Economic and biophysical limits to seaweed farming for climate change mitigation.

Net-zero greenhouse gas (GHG) emissions targets are driving interest in opportunities for biomass-based negative emissions and bioenergy, including from marine sources such as seaweed. Yet the biophysical and economic limits to farming seaweed at scales relevant to the global carbon budget have not been assessed in detail. We use coupled seaweed growth and technoeconomic models to estimate the costs of global seaweed production and related climate benefits, systematically testing the relative importance of model parameters. Under our most optimistic assumptions, sinking farmed seaweed to the deep sea to sequester a gigaton of CO2 per year costs as little as US$480 per tCO2 on average, while using farmed seaweed for products that avoid a gigaton of CO2-equivalent GHG emissions annually could return a profit of $50 per tCO2-eq. However, these costs depend on low farming costs, high seaweed yields, and assumptions that almost all carbon in seaweed is removed from the atmosphere (that is, competition between phytoplankton and seaweed is negligible) and that seaweed products can displace products with substantial embodied non-CO2 GHG emissions. Moreover, the gigaton-scale climate benefits we model would require farming very large areas (>90,000 km2)-a >30-fold increase in the area currently farmed. Our results therefore suggest that seaweed-based climate benefits may be feasible, but targeted research and demonstrations are needed to further reduce economic and biophysical uncertainties.

Global and regional drivers of land-use emissions in 1961-2017.

Historically, human uses of land have transformed and fragmented ecosystems1,2, degraded biodiversity3,4, disrupted carbon and nitrogen cycles5,6 and added prodigious quantities of greenhouse gases (GHGs) to the atmosphere7,8. However, in contrast to fossil-fuel carbon dioxide (CO2) emissions, trends and drivers of GHG emissions from land management and land-use change (together referred to as 'land-use emissions') have not been as comprehensively and systematically assessed. Here we present country-, process-, GHG- and product-specific inventories of global land-use emissions from 1961 to 2017, we decompose key demographic, economic and technical drivers of emissions and we assess the uncertainties and the sensitivity of results to different accounting assumptions. Despite steady increases in population (+144 per cent) and agricultural production per capita (+58 per cent), as well as smaller increases in emissions per land area used (+8 per cent), decreases in land required per unit of agricultural production (-70 per cent) kept global annual land-use emissions relatively constant at about 11 gigatonnes CO2-equivalent until 2001. After 2001, driven by rising emissions per land area, emissions increased by 2.4 gigatonnes CO2-equivalent per decade to 14.6 gigatonnes CO2-equivalent in 2017 (about 25 per cent of total anthropogenic GHG emissions). Although emissions intensity decreased in all regions, large differences across regions persist over time. The three highest-emitting regions (Latin America, Southeast Asia and sub-Saharan Africa) dominate global emissions growth from 1961 to 2017, driven by rapid and extensive growth of agricultural production and related land-use change. In addition, disproportionate emissions are related to certain products: beef and a few other red meats supply only 1 per cent of calories worldwide, but account for 25 per cent of all land-use emissions. Even where land-use change emissions are negligible or negative, total per capita CO2-equivalent land-use emissions remain near 0.5 tonnes per capita, suggesting the current frontier of mitigation efforts. Our results are consistent with existing knowledge-for example, on the role of population and economic growth and dietary choice-but provide additional insight into regional and sectoral trends.

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

Economic uncertainty before and during the COVID-19 pandemic.

We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based policy uncertainty, Twitter chatter about economic uncertainty, subjective uncertainty about business growth, forecaster disagreement about future GDP growth, and a model-based measure of macro uncertainty. Four results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly - from a 35% rise for the model-based measure of US economic uncertainty (relative to January 2020) to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting differences between Wall Street and Main Street uncertainty measures. Fourth, in Cholesky-identified VAR models fit to monthly U.S. data, a COVID-size uncertainty shock foreshadows peak drops in industrial production of 12-19%.

Effects of Deep Reductions in Energy Storage Costs on Highly Reliable Wind and Solar Electricity Systems.

