Study on Transition of Primary Energy Structure and Carbon Emission Reduction Targets in China Based on Markov Chain Model and GM (1, 1)

Ren, Feng; Gu, Lihong

doi:10.1155/2016/4912935

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…e explanations of the parameters in model ( 3) can be referred to as in model (2). e effectiveness of the energy efficiency in model ( 3) is determined based on the following rules: (1) if ρ < 1, then DMU 0 is non-DEA-efficient; (2) if ρ ≥ 1, then DMU 0 is DEA-efficient, and the bigger the value, the higher the efficiency.…”

Section: Super Sbm-undesirable Modelmentioning

confidence: 99%

“…Increasing energy efficiency can help to reduce energy consumption, alleviate the pressure of energy shortage, and contribute to the realization of reliable energy supply in this region. (2) Be helpful to reduce carbon emissions. In 2017, the world's energy-related carbon emissions rebounded, having increased by 1.7% (the average growth rate in the past decade is only 1.3%).…”

Section: Introductionmentioning

confidence: 99%

“…China has been facing the growing international pressure on carbon emission reductions and has set the carbon reduction target for 2030. Carbon emission reduction will undoubtedly be a hard constraint condition on the economic and social development in China [2,3]. Obviously, the increase of the ETFEE in the BTH region can reduce carbon emissions and loosen the constraint for the longterm development in this region.…”

Section: Introductionmentioning

confidence: 99%

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Study on ETFEE in the BTH Region Based on the Window-SBM-Undesirable Model

Ren

2020

Discrete Dynamics in Nature and Society

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Research on ecological total-factor energy efficiency (ETFEE) is conducive to energy conservation, emission reduction, and ecological protection. This paper focuses on the measurement and decomposition of the ETFEE in the Beijing-Tianjin-Hebei (BTH) region in China. In order to measure the ETFEE values, the window technology is combined with a nonradial and nonoriented SBM-undesirable model considering undesirable outputs to overcome the defect of insufficient data of research objects and ensure the calculation process to be implemented. The findings show that Beijing and Tianjin are DEA-efficient, while Hebei is not. The technological progress rates of Beijing and Tianjin reach up to 11.92% and 14.96%, while that of Hebei retrogresses by 4.47%. The scale efficiencies of Beijing, Tianjin, and Hebei are 97.75%, 86.60%, and 93.81%, respectively, which means that there are potentials for further optimization in the energy structures. The impulse response results between the energy structure and the ETFEE show that the proportions of coal and petroleum have negative effects on the ETFEE, while that of natural gas has a positive effect. The research results can provide reference for decision makers to formulate regional development plans.

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Section: Super Sbm-undesirable Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study on ETFEE in the BTH Region Based on the Window-SBM-Undesirable Model

Ren

2020

Discrete Dynamics in Nature and Society

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“…To estimate 2020 for each participant, a grey system model called GM (1, 1) is adopted due to the advantages of less required sample data, high short-term prediction accuracy and testability (Ren and Gu, 2016). Because the prediction of GM (1, 1) is based on the internal growth trend of the data, it can be regarded as a natural growth of carbon emissions without policy constraints.…”

Section: Quota Allocation By CMmentioning

confidence: 99%

A bi-objective programming model for carbon emission quota allocation: Evidence from the Pearl River Delta region

et al. 2018

Journal of Cleaner Production

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As a core component of the emission trading scheme (ETS), the initial allocation of carbon quotas is extremely important. Currently, most allocation methods mainly focus on the realization of a single performance goal, which will result in conflicts between different levels of participants. To overcome this deficiency, this paper develops a bi-objective programming model (BPM) with two sub-objective functions of abatement costs and carbon assets.Meanwhile, cost-oriented model (CM) and asset-oriented model (AM) are implemented as comparison approaches that represent the minimization of regional abatement costs and the maximization of individual interests, respectively. The empirical results of the Pearl River Delta (PRD) region reveal that BPM is the most efficient and feasible approach to some extent. More precisely, BPM can motivate the enthusiasm of all participants while optimizing abatement costs. With the increase of regional total quotas, the advantage of BPM becomes more and more prominent. The contribution of this paper is to present a novel method for carbon emission quota allocation, which fills the gap in the existing literature. Furthermore, the proposed method that can be deployed in other similar regions assists policymakers in enacting an effective emission reduction policy and in better understanding the objectives of economy, energy and environment.

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“…Grey forecasting models can be divided into two categories according to the number of variables, single-variable grey forecasting models where GM(1, 1) is the core [23][24][25][26] and multivariable grey forecasting models, represented by GM (1, ) [27][28][29] and MGM (1, ) [30]. Zeng et al [27] pointed out that there were some structure deficiencies in GM (1, ), which may lead to greater errors.…”

Section: Introductionmentioning

confidence: 99%

A Nonhomogeneous Multivariable Grey Prediction NMGM Modeling Mechanism and Its Application

Wang

Zhao

2018

Mathematical Problems in Engineering

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The purpose of this paper is to explore modeling mechanism of a nonhomogeneous multivariable grey prediction NMGM(1, m, kα) model and its application. Although multi-variable grey prediction MGM(1, m) model has been employed in many fields, its prediction results are not always satisfactory. Traditional MGM(1, m) model is constructed on the hypothesis that original data sequences are in accord with homogeneous index trend; however, the nonhomogeneous index data sequences are the most common data existing in all systems, and how to handle multivariable nonhomogeneous index data sequences is an urgent problem. This paper proposes a novel nonhomogeneous multivariable grey prediction model termed NMGM(1, m, kα) to deal with those data sequences that are not in accord with homogeneous index trend. Based on grey prediction theory, by least square method and solutions of differential equations, the modeling mechanism and time response function of the proposed model are expounded. A case study demonstrates that the novel model provides preferable prediction performance compared with traditional MGM(1, m) model. This work is an extension of the multivariable grey prediction model and enriches the study of grey prediction theory.

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Study on Transition of Primary Energy Structure and Carbon Emission Reduction Targets in China Based on Markov Chain Model and GM (1, 1)

Cited by 14 publications

References 25 publications

Study on ETFEE in the BTH Region Based on the Window-SBM-Undesirable Model

Study on ETFEE in the BTH Region Based on the Window-SBM-Undesirable Model

A bi-objective programming model for carbon emission quota allocation: Evidence from the Pearl River Delta region

A Nonhomogeneous Multivariable Grey Prediction NMGM Modeling Mechanism and Its Application

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