Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China

Feng, Zhiying; Tang, Wenhu; Niu, Zhewen; Wu, Q. H.

doi:10.1016/j.apenergy.2018.07.005

Cited by 48 publications

(19 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), and political acceptability (regarding equity [49], flexibility [108], competitiveness [107], compliance [87,98], feasibility of implementation [80,105], etc.). Different techniques, such as the Wideband Delphi method [49,105], AHP [109] and Euclidean distance methods [80,81]), were used to determine the weights on the various evaluation indexes. The evaluation results for existing policies provide helpful insights for future policy.…”

Section: Quantitative Modelsmentioning

confidence: 99%

“…(4) Statistical models, e.g., GARCHs [110] and other linear regression analyses [53,92], have been introduced to capture the relationship between the ETS rules (e.g., existence of ETS market [92], covered enterprise numbers [92] and free carbon allowances [92], as the explanatory variables) and the associated mitigating effect (emission reduction [92,111] as the dependent variable), as well as the performance of ETS market (carbon prices [92,110] and volatility [97]). Furthermore, cluster analyses have been used to group various existing ETS markets with different policy designs into categories with similar features (e.g., GDP [54,109], carbon emissions [54,109] and carbon intensity [112]), providing policy implications for other markets based on their features.…”

Section: Quantitative Modelsmentioning

confidence: 99%

“…(1) Market factors in the ETS market mainly include carbon price [73], certified emission reduction (CER) price [51,73], futures [41,140], options [141], trading volumes [142], carbon price returns [142] and volatilities [142,143]. On the one hand, existing research has been devoted to capturing the dynamic trends of these market factors in the ETS market [51,64] and their relationships with each other (particularly carbon and CER prices [73]), which provides policy implications for both policy design [109,144] and investment decisions [145]. In addition, existing models have attempted to explore an effective pricing mechanism for ETS markets, exploring a reasonable carbon or CER price to balance the mitigation effect and financial costs of ETS policy [146,147].…”

Section: Research Hotspotsmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative models in emission trading system research: A literature review

Tang

Wang

et al. 2020

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

Diverse quantitative models have been applied to analyse emission trading system, as the top effective climate change policy. This paper is the first attempt to present a comprehensive literature review on full-scale types of quantitative models in emission trading system research. The models dominating emission trading system-related literature could be categorized as optimization models, simulation models, assessment models, statistical models, artificial intelligences and ensemble models. Using different quantification and solution tools, these models complemented and enriched each other in serving the various agents involved in emission trading system and facilitating their respective emission trading system related works: the government to design emission trading system policies, enterprises to participate in emission trading system and goods markets, third parties to regulate emission trading system and emission trading system markets involving different agents. For each agent, a systematic analysis is provided on research hotspots (the challenges to address), quantitative models (to describe the problems and find the results), main findings (the policy implications from the models) and future research (potential improvements on existing models). Some interesting conclusions are obtained. (1) Generally, China was the largest contributor to emission trading system research using quantitative models (representing 35.71% of the total articles). (2) The research hotspots were decision making by enterprises under an emission trading system (20.92%), spillovers amongst emission trading system and other markets (17.54%) and allowance allocation by the government (12.52%). (3) Popular quantitative models included various optimization models (32.00%) and simulation models (29.64%). Highlights：• A variety of quantitative models have been applied to ETS research.• A comprehensive review on full-scale types of models in ETS research is presented.• Optimization, simulation, assessment, statistical, AI & ensemble models were built.• Models helpfully served the government, enterprises, third parties & market in ETS.• For each agent, hotspots, models, main findings and future research are analysed.

show abstract

Section: Quantitative Modelsmentioning

confidence: 99%

Section: Quantitative Modelsmentioning

confidence: 99%

Section: Research Hotspotsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative models in emission trading system research: A literature review

Tang

Wang

et al. 2020

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

show abstract

“…Invention applications and new product sales were used to represent the desirable outputs. We chose industrial SO 2 emissions [114,115] and CO 2 emissions [116][117][118] to represent the undesirable outputs.…”

