Publications
25 results found
Kikstra J, Waidelich P, Batibeniz F, et al., 2024, Climate damage projections beyond annual temperature
<jats:p>Projections of economic damages from climate change are key for evaluating the benefits of climate mitigation and informing discussions around adaptation needs. So far, global and country-level top-down assessments of GDP damages have focused on annual mean temperature changes and annual precipitation. Recent backward-looking studies have identified further impacts of variability and extremes in precipitation and temperatures on income growth.Here, we examine GDP impacts and uncertainties under different global warming levels by combining empirical dose-response functions for temperature variability, rainfall deviations, and extreme precipitation with climate projections of 33 CMIP6 models. The main contribution of this work is to understand the projected relative contributions of multiple climate variables under many possible future climates.We find that at a +3&#176;C global warming level, global average losses reach 10% of GDP, with worst effects (up to 17%) in poorer, low-latitude countries. Relative to annual temperature damages, which find to seemingly capture heat wave impacts, the additional GDP impacts of projecting variability and extremes are relatively small and dominated by inter-annual variability, especially in lower latitudes. However, accounting for variability and extremes when estimating the temperature dose-response function still raises global GDP losses by nearly 2%-pts and exacerbates tail risks for economic growth.Our results call for region-specific risk assessments and complementary research into climatic extremes not considered here, including their indirect effects on temperature dose-response functions. Additionally, it will be very important to further the work on understanding historical and future persistence and adaptive capacities for these different impact channels.</jats:p>
Andrijevic M, Schleussner C-F, Kikstra J, et al., 2024, Towards Evaluating the Financial Responsibility of Carbon Majors for Climate-Related Damages
<jats:p>In light of the global energy crisis and escalating climate change impacts, the liability of major fossil fuel companies is receiving heightened scrutiny, particularly in the context of climate litigation. This study initially establishes the feasibility of attributing climate damages to these companies. Utilizing the social cost of carbon methodology, we evaluate the damages inflicted by the top 25 oil and gas emitters from 1985 to 2018, comparing these to their financial profits. Our central estimate suggests partial damages of approximately 20 trillion USD, with the companies&#8217; financial gains surpassing this by 50%, totaling around 30 trillion USD. This indicates the potential of carbon majors to cover their attributed damages while maintaining significant profits. In our analysis, we also explore how varying approaches to assigning responsibility and handling uncertainties in climate damages can markedly influence these findings. Additionally, we explore the role of sovereign wealth funds in perpetuating fossil-fuel derived wealth and the ensuing liability questions.</jats:p>
Zimm C, Mintz-Woo K, Brutschin E, et al., 2024, Justice considerations in climate research, Nature Climate Change, Vol: 14, Pages: 22-30, ISSN: 1758-678X
Climate change and decarbonization raise complex justice questions that researchers and policymakers must address. The distributions of greenhouse gas emissions rights and mitigation efforts have dominated justice discourses within scenario research, an integrative element of the IPCC. However, the space of justice considerations is much larger. At present, there is no consistent approach to comprehensively incorporate and examine justice considerations. Here we propose a conceptual framework grounded in philosophical theory for this purpose. We apply this framework to climate mitigation scenarios literature as proof of concept, enabling a more holistic and multidimensional investigation of justice. We identify areas of future research, including new metrics of service provisioning essential for human well-being.
Lamboll RD, Nicholls ZRJ, Smith CJ, et al., 2024, Author Correction: Assessing the size and uncertainty of remaining carbon budgets (Nature Climate Change, (2023), 13, 12, (1360-1367), 10.1038/s41558-023-01848-5), Nature Climate Change, Vol: 14, ISSN: 1758-678X
Correction to: Nature Climate Change, published online 30 October 2023. In the version of the article initially published, in the “Comparison of recommended result with AR6 WG1 results” section, the sentence now reading “After making all these changes, our best (50%) RCB estimate starting after 2022…” originally said “starting from 2022”. In the Methods, in the sentence now reading “We use a different approach to MAGICC when processing FaIR data because by default, FaIR includes the effects of a substantial solar cycle in future temperatures…”, “future temperatures” originally said “future emissions”. These updates have been made in the HTML and PDF versions of the article.
