• Energy Research
• 13. Climate action close

Abstract We evaluated three Integrated Assessment Models (IAMs: IGSM, MERGE, MiniCAM) by: (i) comparing their global Primary Energy year-2000 initializations and projections for 2010 and 2015 to historical data; (ii) mapping their CO2 emissions projections against observations; and (iii) examining model-output diagnostics. The IAMs underestimated historical primary energy consumption and initial/projected CO2 emissions in both reference and stabilization scenarios (particularly for combustion fuels) but overestimated usage of non-biomass renewables, causing underestimates of future CO2 emissions that, for the stabilization scenarios, are wildly optimistic. Mitigation technology breakdowns in the policy scenarios vary enormously across IAMs, suggesting that confidence in their projections might be misplaced, or that options for mitigation have greater scope than is supposed. Most increases in carbon-free technologies in the stabilization scenarios are already captured in the reference cases. Energy-conversion efficiencies in electricity generation improve over time, but, (except for gas-powered generation in IGSM), efficiencies in the policy scenarios are less than in the reference. Electrification results diverge widely: IGSM has little change over the 21st century, while MiniCAM and MERGE have major electrification increases in their policy scenarios. We suggest: 1) comprehensive model output suitable for secondary analysis should be more readily available; 2) directly comparable reference and policy-driven mitigation scenarios are essential for assessing mitigation measures; 3) model validation using historical, source-specific energy data is crucial for assessing model credibility; 4) separation of mitigation contributions into no-policy and policy-driven amounts is needed to assess the effectiveness of mitigation policies; and 5) detailed inter-model comparisons can provide important insights into model credibility.

Small modular nuclear reactors (SMRs) offer the promise of providing carbon-free electricity and heat to small islands or isolated electricity grids. However, the economic feasibility of SMRs is highly system-dependent and has not been studied in this context. We selected three case-study islands for such an evaluation: Jeju, Tasmania and Tenerife based on their system complexity. We generated 100,000 electricity-mix cases stochastically for each island and examined the system-level generation-cost changes by incrementing the average generation cost of SMRs from USD$60 to 200 MWh−1. SMRs were found to be economically viable when average generation cost was <$100 MWh−1 for Jeju and <$140 MWh−1 for Tenerife. For Tasmania the situation was complex; hydroelectric power is an established competitor, but SMRs might be complementary in a future “battery of the nation” scenario where most of the island’s hydro capacity was exported to meet peak power demand on the mainland grid. The higher average generation cost of SMRs makes it difficult for them to compete economically with a fossil fuel/renewable mix in many contexts. However, we have demonstrated that SMRs can be an economically viable carbon-free option for a small island with a limited land area and high energy demand.

AbstractAimDeforestation and climate change are two of the most serious threats to tropical birds. Here, we combine fine‐scale climatic and dynamic land cover models to forecast species vulnerability in rain forest habitats.LocationSulawesi, Indonesia.MethodsWe sampled bird communities on four mountains across three seasons in Lore Lindu National Park, Sulawesi, Indonesia (a globally important hotspot of avian endemism), to characterize relationships between elevation and abundance. Deforestation from 2000 to 2010 was quantified, and predictors of deforestation were identified. Future forest area was projected under two land use change scenarios – one assuming current deforestation rates and another assuming a 50% reduction in deforestation. A digital elevation model and an adiabatic lapse rate were used to create a fine‐scale map of temperature in the national park. Then, the effects of climate change were projected by fitting statistical models of species abundance as a function of current temperature and forecasting future abundance based on warming from low‐ and high‐emissions climate change.ResultsThe national park lost 11.8% of its forest from 2000 to 2010. Model‐based projections indicate that high‐elevation species (white‐eared myza Myza sarasinorum and Sulawesi leaf‐warbler Phylloscopus sarasinorum) might be buffered from deforestation because their ranges are isolated from human settlement, but these species may face steep population declines from climate change (by as much as 61%). The middle‐elevation sulphur‐bellied whistler Pachycephala sulfuriventer is predicted to undergo minor declines from climate change (8–11% reduction), while deforestation is predicted to cause larger declines of 13–19%.Main conclusionsThe biological richness and rapid deforestation now occurring inside the national park emphasize the need for increased enforcement, while our modelling suggests that climate change is most threatening to high‐elevation endemics. These findings are likely applicable to other highland tropical sites where deforestation is encroaching from below and climate change is stressing high‐elevation species from above.

Human Impact? Following the arrival of humans in Australia 40- to 50,000 years ago, many species of large vertebrates rapidly became extinct. By analyzing sediment cores from a site in northeastern Australia, Rule et al. (p. 1483 ; see the Perspective by McGlone ) show that the extinction of the Australian megafauna caused important ecosystem shifts. Prominent among these were a shift from rainforest vegetation to sclerophyllous vegetation and a sustained increase in the incidence of fire. The cores also provide evidence of the cause of megafaunal extinction in Australia, ruling out climate and anthropogenic fire as possible causes while confirming that the extinctions closely followed human arrival. These findings show how landscapes sometimes have been fundamentally changed by the indirect effects of early humans—which underscores the impact that even prehistoric human societies had on natural systems.

