Consumption-Based Adjustment of Emissions-Intensity Targets: An Economic Analysis for China’s Provinces

Abstract

China’s Twelfth Five-Year Plan (2011–2015) aims to achieve a national carbon intensity reduction of 17 % through differentiated targets at the provincial level. Allocating the national target among China’s provinces is complicated by the fact that more than half of China’s national carbon emissions are embodied in interprovincial trade, with the relatively developed eastern provinces relying on the center and west for energy-intensive imports. This study develops a consistent methodology to adjust regional emissions-intensity targets for trade-related emissions transfers and assesses its economic effects on China’s provinces using a regional computable-general-equilibrium (CGE) model of the Chinese economy. This study finds that in 2007 China’s eastern provinces outsource 14 % of their territorial emissions to the central and western provinces. Adjusting the provincial targets for those emissions transfers increases the reduction burden for the eastern provinces by 60 %, while alleviating the burden for the central and western provinces by 50 % each. The CGE analysis indicates that this adjustment could double China’s national welfare loss compared to the homogenous and politics-based distribution of reduction targets. A shared-responsibility approach that balances production-based and consumption-based emissions responsibilities is found to alleviate those unbalancing effects and lead to a more equal distribution of economic burden among China’s provinces.

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Notes

  1. 1.

    This commitment was made at the 15th Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC) in Copenhagen in December 2009.

  2. 2.

    Prior to the Twelfth Five-Year Plan, the Eleventh Five-Year Plan and its predecessors focused on energy intensity (and did not set a target for carbon intensity). The Eleventh Five-Year Plan included a target to reduce energy intensity by 20 % nationwide. The target was not formally allocated to provinces but comprised of pledges made by each province (World Bank 2009).

  3. 3.

    A near-homogenous setting of energy (instead of carbon) intensity targets in the Eleventh FYP (by collecting and renegotiating provincial pledges) had pushed some provinces to adopt extreme short-term measures, such as rolling black-outs, to fulfill their target.

  4. 4.

    Their results indicate higher emissions intensities for the emissions-exporting eastern-coastal provinces and lower emissions intensities for the emissions-importing central and western provinces. The analysis is based on data from the year 2002. As a part of this study, we recalculate the emissions embodied in China’s interregional trade using an updated dataset for the year 2007.

  5. 5.

    For example, the consumer of a good gains from its consumption, while the producer gains from its production and sale. Similarly on the regional level, standard trade theory knows many cases in which each trading partner gains. Producing for export raises one region’s GDP, while importing products increases the varieties on offer and may reduce prices for consumers who then increase consumption.

  6. 6.

    For example, one could argue that the emissions-intensity targets should not be adjusted by all of a region’s emissions transfers, but only by some proportion (e.g., by the regional percentage emissions-reduction target \(r_{ EI ,r}^{ PRD }\), such that \(t_{ EI ,r}^{ CON 2} =({1-r_{ EI ,r}^{ PRD }})EI_r^{ PRD } -r_{ EI ,r}^{ RDP } \frac{B_r }{ GDP _r })\). Similarly, one could argue that emissions transfers should be normalized by the GDP of the emissions-exporting regions (\(t_{ EI ,r}^{ CON 3} =\left( {1-r_{ EI ,r}^{ PRD } } \right) EI_r^{ PRD } -\mathop \sum \limits _s \frac{B_{s,r} }{ GDP _s }\)). While those approaches might have some intuitive appeal, they do not preserve the total emissions-intensity target as, unlike in Eq. (8), the adjustments to the production-based emissions-intensity targets do not sum to zero.

  7. 7.

    While the total emissions-intensity target is conserved in the static framework described above, it should be noted that the resulting emissions intensities may differ from the target due to changes in GDP. However, sensitivity analyses conducted for this study indicate that the deviations from the total emissions-intensity target amount to \(<\)0.4 % percentage points (2.5 %) for the model scenarios considered.

  8. 8.

