Taiwan’s 2050 low carbon development roadmap: An evaluation with the MARKAL model

Tsai, Miao-Shan; Chang, Ssu-Li

doi:10.1016/j.rser.2015.04.153

Cited by 54 publications

(15 citation statements)

References 20 publications

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“…In practice, it is common to predict energy demand on the basis of the developed statistical relationship and the identified driving factors. (Tsai and Chang, 2015) TIMES (Comodi et al, 2012) LEAP (Kumar, 2016) High level High level…”

Section: Introductionmentioning

confidence: 99%

Forecasting China’s regional energy demand by 2030: A Bayesian approach

Yuan

Sun

Zhao

et al. 2017

Resources, Conservation and Recycling

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China has been the largest energy consumer in the world, and its future energy demand is of concern to policy makers. With the data from 30 provinces during 1995-2012, this study employs a hierarchical Bayesian approach to present the probabilistic forecasts of energy demand at the provincial and national levels. The results show that the hierarchical Bayesian approach is effective for energy forecasting by taking model uncertainty, regional heterogeneity, and cross-sectional dependence into account. The eastern and central areas would peak their energy demand in all the scenarios, while the western area would continue to increase its demand in the high growth scenario. For the country as a whole, the maximum energy demand could appear before 2030, reaching 4.97/5.25 billion tons of standard coal equivalent in the low/high growth scenario. However, rapid economic development would keep national energy demand growing. It also suggests that most western provinces still have great potential for energy intensity reduction. The energy-intensive industries should be cut down to improve energy efficiency, and the development of renewable energy is essential.

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Section: Introductionmentioning

confidence: 99%

Forecasting China’s regional energy demand by 2030: A Bayesian approach

Yuan

Sun

Zhao

et al. 2017

Resources, Conservation and Recycling

View full text Add to dashboard Cite

show abstract

“…When conducting scenario analyses and policy simulations on carbon emissions at different levels, the common approach is to build a multi-parameter mathematical model in order to distinguish the impact factors of carbon emissions and to design carbon decomposition methods and life cycle assessment to assess components of carbon emissions. These models include the MARKAL model (Tsai and Chang 2015;Deng and Liang 2017), STIRPAT model (Ma et al 2017), BP neural network model, SRIO model, logistic model, system dynamics model, input-output model (Huang et al 2018;Mi et al 2018b) and life cycle assessment model.…”

Section: Decomposition and The Impact Factors Of Carbon Emissionsmentioning

confidence: 99%

Carbon emissions accounting for China’s coal mining sector: invisible sources of climate change

et al. 2018

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Coal is the primary source of China's carbon emissions due to the energy structure and its resource endowment. This reality creates enormous pressure and impetus for low-carbon pathways of coal production and consumption. Based on a literature review on carbon emissions accounting methods, this paper builds a source-driven CO 2 emissions accounting model for the coal development sector using the emissions factor method. Scenario analysis is employed to predict future carbon emission equivalents and to indicate possible implications for climate change mitigation in this sector. Carbon emissions from coal development are mainly derived from coal mine gas emissions, which yield 62% of the sector's total carbon emissions, followed by energy consumption. The recent decline in coal mining-driven CO 2 emissions is mainly due to the strict deployment of coal mine gas and the changing structure of coal mines. The results from the scenarios suggest that the carbon emissions reduction potential will largely be determined by technology innovation in the coal mine gas industry. Policy implications for further addressing carbon emissions from the supply side of the coal industry include improvements in energy efficiency and coal mine gas extraction and utilization.

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“…For example, Tsai and Chang (2015) utilized the MARKAL energy engineering model to simulate the effects of adopting a combination of technology and tax measures under the various carbon reduction targets and low-carbon development scenarios up to 2050 in Taiwan. Bi, Huang and Ye (2015) analyzes the risk of low-carbon technological innovation in emerging economies under globalization, by integrating the method of global value chain (GVC) and technological innovation linear progress into a new analytical framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy evaluation of low carbon development levels for logistic enterprises in China

Zhao

Wang

et al. 2015

JIEM

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Abstract:Purpose: Under the background of global warming, low carbon economy, which is based on low waste and low pollution, has become a globally focused topic. Energy conservation, emission reduction and low carbon development have become irresistible trends of social development. A new set of low carbon evaluation index system for logistics enterprises is proposed. It is helpful for the better monitor of low carbon production and provide the optimization of industrial policies in China.Design/methodology/approach: Based on the problem that logistic analyses are facing under the background of low carbon economy, this paper firstly establishes a set of new evaluation indexes system, including 5 first-level indices and 28 second-level indices, for the low carbon development levels of logistics enterprises in China. To simplify the new evaluation system, the weight judgment method is then used to select indices, the validity judgment method and reliability coefficient judgment method are respectively used to test the validity and reliability of the evaluation systems. The simplified evaluation system consists of 4 first-class indices and 20 second-class indexes. Finally, numerical examples illustrate the validity and operability of the evaluation system. performance. Moreover, The reason for the good low carbon performance of one enterprises is that it is in the lead on the key indexes of high weighs such as Perfection degree of low carbon development strategy, Level of transportation route optimization, etc. The new evaluation index system also helps enterprises to realize the sustainable development by identifying the key factors that affect the low carbon development level. Originality/value:The existing literature that assesses low carbon logistics is mostly focused on the review of the location-based and industry-based aspects, and little attention has been paid to assessing the efficiency of low carbon logistics from the components that are within the logistics enterprises. Therefore, we develop a new low carbon evaluation index system for logistics enterprises in order to better monitor low carbon production and provide the optimization of industrial policies in China.

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Taiwan’s 2050 low carbon development roadmap: An evaluation with the MARKAL model

Cited by 54 publications

References 20 publications

Forecasting China’s regional energy demand by 2030: A Bayesian approach

Forecasting China’s regional energy demand by 2030: A Bayesian approach

Carbon emissions accounting for China’s coal mining sector: invisible sources of climate change

Fuzzy evaluation of low carbon development levels for logistic enterprises in China

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