The rise in investors’ awareness of climate risks after the Paris Agreement and the clean energy-oil-technology prices nexus

Fahmy, Hany

doi:10.1016/j.eneco.2021.105738

Cited by 109 publications

(27 citation statements)

References 46 publications

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“…According to, the Covid-19 outbreak has posed problems, possibilities, and a potential influence on sustainable energy techniques in both present and future situations. Covid-19 consequences on renewable-energy plans, according to the authors, need short-term policy prioritization (Fahmy 2022 ). It is also important to design both short- and long-term action plans to meet renewable energy goals in an effective and sustainable manner.…”

Section: Literature Reviewmentioning

confidence: 99%

Climate risk, natural resources, and climate change mitigation options in BRICS: implications for green recovery

Wei¹,

Khan²

2022

Environ Sci Pollut Res

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The study tests the dynamic nexus between climate risk, natural resource exploration strategy in BRICS economies, and climate change mitigation. The study further extended the research aim and presented the recommendations for greening the globe by suggesting green recovery. Climate change protection and climate risk reduction may be efficiently funded using climate risks and natural resources. Still, it is vital to look at the carbon risk in BRICS countries as an example. The researchers used the GMM analysis technique to infer the study findings. According to the study’s findings, environmental mitigation was significant at 17%, and financial strength and carbon risks were significant at 22.0%. In addition, the 20.5% association between climate risks and environmental drift in the BRICS nations highlights climate change concerns. A state’s financial strength is essential to execute green economic recovery strategies, one of the most highly regarded measures to reducing climate change and guaranteeing long-term economic status at the national level. As a result of the study on green economic growth, decision-makers are provided with specific policy recommendations.

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Section: Literature Reviewmentioning

confidence: 99%

Climate risk, natural resources, and climate change mitigation options in BRICS: implications for green recovery

Wei¹,

Khan²

2022

Environ Sci Pollut Res

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“…The production of all commodities has always been impacted by climate change, and especially by the increasingly frequent extreme weather events. The economic consequences provoked by climate change are considered to be extremely important (Fahmy, 2022; Painter, 2020; Pizer, 2006; Stern, 2008; Surminski, 2013; Zscheischler et al, 2018). As a response to combat climate change, it is necessary to assess the impact of climate risks and maintain the stability of financial markets (Battiston et al, 2017; NGFS, 2019), among which it is outstandingly to find an effective way for hedging the climate risk arising out of climate change and the extreme weather events.…”

Section: Introductionmentioning

confidence: 99%

Global climate change and commodity markets: A hedging perspective

Jia

Chen

Han

et al. 2023

Journal of Futures Markets

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This paper aims to measure the tail‐risk dependence between climate change and commodity futures markets. We utilize Morgan Stanley Capital International Climate Change Index (CCI) to serve as a proxy indicator of the stock market climate change for examining the tail‐risk spillover effect among 16 major commodity futures. Using GARCH–Copula–CoVaR framework, we show that extreme climate change has a significant tail‐risk spillover effect on commodity futures markets, in which agricultural and energy futures are affected by climate change most. We then adopt the copula‐based generalized autoregressive conditional heteroskedasticity (GARCH) model to compute the optimal hedge ratio among each pair of CCI with commodity futures, which performs well economic advantages for all 16 commodities. Compared with hedging against commodity index, CCI has a more significant hedging effect on metal and energy subindex futures. Therefore, we finally suggest an effective hedging portfolio composed of CCI and subindex futures in a significant economic sense.

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“…Distributed generation refers to any power generation unit installed in the power grid by independent power producers or users. It is a new form of clean energy application at present [4,5]. The installed capacity of distributed generation unit is less than 50 MW, which is close to the load center.…”

Section: Introductionmentioning

confidence: 99%

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

Wen¹,

Zhu²,

Li³

et al. 2023

Energy Engineering

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There is instability in the distributed energy storage cloud group end region on the power grid side. In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components show a continuous and stable charging and discharging state, a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed. Firstly, a voltage stability analysis model based on multi-scale and multi feature convolution neural network is constructed, and the multi-scale and multi feature convolution neural network is optimized based on Self-Organizing Maps (SOM) algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility. According to the optimal scheduling objectives and network size, the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales; Finally, the time series characteristics of regional power grid load and distributed generation are analyzed. According to the regional hierarchical time-sharing configuration model of "cloud", "group" and "end" layer, the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized. The experimental results show that after applying this algorithm, the best grid side distributed energy storage configuration scheme can be determined, and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved. KEYWORDSMultiscale and multi feature convolution neural network; distributed energy storage at grid side; cloud group end region; layered time-sharing configuration algorithm

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The rise in investors’ awareness of climate risks after the Paris Agreement and the clean energy-oil-technology prices nexus

Cited by 109 publications

References 46 publications

Climate risk, natural resources, and climate change mitigation options in BRICS: implications for green recovery

Climate risk, natural resources, and climate change mitigation options in BRICS: implications for green recovery

Global climate change and commodity markets: A hedging perspective

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

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