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We estimate the effect of changes in demographic structure on long-term trends of key macroeconomic variables using a panel VAR for 21 OECD economies from 1970–2014. The panel data variation assists the identification of demographic effects, while the dynamic structure, incorporating multiple channels of influence, uncovers long-term effects. We propose a theoretical model, relating demographics, innovation, and growth, whose simulations match our empirical findings. The current trend of population aging and low fertility is projected to reduce output growth, investment, and real interest rates across OECD countries.

Amazonia hosts the Earth’s largest tropical forests and has been shown to be an important carbon sink over recent decades 1 – 3 . This carbon sink seems to be in decline, however, as a result of factors such as deforestation and climate change 1 – 3 . Here we investigate Amazonia’s carbon budget and the main drivers responsible for its change into a carbon source. We performed 590 aircraft vertical profiling measurements of lower-tropospheric concentrations of carbon dioxide and carbon monoxide at four sites in Amazonia from 2010 to 2018 4 . We find that total carbon emissions are greater in eastern Amazonia than in the western part, mostly as a result of spatial differences in carbon-monoxide-derived fire emissions. Southeastern Amazonia, in particular, acts as a net carbon source (total carbon flux minus fire emissions) to the atmosphere. Over the past 40 years, eastern Amazonia has been subjected to more deforestation, warming and moisture stress than the western part, especially during the dry season, with the southeast experiencing the strongest trends 5 – 9 . We explore the effect of climate change and deforestation trends on carbon emissions at our study sites, and find that the intensification of the dry season and an increase in deforestation seem to promote ecosystem stress, increase in fire occurrence, and higher carbon emissions in the eastern Amazon. This is in line with recent studies that indicate an increase in tree mortality and a reduction in photosynthesis as a result of climatic changes across Amazonia 1 , 10 . Aircraft observations of atmospheric carbon dioxide and monoxide concentrations in Brazil show higher carbon emissions in eastern Amazonia than in the western part, which are linked to increased ecosystem stress and fire occurrence.

The identification of the capacity of a run‐of‐river plant which allows for the optimal utilization of the available water resources is a challenging task, mainly because of the inherent temporal variability of river flows. This paper proposes an analytical framework to describe the energy production and the economic profitability of small run‐of‐river power plants on the basis of the underlying streamflow regime. We provide analytical expressions for the capacity which maximize the produced energy as a function of the underlying flow duration curve and minimum environmental flow requirements downstream of the plant intake. Similar analytical expressions are derived for the capacity which maximize the economic return deriving from construction and operation of a new plant. The analytical approach is applied to a minihydro plant recently proposed in a small Alpine catchment in northeastern Italy, evidencing the potential of the method as a flexible and simple design tool for practical application. The analytical model provides useful insight on the major hydrologic and economic controls (e.g., streamflow variability, energy price, costs) on the optimal plant capacity and helps in identifying policy strategies to reduce the current gap between the economic and energy optimizations of run‐of‐river plants. Key Points We set up analytical methods to find the optimal capacity of run‐of‐river plants Expressions for plant capacities maximizing energy & economic reward are derived The dependence of optimal plant capacity on streamflow variability is addressed

We document that government spending multipliers depend on the population age structure. Using the variation in military spending and birth rates across US states, we show that the local fiscal multiplier is 1.5 and increases with the population share of young people, implying multipliers of 1.1–1.9 in the interquartile range. A parsimonious life cycle open economy New Keynesian model with credit market imperfections and age-specific differences in labor supply and demand explains 87 percent of the relationship between local multipliers and demographics. The model implies that the US population aging between 1980 and 2015 caused a 38 percent drop in national government spending multipliers.

