Introducing detailed land-based mitigation measures into a computable general equilibrium model

https://doi.org/10.1016/j.jclepro.2015年03月09日3 Get rights and content

Abstract

We propose a new climate change mitigation assessment method focusing on agriculture, forestry, and land-use change sectors by coupling the computable general equilibrium (CGE) model with a bottom-up type technology model. The CGE model covers the entire economic market, but includes a rough description of mitigation measures , whereas the bottom-up type technology model takes into account abatement cost and mitigation effects of individual mitigation measures, but only focuses on a few sectors. The coupled framework enables us to connect relevant conditions and to complement the shortcomings of each model. As a test, we applied our method to Indonesia, which has set a national greenhouse gas emissions reduction target for 2020. A large proportion of Indonesia's greenhouse gas emissions are from the land-use sector. We assessed the differences in modeling behaviors between the CGE models with and without coupling the bottom-up type model. The two primary findings were: 1) consumption loss estimated by the coupled CGE (1.2%) was larger than the loss estimated by the uncoupled model (0.5%), because the emission reduction estimated by the bottom-up model was less than the standalone CGE's estimate; and 2) consumption loss caused by achieving the reduction target by 2020 in Indonesia strongly depends on the assumption of mitigation costs and available land area for the emission reduction measures.

Introduction

The International Panel on Climate Change (IPCC) recently reported, with medium evidence and high agreement, that the greenhouse gas (GHG) emissions from agriculture, forestry, and other land-use (AFOLU) sectors could change substantially in terms of transformation pathways, with significant mitigation potential from agriculture, forestry, and bioenergy mitigation measures (IPCC, 2014). Similarly, Weyant et al. (2006) conducted a comprehensive analysis on climate change mitigation, and reported that non-energy sectors and non-CO2 GHGs may have great mitigation potential and can play an important role in future climate change. On a national scale, several countries have made pledges to reduce their emissions in the near future and have submitted their own emission reduction targets under the Copenhagen Accord (UNFCCC, 2009). In addition, developing countries have been establishing Nationally Appropriate Mitigation Actions towards a multi-sectoral GHG mitigation framework. For some countries, the AFOLU sectors account for substantial emission mitigation potential (Hasegawa and Matsuoka, 2015, Jilani et al., 2015, Nguyen et al., 2014). Therefore, assessing the abatement costs and economic impacts of mitigation measures is essential for national decision-making.
There are currently two approaches for assessing climate change mitigation potential. First, integrated models are effective tools for comprehensively analyzing mitigation scenarios by incorporating agricultural, forestry, and land-based emission reduction. For example, several studies have combined the Integrated Model to Assess the Global Environment (IMAGE) with an abatement cost model, and examined how abatement costs and environmental impacts differ between a long-term multi-gas mitigation strategy and a CO2-only strategy for climate stabilization targets (Lucas et al., 2007, Strengers et al., 2007, van Vuuren et al., 2006). In addition, the Global Change Assessment Model fully integrates the energy, agriculture, and land-use systems, and is therefore a state-of-art approach for assessing climate change mitigation in all sectors (Wise et al., 2009). Second, a computable general equilibrium (CGE) model has been proposed as an approach for dealing with land-use within an entire economic system (Fujimori et al., 2014a, Golub et al., 2013, Golub and Hertel, 2012, Gurgel et al., 2007, Ronneberger et al., 2008, Sohngen et al., 2009). The CGE model can account for inter-temporal change in carbon stocks accumulated over a long time, which is a challenge when assessing land-use (Sohngen et al., 2009). However, both above approaches; integrated models and CGE models still rely on simplified descriptions of mitigation measures for agricultural and land-use sectors, leaving room for improvement in terms of the descriptions of the mitigation measures.
Here, we propose a new method for assessing climate change mitigation scenarios by coupling a CGE model and a bottom-up type model, and incorporating detailed mitigation measures in the agriculture, forestry, and land-use sectors. We tested the model applying it to Indonesia. Specifically, we examined the differences in modeling behavior between the coupled and uncoupled CGE model under a carbon-constrained scenario. To clarify the influences of assumptions of the abatement costs and applicable area used for implementing land-use mitigation measures, which are largely uncertain, to macroeconomic impacts under the carbon-constrained scenario, we also performed a sensitivity analysis of modeling behavior. Indonesia serves as a good example for examining the model's coupling activity, as it accounts for some of the highest levels of GHG emissions globally and a large portion of the GHG emissions is from land-use changes (Ministry of Environment (MoE), 2010). Recently, under the Copenhagen Accord, the Indonesian government pledged to reduce total GHG emissions by 26% to BaU levels unilaterally, and to 41% with sufficient international support (UNFCCC, 2009).

