The effectiveness of energy service demand reduction: A scenario analysis of global climate change mitigation

doi:10.1016/j.enpol.2014.09.015

Elsevier

Energy Policy

Volume 75, December 2014, Pages 379-391

Energy Policy

https://doi.org/10.1016/j.enpol.2014年09月01日5 Get rights and content

Highlights

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The effectiveness of a reduction in energy service demand is quantified.
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A 25% reduction in energy service demand would be equivalent to 1% of GDP in 2050.
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Stringent mitigation increases the effectiveness of energy service demand reduction.
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Effectiveness of a reduction in energy demand service is higher in the building sector.

Abstract

A reduction of energy service demand is a climate mitigation option, but its effectiveness has never been quantified. We quantify the effectiveness of energy service demand reduction in the building, transport, and industry sectors using the Asia-Pacific Integrated Assessment/Computable General Equilibrium (AIM/CGE) model for the period 2015–2050 under various scenarios. There were two major findings. First, a 25% energy service demand reduction in the building, transport, and basic material industry sectors would reduce the GDP loss induced by climate mitigation from 4.0% to 3.0% and from 1.2% to 0.7% in 2050 under the 450 ppm and 550 ppm CO₂ equivalent concentration stabilization scenarios, respectively. Second, the effectiveness of a reduction in the building sector׳s energy service demand would be higher than those of the other sectors at the same rate of the energy service demand reduction. Furthermore, we also conducted a sensitivity analysis of different socioeconomic conditions, and the climate mitigation target was found to be a key determinant of the effectiveness of energy service demand reduction measures. Therefore, more certain climate mitigation targets would be useful for the decision makers who design energy service demand reduction measures.

Introduction

Integrated assessment models are widely used in climate mitigation analysis. For example, the following integrated assessment models are all well-known: AIM/CGE (Masui et al., 2011), GCAM (Vuuren et al., 2011), IMAGE (van Vuuren et al., 2011), MESSAGE (Riahi et al., 2011), and ReMIND (Kriegler et al., 2013). These models basically couple economic, energy, greenhouse gas (GHG) emissions, agricultural, land use, and climate components. They estimate energy production and consumption as well as CO₂ emissions and climate change mitigation costs. Therefore, final energy consumption is a key element of these models. Final energy consumption is determined by two factors—energy service demand and energy technological choices. The former is an indicator that represents the energy consumption activity level. The latter is the combination of energy technological device selections that satisfies the energy service demand. Furthermore, the energy service demand is affected by basic socioeconomic indicators such as GDP and population.

Reducing energy service demand is a mitigation option, but there are various types of energy service demand and different potential sources to reduce demand. For example, improving land use management and the efficiency of urban structural design could potentially reduce transport demand (IEA, 2009d, Ito et al., 2013). In industrial sectors, extending the lifespan of buildings and infrastructure and facilitating material recycling could reduce the production of basic building materials (IEA, 2009c, Kahn Ribeiro et al., 2012, Shi et al., 2012). Building design, holistic retrofits, and other similar techniques could also reduce energy service demand in the building sectors (Ürge-Vorsatz et al., 2012). Consumer behavioral change might also have broad impacts on energy service demand. Weatherization and proper maintenance and adjustment of electrical equipment are examples of consumer behavioral changes (Dietz et al., 2009). Changing consumption patterns that do not have direct linkages with energy-using equipment can also affect energy service demand. For example, if people were to spend more money on cultural or non-materialized services such as reading books and listening to music rather than buying new cars, the energy service demand for private car usage would be directly reduced. In addition, industrial production (including metal production) would also decrease. Eventually, structural economic changes would be induced. In terms of travel, changes in people׳s location preferences could reduce transport demand even if they spend the same amount of time traveling (Girod et al., 2012). Some technological changes that are not directly related to energy technology and not originally intended to change energy consumption can also affect energy service demand. For example, recent technological progress in information technology could improve the efficiency of various industrial activities.