Lamboll RD, Nicholls ZRJ, Smith CJ, et al., 2023, Assessing the size and uncertainty of remaining carbon budgets, Nature Climate Change, Vol: 13, Pages: 1360-1367, ISSN: 1758-678X
The remaining carbon budget (RCB), the net amount of CO2 humans can still emit without exceeding a chosen global warming limit, is often used to evaluate political action against the goals of the Paris Agreement. RCB estimates for 1.5 °C are small, and minor changes in their calculation can therefore result in large relative adjustments. Here we evaluate recent RCB assessments by the IPCC and present more recent data, calculation refinements and robustness checks that increase confidence in them. We conclude that the RCB for a 50% chance of keeping warming to 1.5 °C is around 250 GtCO2 as of January 2023, equal to around six years of current CO2 emissions. For a 50% chance of 2 °C the RCB is around 1,200 GtCO2. Key uncertainties affecting RCB estimates are the contribution of non-CO2 emissions, which depends on socioeconomic projections as much as on geophysical uncertainty, and potential warming after net zero CO2.
Kikstra J, Li M, Brockway P, et al., 2023, Downscaling Down Under: towards degrowth in integrated assessment models, Economic Systems Research, ISSN: 0953-5314
IPCC reports to date have not featured scenarios of reduced absolute economic output in high-income regions. Here, we change the utility function in the energy-economy model MESSAGEix-Australia to explore an ensemble of 51 emissions scenarios. Total GDP growth in these scenarios goes from continuing historical growth trends (+3%/year) to rapid reductions (-5%/year) in aggregate production andconsumption. We use this experiment as an illustration to investigate the ability of a traditional integrated assessment model (IAM) to capture elements of degrowth startingfrom an economic, rather than energy demand reduction entry -point. Keeping Australian GDP per capita close to current levels reduces the mid-century need for upscaling solar and wind by close to 40% compared to the SSP2 growth baseline, and limits future material needs for renewables, although upscaling needs from 2020 remain11high until 2030 at 4.2x versus 5.6x growth in the SSP2 baseline. More dramatic reductions in energy demand may entail higher socio-cultural feasibility concerns, depending on the policies involved. Strong reductions in inequality reduce the risk of lowered access to decent living services. We end with a discussion on research needs and possible extensions of IAM frameworks to more accurately model post-growth and degrowth scenarios.
Li M, Keysser L, Kikstra JS, et al., 2023, Integrated assessment modelling of degrowth scenarios for Australia, ECONOMIC SYSTEMS RESEARCH, ISSN: 0953-5314
Lamboll R, Nicholls Z, Smith C, et al., 2023, Assessing the size and uncertainty of remaining carbon budgets
<jats:p>The remaining carbon budget (RCB), the net amount of carbon dioxide humans can still emit without exceeding a chosen global warming limit, is often used to evaluate political action against the goals of the Paris Agreement. RCB estimates for 1.5C are small, and minor changes in their calculation can therefore result in large relative shifts. Here we evaluate recent RCB assessments by the IPCC and explain differences between them. We present calculation refinements together with robustness checks that increase confidence in RCB estimates. We conclude that the RCB for a 50% chance of keeping warming to 1.5C is around 250 GtCO2 as of January 2023, around 6 years of current CO2 emissions. This estimate changes to 480 and 60 GtCO2 for a 33% and 66% chance, respectively. Key uncertainties affecting RCB estimates are the contribution of non-CO2 emissions, which depends on socioeconomic projections as much as on geophysical uncertainty, and potential warming after net zero is reached.&#160;</jats:p>
Kikstra JSS, Waidelich P, 2023, Strong climate action is worth it, NATURE CLIMATE CHANGE, ISSN: 1758-678X
Kikstra JS, Nicholls ZRJ, Smith CJ, et al., 2022, The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures, GEOSCIENTIFIC MODEL DEVELOPMENT, Vol: 15, Pages: 9075-9109, ISSN: 1991-959X
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- Citations: 15
Nicholls Z, Meinshausen M, Lewis J, et al., 2022, Changes in IPCC scenario assessment emulators between SR1.5 and AR6 unraveled, Geophysical Research Letters, Vol: 49, ISSN: 0094-8276
The IPCC's scientific assessment of the timing of net-zero emissions and 2030 emission reduction targets consistent with limiting warming to 1.5°C or 2°C rests on large scenario databases. Updates to this assessment, such as between the IPCC's Special Report on Global Warming of 1.5°C (SR1.5) of warming and the Sixth Assessment Report (AR6), are the result of intertwined, sometimes opaque, factors. Here we isolate one factor: the Earth System Model emulators used to estimate the global warming implications of scenarios. We show that warming projections using AR6-calibrated emulators are consistent, to within around 0.1°C, with projections made by the emulators used in SR1.5. The consistency is due to two almost compensating changes: the increase in assessed historical warming between SR1.5 (based on AR5) and AR6, and a reduction in projected warming due to improved agreement between the emulators' response to emissions and the assessment to which it is calibrated.