    The total emissions to be redistributed, \(T^{red}\), are positive because \(r_{EI,r}^{old} \) are negative reductions (i.e., increases) in the provinces whose emissions-reduction targets are to be adjusted.

  9. 9.

    For example, one could define shared-responsibility emission-intensity target by trade-adjusting the production-based target by half of a region’s emissions transfers, i.e., \(t_{EI,r}^{SHR} =({1-r_{EI,r}^{ PRD }})EI_r^{ PRD } -\frac{1}{2}\frac{B_r }{ GDP _r}\). This method also leads to a consistent allocation of emissions-reduction burden in that is preserved the total emissions-intensity target.

  10. 10.

    The model is formulated in the mathematical programming system MPSGE (Rutherford 1999), a subsystem of GAMS, and solved by using PATH (Dirkse and Ferris 1995).

  11. 11.

    The energy goods include coal (COL), crude oil (CRU), refined-oil products (OIL), natural gas (GAS), gas manufacture and distribution (GDT), and electricity (ELE); the non-energy sectors include agriculture (AGR), minerals mining (OMN), light industries (LID), energy-intensive industries (EID), transport equipment (TME), other manufacturing industries (OID), water (WTR), trade (TRD), transport (TRP), other service industry (OTH).

  12. 12.

    Although we adopt the emissions-intensity target of the 12th FYP, we do not aim to simulate its future economic impacts. Instead our objective is to gain insights into the relative economic and distributional impacts of different approaches for allocating emissions-intensity targets. To better isolate the effects relevant for this analysis, we use a static (instead of a dynamic) CGE framework based on data representing economic conditions for the year 2007.

  13. 13.

    Böhringer et al. (2011) build a multiregional input-output model based on the GTAP data base and calculate the emissions embodied in international trade. We apply the same method to calculate the emissions embodied in interregional trade in China. We refer to Böhringer et al. (2011) for a detailed description of the general methodology.

  14. 14.

    The emissions-intensity reduction targets of those provinces would actually become negative, i.e., allow for increases in emissions intensity. However, because we want to preserve incentives for not increasing emissions intensities on the provincial level, we constrain the maximum alleviation for emissions exporting provinces to be the homogenous reduction target of the production-based approach, i.e., 17.4 %. The provinces that would exceed this alleviation are allocated their baseline emissions intensities, i.e., a zero percent reduction target.

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Acknowledgments

The authors thank Eni S.p.A., ICF International, Shell International Limited, and the French Development Agency (AFD), founding sponsors of the China Energy and Climate Project. We are also grateful to the AXA Research Fund, which supported Marco Springmann’s doctoral research. We further acknowledge support provided by the Ministry of Science and Technology of China, the National Development and Reform Commission, and Rio Tinto. This work was also supported by the MIT Joint Program on the Science and Policy of Global Change through a consortium of industrial sponsors and Federal grants. We are also grateful to John Reilly, Sergey Paltsev, Henry Jacoby and Audrey Resutek for helpful comments and suggestions on earlier versions of this manuscript.

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Correspondence to Marco Springmann.

Appendices

Appendix 1: Elasticities of Substitution

Table 5 Reference values of elasticities of substitution in production and consumption

Appendix 2: Emissions-intensity targets by Province

Table 6 Emissions-intensity reduction targets (%)

Appendix 3: Economic Impacts by Province

Table 7 Percentage changes in welfare as measured by equivalent variation of income
Table 8 Percentage changes in GDP

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Springmann, M., Zhang, D. & Karplus, V.J. Consumption-Based Adjustment of Emissions-Intensity Targets: An Economic Analysis for China’s Provinces. Environ Resource Econ 61, 615–640 (2015). https://doi.org/10.1007/s10640-014-9809-5

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Keywords

  • Climate policy
  • China
  • Emissions-intensity targets
  • Regional development
  • Emissions embodied in trade
  • Emission transfers
  • Computable general equilibrium modeling