Methane fluxes from the stems of Amazonian floodplain trees indicate that the escape of soil gas through wetland trees is the dominant source of methane emissions in the Amazon basin. Missing methane in the Amazon Wetlands are the single largest global source of the greenhouse gas methane, but the contribution of the Amazon floodplain, the largest natural geographic source of methane in the tropics, remains poorly understood. Methane emission inventories underestimate the atmospheric burden of methane determined via remote sensing and inversion modelling. This paper reports on methane fluxes from the stems of Amazonian floodplain trees and finds that gas leaving the soil through wetland trees is the dominant source of regional methane emissions. The authors also provide an estimate of methane emission for the Amazon basin based on atmospheric methane profiles and find that it can be reconciled with the combined emission estimate from floodplain trees and other regional methane sources. Overall, the findings suggest that the large methane emission from trees could be what was missing from the Amazon budget. Wetlands are the largest global source of atmospheric methane (CH 4 ) 1 , a potent greenhouse gas. However, methane emission inventories from the Amazon floodplain 2 , 3 , the largest natural geographic source of CH 4 in the tropics, consistently underestimate the atmospheric burden of CH 4 determined via remote sensing and inversion modelling 4 , 5 , pointing to a major gap in our understanding of the contribution of these ecosystems to CH 4 emissions. Here we report CH 4 fluxes from the stems of 2,357 individual Amazonian floodplain trees from 13 locations across the central Amazon basin. We find that escape of soil gas through wetland trees is the dominant source of regional CH 4 emissions. Methane fluxes from Amazon tree stems were up to 200 times larger than emissions reported for temperate wet forests 6 and tropical peat swamp forests 7 , representing the largest non-ebullitive wetland fluxes observed. Emissions from trees had an average stable carbon isotope value (δ 13 C) of −66.2 ± 6.4 per mil, consistent with a soil biogenic origin. We estimate that floodplain trees emit 15.1 ± 1.8 to 21.2 ± 2.5 teragrams of CH 4 a year, in addition to the 20.5 ± 5.3 teragrams a year emitted regionally from other sources. Furthermore, we provide a ‘top-down’ regional estimate of CH 4 emissions of 42.7 ± 5.6 teragrams of CH 4 a year for the Amazon basin, based on regular vertical lower-troposphere CH 4 profiles covering the period 2010–2013. We find close agreement between our ‘top-down’ and combined ‘bottom-up’ estimates, indicating that large CH 4 emissions from trees adapted to permanent or seasonal inundation can account for the emission source that is required to close the Amazon CH 4 budget. Our findings demonstrate the importance of tree stem surfaces in mediating approximately half of all wetland CH 4 emissions in the Amazon floodplain, a region that represents up to one-third of the global wetland CH 4 source when trees are combined with other emission sources.

Increasing the water‐holding capacity of sandy soils will help improve efficiency of water use in agricultural production, and may be critical for providing enough energy and food for an increasing global population. We hypothesized that addition of biochar will increase the water‐holding capacity of a sandy loam soil, and that the depth of biochar incorporation will influence the rate of biochar surface oxidation in the amended soils. Hardwood fast pyrolysis biochar was mixed with soil (0%, 3%, and 6% w/w) and placed into columns in either the bottom 11.4 cm or the top 11.4 cm to simulate deep banding in rows (DBR) and uniform topsoil mixing (UTM) applications, respectively. Four sets of 18 columns were incubated at 30 °C and 80% RH. Every 7 days, 150 mL of 0.001 M calcium chloride solution was added to the columns to produce leaching. Sets of columns were harvested after 1, 15, 29, and 91 days. Addition of biochar increased the gravity‐drained water content 23% relative to the control. Bulk density of the control soils increased with incubation time (from 1.41 to 1.45 g cm−3), whereas bulk density of biochar‐treated soils was up to 9% less than the control and remained constant throughout the incubation period. Biochar did not affect the CEC of the soil. The results suggest that biochar added to sandy loam soil increases water‐holding capacity and might increase water available for crop use.

Magnitude and frequency are prominent features of river floods informing design of engineering structures, insurance premiums and adaptation strategies. Recent advances yielding a formal characterization of these variables from a joint description of soil moisture and daily runoff dynamics in river basins are here systematized to highlight their chief outcome: the PHysically-based Extreme Value (PHEV) distribution of river flows. This is a physically-based alternative to empirical estimates and purely statistical methods hitherto used to characterize extremes of hydro-meteorological variables. Capabilities of PHEV for predicting flood magnitude and frequency are benchmarked against a standard distribution and the latest statistical approach for extreme estimation, by using both an extensive observational dataset and long synthetic series of streamflow generated for river basins from contrasting hydro-climatic regions. The analyses outline the domain of applicability of PHEV and reveal its fairly unbiased capabilities to estimate flood magnitudes with return periods much longer than the sample size used for calibration in a wide range of case studies. The results also emphasize reduced prediction uncertainty of PHEV for rare floods, notably if the flood magnitude-frequency curve displays an inflection point. These features, arising from the mechanistic understanding embedded in the novel distribution of the largest river flows, are key for a reliable assessment of the actual flooding hazard associated to poorly sampled rare events, especially when lacking long observational records.