Section snippets

Modeling framework

Fig. 1 shows the coupling scheme of the AIM/CGE (Fujimori et al., 2012) and a bottom-up type model, named the AFOLU model (Hasegawa and Matsuoka, 2015). The scheme enables us to complement the shortcomings of each model and to connect the relevant conditions to produce an assessment of macroeconomic impacts under various emission scenarios. The CGE model covers the entire economic market and provides estimates of macroeconomic impacts caused by climate change mitigation, but relies on a rough

Effects of coupling on the emission-constrained scenario

To characterize the effects of coupling on the emission-constrained scenario, we compared the two model types (CGE-only, CGE-coupled) with respect to representative elements relating to climate mitigation in agriculture and land-use (GHG emission and reduction potential by sectors, carbon price, consumption and GDP loss, and land-use change) in Fig. 3. In the BaU case without emission constraints, total GHG emissions increased up to 2000 MtCO2eq/year by 2020. Conversely, the GHG reduction

Discussion and conclusions

This paper proposes a new GHG mitigation assessment method by coupling the AIM/CGE model with a bottom-up type technology assessment model focusing on agriculture, forestry, and land-use change sectors. The unified framework enables a detailed description of mitigation technologies for the sectors in the CGE model. We analyzed the differences caused by incorporating descriptions of individual mitigation measures to the CGE model by applying our coupled model to Indonesia. We found that

Acknowledgments

This research was supported by the Environment Research and Technology Development Fund (2–1402) of the Ministry of the Environment, Japan.

References (49)

  • Dewan Nasional Perubahan Iklim (DNPI)

    Indonesia's Greenhouse Gas Abatement Cost Curve

    (2010)
  • B.V. Dimaranan

    Global trade, assistance, and production: the GTAP 6 data base

  • FAO

    FAOSTAT

    (2013)
  • S. Fujimori et al.

    Land use representation in a global CGE model for long-term simulation: CET vs. logit functions

    Food Sec.

    (2014)
  • S. Fujimori et al.

    AIM/CGE [basic] Manual, Discussion Paper Series

    (2012)
  • A.A. Golub et al.

    Global climate policy impacts on livestock, land use, livelihoods, and food security

    Proc. Natl. Acad. Sci. U. S. A.

    (2013)
  • A.A. Golub et al.

    Modeling land-use change impacts of biofuels in the gtap-bio framework

    Clim. Change Econ.

    (2012)
  • A. Grubler

    Technology and Global Change

    (2004)
  • A. Gurgel et al.

    Potential land use implications of a global biofuels industry

    J. Agric. Food Ind. Organ.

    (2007)
  • T. Hasegawa et al.

    Climate change impact and adaptation assessment on food consumption utilizing a new scenario framework

    Environ. Sci. Technol.

    (2014)
  • T. Hasegawa et al.

    Scenarios for the risk of hunger in the twenty-first century using Shared Socioeconomic Pathways

    Environ. Res. Lett.

    (2015)
  • T. Hasegawa et al.

    Climate change mitigation strategies in agriculture and land use in Indonesia

    Mitig. Adapt. Strateg. Glob. Change

    (2015)
  • G.C. Hurtt et al.

    Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands

    Clim. Change

    (2011)
  • IEA

    • Cited by (43)

    • Assessing the impacts of China's environmental tax using a dynamic computable general equilibrium model

      2019, Journal of Cleaner Production
      Citation Excerpt :

      It is very important to have an integrated assessment of the socioeconomic and environmental impacts of the environmental tax and the potential carbon tax. Among all the integrated assessment tools, computable general equilibrium (CGE) solves the general equilibrium situation of an economy where all the markets clear at the same time, and has been widely applied in the policy analysis of international trade (De Melo and Robinson, 1989), energy (Fujimori et al., 2014), and environment (Hasegawa et al., 2016). CGE has also been used to analyse the carbon tax and environmental tax policies in China.

    • SSP3: AIM implementation of Shared Socioeconomic Pathways

      2017, Global Environmental Change
      Citation Excerpt :

      Concluding remarks are provided in Section 5. AIM/CGE (Computable General Equilibrium) was used for quantification of the SSPs, which has been widely applied for the assessment of global and national climate mitigation analysis (e.g., (Fujimori et al., 2014b; Fujimori et al., 2013, 2015; Hasegawa et al., 2016; Hasegawa et al., 2015a; Namazu et al., 2013; Thepkhun et al., 2013). The period from 2005 to 2100 was targeted.

    View all citing articles on Scopus
    1
    Tel./fax: +81 75 383 3367.
    View full text
    Copyright © 2015 Elsevier Ltd. All rights reserved.