would need to be addressed in order to cover the whole range of decarbonization
routes.
In the next section, we introduce the six recent empirical cases in which logistics
reorganization responses have been studied.
3 Overview of Case Studies, Foci and Methods
There is a large body of literature on the evaluation of the impact of carbon taxes.
Our focus here is on predictive studies, not the normative approaches studying
optimal configurations of supply chains (see, e.g. Das and Jharkharia 2018, for a
review of these studies). Predictive evaluation studies have been presented at various
levels of aggregation, including urban (Anand 2015; Waisman et al. 2013; Marcucci
et al. 2018; Cheng et al. 2015), corridor (Winebrake et al. 2008; Zhang 2013),
(inter)national (Piattelli et al. 2002; Raha et al. 2003; De Bok et al. 2019) and global
level (Lee et al. 2013; Tavasszy et al. 2016; Halim et al. 2019). These used different
types of models, falling largely into two categories: transport network models
(including mode choice) and spatial/sectoral-economic interaction models
(a gravity or CGE type approach). We are not aware of any case that considers
decisions on production planning, distribution channel, inventories and fleet technology. The focus, therefore, lies on transport demand and a relatively rich view on
transport organization. We distinguish between the following logistics choices:
Table 2.2 Relevance of logistics decision areas for carbon pricing
Functional area
Carbon performance dimension and measure
Demand for
transport
(tonne-km)
Choice of
mode (tonnekm per mode)
Transportation
efficiency
(vehicle/tonne)
Energy
efficiency
(J/vehicle
km)
Carbon
efficiency of
energy
(CO 2 /J)
Sales and
marketing
+
À
À
À
À
Production
planning
+
+
À
À
À
Sourcing and
material
handling
+
+
+
À
À
Distribution
channels
À
+
+
À
À
Inventory and
packaging
+
+
+
À
À
Transportation
organization
À
+
+
+
+
Legend: + influence, À little or no influence
2 The Influence of Logistics Decisions on Transport Decarbonization: Lessons from. . .
21
routes.
In the next section, we introduce the six recent empirical cases in which logistics
reorganization responses have been studied.
3 Overview of Case Studies, Foci and Methods
There is a large body of literature on the evaluation of the impact of carbon taxes.
Our focus here is on predictive studies, not the normative approaches studying
optimal configurations of supply chains (see, e.g. Das and Jharkharia 2018, for a
review of these studies). Predictive evaluation studies have been presented at various
levels of aggregation, including urban (Anand 2015; Waisman et al. 2013; Marcucci
et al. 2018; Cheng et al. 2015), corridor (Winebrake et al. 2008; Zhang 2013),
(inter)national (Piattelli et al. 2002; Raha et al. 2003; De Bok et al. 2019) and global
level (Lee et al. 2013; Tavasszy et al. 2016; Halim et al. 2019). These used different
types of models, falling largely into two categories: transport network models
(including mode choice) and spatial/sectoral-economic interaction models
(a gravity or CGE type approach). We are not aware of any case that considers
decisions on production planning, distribution channel, inventories and fleet technology. The focus, therefore, lies on transport demand and a relatively rich view on
transport organization. We distinguish between the following logistics choices:
Table 2.2 Relevance of logistics decision areas for carbon pricing
Functional area
Carbon performance dimension and measure
Demand for
transport
(tonne-km)
Choice of
mode (tonnekm per mode)
Transportation
efficiency
(vehicle/tonne)
Energy
efficiency
(J/vehicle
km)
Carbon
efficiency of
energy
(CO 2 /J)
Sales and
marketing
+
À
À
À
À
Production
planning
+
+
À
À
À
Sourcing and
material
handling
+
+
+
À
À
Distribution
channels
À
+
+
À
À
Inventory and
packaging
+
+
+
À
À
Transportation
organization
À
+
+
+
+
Legend: + influence, À little or no influence
2 The Influence of Logistics Decisions on Transport Decarbonization: Lessons from. . .
21