• Energy Research

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.

ABSTRACT. How can one manage wildlife under a suite of competing values? In isolation, the ecological economics of native wildlife harvest, threatened species conservation and control of exotic species are all well established sub‐disciplines of wildlife management. However, the wild banteng (Bos javanicus) population of northern Australia represents an interesting combination of these aspirations. A native bovid of Southeast Asia now ‘endangered’ in its native range, banteng were introduced into northern Australia in 1849. Today, a population of 8,000–10,000 resides on one small, isolated peninsula in western Arnhem Land, Northern Territory and is harvested by both recreational (trophy) and aboriginal subsistence hunters. Indigenous, industry and conservationist stakeholders differ in their requirements for population management. Here we analyze the ecological and economic costs/benefits of a series of potential harvest management options for Australia's banteng population, with the aim being either to: (1) maximize sustainable yield (MSY); (2) maximize harvest of trophy males; (3) maximize indigenous off‐take; (4) suppress density or completely eradicate the population; (5) minimize risk of extinction whilst limiting range expansion; (6) scenarios incorporating two or more of options 1–5. The modeling framework employed stochastic, density‐regulated matrix population models with life‐history parameters derived from (i) allometric relationships (for estimating rmax, generation length, fecundity and densities for a banteng‐sized mammal) and (ii) measured vital rates for wild and captive banteng and other Bos spp. For each management option, we present a simple economic analysis that incorporates estimated costs of management implementation and associated profits projected. Results demonstrate that revenue of >Ä$200,000 is possible from meat production and safari hunting without compromising long‐term population stability or the conservation status of this endangered bovid.

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.

Summary Anthropogenic global change compromises forest resilience, with profound impacts to ecosystem functions and services. This synthesis paper reflects on the current understanding of forest resilience and potential tipping points under environmental change and explores challenges to assessing responses using experiments, observations and models. Forests are changing over a wide range of spatio‐temporal scales, but it is often unclear whether these changes reduce resilience or represent a tipping point. Tipping points may arise from interactions across scales, as processes such as climate change, land‐use change, invasive species or deforestation gradually erode resilience and increase vulnerability to extreme events. Studies covering interactions across different spatio‐temporal scales are needed to further our understanding. Combinations of experiments, observations and process‐based models could improve our ability to project forest resilience and tipping points under global change. We discuss uncertainties in changing CO2 concentration and quantifying tree mortality as examples. Synthesis. As forests change at various scales, it is increasingly important to understand whether and how such changes lead to reduced resilience and potential tipping points. Understanding the mechanisms underlying forest resilience and tipping points would help in assessing risks to ecosystems and presents opportunities for ecosystem restoration and sustainable forest management.

Using the past to inform the future The late Quaternary paleorecord, within the past ∼130,000 years, can help to inform present-day management of the Earth's ecosystems and biota under climate change. Fordham et al. review when and where rapid climate transitions can be found in the paleoclimate record. They show how such events in Earth's history can shape our understanding of the consequences of future global warming, including rates of biodiversity loss, changes in ecosystem structure and function, and degradation in the goods and services that these ecosystems provide to humanity. They also highlight how recent developments at the intersection of paleoecology, paleoclimatology, and macroecology can provide opportunities to anticipate and manage the responses of species and ecosystems to changing climates in the Anthropocene. Science , this issue p. eabc5654

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.