraftigt vilket också har höjt medvetenheten och viljan att köpa bilar med lägre förbrukning. Hade samma styrmedel införts under en annan samhällsdebatt hade deras genomslagskraft kanske inte varit lika stor. Kombinationen av styrmedel har antagligen också varit till nytta även om det har ökat möjligheterna till sk fripassagerare dvs. en del personer hade köpt samma bil oavsett den extra subventioneringen. Detta har ökat de totala kostnaderna för styrmedlen.
Rapporten har utförts på uppdrag av Naturvårdsverket inför arbetet med den 5:e nationalrapporten till UNFCCC
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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