Exploring carbon footprints and carbon intensities of Indonesian provinces in a domestic and global context

Within Indonesia, the structure of consumption and production differs significantly across provinces. This implies that carbon footprints and intensities between provinces are also diverse. This paper calculates historical consumption- and production-based carbon emissions at the provincial level using a multi-scale input-output (IO) database for 2010, in which an environmentally extended multi-regional IO (EE MRIO) table for 34 Indonesian provinces is integrated in the global EE MRIO EXIOBASE with data for 43 countries and 5 rest of the world regions. Emissions from consumption are detailed by product and their points of origin, while emissions from production are detailed by industry and their destinations. Our results show the heterogeneity of Greenhouse Gas (GHG) emissions under both sides. The Java region is a net importer of carbon emission, while Sumatra and Kalimantan are net exporters. In the global context, the Asia Pacific region plays important role in national GHG emissions. Services product contributed 57.1% of national consumption-based GHG emissions, followed by manufacture (30.6%), and agriculture (12.3%). On the national level, 63.5% of national GHG emissions are related to household consumption. There is a high disparity across provinces in Indonesia in carbon footprints. Provincial average per capita carbon footprints vary from 2 t CO 2 e/capita in East Nusa Tenggara to 13.84 t CO 2 e/capita in East Kalimantan. Carbon intensity also varies from 0.83 kt CO 2 e/M Euro in Jakarta to 2.37 kt CO 2 e/M Euro in North Kalimantan. Agriculture and food products dominate household carbon footprints, while construction leads in government carbon footprints. Utilities and transportation services play important roles on national carbon intensities. We further correlated the Human Development Index (HDI) with per capita carbon footprints and expenditure, and find that provinces with similar GHG emissions and expenditure per capita income as Java, tend to have a lower HDI. Understanding development status and province-level characteristics is important for selecting policy strategies.