Structure of Extended Snapshot (ExSS) Tool

The Extended Snapshot tool is a key component of the aforementioned Asia-Pacific Integrated Model (AIM) developed by Kyoto University and NIES, Japan. It is a modelling tool to assess future energy consumption, power generation, technology diffusion, transportation, industrial outputs, residential and commercial activities and waste generation and GHG emissions, coupling with predetermined socioeconomic, industrial and demographic scenarios in a particular future year (the target year).

Figure 7.7 shows the simplified internal working and data structure of the ExSS tool, which comprises four modules (driving forces, energy service demand, primary energy supply and GHG emissions) with input parameters, exogenous variables and variables between modules. ExSS is a system of simultaneous equations. Given a set of exogenous variables and parameters, solution is uniquely defined. In this simulation tool, only CO2 emissions from energy consumption are calculated.

Fig. 7.7 Structure of the ExSS tool (Source: Adapted from Gomi et al. 2010)

In many LCS scenarios, exogenously fixed population data are used. However, people migrate more easily, when the target region is a relatively smaller area such as a state, district, city or town. Population is decided by demand from outside of the region, labour participation ratio, demographic composition and relationship of commuting with outside of the region.

To determine output of industries, the 'export-base' input-output approach is combined in line with the theory of regional economics. Industries producing export goods are called basic industries. Production of basic industries induces other industries, i.e. nonbasic industries, through demand of intermediate input and consumption of their employees. A number of workers must fulfil labour demand of those productions. Given assumptions of where those workers live and labour participation ratio, population living in the region is computed. This model enables us to consider viewpoints of regional economic development to estimate energy demand and CO2 emissions. For future estimation, assumption of export value is especially important if future development of the target region is expected to (or desired to) be led by particular industries, such as automotive manufacturing or petrochemical industries.

Passenger transport demand is estimated from the population and freight transport demand, which is taken as a function of output by manufacturing industries. Floor area of commercial activities is determined from output of tertiary (service) industries. Other than driving force, activity level of each sector and energy demand by fuels are determined with three parameters: energy service demand per driving force, energy efficiency and fuel share. Diffusion of countermeasures changes the value of these parameters and so GHG emissions.

The estimated results of the future socio-economic indicators and energy demand in 2025 are based on the modelling of the socio-economic variables and energy balance table in 2025. Most of the socio-economic indicators and energy balance table for Iskandar Malaysia are obtained from official and published statistics and secondary sources. Assumptions are used where information for macroeconomic analysis is not available for the Iskandar Malaysia region (see Ho et al. 2010).

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