AbstractA large and growing market exists for the management of used nuclear fuel. Urgent need for service lies in Asia, also the region of the fastest growth in fossil fuel consumption. A logical potential provider of this service is acknowledged to be Australia. We describe and assess a service combining approved multinational storage with an advanced fuel reconditioning facility and commercialisation of advanced nuclear reactor technologies. We estimate that this project has the potential to deliver a net present value of (2015) AU$30.9 billion. This economic finding compares favourably with recent assessment based on deep geological repository. Providing service for used nuclear fuel and commercialisation of next generation nuclear technology would catalyse the expansion of nuclear technology for energy requirements across Asia and beyond, aiding efforts to combat climate change. Pathways based on leveraging advanced nuclear technologies are therefore worthy of consideration in the development of policy in this area.

There is wide public debate about which electricity generating technologies will best be suited to reduce greenhouse gas emissions (GHG). Sometimes this debate ignores real-world practicalities and leads to over-optimistic conclusions. Here we define and apply a set of fit-for-service criteria to identify technologies capable of supplying baseload electricity and reducing GHGs by amounts and within the timescale set by the Intergovernmental Panel on Climate Change (IPCC). Only five current technologies meet these criteria: coal (both pulverised fuel and integrated gasification combined cycle) with carbon capture and storage (CCS); combined cycle gas turbine with CCS; Generation III nuclear fission; and solar thermal backed by heat storage and gas turbines. To compare costs and performance, we undertook a meta-review of authoritative peer-reviewed studies of levelised cost of electricity (LCOE) and life-cycle GHG emissions for these technologies. Future baseload electricity technology selection will be influenced by the total cost of technology substitution, including carbon pricing, which is synergistically related to both LCOE and emissions. Nuclear energy is the cheapest option and best able to meet the IPCC timetable for GHG abatement. Solar thermal is the most expensive, while CCS will require rapid major advances in technology to meet that timetable.

Abstract Rising crop production over the last half century has had far-reaching consequences for human welfare and the environment. With food demand projected to rise, one of the central challenges in minimizing agriculture’s impacts on the climate and biodiversity is to increase crop production with higher yields rather than more cropland. However, quantifying progress is challenging. When analyzed at the most aggregated, global level, yields can be defined as the total crop output per unit area per year, but aggregate yields are driven by multiple factors, only some of which have a clear relationship to improved agricultural production. To date, there is no research that simultaneously determines how much of rising crop production has been met by rising aggregate yields versus cropland expansion, while also quantifying the unique contribution of each yield driver. Using LMDI decomposition analysis, we find that rising aggregate yields contributed far more than cropland expansion (89% compared to 11%). That is, growing global food demand has by and large been met by growing more crops on the same amount of land, rather than expanding cropland. Our second-stage decomposition showed that nearly two-thirds of aggregate yield improvements have come from pure yield, or the output of a given crop per unit of harvested cropland area in a given country per unit area per year. The remainder has come from less-discussed drivers of aggregate yields, including cropping intensity, changes in the geographic distribution of cropland, and crop composition. Further, we use attribution analysis to show the contributions to different decomposition factors from countries grouped by climate, income, and region, as well as from different crops. Such granular yet comprehensive breakdowns of crop production and aggregate yields offer more accurate forecasts and can help focus policies on the most promising levers to meet rising food demand sustainably.

Summary Assessing the impacts of multiple, often synergistic, stressors on the population dynamics of long‐lived species is becoming increasingly important due to recent and future global change. Tiliqua rugosa (sleepy lizard) is a long‐lived skink (>30 years) that is adapted to survive in semi‐arid environments with varying levels of parasite exposure and highly seasonal food availability. We used an exhaustive database of 30 years of capture–mark–recapture records to quantify the impacts of both parasite exposure and environmental conditions on the lizard's survival rates and long‐term population dynamics. Lizard abundance was relatively stable throughout the study period; however, there were changing patterns in adult and juvenile apparent survival rates, driven by spatial and temporal variation in levels of tick exposure and temporal variation in environmental conditions. Extreme weather events during the winter and spring seasons were identified as important environmental drivers of survival. Climate models predict a dramatic increase in the frequency of extreme hot and dry winter and spring seasons in our South Australian study region; from a contemporary probability of 0·17 up to 0·47–0·83 in 2080 depending on the emissions scenario. Our stochastic population model projections showed that these future climatic conditions will induce a decline in the abundance of this long‐lived reptile of up to 67% within 30 years from 2080, under worst case scenario modelling. The results have broad implications for future work investigating the drivers of population dynamics and persistence. We highlight the importance of long‐term data sets and accounting for synergistic impacts between multiple stressors. We show that predicted increases in the frequency of extreme climate events have the potential to considerably and negatively influence a long‐lived species, which might previously have been assumed to be resilient to environmental perturbations.