We use 36 years (1980-2015) of hourly weather data over the contiguous United States (CONUS) to assess the impact of low-cost energy storage on highly reliable electricity systems that use only variable renewable energy (VRE; wind and solar photovoltaics). Even assuming perfect transmission of wind and solar generation aggregated over CONUS, energy storage costs would need to decrease several hundred-fold from current costs (to ∼$1/kWh) in fully VRE electricity systems to yield highly reliable electricity without extensive curtailment of VRE generation. The role of energy storage changes from high-cost storage competing with curtailment to fill short-term gaps between VRE generation and hourly demand to near-free storage serving as seasonal storage for VRE resources. Energy storage faces "double penalties" in VRE/storage systems: with increasing capacity, (1) the additional storage is used less frequently and (2) hourly electricity costs would become less volatile, thus reducing price arbitrage opportunities for the additional storage.

Global supply-chain effects of COVID-19 control measures.

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A 'go-slow' approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.

Multispectral singlet oxygen and photosensitizer luminescence dosimeter for continuous photodynamic therapy dose assessment during treatment.

SIGNIFICANCE: Photodynamic therapy (PDT) involves complex light-drug-pathophysiology interactions that can be affected by multiple parameters and often leads to large variations in treatment outcome from patient to patient. Direct PDT dosimetry technologies have been sought to optimize the control variables (e.g., light dose, drug administration, tissue oxygenation, and patient conditioning) for best patient outcomes. In comparison, singlet oxygen (O21) dosimetry has been tested in various forms to provide an accurate and perhaps comprehensive prediction of the treatment efficacy. AIM: We discuss an advanced version of this approach provided by a noninvasive, continuous wave dosimeter that can measure near-infrared spectrally resolved luminescence of both photosensitizer (PS) and O21 generated during PDT cancer treatment. APPROACH: This dosimetry technology uses an amplified, high quantum efficiency InGaAs detector with spectroscopic decomposition during the light treatment to continuously extract the maximum signal of O21 phosphorescence while suppressing the strong PS luminescence background by spectrally fitting the data points across nine narrow band wavelengths. O21 and PS luminescence signals were measured in vivo in FaDu xenograft tumors grown in mice during PDT treatment using Verteporfin as the PS and a continuous laser treatment at 690 nm wavelength. RESULTS: A cohort of 19 mice was used and observations indicate that the tumor growth rate inhibition showed a stronger correlation with O21 than with just the PS signal. CONCLUSIONS: These results suggest that O21 measurement may be a more direct dosimeter of PDT damage, and it has potential value as a definitive diagnostic for PDT treatment, especially with spectral separation of the background luminescence and online estimation of the PS concentration.

Inequality of household consumption and air pollution-related deaths in China.

Substantial quantities of air pollution and related health impacts are ultimately attributable to household consumption. However, how consumption pattern affects air pollution impacts remains unclear. Here we show, of the 1.08 (0.74-1.42) million premature deaths due to anthropogenic PM2.5 exposure in China in 2012, 20% are related to household direct emissions through fuel use and 24% are related to household indirect emissions embodied in consumption of goods and services. Income is strongly associated with air pollution-related deaths for urban residents in which health impacts are dominated by indirect emissions. Despite a larger and wealthier urban population, the number of deaths related to rural consumption is higher than that related to urban consumption, largely due to direct emissions from solid fuel combustion in rural China. Our results provide quantitative insight to consumption-based accounting of air pollution and related deaths and may inform more effective and equitable clean air policies in China.

Committed emissions from existing energy infrastructure jeopardize 1.5 °C climate target.

Net anthropogenic emissions of carbon dioxide (CO2) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts1-5. Yet continued expansion of fossil-fuel-burning energy infrastructure implies already 'committed' future CO2 emissions6-13. Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO2 emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO2 (with a range of 226 to 1,479 gigatonnes CO2, depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37-427) gigatonnes CO2. Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO2) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420-580 gigatonnes CO2)5, and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170-1,500 gigatonnes CO2)5. The remaining carbon budget estimates are varied and nuanced14,15, and depend on the climate target and the availability of large-scale negative emissions16. Nevertheless, our estimates suggest that little or no new CO2-emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals17. Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable4,18.

Economic carbon cycle feedbacks may offset additional warming from natural feedbacks.

