Integrated Assessment Models (IAMs) for Climate Change

2016 ◽  
Author(s):  
Zili Yang ◽  
Yi-Ming Wei ◽  
Zhifu Mi
Keyword(s):  
Climate Change ◽  
Interdisciplinary Research ◽  
Integrated Assessment ◽  
Social Economic ◽  
Methodological Approach ◽  
Integrated Model ◽  
Climate Impacts ◽  
Research Approach ◽  
Climate Change Research ◽  
Integrated Assessment Models

Integrated assessment models (IAMs) for climate change refers to a broad category of research approaches in climate change. Climate change is the most complicated global environmental problem. By the very nature of climate change, research has to be interdisciplinary and multifaceted. IAM is the mainstream methodological approach in climate change research. Most researchers in climate change utilize IAMs directly or indirectly. IAMs draw knowledge and strengths from various disciplines related to climate change; contributions from each discipline rely on the mathematical representations of certain relationships connected to climate change; disciplinary components are linked through a unified modeling platform(s). In particular, IAMs for climate change usually involve social-economic components as well as natural sciences components. The key linkages in IAM platforms are anthropogenic greenhouse gas (GHG) emissions in climate systems and climate change impacts on social-economic systems. The outputs of IAMs are numerical simulation results based on assumptions, historical data, and scenario designs. IAMs are widely used in assessing various GHG mitigation policies and climate impacts. In fact, conclusions in the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports are drawn substantially from numerous IAMs. IAMs for climate change started in the late 1980s. Since then, IAMs for climate change have developed into a full-fledged interdisciplinary research field that involves hundreds of models, thriving online resources, and thousands of academic publications and policy reports around the world. IAM for climate change, as an interdisciplinary research approach, has received recognition by mainstream disciplines. The Dynamic Integrated model of Climate and the Economy (DICE) and the Regional Integrated model of Climate and the Economy (RICE)—two IAMs for climate change—are part of the core contributions in William Nordhaus’s Nobel Prize in Economic Sciences in 2018.

scholarly journals Evaluating process-based integrated assessment models of climate change mitigation

2021 ◽  
Vol 166 (1-2) ◽  
Author(s):  
Charlie Wilson ◽  
Céline Guivarch ◽  
Elmar Kriegler ◽  
Bas van Ruijven ◽  
Detlef P. van Vuuren ◽  
...  
Keyword(s):  
Climate Change ◽  
Integrated Assessment ◽  
Climate Change Mitigation ◽  
Evaluation Method ◽  
Evaluation Research ◽  
Evaluation Framework ◽  
Systematic Evaluation ◽  
Integrated Assessment Models ◽  
Wide Range ◽  
Change Mitigation

AbstractProcess-based integrated assessment models (IAMs) project long-term transformation pathways in energy and land-use systems under what-if assumptions. IAM evaluation is necessary to improve the models’ usefulness as scientific tools applicable in the complex and contested domain of climate change mitigation. We contribute the first comprehensive synthesis of process-based IAM evaluation research, drawing on a wide range of examples across six different evaluation methods including historical simulations, stylised facts, and model diagnostics. For each evaluation method, we identify progress and milestones to date, and draw out lessons learnt as well as challenges remaining. We find that each evaluation method has distinctive strengths, as well as constraints on its application. We use these insights to propose a systematic evaluation framework combining multiple methods to establish the appropriateness, interpretability, credibility, and relevance of process-based IAMs as useful scientific tools for informing climate policy. We also set out a programme of evaluation research to be mainstreamed both within and outside the IAM community.

Systematic exploration of scenario spaces

foresight ◽  
2016 ◽  
Vol 18 (1) ◽  
pp. 59-75 ◽  
Author(s):  
Henrik Carlsen ◽  
E. Anders Eriksson ◽  
Karl Henrik Dreborg ◽  
Bengt Johansson ◽  
Örjan Bodin
Keyword(s):  
Climate Change ◽  
Methodological Approach ◽  
Intergovernmental Panel ◽  
Climate Change Research ◽  
Content Type ◽  
Generating Sets ◽  
Ex Post ◽  
Systematic Exploration ◽  
Emissions Scenarios ◽  
Business As Usual

Purpose – Scenarios have become a vital methodological approach in business as well as in public policy. When scenarios are used to guide analysis and decision-making, the aim is typically robustness and in this context we argue that two main problems at scenario set level is conservatism, i.e. all scenarios are close to a perceived business-as-usual trajectory and lack of balance in the sense of arbitrarily mixing some conservative and some extreme scenarios. The purpose of this paper is to address these shortcomings by proposing a methodology for generating sets of scenarios which are in a mathematical sense maximally diverse. Design/methodology/approach – In this paper, we develop a systematic methodology, Scenario Diversity Analysis (SDA), which addresses the problems of broad span vs conservatism and imbalance. From a given set of variables with associated states, SDA generates scenario sets where the scenarios are in a quantifiable sense maximally different and therefore best span the whole set of feasible scenarios. Findings – The usefulness of the methodology is exemplified by applying it to sets of storylines of the emissions scenarios of the Intergovernmental Panel on Climate Change. This ex-post analysis shows that the storylines were not maximally diverse and given the challenges ahead with regard to emissions reduction and adaptation planning, we argue that it is important to strive for diversity when developing scenario sets for climate change research. Originality/value – The proposed methodology adds significant novel features to the field of systematic scenario generation, especially with regard to scenario diversity. The methodology also enables the combination of systematics with the distinct future logics of good intuitive logics scenarios.

scholarly journals Climate Change Adaptation and Gender Inequality: Insights from Rural Vietnam

