The NextSTEPS program at ITS-Davis convened a one-day workshop on international transportation energy modeling (iTEM), focused on comparing the frameworks and scenario projections from four major global transport models:
-- Global Change Assessment Model (GCAM) by Pacific Northwest National Laboratory (PNNL) and ITS-Davis,
-- MESSAGE-Transport (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) by the International Institute for Applied Systems Analysis (IIASA),
-- Mobility Model (MoMo) by the International Energy Agency, and
-- Roadmap by the International Council on Clean Transportation (ICCT).
Highlights:
-- Projections of "baseline" global transportation energy use rise from 98 EJ in 2010 to 160-250 EJ by 2050.
-- There are considerable differences in historical data for some modes, both globally and for individual countries (particularly non-OECD countries). Variability in estimates of transportation activity are in most cases much larger than energy differences.
-- Global average vehicle ownership rates are projected to range from 270 to 450 per 1,000 people by 2050 with wide ranges across countries: 700-1,075 for the US by the middle of the century (US is around 700 today), 100-650 for China, and 80-380 for India across four models.
-- All models rely mainly on GDP to estimate the future demand for freight and hold the base year modal shares (e.g. truck v. rail) roughly constant through 2050. In reality, future evolution will depend on characteristics of products (e.g. type of commodities) being shipped, technologies available for freight and their efficiencies, and policies and infrastructure.
-- Current policy commitments toward EVs, PHEVs and H2FCVs (and thus baseline projections) maybe below the numbers suggested by iTEM models as required for meeting climate targets (e.g., 2 degrees C).
-- Improvements in data quality and the representation of car ownership and use across the models were identified as priorities.
Modeling transport energy use can either be done by estimating how far people travel and what mode of transportation they choose or by estimating how many vehicles there are and how far each one travels. These are complementary approaches, and in theory they should both lead to the same answer. The former approach, used in "service demand" models, seem more intuitive when one wants to model societal shifts in modes of transportation, either in emerging economies as they develop or in developed economies as they decarbonize; but collecting data on service demand is notoriously difficult. In contrast, vehicle stock models use readily-available vehicle sales data, but are harder to use in future-state, what-if scenarios (particularly in estimating modal shift behaviors) and thus require special attention by experts.
The four iTEM models are different in terms of scope (GCAM and MESSAGE cover all sectors of the energy system vs. MoMo and Roadmap which cover transportation only) and model structure (GCAM and MESSAGE rely on internal drivers, particularly the costs of technology and travel, to project future changes whereas MoMo and Roadmap rely on experts' judgments and detailed analysis of technology and policies to drive long-term changes). Yet, owing to these differences, the models are highly complementary and in some cases can be used jointly to answer questions that no single model can tackle on its own.
The following summary shares some of the comparisons and findings from the workshop