Section: Desirable and Undesirable Outputsmentioning

confidence: 99%

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

Bossink

Chen

2019

Sustainability

View full text Add to dashboard Cite

An efficiency evaluation of China's regional sustainable innovation, evaluating industrial waste and total energy consumption, is the main research subject in this paper. It focuses on a regional measurement and comparison of these undesirable outputs of Chinese firm activities, such as industrial SO 2 and CO 2 emissions. By applying a data envelopment analysis-slack-based measure (DEA-SBM) model with undesirable outputs indicators, the regional innovation efficiency was evaluated for 30 provinces in China, from 2002 to 2014. The results indicate that the sustainable innovation efficiency of overall China is still relatively low, and varies significantly in different regions. Central and Western China have similar sustainable innovation efficiencies, which are much lower than the sustainable innovation efficiency in Eastern China. Furthermore, the data indicate that regional sustainable innovation efficiency disparities among these three areas are decreasing. Based on these findings, reasons for the sustainable innovation efficiency gap among the different regions were analyzed. To scholars, this paper extends the research on regional sustainable innovation efficiency by implementing an undesirable output perspective to the DEA-SBM model. The findings also provide Chinese policy makers with useful decision support insights for regional sustainable innovation, and energy conservation and emission reduction policies.Sustainability 2020, 12, 31 2 of 21 differences can be addressed [15][16][17]. Research on regional innovation efficiency has gained increasing attention in recent years [18][19][20], but to the best of our knowledge, very few studies have been conducted to quantitatively evaluate sustainable innovation efficiency in China, by means of focusing on undesirable outputs like industrial SO 2 and CO 2 emissions [21,22]. Our study aims to contribute to the knowledge in this area, and does this by means of measuring and comparing unsustainable, unwanted, and undesirable outcomes/byproducts of Chinese regions' innovative firm activities. By evaluating the regional sustainable innovation efficiency of 30 provinces in China from 2002 to 2014, using the slack-based measure (SBM) model, this study focuses on the following research question: what are the differences between sustainable innovation efficiencies of different regions in China and which trends in these differences can be estimated?How to deal with SO 2 and CO 2 emissions and with related climate change has become one of the most urgent challenges of today's world. For example, it has been reported that global CO 2 emissions from fuel combustion has increased from 26,177 Mt in 2004 to 32,190 Mt in 2013. Many countries have committed to controlling SO 2 and CO 2 emissions and aim to develop a no-to low-emissions economy. In 2015, the Paris Climate Change Conference adopted the "Paris Agreement" to deal with climate change after 2020 [24]. More than 170 countries jointly signed the Paris Agreement in 2016. China and the United States officially ratified the...

show abstract

“…Some scholars have noticed this flaw and thus, have adopted a combined weight approach. For example, Han et al and Feng et al combined the AHP with the information entropy approach to overcome this problem [29,30].…”

Section: Introductionmentioning

confidence: 99%

How to Allocate Carbon Emission Permits Among China’s Industrial Sectors Under the Constraint of Carbon Intensity?

Zhou

Huo

et al. 2019

Sustainability

View full text Add to dashboard Cite

With the official launch of China’s national unified carbon trading system (ETS) in 2017, it has played an increasingly important role in controlling the growth of carbon dioxide emissions. One of the core issues in carbon trading is the allocation of initial carbon emissions permits. Since the industry emits the largest amount of carbon dioxide in China, a study on the allocation of carbon emission permits among China’s industrial sectors is necessary to promote industry carbon abatement efficiency. In this study, industrial carbon emissions permits are allocated to 37 sub-sectors of China to reach the emission reduction target of 2030 considering the carbon marginal abatement cost, carbon abatement responsibility, carbon abatement potential, and carbon abatement capacity. A hybrid approach that integrates data envelop analysis (DEA), the analytic hierarchy process (AHP), and principal component analysis (PCA) is proposed to allocate carbon emission permits. The results of this study are as follows: First, under the constraint of carbon intensity, the carbon emission permits of the total industry in 2030 will be 8792 Mt with an average growth rate of 3.27%, which is 1.57 times higher than that in 2016. Second, the results of the carbon marginal abatement costs show that light industrial sectors and high-tech industrial sectors have a higher abatement cost, while energy-intensive heavy chemical industries have a lower abatement cost. Third, based on the allocation results, there are six industrial sub-sectors that have obtained major carbon emission permits, including the smelting and pressing of ferrous metals (S24), manufacturing of raw chemical materials and chemical products (S18), manufacturing of non-metallic mineral products (S23), smelting and pressing of non-ferrous metals (S25), production and supply of electric power and heat power (S35), and the processing of petroleum, coking, and processing of nuclear fuel (S19), accounting for 69.23% of the total carbon emissions permits. Furthermore, the study also classifies 37 industrial sectors to explore the emission reduction paths, and proposes corresponding policy recommendations for different categories.

show abstract

Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China

Cited by 48 publications

References 41 publications

Quantitative models in emission trading system research: A literature review

Quantitative models in emission trading system research: A literature review

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

How to Allocate Carbon Emission Permits Among China’s Industrial Sectors Under the Constraint of Carbon Intensity?

Contact Info

Product

Resources

About