2019 ◽  
Vol 11 (10) ◽  
pp. 2805 ◽  
Author(s):  
Josephine Ylipaa ◽  
Sara Gabrielsson ◽  
Anne Jerneck
Keyword(s):  
Climate Change ◽  
Political Ecology ◽  
Climate Change Adaptation ◽  
Climate Adaptation ◽  
Rural Livelihoods ◽  
Climate Impacts ◽  
Extreme Weather Events ◽  
Research Approach ◽  
Gender Inequalities ◽  
Feminist Political Ecology

Vietnam is one of the countries most vulnerable to climate change impacts, especially from extreme weather events such as storms and floods. Thus, climate change adaptation is crucial, especially for natural resource-dependent farmers. Based on a qualitative research approach using a feminist political ecology lens, this article investigates gendered patterns of rural agrarian livelihoods and climate adaptation in the province of Thái Bình. In doing so, we identify differentiated rights and responsibilities between female and male farmers, leading to unequal opportunities and immobility for females, making them more vulnerable to climate impacts and threatening to reduce their capacity to adapt. This research also shows that demands on farmers to contribute to perpetual increases in agricultural output by the state poses a challenge, since farming livelihoods in Vietnam are increasingly becoming feminised, as a result of urbanisation and devaluation of farming. Past and present national strategies and provincial implementation plans linked to climate change do not consider the burden affecting rural female farmers, instead the focus lies on addressing technical solutions to adaptation. With little attention being paid to an increasingly female workforce, existing gender inequalities may be exacerbated, threatening the future existence of rural livelihoods and the viability of Vietnam’s expansion into global markets.

Implications of intrinsic variability for economic assessments of climate change

2020 ◽  
Author(s):  
David Stainforth ◽  
Raphael Calel ◽  
Sandra Chapman ◽  
Nicholas Watkins
Keyword(s):  
Climate Change ◽  
Integrated Assessment ◽  
Radiative Forcing ◽  
Epistemic Uncertainty ◽  
Global Climate Model ◽  
Damage Function ◽  
Climate System ◽  
Social Planner ◽  
Intrinsic Variability ◽  
Integrated Assessment Models

<p>Integrated Assessment Models (IAMs) are widely used to evaluate the economic costs of climate change, the social cost of carbon and the value of mitigation policies. These IAMs include simple energy balance models (EBMs) to represent the physical climate system and to calculate the timeseries of global mean temperature in response to changing radiative forcing[1]. The EBMs are deterministic in nature which leads to smoothly varying GMT trajectories so for simple monotonically increasing forcing scenarios (e.g. representative concentration pathways (RCPs) 8.5, 6.0 and 4.5) the GMT trajectories are also monotonically increasing. By contrast real world, and global-climate-model-derived, timeseries show substantial inter-annual and inter-decadal variability. Here we present an analysis of the implications of this intrinsic variability for the economic consequences of climate change.</p><p>We use a simple stochastic EBM to generate large ensembles of GMT trajectories under each of the RCP forcing scenarios. The damages implied by each trajectory are calculated using the Weitzman damage function. This provides a conditional estimate of the unavoidable uncertainty in implied damages. It turns out to be large and positively skewed due to the shape of the damage function. Under RCP2.6 we calculate a 5-95% range of -30% to +52% of the deterministic value; -13% to +16% under RCP 8.5. The risk premia associated with such unavoidable uncertainty are also significant. Under our economic assumptions a social planner would be willing to pay 32 trillion dollars to avoid just the intrinsic uncertainty in RCP8.5. This figure rises further when allowance is made for epistemic uncertainty in relation to climate sensitivity. We conclude that appropriate representation of stochastic variability in the climate system is important to include in future economic assessments of climate change.</p><p><br>[1] Calel, R. and Stainforth D.A., “On the Physics of Three Integrated Assessment Models”, Bulletin of the American Meteorological Society, 2017.</p><p> </p>

scholarly journals The benefits of climate change mitigation in integrated assessment models: the role of the carbon cycle and climate component

2011 ◽  
Vol 113 (3-4) ◽  
pp. 897-917 ◽  
Author(s):  
Andries F. Hof ◽  
Chris W. Hope ◽  
Jason Lowe ◽  
Michael D. Mastrandrea ◽  
Malte Meinshausen ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Integrated Assessment ◽  
Climate Change Mitigation ◽  
Integrated Assessment Models ◽  
Change Mitigation

scholarly journals A comprehensive view on climate change: coupling of earth system and integrated assessment models

2012 ◽  
Vol 7 (2) ◽  
pp. 024012 ◽  
Author(s):  
Detlef P van Vuuren ◽  
Laura Batlle Bayer ◽  
Clifford Chuwah ◽  
Laurens Ganzeveld ◽  
Wilco Hazeleger ◽  
...  
Keyword(s):  
Climate Change ◽  
Integrated Assessment ◽  
Earth System ◽  
Comprehensive View ◽  
Integrated Assessment Models

scholarly journals Evaluating integrated assessment models of global climate change - From philosophical aspects to practical examples

2021 ◽  
Author(s):  
Valeria Jana Schwanitz
Keyword(s):  
Climate Change ◽  
Model Validation ◽  
Global Climate Change ◽  
Integrated Assessment ◽  
Climate Change Mitigation ◽  
Global Climate ◽  
Integrated Assessment Models ◽  
Mitigation And Adaptation ◽  
Change Mitigation

Integrated Assessment Models of Global Climate Change are an established tool to explore possible pathways of climate change mitigation and adaptation. The models are a quantitative backbone for IPCC reports. But can the models be trusted? This manuscript discusses how the models can be scrutinized and where limits to model validation exist.

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