Although some studies have discussed the potential effectiveness of energy service demand reduction, no study has quantified its effectiveness or value. The majority of previous studies using integrated assessment models have focused on either technological or emission abatement options (Krey, 2014). For example, Energy Modeling Forum (EMF) 27 (Kriegler et al., 2014) dealt with technological constraints and their effects on mitigation costs (e.g., carbon capture and storage [CCS] and nuclear energy). Kriegler et al. (2014) compiled multiple integrated assessment model results and concluded that technology is a key element of climate mitigation. Energy intensity improvements and the electrification of energy end use coupled with a fast decarbonization of the electricity sector are required for the stringent climate mitigation target. Moreover, CCS and the use of bioenergy were found to be the most important elements, in part because of their combined ability to produce negative emissions. Although the scenario framework in EMF 27 distinguished between high and low energy demand, their study did not explicitly deal with energy service demand differences, and the assumptions regarding energy technology and energy service demand were mixed. In a pioneering work, Kainuma et al. (2013) explicitly considered energy service demand assumptions. They utilized two climate mitigation scenarios, one with and one without a reduction in energy service demand. The two scenarios, however, also had different assumptions not only about energy service demand but also about other GHG abatement technologies such as CCS. Therefore, it is difficult to extract information about the effectiveness of energy service demand reduction from that study.

This study aimed to quantify the effectiveness of energy service demand reduction by measuring the effectiveness of the demand reduction as a fraction of the gross domestic product (GDP). Section 2 presents the overall methodology, model, scenario framework, and data settings. In Section 3, we present the results of the analysis. In Section 4, we discuss our interpretations of the results and the limitations of this study. Finally, concluding remarks and policy implications are offered in Section 5.

Section snippets

Overview of the method

Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) was used for the analysis and its scenario analysis was adopted. AIM/CGE has been widely used for the assessment of climate mitigation and its associated impacts (e.g., (Masui et al., 2011; Schmitz et al., 2014; Thepkhun et al., 2013)). This model has the unique characteristic that energy service demand and energy end-use devices have high resolution. CGE models are generally able to assess the energy system, the cost of

GHG emissions and primary energy supply

Fig. 2 illustrates global GHG emissions for the NO case scenarios in which no additional energy service demand reduction is assumed. The total global emissions in the BaU scenario are projected to steadily increase to about 79 Gt-CO₂eq/year in 2050. Both mitigation scenarios show emissions reductions beginning in 2015. Emissions in the 450 ppm and 550 ppm scenarios reach about 19 Gt-CO₂eq/year and 41 Gt-CO₂eq/year in 2050, respectively. As is described in Section 2.4, the emissions of the mitigation

Interpretation of the results and their implications

Three main points should be considered when interpreting the results. The first is the dependency of the effectiveness of energy service demand reduction on climate mitigation policy. As indicated in Section 3, the effectiveness of energy service demand reduction was dependent on the strength of the climate mitigation scenario. Discussion about climate targets remains ongoing. Some researchers and policymakers insist on keeping what we call the 2 degree (°C) target (approximately the 450 ppm

Conclusions and policy implications

This paper quantifies the effectiveness of energy service demand reductions by analyzing multiple scenarios with an AIM/CGE model. The scenario structure had two dimensions: strength of climate mitigation and assumptions on energy service demand reductions. Two indicators were used to measure the effectiveness of energy service demand reduction. One is the recovery rate of GDP losses caused by climate mitigation, and the other is GDP loss rate of energy service demand reduction cases relative

Acknowledgments

This article benefitted greatly from the comments of the anonymous reviewers. This study was supported by the "Global Environmental Research Fund" (2-1402) of the Ministry of the Environment of Japan (MOEJ). The authors gratefully acknowledge MOEJ support.

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View full text

The effectiveness of energy service demand reduction: A scenario analysis of global climate change mitigation

Highlights

Abstract

Introduction

Section snippets

Overview of the method

GHG emissions and primary energy supply

Interpretation of the results and their implications

Conclusions and policy implications

Acknowledgments

Energy Econ.

Appl. Energy

Energy Econ.

Energy Policy

Energy Econ.

Energy Policy

Technological feasibility and costs of achieving a 50% reduction of global GHG emissions by 2050: mid- and long-term perspectives

Sustaina. Sci.

Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions

Proc. Natl. Acad. Sci.

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

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

Food Secur.

Energy Technology Transitions for Industry

Transport, Energy and CO2: Moving Toward Sustainability

Transport, Energy and CO₂: Moving Toward Sustainability