Lamboll R, Nicholls Z, Smith C, et al., 2022, Assessing the size and uncertainty of remaining carbon budgets
<jats:title>Abstract</jats:title> <jats:p>The remaining carbon budget (RCB), the net amount of carbon dioxide humans can still emit without exceeding a chosen global warming limit, is often used to evaluate political action against the goals of the Paris Agreement. RCB estimates for 1.5C are small, and minor changes in their calculation can therefore result in large relative shifts. Here we evaluate recent RCB assessments by the IPCC and explain differences between them. We present calculation refinements together with robustness checks that increase confidence in RCB estimates. We conclude that the RCB for a 50% chance of keeping warming to 1.5C is around 300 GtCO2 as of January 2022, less than 8 years of current emissions. This estimate changes to 530 and 110 GtCO2 for a 33% and 66% chance, respectively. Key uncertainties affecting RCB estimates are the contribution of non-CO2 emissions, which depends on socioeconomic projections as much as on geophysical uncertainty, and the potential warming after net zero is reached.</jats:p>
Kikstra JS, Nicholls ZRJ, Smith CJ, et al., 2022, The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures
<jats:p>Abstract. While the IPCC’s physical science report usually assesses a handful of future scenarios, the IPCC Sixth Assessment Working Group III report (AR6 WGIII) on climate mitigation assesses hundreds to thousands of future emissions scenarios. A key task is to assess the global-mean temperature outcomes of these scenarios in a consistent manner, given the challenge that the emission scenarios from different integrated assessment models come with different sectoral and gas-to-gas coverage and cannot all be assessed consistently by complex Earth System Models. In this work, we describe the “climate assessment” workflow and its methods, including infilling of missing emissions and emissions harmonisation as applied to 1,202 mitigation scenarios in AR6 WGIII. We evaluate the global-mean temperature projections and effective radiative forcing characteristics (ERF) of climate emulators FaIRv1.6.2, MAGICCv7.5.3, and CICERO-SCM, discuss overshoot severity of the mitigation pathways using overshoot degree years, and look at an interpretation of compatibility with the Paris Agreement. We find that the lowest class of emission scenarios that limit global warming to “1.5 °C (with a probability of greater than 50 %) with no or limited overshoot” includes 90 scenarios for MAGICCv7.5.3, and 196 for FaIRv1.6.2. For the MAGICCv7.5.3 results, “limited overshoot” typically implies exceedance of median temperature projections of up to about 0.1 °C for up to a few decades, before returning to below 1.5 °C by or before the year 2100. For more than half of the scenarios of this category that comply with three criteria for being “Paris-compatible”, including net-zero or net-negative greenhouse gas (GHG) emissions, are projected to see median temperatures decline by about 0.3–0.4 °C after peaking at 1.5–1.6 °C in 2035–2055. We compare the methods applied in AR6 with the methods used f
Kikstra JS, Nicholls ZRJ, Smith CJ, et al., 2022, Supplementary material to "The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures"
Kikstra JS, Vinca A, Lovat F, et al., 2021, Climate mitigation scenarios with persistent COVID-19-related energy demand changes, NATURE ENERGY, Vol: 6, Pages: 1114-1123, ISSN: 2058-7546
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- Citations: 35
Kikstra JS, Waidelich P, Rising J, et al., 2021, The social cost of carbon dioxide under climate-economy feedbacks and temperature variability, ENVIRONMENTAL RESEARCH LETTERS, Vol: 16, ISSN: 1748-9326
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- Citations: 35
Kikstra JS, Mastrucci A, Min J, et al., 2021, Decent living gaps and energy needs around the world, Environmental Research Letters, Vol: 16, Pages: 1-12, ISSN: 1748-9326
Interest has grown in recent years to define the terms of decent living standards (DLS), the material underpinnings of human well-being. We assess the gaps in health, shelter, nutrition, socialization and mobility needs within countries, across the world. Our results show that more people are deprived of such provisions than the number of income poor, even when measured against income poverty thresholds. We estimate the energy needs for building out new infrastructure to support the provision of DLS to all by 2040 to be about 290 Exajoules, which is less than three-quarters of annual global energy demand today. The annual energy requirements to support decent living for the global population thereafter is estimated at 156 EJ/year. Present energy demand levels in most countries exceed hypothetical DLS energy needs. Still, the required rate of increase in energy for decent living in the coming two decades would be unprecedented for many countries. Greater attention to equity would significantly reduce the need for growth. Countries' per capita energy needs to provide the same DLS vary by up to a factor of four due to differences in climate, urbanization, diets and transport infrastructure. Transport energy dominates energy for decent living worldwide, while housing requirements additionally dominate upfront energy investment needs. This study supports the claim that the increase in energy provision for eradicating poverty does not in itself pose a threat to mitigating climate change at a global scale. Distinguishing energy for affluence and for decent living could provide a basis to define equitable access to sustainable development in energy terms.
