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[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]This study analyzed the role of low-carbon energy technologies in reducing the greenhouse gas emissions of Indonesia’s energy sector by 2030. The aim of this study was to provide insights into the Indonesian government’s approach to developing a strategy and plan for mitigating emissions and achieving Indonesia’s emission reduction targets by 2030, as pledged in the country’s Intended Nationally Determined Contribution. The Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model was used to quantify three scenarios that had the same socioeconomic assumptions: baseline, countermeasure (CM)1, and CM2, which had a higher emission reduction target than that of CM1. Results of the study showed that an Indonesian low-carbon energy system could be achieved with two pillars, namely, energy efficiency measures and deployment of less carbon-intensive energy systems (i.e., the use of renewable energy in the power and transport sectors, and the use of natural gas in the power sector and in transport). Emission reductions would also be satisfied through the electrification of end-user consumption where the electricity supply becomes decarbonized by deploying renewables for power generation. Under CM1, Indonesia could achieve a 15.5% emission reduction target (compared to the baseline scenario). This reduction could be achieved using efficiency measures that reduce final energy demand by 4%; This would require the deployment of geothermal power plants at a rate six times greater than the baseline scenario and four times the use of hydropower than that used in the baseline scenario. Greater carbon reductions (CM2; i.e., a 27% reduction) could be achieved with similar measures to CM1 but with more intensive penetration. Final energy demand would need to be cut by 13%, deployment of geothermal power plants would need to be seven times greater than at baseline, and hydropower use would need to be five times greater than the baseline case. Carbon prices under CM1 and CM2 were US$16 and US$63 (2005)/tCO2, respectively. The mitigation scenarios for 2030 both had a small positive effect on gross domestic product (GDP) compared to the baseline scenario (0.6% and 0.3% for CM1 and CM2, respectively). This is mainly due to the combination of two assumptions. The first is that there would be a great increase in coal-fired power in the baseline scenario. The other assumption is that there is low productivity in coal-related industries. Eventually, when factors such as capital and labor shift from coal-related industries to other low-carbon-emitting sectors in the CM cases are put in place, the total productivity of the economy would offset low-carbon investment.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Asia-Pacific Integrated Model/Computable General (AIM/CGE) model,Economic development impact,Efficiency measures,Greenhouse gas emission reduction,Indonesia,Intended Nationally Determined Contribution (INDC),Low-carbon energy development,Mitigation cost,Renewable energies[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This work was supported by JSPS KAKENHI, Grant Number JP16K18177, and the Global Environmental Research Fund 2-1402 of the Ministry of Environment, Japan.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.3390/en10010052[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]