Improving poverty and inequality modelling in climate research

As climate change progresses, the risk of adverse impacts on vulnerable populations is growing. As governments seek increased and drastic action, policymakers are likely to seek quantification of climate-change impacts and the consequences of mitigation policies on these populations. Current models used in climate research have a limited ability to represent the poor and vulnerable, or the different dimensions along which they face these risks. Best practices need to be adopted more widely, and new model features that incorporate social heterogeneity and different policy mechanisms need to be developed. Increased collaboration between modellers, economists, and other social scientists could aid these developments. We review the history and state of the art of models used in climate research, including integrated assessment models (IAMs) and national studies, and those that model mitigation and climate-change impacts. We assess how and to what extent they represent distributional impacts within countries. We argue that there is much scope to improve the representation of income distribution and poverty. Given the diversity of models, this endeavour presents fundamental challenges for some models, but possibly requires only incremental changes in others

What do near-term observations tell us about long-term developments in greenhouse gas emissions? A letter

Long-term scenarios developed by integrated assessment models are used in climate research to provide an indication of plausible long-term emissions of greenhouse gases and other radiatively active substances based on developments in the global energy system, land-use and the emissions associated with these systems The phenomena that determine these long-term developments (several decades or even centuries) are very different than those that operate on a shorter time-scales (a few years) Nevertheless, in the literature, we still often find direct comparisons between short-term observations and long-term developments that do not take into account the differing dynamics over these time scales In this letter, we discuss some of the differences between the factors that operate in the short term and those that operate in the long term We use long-term historical emissions trends to show that short-term observations are very poor indicators of long-term future emissions developments Based on this, we conclude that the performance of long-term scenarios should be evaluated against the appropriate, corresponding long-term variables and trends The research community may facilitate this by developing appropriate data sets and protocols that can be used to test the performance of long-term scenarios and the models that produce the

Accounting for finance is key for climate mitigation pathways

The financial system—the ecosystem of investors (e.g., banks, investment funds, insurance), markets, and instruments—is often considered to play an enabling role in climate mitigation pathways to a low-carbon transition. But it can also have a hampering role, e.g., if investors’ perceptions of low risk from a missed transition and low opportunities from a transition fail to trigger a reallocation of capital into low-carbon investments. This increases the chance of the transition not occurring within the time window required to stabilize the climate or occurring in a disorderly fashion. Indeed investors, who can influence the realization of climate mitigation pathways, themselves rely on estimates of climate mitigation pathways from process-based integrated assessment models (IAMs). And IAMs do not model the financial system or investors’ decisions; thus, the feedback loop between the financial system and mitigation pathways is not taken into account, neither by the IAMs nor by the finance community. This limitation to our understanding of the dynamics and the feasibility of the low-carbon transition weakens the ability of IAMs to inform policy and investment decisions. We propose a framework to capture the interdependence between investors’ perception of future climate risk, depending on the credibility of climate policies, and the allocation of investments in the economy

Pathways to achieve universal household access to modern energy by 2030

A lack of access to modern energy impacts health and welfare and impedes development for billions of people. Growing concern about these impacts has mobilized the international community to set new targets for universal modern energy access. However, analyses exploring pathways to achieve these targets and quantifying the potential costs and benefits are limited. Here, we use two modelling frameworks to analyse investments and consequences of achieving total rural electrification and universal access to clean-combusting cooking fuels and stoves by 2030. Our analysis indicates that these targets can be achieved with additional investment of US$(2005)65-86 billion per year until 2030 combined with dedicated policies. Only a combination of policies that lowers costs for modern cooking fuels and stoves, along with more rapid electrification, can enable the realization of these goals. Our results demonstrate the critical importance of accounting for varying demands and affordability across heterogeneous household groups in both analysis and policy setting. While the investments required are significant, improved access to modern cooking fuels alone can avert between 0.6 and 1.8 million premature deaths annually in 2030 and enhance wellbeing substantially

Multi-Objective and Multidisciplinary Design Optimisation of Unmanned Aerial Vehicle Systems using Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithms

The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised

A multidimensional feasibility evaluation of low-carbon scenarios

Long-term mitigation scenarios developed by integrated assessment models underpin major aspects of recent IPCC reports and have been critical to identify the system transformations that are required to meet stringent climate goals. However, they have been criticized for proposing pathways that may prove challenging to implement in the real world and for failing to capture the social and institutional challenges of the transition. There is a growing interest to assess the feasibility of these scenarios, but past research has mostly focused on theoretical considerations. This paper proposes a novel and versatile multidimensional framework that allows evaluating and comparing decarbonization pathways by systematically quantifying feasibility concerns across geophysical, technological, economic, socio-cultural and institutional dimensions. This framework enables to assess the timing, disruptiveness and scale of feasibility concerns, and to identify trade-offs across different feasibility dimensions. As a first implementation of the proposed framework, we map the feasibility concerns of the IPCC 1.5 °C Special Report scenarios. We select 24 quantitative indicators and propose feasibility thresholds based on insights from an extensive analysis of the literature and empirical data. Our framework is, however, flexible and allows evaluations based on different thresholds or aggregation rules. Our analyses show that institutional constraints, which are often not accounted for in scenarios, are key drivers of feasibility concerns. Moreover, we identify a clear intertemporal trade-off, with early mitigation being more disruptive but preventing higher and persistent feasibility concerns produced by postponed mitigation action later in the century

Energy and development : A modelling approach

Rapid economic growth of developing countries like India and China implies that these countries become important actors in the global energy system. Examples of this impact are the present day oil shortages and rapidly increasing emissions of greenhouse gases. Global energy models are used explore possible future developments of the global energy system and identify policies to prevent potential problems. Such estimations of future energy use in developing countries are very uncertain. Crucial factors in the future energy use of these regions are electrification, urbanisation and income distribution, issues that are generally not included in present day global energy models. Model simulations in this thesis show that current insight in developments in low-income regions lead to a wide range of expected energy use in 2030 of the residential and transport sectors. This is mainly caused by many different model calibration options that result from the limited data availability for model development and calibration. We developed a method to identify the impact of model calibration uncertainty on future projections. We developed a new model for residential energy use in India, in collaboration with the Indian Institute of Science. Experiments with this model show that the impact of electrification and income distribution is less univocal than often assumed. The use of fuelwood, with related health risks, can decrease rapidly if the income of poor groups increases. However, there is a trade off in terms of CO2 emissions because these groups gain access to electricity and the ownership of appliances increases. Another issue is the potential role of new technologies in developing countries: will they use the opportunities of leapfrogging? We explored the potential role of hydrogen, an energy carrier that might play a central role in a sustainable energy system. We found that hydrogen only plays a role before 2050 under very optimistic assumptions. Regional energy policies have an important role. For instance, low energy taxes and subsidies in developing countries limit the opportunities to promote alternative energy options. A final issue in this thesis is the impact of the changing development context – depletion of fossil fuels and climate change – on the economic development of low-income regions. We developed a stylized population-economy-energy-climate model (SUSCLIME) in which automated agents can take policy-decisions and develop strategies to cope with resource depletion and climate change. From preliminary model experiments it appears that developing countries are more vulnerable to both resource depletion and climate change. A co-benefit of a long-term focus on avoiding climate change is that it also slows down fossil resource depletion. A short-term focus to reduce impacts from depletion of endogenous fossil resources has probably not much synergy with climate policy because imported fossil energy (or coal) is more attractive than developing alternatives

Model projections for household energy use in developing countries

The residential sector plays an important role in the energy system of developing countries. In this paper we introduce a bottom up simulation model for household energy use. The model describes energy demand for several end-use functions based on a set of physical drivers, such as floor space and heating degree days. The model also recognizes different population groups: i.e. urban and rural households, each distinguishing five income quintiles. The model is applied to analyze possible future developments of residential energy use in five developing world regions: India, China, South East Asia, South Africa and Brazil. We find that in each of these regions cooking is currently the main end-use function, but that other functions, such as space heating, cooling and appliances become more important. At the same time, energy consumption slowly shifts towards modern fuels. The model also shows that climate policy can reduce residential energy emissions, but could also slow down the energy transition away from traditional fuels in low income classes