Modeling deep decarbonization: Robust energy policy and climate action

Duan, Hongbo; Rogelj, Joeri; Veysey, Jason; Wang, Shouyang

doi:10.1016/j.apenergy.2020.114517

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Carbon emissions have become a growing source of concern for governments and businesses as one of the most significant contributors to the rise of environmental issues in the twenty-first century [1]. Global energy-related carbon emissions reached a historic high of 36.3 gigatons in 2021, 6% higher than the emissions in 2020 [2].…”

Section: Introductionmentioning

confidence: 99%

A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network

et al. 2023

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Most previous studies on airline fleet planning have focused solely on economic considerations, neglecting the impact of carbon reduction. This paper presents a novel method for green fleet planning, using a bi-level programming model to balance conflicts among stakeholders while considering uncertain parameters such as demand and operating costs. The upper model aims to reduce carbon emissions by taking into account government constraints, such as carbon allowances and carbon prices, as well as airline satisfaction. The lower model seeks to maximize airline revenue using a space-and-time network model based on given airline flight schedules. To verify the game model, a case study utilizing randomly generated scenarios is employed within the context of China’s aviation-specific emissions trading scheme. Results show that: (1) compared to the scenario without a policy aiming at reducing carbon emissions, this method reduces carbon emissions by 23.03% at the expense of a 6.96% reduction in terms of the airline’s operating profit; (2) when passenger demand levels increase to 160%, the profitability of the proposed fleet increases by 50.83%, while there were only insignificant changes in carbon emissions; (3) the proposed methodology can assist the airlines systematically to reduce carbon emissions and provide valuable strategic guidance for policy makers.

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Section: Introductionmentioning

confidence: 99%

A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network

et al. 2023

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“…For achieving decarbonization and the Paris agreement, focused and strategical investment is of the utmost importance, and most of these investments are required in the developing countries [9]. Further, deep decarbonization is an extremely convoluted and systematic process that requires blended contributions from policies, technologies, people, companies, and markets [10][11][12][13]. The power sector is the most significant sector contributing to India's total GHG emissions [14], and decarbonizing the power sector is closely interlinked with digitalization and decentralization [15].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Clean Energy Transition Potential in Major Power-Producing States of India Using Multi-Criteria Decision Analysis

Indrajayanthan¹,

Mohanty²

2022

Sustainability

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India has an ambitious target to promote clean energy penetration, but as of 2021, the electricity mix of India is dominated by coal to about 71%. Therefore, analyzing the clean energy potential and the ability of the individual states to entrench energy transition in the upcoming years will be supportive for policymakers. This study is propounded to assess the clean energy transition potential with a focused analysis on seven major power-producing states of India. These states include Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, Karnataka, Madhya Pradesh, and Andhra Pradesh. The clean energy transition potential assessment is performed by utilizing multi-criteria decision analysis methodologies such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization Method by Ratio Analysis (MOORA). Further, the analysis is performed against four major criteria that include high carbon energy resource dependency, low carbon energy resource dependency, clean energy potential, and policy support. Altogether, the assessment criteria include four primary level criteria and fourteen secondary level parameters. In order to reflect the significance of each parameter and criterion to the characteristics of clean energy transition potential, appropriate weightage is provided using the Fuzzy Analytic Hierarchy Process (AHP). The results indicate that Gujarat has the highest clean energy transition potential in both the multi-criteria decision analysis methods. On the other hand, Uttar Pradesh exhibited the least performance, and a complete energy transition to clean energy resources is less likely in this state. The rest of the states obtained intermediate ranking, and a comparative analysis between the two methods was also accomplished. This study suggests that India should focus on the clean energy policy with vigorous efforts on top-performing states which will effectively accelerate the power sector decarbonization.

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Full energy sector transition towards 100% renewable energy supply: Integrating power, heat, transport and industry sectors including desalination

et al. 2021

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Modeling deep decarbonization: Robust energy policy and climate action

Cited by 8 publications

References 23 publications

A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network

A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network

Assessment of Clean Energy Transition Potential in Major Power-Producing States of India Using Multi-Criteria Decision Analysis

Full energy sector transition towards 100% renewable energy supply: Integrating power, heat, transport and industry sectors including desalination

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