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Role of endogenous energy efficiency improvement in global climate change mitigation

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Abstract

Improving the energy efficiency of conventional energy services is an essential way to cope with global CO2 emissions mitigation. To date, energy efficiency improvement (EEI) has been broadly introduced exogenously in integrated assessment models (IAMs) by virtue of the autonomous energy efficiency improvement (AEEI) coefficient; however, it is usually good at capturing the EEI driven by non-price factors, while weak in describing the EEI induced by policy incentives. In this paper, we introduce an endogenous EEI (EEEI) mechanism in an IAM, called E3METL, to explore the impacts of EEEI on the global macro-economy, CO2 emission paths, and timing of carbon mitigations. The results reveal that (1) introducing EEEI significantly improves gross world product (GWP) gains, and this positive effect is partly offset when carbon restriction policies are implemented; (2) R&D investment dedicated to enhance energy efficiency limits R&D expenditures for other alternative technologies, and this effect will impede the development of non-fossil technologies; (3) EEEI may perform as one of supporting factors to delay the actions of carbon reduction; moreover, the introduction of EEEI lowers the optimal carbon tax level by 7.8 % on average, as compared to the no EEEI case.

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Notes

  1. According to Fisher-Vanden et al. (2006), around two thirds of energy-consumption changes can be attributed to price factors, while the rest (one third) comes from non-price factors. However, AEEI usually covers the energy efficiency improvement resulting from non-price factors, while captures few EEI induced by price, particularly by policy incentives, which could be largely responsible for the possible deviation.

  2. CES method is first proposed in Arrow et al. (1961), and the general formula is Y = A(αK −ρ + βL −ρ)−1/ρ, here ρ ≤ 1and ρ ≠ 0. We could obtain two representative conclusions from this formula: first, when ρ → 0, it reduces to the classical Cobb-Douglas production function; second, the elasticity of substitution between capital stock and labor factor is constant and equals to 1/(1 − ρ).

  3. Endogenous energy efficiency is enhanced through the substitution of knowledge capital for carbon services, and this substitution may be driven by productivity improvement of current production processes, introduction of more efficient technology, or the adoption of some carbon-control measures (Popp 2004).

  4. Empirical studies suggest that returns to energy R&D are diminishing over time (Popp 2001). On this basis, the function form of the innovation possibility frontier should be satisfied with the following two conditions: first, ∂IPF t /∂RD ee , t  > 0 and second, ∂2 IPF t /∂KDE t ∂RD ee , t  < 0 (Popp 2004).

  5. In general, C f , t /C i , t show a wide frequency distribution; hence, the ratio here means the mean value, so does the relative price P f , t (Anderson and Winne 2004).

  6. In this work, non-carbon energy is measured by carbon ton equivalent (CTE), which can be converted in terms of the equivalent calorific value between fossil and non-fossil energy; therefore, $/tC is also employed to measure the cost of non-fossil energy (Gerlagh et al. 2003; Popp 2004).

  7. We can observe from Fig. 5 that R&D investment on energy efficiency is several fold higher than that on non-fossil technologies. This is consistent with the reality, actually, the amount of R&D investment on new energy technologies only accounts for about 10 % of total energy R&D expenditures, which implies that nearly 90 % of energy R&D investment relates to energy efficiency improvement of conventional fuels (REN21 2015).

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Acknowledgments

Financial support for this work was provided by the Ministry of Education, Humanities and Social Sciences Youth Fund Project, No. 14YJC630029, and the National Natural Science Foundation of China under grant Nos. 71503242, 71210005. The authors would like to express their gratitude to the excellent comments from group members of the Center for Energy and Environmental Policy Research (CEEP), Chinese Academy of Sciences (CAS). Special thanks should also be given to all the anonymous reviewers for their extensive feedback.

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Authors and Affiliations

  1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    Hongbo Duan & Shouyang Wang

  2. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    Gupeng Zhang

  3. School of Economics and Management, Beihang University, Beijing, 100191, China

    Ying Fan

  4. Academy of Mathematics and System Science, Chinese Academy of Sciences, Beijing, 100190, China

    Shouyang Wang

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  1. Hongbo Duan
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Correspondence to Hongbo Duan.

Appendix

Appendix

Summary of abbreviations.

IAMs:

Integrated assessment models

AEEI:

Autonomous energy efficiency improvement

EEI:

Energy efficiency improvement

EEEI:

Endogenous energy efficiency improvement

GWP:

Gross world product

R&D:

Research and development

GHGs:

Greenhouse gases

EU:

European Union

NLP:

Nonlinear programming algorithm

CES:

Constant elasticity substitution

LBD:

Learning-by-doing

LBS:

Learning-by-searching

FBND:

Forgetting-by-not-doing

CTE:

Carbon ton equivalent

$/tC:

US dollar per ton of carbon

BAU:

Business-as-usual

CGE:

Computable general equilibrium

DICE:

Dynamic integrated climate economy model

ENTICE:

Model for endogenous technological change

DEMETER:

De-carbonization model with endogenous technologies for emissions reductions

WITCH:

World-induced technical change hybrid model

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Duan, H., Zhang, G., Fan, Y. et al. Role of endogenous energy efficiency improvement in global climate change mitigation. Energy Efficiency 10, 459–473 (2017). https://doi.org/10.1007/s12053-016-9468-1

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