Huppmann D, Gidden MJ, Nicholls Z, et al., 2021, pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios, Open Research Europe, Vol: 1, Pages: 74-74
<ns4:p>The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages.</ns4:p><ns4:p> </ns4:p><ns4:p> The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for timeseries aggregation, downscaling and unit conversion. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.</ns4:p>
Vinca A, Kikstra JS, Lovat F, et al., 2021, Impacts of COVID-19 induced energy demand changes on emissions and mitigation challenges&#160;
<jats:p>&lt;p&gt;The COVID-19 pandemic is causing radical temporary breaks with past energy use and GHG emissions trends. However, how a post-pandemic recovery will impact longer-term transformations to a low-carbon society is unclear. Here, we present different global COVID-19 shock-and-recovery scenarios that systematically explore economic uncertainty and the demand-side effect on emissions. We consider changes in the residential, industry and transport energy sub-sectors under diverging cases that might lead to a more carbon intensive and individualistic way of consumption, or to a policy-advised new future that supports the emission reduction opportunities seen during the pandemic. The resulting impact on cumulative CO2 emissions over the coming decade can range from 28 to 53 GtCO2 reduction depending on the depth and duration of the economic downturn and the extent and persistence of demand-side changes. Recovering from the pandemic with low energy demand practices - embedded in new patterns of travel, work, consumption, and production &amp;#8211; reduces climate mitigation challenges in the long run. We show that a low energy demand recovery reduces carbon prices for a 1.5&amp;#176;C consistent pathway by 19%, saves energy supply investments until 2030 by 2.1 trillion USD, and lessens pressure on the upscaling of renewable energy technologies.&amp;#160;&amp;#160;&lt;/p&gt;</jats:p>
Huppmann D, Gidden MJ, Nicholls Z, et al., 2021, pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios, Open Research Europe, Vol: 1, ISSN: 2732-5121
The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for working with timeseries data in the context of climate policy and energy systems. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users. The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.
Lamboll RD, Nicholls ZRJ, Kikstra JS, et al., 2020, Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research, GEOSCIENTIFIC MODEL DEVELOPMENT, Vol: 13, Pages: 5259-5275, ISSN: 1991-959X
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- Citations: 13
Lamboll RD, Nicholls ZRJ, Kikstra JS, et al., 2020, Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research
<jats:p>Abstract. Integrated assessment models (IAMs) project future anthropogenic emissions for input into climate models. However, the full list of climate-relevant emissions is lengthy and most IAMs do not model all of them. Here we present silicone, an open-source Python package which infers anthropogenic emissions of missing species based on other known emissions. For example, it can infer nitrous oxide emissions in one scenario based on carbon dioxide emissions from that scenario plus the relationship between nitrous oxide and carbon dioxide emissions in other scenarios. This broadens the range of IAMs available for exploring projections of future climate change. Silicone forms part of the open-source pipeline for assessments of the climate implications of IAMs by the IAM consortium (IAMC). A variety of infilling options are outlined and their suitability for different cases are discussed. The code and notebooks explaining details of the package and how to use it are available from the GitHub repository, https://github.com/GranthamImperial/silicone. There is an additional repository showing uses of the code to complement existing research at https://github.com/GranthamImperial/silicone_examples. </jats:p>
Byers EA, Riahi K, Kriegler E, et al., 2020, Systematic scenario process to support analysis of long-term emissions scenarios and transformation pathways for the IPCC WG3 6th Assessment Report
<jats:p> &lt;p&gt;The assessment of long-term greenhouse gas emissions scenarios and societal transformation pathways is a key component of the IPCC Working Group 3 (WG3) on the Mitigation of Climate Change. A large scientific community, typically using integrated assessment models and econometric frameworks, supports this assessment in understanding both near-term actions and long-term policy responses and goals related to mitigating global warming. WG3 must systematically assess hundreds of scenarios from the literature to gain an in-depth understanding of long-term emissions pathways, across all sectors, leading to various levels of global warming. Systematic assessment and understanding the climate outcomes of each emissions scenario, requires coordinated processes which have developed over consecutive IPCC assessments. Here, we give an overview of the processes involved in the systematic assessment of long-term mitigation pathways as used in recent IPCC Assessments&lt;sup&gt;1&lt;/sup&gt; and being further developed for the IPCC 6&lt;sup&gt;th&lt;/sup&gt; Assessment Report (AR6). The presentation will explain how modelling teams can submit scenarios to AR6 and invite feedback to the process.