How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development

Yi, Wenjing; Zou, Le–Le; Jie, Guo; Wang, Kai; Wei, Yi‐Ming

doi:10.1016/j.enpol.2011.01.063

Cited by 224 publications

(97 citation statements)

References 22 publications

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“…To allocate CO 2 emission reduction quota rationally among Chinese provinces, this paper thoroughly considers characteristics of each province. Based on our findings, the previous studies on CO 2 emission reduction quota allocation typically chose population, CO 2 emission quantity, GDP and carbon intensity as their reduction indexes (Welsch 1993;Baer et al 2008;Yi et al 2011;Zhou et al 2014a;Zhou and Wang 2016). However, the MAC has been ignored by the previous studies as one of the CO 2 emission reduction allocation indexes.…”

Section: Introductionmentioning

confidence: 78%

“…The result is similar to previous studies. From the previous studies, provinces like Shandong, Shanxi and Hebei have larger CO 2 emission reduction quota, while provinces like Hainan, Beijing and Qinghai have smaller CO 2 emission reduction quota (Wang et al 2011;Yi et al 2011;Sun et al 2011;Yu et al 2014;Chen and Lin 2015;Han et al 2016).…”

Section: Co 2 Emissions Reduction Quota Allocation Of Chinese Provincesmentioning

confidence: 99%

“…The result shows that Shanxi is the largest CO 2 emission reducer which will reduce 1.55% of its CO 2 emission in 2020 compared with 2005, while Sichuan has the largest increment and it will increase 1.18% its CO 2 emission in 2020 compared with 2005. Yi et al (2011) selected per capita GDP, accumulative CO 2 emissions and energy consumption per unit of industrial added value as the CO 2 quota allocation indexes based on equity principle. They analyzed the CO 2 emission quota allocation among 30 Chinese provinces under four scenarios with subjectively set biased weight on each index.…”

Section: Carbon Intensitymentioning

confidence: 99%

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Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model

et al. 2017

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Chinese central government made a commitment to achieve a 40-45% reduction in carbon dioxide (CO 2 ) per unit of GDP by 2020 compared with 2005. This targeted reduction was allocated averagely among all the provinces rather than individually according to different situations of each province. Though some research has been done regarding this rough allocation, two shortcomings in previous studies exist: Firstly, CO 2 marginal abatement cost (MAC) has been ignored as one of the CO 2 emission reduction allocation indexes. Secondly, either subjective or objective method has been used rather than comprehensively of both subjective and objective method to calculate the weight of each index in the previous studies. In order to fill the gaps, this paper builds a two-stage Shapley information entropy model to allocate CO 2 emission reduction quota among the Chinese provinces based on the equity and efficiency principles. Afterward, three CO 2 emission reduction quota allocation scenarios have been proposed. The results show that the CO 2 MAC is an indispensable index in CO 2 emission reduction quota allocation, because its value of CO 2 Shapley information entropy is the highest among five indexes. CO 2 emission reduction quota of lower-MAC provinces should be allocated larger, while the quota of higher-MAC provinces should be allocated smaller. Therefore, two suggested policies have been proposed: First, differential CO 2 emission reduction quota allocation should be proposed. Second, synergetic development should be promoted.

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Section: Introductionmentioning

confidence: 78%

Section: Co 2 Emissions Reduction Quota Allocation Of Chinese Provincesmentioning

confidence: 99%

Section: Carbon Intensitymentioning

confidence: 99%

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Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model

et al. 2017

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“…(12) can be generalized to combine different indicators which inform emissions-intensity targets into an aggregate reduction index. Indicators other than the emissions-based ones used in this study may include economic ones, such as per-capita GDP, or temporal ones, such as historical emissions (Yi et al 2011;Wei et al 2011). The general form of a composite indicator is then:…”

Section: Shared Responsibility For Emission-intensity Reductionsmentioning

confidence: 99%

“…Based on time series data from 1995 to 2007, the equity index includes per-capita CO 2 emissions and per-capita GDP, while the efficiency index includes regional emissions intensity and marginal-abatement costs. Yi et al (2011) have constructed an aggregate index for informing the carbon intensity allocation in 2020. Their index is based on per-capita GDP (to indicate the capacity for emissions reduction), accumulated fossil-fuel related CO 2 emissions (to indicate the responsibility for emissions reduction), and energy consumption per unit of industrial value added (to indicate the potential for emissions reduction).…”

Section: Previous Approaches For Target Allocationmentioning

confidence: 99%

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

Springmann

Zhang

Karplus

2014

Environ Resource Econ

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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. M. Springmann (B)

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Life cycle sustainability assessment of pumped hydro energy storage

Guo

Yao

et al. 2019

Int J Energy Res

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How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development

Cited by 224 publications

References 22 publications

Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model

Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model

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

Life cycle sustainability assessment of pumped hydro energy storage

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