Increased wheat price spikes and larger economic inequality with 2°C global warming

Zhang, Tianyi; Wiel, Karin van der; Wei, Taoyuan; Screen, James A.; Yue, Xu; Zheng, Bangyou; Selten, Frank; Anderson, Weston; Blackport, Russell; Glomsrød, Solveig; Liu, Yu; Cui, Xuefeng; Yang, Xiaoguang

doi:10.1016/j.oneear.2022.07.004

Cited by 34 publications

(15 citation statements)

References 36 publications

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“…For projected impacts if climate change on FSN, we find that climate change will pose severe risks in terms of increasing the number of undernourished people, affecting tens to hundreds of millions of people under high vulnerability (Shared Socioeconomic Pathways (SSPs) 3 and 4) and high emission scenarios (Representative Concentration Pathway RCP8.5), particularly among vulnerable populations in low-income countries. Extreme weather events will increase the risks of undernutrition even on a regional scale via spikes in food prices and reduced income (Mbow et al, 2019;FAO, 2018;Hickey and Unwin, 2020;Hasegawa et al, 2021;Zhang et al, 2022). Moreover, climate change risks of micronutrient deficiency will become severe in high vulnerability development pathways (SSPs 3 and 4) in the absence of societal adaptation, leading to hundreds of millions of additional people lacking key nutrients at atmospheric CO 2 levels above 500 ppm (Myers et al, 2017;Mbow et al, 2019;Maire et al, 2021;Semba et al, 2022).…”

Section: Severe Climate Change Risks To Fsnmentioning

confidence: 99%

Severe climate change risks to food security and nutrition

Mirzabaev

Kerr

Hasegawa

et al. 2023

Climate Risk Management

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Section: Severe Climate Change Risks To Fsnmentioning

confidence: 99%

Severe climate change risks to food security and nutrition

Mirzabaev

Kerr

Hasegawa

et al. 2023

Climate Risk Management

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“…However, the literature indicates clearly that the price effects of impacts of climate change are likely to be significant (Wei and Aaheim, 2021; Zhang et al , 2022). At the same time, prices are also a main driver behind autonomous adaptation among people, in businesses and within local administrative institutions.…”

Section: Discussionmentioning

confidence: 99%

Climate change adaptation based on computable general equilibrium models – a systematic review

Wei

Aaheim

2023

IJCCSM

Self Cite

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Purpose This study aims to identify the current state of the art and the gaps in the application of computable general equilibrium (CGE) models on studying climate change adaptation. Design/methodology/approach A systematic review is conducted to select, classify and analyze relevant studies from two databases of Web of Science and Scopus. Findings Totally, 170 articles based on selected keywords were found from both databases, where 56 articles were duplicates. The authors further excluded 17 articles owing to preliminary exclusion criteria. Hence, 97 papers were selected for full-text review and more detailed assessment. Only a few of the studies explicitly have addressed the role of autonomous adaptation embodied in the CGE models. Over one-third of the studies have focused on planned adaptation without explicitly mentioning autonomous adaptation. Agriculture was the most addressed sector, and country-level models are the most adopted. Only one article has focused on South America. Research limitations/implications The review suggests that autonomous adaptation embodied in CGE models was not well addressed in the literature. As the limited studies have shown that autonomous adaptation can dramatically mitigate direct climate change impacts, further studies are needed to examine the importance of the autonomous adaptation for better understanding of climate change impacts. Furthermore, CGE models can provide a joint assessment considering both mitigation and adaptation strategies and management measures as such models have also been widely used to address effects of mitigation measures in the literature. Originality/value The studies on climate change adaptation based on CGE models have been systematically reviewed, and state-of-the-art knowledge and research gaps have been identified.

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“…Tschumi et al, 2021Tschumi et al, , 2022, crop modelling (e.g. Vogel et al, 2021;Goulart et al, 2021;Zhang et al, 2022) or energy modelling (e.g. van der Wiel et al, 2019a, b), were used to assess the influence of (changing) climate variability on various natural and societal systems.…”

Section: Introductionmentioning

confidence: 99%

The KNMI Large Ensemble Time Slice (KNMI-LENTIS)

Muntjewerf

Reerink

Wiel

2023

Preprint

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Abstract. Large-ensemble modelling has become an increasingly popular approach to study the mean climate and the climate system’s internal variability in response to external forcing. Here we present the KNMI Large ENsemble TIme Slice (KNMI-LENTIS): a new large ensemble data set produced with the re-turned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 year each: a present-day time slice and a +2 K warmer future time slice relative to present-day. The initial conditions for the ensemble members are generated with a combination of micro and macro perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160 x 10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time-slice approach makes it possible to study extreme events on sub-annual timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI-LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and those resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The data set is particularly geared towards research in the land-atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI-LENTIS are advised to make in-depth comparisons with observational or reanalysis data especially if their studies focus on ocean processes, on locations in the Southern Hemisphere or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI-LENTIS for extreme and compound event research and for climate-impact modelling.

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Increased wheat price spikes and larger economic inequality with 2°C global warming

Cited by 34 publications

References 36 publications

Severe climate change risks to food security and nutrition

Severe climate change risks to food security and nutrition

Climate change adaptation based on computable general equilibrium models – a systematic review

The KNMI Large Ensemble Time Slice (KNMI-LENTIS)

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