Nontraditional Use of Biomass at Certified Forest Management Units: Forest Biomass for Energy Production and Carbon Emissions Reduction in Indonesia

Abstract

Biomass conversion technologies that produce energy and reduce carbon emissions have become more feasible to develop. This paper analyzes the potential of converting biomass into biomethanol at forest management units experiencing three forest management practices (community-based forest management (CBFM), plantation forest (PF), and natural production forest (NPF)). Dry aboveground biomass collected varied considerably: 0.26–2.16 Mg/ha/year (CBFM), 8.08–8.35 Mg/ha/year (NPF), and 36.48–63.55 Mg/ha/year (PF). If 5% of the biomass was shifted to produce biomethanol for electricity production, the NPF and PF could provide continuous power to 138 and 2,762 households, respectively. Dedicating 5% of the biomass was not a viable option from one CBFM unit. However, if all biomasses were converted, the CBFM could provide electricity to 19–27 households. If 100% biomass from two selected PF was dedicated to biomethanol production: (1) 52,200–72,600 households could be provided electricity for one year; (2) 142–285% of the electricity demand in Jambi province could be satisfied; (3) all gasoline consumed in Jambi, in 2009, would be replaced. The net carbon emissions avoided could vary from 323 to 8,503 Mg when biomethanol was substituted for the natural gas methanol in fuel cells and from 294 to 7,730 Mg when it was used as a gasoline substitute.

Effects of different sampling scales and selection criteria on modelling net primary productivity of Indonesian tropical forests

“…47 707 000 ha (Suntana et al . 2013 b ). Tukey HSD comparisons across columns (*) are significantly different, Tukey HSD comparisons across rows (+) cells with same letter are not significantly different.…”

“…This study was restricted to analysing tropical forest areas in Indonesia that are designated as ‘production forests’, which are unfertilized natural forest areas under selective harvest management (Suntana et al . 2013 a ). We used the randomForest statistical method (Liaw & Wiener 2002) to identify at each scale the significant variables in the predictive models.…”

“…The objective was to examine whether variables identified as significant when predicting NPP would vary with the spatial resolution or cell size used, and whether sampling across the cell sizes or applying different occupancy selection criteria significantly changed the distribution of NPP values. This study was restricted to analysing tropical forest areas in Indonesia that are designated as 'production forests', which are unfertilized natural forest areas under selective harvest management (Suntana et al 2013a). We used the randomForest statistical method (Liaw & Wiener 2002) to identify at each scale the significant variables in the predictive models.…”

A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level

“…Also, the continuous rising of fossil fuels prices has triggered the interest in these sources of energy [2,3]. Among the available options, forest biomass has gained interest in the last years [1,[4][5][6].…”