As the Earth warms, carbon sinks on land and in the ocean will weaken, thereby increasing the rate of warming. Although natural mechanisms contributing to this positive climate-carbon feedback have been evaluated using Earth system models, analogous feedbacks involving human activities have not been systematically quantified. Here we conceptualize and estimate the magnitude of several economic mechanisms that generate a carbon-climate feedback, using the Kaya identity to separate a net economic feedback into components associated with population, GDP, heating and cooling, and the carbon intensity of energy production and transportation. We find that climate-driven decreases in economic activity (GDP) may in turn decrease human energy use and thus fossil fuel CO2 emissions. In a high radiative forcing scenario, such decreases in economic activity reduce fossil fuel emissions by 13% this century, lowering atmospheric CO2 by over 100 ppm in 2100. The natural carbon-climate feedback, in contrast, increases atmospheric CO2 over this period by a similar amount, and thus, the net effect including both feedbacks is nearly zero. Our work highlights the importance of improving the representation of climate-economic feedbacks in scenarios of future change. Although the effects of climate warming on the economy may offset weakening land and ocean carbon sinks, a loss of economic productivity will have high societal costs, potentially increasing wealth inequity and limiting resources available for effective adaptation.

Infrastructure Shapes Differences in the Carbon Intensities of Chinese Cities.

The carbon intensity of economic activity, or CO2 emissions per unit GDP, is a key indicator of the climate impacts of a given activity, business, or region. Although it is well-known that the carbon intensity of countries varies widely according to their level of economic development and dominant industries, few studies have assessed disparities in carbon intensity at the level of cities due to limited availability of data. Here, we present a detailed new inventory of emissions for 337 Chinese cities (every city in mainland China including 333 prefecture-level divisions and 4 province-level cities, Beijing, Tianjin, Shanghai, and Chongqing) in 2013, which we use to evaluate differences of carbon intensity between cities and the causes of those differences. We find that cities' average carbon intensity is 0.84 kg of CO2 per dollar of gross domestic product (kgCO2 per $GDP), but individual cities span a large range: from 0.09 to 7.86 kgCO2 per $GDP (coefficient of variation of 25%). Further analysis of economic and technological drivers of variations in cities' carbon intensity reveals that the differences are largely due to disparities in cities' economic structure that can in turn be traced to past investment-led growth. These patterns suggest that "carbon lock-in" via socio-economic and infrastructural inertia may slow China's efforts to reduce emissions from activities in urban areas. Policy instruments targeted to accelerate the transition of urban economies from investment-led to consumption-led growth may thus be crucial to China meeting both its economic and climate targets.

Evaluation of a proposal for reliable low-cost grid power with 100% wind, water, and solar.

A number of analyses, meta-analyses, and assessments, including those performed by the Intergovernmental Panel on Climate Change, the National Oceanic and Atmospheric Administration, the National Renewable Energy Laboratory, and the International Energy Agency, have concluded that deployment of a diverse portfolio of clean energy technologies makes a transition to a low-carbon-emission energy system both more feasible and less costly than other pathways. In contrast, Jacobson et al. [Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015) Proc Natl Acad Sci USA 112(49):15060-15065] argue that it is feasible to provide "low-cost solutions to the grid reliability problem with 100% penetration of WWS [wind, water and solar power] across all energy sectors in the continental United States between 2050 and 2055", with only electricity and hydrogen as energy carriers. In this paper, we evaluate that study and find significant shortcomings in the analysis. In particular, we point out that this work used invalid modeling tools, contained modeling errors, and made implausible and inadequately supported assumptions. Policy makers should treat with caution any visions of a rapid, reliable, and low-cost transition to entire energy systems that relies almost exclusively on wind, solar, and hydroelectric power.

Outsourcing CO2 within China.

Recent studies have shown that the high standard of living enjoyed by people in the richest countries often comes at the expense of CO2 emissions produced with technologies of low efficiency in less affluent, developing countries. Less apparent is that this relationship between developed and developing can exist within a single country's borders, with rich regions consuming and exporting high-value goods and services that depend upon production of low-cost and emission-intensive goods and services from poorer regions in the same country. As the world's largest emitter of CO2, China is a prominent and important example, struggling to balance rapid economic growth and environmental sustainability across provinces that are in very different stages of development. In this study, we track CO2 emissions embodied in products traded among Chinese provinces and internationally. We find that 57% of China's emissions are related to goods that are consumed outside of the province where they are produced. For instance, up to 80% of the emissions related to goods consumed in the highly developed coastal provinces are imported from less developed provinces in central and western China where many low-value-added but high-carbon-intensive goods are produced. Without policy attention to this sort of interprovincial carbon leakage, the less developed provinces will struggle to meet their emissions intensity targets, whereas the more developed provinces might achieve their own targets by further outsourcing. Consumption-based accounting of emissions can thus inform effective and equitable climate policy within China.