The Impact of Implementing Carbon Tax and Feed-in Tariff
: A CGE Analysis of the Indonesian Case

  • Herbert Wibert Victor Hasudungan

    Student thesis: Doctoral ThesisDoctor of Philosophy


    This thesis focuses on the two main works that related in assessing the implications of (i) fiscal expansion (or contraction) and (ii) implementing a carbon tax on carbon-based fuels as well as the feed-in tariffs (subsidies to clean energy production) on Indonesia’s economy, within the context of static computable general equilibrium (CGE) analysis.

    In the first study, we investigate the impacts of increasing the public consumptions on Indonesia’s main macroeconomic indicators and to their consequences by examining how different institutions and sectors in the economy are affected. Three scenarios are carried out under different financing options to budgeting neutral the additional public spending. The results suggest that the increase of government expenditure on goods under the adjusted government saving generates the highest improvement on Indonesia’s GDP but results in a rise of budget deficit. In contrast, under the budget-neutral scheme of either reducing the subsidy rates across activities or increasing the output tax rates would result in less improvement to the Indonesia’s GDP. This is because a subsidy cut (or higher output tax) immediately escalates the production costs and, thus, increases the prices of final goods purchased by the households. These changes result in a fall of their real consumption that eventually leads to a drop in aggregate demand. However, compared to the scenario of subsidy cut, a higher output tax has the most adverse effects on national income. The industry’s production costs are more pressurized by a higher output tax. which in turn, creates deindustrialization, lower employment, and thus reduces the national income and output.

    In the second study, we investigate the two key frameworks to reduce Indonesia’s greenhouse gas (GHG) emissions: (i) implementing a carbon tax on fossil fuels; and (ii) promoting clean (renewable) energy production through the feed-in tariff (subsidy) scheme. In the carbon tax implementation, we assume that the government levies a tax of Rp. 100,000/ton CO2e with three possible revenue-recycling scenarios. In a first scenario, we allow the carbon tax to be recycled through adjustment of the labour (income) tax rate. In a second scenario, we allow the government to increase their spending on goods proportionally to compensate the revenue raised from a carbon tax. And finally, in the third scenario, we assume that the additional revenue from carbon tax is kept to run a budget surplus (government saving adjusts). Whilst, in the feed-in tariff (FIT) scenario, we assume that the government sets a 13.14% subsidy rate to renewable generations (hydro and geothermal generation) where the support payments are distributed equally among electricity consumers through a higher electricity tax rate. Overall, the results suggested that the carbon tax, in the short run, reduces the national emissions but raises costs to the economy, resulting a fall in GDP. In terms of income distribution, the carbon tax tends to be progressive in both (first and second) scenarios of revenue-recycling. However, when there is no compensating (recycling) mechanism (third scenario), the carbon tax tends to be regressive - the poorer households carry a higher share of the carbon tax burden. On the other hand, in case of the FIT scheme (15% subsidy to renewable generation), the impacts are negligible on national income and emissions. This is because the initial renewable shares in the electricity mix are small (a 11% share from hydro generation and a 5% share from geothermal generation); and these technology outputs are only utilized in the electricity industry. Therefore, we argue that the current Indonesia’s FIT regulation – about 13.14% subsidy rate for renewable generation technologies – is ineffective to reduce the national emissions.
    Date of Award2016
    Original languageEnglish
    SupervisorHassan Molana (Supervisor) & Omar Feraboli (Supervisor)


    • Energy CGE modeling

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