The implementation of large-scale containment measures by governments to contain the spread of the COVID-19 virus has resulted in large impacts to the global economy. Here, we derive a new ...high-frequency indicator of economic activity using empirical vessel tracking data, and use it to estimate the global maritime trade losses during the first eight months of the pandemic. We go on to use this high-frequency dataset to infer the effect of individual non-pharmaceutical interventions on maritime exports, which we use as a proxy of economic activity. Our results show widespread port-level trade losses, with the largest absolute losses found for ports in China, the Middle-East and Western Europe, associated with the collapse of specific supply-chains (e.g. oil, vehicle manufacturing). In total, we estimate that global maritime trade reduced by -7.0% to -9.6% during the first eight months of 2020, which is equal to around 206-286 million tonnes in volume losses and up to 225-412 billion USD in value losses. We find large sectoral and geographical disparities in impacts. Manufacturing sectors are hit hardest, with losses up to 11.8%, whilst some small islands developing states and low-income economies suffered the largest relative trade losses. Moreover, we find a clear negative impact of COVID-19 related school and public transport closures on country-wide exports. Overall, we show how real-time indicators of economic activity can inform policy-makers about the impacts of individual policies on the economy, and can support economic recovery efforts by allocating funds to the hardest hit economies and sectors.
Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst ...there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2 °C global warming scenario (by ∼2050 s). Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 °C (by ∼2040 s) and 2 °C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios.
The risks of cooling water shortages to thermo-electric power plants are increasingly studied as an important climate risk to the energy sector. Whilst electricity transmission networks reduce the ...risks during disruptions, more costly plants must provide alternative supplies. Here, we investigate the electricity price impacts of cooling water shortages on Britain's power supplies using a probabilistic spatial risk model of regional climate, hydrological droughts and cooling water shortages, coupled with an economic model of electricity supply, demand and prices. We find that on extreme days (p99), almost 50% (7GW
) of freshwater thermal capacity is unavailable. Annualized cumulative costs on electricity prices range from £29-66m.yr
GBP2018, whilst in 20% of cases from £66-95m.yr
. With climate change, the median annualized impact exceeds £100m.yr
. The single year impacts of a 1-in-25 year event exceed >£200m, indicating the additional investments justifiable to mitigate the 1
-order economic risks of cooling water shortage during droughts.
Meat consumption, health, and the environment Godfray, H Charles J; Aveyard, Paul; Garnett, Tara ...
Science (American Association for the Advancement of Science),
07/2018, Volume:
361, Issue:
6399
Journal Article
Peer reviewed
Open access
Both the global average per capita consumption of meat and the total amount of meat consumed are rising, driven by increasing average individual incomes and by population growth. The consumption of ...different types of meat and meat products has substantial effects on people's health, and livestock production can have major negative effects on the environment. Here, we explore the evidence base for these assertions and the options policy-makers have should they wish to intervene to affect population meat consumption. We highlight where more research is required and the great importance of integrating insights from the natural and social sciences.
•The current status of China’s thermoelectric power sector’s water uses has been presented.•The implications of China’s different future energy scenarios and cooling technology portfolios on its ...thermoelectric power sector’s water withdrawal and consumption have been assessed on both regional and national levels.•China’s energy sector’s compliance with related water policy has been examined on a regional scale and potential conflicts are revealed.•The temporal patterns of China’s thermoelectric power sector’s water uses are discussed.
We quantify the current water use of China’s thermoelectric power sector with plant-level data. We also quantify the future implications for cooling water use of different energy supply scenarios at both a regional and national levels. Within China, water withdrawal and consumption are projected to exceed 280 and 15 billion m3 respectively by 2050 if China does not implement any new policies, up from current levels of 65.2 and 4.64 billion m3. Improving energy efficiency or transforming the energy infrastructure to renewable, or low-carbon, sources provides the opportunity to reduce water use by over 50%. At a regional level, central and eastern China account for the majority of the power sector’s water withdrawals, but water consumption is projected to increase in many regions under most scenarios. In high-renewable and low-carbon scenarios, concentrated solar power and inland nuclear power, respectively, constitute the primary fresh water users. Changing cooling technology, from open-loop to closed-loop in the south and from closed-loop to air cooling in the north, curtails the power sector’s water withdrawal considerably while increasing water consumption, particularly in eastern and central China. The power sector’s water use is predicted to exceed the regional industrial water quota under the ‘3 Red Line’ policy in the east under all scenarios, unless cooling technology change is facilitated. The industrial water quota is also likely to be violated in the central and the northern regions under a baseline scenario. Moreover, in line with electricity production, the power sector’s water use peaks in the winter when water availability is lowest. Water-for-energy is a highly contextual issue – a better understanding of its spatio-temporal characteristics is therefore critical for development of policies for sustainable cooling water use in the power sector.