We use a global inverse model, satellite data and flask measurements to estimate methane (CH4) emissions from South America, Brazil and the basin of the Amazon River for the period 2010–2018. We find that emissions from Brazil have risen during this period, most quickly in the eastern Amazon basin, and that this is concurrent with increasing surface temperatures in this region. Brazilian CH4 emissions rose from 49.8 ± 5.4 Tg yr−1 in 2010–2013 to 55.6 ± 5.2 Tg yr−1 in 2014–2017, with the wet season of December–March having the largest positive trend in emissions. Amazon basin emissions grew from 41.7 ± 5.3 to 49.3 ± 5.1 Tg yr−1 during the same period. We derive no significant trend in regional emissions from fossil fuels during this period. We find that our posterior distribution of emissions within South America is significantly and consistently changed from our prior estimates, with the strongest emission sources being in the far north of the continent and to the south and south-east of the Amazon basin, at the mouth of the Amazon River and nearby marsh, swamp and mangrove regions. We derive particularly large emissions during the wet season of 2013/14, when flooding was prevalent over larger regions than normal within the Amazon basin. We compare our posterior CH4 mole fractions, derived from posterior fluxes, to independent observations of CH4 mole fraction taken at five lower- to mid-tropospheric vertical profiling sites over the Amazon and find that our posterior fluxes outperform prior fluxes at all locations. In particular the large emissions from the eastern Amazon basin are shown to be in good agreement with independent observations made at Santarém, a location which has long displayed higher mole fractions of atmospheric CH4 in contrast with other basin locations. We show that a bottom-up wetland flux model can match neither the variation in annual fluxes nor the positive trend in emissions produced by the inversion. Our results show that the Amazon alone was responsible for 24 ± 18 % of the total global increase in CH4 flux during the study period, and it may contribute further in future due to its sensitivity to temperature changes.

Carbon dioxide and carbon monoxide measurements across the Amazon basin for 2010 and 2011 reveal that drought rather than temperature caused the observed halt in forest productivity during the anomalously dry year of 2010. The Amazon basin — sink or source? Amazonia stores large amounts of carbon, but our understanding of the sensitivity of the tropical terrestrial carbon budget to climate anomalies remains uncertain. An analysis of seasonal and annual carbon balances based on basin-wide atmospheric measurements of carbon dioxide and monoxide for anomalously dry and wet years together with forest plot data suggest that water availability has an important role in determining the carbon balance in the Amazon basin. Drought reduced plant production and limited the amount of carbon that could be stored in vegetation; at the same time large amounts of carbon were released by fire during the dry year. The region was carbon neutral during the wet year, because of reduced carbon loss through fires and increased carbon uptake by vegetation. Feedbacks between land carbon pools and climate provide one of the largest sources of uncertainty in our predictions of global climate 1 , 2 . Estimates of the sensitivity of the terrestrial carbon budget to climate anomalies in the tropics and the identification of the mechanisms responsible for feedback effects remain uncertain 3 , 4 . The Amazon basin stores a vast amount of carbon 5 , and has experienced increasingly higher temperatures and more frequent floods and droughts over the past two decades 6 . Here we report seasonal and annual carbon balances across the Amazon basin, based on carbon dioxide and carbon monoxide measurements for the anomalously dry and wet years 2010 and 2011, respectively. We find that the Amazon basin lost 0.48 ± 0.18 petagrams of carbon per year (Pg C yr −1 ) during the dry year but was carbon neutral (0.06 ± 0.1 Pg C yr −1 ) during the wet year. Taking into account carbon losses from fire by using carbon monoxide measurements, we derived the basin net biome exchange (that is, the carbon flux between the non-burned forest and the atmosphere) revealing that during the dry year, vegetation was carbon neutral. During the wet year, vegetation was a net carbon sink of 0.25 ± 0.14 Pg C yr −1 , which is roughly consistent with the mean long-term intact-forest biomass sink of 0.39 ± 0.10 Pg C yr −1 previously estimated from forest censuses 7 . Observations from Amazonian forest plots suggest the suppression of photosynthesis during drought as the primary cause for the 2010 sink neutralization. Overall, our results suggest that moisture has an important role in determining the Amazonian carbon balance. If the recent trend of increasing precipitation extremes persists 6 , the Amazon may become an increasing carbon source as a result of both emissions from fires and the suppression of net biome exchange by drought.

Motivated by its implications in the development of general purpose solvers for decomposable Mixed Integer Programs (MIPs), we address a fundamental research question, that is how to exploit data-driven techniques to obtain automatic decomposition methods. We preliminary investigate the link between static properties of MIP input instances and good decomposition patterns. We devise a random sampling algorithm, considering a set of generic MIP base instances, and generate a large, balanced and well diversified set of decomposition patterns, that we analyze with machine learning tools. We also propose and test a minimal proof of concept framework performing data-driven automatic decomposition. The use of supervised techniques highlights interesting structures of random decompositions, as well as proving (under certain conditions) that data-driven methods are fruitful in our context, triggering at the same time perspectives for future research.