The sensitivity of the costs of reducing emissions from deforestation and degradation (REDD) to future socioeconomic drivers and its implications for mitigation policy design

Abstract

Climate change mitigation policies for the land use, land use change, and forestry (LULUCF) sector are commonly assessed based on marginal abatement cost curves (MACC) derived from optimization models or engineering approaches. Yet, little is known about the space of validity of MACCs and how they are influenced by changes in main underlying drivers. In this study, we apply the Global Forest Model (G4M) to explore the sensitivity of MACCs to variation of socioeconomic drivers of deforestation, afforestation, and forest management activities. Particularly, three key factors are considered: (I) wood price, as an indicator of timber market developments; (II) agricultural land price, as a proxy representing the developments on agricultural markets; and (III) corruption coefficient, representing the progress in institutional development and measuring abatement costs use efficiency. The results indicate that the MACCs are more sensitive to the corruption coefficient than to agricultural land price and wood price. Furthermore, we find that the MACCs are more robust with high carbon dioxide (CO 2 ) price and that the sensitivity of the MACCs is higher at low CO 2 prices. In general, it can be concluded that when assessing medium-term mitigation policies characterized by low CO 2 prices, MACCs need to be developed in-line with institutions currently in place. When designing long-term mitigation policy characterized by high CO 2 prices, the role of the analyzed drivers in MACCs estimation is less important.Mitig Adapt Strateg Glob Change

Quantifying greenhouse gas emissions

“…The papers by La Notte et al (2018), Verstraete (2018), Gusti et al (2018), Jarnicka and Żebrowski (2019), and Jonas and Żebrowski (2018) provide beyond-inventory or methodological support on aspects of uncertainty that have not been addressed to date, have been overlooked, or have emerged over time. The research described in these papers contributes equally to key issues 1 (one-sided perspective), 3, and 4.…”

“…Fuzzy rule-based methods are elaborated and tested for regridding using additionally available knowledge in order to obtain better results, particularly during spatial disaggregation processes. Gusti et al (2018) address the uncertainty underlying marginal abatement cost curves (MACCs) derived for the LULUCF sector, by studying their sensitivity to uncertainty in the price of agricultural land and forestry commodities and to uncertainty in the quality of governance-stability, effectiveness, and assertiveness (simplified and summarized by means of a so-called "corruption" coefficient)-in the regions studied, ranging from individual countries to the global level. The uncertainty in MACCs, if not considered, may crucially influence the trade of emission permits or undermine decisions based on MACCs to mitigate GHG emissions.…”

Binary Choices of Households about REDD Regime in Nepalese Forest

“…In this regard, Levin, McDermott, & Cashore, (2008) argues that reduced emissions from deforestation and forest degradation (REDD) initiatives will yield significant impacts only if decision makers are committed to a results-based dual effectiveness test, addressing both forest degradation and global emissions reductions, and involving significant and measurable global-scale targets. Gusti, Forsell, Havlik, Khabarov, Kraxner, & Obersteiner, (2019) examined the sensitivity of cost of REDD to variation of socio-economic drivers of deforestation, afforestation and forest management activities through the global forest model (GFM). In the study, three variables are considered: wood price, agricultural land price and corruption coefficient.…”

A cross-sectoral integrated assessment of alternatives for climate mitigation in Madagascar

“…This assumption reflects the difficulties that Madagascar faces with regards to corruption, which includes land property issues and illegal logging practices (EC, 2014;Scales, 2014;Vieilledent et al, 2018). It draws on the work of Gusti et al (2019) on the estimation of mitigation costs in contexts where institutions are weak and corruption is rife. Another reason for considering two distinct NDC scenarios is the high uncertainty of mitigation costs and potentials from AFOLU in Madagascar.…”