Inadequate water quality can mean that water is unsuitable for a variety of human uses, thus exacerbating freshwater scarcity. Previous large-scale water scarcity assessments mostly focused on the ...availability of sufficient freshwater quantity for providing supplies, but neglected the quality constraints on water usability. Here we report a comprehensive nationwide water scarcity assessment in China, which explicitly includes quality requirements for human water uses. We highlight the necessity of incorporating water scarcity assessment at multiple temporal and geographic scales. Our results show that inadequate water quality exacerbates China's water scarcity, which is unevenly distributed across the country. North China often suffers water scarcity throughout the year, whereas South China, despite sufficient quantities, experiences seasonal water scarcity due to inadequate quality. Over half of the population are affected by water scarcity, pointing to an urgent need for improving freshwater quantity and quality management to cope with water scarcity.
Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental ...modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research.
•We present an overview of SA and its link to uncertainty analysis, model calibration and evaluation, robust decision-making.•We provide a systematic review of existing approaches, which can support users in the choice of an SA method.•We provide practical guidelines by developing a workflow for the application of SA and discuss critical choices.•We give best practice examples from the literature and highlight trends and gaps for future research.
Abstract
When construction of the Grand Ethiopian Renaissance Dam (GERD) is completed, the Nile will have two of the world’s largest dams—the High Aswan Dam (HAD) and the GERD—in two different ...countries (Egypt and Ethiopia). There is not yet agreement on how these dams will operate to manage scarce water resources. We elucidate the potential risks and opportunities to Egypt, Sudan and Ethiopia by simulating the filling period of the reservoir; a
new normal
period after the reservoir fills; and a severe multi-year drought after the filling. Our analysis illustrates how during filling the HAD reservoir could fall to levels not seen in recent decades, although the risk of water shortage in Egypt is relatively low. The
new normal
will benefit Ethiopia and Sudan without significantly affecting water users in Egypt. Management of multi-year droughts will require careful coordination if risks of harmful impacts are to be minimized.
•We present a spatiotemporal appraisal of poverty in the coastal zone in Bangladesh.•Flood risk is positively correlated with land use/land cover (LULC) change.•The expected annual damage of 2005 is ...estimated to be more than double by 2030.•Flood risk and patterns of LULC change have a negative effect on wealth index.•The rate of increase of wealth index is likely to be low in the future.
The construction of polders in the coastal region of Bangladesh has significantly modified the patterns of flooding, as well as leading to significant land use/land cover (hereinafter, LULC) changes. The impact of LULC change and flooding on poverty is complex and poorly understood. This study presents a spatiotemporal appraisal of poverty in relation to LULC change and pluvial flood risk in the south western embanked area of Bangladesh. A combination of logistic regression (LR), cellular automata (CA), and Markov Chain models were utilised to predict future LULC based on historical data. Flood risk assessment was performed at present and for future LULC scenarios. A spatial regression model was developed, incorporating multiple parameters to estimate the wealth index (WI) for present-day and future scenarios. In the study area, agricultural lands reduced from 34 % in 2005 to 8% in 2010, while aquaculture land cover increased from 17 % to 39 % during the same time. The rate of LULC change was relatively low between 2010 and 2019. Based on the recent trend, LULC was predicted for the year 2030. Flood risk was positively correlated with LULC and the expected annual damage (EAD) was estimated at $903 million in 2005, which is likely to increase to $2096 million by 2030, considering changes in LULC scenarios. The analysis further showed that the EAD and LULC change were negatively associated with the WI. Despite consistent national GDP growth in Bangladesh in recent years, the rate of increase of WI is likely to be low in the future because flood risk and patterns of LULC change have a negative effect on WI.
•Resilience in rail networks is influenced by complexity of their interdependencies.•Risk to electrical systems result in widescale effects in interdependent networks.•Resilience can be assessed ...using metrics of train and passenger delay minutes.
Rail networks entail multiple interdependencies which can initiate or propagate network failures with serious consequences for the movement of trains and passengers. In this study, we present a rail infrastructure system-of-systems model which can be used to simulate disruptions to the network's operations and propose a performance metric based on train and passenger delay minutes. We demonstrate the applicability of our model by evaluating the resilience of the southern region of Great Britain's rail network in scenarios of failure initiated in the traction power supply system. The results highlight the sensitivity of the rail network to a small number of traction assets concentrated in the London area, where we find that failure of one of the region's most critical electricity traction power grid could disrupt 75% of all trains in the southern region and 25% of all trains nationally. Our analysis demonstrates how the resilience of the rail network is influenced by the speed of backup restoration following a failure event as well as the network's vulnerability to failure at peak travel times with a delay during peak travel resulting in about 63% increase in passenger delay minutes compared with off-peak travel times for the same duration.
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