Assessment of long-term energy scenarios for New South Wales (NSW)
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This research analyses the economy-wide impacts of three energy scenarios (Base, Moderate and Advanced) for NSW for the period 2000-2040. These scenarios represent a suite of energy policy measures that the state could adopt in order to achieve its energy, environmental and economic goals. The Base scenario largely reflects the continuation of the current policy trends. In the Moderate and Advanced scenarios, CO2 emissions, in the year 2040, are restricted to 8 percent above, and 25 percent below, the 1990 levels, respectively. The scenario impacts are analysed in this research using a modelling framework that combines an optimisation-based energy sector model (MARKAL model) and an energy-oriented input- output economic model. The energy impacts are analysed in terms of how the state's primary and final energy requirements would evolve in response to alternative scenario-specific policies. And, the economic impact analysis focuses on how would such evolution affect sectoral outputs, wages and salaries, employment, and energy and CO2 intensities. The analysis suggests that a continuation of current policy trends (Base scenario) would result, by 2040, in a doubling of primary energy requirements, 80 percent increase in CO2 emissions, and a markedly increased dependency on imported oil. The CO2- restricting policies specific to the Moderate and Advanced scenarios could however result in significantly lower primary energy requirements - approximately 17 and 28 percent, respectively, below the Base level. Further, the economy wide impacts of these reduced energy requirements are likely to be minimal at the aggregate (State) level. For example, the Gross State Product, wages and salaries, and employment in 2040 would be lower by merely 0.28, 0.09 and 0.003 percent, respectively, in the Moderate scenario as compared with the Base scenario. These impacts at disaggregated (sectoral) levels would, however, be rather significant and mixed. The main beneficiaries, for example, in terms of wages and salaries and employment, would be the agriculture, other equipment, and construction sectors, and the main losers would be electricity, coal, and petroleum sectors. This shows the importance - in a policy context - of undertaking disaggregated analysis and the pitfalls of basing policy decisions on aggregate analyses alone. Such disaggregate analysis also makes transparent the inter- and intra-sectoral linkages and provides more robust bases for developing trade-offs and compromises to achieve desirable policy outcomes.
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