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Number of Stakeholders

The number of stakeholders in problems in this area is significant and may typically include (list is not exhaustive):

• Transportation operators

• Terminal and warehouse operators

• Infrastructure operators

• Cargo owners (shippers)

• Non governmental organizations (NGOs)

• Environmental organizations

• Authorities responsible for social and spatial planning

• Public officials and politicians

• Other industries (e.g., manufacturing, retailing, recycling)

• R&D organizations and universities

Each of the above stakeholders may have their own agenda and objectives that are many times conflicting with the objectives of other stakeholders. It thus may be difficult to reach consensus solutions, and political considerations may sometimes prevail. Determining the final set of corridor KPIs in the SuperGreen project involved several stakeholder workshops and extensive consultation with these stakeholders. Adopting the Energy Efficiency Design Index (EEDI) for maritime CO2 emissions at the International Maritime Organization (IMO) revealed widely different views between industrialized and developing countries and the solution obtained was not a consensus solution.

Elusive Data

In the quest to reduce emissions, one may pose the na¨ıve question; can we at least measure them? It is clear that to reduce anything, one should be able to measure it first. However, it turns out that emissions from various sources are not being measured directly and the only data that exist are estimates of these emissions. Even for past emissions, these estimates are produced by specific methods, most of which involve modeling and various assumptions on model inputs such as fuel consumptions and speeds of vehicles, activity profile of fleet, fuel sales, and others. These estimates can vary significantly, depending on the method. This problem has been recognized and significant regulatory activity is in place to monitor, report, and verify emissions, as a basis for further action to reduce them.

Perhaps more fundamentally, lack of data, or data of questionable quality, is also a problem. This is mainly as regards freight flow data, especially in multimodal scenarios. This may come as a surprise to Operations Research/Management Science (OR/MS) analysts if they assume that the data that is necessary to feed their OR/MS model is readily available. Nothing can be further from the truth in many cases. Origin-Destination (OD) data, transshipment data, or simply flow data in the links of a network or through the nodes of that network may be either elusive or of questionable quality. Some other data may be considered proprietary by carriers or other stakeholders. Then the question is, if you do not have the data, what do you do? As with emissions, one solution is modeling, that is, try to estimate, that is, generate data that emulates the missing data by running and calibrating a model. Many times the methodology that is used depends on the kinds of data that are available.

The flow data availability problem has been recognized as such in at least the

EU, and various efforts to address it have been made through the years. Progress has been questionable, and has been exacerbated by some institutional developments. For instance, the abolition of customs in intra-EU road border crossings has removed a monitoring checkpoint along the supply chain, and no adequate replacements have been found as of yet. It is hoped that the use of Information and Communication Technologies (ICT) will help alleviate this problem, but thus far this has not materialized in any substantial way.

 
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