&lt;/p&gt;&lt;p&gt;Following discussions amongst IPCC Lead Authors to define the scope of scenarios desired and variables requested, a call for scenarios to support AR6 was launched in September 2019. Modelling teams have registered and submitted scenarios through Autumn 2019 using a new and secure online submission portal, from which authorised Lead Authors can interrogate the scenarios interactively.&lt;/p&gt;&lt;p&gt;This analysis is underpinned by the open-source software pyam, a Python package specifically designed for analysis and visualisation of integrated assessment scenarios&lt;sup&gt;2&
Kikstra J, Waidelich P, Rising J, et al., 2020, Climate-economy feedbacks, temperature variability, and the social cost of carbon
<jats:p> &lt;p&gt;A key statistic describing climate change impacts is the &amp;#8220;social cost of carbon&amp;#8221; (SCC), the total market and non-market costs to society incurred by releasing a ton of CO&lt;sub&gt;2&lt;/sub&gt;. Estimates of the SCC have risen in recent years, with improved understanding of the risk of climate change to various sectors, including agriculture [1], mortality [2], and economic growth [3].&lt;/p&gt;&lt;p&gt;The total risks of climate impacts also depend on the representation of human-climate feedbacks such as the effect of climate impacts on GDP growth and extremes (rather than a focus only on means), but this relationship has not been extensively studied [4-7]. In this paper, we update the widely used PAGE IAM to investigate how SCC distributions change with the inclusion of climate-economy feedbacks and temperature variability. The PAGE model has recently been improved with representations of permafrost thawing and surface albedo feedback, CMIP6 scenarios, and empirical market damage estimates [8]. We study how changes from PAGE09 to PAGE-ICE affected the SCC, increasing it up to 75%, with a SCC distribution with a mean around $300 for the central SSP2-4.5 scenario. Then we model the effects of different levels of the persistence of damages, for which the persistence parameter is shown to have enormous effects. Adding stochastic interannual regional temperature variations based on an analysis of observational temperature data [9] can increase the hazard rate of economic catastrophes changes the form of the distribution of SCC values. Both the effects of temperature variability and climate-economy feedbacks are region-dependent. Our results highlight the importance of feedbacks and extremes for the understanding of the expected value, distribution, and heterogeneity of climate impacts.&lt;/p&gt;&lt;p
Gidden M, Nicholls Z, Byers E, et al., 2020, Climate assessment of emissions scenarios for use in WG3 of the IPCC&#8217;s Sixth Assessment Report
<jats:p> &lt;p&gt;Consistent and comparable climate assessments of scenarios are critical within the context of IPCC assessment reports. Given the number of scenarios assessed by WG3, the assessment &amp;#8220;pipeline&amp;#8221; must be almost completely automated. Here, we present the application of a new assessment pipeline which combines state-of-the-art components into a single workflow in order to derive climate outcomes for integrated assessment model (IAM) scenarios assessed by WG3 of the IPCC. A consistent analysis ensures that WG3&amp;#8217;s conclusions about the socioeconomic transformations required to maintain a safe climate are based on the best understanding of our planetary boundaries from WG1. For example, if WG1 determines that climate sensitivity is higher than previously considered, then WG3 could incorporate this insight by e.g. considering much smaller remaining carbon budgets for any given temperature target.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The scenario-climate assessment pipeline is comprised of three primary components. First, a consistent harmonization algorithm which maintains critical model characteristics between harmonized and unharmonized scenarios [1] is employed to harmonize emissions trajectories to a common and consistent historical dataset as used in CMIP6 [2]. Next, a scenario&amp;#8217;s reported emissions trajectories are analyzed as to the completeness of its species and sectoral coverage. A consistent set of 14 emissions species are expected, aligning with published work within ScenarioMIP and CMIP6 (see ref [2], Table 2). Should any component of this full set of emissions trajectories be absent for a given scenario, an algorithm (e.g., generalised quantile walk [3]) is employed in order to &amp;#8220;back-fill&amp;#8221; missing species at the native model region
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